From c332a86c5d362e567b52a0b8036e36e33629c649 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 10 Oct 2024 01:35:23 -0400 Subject: [PATCH 01/55] Added a clustering branch --- experiments/clustering/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 experiments/clustering/.gitkeep diff --git a/experiments/clustering/.gitkeep b/experiments/clustering/.gitkeep new file mode 100644 index 0000000..e69de29 From f64a35636d281d192009d15e06f80bbdca1b1aff Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 10 Oct 2024 01:49:34 -0400 Subject: [PATCH 02/55] Added a template notebook file --- experiments/clustering/clustering.ipynb | 52 +++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 experiments/clustering/clustering.ipynb diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb new file mode 100644 index 0000000..139d165 --- /dev/null +++ b/experiments/clustering/clustering.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clustering Experiment\n", + "\n", + "In this notebook we collab to create a solution to determining the BP and HR values as well as times using clustering. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Before we start, we need to be able to do some collaboration. \n", + "\n", + "Lets do this using the CodeTogether Live VS code plug in.\n", + "\n", + "- Search `CodeTogether Live VS code` in the extensions tab on the left-hand side. Download the add-on. \n", + "- When you're coding if you want to live collaborate with someone else send them the link that you created when you create a session\n", + "\n", + "Note: This will not allow for dual cursors. It only allows you guys to both follow along and edit with one another on the same cursor.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Collab\n", + "\n", + "We should consider collab in my opinion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We cook" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 570007e28103d86b5481e4d3fbba05096f6b85c0 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Wed, 16 Oct 2024 21:54:56 -0400 Subject: [PATCH 03/55] Adding poetry and an empty "Data" file by default. The data with in this file will be ignored by git (all files within this directory will be ignored by git with exception of ".gitkeep"). Additionally adding details to README.md to ease set up. --- .gitignore | 4 +- README.md | 32 + data/.gitkeep | 0 experiments/clustering/clustering.ipynb | 26 +- poetry.lock | 1634 +++++++++++++++++ pyproject.toml | 27 + requirements.txt | 1 - .../apply_homography_to_labels.ipynb | 319 ++++ 8 files changed, 2025 insertions(+), 18 deletions(-) create mode 100644 data/.gitkeep create mode 100644 poetry.lock create mode 100644 pyproject.toml delete mode 100644 requirements.txt create mode 100644 utilities/conversion/apply_homography_to_labels.ipynb diff --git a/.gitignore b/.gitignore index 49aaa61..e33d381 100644 --- a/.gitignore +++ b/.gitignore @@ -160,4 +160,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -/data +#/data +/data/* +!/data/.gitkeep \ No newline at end of file diff --git a/README.md b/README.md index b7d3c9d..7bb73c2 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,34 @@ # ChartExtractorSupplements + This repository houses two types of content: (1) jupyter notebooks that run experiments to improve ChartExtractor and (2) useful scripts for working with ChartExtractor. + +### Getting Set Up + +#### Where To Place Data + +- When you pull this repository, there will be an empty directory called `Data` that contains a `.gitkeep` file. This file should remain in this directory, do not delete it. +- Add your data files to this directory. These files should and will be ignored by git. + +#### Downloading Necessary Packages + +- Install poetry using pip to start + ```bash + pip install poetry + ``` +- I have created the pyproject.toml files so you don't have to worry about any of that. Just do the below. +- Add configuration to have venv in project directory + ```bash + poetry config virtualenvs.in-project true + ``` +- Set up venv using poetry + ```bash + poetry install + ``` +- Now you should have a created venv that you can switch into with the following command and run the python scripts + ```bash + poetry shell + ``` +- As you develop you can add packages with the following command + ```bash + poetry add + ``` diff --git a/data/.gitkeep b/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 139d165..8a438c2 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -4,32 +4,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Clustering Experiment\n", + "# Clustering Experiment \n", + "#### By: Charbel Marche\n", "\n", - "In this notebook we collab to create a solution to determining the BP and HR values as well as times using clustering. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." + "We decided to individually tackle the problem using 1 method, and by the time we are all done we will be able to merge our techniques and select the optimal techinque. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Before we start, we need to be able to do some collaboration. \n", + "### Register Images to Start\n", "\n", - "Lets do this using the CodeTogether Live VS code plug in.\n", - "\n", - "- Search `CodeTogether Live VS code` in the extensions tab on the left-hand side. Download the add-on. \n", - "- When you're coding if you want to live collaborate with someone else send them the link that you created when you create a session\n", - "\n", - "Note: This will not allow for dual cursors. It only allows you guys to both follow along and edit with one another on the same cursor.\n" + "To start, we need to register images using a completed version of the NoteBook Ryan shared on email." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### Collab\n", - "\n", - "We should consider collab in my opinion." + "# We cook" ] }, { @@ -37,9 +33,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# We cook" - ] + "source": [] } ], "metadata": { diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..250d90f --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1634 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2024.8.30" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, + {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.0" +description = "The Real First Universal Charset Detector. 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b/pyproject.toml @@ -0,0 +1,27 @@ +[tool.poetry] +name = "supplements" +version = "0.1.0" +description = "" +authors = [ + "RyanDoesMath ", + "mattbeck1 ", + "hvalenty ", + "charbelmarche33 ", +] +license = "GPL-3.0-or-later" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.12" +pillow = "^11.0.0" +numpy = "^2.1.2" +ruff = "^0.6.9" +ultralytics = "^8.3.15" +opencv-python = "^4.10.0.84" +pandas = "^2.2.3" +tqdm = "^4.66.5" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 3e338bf..0000000 --- a/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -python-dotenv \ No newline at end of file diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb new file mode 100644 index 0000000..e5a3125 --- /dev/null +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6b9d8dbd-5c0d-44c4-8054-ceb6d1f130fb", + "metadata": {}, + "source": [ + "# Apply Homography to Labels" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5f322da5-10f8-49ee-a81a-5edc7bac12cd", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "sys.path.append(os.path.join(\"..\", \"..\", \"..\", \"ChartExtractor\", \"src\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "95997450-a2a0-4035-b040-3c8fb532836b", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import random\n", + "from PIL import Image, ImageDraw\n", + "from pathlib import Path\n", + "from typing import Dict, List, Tuple\n", + "from tqdm import tqdm\n", + "from utilities.annotations import BoundingBox, Point\n", + "from utilities.image_conversion import pil_to_cv2, cv2_to_pil\n", + "import cv2\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", + "metadata": {}, + "outputs": [], + "source": [ + "from operator import attrgetter" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", + "metadata": {}, + "outputs": [], + "source": [ + "Point.__repr__ = lambda self: f\"Point({self.x}, {self.y})\"" + ] + }, + { + "cell_type": "markdown", + "id": "ddfd5339-e298-4223-a19f-94203044e543", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "3dd0d783-7093-4e21-9907-fa112f6deb57", + "metadata": {}, + "source": [ + "## 1 - Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cd2294bd-3749-4872-b7e8-918218191c88", + "metadata": {}, + "outputs": [], + "source": [ + "def label_studio_to_bboxes(path_to_json_data: Path) -> List[BoundingBox]:\n", + " \"\"\"Loads data from LabelStudio's json format into BoundingBoxes.\"\"\"\n", + " json_data: List[Dict] = json.loads(open(str(path_to_json_data)).read())\n", + " return {\n", + " sheet_data['data']['image'].split(\"-\")[-1]:[\n", + " BoundingBox(\n", + " category=label['value']['rectanglelabels'][0],\n", + " left=label['value']['x']/100,\n", + " top=label['value']['y']/100,\n", + " right=label['value']['x']/100+label['value']['width']/100,\n", + " bottom=label['value']['y']/100+label['value']['height']/100,\n", + " )\n", + " for label in sheet_data['annotations'][0]['result']\n", + " ]\n", + " for sheet_data in json_data\n", + " }\n", + "\n", + "\n", + "data_path: Path = Path(\"..\")/\"..\"/\"data\"\n", + "landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(data_path/\"intraop_document_landmarks.json\")\n", + "checkbox_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(data_path/\"intraop_checkbox_names.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "c169a1f4-dc7f-4242-b8a4-bb5062fa6cdc", + "metadata": {}, + "source": [ + "This is a slightly different version of the homography function from the main program. The only thing it changes is to return the homography matrix along with the image." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", + "metadata": {}, + "outputs": [], + "source": [ + "def homography_transform(\n", + " src_image: Image.Image,\n", + " src_points: List[Tuple[float, float]],\n", + " dest_points: List[Tuple[float, float]],\n", + " original_image_size: Tuple[float, float] = (3300, 2250),\n", + ") -> Tuple[List[List[float]], Image.Image]:\n", + " \"\"\"Performs homography transformation on an image.\n", + "\n", + " This function transforms an image (src_image) based on corresponding points\n", + " between the source and destination images. It calculates the homography matrix\n", + " and uses it to warp the source image to the perspective of the destination points.\n", + "\n", + " Args:\n", + " src_image (Image.Image):\n", + " A PIL image object representing the source image.\n", + " src_points (List[Tuple[int, int]]):\n", + " A list of tuples (x, y) representing points in the source image that correspond\n", + " to points in the destination image.\n", + " dest_points (List[Tuple[int, int]]):\n", + " A list of tuples (x, y) representing points in the destination image that points\n", + " in the source image correspond to (where the source image should be warped to).\n", + " original_image_size (Tuple[float, float]):\n", + " A tuple (width, height) representing the size of the control image.\n", + " Defaults to (3300, 2250).\n", + "\n", + " Returns:\n", + " A PIL image object representing the transformed source image.\n", + "\n", + " Raises:\n", + " ValueError:\n", + " If the length of src_points and dest_points don't match (must have the same\n", + " number of corresponding points), or if there are less than 4 points.\n", + " \"\"\"\n", + " src_points: np.ndarray = np.array(src_points)\n", + " dest_points: np.ndarray = np.array(dest_points)\n", + "\n", + " if len(src_points) != len(dest_points):\n", + " raise ValueError(\n", + " \"Source and destination points must have the same number of elements.\"\n", + " )\n", + " if len(src_points) < 4 or len(dest_points) < 4:\n", + " raise ValueError(\"Must have 4 or more points to compute the homography.\")\n", + "\n", + " src_image = pil_to_cv2(src_image)\n", + " h, _ = cv2.findHomography(src_points, dest_points)\n", + " dest_image = cv2.warpPerspective(src_image, h, original_image_size)\n", + " return h, cv2_to_pil(dest_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_corresponding_points(bboxes, imsize) -> List[Tuple[float, float]]:\n", + " \"\"\"Gets and sorts the points used for the homography from all the bounding boxes that are labeled.\"\"\"\n", + " categories_to_get = ['anesthesia_start', 'lateral', 'safety_checklist', 'units']\n", + " if not all([c in [bb.category for bb in bboxes] for c in categories_to_get]):\n", + " raise ValueError(f\"Necessary labels not found: {categories_to_get}\")\n", + " \n", + " points = list(map(\n", + " attrgetter('center'),\n", + " sorted(\n", + " list(filter(lambda bb: bb.category in categories_to_get, bboxes)), \n", + " key=lambda bb: bb.category\n", + " )\n", + " ))\n", + " return [(p[0]*imsize[0], p[1]*imsize[1]) for p in points]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a18e97c6-e438-461a-a701-2bf320da275f", + "metadata": {}, + "outputs": [], + "source": [ + "remap_point = lambda p, h: cv2.perspectiveTransform(np.array(p, dtype=np.float32).reshape(-1, 1, 2), h).tolist()[0][0]\n", + "\n", + "\n", + "def remap_bbox(\n", + " bbox: BoundingBox, \n", + " h, \n", + " original_width:int=4032, \n", + " original_height:int=3024,\n", + " new_width:int=3300,\n", + " new_height:int=2250,\n", + ") -> BoundingBox:\n", + " \"\"\"Maps boundingboxes to a new space using the homography matrix.\n", + " \n", + " Given a bounding box, homography matrix, and the image sizes of the original \n", + " and destination (new) image, this function returns a remapped bounding box.\n", + " \"\"\"\n", + " new_left, new_top = remap_point((bbox.left*original_width, bbox.top*original_height), h)\n", + " new_right, new_bottom = remap_point((bbox.right*original_width, bbox.bottom*original_height), h)\n", + " return BoundingBox(bbox.category, new_left/new_width, new_top/new_height, new_right/new_width, new_bottom/new_height)\n", + "\n", + "\n", + "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]\n", + "\n", + "\n", + "locations = landmark_location_data[sheet]\n", + "remapped_locations = remap_all_bboxes(locations, h)" + ] + }, + { + "cell_type": "markdown", + "id": "2414bd30-4ba1-490e-b3cd-4aeedf9e0397", + "metadata": {}, + "source": [ + "Get landmarks that show up only once." + ] + }, + { + "cell_type": "markdown", + "id": "990d45a9-9aac-4765-99f1-50a0d44447b7", + "metadata": {}, + "source": [ + "Check labels." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6fd84989-134e-441f-a7ae-b86cc8e61ae4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", + "sheet = \"RC_0001_intraoperative.JPG\"\n", + "img = Image.open(str(data_path/\"chart_images\"/sheet))\n", + "original_width, original_height = img.size\n", + "img = transformed_img.copy()\n", + "width, height = img.size\n", + "draw = ImageDraw.Draw(img)\n", + "\n", + "for bounding_box in remapped_locations:\n", + " box = [\n", + " bounding_box.left*width,\n", + " bounding_box.top*height,\n", + " bounding_box.right*width,\n", + " bounding_box.bottom*height,\n", + " ]\n", + " draw.rectangle(box, outline=generate_color(), width=3)\n", + "img.resize((800, 600))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02674b37-4648-46e5-b6fd-ec681e7664dc", + "metadata": {}, + "outputs": [], + "source": [ + "# dump your BoundingBoxes to the format of your choosing here." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 23e2b5962ead876d4e2ad47d2e2020f23d7a3ee7 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 02:13:07 -0400 Subject: [PATCH 04/55] Working on homography and registering. Added utils to conversion folder. May want to take some of these functions and turn them into a package to use in various microservices? --- .gitignore | 2 +- poetry.lock | 664 +++++++++++++++++- pyproject.toml | 3 + .../apply_homography_to_labels.ipynb | 139 +++- utilities/conversion/utils/annotations.py | 445 ++++++++++++ .../conversion/utils/image_conversion.py | 48 ++ 6 files changed, 1282 insertions(+), 19 deletions(-) create mode 100644 utilities/conversion/utils/annotations.py create mode 100644 utilities/conversion/utils/image_conversion.py diff --git a/.gitignore b/.gitignore index e33d381..dbf7860 100644 --- a/.gitignore +++ b/.gitignore @@ -160,6 +160,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -#/data + /data/* !/data/.gitkeep \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 250d90f..6cfdbd2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,34 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "appnope" +version = "0.1.4" +description = "Disable App Nap on macOS >= 10.9" +optional = false +python-versions = ">=3.6" +files = [ + {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, + {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, +] + +[[package]] +name = "asttokens" +version = "2.4.1" +description = "Annotate AST trees with source code positions" +optional = false +python-versions = "*" +files = [ + {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"}, + {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"}, +] + +[package.dependencies] +six = ">=1.12.0" + +[package.extras] +astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"] +test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] + [[package]] name = "certifi" version = "2024.8.30" @@ -11,6 +40,85 @@ files = [ {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, ] +[[package]] +name = "cffi" +version = "1.17.1" +description = "Foreign Function Interface for Python calling C code." +optional = false +python-versions = ">=3.8" +files = [ + {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"}, + {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", 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9, "id": "95997450-a2a0-4035-b040-3c8fb532836b", "metadata": {}, "outputs": [], @@ -33,8 +33,13 @@ "from pathlib import Path\n", "from typing import Dict, List, Tuple\n", "from tqdm import tqdm\n", - "from utilities.annotations import BoundingBox, Point\n", - "from utilities.image_conversion import pil_to_cv2, cv2_to_pil\n", + "# Created a folder utils in the conversion folder and moved these files into there so we can call their functions\n", + "# There should be a better way to do this perhaps, if this is something we will use across various microservices\n", + "# Perhaps they can be a part of a package.\n", + "from utils.annotations import BoundingBox, Point\n", + "from utils.image_conversion import pil_to_cv2, cv2_to_pil\n", + "\n", + "\n", "import cv2\n", "import numpy as np\n", "import pandas as pd" @@ -42,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 10, "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", "metadata": {}, "outputs": [], @@ -52,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", "metadata": {}, "outputs": [], @@ -78,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], @@ -116,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -172,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -198,14 +203,87 @@ "execution_count": 31, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['unified_intraoperative_preoperative_flowsheet_v1_1_front.png', 'RC_0001_intraoperative.JPG', 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Image: \n", + "Homography matrix: [[ 9.36855166e-01 -2.36434063e-03 -3.30552699e+02]\n", + " [-1.97486704e-02 7.08216501e-01 -4.84753628e+01]\n", + " [-9.74865835e-06 -3.47074295e-05 1.00000000e+00]]\n", + "Remapped locations: [BoundingBox(category='mg', left=0.9986187559185606, top=0.0421971435546875, right=1.013432099313447, bottom=0.05060342068142361), BoundingBox(category='5', left=0.9441225733901515, top=-0.003222407235039605, right=0.9504044596354166, bottom=0.006365236070421007), BoundingBox(category='2', left=0.9379266542376894, top=-0.0030366956922743054, right=0.9443192915482954, bottom=0.00621986346774631), BoundingBox(category='0', left=0.9236537494081439, top=-0.0025744647979736328, right=0.9301299124053031, bottom=0.006803653717041016), BoundingBox(category='2', left=0.9174038973721591, top=-0.002383659150865343, right=0.9240199603456439, bottom=0.006952469295925565), BoundingBox(category='5', left=0.9028962476325758, top=-0.0018002317216661242, right=0.9093166281960228, 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Image: \n", + "Homography matrix: [[ 9.75470550e-01 -1.79863071e-02 -3.90062022e+02]\n", + " [-7.57438267e-03 7.25311701e-01 -8.95365840e+01]\n", + " [-5.04203303e-06 -4.05838556e-05 1.00000000e+00]]\n", + "Remapped locations: [BoundingBox(category='temperature', left=0.047746401700106536, top=0.7597625868055555, right=0.1279843602035985, bottom=0.7762079535590278), BoundingBox(category='anesthesia_start', left=-0.03403087269176136, top=-0.004548900604248047, right=0.034112981160481774, bottom=0.0045598759121365014), BoundingBox(category='hour_24hr', left=0.04398478652491714, top=-0.005701022254096137, right=0.08739609227035985, bottom=0.00485937245686849), BoundingBox(category='minute', left=0.13297152432528409, top=-0.006321883307562934, right=0.1619772431344697, bottom=0.0019382877349853516), BoundingBox(category='surgery_start', left=0.2402159904711174, top=-0.008370070563422309, right=0.2955367209694602, bottom=0.0015943449868096246), BoundingBox(category='hour_24hr', 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Image: \n", + "Homography matrix: [[ 9.83575577e-01 -9.20876349e-03 -4.03199773e+02]\n", + " [-1.86956570e-02 7.39524829e-01 -1.41407587e+02]\n", + " [-9.94587348e-06 -4.00269974e-05 1.00000000e+00]]\n", + "Remapped locations: [BoundingBox(category='ml', left=1.0975272993607954, top=0.8344891493055555, right=1.1105656664299242, bottom=0.8453080512152777), BoundingBox(category='lateral', left=0.9821175870028409, top=0.9936442057291667, right=1.0179267282196969, bottom=1.0063715277777778), BoundingBox(category='fowler', left=0.9788873106060606, top=0.9683224826388889, right=1.0145008433948863, bottom=0.9804342447916666), BoundingBox(category='reverse_trendelenburg', left=0.9761582623106061, top=0.9428984375, right=1.0162690133759469, bottom=0.9549292534722222), BoundingBox(category='trendeleburg', left=1.0170803000710227, top=0.9426698133680556, right=1.0937247721354166, bottom=0.95826171875), BoundingBox(category='trendeleburg', left=0.9724749940814394, top=0.9158442925347222, 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Image: \n", + "Homography matrix: [[ 9.57726370e-01 -8.43922996e-03 -3.23262281e+02]\n", + " [-1.49053707e-02 7.35950271e-01 -6.02761454e+01]\n", + " [-1.47324621e-05 -3.20157811e-05 1.00000000e+00]]\n", + "Remapped locations: [BoundingBox(category='0', left=0.10218864672111742, top=0.64888671875, right=0.10797068277994791, bottom=0.65937890625), BoundingBox(category='3', left=0.09596095229640152, top=0.6657893337673612, right=0.1015533077355587, bottom=0.6764264322916667), BoundingBox(category='0', left=0.10228736646247633, top=0.66574072265625, right=0.10778247255267519, bottom=0.6763467881944445), BoundingBox(category='fentanyl', left=0.023342458551580257, top=0.08010750325520834, right=0.06597223455255682, bottom=0.09219219292534722), BoundingBox(category='rocuronium', left=0.02356424042672822, top=0.059096442328559026, right=0.08423786510120738, bottom=0.06790466986762153), BoundingBox(category='propofol', left=0.023859067974668562, top=0.03745025973849826, 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fp\u001b[38;5;241m.\u001b[39mflush()\n", + "\u001b[1;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[31], line 51\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemapped locations: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mremapped_locations\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 50\u001b[0m \u001b[38;5;66;03m# View the image\u001b[39;00m\n\u001b[1;32m---> 51\u001b[0m \u001b[43mpil_img\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2660\u001b[0m, in 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This method is mainly intended for debugging purposes.\u001b[39;00m\n\u001b[0;32m 2643\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[38;5;124;03m :param title: Optional title to use for the image window, where possible.\u001b[39;00m\n\u001b[0;32m 2658\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2660\u001b[0m \u001b[43m_show\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:3775\u001b[0m, in \u001b[0;36m_show\u001b[1;34m(image, **options)\u001b[0m\n\u001b[0;32m 3772\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_show\u001b[39m(image: Image, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 3773\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageShow\n\u001b[1;32m-> 3775\u001b[0m \u001b[43mImageShow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:61\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(image, title, **options)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124;03mDisplay a given image.\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;124;03m:returns: ``True`` if a suitable viewer was found, ``False`` otherwise.\u001b[39;00m\n\u001b[0;32m 59\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m viewer \u001b[38;5;129;01min\u001b[39;00m _viewers:\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mviewer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:85\u001b[0m, in \u001b[0;36mViewer.show\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m!=\u001b[39m base:\n\u001b[0;32m 83\u001b[0m image \u001b[38;5;241m=\u001b[39m image\u001b[38;5;241m.\u001b[39mconvert(base)\n\u001b[1;32m---> 85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:112\u001b[0m, in \u001b[0;36mViewer.show_image\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[0;32m 111\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Display the given image.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_file(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:108\u001b[0m, in \u001b[0;36mViewer.save_image\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Save to temporary file and return filename.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dump\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:678\u001b[0m, in \u001b[0;36mImage._dump\u001b[1;34m(self, file, format, **options)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mim\u001b[38;5;241m.\u001b[39msave_ppm(filename)\n\u001b[0;32m 677\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filename\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2605\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n\u001b[0;32m 2604\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2605\u001b[0m \u001b[43msave_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2606\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 2607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m open_fp:\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1297\u001b[0m, in \u001b[0;36m_save_all\u001b[1;34m(im, fp, filename)\u001b[0m\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_save_all\u001b[39m(im: Image\u001b[38;5;241m.\u001b[39mImage, fp: IO[\u001b[38;5;28mbytes\u001b[39m], filename: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1297\u001b[0m \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1488\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[0;32m 1484\u001b[0m single_im \u001b[38;5;241m=\u001b[39m _write_multiple_frames(\n\u001b[0;32m 1485\u001b[0m im, fp, chunk, mode, rawmode, default_image, append_images\n\u001b[0;32m 1486\u001b[0m )\n\u001b[0;32m 1487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m single_im:\n\u001b[1;32m-> 1488\u001b[0m \u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1489\u001b[0m \u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1490\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIO\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_idat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1491\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Tile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1492\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info:\n\u001b[0;32m 1495\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m info_chunk \u001b[38;5;129;01min\u001b[39;00m info\u001b[38;5;241m.\u001b[39mchunks:\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:558\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 556\u001b[0m _encode_tile(im, fp, tile, bufsize, fh)\n\u001b[0;32m 557\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, io\u001b[38;5;241m.\u001b[39mUnsupportedOperation) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m--> 558\u001b[0m \u001b[43m_encode_tile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflush\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 560\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:584\u001b[0m, in \u001b[0;36m_encode_tile\u001b[1;34m(im, fp, tile, bufsize, fh, exc)\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc:\n\u001b[0;32m 582\u001b[0m \u001b[38;5;66;03m# compress to Python file-compatible object\u001b[39;00m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 584\u001b[0m errcode, data \u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m 585\u001b[0m fp\u001b[38;5;241m.\u001b[39mwrite(data)\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errcode:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "remap_point = lambda p, h: cv2.perspectiveTransform(np.array(p, dtype=np.float32).reshape(-1, 1, 2), h).tolist()[0][0]\n", "\n", "\n", "def remap_bbox(\n", " bbox: BoundingBox, \n", - " h, \n", + " homography_matrix: List[List[float]], \n", " original_width:int=4032, \n", " original_height:int=3024,\n", " new_width:int=3300,\n", @@ -216,16 +294,43 @@ " Given a bounding box, homography matrix, and the image sizes of the original \n", " and destination (new) image, this function returns a remapped bounding box.\n", " \"\"\"\n", - " new_left, new_top = remap_point((bbox.left*original_width, bbox.top*original_height), h)\n", - " new_right, new_bottom = remap_point((bbox.right*original_width, bbox.bottom*original_height), h)\n", + " new_left, new_top = remap_point((bbox.left*original_width, bbox.top*original_height), homography_matrix)\n", + " new_right, new_bottom = remap_point((bbox.right*original_width, bbox.bottom*original_height), homography_matrix)\n", " return BoundingBox(bbox.category, new_left/new_width, new_top/new_height, new_right/new_width, new_bottom/new_height)\n", "\n", "\n", "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]\n", "\n", + "print(landmark_location_data.keys())\n", + "for sheet in landmark_location_data:\n", + " print(f\"Sheet: {sheet}\")\n", + "\n", + " locations = landmark_location_data[sheet]\n", + " print(f\"Locations for {sheet}: {locations}\")\n", + "\n", + " try:\n", + " image = Image.open(data_path/f\"chart_images/{sheet}\")\n", + " print(f\"Able to obtain image. Image: {image}\")\n", + " except:\n", + " print(f\"Unable to obtain image for sheet {sheet}. Likely in main directory and png format.\")\n", + " continue\n", + " \n", + " h, pil_img = homography_transform(\n", + " src_image=image,\n", + " src_points=get_corresponding_points(locations, (4032, 3024)),\n", + " dest_points=[(0, 0), (3300, 2250), (0, 2250), (3300, 0)]\n", + " )\n", + "\n", + " print(f\"Homography matrix: {h}\")\n", + "\n", + " remapped_locations = remap_all_bboxes(locations, h)\n", + "\n", + " print(f\"Remapped locations: {remapped_locations}\")\n", + "\n", + " # View the image\n", + " pil_img.show()\n", "\n", - "locations = landmark_location_data[sheet]\n", - "remapped_locations = remap_all_bboxes(locations, h)" + " break" ] }, { @@ -297,7 +402,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -311,7 +416,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/utilities/conversion/utils/annotations.py b/utilities/conversion/utils/annotations.py new file mode 100644 index 0000000..8cb29a7 --- /dev/null +++ b/utilities/conversion/utils/annotations.py @@ -0,0 +1,445 @@ +"""This module defines classes for representing bounding boxes and keypoints associated with objects in images. + +It also provides helper functions for constructing these objects from YOLO formatted labels. +""" + +from dataclasses import dataclass +from typing import Dict, List, Tuple +import warnings + + +class Point: + """The `Point` class is a struct which contains an x and y value for a point. + + Attributes : + `x` (float): + The x coordinate for the point. + `y` (float): + The y coordinate for the point. + """ + + def __init__(self, x:float, y:float): + """inits this point.""" + self.x = x + self.y = y + + def __eq__(self, other): + """Determines if two points are the same.""" + return self.x == other.x and self.y == other.y + + +@dataclass +class BoundingBox: + """The `BoundingBox` class represents a bounding box around an object in an image. + + + Attributes : + `category` (str): + The category of the object within the bounding box. + `left` (float): + The x-coordinate of the top-left corner of the bounding box. + `top` (float): + The y-coordinate of the top-left corner of the bounding box. + `right` (float): + The x-coordinate of the bottom-right corner of the bounding box. + `bottom` (float): + The y-coordinate of the bottom-right corner of the bounding box. + + + Constructors : + `from_yolo(yolo_line: str, image_width: int, image_height: int, int_to_category: Dict[int, str])`: + Constructs a `BoundingBox` from a line in a YOLO formatted labels file. It requires the original image dimensions and a dictionary mapping category IDs to category names. + + `from_coco(coco_annotation: Dict, categories: List[Dict])`: + Constructs a `BoundingBox` from an annotation in a COCO data JSON file. It requires the annotation dictionary and a list of category dictionaries. + + + Properties : + `center` (Tuple[int]): + A tuple containing the (x, y) coordinates of the bounding box's center. + `box` (List[int]): + A list containing the bounding box coordinates as [left, top, right, bottom]. + + + Methods : + `to_yolo(image_width: int, image_height: int, category_to_int: Dict[str, int]) -> str`: + Writes a yolo formatted string using this bounding box's data. + `validate_box_values(cls, left: float, top: float, right: float, bottom: float) -> None`: + Validates the box parameters and throws a value error if left > right or top > bottom. + Also issues a warning for the case when left == right or top == bottom letting the user + know that they are constructing a degenerate rectangle. + """ + + category: str + left: float + top: float + right: float + bottom: float + + def __init__( + self, category: str, left: float, top: float, right: float, bottom: float + ): + """Overrides the default constructor from dataclass to validate the parameters before constructing.""" + BoundingBox.validate_box_values(left, top, right, bottom) + self.category = category + self.left = left + self.top = top + self.right = right + self.bottom = bottom + + @staticmethod + def from_yolo( + yolo_line: str, + image_width: int, + image_height: int, + id_to_category: Dict[int, str], + ): + """Constructs a `BoundingBox` from a line in a yolo formatted labels file. + + Because the yolo format stores data in normalized xywh format (from 0 to 1), this method + requires the original image's width and height. + + Args : + `yolo_line` (str): + A string in the yolo label format (c x y w h). + `image_width` (int): + The original image's width. + `image_height` (int): + The original image's height. + `id_to_category` (Dict): + A dictionary that maps the number id in the label to the category. + + Returns: + A `BoundingBox` object containing the yolo_line's data. + """ + data = yolo_line.split() + x, y, w, h = float(data[1]), float(data[2]), float(data[3]), float(data[4]) + x, y, w, h = ( + x * image_width, + y * image_height, + w * image_width, + h * image_height, + ) + left, top, right, bottom = ( + x - (1 / 2) * w, + y - (1 / 2) * h, + x + (1 / 2) * w, + y + (1 / 2) * h, + ) + category = id_to_category.get(int(data[0])) + if category == None: + raise ValueError( + f"Category {int(data[0])} not found in the id_to_category dictionary." + ) + return BoundingBox(category, left, top, right, bottom) + + @staticmethod + def from_coco(coco_annotation: Dict, categories: List[Dict]): + """Constructs a `BoundingBox` from an annotation in a coco data json file. + + Args : + `coco_annotation` (Dict): A bounding box annotation from the 'annotations' section. + `categories` (List[Dict]): A list of dictionaries containing their numeric ids and categories. + + Returns: + A `BoundingBox` object containing the coco annotation's data. + """ + left, top, w, h = coco_annotation["bbox"] + right, bottom = left + w, top + h + try: + category = list( + filter(lambda c: c["id"] == coco_annotation["category_id"], categories) + )[0].get("name") + except IndexError: + raise ValueError( + f"Category {int(coco_annotation['category_id'])} not found in the categories list." + ) + return BoundingBox(category, left, top, right, bottom) + + @classmethod + def validate_box_values( + cls, left: float, top: float, right: float, bottom: float + ) -> None: + """Validates the coordinates of a rectangle (bounding box). + + This classmethod ensures that the left coordinate is less than the right coordinate, and + the top coordinate is less than the bottom coordinate. It raises a `ValueError` if these + conditions are not met, indicating an invalid box configuration. If the left coordinate + is equal to the right coordinate or if the top coordinate is equal to the bottom + coordinate, this method issues a warning. + + Args: + `left` (float): + The left x-coordinate of the box. + `top` (float): + The top y-coordinate of the box. + `right` (float): + The right x-coordinate of the box. + `bottom` (float): + The bottom y-coordinate of the box. + + Raises: + ValueError: If `left > right` or `top > bottom`. + """ + if left > right: + raise ValueError( + f"Box's left side greater than its right side (Left:{left} > Right:{right})." + ) + if top > bottom: + raise ValueError( + f"Box's top side greater than its bottom side (Top:{top} > Bottom:{bottom})." + ) + if left == right and bottom == top: + warnings.warn( + f"Degenerate rectangle detected. All of the box's parameters are equal (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + elif left == right: + warnings.warn( + f"Degenerate rectangle detected. The box's left side equals its right side (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + elif top == bottom: + warnings.warn( + f"Degenerate rectangle detected. The box's top side equals its bottom side (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + + @property + def center(self) -> Tuple[float]: + """This `BoundingBox`'s center.""" + return ( + self.left + (1 / 2) * (self.right - self.left), + self.top + (1 / 2) * (self.bottom - self.top), + ) + + @property + def box(self) -> List[int]: + """A list containing this `BoundingBox`'s [left, top, right, bottom].""" + return [self.left, self.top, self.right, self.bottom] + + def set_box(self, new_left: int, new_top: int, new_right: int, new_bottom: int): + """Sets this BoundingBox's values for left, top, right, bottom. + + Args : + new_left (int): + The new left side for the box. + new_top (int): + The new top side for the box. + new_right (int): + The new right side for the box. + new_bottom (int): + The new bottom side for the box. + """ + self.validate_box_values(new_left, new_top, new_right, new_bottom) + return BoundingBox( + category=self.category, + left=new_left, + top=new_top, + right=new_right, + bottom=new_bottom, + ) + + def to_yolo( + self, image_width: int, image_height: int, category_to_id: Dict[str, int] + ) -> str: + """Writes the data from this `BoundingBox` into a yolo formatted string. + + Args : + `image_width` (int): + The image's width that this boundingbox belongs to. + `image_height` (int): + The image's height that this boundingbox belongs to. + `category_to_id` (Dict[str, int]): + A dictionary that maps the category string to an id (integer). + + Returns: + A string that encodes this `BoundingBox`'s data for a single line in a yolo label file. + """ + c = category_to_id[self.category] + x, y = self.center + x /= image_width + y /= image_height + w = (self.right - self.left) / image_width + h = (self.bottom - self.top) / image_height + return f"{c} {x} {y} {w} {h}" + + +@dataclass +class Keypoint: + """The `Keypoint` class represents a keypoint associated with an object in an image. + + Attributes : + `keypoint` (Tuple[float]): + A tuple containing the (x, y) coordinates of the keypoint relative to the top-left corner of the image. + `bounding_box` (BoundingBox): + A `BoundingBox` object that defines the bounding box around the object containing the keypoint. + + + Constructors : + `from_yolo(yolo_line: str, image_width: int, image_height: int, id_to_category: Dict[int, str])`: + Constructs a Keypoint from a line in a YOLO formatted labels file. It requires the original image dimensions and a dictionary mapping category IDs to category names. + **Note:** This method ignores the "visibility" information (denoted by 'v') in the YOLO format. + + + Properties : + `category` (str): + The category of the object the keypoint belongs to (inherited from the `bounding_box`). + `center` (Tuple[float]): + The (x, y) coordinates of the bounding box's center (inherited from the `bounding_box`). + `box` (Tuple[float]): + A list containing the bounding box coordinates as [left, top, right, bottom] (inherited from the `bounding_box`). + + + Methods : + `to_yolo(self, image_width: int, image_height: int, category_to_id: Dict[str, int]) -> str`: + Generates a YOLO formatted string representation of this `Keypoint` object. It requires the image dimensions and a dictionary mapping category strings to integer labels. + `validate_keypoint(cls, bounding_box: BoundingBox, keypoint: Point) -> None`: + Validates that a keypoint lies within the specified bounding box. Raises a ValueError if the keypoint is outside the bounding box. + """ + + keypoint: Point + bounding_box: BoundingBox + + def __init__(self, keypoint: Point, bounding_box: BoundingBox): + """Overrides the default constructor from dataclass to validate the parameters before constructing.""" + Keypoint.validate_keypoint(bounding_box, keypoint) + self.keypoint = keypoint + self.bounding_box = bounding_box + + @staticmethod + def from_yolo( + yolo_line: str, + image_width: int, + image_height: int, + id_to_category: Dict[int, str], + ): + """Constructs a `Keypoint` from a line in a yolo formatted labels file. + + Because the yolo format stores data in normalized xywh format (from 0 to 1), this method + requires the original image's width and height. The 'visible' data is optional, and is not + read to create the object. + + Args : + `yolo_line` (str): + A string in the yolo label format (c x y w h kpx kpy v). + `image_width` (int): + The original image's width. + `image_height` (int): + The original image's height. + `id_to_category` (Dict): + A dictionary that maps the id number in the label to the category. + + Returns: + A `BoundingBox` object containing the yolo_line's data. + """ + bounding_box = BoundingBox.from_yolo( + yolo_line, image_width, image_height, id_to_category + ) + keypoint_x = float(yolo_line.split()[5]) + keypoint_y = float(yolo_line.split()[6]) + keypoint = Point(keypoint_x * image_width, keypoint_y * image_height) + return Keypoint(keypoint, bounding_box) + + @classmethod + def validate_keypoint(cls, bounding_box: BoundingBox, keypoint: Point) -> None: + """Validates that a keypoint lies within the specified bounding box. + + This classmethod ensures that the `keypoint` (represented by a `Point` object) + falls within the confines of the provided `bounding_box` (represented by a + `BoundingBox` object). It checks both the x and y coordinates of the keypoint + against the left, top, right, and bottom boundaries of the bounding box. + + Args: + bounding_box: + The `BoundingBox` object representing the enclosing region. + keypoint: + The `Point` object representing the keypoint to be validated. + + Raises: + ValueError: If the keypoint's coordinates are not within the bounding box. + """ + in_bounds_x: bool = bounding_box.left <= keypoint.x <= bounding_box.right + in_bounds_y: bool = bounding_box.top <= keypoint.y <= bounding_box.bottom + in_bounds: bool = in_bounds_x and in_bounds_y + if not in_bounds: + raise ValueError( + f"Keypoint is not in the bounding box intended to enclose it (Keypoint:{(keypoint.x, keypoint.y)}, BoundingBox:{str(bounding_box)})" + ) + + @property + def category(self) -> str: + """This `Keypoint`'s category.""" + return self.bounding_box.category + + @property + def center(self) -> Tuple[float]: + """This `Keypoint`'s boundingbox center.""" + return self.bounding_box.center + + @property + def box(self) -> Tuple[float]: + """This keypoints boundingbox's [left, top, right, bottom].""" + return self.bounding_box.box + + def set_box( + self, new_left: int, new_top: int, new_right: int, new_bottom: int + ) -> BoundingBox: + """Sets this Keypoints's BoundingBox's values for left, top, right, bottom. + + Args: + new_left (int): + The new left side for the box. + new_top (int): + The new top side for the box. + new_right (int): + The new right side for the box. + new_bottom (int): + The new bottom side for the box. + + Returns: A new Keypoint with a new bounding box. + """ + return Keypoint( + point=self.point, + bounding_box=self.bounding_box.set_box( + new_left, new_top, new_right, new_bottom + ), + ) + + def set_keypoint(self, new_x: int, new_y: int) -> "Keypoint": + """Sets this Keypoint's Keypoint to a new point. + + Args: + new_x (int): + The new x value for the Keypoint. + new_y (int): + The new y value for the Keypoint. + + Returns: A new Keypoint with a new Point as its keypoint. + """ + self.validate_keypoint(self.bounding_box, Point(new_x, new_y)) + return Keypoint(Point(new_x, new_y), self.bounding_box) + + def to_yolo( + self, image_width: int, image_height: int, category_to_id: Dict[str, int] + ) -> str: + """Writes the data from this `Keypoint` into a yolo formatted string. + + Args : + `image_width` (int): + The image's width that this `Keypoint` belongs to. + `image_height` (int): + The image's height that this `Keypoint` belongs to. + `category_to_id` (Dict[str, int]): + A dictionary that maps the category string to an id (int). + + Returns: + A string that encodes this `Keypoint`'s data for a single line in a yolo label file. + """ + yolo_line = self.bounding_box.to_yolo(image_width, image_height, category_to_id) + keypoint_x, keypoint_y = ( + self.keypoint.x / image_width, + self.keypoint.y / image_height, + ) + yolo_line += f" {keypoint_x} {keypoint_y}" + return yolo_line diff --git a/utilities/conversion/utils/image_conversion.py b/utilities/conversion/utils/image_conversion.py new file mode 100644 index 0000000..23aaba9 --- /dev/null +++ b/utilities/conversion/utils/image_conversion.py @@ -0,0 +1,48 @@ +"""Converts between PIL and OpenCV image formats. + +This module provides functions to convert between Python Imaging Library (PIL) +image format and OpenCV image format. +""" + +import cv2 +from PIL import Image +import numpy as np + + +def pil_to_cv2(pil_image: Image.Image) -> np.ndarray: + """Converts a PIL image to OpenCV image format. + + Args: + pil_image (Image.Image): + A PIL image object. + + Returns: + A NumPy array representing the image in OpenCV format (BGR channel order). + + Raises: + ValueError: + If the input image mode is not compatible with RGB or BGR. + """ + cv2_image = np.array(pil_image) + if pil_image.mode not in ("RGB", "BGR"): + raise ValueError( + f"Unsupported image mode: {pil_image.mode}. Only RGB and BGR modes are supported." + ) + if pil_image.mode == "RGB": + cv2_image = cv2_image[:, :, ::-1].copy() + return cv2_image + + +def cv2_to_pil(cv2_image: np.ndarray) -> Image.Image: + """Converts an OpenCV image to PIL image format. + + Args: + cv2_image (np.ndarray): + A NumPy array representing an image in OpenCV format (BGR channel order). + + Returns: + A PIL image object. + """ + img = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB) + pil_image = Image.fromarray(img) + return pil_image From 46cce3f6222d30f73adb1f3bec09af8fc8b93dbc Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 02:49:03 -0400 Subject: [PATCH 05/55] Homography working, need to tweak bounding boxes remapping as this is not quite accurate. --- .../apply_homography_to_labels.ipynb | 209 ++++++++---------- 1 file changed, 96 insertions(+), 113 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index df25268..4a75344 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -78,18 +78,27 @@ "id": "3dd0d783-7093-4e21-9907-fa112f6deb57", "metadata": {}, "source": [ - "## 1 - Load Data" + "## 1 - Load Data\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 40, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], "source": [ "def label_studio_to_bboxes(path_to_json_data: Path) -> List[BoundingBox]:\n", - " \"\"\"Loads data from LabelStudio's json format into BoundingBoxes.\"\"\"\n", + " \"\"\"\n", + " Loads data from LabelStudio's json format into BoundingBoxes.\n", + "\n", + " Args:\n", + " path_to_json_data (Path) \n", + " A path to the json file containing the data.\n", + " Returns:\n", + " A list of BoundingBoxes.\n", + " \"\"\"\n", " json_data: List[Dict] = json.loads(open(str(path_to_json_data)).read())\n", " return {\n", " sheet_data['data']['image'].split(\"-\")[-1]:[\n", @@ -121,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -162,6 +171,9 @@ " src_points: np.ndarray = np.array(src_points)\n", " dest_points: np.ndarray = np.array(dest_points)\n", "\n", + " print(f\"Source Points: {src_points}\")\n", + " print(f\"Destination Points: {dest_points}\")\n", + "\n", " if len(src_points) != len(dest_points):\n", " raise ValueError(\n", " \"Source and destination points must have the same number of elements.\"\n", @@ -170,14 +182,18 @@ " raise ValueError(\"Must have 4 or more points to compute the homography.\")\n", "\n", " src_image = pil_to_cv2(src_image)\n", - " h, _ = cv2.findHomography(src_points, dest_points)\n", + " h, status = cv2.findHomography(src_points, dest_points)\n", + " \n", + " print(f\"Homography Matrix: {h}\")\n", + " print(f\"Status: {status}\")\n", + "\n", " dest_image = cv2.warpPerspective(src_image, h, original_image_size)\n", " return h, cv2_to_pil(dest_image)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 49, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -200,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 61, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, "outputs": [ @@ -208,72 +224,35 @@ "name": "stdout", "output_type": "stream", "text": [ + "Locations unified front: [BoundingBox(category='anesthesia_start', left=0.01272509471128841, top=0.0074461197571953, right=0.07575473602069113, bottom=0.016866305933318605), BoundingBox(category='units', left=0.9460597274343558, top=0.029801815290808963, right=0.9706124622802825, bottom=0.04156358205163003), BoundingBox(category='safety_checklist', left=0.030913069333097596, top=0.980779480372136, right=0.09272854168786186, bottom=0.9925825938048181), BoundingBox(category='lateral', left=0.8478774710612861, top=0.9807845444628994, right=0.8745474031974078, bottom=0.9901969156805276)]\n", + "Unified width: 3300, Unified height: 2550\n", "dict_keys(['unified_intraoperative_preoperative_flowsheet_v1_1_front.png', 'RC_0001_intraoperative.JPG', 'RC_0002_intraoperative.JPG', 'RC_0003_intraoperative.JPG', 'RC_0004_intraoperative.JPG', 'RC_0005_intraoperative.JPG', 'RC_0006_intraoperative.JPG', 'RC_0007_intraoperative.JPG', 'RC_0008_intraoperative.JPG', 'RC_0009_intraoperative.JPG', 'RC_0010_intraoperative.JPG', 'RC_0011_intraoperative.JPG', 'RC_0012_intraoperative.JPG', 'RC_0013_intraoperative.JPG', 'RC_0014_intraoperative.JPG', 'RC_0015_intraoperative.JPG', 'RC_0016_intraoperative.JPG', 'RC_0017_intraoperative.JPG', 'RC_0018_intraoperative.JPG', 'RC_0019_intraoperative.JPG'])\n", "Sheet: unified_intraoperative_preoperative_flowsheet_v1_1_front.png\n", "Locations for unified_intraoperative_preoperative_flowsheet_v1_1_front.png: [BoundingBox(category='anesthesia_start', left=0.01272509471128841, top=0.0074461197571953, right=0.07575473602069113, bottom=0.016866305933318605), BoundingBox(category='units', left=0.9460597274343558, top=0.029801815290808963, right=0.9706124622802825, bottom=0.04156358205163003), BoundingBox(category='safety_checklist', left=0.030913069333097596, top=0.980779480372136, right=0.09272854168786186, bottom=0.9925825938048181), BoundingBox(category='lateral', left=0.8478774710612861, top=0.9807845444628994, right=0.8745474031974078, bottom=0.9901969156805276)]\n", "Unable to obtain image for sheet unified_intraoperative_preoperative_flowsheet_v1_1_front.png. Likely in main directory and png format.\n", "Sheet: RC_0001_intraoperative.JPG\n", "Locations for RC_0001_intraoperative.JPG: [BoundingBox(category='5', left=0.8502850000000001, top=0.3911735, right=0.8547370000000001, bottom=0.4002345), BoundingBox(category='mg', left=0.8837505, top=0.09925600000000001, right=0.8948615, bottom=0.10798600000000001), BoundingBox(category='mg', left=0.8841315000000001, top=0.1206155, right=0.8951185, bottom=0.1292465), BoundingBox(category='micro_g', left=0.885347, top=0.14174599999999998, right=0.894449, bottom=0.15103799999999998), BoundingBox(category='pcnt', left=0.897607, top=0.6939615, right=0.904663, bottom=0.7023945), BoundingBox(category='mmHg', left=0.889846, top=0.7150265, right=0.91368, bottom=0.7256415), BoundingBox(category='pcnt', left=0.898723, top=0.7358294999999999, right=0.905841, bottom=0.7440604999999999), BoundingBox(category='degree_C', left=0.8989705000000001, top=0.756528, right=0.9064355000000002, bottom=0.765324), 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Image: \n", - "Homography matrix: [[ 9.36855166e-01 -2.36434063e-03 -3.30552699e+02]\n", - " [-1.97486704e-02 7.08216501e-01 -4.84753628e+01]\n", - " [-9.74865835e-06 -3.47074295e-05 1.00000000e+00]]\n", - "Remapped locations: [BoundingBox(category='mg', left=0.9986187559185606, top=0.0421971435546875, right=1.013432099313447, bottom=0.05060342068142361), BoundingBox(category='5', left=0.9441225733901515, top=-0.003222407235039605, right=0.9504044596354166, bottom=0.006365236070421007), BoundingBox(category='2', left=0.9379266542376894, top=-0.0030366956922743054, right=0.9443192915482954, bottom=0.00621986346774631), BoundingBox(category='0', left=0.9236537494081439, top=-0.0025744647979736328, right=0.9301299124053031, bottom=0.006803653717041016), BoundingBox(category='2', left=0.9174038973721591, top=-0.002383659150865343, right=0.9240199603456439, bottom=0.006952469295925565), BoundingBox(category='5', left=0.9028962476325758, top=-0.0018002317216661242, right=0.9093166281960228, 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Image: \n", - "Homography matrix: [[ 9.75470550e-01 -1.79863071e-02 -3.90062022e+02]\n", - " [-7.57438267e-03 7.25311701e-01 -8.95365840e+01]\n", - " [-5.04203303e-06 -4.05838556e-05 1.00000000e+00]]\n", - "Remapped locations: [BoundingBox(category='temperature', left=0.047746401700106536, top=0.7597625868055555, right=0.1279843602035985, bottom=0.7762079535590278), BoundingBox(category='anesthesia_start', left=-0.03403087269176136, top=-0.004548900604248047, right=0.034112981160481774, bottom=0.0045598759121365014), BoundingBox(category='hour_24hr', left=0.04398478652491714, top=-0.005701022254096137, right=0.08739609227035985, bottom=0.00485937245686849), BoundingBox(category='minute', left=0.13297152432528409, top=-0.006321883307562934, right=0.1619772431344697, bottom=0.0019382877349853516), BoundingBox(category='surgery_start', left=0.2402159904711174, top=-0.008370070563422309, right=0.2955367209694602, bottom=0.0015943449868096246), BoundingBox(category='hour_24hr', 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fp\u001b[38;5;241m.\u001b[39mflush()\n", - "\u001b[1;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[31], line 51\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRemapped locations: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mremapped_locations\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 50\u001b[0m \u001b[38;5;66;03m# View the image\u001b[39;00m\n\u001b[1;32m---> 51\u001b[0m \u001b[43mpil_img\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2660\u001b[0m, in \u001b[0;36mImage.show\u001b[1;34m(self, title)\u001b[0m\n\u001b[0;32m 2640\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow\u001b[39m(\u001b[38;5;28mself\u001b[39m, title: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2641\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2642\u001b[0m \u001b[38;5;124;03m Displays this image. This method is mainly intended for debugging purposes.\u001b[39;00m\n\u001b[0;32m 2643\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[38;5;124;03m :param title: Optional title to use for the image window, where possible.\u001b[39;00m\n\u001b[0;32m 2658\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2660\u001b[0m \u001b[43m_show\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:3775\u001b[0m, in \u001b[0;36m_show\u001b[1;34m(image, **options)\u001b[0m\n\u001b[0;32m 3772\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_show\u001b[39m(image: Image, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 3773\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageShow\n\u001b[1;32m-> 3775\u001b[0m \u001b[43mImageShow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:61\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(image, title, **options)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124;03mDisplay a given image.\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;124;03m:returns: ``True`` if a suitable viewer was found, ``False`` otherwise.\u001b[39;00m\n\u001b[0;32m 59\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m viewer \u001b[38;5;129;01min\u001b[39;00m _viewers:\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mviewer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:85\u001b[0m, in \u001b[0;36mViewer.show\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m!=\u001b[39m base:\n\u001b[0;32m 83\u001b[0m image \u001b[38;5;241m=\u001b[39m image\u001b[38;5;241m.\u001b[39mconvert(base)\n\u001b[1;32m---> 85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:112\u001b[0m, in \u001b[0;36mViewer.show_image\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[0;32m 111\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Display the given image.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_file(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:108\u001b[0m, in \u001b[0;36mViewer.save_image\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Save to temporary file and return filename.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dump\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:678\u001b[0m, in \u001b[0;36mImage._dump\u001b[1;34m(self, file, format, **options)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mim\u001b[38;5;241m.\u001b[39msave_ppm(filename)\n\u001b[0;32m 677\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filename\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2605\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n\u001b[0;32m 2604\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2605\u001b[0m \u001b[43msave_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2606\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 2607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m open_fp:\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1297\u001b[0m, in \u001b[0;36m_save_all\u001b[1;34m(im, fp, filename)\u001b[0m\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_save_all\u001b[39m(im: Image\u001b[38;5;241m.\u001b[39mImage, fp: IO[\u001b[38;5;28mbytes\u001b[39m], filename: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1297\u001b[0m \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1488\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[0;32m 1484\u001b[0m single_im \u001b[38;5;241m=\u001b[39m _write_multiple_frames(\n\u001b[0;32m 1485\u001b[0m im, fp, chunk, mode, rawmode, default_image, append_images\n\u001b[0;32m 1486\u001b[0m )\n\u001b[0;32m 1487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m single_im:\n\u001b[1;32m-> 1488\u001b[0m \u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1489\u001b[0m \u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1490\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIO\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_idat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1491\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Tile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1492\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info:\n\u001b[0;32m 1495\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m info_chunk \u001b[38;5;129;01min\u001b[39;00m info\u001b[38;5;241m.\u001b[39mchunks:\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:558\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 556\u001b[0m _encode_tile(im, fp, tile, bufsize, fh)\n\u001b[0;32m 557\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, io\u001b[38;5;241m.\u001b[39mUnsupportedOperation) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m--> 558\u001b[0m \u001b[43m_encode_tile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflush\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 560\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:584\u001b[0m, in \u001b[0;36m_encode_tile\u001b[1;34m(im, fp, tile, bufsize, fh, exc)\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc:\n\u001b[0;32m 582\u001b[0m \u001b[38;5;66;03m# compress to Python file-compatible object\u001b[39;00m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 584\u001b[0m errcode, data \u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m 585\u001b[0m fp\u001b[38;5;241m.\u001b[39mwrite(data)\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errcode:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Able to obtain image. Image: \n", + "Intraoperative sheet\n", + "Source Points: [[ 337.623552 262.71 ]\n", + " [3310.610688 2874.75048 ]\n", + " [ 456.450624 2940.455952]\n", + " [3583.149696 239.845536]]\n", + "Destination Points: [[ 145.99172071 30.99834276]\n", + " [2842.00104253 2513.00136168]\n", + " [ 204.00865818 2516.03664458]\n", + " [3162.50911303 90.99088161]]\n", + "Homography Matrix: [[ 9.36717731e-01 -1.93593018e-02 -1.64989688e+02]\n", + " [ 2.52704454e-02 9.34459112e-01 -2.22984810e+02]\n", + " [ 1.97804479e-06 2.42995900e-06 1.00000000e+00]]\n", + "Status: [[1]\n", + " [1]\n", + " [1]\n", + " [1]]\n", + "Homography matrix: [[ 9.36717731e-01 -1.93593018e-02 -1.64989688e+02]\n", + " [ 2.52704454e-02 9.34459112e-01 -2.22984810e+02]\n", + " [ 1.97804479e-06 2.42995900e-06 1.00000000e+00]]\n", + "Remapped locations: [BoundingBox(category='5', left=0.9074515417850378, top=0.42656157769097225, right=0.9122466856060606, bottom=0.43798795572916666), BoundingBox(category='mg', left=0.952287079782197, 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new_right/new_width, new_bottom/new_height)\n", "\n", - "\n", + "# Remap all bounding boxes\n", "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]\n", "\n", + "# Get location data for unified chart frontside\n", + "locations_unified_front = landmark_location_data['unified_intraoperative_preoperative_flowsheet_v1_1_front.png']\n", + "print(f\"Locations unified front: {locations_unified_front}\")\n", + "image_unified_front = Image.open(data_path/\"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\")\n", + "image_unified_front.show()\n", + "unified_width, unified_height = image_unified_front.size\n", + "print(f\"Unified width: {unified_width}, Unified height: {unified_height}\")\n", + "\n", + "# Get location data for unified chart backside... will have to do something similar to the above when ready for this\n", + "#locations_unified_back = landmark_location_data['unified_intraoperative_preoperative_flowsheet_v1_1_back.png']\n", + "\n", "print(landmark_location_data.keys())\n", "for sheet in landmark_location_data:\n", " print(f\"Sheet: {sheet}\")\n", @@ -310,16 +300,35 @@ "\n", " try:\n", " image = Image.open(data_path/f\"chart_images/{sheet}\")\n", + "\n", + " # View image\n", + " image.show()\n", + "\n", + " # Get image dimensions\n", + " width, height = image.size\n", + "\n", " print(f\"Able to obtain image. Image: {image}\")\n", " except:\n", " print(f\"Unable to obtain image for sheet {sheet}. Likely in main directory and png format.\")\n", " continue\n", " \n", - " h, pil_img = homography_transform(\n", - " src_image=image,\n", - " src_points=get_corresponding_points(locations, (4032, 3024)),\n", - " dest_points=[(0, 0), (3300, 2250), (0, 2250), (3300, 0)]\n", - " )\n", + " if \"intraoperative\" in sheet:\n", + " print(\"Intraoperative sheet\")\n", + " h, pil_img = homography_transform(\n", + " src_image=image,\n", + " src_points=get_corresponding_points(locations, (width, height)),\n", + " dest_points=get_corresponding_points(locations_unified_front, (unified_width, unified_height)),\n", + " original_image_size=(unified_width, unified_height),\n", + " )\n", + " # Seems like currently there is no homography in here for the preoperative sheet\n", + " # else:\n", + " # print(\"Preoperative sheet\")\n", + " # h, pil_img = homography_transform(\n", + " # src_image=image,\n", + " # src_points=get_corresponding_points(locations, (width, height)),\n", + " # dest_points=get_corresponding_points(locations_unified_back, (3300, 2250)),\n", + " # original_image_size=(width, height),\n", + " # )\n", "\n", " print(f\"Homography matrix: {h}\")\n", "\n", @@ -330,6 +339,20 @@ " # View the image\n", " pil_img.show()\n", "\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + " draw = ImageDraw.Draw(pil_img)\n", + "\n", + " for bounding_box in remapped_locations:\n", + " box = [\n", + " bounding_box.left*unified_width,\n", + " bounding_box.top*unified_height,\n", + " bounding_box.right*unified_width,\n", + " bounding_box.bottom*unified_height,\n", + " ]\n", + " draw.rectangle(box, outline=generate_color(), width=3)\n", + " pil_img.resize((800, 600))\n", + "\n", + " pil_img.show()\n", " break" ] }, @@ -349,46 +372,6 @@ "Check labels." ] }, - { - "cell_type": "code", - "execution_count": 32, - "id": "6fd84989-134e-441f-a7ae-b86cc8e61ae4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - "\n", - "sheet = \"RC_0001_intraoperative.JPG\"\n", - "img = Image.open(str(data_path/\"chart_images\"/sheet))\n", - "original_width, original_height = img.size\n", - "img = transformed_img.copy()\n", - "width, height = img.size\n", - "draw = ImageDraw.Draw(img)\n", - "\n", - "for bounding_box in remapped_locations:\n", - " box = [\n", - " bounding_box.left*width,\n", - " bounding_box.top*height,\n", - " bounding_box.right*width,\n", - " bounding_box.bottom*height,\n", - " ]\n", - " draw.rectangle(box, outline=generate_color(), width=3)\n", - "img.resize((800, 600))" - ] - }, { "cell_type": "code", "execution_count": null, From 5e307898d551c28b7fce29d7cf1471027f985aa7 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 03:34:59 -0400 Subject: [PATCH 06/55] Fixed a typo that caused for improper bounding box remapping. Ready to convert to YOLO. --- .../apply_homography_to_labels.ipynb | 222 ++++++++++-------- 1 file changed, 119 insertions(+), 103 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 4a75344..54b0f63 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "id": "5f322da5-10f8-49ee-a81a-5edc7bac12cd", "metadata": {}, "outputs": [], @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 22, "id": "95997450-a2a0-4035-b040-3c8fb532836b", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ "import random\n", "from PIL import Image, ImageDraw\n", "from pathlib import Path\n", - "from typing import Dict, List, Tuple\n", + "from typing import Dict, List, Tuple, Optional\n", "from tqdm import tqdm\n", "# Created a folder utils in the conversion folder and moved these files into there so we can call their functions\n", "# There should be a better way to do this perhaps, if this is something we will use across various microservices\n", @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", "metadata": {}, "outputs": [], @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", "metadata": {}, "outputs": [], @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 25, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 26, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -171,9 +171,6 @@ " src_points: np.ndarray = np.array(src_points)\n", " dest_points: np.ndarray = np.array(dest_points)\n", "\n", - " print(f\"Source Points: {src_points}\")\n", - " print(f\"Destination Points: {dest_points}\")\n", - "\n", " if len(src_points) != len(dest_points):\n", " raise ValueError(\n", " \"Source and destination points must have the same number of elements.\"\n", @@ -183,9 +180,6 @@ "\n", " src_image = pil_to_cv2(src_image)\n", " h, status = cv2.findHomography(src_points, dest_points)\n", - " \n", - " print(f\"Homography Matrix: {h}\")\n", - " print(f\"Status: {status}\")\n", "\n", " dest_image = cv2.warpPerspective(src_image, h, original_image_size)\n", " return h, cv2_to_pil(dest_image)" @@ -193,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 27, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -216,46 +210,10 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 44, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Locations unified front: [BoundingBox(category='anesthesia_start', left=0.01272509471128841, top=0.0074461197571953, right=0.07575473602069113, bottom=0.016866305933318605), BoundingBox(category='units', left=0.9460597274343558, top=0.029801815290808963, right=0.9706124622802825, bottom=0.04156358205163003), BoundingBox(category='safety_checklist', left=0.030913069333097596, top=0.980779480372136, right=0.09272854168786186, bottom=0.9925825938048181), BoundingBox(category='lateral', left=0.8478774710612861, top=0.9807845444628994, right=0.8745474031974078, bottom=0.9901969156805276)]\n", - "Unified width: 3300, Unified height: 2550\n", - "dict_keys(['unified_intraoperative_preoperative_flowsheet_v1_1_front.png', 'RC_0001_intraoperative.JPG', 'RC_0002_intraoperative.JPG', 'RC_0003_intraoperative.JPG', 'RC_0004_intraoperative.JPG', 'RC_0005_intraoperative.JPG', 'RC_0006_intraoperative.JPG', 'RC_0007_intraoperative.JPG', 'RC_0008_intraoperative.JPG', 'RC_0009_intraoperative.JPG', 'RC_0010_intraoperative.JPG', 'RC_0011_intraoperative.JPG', 'RC_0012_intraoperative.JPG', 'RC_0013_intraoperative.JPG', 'RC_0014_intraoperative.JPG', 'RC_0015_intraoperative.JPG', 'RC_0016_intraoperative.JPG', 'RC_0017_intraoperative.JPG', 'RC_0018_intraoperative.JPG', 'RC_0019_intraoperative.JPG'])\n", - "Sheet: unified_intraoperative_preoperative_flowsheet_v1_1_front.png\n", - "Locations for unified_intraoperative_preoperative_flowsheet_v1_1_front.png: [BoundingBox(category='anesthesia_start', left=0.01272509471128841, top=0.0074461197571953, right=0.07575473602069113, bottom=0.016866305933318605), BoundingBox(category='units', left=0.9460597274343558, top=0.029801815290808963, right=0.9706124622802825, bottom=0.04156358205163003), BoundingBox(category='safety_checklist', left=0.030913069333097596, top=0.980779480372136, right=0.09272854168786186, bottom=0.9925825938048181), BoundingBox(category='lateral', left=0.8478774710612861, top=0.9807845444628994, right=0.8745474031974078, bottom=0.9901969156805276)]\n", - "Unable to obtain image for sheet unified_intraoperative_preoperative_flowsheet_v1_1_front.png. Likely in main directory and png format.\n", - "Sheet: RC_0001_intraoperative.JPG\n", - "Locations for RC_0001_intraoperative.JPG: [BoundingBox(category='5', left=0.8502850000000001, top=0.3911735, right=0.8547370000000001, bottom=0.4002345), BoundingBox(category='mg', left=0.8837505, top=0.09925600000000001, right=0.8948615, bottom=0.10798600000000001), BoundingBox(category='mg', left=0.8841315000000001, top=0.1206155, right=0.8951185, bottom=0.1292465), BoundingBox(category='micro_g', left=0.885347, top=0.14174599999999998, right=0.894449, bottom=0.15103799999999998), BoundingBox(category='pcnt', left=0.897607, top=0.6939615, right=0.904663, bottom=0.7023945), BoundingBox(category='mmHg', left=0.889846, top=0.7150265, right=0.91368, bottom=0.7256415), BoundingBox(category='pcnt', left=0.898723, top=0.7358294999999999, right=0.905841, bottom=0.7440604999999999), BoundingBox(category='degree_C', left=0.8989705000000001, top=0.756528, right=0.9064355000000002, bottom=0.765324), 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obtain image. Image: \n", - "Intraoperative sheet\n", - "Source Points: [[ 337.623552 262.71 ]\n", - " [3310.610688 2874.75048 ]\n", - " [ 456.450624 2940.455952]\n", - " [3583.149696 239.845536]]\n", - "Destination Points: [[ 145.99172071 30.99834276]\n", - " [2842.00104253 2513.00136168]\n", - " [ 204.00865818 2516.03664458]\n", - " [3162.50911303 90.99088161]]\n", - "Homography Matrix: [[ 9.36717731e-01 -1.93593018e-02 -1.64989688e+02]\n", - " [ 2.52704454e-02 9.34459112e-01 -2.22984810e+02]\n", - " [ 1.97804479e-06 2.42995900e-06 1.00000000e+00]]\n", - "Status: [[1]\n", - " [1]\n", - " [1]\n", - " [1]]\n", - "Homography matrix: [[ 9.36717731e-01 -1.93593018e-02 -1.64989688e+02]\n", - " [ 2.52704454e-02 9.34459112e-01 -2.22984810e+02]\n", - " [ 1.97804479e-06 2.42995900e-06 1.00000000e+00]]\n", - "Remapped locations: [BoundingBox(category='5', left=0.9074515417850378, top=0.42656157769097225, right=0.9122466856060606, bottom=0.43798795572916666), BoundingBox(category='mg', left=0.952287079782197, 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original_height:int=3024,\n", " new_width:int=3300,\n", - " new_height:int=2250,\n", + " new_height:int=2550,\n", ") -> BoundingBox:\n", " \"\"\"Maps boundingboxes to a new space using the homography matrix.\n", " \n", @@ -278,67 +236,95 @@ " return BoundingBox(bbox.category, new_left/new_width, new_top/new_height, new_right/new_width, new_bottom/new_height)\n", "\n", "# Remap all bounding boxes\n", - "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]\n", + "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "7bb3dbbb", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Functions added that allow for the completion of the homography transformation and the bounding boxes for the sheet.\n", + "To be called from a for loop to complete for all documents, then exported to YOLO format.\n", + "\"\"\"\n", "\n", - "# Get location data for unified chart frontside\n", - "locations_unified_front = landmark_location_data['unified_intraoperative_preoperative_flowsheet_v1_1_front.png']\n", - "print(f\"Locations unified front: {locations_unified_front}\")\n", - "image_unified_front = Image.open(data_path/\"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\")\n", - "image_unified_front.show()\n", - "unified_width, unified_height = image_unified_front.size\n", - "print(f\"Unified width: {unified_width}, Unified height: {unified_height}\")\n", + "def __get_image_size(path: Path) -> Tuple[int, int]:\n", + " \"\"\"\n", + " Returns the size of the image based on path\n", "\n", - "# Get location data for unified chart backside... will have to do something similar to the above when ready for this\n", - "#locations_unified_back = landmark_location_data['unified_intraoperative_preoperative_flowsheet_v1_1_back.png']\n", + " Args:\n", + " path (Path): The path to the image file\n", + " Returns:\n", + " Tuple[int, int]: The width and height of the image\n", + " \"\"\"\n", + " return Image.open(data_path/\"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\").size\n", "\n", - "print(landmark_location_data.keys())\n", - "for sheet in landmark_location_data:\n", - " print(f\"Sheet: {sheet}\")\n", "\n", - " locations = landmark_location_data[sheet]\n", - " print(f\"Locations for {sheet}: {locations}\")\n", + "def complete_homography_and_get_bounding_boxes(\n", + " path_to_sheet: Path, \n", + " path_to_landmarks: Path,\n", + " intraoperative: bool = True,\n", + " show_images: bool = False,\n", + ") -> Optional[List[BoundingBox]]:\n", + " \"\"\"\n", + " Function that completes the homography transformation and returns the bounding boxes for the sheet.\n", "\n", - " try:\n", - " image = Image.open(data_path/f\"chart_images/{sheet}\")\n", + " Args:\n", + " path_to_sheet (Path): The path to the sheet image\n", + " path_to_landmarks (Path): The path to the landmark json file\n", + " intraoperative (bool, optional): Whether the sheet is intraoperative or not. Defaults to True.\n", + " show_images (bool, optional): Whether to show the images or not. Defaults to False\n", + " Returns:\n", + " Optional[List[BoundingBox]]: The bounding boxes for the sheet. If None then the image could not be opened.\n", + " \"\"\"\n", + " # Get the landmark location data\n", + " data_path: Path = Path(\"..\")/\"..\"/\"data\"\n", + " landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(path_to_landmarks)\n", + " # Check if the sheet is in the landmark data\n", + " if path_to_sheet.name not in landmark_location_data:\n", + " print(f\"Sheet {path_to_sheet.name} not found in landmark data.\")\n", + " return None\n", + " # Get locations of landmarks for this sheet by getting the file name from the path\n", + " locations = landmark_location_data[path_to_sheet.name]\n", + " \n", + " # Get the unified front/back image\n", + " unified_file = f\"unified_intraoperative_preoperative_flowsheet_v1_1_{'front' if intraoperative else 'back'}.png\"\n", + " locations_unified = landmark_location_data[unified_file]\n", + " unified_width, unified_height = __get_image_size(data_path/unified_file)\n", "\n", - " # View image\n", - " image.show()\n", + " # Get the image\n", + " try:\n", + " image = Image.open(path_to_sheet)\n", + " except:\n", + " print(f\"Unable to obtain image for sheet {path_to_sheet}. Likely in main directory and png format.\")\n", + " return None\n", "\n", - " # Get image dimensions\n", - " width, height = image.size\n", + " # Get image dimensions\n", + " width, height = image.size\n", "\n", - " print(f\"Able to obtain image. Image: {image}\")\n", - " except:\n", - " print(f\"Unable to obtain image for sheet {sheet}. Likely in main directory and png format.\")\n", - " continue\n", - " \n", - " if \"intraoperative\" in sheet:\n", - " print(\"Intraoperative sheet\")\n", - " h, pil_img = homography_transform(\n", - " src_image=image,\n", - " src_points=get_corresponding_points(locations, (width, height)),\n", - " dest_points=get_corresponding_points(locations_unified_front, (unified_width, unified_height)),\n", - " original_image_size=(unified_width, unified_height),\n", - " )\n", - " # Seems like currently there is no homography in here for the preoperative sheet\n", - " # else:\n", - " # print(\"Preoperative sheet\")\n", - " # h, pil_img = homography_transform(\n", - " # src_image=image,\n", - " # src_points=get_corresponding_points(locations, (width, height)),\n", - " # dest_points=get_corresponding_points(locations_unified_back, (3300, 2250)),\n", - " # original_image_size=(width, height),\n", - " # )\n", + " # If show_images is true show image\n", + " if show_images:\n", + " image.show()\n", "\n", - " print(f\"Homography matrix: {h}\")\n", + " # Perform homography transformation\n", + " h, pil_img = homography_transform(\n", + " src_image=image,\n", + " src_points=get_corresponding_points(locations, (width, height)),\n", + " dest_points=get_corresponding_points(locations_unified, (unified_width, unified_height)),\n", + " original_image_size=(unified_width, unified_height),\n", + " )\n", "\n", + " # Use the homography matrix to remap all bounding boxes\n", " remapped_locations = remap_all_bboxes(locations, h)\n", "\n", - " print(f\"Remapped locations: {remapped_locations}\")\n", - "\n", - " # View the image\n", - " pil_img.show()\n", + " # If show_images is true show image\n", + " if show_images:\n", + " pil_img.show()\n", "\n", + " # Draw bounding boxes on the image\n", " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", " draw = ImageDraw.Draw(pil_img)\n", "\n", @@ -352,7 +338,37 @@ " draw.rectangle(box, outline=generate_color(), width=3)\n", " pil_img.resize((800, 600))\n", "\n", - " pil_img.show()\n", + " # If show_images is true show image\n", + " if show_images:\n", + " pil_img.show()\n", + "\n", + " return remapped_locations" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "77c8599f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unable to obtain image for sheet ..\\..\\data\\chart_images\\unified_intraoperative_preoperative_flowsheet_v1_1_front.png. 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BoundingBox(category='0', left=0.7210614198626893, top=0.29564344618055555, right=0.7249716648910984, bottom=0.3039699435763889), BoundingBox(category='2', left=0.7312776692708334, top=0.29562974717881946, right=0.735227568655303, bottom=0.3042026638454861)]\n" + ] + } + ], + "source": [ + "for sheet in landmark_location_data:\n", + " bounding_boxes = complete_homography_and_get_bounding_boxes(\n", + " data_path/f\"chart_images/{sheet}\", \n", + " data_path/\"intraop_document_landmarks.json\", \n", + " show_images=True,\n", + " )\n", + " if bounding_boxes is None:\n", + " continue\n", " break" ] }, @@ -374,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "02674b37-4648-46e5-b6fd-ec681e7664dc", "metadata": {}, "outputs": [], From 08a12fda5a524df8a717b1c916cb4b040f0ea29e Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 04:05:21 -0400 Subject: [PATCH 07/55] Saving YOLO bounding boxes to a json file for each sheet. --- .../apply_homography_to_labels.ipynb | 114 +++++++++++++----- 1 file changed, 87 insertions(+), 27 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 54b0f63..56698e7 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -5,7 +5,9 @@ "id": "6b9d8dbd-5c0d-44c4-8054-ceb6d1f130fb", "metadata": {}, "source": [ - "# Apply Homography to Labels" + "# Apply Homography to Labels\n", + "\n", + "This script applies homography to the labels from anesthesia data flowsheets and maps the bounding boxes into the unified space." ] }, { @@ -78,8 +80,7 @@ "id": "3dd0d783-7093-4e21-9907-fa112f6deb57", "metadata": {}, "source": [ - "## 1 - Load Data\n", - "\n" + "## Load Data" ] }, { @@ -210,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 47, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, "outputs": [], @@ -239,9 +240,17 @@ "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]" ] }, + { + "cell_type": "markdown", + "id": "1c2c1fd7", + "metadata": {}, + "source": [ + "### Functions To Quickly Grab the Remapped Bounding Boxes In YOLO Format" + ] + }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 56, "id": "7bb3dbbb", "metadata": {}, "outputs": [], @@ -262,13 +271,24 @@ " \"\"\"\n", " return Image.open(data_path/\"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\").size\n", "\n", + "# Function to convert bounding boxes to YOLO format\n", + "def convert_to_yolo_format(bbox_list):\n", + " yolo_format = []\n", + " for bbox in bbox_list:\n", + " x_center = (bbox.left + bbox.right) / 2\n", + " y_center = (bbox.top + bbox.bottom) / 2\n", + " width = bbox.right - bbox.left\n", + " height = bbox.bottom - bbox.top\n", + " yolo_format.append(f\"{bbox.category} {x_center} {y_center} {width} {height}\")\n", + " return yolo_format\n", + "\n", "\n", "def complete_homography_and_get_bounding_boxes(\n", " path_to_sheet: Path, \n", " path_to_landmarks: Path,\n", " intraoperative: bool = True,\n", " show_images: bool = False,\n", - ") -> Optional[List[BoundingBox]]:\n", + ") -> Optional[str]:\n", " \"\"\"\n", " Function that completes the homography transformation and returns the bounding boxes for the sheet.\n", "\n", @@ -278,7 +298,7 @@ " intraoperative (bool, optional): Whether the sheet is intraoperative or not. Defaults to True.\n", " show_images (bool, optional): Whether to show the images or not. Defaults to False\n", " Returns:\n", - " Optional[List[BoundingBox]]: The bounding boxes for the sheet. If None then the image could not be opened.\n", + " Optional[str]: The bounding boxes for the sheet in YOLO format. If None then the image could not be opened.\n", " \"\"\"\n", " # Get the landmark location data\n", " data_path: Path = Path(\"..\")/\"..\"/\"data\"\n", @@ -345,9 +365,19 @@ " return remapped_locations" ] }, + { + "cell_type": "markdown", + "id": "6545b260", + "metadata": {}, + "source": [ + "### Iterate Over All Sheets, Get Bounding Boxes in YOLO For Registered Images\n", + "\n", + "For each sheet, get the bounding box data in YOLO format." + ] + }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 63, "id": "77c8599f", "metadata": {}, "outputs": [ @@ -356,36 +386,66 @@ "output_type": "stream", "text": [ "Unable to obtain image for sheet ..\\..\\data\\chart_images\\unified_intraoperative_preoperative_flowsheet_v1_1_front.png. 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0.01427562040441177', 'procedure_details 0.05623511516686642 0.896137025122549 0.08675795237223306 0.017058057598039245', 'blood_loss 0.13613868482185132 0.8742905560661764 0.05603413899739583 0.014708371629901906', 'urine_output 0.1305666836825284 0.8512287932751226 0.06729462594696968 0.01596823299632355', 'respiratory_rate 0.12145679358280066 0.8292753810508577 0.08508386785333807 0.018084501378676454', 'tidal_volume 0.13018812237363872 0.8048292691099878 0.06770466197620738 0.015050216375612768', 'temperature 0.13126064878521543 0.7843726543351716 0.0684715409712358 0.01642310049019602', 'fio2 0.15244615959398672 0.7598494944852942 0.02264659534801136 0.01261326210171565', 'etco2 0.14857745546283146 0.7373801796109068 0.03143107096354167 0.012793447457107865', 'spo2 0.14996243563565342 0.7163547650505515 0.02787261038115532 0.01564410041360287', 'diastolic 0.07815663655598958 0.5714819096583946 0.055339484937263254 0.017088359757965632', 'heart_rate 0.07164241675174597 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0.008998018152573506', 'degree_C 0.9567832623106061 0.7821016438802083 0.008104137073863704 0.009192947686887276', 'ml 0.9576332785866477 0.8047254854090073 0.009432336055871238 0.009385387944240264', 'BPM 0.9569930382930871 0.8275998104319853 0.0164078036221591 0.009439242493872513', 'ml 0.9577046342329545 0.8500433229932598 0.009065459280303112 0.009826995251225545', 'ml 0.9579569868607954 0.8728534294577206 0.00922807173295448 0.009747338388480409']\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:554\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 554\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileno\u001b[49m()\n\u001b[0;32m 555\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", + "\u001b[1;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[63], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m yolo_dict \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sheet \u001b[38;5;129;01min\u001b[39;00m landmark_location_data:\n\u001b[1;32m----> 3\u001b[0m bounding_boxes \u001b[38;5;241m=\u001b[39m \u001b[43mcomplete_homography_and_get_bounding_boxes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_path\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchart_images/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msheet\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_path\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mintraop_document_landmarks.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mshow_images\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bounding_boxes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", + "Cell \u001b[1;32mIn[56], line 73\u001b[0m, in \u001b[0;36mcomplete_homography_and_get_bounding_boxes\u001b[1;34m(path_to_sheet, path_to_landmarks, intraoperative, show_images)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# If show_images is true show image\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m show_images:\n\u001b[1;32m---> 73\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;66;03m# Perform homography transformation\u001b[39;00m\n\u001b[0;32m 76\u001b[0m h, pil_img \u001b[38;5;241m=\u001b[39m homography_transform(\n\u001b[0;32m 77\u001b[0m src_image\u001b[38;5;241m=\u001b[39mimage,\n\u001b[0;32m 78\u001b[0m src_points\u001b[38;5;241m=\u001b[39mget_corresponding_points(locations, (width, height)),\n\u001b[0;32m 79\u001b[0m dest_points\u001b[38;5;241m=\u001b[39mget_corresponding_points(locations_unified, (unified_width, unified_height)),\n\u001b[0;32m 80\u001b[0m original_image_size\u001b[38;5;241m=\u001b[39m(unified_width, unified_height),\n\u001b[0;32m 81\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2660\u001b[0m, in \u001b[0;36mImage.show\u001b[1;34m(self, title)\u001b[0m\n\u001b[0;32m 2640\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow\u001b[39m(\u001b[38;5;28mself\u001b[39m, title: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2641\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2642\u001b[0m \u001b[38;5;124;03m Displays this image. This method is mainly intended for debugging purposes.\u001b[39;00m\n\u001b[0;32m 2643\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[38;5;124;03m :param title: Optional title to use for the image window, where possible.\u001b[39;00m\n\u001b[0;32m 2658\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2660\u001b[0m \u001b[43m_show\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:3775\u001b[0m, in \u001b[0;36m_show\u001b[1;34m(image, **options)\u001b[0m\n\u001b[0;32m 3772\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_show\u001b[39m(image: Image, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 3773\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageShow\n\u001b[1;32m-> 3775\u001b[0m \u001b[43mImageShow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:61\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(image, title, **options)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124;03mDisplay a given image.\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;124;03m:returns: ``True`` if a suitable viewer was found, ``False`` otherwise.\u001b[39;00m\n\u001b[0;32m 59\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m viewer \u001b[38;5;129;01min\u001b[39;00m _viewers:\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mviewer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:85\u001b[0m, in \u001b[0;36mViewer.show\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m!=\u001b[39m base:\n\u001b[0;32m 83\u001b[0m image \u001b[38;5;241m=\u001b[39m image\u001b[38;5;241m.\u001b[39mconvert(base)\n\u001b[1;32m---> 85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:112\u001b[0m, in \u001b[0;36mViewer.show_image\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[0;32m 111\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Display the given image.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_file(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:108\u001b[0m, in \u001b[0;36mViewer.save_image\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Save to temporary file and return filename.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dump\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:678\u001b[0m, in \u001b[0;36mImage._dump\u001b[1;34m(self, file, format, **options)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mim\u001b[38;5;241m.\u001b[39msave_ppm(filename)\n\u001b[0;32m 677\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filename\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2605\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n\u001b[0;32m 2604\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2605\u001b[0m \u001b[43msave_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2606\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 2607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m open_fp:\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1297\u001b[0m, in \u001b[0;36m_save_all\u001b[1;34m(im, fp, filename)\u001b[0m\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_save_all\u001b[39m(im: Image\u001b[38;5;241m.\u001b[39mImage, fp: IO[\u001b[38;5;28mbytes\u001b[39m], filename: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1297\u001b[0m \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1488\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[0;32m 1484\u001b[0m single_im \u001b[38;5;241m=\u001b[39m _write_multiple_frames(\n\u001b[0;32m 1485\u001b[0m im, fp, chunk, mode, rawmode, default_image, append_images\n\u001b[0;32m 1486\u001b[0m )\n\u001b[0;32m 1487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m single_im:\n\u001b[1;32m-> 1488\u001b[0m \u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1489\u001b[0m \u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1490\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIO\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_idat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1491\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Tile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1492\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info:\n\u001b[0;32m 1495\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m info_chunk \u001b[38;5;129;01min\u001b[39;00m info\u001b[38;5;241m.\u001b[39mchunks:\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:558\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 556\u001b[0m _encode_tile(im, fp, tile, bufsize, fh)\n\u001b[0;32m 557\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, io\u001b[38;5;241m.\u001b[39mUnsupportedOperation) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m--> 558\u001b[0m \u001b[43m_encode_tile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflush\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 560\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:584\u001b[0m, in \u001b[0;36m_encode_tile\u001b[1;34m(im, fp, tile, bufsize, fh, exc)\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc:\n\u001b[0;32m 582\u001b[0m \u001b[38;5;66;03m# compress to Python file-compatible object\u001b[39;00m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 584\u001b[0m errcode, data \u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m 585\u001b[0m fp\u001b[38;5;241m.\u001b[39mwrite(data)\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errcode:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ + "yolo_dict = {}\n", "for sheet in landmark_location_data:\n", " bounding_boxes = complete_homography_and_get_bounding_boxes(\n", " data_path/f\"chart_images/{sheet}\", \n", " data_path/\"intraop_document_landmarks.json\", \n", - " show_images=True,\n", + " show_images=False,\n", " )\n", " if bounding_boxes is None:\n", " continue\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "2414bd30-4ba1-490e-b3cd-4aeedf9e0397", - "metadata": {}, - "source": [ - "Get landmarks that show up only once." - ] - }, - { - "cell_type": "markdown", - "id": "990d45a9-9aac-4765-99f1-50a0d44447b7", - "metadata": {}, - "source": [ - "Check labels." + " print(bounding_boxes)\n", + " yolo_boxes = convert_to_yolo_format(bounding_boxes)\n", + " print(yolo_boxes)\n", + " yolo_dict[sheet] = yolo_boxes\n", + "\n", + "print(yolo_dict)\n", + "# Save the yolo_dict to a json file\n", + "with open(data_path/\"yolo_data.json\", \"w\") as f:\n", + " json.dump(yolo_dict, f, indent=4)" ] }, { From af7ab84d97ab9b8ba9b601e5afcae2d8e333ae71 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 04:10:34 -0400 Subject: [PATCH 08/55] Removed print statements --- .../apply_homography_to_labels.ipynb | 53 +------------------ 1 file changed, 2 insertions(+), 51 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 56698e7..7ba4ee9 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "id": "77c8599f", "metadata": {}, "outputs": [ @@ -386,44 +386,7 @@ "output_type": "stream", "text": [ "Unable to obtain image for sheet ..\\..\\data\\chart_images\\unified_intraoperative_preoperative_flowsheet_v1_1_front.png. 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0.9642003676470589 0.0041905628551136 0.00857575061274507', '2 0.3523120302142519 0.9426456227022059 0.0036007412997158816 0.008718405330882417', '5 0.35944302645596593 0.9425743910845588 0.004297022964015185 0.008635876225490224', '5 0.3561116536458333 0.9642716471354167 0.00399887547348482 0.008408490349264719', '1 0.5001259913589015 0.942331973805147 0.00338748816287876 0.008615962009803968', '2 0.49811356977982957 0.964028751148897 0.0040565074573863935 0.008996342677696112', '3 0.5369079774798768 0.9425435623468137 0.0042153098366476405 0.009094669117647114', '4 0.5368603885535038 0.9654656384037991 0.004437810724431834 0.00815707337622551', '2 0.4983538448449337 0.9850258023131127 0.004213016394412905 0.009054744944852922', 'pcnt 0.9571518406723485 0.7141788497625612 0.007592033617424221 0.00922157437193627', 'mmHg 0.957242061730587 0.7380800972732844 0.026308667732007573 0.011780311734068571', 'pcnt 0.9571453302556818 0.7596277812882966 0.007697827888257569 0.008998018152573506', 'degree_C 0.9567832623106061 0.7821016438802083 0.008104137073863704 0.009192947686887276', 'ml 0.9576332785866477 0.8047254854090073 0.009432336055871238 0.009385387944240264', 'BPM 0.9569930382930871 0.8275998104319853 0.0164078036221591 0.009439242493872513', 'ml 0.9577046342329545 0.8500433229932598 0.009065459280303112 0.009826995251225545', 'ml 0.9579569868607954 0.8728534294577206 0.00922807173295448 0.009747338388480409']\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:554\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 554\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileno\u001b[49m()\n\u001b[0;32m 555\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", - "\u001b[1;31mAttributeError\u001b[0m: '_idat' object has no attribute 'fileno'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[63], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m yolo_dict \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sheet \u001b[38;5;129;01min\u001b[39;00m landmark_location_data:\n\u001b[1;32m----> 3\u001b[0m bounding_boxes \u001b[38;5;241m=\u001b[39m \u001b[43mcomplete_homography_and_get_bounding_boxes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_path\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchart_images/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msheet\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_path\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mintraop_document_landmarks.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mshow_images\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bounding_boxes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", - "Cell \u001b[1;32mIn[56], line 73\u001b[0m, in \u001b[0;36mcomplete_homography_and_get_bounding_boxes\u001b[1;34m(path_to_sheet, path_to_landmarks, intraoperative, show_images)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# If show_images is true show image\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m show_images:\n\u001b[1;32m---> 73\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;66;03m# Perform homography transformation\u001b[39;00m\n\u001b[0;32m 76\u001b[0m h, pil_img \u001b[38;5;241m=\u001b[39m homography_transform(\n\u001b[0;32m 77\u001b[0m src_image\u001b[38;5;241m=\u001b[39mimage,\n\u001b[0;32m 78\u001b[0m src_points\u001b[38;5;241m=\u001b[39mget_corresponding_points(locations, (width, height)),\n\u001b[0;32m 79\u001b[0m dest_points\u001b[38;5;241m=\u001b[39mget_corresponding_points(locations_unified, (unified_width, unified_height)),\n\u001b[0;32m 80\u001b[0m original_image_size\u001b[38;5;241m=\u001b[39m(unified_width, unified_height),\n\u001b[0;32m 81\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2660\u001b[0m, in \u001b[0;36mImage.show\u001b[1;34m(self, title)\u001b[0m\n\u001b[0;32m 2640\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow\u001b[39m(\u001b[38;5;28mself\u001b[39m, title: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2641\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2642\u001b[0m \u001b[38;5;124;03m Displays this image. This method is mainly intended for debugging purposes.\u001b[39;00m\n\u001b[0;32m 2643\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[38;5;124;03m :param title: Optional title to use for the image window, where possible.\u001b[39;00m\n\u001b[0;32m 2658\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2660\u001b[0m \u001b[43m_show\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:3775\u001b[0m, in \u001b[0;36m_show\u001b[1;34m(image, **options)\u001b[0m\n\u001b[0;32m 3772\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_show\u001b[39m(image: Image, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 3773\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ImageShow\n\u001b[1;32m-> 3775\u001b[0m \u001b[43mImageShow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:61\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(image, title, **options)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124;03mDisplay a given image.\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;124;03m:returns: ``True`` if a suitable viewer was found, ``False`` otherwise.\u001b[39;00m\n\u001b[0;32m 59\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m viewer \u001b[38;5;129;01min\u001b[39;00m _viewers:\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mviewer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:85\u001b[0m, in \u001b[0;36mViewer.show\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m!=\u001b[39m base:\n\u001b[0;32m 83\u001b[0m image \u001b[38;5;241m=\u001b[39m image\u001b[38;5;241m.\u001b[39mconvert(base)\n\u001b[1;32m---> 85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:112\u001b[0m, in \u001b[0;36mViewer.show_image\u001b[1;34m(self, image, **options)\u001b[0m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[0;32m 111\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Display the given image.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_file(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageShow.py:108\u001b[0m, in \u001b[0;36mViewer.save_image\u001b[1;34m(self, image)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_image\u001b[39m(\u001b[38;5;28mself\u001b[39m, image: Image\u001b[38;5;241m.\u001b[39mImage) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Save to temporary file and return filename.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dump\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:678\u001b[0m, in \u001b[0;36mImage._dump\u001b[1;34m(self, file, format, **options)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mim\u001b[38;5;241m.\u001b[39msave_ppm(filename)\n\u001b[0;32m 677\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filename\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2605\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n\u001b[0;32m 2604\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2605\u001b[0m \u001b[43msave_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2606\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 2607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m open_fp:\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1297\u001b[0m, in \u001b[0;36m_save_all\u001b[1;34m(im, fp, filename)\u001b[0m\n\u001b[0;32m 1296\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_save_all\u001b[39m(im: Image\u001b[38;5;241m.\u001b[39mImage, fp: IO[\u001b[38;5;28mbytes\u001b[39m], filename: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1297\u001b[0m \u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\PngImagePlugin.py:1488\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, filename, chunk, save_all)\u001b[0m\n\u001b[0;32m 1484\u001b[0m single_im \u001b[38;5;241m=\u001b[39m _write_multiple_frames(\n\u001b[0;32m 1485\u001b[0m im, fp, chunk, mode, rawmode, default_image, append_images\n\u001b[0;32m 1486\u001b[0m )\n\u001b[0;32m 1487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m single_im:\n\u001b[1;32m-> 1488\u001b[0m \u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_save\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1489\u001b[0m \u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1490\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIO\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_idat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1491\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mImageFile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Tile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msingle_im\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrawmode\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1492\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info:\n\u001b[0;32m 1495\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m info_chunk \u001b[38;5;129;01min\u001b[39;00m info\u001b[38;5;241m.\u001b[39mchunks:\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:558\u001b[0m, in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m 556\u001b[0m _encode_tile(im, fp, tile, bufsize, fh)\n\u001b[0;32m 557\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mAttributeError\u001b[39;00m, io\u001b[38;5;241m.\u001b[39mUnsupportedOperation) \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m--> 558\u001b[0m \u001b[43m_encode_tile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflush\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 560\u001b[0m fp\u001b[38;5;241m.\u001b[39mflush()\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\ImageFile.py:584\u001b[0m, in \u001b[0;36m_encode_tile\u001b[1;34m(im, fp, tile, bufsize, fh, exc)\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc:\n\u001b[0;32m 582\u001b[0m \u001b[38;5;66;03m# compress to Python file-compatible object\u001b[39;00m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 584\u001b[0m errcode, data \u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m 585\u001b[0m fp\u001b[38;5;241m.\u001b[39mwrite(data)\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errcode:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "{'RC_0001_intraoperative.JPG': ['5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088', 'mg 0.9584463038589015 0.06242681765088848 0.0123184481534091 0.010029871323529414', 'mg 0.9582821377840909 0.08585675108666513 0.01217921401515154 0.009911145976945465', 'micro_g 0.9580625961766098 0.1094433474073223 0.01003543738162882 0.010558986289828431', 'pcnt 0.9571751450047348 0.7134653128829657 0.007717803030303005 0.009426604626225465', 'mmHg 0.9573366477272727 0.7376209214154412 0.02650213068181817 0.012363281249999969', 'pcnt 0.9574410363399621 0.7589949544270833 0.007789861505681839 0.009200463388480351', 'degree_C 0.9574003832267992 0.7818501311657475 0.008164284446022685 0.009822926240808827', 'ml 0.9580979965672349 0.8047243365119485 0.009312411221590877 0.00949319278492644', 'BPM 0.9578602183948863 0.8276670209099265 0.01698715672348483 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0.014848441329656903', 'airway_device 0.4011843964547822 0.8981724398743873 0.06690085671164775 0.015815812653186323', 'anesthesia_end 0.7764114287405303 0.013965979557411344 0.05918930516098486 0.011562149197447535', 'anesthesia_start 0.04422839135834665 0.012154220132266774 0.06359123345577355 0.011001755957509955', 'blood_loss 0.13546287767814869 0.8743980258118873 0.055907518791429925 0.013480296415441129', 'bronchoscope 0.414197998046875 0.9645041073069853 0.05556477864583331 0.01228716681985298', 'capnography 0.7012774473248107 0.9857208850337009 0.05193566524621207 0.013379767922794161', 'central_iv_line 0.594432188091856 0.9419236845128676 0.05524221709280308 0.01096670113357845', 'code 0.03762004621101148 0.03569042729396446 0.025044238928592564 0.012141059426700365', 'code 0.03861735488429214 0.3141523652918199 0.0255311630711411 0.012213469860600512', 'degree_C 0.9573938358191287 0.7821252202052695 0.00782744436553029 0.009254844515931393', 'des 0.1566104542125355 0.29282917097503064 0.013283330743963068 0.008629198261335791', 'diastolic 0.07821099021218039 0.5714296348422181 0.05463807077118844 0.01667178883272058', 'difficult_ventilation 0.16918288722182764 0.9642635091145833 0.07419607451467802 0.011181736366421613', 'direct_laryngoscopy 0.4256149754379735 0.9217474724264706 0.07835449218750001 0.011856426164215783', 'dl_view 0.5104369377367424 0.9201161822150735 0.029849520596590906 0.009550302542892242', 'drug_name 0.11081850456468986 0.03784934399174709 0.054129019072561554 0.014739352955537684', 'easy_ventilation 0.16305257161458334 0.9214770029105392 0.061584398674242424 0.013540900735294126', 'ecg 0.6830250503077652 0.9198758712469364 0.014712062026515094 0.009263269761029425', 'etco2 0.14805506850733902 0.7374595971200981 0.030532041607481075 0.012019952512254961', 'ett_n 0.2612961277817235 0.9854319852941177 0.022035023082386362 0.009514590992647065', 'eye_protection 0.05844277121803977 0.9224428902420343 0.05612983472419508 0.01305501302083334', 'fentanyl 0.08761686151677911 0.10849687164905025 0.03860956827799479 0.014066090303308812', 'fio2 0.1518309714577415 0.7599672324984681 0.02222898541074811 0.012498803232230404', 'fluid_blood_product 0.11582237937233665 0.3151092529296875 0.08470826120087596 0.014186604817708282', 'fowler 0.8608140980113637 0.964118891697304 0.025913677793560574 0.009895258884803915', 'gastric_tube 0.5907611638849433 0.9846837660845589 0.04791267163825752 0.010535194546568705', 'halo 0.07745412190755208 0.29258195465686276 0.01741963704427084 0.00986179725796571', 'heart_rate 0.07192256811893348 0.5388858570772059 0.06717196840228457 0.015076497395833388', 'hour_24hr 0.5866448419744318 0.014388614355349074 0.03933305220170458 0.011760099074419808', 'hour_24hr 0.3520171009410511 0.014634702719894109 0.03943104137073866 0.011984878988826974', 'hour_24hr 0.8401535496567235 0.013563196518841912 0.04000791607481058 0.01165563246783088', 'hour_24hr 0.10563173698656486 0.0133741917329676 0.040325141675544504 0.011686474669213388', 'inhaled_exhaled 0.9573897668087121 0.2705905211205576 0.05732362689393944 0.010169917087928915', 'inhaled_volatile 0.08920616149902344 0.2704135071997549 0.07018765998609139 0.012696796492034312', 'iso 0.037621627576423414 0.2928338503370098 0.011119761611476084 0.00858790977328433', 'lateral 0.8612083851207386 0.9854891907935049 0.02654067530776516 0.010363147212009816', 'lithotomy 0.7947796075994318 0.9647503063725491 0.039870383522727315 0.012774394914215725', 'lma_n 0.2611172485351563 0.9529718615962011 0.02141592314749058 0.008837028952205839', 'mask_ventilation 0.15410254276160038 0.8967477117800245 0.07930001923532197 0.013331705729166643', 'mg 0.9584787079782198 0.06249830058976716 0.01176595052083329 0.009736675187653178', 'mg 0.9582909046519886 0.08601959527707567 0.011691302675189474 0.009503790163526343', 'micro_g 0.9581771203243372 0.10944538789636948 0.009434555516098508 0.009816738951439946', 'minute 0.18235370982776988 0.013375766604554421 0.02684709028764204 0.009463299769981235', 'minute 0.6611589651396781 0.014221066493614048 0.02608746152935615 0.00993081335927926', 'minute 0.9156442723129734 0.012333948097976983 0.025829856178977262 0.01003531437294156', 'minute 0.4264068788470644 0.013321072821523628 0.026034342447916692 0.009637988969391467', 'ml 0.9583071437026515 0.8729661171109069 0.009406812263257569 0.009451497395833286', 'ml 0.9580873801491477 0.8050824094286152 0.009257886482007649 0.008987486596200966', 'ml 0.9580792421283144 0.8500218290441176 0.009095865885416665 0.009539292279411749', 'mmHg 0.9575336988044507 0.7381407973345588 0.02592262961647729 0.01146541819852942', 'monitoring_details 0.7014835242069128 0.8973393458946078 0.0875117631392045 0.016778301164215748', 'natural 0.26478233568596116 0.9201625689338235 0.02835218024976327 0.010109911151960693', 'nibp 0.684616514263731 0.9417972579656863 0.018203198982007507 0.008845549938725439', 'other_airway_device 0.42496718897964014 0.9857038909313726 0.0771639737215909 0.013096086090686354', 'pcnt 0.9572666237571023 0.7140571384803922 0.007395315459280294 0.008924823835784301', 'pcnt 0.9576054983428031 0.7596706016390932 0.007256895123106122 0.008760340073529438', 'peripheral_iv_line 0.6004028875177556 0.9210951382506127 0.06661373254024616 0.013003887101715739', 'position 0.7765677527225379 0.8960735006893382 0.03857747395833333 0.012133597579656819', 'procedure_details 0.05542940775553386 0.897071413526348 0.0857207743326823 0.01376637178308826', 'prone 0.7861633670691288 0.942042116651348 0.022626509232954506 0.009004576439951006', 'propofol 0.087458540020567 0.061608078900505514 0.03890899195815578 0.014648497338388487', 'respiratory_rate 0.12102766557173295 0.8295408241421569 0.08493737423058711 0.016221852022058836', 'reverse_trendelenburg 0.8925144634824811 0.9436308498008579 0.0894375147964015 0.012843807444852917', 'rocuronium 0.0958598720666134 0.08371035706763175 0.0555763984448982 0.011724841547947298', 'safety_checklist 0.06180783705277876 0.986678347120098 0.06162082787716027 0.012717141544117627', 'sev 0.12151231245561081 0.29306958965226715 0.013225726503314397 0.008674412147671562', 'sitting 0.7876835123697916 0.9863052428002451 0.02503425366950751 0.012742225796568585', 'spo2 0.14940772779060132 0.7163994523590687 0.027246815074573882 0.015149069393382342', 'supine 0.7881055427320076 0.920924622778799 0.026608812736742427 0.011635167738970553', 'surgery_end 0.5358614095052083 0.014109206480138442 0.046074662642045405 0.012619483050178078', 'surgery_start 0.2939803429805871 0.015071311651491653 0.05048687559185605 0.01238004946241192', 'systolic 0.08114731528542259 0.5071451344209559 0.04936831849994081 0.020476122089460758', 'ted_stockings 0.058208673650568185 0.9656909179687501 0.0554925537109375 0.012300379136029327', 'temperature 0.13076041944099195 0.7844244025735294 0.06755098285097065 0.015194546568627398', 'temperature 0.700289824514678 0.9648336014093137 0.05104854699337125 0.011948720894607856', 'tidal_volume 0.12949113325639205 0.8049917183670343 0.06762399384469699 0.013324812346813708', 'total 0.9576194069602273 0.31442962048100487 0.02458836410984855 0.012173138786764681', 'trendeleburg 0.8763470274029357 0.9214481368719363 0.05876161517518941 0.012545094209558849', 'tubes_and_lines 0.5816712165601325 0.8964221430759804 0.06575365471117423 0.01267290900735285', 'units 0.9583320756392045 0.03568070205987668 0.02411413944128782 0.012111723656747852', 'urinary_catheter 0.5990088815400094 0.964529239430147 0.06389881480823856 0.012913602941176383', 'urine_output 0.130001220703125 0.8516700176164216 0.0673636141690341 0.01518918504901956', 'ventilation_w_adjunct 0.17405698371656014 0.9429129327512256 0.08521762732303503 0.012989621629902026', 'video_laryngoscopy 0.42470575506036934 0.9429613300398284 0.07800008138020836 0.012566923253676476', 'warming 0.04784219221635298 0.9440842333026961 0.03525395711263021 0.011748812806372522']}\n" ] } ], @@ -437,9 +400,7 @@ " )\n", " if bounding_boxes is None:\n", " continue\n", - " print(bounding_boxes)\n", " yolo_boxes = convert_to_yolo_format(bounding_boxes)\n", - " print(yolo_boxes)\n", " yolo_dict[sheet] = yolo_boxes\n", "\n", "print(yolo_dict)\n", @@ -447,16 +408,6 @@ "with open(data_path/\"yolo_data.json\", \"w\") as f:\n", " json.dump(yolo_dict, f, indent=4)" ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "02674b37-4648-46e5-b6fd-ec681e7664dc", - "metadata": {}, - "outputs": [], - "source": [ - "# dump your BoundingBoxes to the format of your choosing here." - ] } ], "metadata": { From 322d827fb9abed1237092e34231f9ea79e1c499b Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 14:42:41 -0400 Subject: [PATCH 09/55] Save registered image to "data/registered_images" directory --- .../apply_homography_to_labels.ipynb | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 7ba4ee9..7ac55a0 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "id": "5f322da5-10f8-49ee-a81a-5edc7bac12cd", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "id": "95997450-a2a0-4035-b040-3c8fb532836b", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", "metadata": {}, "outputs": [], @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 8, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "id": "7bb3dbbb", "metadata": {}, "outputs": [], @@ -286,6 +286,7 @@ "def complete_homography_and_get_bounding_boxes(\n", " path_to_sheet: Path, \n", " path_to_landmarks: Path,\n", + " path_to_registered: Path,\n", " intraoperative: bool = True,\n", " show_images: bool = False,\n", ") -> Optional[str]:\n", @@ -295,6 +296,7 @@ " Args:\n", " path_to_sheet (Path): The path to the sheet image\n", " path_to_landmarks (Path): The path to the landmark json file\n", + " path_to_registered (Path): The path to the registered image directory\n", " intraoperative (bool, optional): Whether the sheet is intraoperative or not. Defaults to True.\n", " show_images (bool, optional): Whether to show the images or not. Defaults to False\n", " Returns:\n", @@ -361,6 +363,9 @@ " # If show_images is true show image\n", " if show_images:\n", " pil_img.show()\n", + " \n", + " # Save the image\n", + " pil_img.save(path_to_registered/path_to_sheet.name)\n", "\n", " return remapped_locations" ] @@ -372,12 +377,12 @@ "source": [ "### Iterate Over All Sheets, Get Bounding Boxes in YOLO For Registered Images\n", "\n", - "For each sheet, get the bounding box data in YOLO format." + "For each sheet, get the bounding box data in YOLO format. Make sure to create the `registered_images` directory." ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 11, "id": "77c8599f", "metadata": {}, "outputs": [ @@ -386,7 +391,7 @@ "output_type": "stream", "text": [ "Unable to obtain image for sheet ..\\..\\data\\chart_images\\unified_intraoperative_preoperative_flowsheet_v1_1_front.png. Likely in main directory and png format.\n", - "{'RC_0001_intraoperative.JPG': ['5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088', 'mg 0.9584463038589015 0.06242681765088848 0.0123184481534091 0.010029871323529414', 'mg 0.9582821377840909 0.08585675108666513 0.01217921401515154 0.009911145976945465', 'micro_g 0.9580625961766098 0.1094433474073223 0.01003543738162882 0.010558986289828431', 'pcnt 0.9571751450047348 0.7134653128829657 0.007717803030303005 0.009426604626225465', 'mmHg 0.9573366477272727 0.7376209214154412 0.02650213068181817 0.012363281249999969', 'pcnt 0.9574410363399621 0.7589949544270833 0.007789861505681839 0.009200463388480351', 'degree_C 0.9574003832267992 0.7818501311657475 0.008164284446022685 0.009822926240808827', 'ml 0.9580979965672349 0.8047243365119485 0.009312411221590877 0.00949319278492644', 'BPM 0.9578602183948863 0.8276670209099265 0.01698715672348483 0.009932693780637214', 'ml 0.9589633271188447 0.8504820101868873 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0.9045032108191289 0.38140860763250617 0.004780421401515134 0.010118120978860279']}\n" ] } ], @@ -396,6 +401,7 @@ " bounding_boxes = complete_homography_and_get_bounding_boxes(\n", " data_path/f\"chart_images/{sheet}\", \n", " data_path/\"intraop_document_landmarks.json\", \n", + " data_path/\"registered_images\",\n", " show_images=False,\n", " )\n", " if bounding_boxes is None:\n", @@ -403,6 +409,8 @@ " yolo_boxes = convert_to_yolo_format(bounding_boxes)\n", " yolo_dict[sheet] = yolo_boxes\n", "\n", + " break\n", + "\n", "print(yolo_dict)\n", "# Save the yolo_dict to a json file\n", "with open(data_path/\"yolo_data.json\", \"w\") as f:\n", From 2c82cf6bbe860cf55c2e7f8e0903816e8b33691a Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 14:43:27 -0400 Subject: [PATCH 10/55] Removed loop break --- utilities/conversion/apply_homography_to_labels.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 7ac55a0..c2bfaf8 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -382,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "77c8599f", "metadata": {}, "outputs": [ @@ -391,7 +391,7 @@ "output_type": "stream", "text": [ "Unable to obtain image for sheet ..\\..\\data\\chart_images\\unified_intraoperative_preoperative_flowsheet_v1_1_front.png. Likely in main directory and png format.\n", - "{'RC_0001_intraoperative.JPG': ['5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088', 'mg 0.9584463038589015 0.06242681765088848 0.0123184481534091 0.010029871323529414', 'mg 0.9582821377840909 0.08585675108666513 0.01217921401515154 0.009911145976945465', 'micro_g 0.9580625961766098 0.1094433474073223 0.01003543738162882 0.010558986289828431', 'pcnt 0.9571751450047348 0.7134653128829657 0.007717803030303005 0.009426604626225465', 'mmHg 0.9573366477272727 0.7376209214154412 0.02650213068181817 0.012363281249999969', 'pcnt 0.9574410363399621 0.7589949544270833 0.007789861505681839 0.009200463388480351', 'degree_C 0.9574003832267992 0.7818501311657475 0.008164284446022685 0.009822926240808827', 'ml 0.9580979965672349 0.8047243365119485 0.009312411221590877 0.00949319278492644', 'BPM 0.9578602183948863 0.8276670209099265 0.01698715672348483 0.009932693780637214', 'ml 0.9589633271188447 0.8504820101868873 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0.01305501302083334', 'fentanyl 0.08761686151677911 0.10849687164905025 0.03860956827799479 0.014066090303308812', 'fio2 0.1518309714577415 0.7599672324984681 0.02222898541074811 0.012498803232230404', 'fluid_blood_product 0.11582237937233665 0.3151092529296875 0.08470826120087596 0.014186604817708282', 'fowler 0.8608140980113637 0.964118891697304 0.025913677793560574 0.009895258884803915', 'gastric_tube 0.5907611638849433 0.9846837660845589 0.04791267163825752 0.010535194546568705', 'halo 0.07745412190755208 0.29258195465686276 0.01741963704427084 0.00986179725796571', 'heart_rate 0.07192256811893348 0.5388858570772059 0.06717196840228457 0.015076497395833388', 'hour_24hr 0.5866448419744318 0.014388614355349074 0.03933305220170458 0.011760099074419808', 'hour_24hr 0.3520171009410511 0.014634702719894109 0.03943104137073866 0.011984878988826974', 'hour_24hr 0.8401535496567235 0.013563196518841912 0.04000791607481058 0.01165563246783088', 'hour_24hr 0.10563173698656486 0.0133741917329676 0.040325141675544504 0.011686474669213388', 'inhaled_exhaled 0.9573897668087121 0.2705905211205576 0.05732362689393944 0.010169917087928915', 'inhaled_volatile 0.08920616149902344 0.2704135071997549 0.07018765998609139 0.012696796492034312', 'iso 0.037621627576423414 0.2928338503370098 0.011119761611476084 0.00858790977328433', 'lateral 0.8612083851207386 0.9854891907935049 0.02654067530776516 0.010363147212009816', 'lithotomy 0.7947796075994318 0.9647503063725491 0.039870383522727315 0.012774394914215725', 'lma_n 0.2611172485351563 0.9529718615962011 0.02141592314749058 0.008837028952205839', 'mask_ventilation 0.15410254276160038 0.8967477117800245 0.07930001923532197 0.013331705729166643', 'mg 0.9584787079782198 0.06249830058976716 0.01176595052083329 0.009736675187653178', 'mg 0.9582909046519886 0.08601959527707567 0.011691302675189474 0.009503790163526343', 'micro_g 0.9581771203243372 0.10944538789636948 0.009434555516098508 0.009816738951439946', 'minute 0.18235370982776988 0.013375766604554421 0.02684709028764204 0.009463299769981235', 'minute 0.6611589651396781 0.014221066493614048 0.02608746152935615 0.00993081335927926', 'minute 0.9156442723129734 0.012333948097976983 0.025829856178977262 0.01003531437294156', 'minute 0.4264068788470644 0.013321072821523628 0.026034342447916692 0.009637988969391467', 'ml 0.9583071437026515 0.8729661171109069 0.009406812263257569 0.009451497395833286', 'ml 0.9580873801491477 0.8050824094286152 0.009257886482007649 0.008987486596200966', 'ml 0.9580792421283144 0.8500218290441176 0.009095865885416665 0.009539292279411749', 'mmHg 0.9575336988044507 0.7381407973345588 0.02592262961647729 0.01146541819852942', 'monitoring_details 0.7014835242069128 0.8973393458946078 0.0875117631392045 0.016778301164215748', 'natural 0.26478233568596116 0.9201625689338235 0.02835218024976327 0.010109911151960693', 'nibp 0.684616514263731 0.9417972579656863 0.018203198982007507 0.008845549938725439', 'other_airway_device 0.42496718897964014 0.9857038909313726 0.0771639737215909 0.013096086090686354', 'pcnt 0.9572666237571023 0.7140571384803922 0.007395315459280294 0.008924823835784301', 'pcnt 0.9576054983428031 0.7596706016390932 0.007256895123106122 0.008760340073529438', 'peripheral_iv_line 0.6004028875177556 0.9210951382506127 0.06661373254024616 0.013003887101715739', 'position 0.7765677527225379 0.8960735006893382 0.03857747395833333 0.012133597579656819', 'procedure_details 0.05542940775553386 0.897071413526348 0.0857207743326823 0.01376637178308826', 'prone 0.7861633670691288 0.942042116651348 0.022626509232954506 0.009004576439951006', 'propofol 0.087458540020567 0.061608078900505514 0.03890899195815578 0.014648497338388487', 'respiratory_rate 0.12102766557173295 0.8295408241421569 0.08493737423058711 0.016221852022058836', 'reverse_trendelenburg 0.8925144634824811 0.9436308498008579 0.0894375147964015 0.012843807444852917', 'rocuronium 0.0958598720666134 0.08371035706763175 0.0555763984448982 0.011724841547947298', 'safety_checklist 0.06180783705277876 0.986678347120098 0.06162082787716027 0.012717141544117627', 'sev 0.12151231245561081 0.29306958965226715 0.013225726503314397 0.008674412147671562', 'sitting 0.7876835123697916 0.9863052428002451 0.02503425366950751 0.012742225796568585', 'spo2 0.14940772779060132 0.7163994523590687 0.027246815074573882 0.015149069393382342', 'supine 0.7881055427320076 0.920924622778799 0.026608812736742427 0.011635167738970553', 'surgery_end 0.5358614095052083 0.014109206480138442 0.046074662642045405 0.012619483050178078', 'surgery_start 0.2939803429805871 0.015071311651491653 0.05048687559185605 0.01238004946241192', 'systolic 0.08114731528542259 0.5071451344209559 0.04936831849994081 0.020476122089460758', 'ted_stockings 0.058208673650568185 0.9656909179687501 0.0554925537109375 0.012300379136029327', 'temperature 0.13076041944099195 0.7844244025735294 0.06755098285097065 0.015194546568627398', 'temperature 0.700289824514678 0.9648336014093137 0.05104854699337125 0.011948720894607856', 'tidal_volume 0.12949113325639205 0.8049917183670343 0.06762399384469699 0.013324812346813708', 'total 0.9576194069602273 0.31442962048100487 0.02458836410984855 0.012173138786764681', 'trendeleburg 0.8763470274029357 0.9214481368719363 0.05876161517518941 0.012545094209558849', 'tubes_and_lines 0.5816712165601325 0.8964221430759804 0.06575365471117423 0.01267290900735285', 'units 0.9583320756392045 0.03568070205987668 0.02411413944128782 0.012111723656747852', 'urinary_catheter 0.5990088815400094 0.964529239430147 0.06389881480823856 0.012913602941176383', 'urine_output 0.130001220703125 0.8516700176164216 0.0673636141690341 0.01518918504901956', 'ventilation_w_adjunct 0.17405698371656014 0.9429129327512256 0.08521762732303503 0.012989621629902026', 'video_laryngoscopy 0.42470575506036934 0.9429613300398284 0.07800008138020836 0.012566923253676476', 'warming 0.04784219221635298 0.9440842333026961 0.03525395711263021 0.011748812806372522']}\n" ] } ], @@ -409,8 +409,6 @@ " yolo_boxes = convert_to_yolo_format(bounding_boxes)\n", " yolo_dict[sheet] = yolo_boxes\n", "\n", - " break\n", - "\n", "print(yolo_dict)\n", "# Save the yolo_dict to a json file\n", "with open(data_path/\"yolo_data.json\", \"w\") as f:\n", From e2992c1142ca12cfb1d9cd79c4d041c9793556dc Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 16:25:47 -0400 Subject: [PATCH 11/55] Saving images without bounding boxes. Working on clustering problem. --- experiments/clustering/clustering.ipynb | 427 +++++++++++++++++- .../apply_homography_to_labels.ipynb | 58 +-- 2 files changed, 451 insertions(+), 34 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 8a438c2..3836b91 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -16,29 +16,442 @@ "source": [ "### Register Images to Start\n", "\n", - "To start, we need to register images using a completed version of the NoteBook Ryan shared on email." + "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Install Packages\n", + "\n", + "This will be added to as I develop." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "# We cook" + "import os\n", + "import json\n", + "\n", + "import cv2\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start By Loading YOLO Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 19 sheets in yolo_data.json\n", + "Sheet: RC_0001_intraoperative.JPG\n", + " 5 ['0.9098491136955492', '0.38141891180300247', '0.0047951438210227515', '0.010082098268995088']\n", + " mg ['0.9584463038589015', '0.06242681765088848', '0.0123184481534091', '0.010029871323529414']\n", + " mg ['0.9582821377840909', '0.08585675108666513', '0.01217921401515154', '0.009911145976945465']\n", + " micro_g ['0.9580625961766098', '0.1094433474073223', '0.01003543738162882', '0.010558986289828431']\n", + " pcnt ['0.9571751450047348', '0.7134653128829657', '0.007717803030303005', '0.009426604626225465']\n", + " mmHg ['0.9573366477272727', '0.7376209214154412', '0.02650213068181817', '0.012363281249999969']\n", + " pcnt ['0.9574410363399621', '0.7589949544270833', '0.007789861505681839', '0.009200463388480351']\n", + " degree_C ['0.9574003832267992', '0.7818501311657475', '0.008164284446022685', '0.009822926240808827']\n", + " ml ['0.9580979965672349', '0.8047243365119485', '0.009312411221590877', '0.00949319278492644']\n", + " BPM ['0.9578602183948863', '0.8276670209099265', '0.01698715672348483', '0.009932693780637214']\n", + " ml ['0.9589633271188447', '0.8504820101868873', '0.009458821614583335', '0.010276405484068607']\n", + " ml ['0.9594578968394887', '0.8737547870710785', '0.009293249881628829', '0.01012657015931373']\n", + " 0 ['0.1665725985440341', '0.036909315819833796', '0.005250466086647726', '0.010752225389667587']\n", + " 5 ['0.1851103349165483', '0.03705927830116422', '0.004877615263967794', '0.010847491096047789']\n", + " 1 ['0.20007650664358428', '0.0371429443359375', '0.004374796549479171', '0.010383378571155025']\n", + " 0 ['0.20520766749526514', '0.03724801905014936', '0.004801617246685597', '0.010397838517731309']\n", + " 1 ['0.21814998742305872', '0.037272623099532776', '0.003926890980113618', '0.009971923828125']\n", + " 5 ['0.22338938395182292', '0.037180241603477326', '0.004642907344933722', '0.0103977996227788']\n", + " 2 ['0.2363513553503788', '0.037137047262752756', '0.004731112393465908', '0.010729459874770225']\n", + " 0 ['0.24186424486564867', '0.03710954703536688', '0.004744077740293562', '0.010144740085975795']\n", + " 2 ['0.2541924678918087', '0.03714175355200674', '0.004789558179450759', '0.010510301776960787']\n", + " 5 ['0.2598185683741714', '0.03701983732335708', '0.004509036902225372', '0.010537800508386944']\n", + " 3 ['0.2722429217714252', '0.037052179972330734', '0.004930401426373077', '0.010397964178347117']\n", + " 0 ['0.2779369284889915', '0.03700894673665364', '0.004563117749763257', '0.010500898174211092']\n", + " 3 ['0.2903317353219697', '0.036930962356866576', '0.004756266276041643', '0.010500162162032783']\n", + " 5 ['0.29597985469933713', '0.037017686133291205', '0.00469915216619321', '0.01065802779852175']\n", + " 4 ['0.3085315218838779', '0.036664605234183516', '0.005150719844933704', '0.010409946815640322']\n", + " 0 ['0.3140088260535038', '0.036799597646675855', '0.004754305752840915', '0.010432248583026958']\n", + " 4 ['0.3264266875295928', '0.036628768023322614', '0.004791037819602273', '0.009884727328431368']\n", + " 5 ['0.33198014692826705', '0.0366968027750651', '0.004797548236268934', '0.010425962560317095']\n", + " 5 ['0.3448106800426136', '0.03664538813572304', '0.004442915482954557', '0.010237163468903188']\n", + " 0 ['0.35004705255681823', '0.03667043498918122', '0.004696377840909094', '0.010282069187538295']\n", + " 5 ['0.3628356378728693', '0.03668723012886795', '0.0045721990411932145', '0.010751899270450366']\n", + " 5 ['0.36815568403764204', '0.03667215534285003', '0.004776167436079559', '0.010576378317440252']\n", + " 0 ['0.3833189993193655', '0.036708647784064796', '0.004590694543087137', '0.010524743772020527']\n", + " 5 ['0.4018963623046875', '0.03663860246246936', '0.004569017814867404', '0.010639815984987748']\n", + " 1 ['0.41693431507457385', '0.03656277525658701', '0.004060613458806817', '0.010591340906479778']\n", + " 0 ['0.4220230379971591', '0.03660824495203355', '0.004599461410984829', '0.010566666546989886']\n", + " 1 ['0.4346909216678504', '0.03651862499760647', '0.0040300588896780565', '0.010582463881548716']\n", + " 5 ['0.4400276877663352', '0.03643033046348422', '0.004886844519412903', '0.010575486725451898']\n", + " 2 ['0.4529890580610796', '0.036648007561178766', '0.004592507102272714', '0.010534539316214765']\n", + " 0 ['0.4585018273555871', '0.03661999870749081', '0.004566169507575768', '0.010387543322993256']\n", + " 2 ['0.47117030288233896', '0.03676462958840763', '0.005156360973011365', '0.01039947808957567']\n", + " 5 ['0.47654476281368374', '0.03676936878877528', '0.004908077355587126', '0.01068300434187347']\n", + " 3 ['0.4891221294981061', '0.03682373944450827', '0.004872972892992433', '0.010352029239430141']\n", + " 0 ['0.49471492882930873', '0.03692662856158088', '0.004562951290246198', '0.010304433785232839']\n", + " 3 ['0.5074373187440815', '0.0368404343548943', '0.004527661872632538', '0.010631253111596198']\n", + " 5 ['0.5127920069839015', '0.036789404177198226', '0.004920099431818148', '0.010680966844745711']\n", + " 4 ['0.5253429620916193', '0.03671505647547105', '0.00529233990293565', '0.010255034203622851']\n", + " 0 ['0.5309896943063447', '0.036765827852136945', '0.004583629261363686', '0.01044847675398284']\n", + " 4 ['0.5433842884410511', '0.03657579758588006', '0.005311057350852355', '0.010619725246055457']\n", + " 5 ['0.5489575010357481', '0.036715633915919886', '0.0046344179095644256', '0.010632207533892463']\n", + " 5 ['0.5615156693892045', '0.036714577768363205', '0.004632827296401465', '0.010631297990387562']\n", + " 0 ['0.5670027484315814', '0.03683267780378753', '0.0046861313328597776', '0.010705569398169426']\n", + " 5 ['0.579657500295928', '0.03672918431899127', '0.0046602746212121016', '0.01055050419826134']\n", + " 5 ['0.5848619865648674', '0.03683747983446308', '0.004656649502840837', '0.010695606306487443']\n", + " 0 ['0.599976788145123', '0.03673920874502144', '0.004711433179450775', '0.010631444594439342']\n", + " 5 ['0.6187065170750473', '0.03671726451200598', '0.004695601029829577', '0.01044658286898744']\n", + " 1 ['0.6336247484611742', '0.03668961319268919', '0.0038060783617424043', '0.01070683797200521']\n", + " 0 ['0.6386852287523674', '0.03671912249396829', '0.00494059244791667', '0.01030826045017616']\n", + " 1 ['0.6518332001657197', '0.036740234973383884', '0.004288145123106046', '0.010357450597426474']\n", + " 5 ['0.6568637917258522', '0.03684149648628983', '0.004623653527462079', '0.010441553452435665']\n", + " 2 ['0.6698687189275568', '0.036766216801662074', '0.004814083214962128', '0.010630319632735911']\n", + " 0 ['0.6754182202888257', '0.03687235514322917', '0.004730409564393967', '0.010582927629059435']\n", + " 2 ['0.6878911798650569', '0.036828288657992495', '0.005056596235795463', '0.010236328723383883']\n", + " 5 ['0.6935938239820076', '0.036917742560891545', '0.00460020123106053', '0.010512132831648285']\n", + " 3 ['0.7061836751302083', '0.0368811663459329', '0.004490189985795423', '0.010327836578967527']\n", + " 0 ['0.7114165704900568', '0.03698845358455882', '0.004443877249053041', '0.010614600088082106']\n", + " 3 ['0.7239361757220644', '0.03707954855526195', '0.004705181699810601', '0.010655900543811277']\n", + " 5 ['0.7294049997040719', '0.03721712897805607', '0.004972996567234822', '0.010704513250612745']\n", + " 4 ['0.7420310650449811', '0.03721283856560202', '0.005110307173295414', '0.010773441090303308']\n", + " 0 ['0.7475331809303978', '0.037318575989966296', '0.004723529237689372', '0.010443366555606619']\n", + " 4 ['0.7600825269294508', '0.03742630902458639', '0.0050070282907197505', '0.010229845233992028']\n", + " 5 ['0.7655023378314394', '0.037398062313304226', '0.004432853929924319', '0.01079559326171875']\n", + " anesthesia_start ['0.04423125411524917', '0.012154400956396964', '0.06517982020522609', '0.013665486130059934']\n", + " hour_24hr ['0.10616891571969697', '0.012932413699580174', '0.0404559141216856', '0.013883381637872432']\n", + " minute ['0.18266698663884945', '0.013347578983680875', '0.02686847108783144', '0.010612991183411843']\n", + " surgery_start ['0.2942826242157907', '0.014566413281010646', '0.050555179480350376', '0.014232577155618105']\n", + " hour_24hr ['0.35220016941879734', '0.013626822677313112', '0.039727487275094675', '0.01247652240827972']\n", + " minute ['0.42666962594696967', '0.012999167348824295', '0.026373328006628782', '0.01049810671338848']\n", + " surgery_end ['0.5361241566051136', '0.013983676012824563', '0.046376583214962186', '0.014492918276319319']\n", + " hour_24hr ['0.5869885623816288', '0.01368722205068551', '0.03977450284090911', '0.012985331217447918']\n", + " minute ['0.6615624630089962', '0.013715349833170572', '0.026395818536931848', '0.010904581406537224']\n", + " anesthesia_end ['0.7766264944365531', '0.013175007128248028', '0.0587158203125', '0.014059343151017731']\n", + " hour_24hr ['0.8398696437026515', '0.013952162499521293', '0.04027166193181819', '0.012734814812155333']\n", + " minute ['0.9153134617660985', '0.013089180366665709', '0.026545632102272676', '0.01098211924235026']\n", + " code ['0.03777714816006747', '0.03570797938926547', '0.02570743907581676', '0.013150787353515624']\n", + " drug_name ['0.11120273474491005', '0.038290484559302236', '0.054133356267755686', '0.016672557756012563']\n", + " units ['0.9583345170454546', '0.035681870404411765', '0.024771987452651523', '0.012977899289598652']\n", + " propofol ['0.08788431340997868', '0.06134477203967524', '0.039359177098129736', '0.016465549842984067']\n", + " rocuronium ['0.09623697685472893', '0.08407759722541361', '0.056171324758818655', '0.013886515299479166']\n", + " fentanyl ['0.0880720820571437', '0.10810244691138174', '0.03905493996360085', '0.015442241593903186']\n", + " inhaled_volatile ['0.08835869991418087', '0.2702792537913603', '0.07045818906841855', '0.015514849494485283']\n", + " iso ['0.03687867366906369', '0.29292378743489583', '0.011540716922644415', '0.009134904450061265']\n", + " halo ['0.0766285751805161', '0.2924439673330269', '0.018493966767282197', '0.010673923866421575']\n", + " sev ['0.12123202237215909', '0.2929575243183211', '0.013491765802556815', '0.009693914675245108']\n", + " des ['0.15673045302882338', '0.29311023188572305', '0.014003027713660049', '0.00906273935355395']\n", + " inhaled_exhaled ['0.9576137103456439', '0.27019542020909926', '0.05838600852272724', '0.013903401692708317']\n", + " code ['0.0376793081110174', '0.3141704484528186', '0.025400244972922585', '0.013120930989583335']\n", + " fluid_blood_product ['0.11558296434807054', '0.3152627503638174', '0.0857542003284801', '0.016596464269301503']\n", + " total ['0.9574660792495264', '0.3139131553500306', '0.025002293442234813', '0.013003336588541636']\n", + " systolic ['0.08106112855853456', '0.5074095004212622', '0.049817384662050185', '0.022406987208946016']\n", + " heart_rate ['0.07192837917443477', '0.5393827789905025', '0.06801822315562855', '0.017495643765318647']\n", + " diastolic ['0.0781756776751894', '0.571723991842831', '0.05540490352746212', '0.01844176049325974']\n", + " spo2 ['0.14991743145567', '0.7161638805912991', '0.027320232969341857', '0.015954159007352975']\n", + " etco2 ['0.14879550818241005', '0.737265194163603', '0.03091262354995264', '0.013269952512254934']\n", + " fio2 ['0.15255228215997868', '0.7594978841145834', '0.022351305412523698', '0.012732364430147025']\n", + " temperature ['0.13156212084221117', '0.783726926317402', '0.06728572961055872', '0.016878350949754872']\n", + " tidal_volume ['0.1300897725423177', '0.804301326976103', '0.06770186915542141', '0.015436102175245048']\n", + " respiratory_rate ['0.12178127404415245', '0.8291605392156862', '0.08465285792495264', '0.019581418504902026']\n", + " urine_output ['0.13068224357836175', '0.8503572591145834', '0.06768195874763257', '0.016968922334558822']\n", + " blood_loss ['0.13629662716027463', '0.8738881548713235', '0.056006266276041675', '0.015490196078431384']\n", + " procedure_details ['0.05553385012077562', '0.8968328737745098', '0.08665008429324988', '0.018058746936274517']\n", + " eye_protection ['0.05868822733561198', '0.9225956456801471', '0.05675207427053741', '0.015555108762254966']\n", + " warming ['0.047794066920424955', '0.9442502967984069', '0.03620337746360086', '0.013022269454656898']\n", + " ted_stockings ['0.0583714248194839', '0.9652882774203431', '0.05588447339607008', '0.014067287071078405']\n", + " safety_checklist ['0.06181244012081262', '0.9866788736979166', '0.06275347854151869', '0.01586923636642157']\n", + " mask_ventilation ['0.1549157206217448', '0.8958708160998774', '0.0796239032167377', '0.01613961014093135']\n", + " easy_ventilation ['0.1640542695016572', '0.9204615693933824', '0.06189412434895833', '0.015705422794117685']\n", + " ventilation_w_adjunct ['0.17499458544182056', '0.9412722598805148', '0.08532983953302556', '0.015096220128676507']\n", + " difficult_ventilation ['0.16996276393081203', '0.9629715265012255', '0.07447751131924718', '0.01423502604166671']\n", + " airway ['0.24857546719637783', '0.8971833352481617', '0.032194472804214', '0.014762274050245106']\n", + " lma_n ['0.26157205292672825', '0.951789981617647', '0.021482950846354154', '0.009777879901960773']\n", + " ett_n ['0.2617069961085464', '0.984241823682598', '0.022160071170691298', '0.011087622549019627']\n", + " airway_device ['0.4011493474786932', '0.8981135589001226', '0.06660422585227277', '0.017322399662990207']\n", + " direct_laryngoscopy ['0.4253016246448864', '0.921787348728554', '0.07861202355587121', '0.014216356464460844']\n", + " video_laryngoscopy ['0.42448978308475377', '0.9431146599264706', '0.0780193536931818', '0.014271599264705781']\n", + " bronchoscope ['0.41401872114701704', '0.9638669481464461', '0.05566905628551133', '0.01295065487132352']\n", + " other_airway_device ['0.4246936405066288', '0.9845426910998775', '0.07751361268939394', '0.015251512714460791']\n", + " dl_view ['0.5099951541785037', '0.9206277765012255', '0.02992956912878786', '0.010807866115196019']\n", + " tubes_and_lines ['0.581489276308002', '0.8972083237591912', '0.06617583303740526', '0.015424038756127412']\n", + " peripheral_iv_line ['0.6003280732125946', '0.9220079369638481', '0.06681292909564396', '0.01598278569240197']\n", + " central_iv_line ['0.5943357895359849', '0.9425973690257352', '0.05552364464962123', '0.012337431066176463']\n", + " urinary_catheter ['0.5990971235795455', '0.9650846832873774', '0.06454131155303033', '0.014709616268382297']\n", + " gastric_tube ['0.5905833851207387', '0.9849417413449755', '0.04816613399621217', '0.011996687346813695']\n", + " monitoring_details ['0.7012158203125001', '0.8965260225183824', '0.08740367542613636', '0.01962507659313728']\n", + " ecg ['0.6828755326704545', '0.9199970798866421', '0.015210256865530347', '0.010160558363970562']\n", + " nibp ['0.6845444187973485', '0.9419569546568627', '0.018625858191287925', '0.009639437806372553']\n", + " temperature ['0.7001318359375', '0.965051030177696', '0.051139322916666674', '0.01373161764705888']\n", + " capnography ['0.7011876701586175', '0.9858335248161765', '0.05190111564867428', '0.015346966911764737']\n", + " position ['0.776318359375', '0.8956819661458333', '0.03877145478219701', '0.01335611979166662']\n", + " supine ['0.7877691095525569', '0.920245911841299', '0.026884247750947', '0.012637005974264648']\n", + " prone ['0.7860235410748106', '0.9418063534007353', '0.023317353219696968', '0.010354051776960804']\n", + " lithotomy ['0.7947135416666666', '0.9644064989276961', '0.04039136482007577', '0.013269378063725523']\n", + " sitting ['0.7874673739346592', '0.9860538736979166', '0.025608575994318206', '0.013563208486519596']\n", + " trendeleburg ['0.8767381332859849', '0.921377383961397', '0.05895330255681819', '0.01418246400122547']\n", + " fowler ['0.8608700284090909', '0.9643896005667891', '0.0263939689867424', '0.010959616268382377']\n", + " lateral ['0.8612107155539772', '0.9854900524662991', '0.026830314867424154', '0.011208352481617667']\n", + " 5 ['0.7780364435369318', '0.03743594300513174', '0.004910037878787854', '0.01073534797219669']\n", + " 0 ['0.7835088926373106', '0.037479007197361365', '0.004690607244318246', '0.010432095995136337']\n", + " 5 ['0.7960588304924243', '0.0375362590714997', '0.004473839962121251', '0.010537157245710783']\n", + " 5 ['0.8014255593039773', '0.03764395021924785', '0.004713689630681861', '0.010556155934053311']\n", + " 0 ['0.8165661621093749', '0.03749972923129213', '0.004744096235795525', '0.01036239624023437']\n", + " 5 ['0.835040283203125', '0.037451981189204196', '0.004987349076704617', '0.010661492441214768']\n", + " 1 ['0.8502111076586174', '0.03757998597388174', '0.004039047703598531', '0.010335603601792281']\n", + " 0 ['0.8552762118252841', '0.03733592313878677', '0.0048515181107954275', '0.010483649758731617']\n", + " 1 ['0.8685168457031249', '0.037145743276558674', '0.003946422230113655', '0.01058996163162531']\n", + " 5 ['0.8736554140033144', '0.03705186731675092', '0.004576305042613638', '0.010494899375765934']\n", + " 2 ['0.8868139278527463', '0.03690080380907246', '0.004929199218750013', '0.010400686825022973']\n", + " 0 ['0.8924439216382576', '0.036706355973786', '0.004691051136363589', '0.01025004368202359']\n", + " 2 ['0.9052115885416667', '0.03660564347809436', '0.00492986505681825', '0.010767181994868258']\n", + " 5 ['0.910719881924716', '0.036490683462105544', '0.004508389559659043', '0.010785142487170649']\n", + " 2 ['0.13794031316583807', '0.39902471804151346', '0.00507585005326705', '0.010464657054227944']\n", + " 2 ['0.1434342216722893', '0.39900106991038603', '0.005159921357125952', '0.010430668849571112']\n", + " 0 ['0.14874451145981296', '0.3989715576171875', '0.004878808223839959', '0.010312284581801445']\n", + " 2 ['0.13839600996537643', '0.41458115521599265', '0.005050483472419515', '0.010243135340073484']\n", + " 1 ['0.14340712576201467', '0.4147241689644608', '0.004516906738281257', '0.010044136795343106']\n", + " 0 ['0.1484095810398911', '0.4144839298023897', '0.004963064482717799', '0.010348642386642182']\n", + " 2 ['0.13811845259232955', '0.4301294424019608', '0.0047374933416193254', '0.010339403339460762']\n", + " 0 ['0.14345488114790483', '0.4301910041360294', '0.005082036798650574', '0.009986979166666687']\n", + " 0 ['0.14887493711529357', '0.4302002192478554', '0.004909926905776518', '0.010090140548406845']\n", + " 1 ['0.1376401912804806', '0.4458321126302084', '0.004371883507930857', '0.010252517999387256']\n", + " 9 ['0.14284934303977273', '0.4458483408011642', '0.004968835079308731', '0.009987410003063746']\n", + " 0 ['0.14849560824307528', '0.4458433143765319', '0.005082073789654362', '0.00991033815870096']\n", + " 1 ['0.13771269364790484', '0.4615478994332108', '0.004487739331794499', '0.010035807291666643']\n", + " 8 ['0.14295229825106534', '0.4613467467064951', '0.004853219696969724', '0.010126282935049025']\n", + " 0 ['0.1485103260387074', '0.4615417959175858', '0.0049125347715435475', '0.009873764935661777']\n", + " 1 ['0.1379021708170573', '0.47700669232536763', '0.004599766586766113', '0.010043370863970613']\n", + " 7 ['0.14299120353929923', '0.47685860428155635', '0.0051032603870738436', '0.009644464231004901']\n", + " 0 ['0.14867162068684897', '0.47691504384957106', '0.00507972486091382', '0.009978697533701009']\n", + " 1 ['0.1378735536517519', '0.49277197744332113', '0.004180575284090909', '0.009765864353553921']\n", + " 6 ['0.14319538925633285', '0.49268571442248776', '0.004992666533499057', '0.010155053232230371']\n", + " 0 ['0.14871603763464725', '0.49253202550551467', '0.00507732044566761', '0.010084826899509791']\n", + " 1 ['0.13820145578095405', '0.5083780445772059', '0.004347099535393001', '0.009914790134803897']\n", + " 5 ['0.14322742808948863', '0.508258846507353', '0.004736642548532205', '0.01018832337622555']\n", + " 0 ['0.14874616680723246', '0.5081815353094363', '0.00521840413411459', '0.010050359987745172']\n", + " 1 ['0.13802489309599908', '0.523800048828125', '0.00428908839370265', '0.010019291896446125']\n", + " 4 ['0.14306269327799478', '0.5237577071844363', '0.004656455300071027', '0.009921683517156943']\n", + " 0 ['0.14873589717980587', '0.5236627077588848', '0.004765218098958357', '0.010106033624387223']\n", + " 1 ['0.13793073711973247', '0.5392588895909927', '0.004460033069957375', '0.009839680989583321']\n", + " 3 ['0.14305358424331202', '0.5392010857077206', '0.005218737053148681', '0.010009191176470589']\n", + " 0 ['0.14886316472833808', '0.5393448893229167', '0.005275989879261367', '0.009938055300245052']\n", + " 1 ['0.1380303446451823', '0.555029177198223', '0.004406285141453581', '0.009690515854779425']\n", + " 2 ['0.14305828672466858', '0.5549668016620711', '0.005177131421638254', '0.010077694163602935']\n", + " 0 ['0.1487662529222893', '0.5549629480698529', '0.005158053311434679', '0.010149260876225474']\n", + " 1 ['0.13802087032433713', '0.5705203067555147', '0.00423336144649622', '0.009863855698529433']\n", + " 1 ['0.1427608975497159', '0.5705034562653186', '0.00451282848011364', '0.009982000612745123']\n", + " 0 ['0.14811153989849668', '0.570541752833946', '0.005231387976444124', '0.009821633731617596']\n", + " 1 ['0.13790817723129734', '0.5862586885340073', '0.0043769605232007736', '0.009684579886642175']\n", + " 0 ['0.14295925255977748', '0.5861784572227329', '0.004822036280776515', '0.010025227864583375']\n", + " 0 ['0.14861735488429212', '0.5860258453967524', '0.0050772279681581545', '0.009886690027573475']\n", + " 9 ['0.14040775645862924', '0.6018566415824143', '0.004934387207031238', '0.01002618527879895']\n", + " 0 ['0.14577925710967093', '0.6019493432138481', '0.0050696448123816185', '0.010319776348039267']\n", + " 8 ['0.140310234301018', '0.6175523226868873', '0.0050445371685606255', '0.010135569852941173']\n", + " 0 ['0.14575131965406013', '0.6176652736289829', '0.004873851429332388', '0.010166159237132377']\n", + " 7 ['0.14023251157818417', '0.6328832289751838', '0.005249772505326683', '0.009667442172181406']\n", + " 0 ['0.1457973410866477', '0.6331068809359681', '0.004873953154592797', '0.010127383961397118']\n", + " 6 ['0.1402722352923769', '0.6487937777650122', '0.00507311271898675', '0.010058737362132364']\n", + " 0 ['0.14590905391808712', '0.6488928462009804', '0.004736217151988631', '0.009903301164215672']\n", + " 5 ['0.1405757834694602', '0.6640935441559437', '0.004903878876657192', '0.010014792049632404']\n", + " 0 ['0.14603031042850378', '0.6641141285615808', '0.004874729965672342', '0.010013547411151902']\n", + " 4 ['0.14035383744673297', '0.6796077952665441', '0.005218505859375', '0.009727807138480316']\n", + " 0 ['0.14601876923532198', '0.6796538947610294', '0.004878577030066278', '0.009756625306372557']\n", + " 3 ['0.14052866155450994', '0.69531494140625', '0.004854838053385407', '0.010225183823529327']\n", + " 0 ['0.14593774044152463', '0.6951335114123774', '0.004789058800899609', '0.01004193474264703']\n", + " 1 ['0.29763186368075284', '0.9410923617493872', '0.003879487008759508', '0.009026405484068634']\n", + " 3 ['0.297940146706321', '0.9628392118566177', '0.0040386962890625044', '0.009511144301470598']\n", + " 2 ['0.3250034031723485', '0.9413646503523284', '0.004276012073863633', '0.00932531020220595']\n", + " 4 ['0.3249136075106534', '0.9628694661458332', '0.004107444069602284', '0.008666896446078431']\n", + " 2 ['0.35265292080965904', '0.9414640299479167', '0.004622765743371227', '0.009477539062499929']\n", + " 5 ['0.3596263538707386', '0.9413741766237744', '0.0042079486268939426', '0.009104243259803968']\n", + " 5 ['0.35622773141571973', '0.9631229894301471', '0.0038008996212121615', '0.00926949295343138']\n", + " 1 ['0.4999950247099905', '0.9423159849877452', '0.0036355868252840873', '0.008874655330882386']\n", + " 2 ['0.49811638109611744', '0.9633417585784314', '0.0041074810606061', '0.009086626838235357']\n", + " 2 ['0.4981499874230587', '0.9843756223192401', '0.004206284031723462', '0.009136316636029318']\n", + " natural ['0.2652316191702178', '0.9189152496936275', '0.028457068241003802', '0.011214575674019622']\n", + " 3 ['0.5366208718039773', '0.9426712335324754', '0.004193300189394011', '0.00876943550857845']\n", + " reverse_trendelenburg ['0.8627786902225378', '0.9427326516544118', '0.03070800781250005', '0.010441367953431424']\n", + " 4 ['0.536828261866714', '0.9648567708333333', '0.00443792169744317', '0.007655292585784235']\n", + " trendeleburg ['0.9092449766216857', '0.9440838982077207', '0.05851126006155305', '0.01419031479779409']\n", + " 0 ['0.16587118437795928', '0.3818634033203125', '0.004725952148437518', '0.010094520718443634']\n", + " 5 ['0.18457039572975853', '0.3817957141352635', '0.005019956646543561', '0.010063165402879881']\n", + " 1 ['0.19968412457090434', '0.38190079034543506', '0.004037623549952657', '0.009629887599571063']\n", + " 0 ['0.2048508615204782', '0.3818134023628983', '0.00521956010298294', '0.009812993068321063']\n", + " 1 ['0.2179007050485322', '0.3819432756012561', '0.004511496803977277', '0.009861916934742698']\n", + " 5 ['0.22321859648733428', '0.3819884535845588', '0.005096158114346605', '0.010281048943014681']\n", + " 2 ['0.2363887255119555', '0.3818835209865196', '0.004943126331676123', '0.010016659007352935']\n", + " 0 ['0.24191182454427085', '0.38182753619025733', '0.005009617660984872', '0.010204407935049009']\n", + " 2 ['0.2546861775716146', '0.3819860720166973', '0.0053478449041193254', '0.010141864851409332']\n", + " 5 ['0.2601831609552557', '0.3821086689070159', '0.004950764973958299', '0.010237606272977928']\n", + " 3 ['0.27290805701053505', '0.3821193321078431', '0.0050712631687973575', '0.010458505667892137']\n", + " 0 ['0.2786404511422822', '0.38203175264246325', '0.005030369614109853', '0.010347876455269633']\n", + " 3 ['0.29124179724491006', '0.3820396513097426', '0.004749552408854163', '0.010192823223039216']\n", + " 5 ['0.2966511396928267', '0.3821407781862745', '0.005036565607244303', '0.010056487438725448']\n", + " 4 ['0.30929721716678504', '0.38233169854856003', '0.0047427460641571995', '0.009682114545036757']\n", + " 0 ['0.31502907492897725', '0.3821165915096507', '0.004972330729166641', '0.010198495902267124']\n", + " 4 ['0.3276321688565341', '0.3824337828393076', '0.005286532315340875', '0.009880059934129937']\n", + " 5 ['0.33330375902580495', '0.38239826277190564', '0.0047296327533143945', '0.010225686465992645']\n", + " 5 ['0.34590320933948865', '0.38236894196155025', '0.004852627840909118', '0.010301417930453471']\n", + " 0 ['0.35132405598958333', '0.3824903181487439', '0.004711840080492413', '0.010260201248468104']\n", + " 5 ['0.36408240116003787', '0.38248385560278797', '0.0049909002130681945', '0.010304673138786746']\n", + " 5 ['0.3695796157374527', '0.3825164794921875', '0.004559770063920443', '0.010000119676776997']\n", + " 0 ['0.3845553496389678', '0.38274670170802694', '0.004613185073390147', '0.010073433670343135']\n", + " 5 ['0.40287364612926135', '0.38273882697610295', '0.004986239346590926', '0.010265634574142146']\n", + " 1 ['0.4178893488103693', '0.38271556181066174', '0.0043999689275567855', '0.009955623851102935']\n", + " 0 ['0.422972412109375', '0.3825348379097733', '0.0045865885416666585', '0.010432823031556349']\n", + " 1 ['0.43551524769176136', '0.3828317200903799', '0.00453901811079549', '0.009922688802083357']\n", + " 5 ['0.4408444121389678', '0.3829491230085784', '0.004507908676609884', '0.010030110677083315']\n", + " 2 ['0.4537920587713068', '0.3825503958907782', '0.005120220762310612', '0.010302997663909352']\n", + " 0 ['0.4591027647076231', '0.38279470406326593', '0.004502064098011349', '0.010210080614276973']\n", + " 2 ['0.4715062921697443', '0.3827597704120711', '0.004608450224905303', '0.010430261948529418']\n", + " 5 ['0.4769446540601326', '0.3828994571461397', '0.004414802320075739', '0.010206083409926459']\n", + " 3 ['0.4893110240589489', '0.38275467218137255', '0.004593875769412892', '0.010356110217524472']\n", + " 0 ['0.4946734804095644', '0.3827644856770833', '0.004666933001893914', '0.010176882276348054']\n", + " 3 ['0.5070133463541666', '0.3827826167087929', '0.00477250532670459', '0.010433205997242623']\n", + " 5 ['0.5126307077118845', '0.38296329273897056', '0.004330573804450788', '0.009986835554534335']\n", + " 4 ['0.5249030095880682', '0.38268938849954043', '0.0051524029356061485', '0.009537162032781876']\n", + " 0 ['0.5305247173887311', '0.3826352347579657', '0.004638819839015151', '0.009919960171568598']\n", + " 4 ['0.5427307683771307', '0.3827019545611213', '0.0048346132220643545', '0.0098486567478554']\n", + " 5 ['0.5481516150272254', '0.3827708524816177', '0.004657796223958344', '0.01028181487438723']\n", + " 5 ['0.5604993045691288', '0.38262589996936275', '0.004909889914772814', '0.010216710707720567']\n", + " 0 ['0.565989472360322', '0.38267627192478554', '0.004707179214015089', '0.010464513442095535']\n", + " 5 ['0.5785705381451232', '0.38266199448529414', '0.004601495916193188', '0.01006065219056368']\n", + " 5 ['0.583848359079072', '0.3826736869064032', '0.004487970525568152', '0.010092893114276968']\n", + " 0 ['0.5987218683416193', '0.3823198744829963', '0.004796364524147778', '0.010029464422487755']\n", + " 5 ['0.617369902639678', '0.38252314548866423', '0.004818004261363584', '0.01017716950061276']\n", + " 1 ['0.6322139855587121', '0.38256816789215686', '0.004319069602272796', '0.00979784198835787']\n", + " 0 ['0.6372403231534092', '0.38235124176623775', '0.004644442471590904', '0.010348067938112715']\n", + " 1 ['0.6502870131983902', '0.3823184622970282', '0.004205063328598491', '0.009785419538909323']\n", + " 5 ['0.6552334502249053', '0.38218898399203427', '0.004599831321022707', '0.010305989583333341']\n", + " 2 ['0.6682829145951705', '0.3821959013097427', '0.00478641394412882', '0.010060987285539225']\n", + " 0 ['0.6739927719578598', '0.38205502977558214', '0.004979654947916745', '0.010139327703737766']\n", + " 2 ['0.6866014145359849', '0.3818985404220282', '0.005006806344696968', '0.010106488396139701']\n", + " 5 ['0.6921630859375', '0.3820809757008272', '0.004916548295454515', '0.010389547909007368']\n", + " 3 ['0.7047801994554924', '0.38196439855238973', '0.004862097537878807', '0.010205939797794106']\n", + " 0 ['0.7103043249881629', '0.3818713737936581', '0.0047473514441287445', '0.010310465494791643']\n", + " 3 ['0.7228476784446023', '0.38184248381969976', '0.004995930989583286', '0.010390074486825995']\n", + " 5 ['0.728689667672822', '0.38188597436044736', '0.00474779533617431', '0.010164507697610292']\n", + " 4 ['0.7413370768229166', '0.3817519962086397', '0.005275065104166754', '0.00967304304534311']\n", + " 0 ['0.7469070342092803', '0.38166058708639705', '0.004968187736742458', '0.010206705729166654']\n", + " 4 ['0.7595242956912879', '0.3817976888020833', '0.005004586884469697', '0.009735514322916694']\n", + " 5 ['0.7650375458688448', '0.38170449649586397', '0.004754305752840859', '0.010270972158394565']\n", + " 5 ['0.7778333999171402', '0.3815218457988664', '0.004798103101325779', '0.010090643190870108']\n", + " 0 ['0.783281434955019', '0.3814627853094363', '0.004878151633522676', '0.010273820465686256']\n", + " 5 ['0.7958795720880683', '0.38156327789905026', '0.004940222537878847', '0.009875943053002434']\n", + " 5 ['0.8014029947916667', '0.3815045285692402', '0.004948656486742475', '0.010133463541666665']\n", + " 0 ['0.8166511304450758', '0.38151131424249385', '0.004765920928030409', '0.0100871486289828']\n", + " 5 ['0.8354467033617424', '0.38141405292585784', '0.004737511837121122', '0.009976495481004877']\n", + " 1 ['0.8502276056463068', '0.3816766237745098', '0.004263065222537943', '0.009814405254289227']\n", + " 0 ['0.855240996389678', '0.3814403578814338', '0.004703554095643936', '0.010118815104166679']\n", + " 1 ['0.8682825816761364', '0.38148327397365195', '0.0044398082386363225', '0.009928624770220607']\n", + " 5 ['0.8734008419152461', '0.38144994399126836', '0.00466108842329549', '0.010043921377144605']\n", + " 2 ['0.8865554717092803', '0.3812796678730086', '0.0048915423768939315', '0.010303715724571061']\n", + " 0 ['0.8920348011363637', '0.38133043476179534', '0.004733220880681732', '0.009827067057291639']\n", + " 2 ['0.9045032108191289', '0.38140860763250617', '0.004780421401515134', '0.010118120978860279']\n" + ] + } + ], + "source": [ + "# Load yolo_data.json\n", + "PATH_TO_YOLO_DATA = '../../data/yolo_data.json'\n", + "PATH_TO_REGISTERED_IMAGES = '../../data/registered_images'\n", + "CROPPED_IMAGE_PATH = '../../data/cropped_blood_press_and_hr.jpg'\n", + "with open(PATH_TO_YOLO_DATA) as json_file:\n", + " yolo_data = json.load(json_file)\n", + "\n", + "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")\n", + "\n", + "# Iterate over all images\n", + "for sheet, bounding_boxes in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " for bounding_box in bounding_boxes:\n", + " print(f\" {bounding_box.split(' ')[0]} {bounding_box.split(' ')[1:]}\")\n", + "\n", + " # Load the image\n", + " registered_image = Image.open(full_image_path)\n", + " registered_image.show() # Take a look at the image\n", + "\n", + " import cv2\n", + " import numpy as np\n", + "\n", + " # Load full and cropped images using OpenCV\n", + " full_image_cv = cv2.imread(full_image_path)\n", + " cropped_template_cv = cv2.imread(CROPPED_IMAGE_PATH)\n", + "\n", + " # Perform template matching to detect the cropped section in the full image\n", + " result = cv2.matchTemplate(full_image_cv, cropped_template_cv, cv2.TM_CCOEFF_NORMED)\n", + "\n", + " # Get the best match position\n", + " min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\n", + "\n", + " # Extract the top-left corner of the matching region\n", + " top_left = max_loc\n", + " height, width, _ = cropped_template_cv.shape\n", + "\n", + " # Define the bottom-right corner based on the cropped image's dimensions\n", + " bottom_right = (top_left[0] + width, top_left[1] + height)\n", + "\n", + " # Draw a rectangle around the detected region\n", + " detected_image = full_image_cv.copy()\n", + " cv2.rectangle(detected_image, top_left, bottom_right, (0, 255, 0), 3)\n", + "\n", + " # Display the detected image\n", + " detected_image = cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB)\n", + " detected_image = Image.fromarray(detected_image)\n", + " detected_image.show()\n", + " \n", + " break\n", + "\n", + " # # Save and display the result\n", + " # detected_output_path = '/mnt/data/detected_section.png'\n", + " # cv2.imwrite(detected_output_path, detected_image)\n", + "\n", + " # detected_output_path\n", + "\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" } }, "nbformat": 4, diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index c2bfaf8..7e5243a 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "id": "5f322da5-10f8-49ee-a81a-5edc7bac12cd", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "id": "95997450-a2a0-4035-b040-3c8fb532836b", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", "metadata": {}, "outputs": [], @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "id": "7bb3dbbb", "metadata": {}, "outputs": [], @@ -346,26 +346,30 @@ " if show_images:\n", " pil_img.show()\n", "\n", - " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - " draw = ImageDraw.Draw(pil_img)\n", + " # Save original image wih bounding boxes\n", + " # Make a copy of the image\n", + " pil_img_no_boxes = pil_img.copy()\n", + " pil_img_no_boxes = pil_img.resize((800, 600))\n", "\n", - " for bounding_box in remapped_locations:\n", - " box = [\n", - " bounding_box.left*unified_width,\n", - " bounding_box.top*unified_height,\n", - " bounding_box.right*unified_width,\n", - " bounding_box.bottom*unified_height,\n", - " ]\n", - " draw.rectangle(box, outline=generate_color(), width=3)\n", - " pil_img.resize((800, 600))\n", - "\n", - " # If show_images is true show image\n", + " # You only need to do this drawing if we are intentionally being visual\n", " if show_images:\n", + " # Draw bounding boxes on the image\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + " draw = ImageDraw.Draw(pil_img)\n", + "\n", + " for bounding_box in remapped_locations:\n", + " box = [\n", + " bounding_box.left*unified_width,\n", + " bounding_box.top*unified_height,\n", + " bounding_box.right*unified_width,\n", + " bounding_box.bottom*unified_height,\n", + " ]\n", + " draw.rectangle(box, outline=generate_color(), width=3)\n", + " pil_img.resize((800, 600))\n", + "\n", " pil_img.show()\n", - " \n", - " # Save the image\n", - " pil_img.save(path_to_registered/path_to_sheet.name)\n", + "\n", + " pil_img_no_boxes.save(path_to_registered/path_to_sheet.name)\n", "\n", " return remapped_locations" ] @@ -382,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "id": "77c8599f", "metadata": {}, "outputs": [ From 8dfb22ffeed3db3ebd1a0bf91def37c33442c870 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Thu, 17 Oct 2024 23:37:26 -0400 Subject: [PATCH 12/55] Selecting region of interest and relevant bounding boxes. --- experiments/clustering/clustering.ipynb | 613 ++++++++---------- .../apply_homography_to_labels.ipynb | 22 +- 2 files changed, 276 insertions(+), 359 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 3836b91..614cfd8 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -30,12 +30,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", + "import random\n", + "from pathlib import Path\n", + "from typing import List\n", "\n", "import cv2\n", "from PIL import Image\n" @@ -57,320 +60,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 19 sheets in yolo_data.json\n", - "Sheet: RC_0001_intraoperative.JPG\n", - " 5 ['0.9098491136955492', '0.38141891180300247', '0.0047951438210227515', '0.010082098268995088']\n", - " mg ['0.9584463038589015', '0.06242681765088848', '0.0123184481534091', '0.010029871323529414']\n", - " mg ['0.9582821377840909', '0.08585675108666513', '0.01217921401515154', '0.009911145976945465']\n", - " micro_g ['0.9580625961766098', '0.1094433474073223', '0.01003543738162882', '0.010558986289828431']\n", - " pcnt ['0.9571751450047348', '0.7134653128829657', '0.007717803030303005', '0.009426604626225465']\n", - " mmHg ['0.9573366477272727', '0.7376209214154412', '0.02650213068181817', '0.012363281249999969']\n", - " pcnt ['0.9574410363399621', '0.7589949544270833', '0.007789861505681839', '0.009200463388480351']\n", - " degree_C ['0.9574003832267992', '0.7818501311657475', '0.008164284446022685', '0.009822926240808827']\n", - " ml ['0.9580979965672349', '0.8047243365119485', '0.009312411221590877', '0.00949319278492644']\n", - " BPM ['0.9578602183948863', '0.8276670209099265', '0.01698715672348483', '0.009932693780637214']\n", - " ml ['0.9589633271188447', '0.8504820101868873', '0.009458821614583335', '0.010276405484068607']\n", - " ml ['0.9594578968394887', '0.8737547870710785', '0.009293249881628829', '0.01012657015931373']\n", - " 0 ['0.1665725985440341', '0.036909315819833796', '0.005250466086647726', '0.010752225389667587']\n", - " 5 ['0.1851103349165483', '0.03705927830116422', '0.004877615263967794', '0.010847491096047789']\n", - " 1 ['0.20007650664358428', '0.0371429443359375', '0.004374796549479171', '0.010383378571155025']\n", - " 0 ['0.20520766749526514', '0.03724801905014936', '0.004801617246685597', '0.010397838517731309']\n", - " 1 ['0.21814998742305872', '0.037272623099532776', '0.003926890980113618', '0.009971923828125']\n", - " 5 ['0.22338938395182292', '0.037180241603477326', '0.004642907344933722', '0.0103977996227788']\n", - " 2 ['0.2363513553503788', '0.037137047262752756', '0.004731112393465908', '0.010729459874770225']\n", - " 0 ['0.24186424486564867', '0.03710954703536688', '0.004744077740293562', '0.010144740085975795']\n", - " 2 ['0.2541924678918087', '0.03714175355200674', '0.004789558179450759', '0.010510301776960787']\n", - " 5 ['0.2598185683741714', '0.03701983732335708', '0.004509036902225372', '0.010537800508386944']\n", - " 3 ['0.2722429217714252', '0.037052179972330734', '0.004930401426373077', '0.010397964178347117']\n", - " 0 ['0.2779369284889915', '0.03700894673665364', '0.004563117749763257', '0.010500898174211092']\n", - " 3 ['0.2903317353219697', '0.036930962356866576', '0.004756266276041643', '0.010500162162032783']\n", - " 5 ['0.29597985469933713', '0.037017686133291205', '0.00469915216619321', '0.01065802779852175']\n", - " 4 ['0.3085315218838779', '0.036664605234183516', '0.005150719844933704', '0.010409946815640322']\n", - " 0 ['0.3140088260535038', '0.036799597646675855', '0.004754305752840915', '0.010432248583026958']\n", - " 4 ['0.3264266875295928', '0.036628768023322614', '0.004791037819602273', '0.009884727328431368']\n", - " 5 ['0.33198014692826705', '0.0366968027750651', '0.004797548236268934', '0.010425962560317095']\n", - " 5 ['0.3448106800426136', '0.03664538813572304', '0.004442915482954557', '0.010237163468903188']\n", - " 0 ['0.35004705255681823', '0.03667043498918122', '0.004696377840909094', '0.010282069187538295']\n", - " 5 ['0.3628356378728693', '0.03668723012886795', '0.0045721990411932145', '0.010751899270450366']\n", - " 5 ['0.36815568403764204', '0.03667215534285003', '0.004776167436079559', '0.010576378317440252']\n", - " 0 ['0.3833189993193655', '0.036708647784064796', '0.004590694543087137', '0.010524743772020527']\n", - " 5 ['0.4018963623046875', '0.03663860246246936', '0.004569017814867404', '0.010639815984987748']\n", - " 1 ['0.41693431507457385', '0.03656277525658701', '0.004060613458806817', '0.010591340906479778']\n", - " 0 ['0.4220230379971591', '0.03660824495203355', '0.004599461410984829', '0.010566666546989886']\n", - " 1 ['0.4346909216678504', '0.03651862499760647', '0.0040300588896780565', '0.010582463881548716']\n", - " 5 ['0.4400276877663352', '0.03643033046348422', '0.004886844519412903', '0.010575486725451898']\n", - " 2 ['0.4529890580610796', '0.036648007561178766', '0.004592507102272714', '0.010534539316214765']\n", - " 0 ['0.4585018273555871', '0.03661999870749081', '0.004566169507575768', '0.010387543322993256']\n", - " 2 ['0.47117030288233896', '0.03676462958840763', '0.005156360973011365', '0.01039947808957567']\n", - " 5 ['0.47654476281368374', '0.03676936878877528', '0.004908077355587126', '0.01068300434187347']\n", - " 3 ['0.4891221294981061', '0.03682373944450827', '0.004872972892992433', '0.010352029239430141']\n", - " 0 ['0.49471492882930873', '0.03692662856158088', '0.004562951290246198', '0.010304433785232839']\n", - " 3 ['0.5074373187440815', '0.0368404343548943', '0.004527661872632538', '0.010631253111596198']\n", - " 5 ['0.5127920069839015', '0.036789404177198226', '0.004920099431818148', '0.010680966844745711']\n", - " 4 ['0.5253429620916193', '0.03671505647547105', '0.00529233990293565', '0.010255034203622851']\n", - " 0 ['0.5309896943063447', '0.036765827852136945', '0.004583629261363686', '0.01044847675398284']\n", - " 4 ['0.5433842884410511', '0.03657579758588006', '0.005311057350852355', '0.010619725246055457']\n", - " 5 ['0.5489575010357481', '0.036715633915919886', '0.0046344179095644256', '0.010632207533892463']\n", - " 5 ['0.5615156693892045', '0.036714577768363205', '0.004632827296401465', '0.010631297990387562']\n", - " 0 ['0.5670027484315814', '0.03683267780378753', '0.0046861313328597776', '0.010705569398169426']\n", - " 5 ['0.579657500295928', '0.03672918431899127', '0.0046602746212121016', '0.01055050419826134']\n", - " 5 ['0.5848619865648674', '0.03683747983446308', '0.004656649502840837', '0.010695606306487443']\n", - " 0 ['0.599976788145123', '0.03673920874502144', '0.004711433179450775', '0.010631444594439342']\n", - " 5 ['0.6187065170750473', '0.03671726451200598', '0.004695601029829577', '0.01044658286898744']\n", - " 1 ['0.6336247484611742', '0.03668961319268919', '0.0038060783617424043', '0.01070683797200521']\n", - " 0 ['0.6386852287523674', '0.03671912249396829', '0.00494059244791667', '0.01030826045017616']\n", - " 1 ['0.6518332001657197', '0.036740234973383884', '0.004288145123106046', '0.010357450597426474']\n", - " 5 ['0.6568637917258522', '0.03684149648628983', '0.004623653527462079', '0.010441553452435665']\n", - " 2 ['0.6698687189275568', '0.036766216801662074', '0.004814083214962128', '0.010630319632735911']\n", - " 0 ['0.6754182202888257', '0.03687235514322917', '0.004730409564393967', '0.010582927629059435']\n", - " 2 ['0.6878911798650569', '0.036828288657992495', '0.005056596235795463', '0.010236328723383883']\n", - " 5 ['0.6935938239820076', '0.036917742560891545', '0.00460020123106053', '0.010512132831648285']\n", - " 3 ['0.7061836751302083', '0.0368811663459329', '0.004490189985795423', '0.010327836578967527']\n", - " 0 ['0.7114165704900568', '0.03698845358455882', '0.004443877249053041', '0.010614600088082106']\n", - " 3 ['0.7239361757220644', '0.03707954855526195', '0.004705181699810601', '0.010655900543811277']\n", - " 5 ['0.7294049997040719', '0.03721712897805607', '0.004972996567234822', '0.010704513250612745']\n", - " 4 ['0.7420310650449811', '0.03721283856560202', '0.005110307173295414', '0.010773441090303308']\n", - " 0 ['0.7475331809303978', '0.037318575989966296', '0.004723529237689372', '0.010443366555606619']\n", - " 4 ['0.7600825269294508', '0.03742630902458639', '0.0050070282907197505', '0.010229845233992028']\n", - " 5 ['0.7655023378314394', '0.037398062313304226', '0.004432853929924319', '0.01079559326171875']\n", - " anesthesia_start ['0.04423125411524917', '0.012154400956396964', '0.06517982020522609', '0.013665486130059934']\n", - " hour_24hr ['0.10616891571969697', '0.012932413699580174', '0.0404559141216856', '0.013883381637872432']\n", - " minute ['0.18266698663884945', '0.013347578983680875', '0.02686847108783144', '0.010612991183411843']\n", - " surgery_start ['0.2942826242157907', '0.014566413281010646', '0.050555179480350376', '0.014232577155618105']\n", - " hour_24hr ['0.35220016941879734', '0.013626822677313112', '0.039727487275094675', '0.01247652240827972']\n", - " minute ['0.42666962594696967', '0.012999167348824295', '0.026373328006628782', '0.01049810671338848']\n", - " surgery_end ['0.5361241566051136', '0.013983676012824563', '0.046376583214962186', '0.014492918276319319']\n", - " hour_24hr ['0.5869885623816288', '0.01368722205068551', '0.03977450284090911', '0.012985331217447918']\n", - " minute ['0.6615624630089962', '0.013715349833170572', '0.026395818536931848', '0.010904581406537224']\n", - " anesthesia_end ['0.7766264944365531', '0.013175007128248028', '0.0587158203125', '0.014059343151017731']\n", - " hour_24hr ['0.8398696437026515', '0.013952162499521293', '0.04027166193181819', '0.012734814812155333']\n", - " minute ['0.9153134617660985', '0.013089180366665709', '0.026545632102272676', '0.01098211924235026']\n", - " code ['0.03777714816006747', '0.03570797938926547', '0.02570743907581676', '0.013150787353515624']\n", - " drug_name ['0.11120273474491005', '0.038290484559302236', '0.054133356267755686', '0.016672557756012563']\n", - " units ['0.9583345170454546', '0.035681870404411765', '0.024771987452651523', '0.012977899289598652']\n", - " propofol ['0.08788431340997868', '0.06134477203967524', '0.039359177098129736', '0.016465549842984067']\n", - " rocuronium ['0.09623697685472893', '0.08407759722541361', '0.056171324758818655', '0.013886515299479166']\n", - " fentanyl ['0.0880720820571437', '0.10810244691138174', '0.03905493996360085', '0.015442241593903186']\n", - " inhaled_volatile ['0.08835869991418087', '0.2702792537913603', '0.07045818906841855', '0.015514849494485283']\n", - " iso ['0.03687867366906369', '0.29292378743489583', '0.011540716922644415', '0.009134904450061265']\n", - " halo ['0.0766285751805161', '0.2924439673330269', '0.018493966767282197', '0.010673923866421575']\n", - " sev ['0.12123202237215909', '0.2929575243183211', '0.013491765802556815', '0.009693914675245108']\n", - " des ['0.15673045302882338', '0.29311023188572305', '0.014003027713660049', '0.00906273935355395']\n", - " inhaled_exhaled ['0.9576137103456439', '0.27019542020909926', '0.05838600852272724', '0.013903401692708317']\n", - " code ['0.0376793081110174', '0.3141704484528186', '0.025400244972922585', '0.013120930989583335']\n", - " fluid_blood_product ['0.11558296434807054', '0.3152627503638174', '0.0857542003284801', '0.016596464269301503']\n", - " total ['0.9574660792495264', '0.3139131553500306', '0.025002293442234813', '0.013003336588541636']\n", - " systolic ['0.08106112855853456', '0.5074095004212622', '0.049817384662050185', '0.022406987208946016']\n", - " heart_rate ['0.07192837917443477', '0.5393827789905025', '0.06801822315562855', '0.017495643765318647']\n", - " diastolic ['0.0781756776751894', '0.571723991842831', '0.05540490352746212', '0.01844176049325974']\n", - " spo2 ['0.14991743145567', '0.7161638805912991', '0.027320232969341857', '0.015954159007352975']\n", - " etco2 ['0.14879550818241005', '0.737265194163603', '0.03091262354995264', '0.013269952512254934']\n", - " fio2 ['0.15255228215997868', '0.7594978841145834', '0.022351305412523698', '0.012732364430147025']\n", - " temperature ['0.13156212084221117', '0.783726926317402', '0.06728572961055872', '0.016878350949754872']\n", - " tidal_volume ['0.1300897725423177', '0.804301326976103', '0.06770186915542141', '0.015436102175245048']\n", - " respiratory_rate ['0.12178127404415245', '0.8291605392156862', '0.08465285792495264', '0.019581418504902026']\n", - " urine_output ['0.13068224357836175', '0.8503572591145834', '0.06768195874763257', '0.016968922334558822']\n", - " blood_loss ['0.13629662716027463', '0.8738881548713235', '0.056006266276041675', '0.015490196078431384']\n", - " procedure_details ['0.05553385012077562', '0.8968328737745098', '0.08665008429324988', '0.018058746936274517']\n", - " eye_protection ['0.05868822733561198', '0.9225956456801471', '0.05675207427053741', '0.015555108762254966']\n", - " warming ['0.047794066920424955', '0.9442502967984069', '0.03620337746360086', '0.013022269454656898']\n", - " ted_stockings ['0.0583714248194839', '0.9652882774203431', '0.05588447339607008', '0.014067287071078405']\n", - " safety_checklist ['0.06181244012081262', '0.9866788736979166', '0.06275347854151869', '0.01586923636642157']\n", - " mask_ventilation ['0.1549157206217448', '0.8958708160998774', '0.0796239032167377', '0.01613961014093135']\n", - " easy_ventilation ['0.1640542695016572', '0.9204615693933824', '0.06189412434895833', '0.015705422794117685']\n", - " ventilation_w_adjunct ['0.17499458544182056', '0.9412722598805148', '0.08532983953302556', '0.015096220128676507']\n", - " difficult_ventilation ['0.16996276393081203', '0.9629715265012255', '0.07447751131924718', '0.01423502604166671']\n", - " airway ['0.24857546719637783', '0.8971833352481617', '0.032194472804214', '0.014762274050245106']\n", - " lma_n ['0.26157205292672825', '0.951789981617647', '0.021482950846354154', '0.009777879901960773']\n", - " ett_n ['0.2617069961085464', '0.984241823682598', '0.022160071170691298', '0.011087622549019627']\n", - " airway_device ['0.4011493474786932', '0.8981135589001226', '0.06660422585227277', '0.017322399662990207']\n", - " direct_laryngoscopy ['0.4253016246448864', '0.921787348728554', '0.07861202355587121', '0.014216356464460844']\n", - " video_laryngoscopy ['0.42448978308475377', '0.9431146599264706', '0.0780193536931818', '0.014271599264705781']\n", - " bronchoscope ['0.41401872114701704', '0.9638669481464461', '0.05566905628551133', '0.01295065487132352']\n", - " other_airway_device ['0.4246936405066288', '0.9845426910998775', '0.07751361268939394', '0.015251512714460791']\n", - " dl_view ['0.5099951541785037', '0.9206277765012255', '0.02992956912878786', '0.010807866115196019']\n", - " tubes_and_lines ['0.581489276308002', '0.8972083237591912', '0.06617583303740526', '0.015424038756127412']\n", - " peripheral_iv_line ['0.6003280732125946', '0.9220079369638481', '0.06681292909564396', '0.01598278569240197']\n", - " central_iv_line ['0.5943357895359849', '0.9425973690257352', '0.05552364464962123', '0.012337431066176463']\n", - " urinary_catheter ['0.5990971235795455', '0.9650846832873774', '0.06454131155303033', '0.014709616268382297']\n", - " gastric_tube ['0.5905833851207387', '0.9849417413449755', '0.04816613399621217', '0.011996687346813695']\n", - " monitoring_details ['0.7012158203125001', '0.8965260225183824', '0.08740367542613636', '0.01962507659313728']\n", - " ecg ['0.6828755326704545', '0.9199970798866421', '0.015210256865530347', '0.010160558363970562']\n", - " nibp ['0.6845444187973485', '0.9419569546568627', '0.018625858191287925', '0.009639437806372553']\n", - " temperature ['0.7001318359375', '0.965051030177696', '0.051139322916666674', '0.01373161764705888']\n", - " capnography ['0.7011876701586175', '0.9858335248161765', '0.05190111564867428', '0.015346966911764737']\n", - " position ['0.776318359375', '0.8956819661458333', '0.03877145478219701', '0.01335611979166662']\n", - " supine ['0.7877691095525569', '0.920245911841299', '0.026884247750947', '0.012637005974264648']\n", - " prone ['0.7860235410748106', '0.9418063534007353', '0.023317353219696968', '0.010354051776960804']\n", - " lithotomy ['0.7947135416666666', '0.9644064989276961', '0.04039136482007577', '0.013269378063725523']\n", - " sitting ['0.7874673739346592', '0.9860538736979166', '0.025608575994318206', '0.013563208486519596']\n", - " trendeleburg ['0.8767381332859849', '0.921377383961397', '0.05895330255681819', '0.01418246400122547']\n", - " fowler ['0.8608700284090909', '0.9643896005667891', '0.0263939689867424', '0.010959616268382377']\n", - " lateral ['0.8612107155539772', '0.9854900524662991', '0.026830314867424154', '0.011208352481617667']\n", - " 5 ['0.7780364435369318', '0.03743594300513174', '0.004910037878787854', '0.01073534797219669']\n", - " 0 ['0.7835088926373106', '0.037479007197361365', '0.004690607244318246', '0.010432095995136337']\n", - " 5 ['0.7960588304924243', '0.0375362590714997', '0.004473839962121251', '0.010537157245710783']\n", - " 5 ['0.8014255593039773', '0.03764395021924785', '0.004713689630681861', '0.010556155934053311']\n", - " 0 ['0.8165661621093749', '0.03749972923129213', '0.004744096235795525', '0.01036239624023437']\n", - " 5 ['0.835040283203125', '0.037451981189204196', '0.004987349076704617', '0.010661492441214768']\n", - " 1 ['0.8502111076586174', '0.03757998597388174', '0.004039047703598531', '0.010335603601792281']\n", - " 0 ['0.8552762118252841', '0.03733592313878677', '0.0048515181107954275', '0.010483649758731617']\n", - " 1 ['0.8685168457031249', '0.037145743276558674', '0.003946422230113655', '0.01058996163162531']\n", - " 5 ['0.8736554140033144', '0.03705186731675092', '0.004576305042613638', '0.010494899375765934']\n", - " 2 ['0.8868139278527463', '0.03690080380907246', '0.004929199218750013', '0.010400686825022973']\n", - " 0 ['0.8924439216382576', '0.036706355973786', '0.004691051136363589', '0.01025004368202359']\n", - " 2 ['0.9052115885416667', '0.03660564347809436', '0.00492986505681825', '0.010767181994868258']\n", - " 5 ['0.910719881924716', '0.036490683462105544', '0.004508389559659043', '0.010785142487170649']\n", - " 2 ['0.13794031316583807', '0.39902471804151346', '0.00507585005326705', '0.010464657054227944']\n", - " 2 ['0.1434342216722893', '0.39900106991038603', '0.005159921357125952', '0.010430668849571112']\n", - " 0 ['0.14874451145981296', '0.3989715576171875', '0.004878808223839959', '0.010312284581801445']\n", - " 2 ['0.13839600996537643', '0.41458115521599265', '0.005050483472419515', '0.010243135340073484']\n", - " 1 ['0.14340712576201467', '0.4147241689644608', '0.004516906738281257', '0.010044136795343106']\n", - " 0 ['0.1484095810398911', '0.4144839298023897', '0.004963064482717799', '0.010348642386642182']\n", - " 2 ['0.13811845259232955', '0.4301294424019608', '0.0047374933416193254', '0.010339403339460762']\n", - " 0 ['0.14345488114790483', '0.4301910041360294', '0.005082036798650574', '0.009986979166666687']\n", - " 0 ['0.14887493711529357', '0.4302002192478554', '0.004909926905776518', '0.010090140548406845']\n", - " 1 ['0.1376401912804806', '0.4458321126302084', '0.004371883507930857', '0.010252517999387256']\n", - " 9 ['0.14284934303977273', '0.4458483408011642', '0.004968835079308731', '0.009987410003063746']\n", - " 0 ['0.14849560824307528', '0.4458433143765319', '0.005082073789654362', '0.00991033815870096']\n", - " 1 ['0.13771269364790484', '0.4615478994332108', '0.004487739331794499', '0.010035807291666643']\n", - " 8 ['0.14295229825106534', '0.4613467467064951', '0.004853219696969724', '0.010126282935049025']\n", - " 0 ['0.1485103260387074', '0.4615417959175858', '0.0049125347715435475', '0.009873764935661777']\n", - " 1 ['0.1379021708170573', '0.47700669232536763', '0.004599766586766113', '0.010043370863970613']\n", - " 7 ['0.14299120353929923', '0.47685860428155635', '0.0051032603870738436', '0.009644464231004901']\n", - " 0 ['0.14867162068684897', '0.47691504384957106', '0.00507972486091382', '0.009978697533701009']\n", - " 1 ['0.1378735536517519', '0.49277197744332113', '0.004180575284090909', '0.009765864353553921']\n", - " 6 ['0.14319538925633285', '0.49268571442248776', '0.004992666533499057', '0.010155053232230371']\n", - " 0 ['0.14871603763464725', '0.49253202550551467', '0.00507732044566761', '0.010084826899509791']\n", - " 1 ['0.13820145578095405', '0.5083780445772059', '0.004347099535393001', '0.009914790134803897']\n", - " 5 ['0.14322742808948863', '0.508258846507353', '0.004736642548532205', '0.01018832337622555']\n", - " 0 ['0.14874616680723246', '0.5081815353094363', '0.00521840413411459', '0.010050359987745172']\n", - " 1 ['0.13802489309599908', '0.523800048828125', '0.00428908839370265', '0.010019291896446125']\n", - " 4 ['0.14306269327799478', '0.5237577071844363', '0.004656455300071027', '0.009921683517156943']\n", - " 0 ['0.14873589717980587', '0.5236627077588848', '0.004765218098958357', '0.010106033624387223']\n", - " 1 ['0.13793073711973247', '0.5392588895909927', '0.004460033069957375', '0.009839680989583321']\n", - " 3 ['0.14305358424331202', '0.5392010857077206', '0.005218737053148681', '0.010009191176470589']\n", - " 0 ['0.14886316472833808', '0.5393448893229167', '0.005275989879261367', '0.009938055300245052']\n", - " 1 ['0.1380303446451823', '0.555029177198223', '0.004406285141453581', '0.009690515854779425']\n", - " 2 ['0.14305828672466858', '0.5549668016620711', '0.005177131421638254', '0.010077694163602935']\n", - " 0 ['0.1487662529222893', '0.5549629480698529', '0.005158053311434679', '0.010149260876225474']\n", - " 1 ['0.13802087032433713', '0.5705203067555147', '0.00423336144649622', '0.009863855698529433']\n", - " 1 ['0.1427608975497159', '0.5705034562653186', '0.00451282848011364', '0.009982000612745123']\n", - " 0 ['0.14811153989849668', '0.570541752833946', '0.005231387976444124', '0.009821633731617596']\n", - " 1 ['0.13790817723129734', '0.5862586885340073', '0.0043769605232007736', '0.009684579886642175']\n", - " 0 ['0.14295925255977748', '0.5861784572227329', '0.004822036280776515', '0.010025227864583375']\n", - " 0 ['0.14861735488429212', '0.5860258453967524', '0.0050772279681581545', '0.009886690027573475']\n", - " 9 ['0.14040775645862924', '0.6018566415824143', '0.004934387207031238', '0.01002618527879895']\n", - " 0 ['0.14577925710967093', '0.6019493432138481', '0.0050696448123816185', '0.010319776348039267']\n", - " 8 ['0.140310234301018', '0.6175523226868873', '0.0050445371685606255', '0.010135569852941173']\n", - " 0 ['0.14575131965406013', '0.6176652736289829', '0.004873851429332388', '0.010166159237132377']\n", - " 7 ['0.14023251157818417', '0.6328832289751838', '0.005249772505326683', '0.009667442172181406']\n", - " 0 ['0.1457973410866477', '0.6331068809359681', '0.004873953154592797', '0.010127383961397118']\n", - " 6 ['0.1402722352923769', '0.6487937777650122', '0.00507311271898675', '0.010058737362132364']\n", - " 0 ['0.14590905391808712', '0.6488928462009804', '0.004736217151988631', '0.009903301164215672']\n", - " 5 ['0.1405757834694602', '0.6640935441559437', '0.004903878876657192', '0.010014792049632404']\n", - " 0 ['0.14603031042850378', '0.6641141285615808', '0.004874729965672342', '0.010013547411151902']\n", - " 4 ['0.14035383744673297', '0.6796077952665441', '0.005218505859375', '0.009727807138480316']\n", - " 0 ['0.14601876923532198', '0.6796538947610294', '0.004878577030066278', '0.009756625306372557']\n", - " 3 ['0.14052866155450994', '0.69531494140625', '0.004854838053385407', '0.010225183823529327']\n", - " 0 ['0.14593774044152463', '0.6951335114123774', '0.004789058800899609', '0.01004193474264703']\n", - " 1 ['0.29763186368075284', '0.9410923617493872', '0.003879487008759508', '0.009026405484068634']\n", - " 3 ['0.297940146706321', '0.9628392118566177', '0.0040386962890625044', '0.009511144301470598']\n", - " 2 ['0.3250034031723485', '0.9413646503523284', '0.004276012073863633', '0.00932531020220595']\n", - " 4 ['0.3249136075106534', '0.9628694661458332', '0.004107444069602284', '0.008666896446078431']\n", - " 2 ['0.35265292080965904', '0.9414640299479167', '0.004622765743371227', '0.009477539062499929']\n", - " 5 ['0.3596263538707386', '0.9413741766237744', '0.0042079486268939426', '0.009104243259803968']\n", - " 5 ['0.35622773141571973', '0.9631229894301471', '0.0038008996212121615', '0.00926949295343138']\n", - " 1 ['0.4999950247099905', '0.9423159849877452', '0.0036355868252840873', '0.008874655330882386']\n", - " 2 ['0.49811638109611744', '0.9633417585784314', '0.0041074810606061', '0.009086626838235357']\n", - " 2 ['0.4981499874230587', '0.9843756223192401', '0.004206284031723462', '0.009136316636029318']\n", - " natural ['0.2652316191702178', '0.9189152496936275', '0.028457068241003802', '0.011214575674019622']\n", - " 3 ['0.5366208718039773', '0.9426712335324754', '0.004193300189394011', '0.00876943550857845']\n", - " reverse_trendelenburg ['0.8627786902225378', '0.9427326516544118', '0.03070800781250005', '0.010441367953431424']\n", - " 4 ['0.536828261866714', '0.9648567708333333', '0.00443792169744317', '0.007655292585784235']\n", - " trendeleburg ['0.9092449766216857', '0.9440838982077207', '0.05851126006155305', '0.01419031479779409']\n", - " 0 ['0.16587118437795928', '0.3818634033203125', '0.004725952148437518', '0.010094520718443634']\n", - " 5 ['0.18457039572975853', '0.3817957141352635', '0.005019956646543561', '0.010063165402879881']\n", - " 1 ['0.19968412457090434', '0.38190079034543506', '0.004037623549952657', '0.009629887599571063']\n", - " 0 ['0.2048508615204782', '0.3818134023628983', '0.00521956010298294', '0.009812993068321063']\n", - " 1 ['0.2179007050485322', '0.3819432756012561', '0.004511496803977277', '0.009861916934742698']\n", - " 5 ['0.22321859648733428', '0.3819884535845588', '0.005096158114346605', '0.010281048943014681']\n", - " 2 ['0.2363887255119555', '0.3818835209865196', '0.004943126331676123', '0.010016659007352935']\n", - " 0 ['0.24191182454427085', '0.38182753619025733', '0.005009617660984872', '0.010204407935049009']\n", - " 2 ['0.2546861775716146', '0.3819860720166973', '0.0053478449041193254', '0.010141864851409332']\n", - " 5 ['0.2601831609552557', '0.3821086689070159', '0.004950764973958299', '0.010237606272977928']\n", - " 3 ['0.27290805701053505', '0.3821193321078431', '0.0050712631687973575', '0.010458505667892137']\n", - " 0 ['0.2786404511422822', '0.38203175264246325', '0.005030369614109853', '0.010347876455269633']\n", - " 3 ['0.29124179724491006', '0.3820396513097426', '0.004749552408854163', '0.010192823223039216']\n", - " 5 ['0.2966511396928267', '0.3821407781862745', '0.005036565607244303', '0.010056487438725448']\n", - " 4 ['0.30929721716678504', '0.38233169854856003', '0.0047427460641571995', '0.009682114545036757']\n", - " 0 ['0.31502907492897725', '0.3821165915096507', '0.004972330729166641', '0.010198495902267124']\n", - " 4 ['0.3276321688565341', '0.3824337828393076', '0.005286532315340875', '0.009880059934129937']\n", - " 5 ['0.33330375902580495', '0.38239826277190564', '0.0047296327533143945', '0.010225686465992645']\n", - " 5 ['0.34590320933948865', '0.38236894196155025', '0.004852627840909118', '0.010301417930453471']\n", - " 0 ['0.35132405598958333', '0.3824903181487439', '0.004711840080492413', '0.010260201248468104']\n", - " 5 ['0.36408240116003787', '0.38248385560278797', '0.0049909002130681945', '0.010304673138786746']\n", - " 5 ['0.3695796157374527', '0.3825164794921875', '0.004559770063920443', '0.010000119676776997']\n", - " 0 ['0.3845553496389678', '0.38274670170802694', '0.004613185073390147', '0.010073433670343135']\n", - " 5 ['0.40287364612926135', '0.38273882697610295', '0.004986239346590926', '0.010265634574142146']\n", - " 1 ['0.4178893488103693', '0.38271556181066174', '0.0043999689275567855', '0.009955623851102935']\n", - " 0 ['0.422972412109375', '0.3825348379097733', '0.0045865885416666585', '0.010432823031556349']\n", - " 1 ['0.43551524769176136', '0.3828317200903799', '0.00453901811079549', '0.009922688802083357']\n", - " 5 ['0.4408444121389678', '0.3829491230085784', '0.004507908676609884', '0.010030110677083315']\n", - " 2 ['0.4537920587713068', '0.3825503958907782', '0.005120220762310612', '0.010302997663909352']\n", - " 0 ['0.4591027647076231', '0.38279470406326593', '0.004502064098011349', '0.010210080614276973']\n", - " 2 ['0.4715062921697443', '0.3827597704120711', '0.004608450224905303', '0.010430261948529418']\n", - " 5 ['0.4769446540601326', '0.3828994571461397', '0.004414802320075739', '0.010206083409926459']\n", - " 3 ['0.4893110240589489', '0.38275467218137255', '0.004593875769412892', '0.010356110217524472']\n", - " 0 ['0.4946734804095644', '0.3827644856770833', '0.004666933001893914', '0.010176882276348054']\n", - " 3 ['0.5070133463541666', '0.3827826167087929', '0.00477250532670459', '0.010433205997242623']\n", - " 5 ['0.5126307077118845', '0.38296329273897056', '0.004330573804450788', '0.009986835554534335']\n", - " 4 ['0.5249030095880682', '0.38268938849954043', '0.0051524029356061485', '0.009537162032781876']\n", - " 0 ['0.5305247173887311', '0.3826352347579657', '0.004638819839015151', '0.009919960171568598']\n", - " 4 ['0.5427307683771307', '0.3827019545611213', '0.0048346132220643545', '0.0098486567478554']\n", - " 5 ['0.5481516150272254', '0.3827708524816177', '0.004657796223958344', '0.01028181487438723']\n", - " 5 ['0.5604993045691288', '0.38262589996936275', '0.004909889914772814', '0.010216710707720567']\n", - " 0 ['0.565989472360322', '0.38267627192478554', '0.004707179214015089', '0.010464513442095535']\n", - " 5 ['0.5785705381451232', '0.38266199448529414', '0.004601495916193188', '0.01006065219056368']\n", - " 5 ['0.583848359079072', '0.3826736869064032', '0.004487970525568152', '0.010092893114276968']\n", - " 0 ['0.5987218683416193', '0.3823198744829963', '0.004796364524147778', '0.010029464422487755']\n", - " 5 ['0.617369902639678', '0.38252314548866423', '0.004818004261363584', '0.01017716950061276']\n", - " 1 ['0.6322139855587121', '0.38256816789215686', '0.004319069602272796', '0.00979784198835787']\n", - " 0 ['0.6372403231534092', '0.38235124176623775', '0.004644442471590904', '0.010348067938112715']\n", - " 1 ['0.6502870131983902', '0.3823184622970282', '0.004205063328598491', '0.009785419538909323']\n", - " 5 ['0.6552334502249053', '0.38218898399203427', '0.004599831321022707', '0.010305989583333341']\n", - " 2 ['0.6682829145951705', '0.3821959013097427', '0.00478641394412882', '0.010060987285539225']\n", - " 0 ['0.6739927719578598', '0.38205502977558214', '0.004979654947916745', '0.010139327703737766']\n", - " 2 ['0.6866014145359849', '0.3818985404220282', '0.005006806344696968', '0.010106488396139701']\n", - " 5 ['0.6921630859375', '0.3820809757008272', '0.004916548295454515', '0.010389547909007368']\n", - " 3 ['0.7047801994554924', '0.38196439855238973', '0.004862097537878807', '0.010205939797794106']\n", - " 0 ['0.7103043249881629', '0.3818713737936581', '0.0047473514441287445', '0.010310465494791643']\n", - " 3 ['0.7228476784446023', '0.38184248381969976', '0.004995930989583286', '0.010390074486825995']\n", - " 5 ['0.728689667672822', '0.38188597436044736', '0.00474779533617431', '0.010164507697610292']\n", - " 4 ['0.7413370768229166', '0.3817519962086397', '0.005275065104166754', '0.00967304304534311']\n", - " 0 ['0.7469070342092803', '0.38166058708639705', '0.004968187736742458', '0.010206705729166654']\n", - " 4 ['0.7595242956912879', '0.3817976888020833', '0.005004586884469697', '0.009735514322916694']\n", - " 5 ['0.7650375458688448', '0.38170449649586397', '0.004754305752840859', '0.010270972158394565']\n", - " 5 ['0.7778333999171402', '0.3815218457988664', '0.004798103101325779', '0.010090643190870108']\n", - " 0 ['0.783281434955019', '0.3814627853094363', '0.004878151633522676', '0.010273820465686256']\n", - " 5 ['0.7958795720880683', '0.38156327789905026', '0.004940222537878847', '0.009875943053002434']\n", - " 5 ['0.8014029947916667', '0.3815045285692402', '0.004948656486742475', '0.010133463541666665']\n", - " 0 ['0.8166511304450758', '0.38151131424249385', '0.004765920928030409', '0.0100871486289828']\n", - " 5 ['0.8354467033617424', '0.38141405292585784', '0.004737511837121122', '0.009976495481004877']\n", - " 1 ['0.8502276056463068', '0.3816766237745098', '0.004263065222537943', '0.009814405254289227']\n", - " 0 ['0.855240996389678', '0.3814403578814338', '0.004703554095643936', '0.010118815104166679']\n", - " 1 ['0.8682825816761364', '0.38148327397365195', '0.0044398082386363225', '0.009928624770220607']\n", - " 5 ['0.8734008419152461', '0.38144994399126836', '0.00466108842329549', '0.010043921377144605']\n", - " 2 ['0.8865554717092803', '0.3812796678730086', '0.0048915423768939315', '0.010303715724571061']\n", - " 0 ['0.8920348011363637', '0.38133043476179534', '0.004733220880681732', '0.009827067057291639']\n", - " 2 ['0.9045032108191289', '0.38140860763250617', '0.004780421401515134', '0.010118120978860279']\n" + "Found 19 sheets in yolo_data.json\n" ] } ], @@ -378,60 +75,280 @@ "# Load yolo_data.json\n", "PATH_TO_YOLO_DATA = '../../data/yolo_data.json'\n", "PATH_TO_REGISTERED_IMAGES = '../../data/registered_images'\n", - "CROPPED_IMAGE_PATH = '../../data/cropped_blood_press_and_hr.jpg'\n", + "UNIFIED_IMAGE_PATH = '../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png'\n", "with open(PATH_TO_YOLO_DATA) as json_file:\n", " yolo_data = json.load(json_file)\n", "\n", - "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")\n", + "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's select relevant bounding boxes from the blood pressure and HR zone. \n", "\n", - "# Iterate over all images\n", - "for sheet, bounding_boxes in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " for bounding_box in bounding_boxes:\n", - " print(f\" {bounding_box.split(' ')[0]} {bounding_box.split(' ')[1:]}\")\n", + "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def YOLO_to_pixels(x_center, y_center, width, height, image_width, image_height):\n", + " \"\"\"\n", + " Convert YOLO bounding box format to pixel coordinates\n", + "\n", + " Args:\n", + " x_center: float, x center of the bounding box\n", + " y_center: float, y center of the bounding box\n", + " width: float, width of the bounding box\n", + " height: float, height of the bounding box\n", + " image_width: int, width of the image\n", + " image_height: int, height of the image\n", + "\n", + " Returns:\n", + " A single tuple with the following values:\n", + " x_min: int, minimum x coordinate of the bounding box in pixels\n", + " y_min: int, minimum y coordinate of the bounding box in pixels\n", + " x_max: int, maximum x coordinate of the bounding box in pixels\n", + " y_max: int, maximum y coordinate of the bounding box in pixels\n", + " \"\"\"\n", + " x_min = int((float(x_center) * image_width) - (width * image_width) / 2)\n", + " y_min = int((float(y_center) * image_height) - (height * image_height) / 2)\n", + " x_max = int((float(x_center) * image_width) + (width * image_width) / 2)\n", + " y_max = int((float(y_center) * image_height) + (height * image_height) / 2)\n", + " return x_min, y_min, x_max, y_max\n", + "\n", + "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path) -> List[str]:\n", + " \"\"\"\n", + " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", + " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", + " Return the bounding boxes that are within the selected region.\n", + "\n", + " Args:\n", + " sheet_data: List of bounding boxes in YOLO format.\n", + " path_to_image: Path to the image file.\n", + "\n", + " Returns:\n", + " Bounding boxes that are within the selected region, in YOLO format.\n", + " \"\"\"\n", + " # Load the image\n", + " image = cv2.imread(path_to_image)\n", + "\n", + " # Display the image and allow the user to select a ROI\n", + " resized_image = cv2.resize(image, (800, 600))\n", + " roi = cv2.selectROI(\"Select ROI\", resized_image)\n", + " print(f\"ROI selected: {roi}\")\n", + "\n", + " # The function returns a tuple (x, y, width, height)\n", + " x, y, w, h = roi\n", + " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", + "\n", + " # Draw a rectangle around the selected region\n", + " cv2.rectangle(img=resized_image, pt1=(x, y), pt2=(x + w, y + h), color=(0, 255, 0), thickness=1)\n", "\n", - " # Load the image\n", - " registered_image = Image.open(full_image_path)\n", - " registered_image.show() # Take a look at the image\n", + " # Close all OpenCV windows\n", + " cv2.destroyAllWindows()\n", "\n", - " import cv2\n", - " import numpy as np\n", + " # List of bounding boxes that are within the selected region\n", + " bounding_boxes_within_region = []\n", "\n", - " # Load full and cropped images using OpenCV\n", - " full_image_cv = cv2.imread(full_image_path)\n", - " cropped_template_cv = cv2.imread(CROPPED_IMAGE_PATH)\n", + " # Identify bounding boxes that are within the selected region\n", + " for bounding_box in sheet_data:\n", + " # Bounding boxes are in YOLO format, let's convert to pixels and see if they are within the selected region\n", + " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ')\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", + " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", + " print(f\"Bounding box {bounding_box} is within the selected region\")\n", "\n", - " # Perform template matching to detect the cropped section in the full image\n", - " result = cv2.matchTemplate(full_image_cv, cropped_template_cv, cv2.TM_CCOEFF_NORMED)\n", + " # Generate a random color for the bounding box in (0, 255, 0) scalar format\n", + " generate_color = lambda: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n", "\n", - " # Get the best match position\n", - " min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\n", + " # Draw a rectangle around the bounding box\n", + " cv2.rectangle(img=resized_image, pt1=(x_min, y_min), pt2=(x_max, y_max), color=generate_color(), thickness=1)\n", "\n", - " # Extract the top-left corner of the matching region\n", - " top_left = max_loc\n", - " height, width, _ = cropped_template_cv.shape\n", + " # Append the bounding box to the list of bounding boxes within the selected region\n", + " bounding_boxes_within_region.append(bounding_box)\n", "\n", - " # Define the bottom-right corner based on the cropped image's dimensions\n", - " bottom_right = (top_left[0] + width, top_left[1] + height)\n", + " # Display the image with the selected region and bounding boxes\n", + " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + " resized_image = Image.fromarray(resized_image)\n", + " resized_image.show()\n", "\n", - " # Draw a rectangle around the detected region\n", - " detected_image = full_image_cv.copy()\n", - " cv2.rectangle(detected_image, top_left, bottom_right, (0, 255, 0), 3)\n", + " return bounding_boxes_within_region\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets use these functions to get the relevant bounding boxes for clustering." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "ROI selected: (103, 220, 632, 203)\n", + "Selected region: x=103, y=220, w=632, h=203\n", + "Bounding box 5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088 is within the selected region\n", + "Bounding box 2 0.13794031316583807 0.39902471804151346 0.00507585005326705 0.010464657054227944 is within the selected region\n", + "Bounding box 2 0.1434342216722893 0.39900106991038603 0.005159921357125952 0.010430668849571112 is within the selected region\n", + "Bounding box 0 0.14874451145981296 0.3989715576171875 0.004878808223839959 0.010312284581801445 is within the selected region\n", + "Bounding box 2 0.13839600996537643 0.41458115521599265 0.005050483472419515 0.010243135340073484 is within the selected region\n", + "Bounding box 1 0.14340712576201467 0.4147241689644608 0.004516906738281257 0.010044136795343106 is within the selected region\n", + "Bounding box 0 0.1484095810398911 0.4144839298023897 0.004963064482717799 0.010348642386642182 is within the selected region\n", + "Bounding box 2 0.13811845259232955 0.4301294424019608 0.0047374933416193254 0.010339403339460762 is within the selected region\n", + "Bounding box 0 0.14345488114790483 0.4301910041360294 0.005082036798650574 0.009986979166666687 is within the selected region\n", + "Bounding box 0 0.14887493711529357 0.4302002192478554 0.004909926905776518 0.010090140548406845 is within the selected region\n", + "Bounding box 1 0.1376401912804806 0.4458321126302084 0.004371883507930857 0.010252517999387256 is within the selected region\n", + "Bounding box 9 0.14284934303977273 0.4458483408011642 0.004968835079308731 0.009987410003063746 is within the selected region\n", + "Bounding box 0 0.14849560824307528 0.4458433143765319 0.005082073789654362 0.00991033815870096 is within the selected region\n", + "Bounding box 1 0.13771269364790484 0.4615478994332108 0.004487739331794499 0.010035807291666643 is within the selected region\n", + "Bounding box 8 0.14295229825106534 0.4613467467064951 0.004853219696969724 0.010126282935049025 is within the selected region\n", + "Bounding box 0 0.1485103260387074 0.4615417959175858 0.0049125347715435475 0.009873764935661777 is within the selected region\n", + "Bounding box 1 0.1379021708170573 0.47700669232536763 0.004599766586766113 0.010043370863970613 is within the selected region\n", + "Bounding box 7 0.14299120353929923 0.47685860428155635 0.0051032603870738436 0.009644464231004901 is within the selected region\n", + "Bounding box 0 0.14867162068684897 0.47691504384957106 0.00507972486091382 0.009978697533701009 is within the selected region\n", + "Bounding box 1 0.1378735536517519 0.49277197744332113 0.004180575284090909 0.009765864353553921 is within the selected region\n", + "Bounding box 6 0.14319538925633285 0.49268571442248776 0.004992666533499057 0.010155053232230371 is within the selected region\n", + "Bounding box 0 0.14871603763464725 0.49253202550551467 0.00507732044566761 0.010084826899509791 is within the selected region\n", + "Bounding box 1 0.13820145578095405 0.5083780445772059 0.004347099535393001 0.009914790134803897 is within the selected region\n", + "Bounding box 5 0.14322742808948863 0.508258846507353 0.004736642548532205 0.01018832337622555 is within the selected region\n", + "Bounding box 0 0.14874616680723246 0.5081815353094363 0.00521840413411459 0.010050359987745172 is within the selected region\n", + "Bounding box 1 0.13802489309599908 0.523800048828125 0.00428908839370265 0.010019291896446125 is within the selected region\n", + "Bounding box 4 0.14306269327799478 0.5237577071844363 0.004656455300071027 0.009921683517156943 is within the selected region\n", + "Bounding box 0 0.14873589717980587 0.5236627077588848 0.004765218098958357 0.010106033624387223 is within the selected region\n", + "Bounding box 1 0.13793073711973247 0.5392588895909927 0.004460033069957375 0.009839680989583321 is within the selected region\n", + "Bounding box 3 0.14305358424331202 0.5392010857077206 0.005218737053148681 0.010009191176470589 is within the selected region\n", + "Bounding box 0 0.14886316472833808 0.5393448893229167 0.005275989879261367 0.009938055300245052 is within the selected region\n", + "Bounding box 1 0.1380303446451823 0.555029177198223 0.004406285141453581 0.009690515854779425 is within the selected region\n", + "Bounding box 2 0.14305828672466858 0.5549668016620711 0.005177131421638254 0.010077694163602935 is within the selected region\n", + "Bounding box 0 0.1487662529222893 0.5549629480698529 0.005158053311434679 0.010149260876225474 is within the selected region\n", + "Bounding box 1 0.13802087032433713 0.5705203067555147 0.00423336144649622 0.009863855698529433 is within the selected region\n", + "Bounding box 1 0.1427608975497159 0.5705034562653186 0.00451282848011364 0.009982000612745123 is within the selected region\n", + "Bounding box 0 0.14811153989849668 0.570541752833946 0.005231387976444124 0.009821633731617596 is within the selected region\n", + "Bounding box 1 0.13790817723129734 0.5862586885340073 0.0043769605232007736 0.009684579886642175 is within the selected region\n", + "Bounding box 0 0.14295925255977748 0.5861784572227329 0.004822036280776515 0.010025227864583375 is within the selected region\n", + "Bounding box 0 0.14861735488429212 0.5860258453967524 0.0050772279681581545 0.009886690027573475 is within the selected region\n", + "Bounding box 9 0.14040775645862924 0.6018566415824143 0.004934387207031238 0.01002618527879895 is within the selected region\n", + "Bounding box 0 0.14577925710967093 0.6019493432138481 0.0050696448123816185 0.010319776348039267 is within the selected region\n", + "Bounding box 8 0.140310234301018 0.6175523226868873 0.0050445371685606255 0.010135569852941173 is within the selected region\n", + "Bounding box 0 0.14575131965406013 0.6176652736289829 0.004873851429332388 0.010166159237132377 is within the selected region\n", + "Bounding box 7 0.14023251157818417 0.6328832289751838 0.005249772505326683 0.009667442172181406 is within the selected region\n", + "Bounding box 0 0.1457973410866477 0.6331068809359681 0.004873953154592797 0.010127383961397118 is within the selected region\n", + "Bounding box 6 0.1402722352923769 0.6487937777650122 0.00507311271898675 0.010058737362132364 is within the selected region\n", + "Bounding box 0 0.14590905391808712 0.6488928462009804 0.004736217151988631 0.009903301164215672 is within the selected region\n", + "Bounding box 5 0.1405757834694602 0.6640935441559437 0.004903878876657192 0.010014792049632404 is within the selected region\n", + "Bounding box 0 0.14603031042850378 0.6641141285615808 0.004874729965672342 0.010013547411151902 is within the selected region\n", + "Bounding box 4 0.14035383744673297 0.6796077952665441 0.005218505859375 0.009727807138480316 is within the selected region\n", + "Bounding box 0 0.14601876923532198 0.6796538947610294 0.004878577030066278 0.009756625306372557 is within the selected region\n", + "Bounding box 3 0.14052866155450994 0.69531494140625 0.004854838053385407 0.010225183823529327 is within the selected region\n", + "Bounding box 0 0.14593774044152463 0.6951335114123774 0.004789058800899609 0.01004193474264703 is within the selected region\n", + "Bounding box 0 0.16587118437795928 0.3818634033203125 0.004725952148437518 0.010094520718443634 is within the selected region\n", + "Bounding box 5 0.18457039572975853 0.3817957141352635 0.005019956646543561 0.010063165402879881 is within the selected region\n", + "Bounding box 1 0.19968412457090434 0.38190079034543506 0.004037623549952657 0.009629887599571063 is within the selected region\n", + "Bounding box 0 0.2048508615204782 0.3818134023628983 0.00521956010298294 0.009812993068321063 is within the selected region\n", + "Bounding box 1 0.2179007050485322 0.3819432756012561 0.004511496803977277 0.009861916934742698 is within the selected region\n", + "Bounding box 5 0.22321859648733428 0.3819884535845588 0.005096158114346605 0.010281048943014681 is within the selected region\n", + "Bounding box 2 0.2363887255119555 0.3818835209865196 0.004943126331676123 0.010016659007352935 is within the selected region\n", + "Bounding box 0 0.24191182454427085 0.38182753619025733 0.005009617660984872 0.010204407935049009 is within the selected region\n", + "Bounding box 2 0.2546861775716146 0.3819860720166973 0.0053478449041193254 0.010141864851409332 is within the selected region\n", + "Bounding box 5 0.2601831609552557 0.3821086689070159 0.004950764973958299 0.010237606272977928 is within the selected region\n", + "Bounding box 3 0.27290805701053505 0.3821193321078431 0.0050712631687973575 0.010458505667892137 is within the selected region\n", + "Bounding box 0 0.2786404511422822 0.38203175264246325 0.005030369614109853 0.010347876455269633 is within the selected region\n", + "Bounding box 3 0.29124179724491006 0.3820396513097426 0.004749552408854163 0.010192823223039216 is within the selected region\n", + "Bounding box 5 0.2966511396928267 0.3821407781862745 0.005036565607244303 0.010056487438725448 is within the selected region\n", + "Bounding box 4 0.30929721716678504 0.38233169854856003 0.0047427460641571995 0.009682114545036757 is within the selected region\n", + "Bounding box 0 0.31502907492897725 0.3821165915096507 0.004972330729166641 0.010198495902267124 is within the selected region\n", + "Bounding box 4 0.3276321688565341 0.3824337828393076 0.005286532315340875 0.009880059934129937 is within the selected region\n", + "Bounding box 5 0.33330375902580495 0.38239826277190564 0.0047296327533143945 0.010225686465992645 is within the selected region\n", + "Bounding box 5 0.34590320933948865 0.38236894196155025 0.004852627840909118 0.010301417930453471 is within the selected region\n", + "Bounding box 0 0.35132405598958333 0.3824903181487439 0.004711840080492413 0.010260201248468104 is within the selected region\n", + "Bounding box 5 0.36408240116003787 0.38248385560278797 0.0049909002130681945 0.010304673138786746 is within the selected region\n", + "Bounding box 5 0.3695796157374527 0.3825164794921875 0.004559770063920443 0.010000119676776997 is within the selected region\n", + "Bounding box 0 0.3845553496389678 0.38274670170802694 0.004613185073390147 0.010073433670343135 is within the selected region\n", + "Bounding box 5 0.40287364612926135 0.38273882697610295 0.004986239346590926 0.010265634574142146 is within the selected region\n", + "Bounding box 1 0.4178893488103693 0.38271556181066174 0.0043999689275567855 0.009955623851102935 is within the selected region\n", + "Bounding box 0 0.422972412109375 0.3825348379097733 0.0045865885416666585 0.010432823031556349 is within the selected region\n", + "Bounding box 1 0.43551524769176136 0.3828317200903799 0.00453901811079549 0.009922688802083357 is within the selected region\n", + "Bounding box 5 0.4408444121389678 0.3829491230085784 0.004507908676609884 0.010030110677083315 is within the selected region\n", + "Bounding box 2 0.4537920587713068 0.3825503958907782 0.005120220762310612 0.010302997663909352 is within the selected region\n", + "Bounding box 0 0.4591027647076231 0.38279470406326593 0.004502064098011349 0.010210080614276973 is within the selected region\n", + "Bounding box 2 0.4715062921697443 0.3827597704120711 0.004608450224905303 0.010430261948529418 is within the selected region\n", + "Bounding box 5 0.4769446540601326 0.3828994571461397 0.004414802320075739 0.010206083409926459 is within the selected region\n", + "Bounding box 3 0.4893110240589489 0.38275467218137255 0.004593875769412892 0.010356110217524472 is within the selected region\n", + "Bounding box 0 0.4946734804095644 0.3827644856770833 0.004666933001893914 0.010176882276348054 is within the selected region\n", + "Bounding box 3 0.5070133463541666 0.3827826167087929 0.00477250532670459 0.010433205997242623 is within the selected region\n", + "Bounding box 5 0.5126307077118845 0.38296329273897056 0.004330573804450788 0.009986835554534335 is within the selected region\n", + "Bounding box 4 0.5249030095880682 0.38268938849954043 0.0051524029356061485 0.009537162032781876 is within the selected region\n", + "Bounding box 0 0.5305247173887311 0.3826352347579657 0.004638819839015151 0.009919960171568598 is within the selected region\n", + "Bounding box 4 0.5427307683771307 0.3827019545611213 0.0048346132220643545 0.0098486567478554 is within the selected region\n", + "Bounding box 5 0.5481516150272254 0.3827708524816177 0.004657796223958344 0.01028181487438723 is within the selected region\n", + "Bounding box 5 0.5604993045691288 0.38262589996936275 0.004909889914772814 0.010216710707720567 is within the selected region\n", + "Bounding box 0 0.565989472360322 0.38267627192478554 0.004707179214015089 0.010464513442095535 is within the selected region\n", + "Bounding box 5 0.5785705381451232 0.38266199448529414 0.004601495916193188 0.01006065219056368 is within the selected region\n", + "Bounding box 5 0.583848359079072 0.3826736869064032 0.004487970525568152 0.010092893114276968 is within the selected region\n", + "Bounding box 0 0.5987218683416193 0.3823198744829963 0.004796364524147778 0.010029464422487755 is within the selected region\n", + "Bounding box 5 0.617369902639678 0.38252314548866423 0.004818004261363584 0.01017716950061276 is within the selected region\n", + "Bounding box 1 0.6322139855587121 0.38256816789215686 0.004319069602272796 0.00979784198835787 is within the selected region\n", + "Bounding box 0 0.6372403231534092 0.38235124176623775 0.004644442471590904 0.010348067938112715 is within the selected region\n", + "Bounding box 1 0.6502870131983902 0.3823184622970282 0.004205063328598491 0.009785419538909323 is within the selected region\n", + "Bounding box 5 0.6552334502249053 0.38218898399203427 0.004599831321022707 0.010305989583333341 is within the selected region\n", + "Bounding box 2 0.6682829145951705 0.3821959013097427 0.00478641394412882 0.010060987285539225 is within the selected region\n", + "Bounding box 0 0.6739927719578598 0.38205502977558214 0.004979654947916745 0.010139327703737766 is within the selected region\n", + "Bounding box 2 0.6866014145359849 0.3818985404220282 0.005006806344696968 0.010106488396139701 is within the selected region\n", + "Bounding box 5 0.6921630859375 0.3820809757008272 0.004916548295454515 0.010389547909007368 is within the selected region\n", + "Bounding box 3 0.7047801994554924 0.38196439855238973 0.004862097537878807 0.010205939797794106 is within the selected region\n", + "Bounding box 0 0.7103043249881629 0.3818713737936581 0.0047473514441287445 0.010310465494791643 is within the selected region\n", + "Bounding box 3 0.7228476784446023 0.38184248381969976 0.004995930989583286 0.010390074486825995 is within the selected region\n", + "Bounding box 5 0.728689667672822 0.38188597436044736 0.00474779533617431 0.010164507697610292 is within the selected region\n", + "Bounding box 4 0.7413370768229166 0.3817519962086397 0.005275065104166754 0.00967304304534311 is within the selected region\n", + "Bounding box 0 0.7469070342092803 0.38166058708639705 0.004968187736742458 0.010206705729166654 is within the selected region\n", + "Bounding box 4 0.7595242956912879 0.3817976888020833 0.005004586884469697 0.009735514322916694 is within the selected region\n", + "Bounding box 5 0.7650375458688448 0.38170449649586397 0.004754305752840859 0.010270972158394565 is within the selected region\n", + "Bounding box 5 0.7778333999171402 0.3815218457988664 0.004798103101325779 0.010090643190870108 is within the selected region\n", + "Bounding box 0 0.783281434955019 0.3814627853094363 0.004878151633522676 0.010273820465686256 is within the selected region\n", + "Bounding box 5 0.7958795720880683 0.38156327789905026 0.004940222537878847 0.009875943053002434 is within the selected region\n", + "Bounding box 5 0.8014029947916667 0.3815045285692402 0.004948656486742475 0.010133463541666665 is within the selected region\n", + "Bounding box 0 0.8166511304450758 0.38151131424249385 0.004765920928030409 0.0100871486289828 is within the selected region\n", + "Bounding box 5 0.8354467033617424 0.38141405292585784 0.004737511837121122 0.009976495481004877 is within the selected region\n", + "Bounding box 1 0.8502276056463068 0.3816766237745098 0.004263065222537943 0.009814405254289227 is within the selected region\n", + "Bounding box 0 0.855240996389678 0.3814403578814338 0.004703554095643936 0.010118815104166679 is within the selected region\n", + "Bounding box 1 0.8682825816761364 0.38148327397365195 0.0044398082386363225 0.009928624770220607 is within the selected region\n", + "Bounding box 5 0.8734008419152461 0.38144994399126836 0.00466108842329549 0.010043921377144605 is within the selected region\n", + "Bounding box 2 0.8865554717092803 0.3812796678730086 0.0048915423768939315 0.010303715724571061 is within the selected region\n", + "Bounding box 0 0.8920348011363637 0.38133043476179534 0.004733220880681732 0.009827067057291639 is within the selected region\n", + "Bounding box 2 0.9045032108191289 0.38140860763250617 0.004780421401515134 0.010118120978860279 is within the selected region\n" + ] + } + ], + "source": [ + "# Iterate over all images\n", + "for sheet, bounding_boxes in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " print(f\"Full image path: {full_image_path}\")\n", "\n", - " # Display the detected image\n", - " detected_image = cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB)\n", - " detected_image = Image.fromarray(detected_image)\n", - " detected_image.show()\n", - " \n", - " break\n", + " # Call the analyze_sheet function with data from the loop\n", + " relevant_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", "\n", - " # # Save and display the result\n", - " # detected_output_path = '/mnt/data/detected_section.png'\n", - " # cv2.imwrite(detected_output_path, detected_image)\n", + " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", + " # For now, we will just print the relevant bounding boxes\n", + " print(f\"Relevant bounding boxes: {relevant_bounding_boxes}\")\n", "\n", - " # detected_output_path\n", - "\n" + " # Break after the first image\n", + " break" ] } ], diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 7e5243a..917e269 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "id": "5f322da5-10f8-49ee-a81a-5edc7bac12cd", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "id": "95997450-a2a0-4035-b040-3c8fb532836b", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "id": "820c4efa-bb9c-489c-9e44-07417836f3e4", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "id": "7ca02ed3-a7fc-44ea-9f47-2c3b90a0ea48", "metadata": {}, "outputs": [], @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "id": "cd2294bd-3749-4872-b7e8-918218191c88", "metadata": {}, "outputs": [], @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 39, "id": "3a24eb52-c5f0-4c69-972f-6193117efe19", "metadata": {}, "outputs": [], @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 40, "id": "36bfc243-75e8-4aff-ba61-5a92a06ba7b6", "metadata": {}, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 41, "id": "a18e97c6-e438-461a-a701-2bf320da275f", "metadata": {}, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 42, "id": "7bb3dbbb", "metadata": {}, "outputs": [], @@ -349,7 +349,7 @@ " # Save original image wih bounding boxes\n", " # Make a copy of the image\n", " pil_img_no_boxes = pil_img.copy()\n", - " pil_img_no_boxes = pil_img.resize((800, 600))\n", + " #pil_img_no_boxes = pil_img.resize((800, 600))\n", "\n", " # You only need to do this drawing if we are intentionally being visual\n", " if show_images:\n", @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 43, "id": "77c8599f", "metadata": {}, "outputs": [ From 563b20be4fccd1241bb60dc6231f687cf84f27b2 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Fri, 18 Oct 2024 00:59:56 -0400 Subject: [PATCH 13/55] K-Means clustering completed. Need to test and tune model. --- experiments/clustering/clustering.ipynb | 582 ++++++++++++++++++------ poetry.lock | 74 ++- pyproject.toml | 1 + 3 files changed, 517 insertions(+), 140 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 614cfd8..f7e972f 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,9 @@ "from typing import List\n", "\n", "import cv2\n", - "from PIL import Image\n" + "import numpy as np\n", + "from PIL import Image, ImageDraw\n", + "from sklearn.cluster import KMeans" ] }, { @@ -60,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -93,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +164,6 @@ " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ')\n", " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", - " print(f\"Bounding box {bounding_box} is within the selected region\")\n", "\n", " # Generate a random color for the bounding box in (0, 255, 0) scalar format\n", " generate_color = lambda: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n", @@ -181,6 +182,43 @@ " return bounding_boxes_within_region\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a function for K-means clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def cluster_kmeans(bounding_boxes: List[str], number_of_clusters: int) -> List[int]:\n", + " \"\"\"\n", + " Cluster bounding boxes using K-Means clustering algorithm.\n", + "\n", + " Args:\n", + " bounding_boxes: List of bounding boxes in YOLO format.\n", + " number_of_clusters: Number of clusters to use in K-Means clustering.\n", + "\n", + " Returns:\n", + " List of cluster labels.\n", + " \"\"\"\n", + " # Convert to a NumPy array (using only x_center and y_center)\n", + " data = np.array([[float(box.split(' ')[1]), float(box.split(' ')[2])] for box in bounding_boxes])\n", + "\n", + " # Apply K-Means\n", + " kmeans = KMeans(n_clusters=number_of_clusters, init='k-means++', n_init=10, max_iter=300, tol=1e-8, random_state=42)\n", + " kmeans.fit(data)\n", + "\n", + " # Get cluster labels\n", + " labels = kmeans.predict(data)\n", + "\n", + " return labels" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -190,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -199,137 +237,137 @@ "text": [ "Sheet: RC_0001_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "ROI selected: (103, 220, 632, 203)\n", - "Selected region: x=103, y=220, w=632, h=203\n", - "Bounding box 5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088 is within the selected region\n", - "Bounding box 2 0.13794031316583807 0.39902471804151346 0.00507585005326705 0.010464657054227944 is within the selected region\n", - "Bounding box 2 0.1434342216722893 0.39900106991038603 0.005159921357125952 0.010430668849571112 is within the selected region\n", - "Bounding box 0 0.14874451145981296 0.3989715576171875 0.004878808223839959 0.010312284581801445 is within the selected region\n", - "Bounding box 2 0.13839600996537643 0.41458115521599265 0.005050483472419515 0.010243135340073484 is within the selected region\n", - "Bounding box 1 0.14340712576201467 0.4147241689644608 0.004516906738281257 0.010044136795343106 is within the selected region\n", - "Bounding box 0 0.1484095810398911 0.4144839298023897 0.004963064482717799 0.010348642386642182 is within the selected region\n", - "Bounding box 2 0.13811845259232955 0.4301294424019608 0.0047374933416193254 0.010339403339460762 is within the selected region\n", - "Bounding box 0 0.14345488114790483 0.4301910041360294 0.005082036798650574 0.009986979166666687 is within the selected region\n", - "Bounding box 0 0.14887493711529357 0.4302002192478554 0.004909926905776518 0.010090140548406845 is within the selected region\n", - "Bounding box 1 0.1376401912804806 0.4458321126302084 0.004371883507930857 0.010252517999387256 is within the selected region\n", - "Bounding box 9 0.14284934303977273 0.4458483408011642 0.004968835079308731 0.009987410003063746 is within the selected region\n", - "Bounding box 0 0.14849560824307528 0.4458433143765319 0.005082073789654362 0.00991033815870096 is within the selected region\n", - "Bounding box 1 0.13771269364790484 0.4615478994332108 0.004487739331794499 0.010035807291666643 is within the selected region\n", - "Bounding box 8 0.14295229825106534 0.4613467467064951 0.004853219696969724 0.010126282935049025 is within the selected region\n", - "Bounding box 0 0.1485103260387074 0.4615417959175858 0.0049125347715435475 0.009873764935661777 is within the selected region\n", - "Bounding box 1 0.1379021708170573 0.47700669232536763 0.004599766586766113 0.010043370863970613 is within the selected region\n", - "Bounding box 7 0.14299120353929923 0.47685860428155635 0.0051032603870738436 0.009644464231004901 is within the selected region\n", - "Bounding box 0 0.14867162068684897 0.47691504384957106 0.00507972486091382 0.009978697533701009 is within the selected region\n", - "Bounding box 1 0.1378735536517519 0.49277197744332113 0.004180575284090909 0.009765864353553921 is within the selected region\n", - "Bounding box 6 0.14319538925633285 0.49268571442248776 0.004992666533499057 0.010155053232230371 is within the selected region\n", - "Bounding box 0 0.14871603763464725 0.49253202550551467 0.00507732044566761 0.010084826899509791 is within the selected region\n", - "Bounding box 1 0.13820145578095405 0.5083780445772059 0.004347099535393001 0.009914790134803897 is within the selected region\n", - "Bounding box 5 0.14322742808948863 0.508258846507353 0.004736642548532205 0.01018832337622555 is within the selected region\n", - "Bounding box 0 0.14874616680723246 0.5081815353094363 0.00521840413411459 0.010050359987745172 is within the selected region\n", - "Bounding box 1 0.13802489309599908 0.523800048828125 0.00428908839370265 0.010019291896446125 is within the selected region\n", - "Bounding box 4 0.14306269327799478 0.5237577071844363 0.004656455300071027 0.009921683517156943 is within the selected region\n", - "Bounding box 0 0.14873589717980587 0.5236627077588848 0.004765218098958357 0.010106033624387223 is within the selected region\n", - "Bounding box 1 0.13793073711973247 0.5392588895909927 0.004460033069957375 0.009839680989583321 is within the selected region\n", - "Bounding box 3 0.14305358424331202 0.5392010857077206 0.005218737053148681 0.010009191176470589 is within the selected region\n", - "Bounding box 0 0.14886316472833808 0.5393448893229167 0.005275989879261367 0.009938055300245052 is within the selected region\n", - "Bounding box 1 0.1380303446451823 0.555029177198223 0.004406285141453581 0.009690515854779425 is within the selected region\n", - "Bounding box 2 0.14305828672466858 0.5549668016620711 0.005177131421638254 0.010077694163602935 is within the selected region\n", - "Bounding box 0 0.1487662529222893 0.5549629480698529 0.005158053311434679 0.010149260876225474 is within the selected region\n", - "Bounding box 1 0.13802087032433713 0.5705203067555147 0.00423336144649622 0.009863855698529433 is within the selected region\n", - "Bounding box 1 0.1427608975497159 0.5705034562653186 0.00451282848011364 0.009982000612745123 is within the selected region\n", - "Bounding box 0 0.14811153989849668 0.570541752833946 0.005231387976444124 0.009821633731617596 is within the selected region\n", - "Bounding box 1 0.13790817723129734 0.5862586885340073 0.0043769605232007736 0.009684579886642175 is within the selected region\n", - "Bounding box 0 0.14295925255977748 0.5861784572227329 0.004822036280776515 0.010025227864583375 is within the selected region\n", - "Bounding box 0 0.14861735488429212 0.5860258453967524 0.0050772279681581545 0.009886690027573475 is within the selected region\n", - "Bounding box 9 0.14040775645862924 0.6018566415824143 0.004934387207031238 0.01002618527879895 is within the selected region\n", - "Bounding box 0 0.14577925710967093 0.6019493432138481 0.0050696448123816185 0.010319776348039267 is within the selected region\n", - "Bounding box 8 0.140310234301018 0.6175523226868873 0.0050445371685606255 0.010135569852941173 is within the selected region\n", - "Bounding box 0 0.14575131965406013 0.6176652736289829 0.004873851429332388 0.010166159237132377 is within the selected region\n", - "Bounding box 7 0.14023251157818417 0.6328832289751838 0.005249772505326683 0.009667442172181406 is within the selected region\n", - "Bounding box 0 0.1457973410866477 0.6331068809359681 0.004873953154592797 0.010127383961397118 is within the selected region\n", - "Bounding box 6 0.1402722352923769 0.6487937777650122 0.00507311271898675 0.010058737362132364 is within the selected region\n", - "Bounding box 0 0.14590905391808712 0.6488928462009804 0.004736217151988631 0.009903301164215672 is within the selected region\n", - "Bounding box 5 0.1405757834694602 0.6640935441559437 0.004903878876657192 0.010014792049632404 is within the selected region\n", - "Bounding box 0 0.14603031042850378 0.6641141285615808 0.004874729965672342 0.010013547411151902 is within the selected region\n", - "Bounding box 4 0.14035383744673297 0.6796077952665441 0.005218505859375 0.009727807138480316 is within the selected region\n", - "Bounding box 0 0.14601876923532198 0.6796538947610294 0.004878577030066278 0.009756625306372557 is within the selected region\n", - "Bounding box 3 0.14052866155450994 0.69531494140625 0.004854838053385407 0.010225183823529327 is within the selected region\n", - "Bounding box 0 0.14593774044152463 0.6951335114123774 0.004789058800899609 0.01004193474264703 is within the selected region\n", - "Bounding box 0 0.16587118437795928 0.3818634033203125 0.004725952148437518 0.010094520718443634 is within the selected region\n", - "Bounding box 5 0.18457039572975853 0.3817957141352635 0.005019956646543561 0.010063165402879881 is within the selected region\n", - "Bounding box 1 0.19968412457090434 0.38190079034543506 0.004037623549952657 0.009629887599571063 is within the selected region\n", - "Bounding box 0 0.2048508615204782 0.3818134023628983 0.00521956010298294 0.009812993068321063 is within the selected region\n", - "Bounding box 1 0.2179007050485322 0.3819432756012561 0.004511496803977277 0.009861916934742698 is within the selected region\n", - "Bounding box 5 0.22321859648733428 0.3819884535845588 0.005096158114346605 0.010281048943014681 is within the selected region\n", - "Bounding box 2 0.2363887255119555 0.3818835209865196 0.004943126331676123 0.010016659007352935 is within the selected region\n", - "Bounding box 0 0.24191182454427085 0.38182753619025733 0.005009617660984872 0.010204407935049009 is within the selected region\n", - "Bounding box 2 0.2546861775716146 0.3819860720166973 0.0053478449041193254 0.010141864851409332 is within the selected region\n", - "Bounding box 5 0.2601831609552557 0.3821086689070159 0.004950764973958299 0.010237606272977928 is within the selected region\n", - "Bounding box 3 0.27290805701053505 0.3821193321078431 0.0050712631687973575 0.010458505667892137 is within the selected region\n", - "Bounding box 0 0.2786404511422822 0.38203175264246325 0.005030369614109853 0.010347876455269633 is within the selected region\n", - "Bounding box 3 0.29124179724491006 0.3820396513097426 0.004749552408854163 0.010192823223039216 is within the selected region\n", - "Bounding box 5 0.2966511396928267 0.3821407781862745 0.005036565607244303 0.010056487438725448 is within the selected region\n", - "Bounding box 4 0.30929721716678504 0.38233169854856003 0.0047427460641571995 0.009682114545036757 is within the selected region\n", - "Bounding box 0 0.31502907492897725 0.3821165915096507 0.004972330729166641 0.010198495902267124 is within the selected region\n", - "Bounding box 4 0.3276321688565341 0.3824337828393076 0.005286532315340875 0.009880059934129937 is within the selected region\n", - "Bounding box 5 0.33330375902580495 0.38239826277190564 0.0047296327533143945 0.010225686465992645 is within the selected region\n", - "Bounding box 5 0.34590320933948865 0.38236894196155025 0.004852627840909118 0.010301417930453471 is within the selected region\n", - "Bounding box 0 0.35132405598958333 0.3824903181487439 0.004711840080492413 0.010260201248468104 is within the selected region\n", - "Bounding box 5 0.36408240116003787 0.38248385560278797 0.0049909002130681945 0.010304673138786746 is within the selected region\n", - "Bounding box 5 0.3695796157374527 0.3825164794921875 0.004559770063920443 0.010000119676776997 is within the selected region\n", - "Bounding box 0 0.3845553496389678 0.38274670170802694 0.004613185073390147 0.010073433670343135 is within the selected region\n", - "Bounding box 5 0.40287364612926135 0.38273882697610295 0.004986239346590926 0.010265634574142146 is within the selected region\n", - "Bounding box 1 0.4178893488103693 0.38271556181066174 0.0043999689275567855 0.009955623851102935 is within the selected region\n", - "Bounding box 0 0.422972412109375 0.3825348379097733 0.0045865885416666585 0.010432823031556349 is within the selected region\n", - "Bounding box 1 0.43551524769176136 0.3828317200903799 0.00453901811079549 0.009922688802083357 is within the selected region\n", - "Bounding box 5 0.4408444121389678 0.3829491230085784 0.004507908676609884 0.010030110677083315 is within the selected region\n", - "Bounding box 2 0.4537920587713068 0.3825503958907782 0.005120220762310612 0.010302997663909352 is within the selected region\n", - "Bounding box 0 0.4591027647076231 0.38279470406326593 0.004502064098011349 0.010210080614276973 is within the selected region\n", - "Bounding box 2 0.4715062921697443 0.3827597704120711 0.004608450224905303 0.010430261948529418 is within the selected region\n", - "Bounding box 5 0.4769446540601326 0.3828994571461397 0.004414802320075739 0.010206083409926459 is within the selected region\n", - "Bounding box 3 0.4893110240589489 0.38275467218137255 0.004593875769412892 0.010356110217524472 is within the selected region\n", - "Bounding box 0 0.4946734804095644 0.3827644856770833 0.004666933001893914 0.010176882276348054 is within the selected region\n", - "Bounding box 3 0.5070133463541666 0.3827826167087929 0.00477250532670459 0.010433205997242623 is within the selected region\n", - "Bounding box 5 0.5126307077118845 0.38296329273897056 0.004330573804450788 0.009986835554534335 is within the selected region\n", - "Bounding box 4 0.5249030095880682 0.38268938849954043 0.0051524029356061485 0.009537162032781876 is within the selected region\n", - "Bounding box 0 0.5305247173887311 0.3826352347579657 0.004638819839015151 0.009919960171568598 is within the selected region\n", - "Bounding box 4 0.5427307683771307 0.3827019545611213 0.0048346132220643545 0.0098486567478554 is within the selected region\n", - "Bounding box 5 0.5481516150272254 0.3827708524816177 0.004657796223958344 0.01028181487438723 is within the selected region\n", - "Bounding box 5 0.5604993045691288 0.38262589996936275 0.004909889914772814 0.010216710707720567 is within the selected region\n", - "Bounding box 0 0.565989472360322 0.38267627192478554 0.004707179214015089 0.010464513442095535 is within the selected region\n", - "Bounding box 5 0.5785705381451232 0.38266199448529414 0.004601495916193188 0.01006065219056368 is within the selected region\n", - "Bounding box 5 0.583848359079072 0.3826736869064032 0.004487970525568152 0.010092893114276968 is within the selected region\n", - "Bounding box 0 0.5987218683416193 0.3823198744829963 0.004796364524147778 0.010029464422487755 is within the selected region\n", - "Bounding box 5 0.617369902639678 0.38252314548866423 0.004818004261363584 0.01017716950061276 is within the selected region\n", - "Bounding box 1 0.6322139855587121 0.38256816789215686 0.004319069602272796 0.00979784198835787 is within the selected region\n", - "Bounding box 0 0.6372403231534092 0.38235124176623775 0.004644442471590904 0.010348067938112715 is within the selected region\n", - "Bounding box 1 0.6502870131983902 0.3823184622970282 0.004205063328598491 0.009785419538909323 is within the selected region\n", - "Bounding box 5 0.6552334502249053 0.38218898399203427 0.004599831321022707 0.010305989583333341 is within the selected region\n", - "Bounding box 2 0.6682829145951705 0.3821959013097427 0.00478641394412882 0.010060987285539225 is within the selected region\n", - "Bounding box 0 0.6739927719578598 0.38205502977558214 0.004979654947916745 0.010139327703737766 is within the selected region\n", - "Bounding box 2 0.6866014145359849 0.3818985404220282 0.005006806344696968 0.010106488396139701 is within the selected region\n", - "Bounding box 5 0.6921630859375 0.3820809757008272 0.004916548295454515 0.010389547909007368 is within the selected region\n", - "Bounding box 3 0.7047801994554924 0.38196439855238973 0.004862097537878807 0.010205939797794106 is within the selected region\n", - "Bounding box 0 0.7103043249881629 0.3818713737936581 0.0047473514441287445 0.010310465494791643 is within the selected region\n", - "Bounding box 3 0.7228476784446023 0.38184248381969976 0.004995930989583286 0.010390074486825995 is within the selected region\n", - "Bounding box 5 0.728689667672822 0.38188597436044736 0.00474779533617431 0.010164507697610292 is within the selected region\n", - "Bounding box 4 0.7413370768229166 0.3817519962086397 0.005275065104166754 0.00967304304534311 is within the selected region\n", - "Bounding box 0 0.7469070342092803 0.38166058708639705 0.004968187736742458 0.010206705729166654 is within the selected region\n", - "Bounding box 4 0.7595242956912879 0.3817976888020833 0.005004586884469697 0.009735514322916694 is within the selected region\n", - "Bounding box 5 0.7650375458688448 0.38170449649586397 0.004754305752840859 0.010270972158394565 is within the selected region\n", - "Bounding box 5 0.7778333999171402 0.3815218457988664 0.004798103101325779 0.010090643190870108 is within the selected region\n", - "Bounding box 0 0.783281434955019 0.3814627853094363 0.004878151633522676 0.010273820465686256 is within the selected region\n", - "Bounding box 5 0.7958795720880683 0.38156327789905026 0.004940222537878847 0.009875943053002434 is within the selected region\n", - "Bounding box 5 0.8014029947916667 0.3815045285692402 0.004948656486742475 0.010133463541666665 is within the selected region\n", - "Bounding box 0 0.8166511304450758 0.38151131424249385 0.004765920928030409 0.0100871486289828 is within the selected region\n", - "Bounding box 5 0.8354467033617424 0.38141405292585784 0.004737511837121122 0.009976495481004877 is within the selected region\n", - "Bounding box 1 0.8502276056463068 0.3816766237745098 0.004263065222537943 0.009814405254289227 is within the selected region\n", - "Bounding box 0 0.855240996389678 0.3814403578814338 0.004703554095643936 0.010118815104166679 is within the selected region\n", - "Bounding box 1 0.8682825816761364 0.38148327397365195 0.0044398082386363225 0.009928624770220607 is within the selected region\n", - "Bounding box 5 0.8734008419152461 0.38144994399126836 0.00466108842329549 0.010043921377144605 is within the selected region\n", - "Bounding box 2 0.8865554717092803 0.3812796678730086 0.0048915423768939315 0.010303715724571061 is within the selected region\n", - "Bounding box 0 0.8920348011363637 0.38133043476179534 0.004733220880681732 0.009827067057291639 is within the selected region\n", - "Bounding box 2 0.9045032108191289 0.38140860763250617 0.004780421401515134 0.010118120978860279 is within the selected region\n" + "ROI selected: (104, 222, 631, 201)\n", + "Selected region: x=104, y=222, w=631, h=201\n", + "Cluster 28: 5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088\n", + "Cluster 10: 2 0.13794031316583807 0.39902471804151346 0.00507585005326705 0.010464657054227944\n", + "Cluster 10: 2 0.1434342216722893 0.39900106991038603 0.005159921357125952 0.010430668849571112\n", + "Cluster 10: 0 0.14874451145981296 0.3989715576171875 0.004878808223839959 0.010312284581801445\n", + "Cluster 39: 2 0.13839600996537643 0.41458115521599265 0.005050483472419515 0.010243135340073484\n", + "Cluster 39: 1 0.14340712576201467 0.4147241689644608 0.004516906738281257 0.010044136795343106\n", + "Cluster 39: 0 0.1484095810398911 0.4144839298023897 0.004963064482717799 0.010348642386642182\n", + "Cluster 19: 2 0.13811845259232955 0.4301294424019608 0.0047374933416193254 0.010339403339460762\n", + "Cluster 19: 0 0.14345488114790483 0.4301910041360294 0.005082036798650574 0.009986979166666687\n", + "Cluster 19: 0 0.14887493711529357 0.4302002192478554 0.004909926905776518 0.010090140548406845\n", + "Cluster 37: 1 0.1376401912804806 0.4458321126302084 0.004371883507930857 0.010252517999387256\n", + "Cluster 37: 9 0.14284934303977273 0.4458483408011642 0.004968835079308731 0.009987410003063746\n", + "Cluster 37: 0 0.14849560824307528 0.4458433143765319 0.005082073789654362 0.00991033815870096\n", + "Cluster 1: 1 0.13771269364790484 0.4615478994332108 0.004487739331794499 0.010035807291666643\n", + "Cluster 1: 8 0.14295229825106534 0.4613467467064951 0.004853219696969724 0.010126282935049025\n", + "Cluster 1: 0 0.1485103260387074 0.4615417959175858 0.0049125347715435475 0.009873764935661777\n", + "Cluster 29: 1 0.1379021708170573 0.47700669232536763 0.004599766586766113 0.010043370863970613\n", + "Cluster 29: 7 0.14299120353929923 0.47685860428155635 0.0051032603870738436 0.009644464231004901\n", + "Cluster 29: 0 0.14867162068684897 0.47691504384957106 0.00507972486091382 0.009978697533701009\n", + "Cluster 33: 1 0.1378735536517519 0.49277197744332113 0.004180575284090909 0.009765864353553921\n", + "Cluster 33: 6 0.14319538925633285 0.49268571442248776 0.004992666533499057 0.010155053232230371\n", + "Cluster 33: 0 0.14871603763464725 0.49253202550551467 0.00507732044566761 0.010084826899509791\n", + "Cluster 11: 1 0.13820145578095405 0.5083780445772059 0.004347099535393001 0.009914790134803897\n", + "Cluster 11: 5 0.14322742808948863 0.508258846507353 0.004736642548532205 0.01018832337622555\n", + "Cluster 11: 0 0.14874616680723246 0.5081815353094363 0.00521840413411459 0.010050359987745172\n", + "Cluster 38: 1 0.13802489309599908 0.523800048828125 0.00428908839370265 0.010019291896446125\n", + "Cluster 38: 4 0.14306269327799478 0.5237577071844363 0.004656455300071027 0.009921683517156943\n", + "Cluster 38: 0 0.14873589717980587 0.5236627077588848 0.004765218098958357 0.010106033624387223\n", + "Cluster 21: 1 0.13793073711973247 0.5392588895909927 0.004460033069957375 0.009839680989583321\n", + "Cluster 21: 3 0.14305358424331202 0.5392010857077206 0.005218737053148681 0.010009191176470589\n", + "Cluster 21: 0 0.14886316472833808 0.5393448893229167 0.005275989879261367 0.009938055300245052\n", + "Cluster 36: 1 0.1380303446451823 0.555029177198223 0.004406285141453581 0.009690515854779425\n", + "Cluster 36: 2 0.14305828672466858 0.5549668016620711 0.005177131421638254 0.010077694163602935\n", + "Cluster 36: 0 0.1487662529222893 0.5549629480698529 0.005158053311434679 0.010149260876225474\n", + "Cluster 20: 1 0.13802087032433713 0.5705203067555147 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"Cluster 43: 3 0.27290805701053505 0.3821193321078431 0.0050712631687973575 0.010458505667892137\n", + "Cluster 43: 0 0.2786404511422822 0.38203175264246325 0.005030369614109853 0.010347876455269633\n", + "Cluster 55: 3 0.29124179724491006 0.3820396513097426 0.004749552408854163 0.010192823223039216\n", + "Cluster 55: 5 0.2966511396928267 0.3821407781862745 0.005036565607244303 0.010056487438725448\n", + "Cluster 4: 4 0.30929721716678504 0.38233169854856003 0.0047427460641571995 0.009682114545036757\n", + "Cluster 4: 0 0.31502907492897725 0.3821165915096507 0.004972330729166641 0.010198495902267124\n", + "Cluster 48: 4 0.3276321688565341 0.3824337828393076 0.005286532315340875 0.009880059934129937\n", + "Cluster 48: 5 0.33330375902580495 0.38239826277190564 0.0047296327533143945 0.010225686465992645\n", + "Cluster 15: 5 0.34590320933948865 0.38236894196155025 0.004852627840909118 0.010301417930453471\n", + "Cluster 15: 0 0.35132405598958333 0.3824903181487439 0.004711840080492413 0.010260201248468104\n", + "Cluster 51: 5 0.36408240116003787 0.38248385560278797 0.0049909002130681945 0.010304673138786746\n", + "Cluster 51: 5 0.3695796157374527 0.3825164794921875 0.004559770063920443 0.010000119676776997\n", + "Cluster 26: 0 0.3845553496389678 0.38274670170802694 0.004613185073390147 0.010073433670343135\n", + "Cluster 56: 5 0.40287364612926135 0.38273882697610295 0.004986239346590926 0.010265634574142146\n", + "Cluster 8: 1 0.4178893488103693 0.38271556181066174 0.0043999689275567855 0.009955623851102935\n", + "Cluster 8: 0 0.422972412109375 0.3825348379097733 0.0045865885416666585 0.010432823031556349\n", + "Cluster 45: 1 0.43551524769176136 0.3828317200903799 0.00453901811079549 0.009922688802083357\n", + "Cluster 45: 5 0.4408444121389678 0.3829491230085784 0.004507908676609884 0.010030110677083315\n", + "Cluster 14: 2 0.4537920587713068 0.3825503958907782 0.005120220762310612 0.010302997663909352\n", + "Cluster 14: 0 0.4591027647076231 0.38279470406326593 0.004502064098011349 0.010210080614276973\n", + "Cluster 50: 2 0.4715062921697443 0.3827597704120711 0.004608450224905303 0.010430261948529418\n", + "Cluster 50: 5 0.4769446540601326 0.3828994571461397 0.004414802320075739 0.010206083409926459\n", + "Cluster 22: 3 0.4893110240589489 0.38275467218137255 0.004593875769412892 0.010356110217524472\n", + "Cluster 22: 0 0.4946734804095644 0.3827644856770833 0.004666933001893914 0.010176882276348054\n", + "Cluster 41: 3 0.5070133463541666 0.3827826167087929 0.00477250532670459 0.010433205997242623\n", + "Cluster 41: 5 0.5126307077118845 0.38296329273897056 0.004330573804450788 0.009986835554534335\n", + "Cluster 2: 4 0.5249030095880682 0.38268938849954043 0.0051524029356061485 0.009537162032781876\n", + "Cluster 2: 0 0.5305247173887311 0.3826352347579657 0.004638819839015151 0.009919960171568598\n", + "Cluster 30: 4 0.5427307683771307 0.3827019545611213 0.0048346132220643545 0.0098486567478554\n", + "Cluster 30: 5 0.5481516150272254 0.3827708524816177 0.004657796223958344 0.01028181487438723\n", + "Cluster 49: 5 0.5604993045691288 0.38262589996936275 0.004909889914772814 0.010216710707720567\n", + "Cluster 49: 0 0.565989472360322 0.38267627192478554 0.004707179214015089 0.010464513442095535\n", + "Cluster 12: 5 0.5785705381451232 0.38266199448529414 0.004601495916193188 0.01006065219056368\n", + "Cluster 12: 5 0.583848359079072 0.3826736869064032 0.004487970525568152 0.010092893114276968\n", + "Cluster 61: 0 0.5987218683416193 0.3823198744829963 0.004796364524147778 0.010029464422487755\n", + "Cluster 59: 5 0.617369902639678 0.38252314548866423 0.004818004261363584 0.01017716950061276\n", + "Cluster 23: 1 0.6322139855587121 0.38256816789215686 0.004319069602272796 0.00979784198835787\n", + "Cluster 23: 0 0.6372403231534092 0.38235124176623775 0.004644442471590904 0.010348067938112715\n", + "Cluster 31: 1 0.6502870131983902 0.3823184622970282 0.004205063328598491 0.009785419538909323\n", + "Cluster 31: 5 0.6552334502249053 0.38218898399203427 0.004599831321022707 0.010305989583333341\n", + "Cluster 5: 2 0.6682829145951705 0.3821959013097427 0.00478641394412882 0.010060987285539225\n", + "Cluster 5: 0 0.6739927719578598 0.38205502977558214 0.004979654947916745 0.010139327703737766\n", + "Cluster 53: 2 0.6866014145359849 0.3818985404220282 0.005006806344696968 0.010106488396139701\n", + "Cluster 53: 5 0.6921630859375 0.3820809757008272 0.004916548295454515 0.010389547909007368\n", + "Cluster 16: 3 0.7047801994554924 0.38196439855238973 0.004862097537878807 0.010205939797794106\n", + "Cluster 16: 0 0.7103043249881629 0.3818713737936581 0.0047473514441287445 0.010310465494791643\n", + "Cluster 57: 3 0.7228476784446023 0.38184248381969976 0.004995930989583286 0.010390074486825995\n", + "Cluster 57: 5 0.728689667672822 0.38188597436044736 0.00474779533617431 0.010164507697610292\n", + "Cluster 24: 4 0.7413370768229166 0.3817519962086397 0.005275065104166754 0.00967304304534311\n", + "Cluster 24: 0 0.7469070342092803 0.38166058708639705 0.004968187736742458 0.010206705729166654\n", + "Cluster 47: 4 0.7595242956912879 0.3817976888020833 0.005004586884469697 0.009735514322916694\n", + "Cluster 47: 5 0.7650375458688448 0.38170449649586397 0.004754305752840859 0.010270972158394565\n", + "Cluster 0: 5 0.7778333999171402 0.3815218457988664 0.004798103101325779 0.010090643190870108\n", + "Cluster 0: 0 0.783281434955019 0.3814627853094363 0.004878151633522676 0.010273820465686256\n", + "Cluster 32: 5 0.7958795720880683 0.38156327789905026 0.004940222537878847 0.009875943053002434\n", + "Cluster 32: 5 0.8014029947916667 0.3815045285692402 0.004948656486742475 0.010133463541666665\n", + "Cluster 58: 0 0.8166511304450758 0.38151131424249385 0.004765920928030409 0.0100871486289828\n", + "Cluster 18: 5 0.8354467033617424 0.38141405292585784 0.004737511837121122 0.009976495481004877\n", + "Cluster 35: 1 0.8502276056463068 0.3816766237745098 0.004263065222537943 0.009814405254289227\n", + "Cluster 35: 0 0.855240996389678 0.3814403578814338 0.004703554095643936 0.010118815104166679\n", + "Cluster 52: 1 0.8682825816761364 0.38148327397365195 0.0044398082386363225 0.009928624770220607\n", + "Cluster 52: 5 0.8734008419152461 0.38144994399126836 0.00466108842329549 0.010043921377144605\n", + "Cluster 6: 2 0.8865554717092803 0.3812796678730086 0.0048915423768939315 0.010303715724571061\n", + "Cluster 6: 0 0.8920348011363637 0.38133043476179534 0.004733220880681732 0.009827067057291639\n", + "Cluster 28: 2 0.9045032108191289 0.38140860763250617 0.004780421401515134 0.010118120978860279\n" ] } ], @@ -344,12 +382,278 @@ " relevant_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", "\n", " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", - " # For now, we will just print the relevant bounding boxes\n", - " print(f\"Relevant bounding boxes: {relevant_bounding_boxes}\")\n", + " labels = cluster_kmeans(relevant_bounding_boxes, 62)\n", + "\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(labels):\n", + "\n", + " # Get the bounding box\n", + " bounding_box = relevant_bounding_boxes[i]\n", + " value, x_center, y_center, width, height = bounding_box.split(' ')\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", + "\n", + " # Draw bounding boxes on the image\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " box = [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ]\n", + " draw.rectangle(box, outline=label_color_map[label], width=3)\n", + "\n", + " # Display the image\n", + " image.show()\n", + "\n", + " # Print numbers for each cluster\n", + " for i, label in enumerate(labels):\n", + " print(f\"Cluster {label}: {relevant_bounding_boxes[i]}\")\n", "\n", " # Break after the first image\n", " break" ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class 28:\n", + " Index 0: 5\n", + " Index 128: 2\n", + "Class 10:\n", + " Index 1: 2\n", + " Index 2: 2\n", + " Index 3: 0\n", + "Class 39:\n", + " Index 4: 2\n", + " Index 5: 1\n", + " Index 6: 0\n", + "Class 19:\n", + " Index 7: 2\n", + " Index 8: 0\n", + " Index 9: 0\n", + "Class 37:\n", + " Index 10: 1\n", + " Index 11: 9\n", + " Index 12: 0\n", + "Class 1:\n", + " Index 13: 1\n", + " Index 14: 8\n", + " Index 15: 0\n", + "Class 29:\n", + " Index 16: 1\n", + " Index 17: 7\n", + " Index 18: 0\n", + "Class 33:\n", + " Index 19: 1\n", + " Index 20: 6\n", + " Index 21: 0\n", + "Class 11:\n", + " Index 22: 1\n", + " Index 23: 5\n", + " Index 24: 0\n", + "Class 38:\n", + " Index 25: 1\n", + " Index 26: 4\n", + " Index 27: 0\n", + "Class 21:\n", + " Index 28: 1\n", + " Index 29: 3\n", + " Index 30: 0\n", + "Class 36:\n", + " Index 31: 1\n", + " Index 32: 2\n", + " Index 33: 0\n", + "Class 20:\n", + " Index 34: 1\n", + " Index 35: 1\n", + " Index 36: 0\n", + "Class 3:\n", + " Index 37: 1\n", + " Index 38: 0\n", + " Index 39: 0\n", + "Class 40:\n", + " Index 40: 9\n", + " Index 41: 0\n", + "Class 46:\n", + " Index 42: 8\n", + " Index 43: 0\n", + "Class 17:\n", + " Index 44: 7\n", + " Index 45: 0\n", + "Class 44:\n", + " Index 46: 6\n", + " Index 47: 0\n", + "Class 25:\n", + " Index 48: 5\n", + " Index 49: 0\n", + "Class 42:\n", + " Index 50: 4\n", + " Index 51: 0\n", + "Class 7:\n", + " Index 52: 3\n", + " Index 53: 0\n", + "Class 60:\n", + " Index 54: 0\n", + "Class 34:\n", + " Index 55: 5\n", + "Class 13:\n", + " Index 56: 1\n", + " Index 57: 0\n", + "Class 54:\n", + " Index 58: 1\n", + " Index 59: 5\n", + "Class 27:\n", + " Index 60: 2\n", + " Index 61: 0\n", + "Class 9:\n", + " Index 62: 2\n", + " Index 63: 5\n", + "Class 43:\n", + " Index 64: 3\n", + " Index 65: 0\n", + "Class 55:\n", + " Index 66: 3\n", + " Index 67: 5\n", + "Class 4:\n", + " Index 68: 4\n", + " Index 69: 0\n", + "Class 48:\n", + " Index 70: 4\n", + " Index 71: 5\n", + "Class 15:\n", + " Index 72: 5\n", + " Index 73: 0\n", + "Class 51:\n", + " Index 74: 5\n", + " Index 75: 5\n", + "Class 26:\n", + " Index 76: 0\n", + "Class 56:\n", + " Index 77: 5\n", + "Class 8:\n", + " Index 78: 1\n", + " Index 79: 0\n", + "Class 45:\n", + " Index 80: 1\n", + " Index 81: 5\n", + "Class 14:\n", + " Index 82: 2\n", + " Index 83: 0\n", + "Class 50:\n", + " Index 84: 2\n", + " Index 85: 5\n", + "Class 22:\n", + " Index 86: 3\n", + " Index 87: 0\n", + "Class 41:\n", + " Index 88: 3\n", + " Index 89: 5\n", + "Class 2:\n", + " Index 90: 4\n", + " Index 91: 0\n", + "Class 30:\n", + " Index 92: 4\n", + " Index 93: 5\n", + "Class 49:\n", + " Index 94: 5\n", + " Index 95: 0\n", + "Class 12:\n", + " Index 96: 5\n", + " Index 97: 5\n", + "Class 61:\n", + " Index 98: 0\n", + "Class 59:\n", + " Index 99: 5\n", + "Class 23:\n", + " Index 100: 1\n", + " Index 101: 0\n", + "Class 31:\n", + " Index 102: 1\n", + " Index 103: 5\n", + "Class 5:\n", + " Index 104: 2\n", + " Index 105: 0\n", + "Class 53:\n", + " Index 106: 2\n", + " Index 107: 5\n", + "Class 16:\n", + " Index 108: 3\n", + " Index 109: 0\n", + "Class 57:\n", + " Index 110: 3\n", + " Index 111: 5\n", + "Class 24:\n", + " Index 112: 4\n", + " Index 113: 0\n", + "Class 47:\n", + " Index 114: 4\n", + " Index 115: 5\n", + "Class 0:\n", + " Index 116: 5\n", + " Index 117: 0\n", + "Class 32:\n", + " Index 118: 5\n", + " Index 119: 5\n", + "Class 58:\n", + " Index 120: 0\n", + "Class 18:\n", + " Index 121: 5\n", + "Class 35:\n", + " Index 122: 1\n", + " Index 123: 0\n", + "Class 52:\n", + " Index 124: 1\n", + " Index 125: 5\n", + "Class 6:\n", + " Index 126: 2\n", + " Index 127: 0\n" + ] + } + ], + "source": [ + "def print_classes_and_values(classes, values):\n", + " \"\"\"Prints the class number followed by all the indices and values belonging to that class.\n", + "\n", + " Args:\n", + " classes: A list of class labels for each value.\n", + " values: A list of values.\n", + " \"\"\"\n", + "\n", + " # Create a dictionary to store indices and values by class\n", + " class_data = {}\n", + " for i, class_label in enumerate(classes):\n", + " if class_label not in class_data:\n", + " class_data[class_label] = []\n", + " class_data[class_label].append((i, values[i]))\n", + "\n", + " # Print the class number followed by the indices and values\n", + " for class_label, data in class_data.items():\n", + " print(f\"Class {class_label}:\")\n", + " for index, value in data:\n", + " print(f\" Index {index}: {value}\")\n", + "\n", + "\n", + "relevant_numbers = [box.split(\" \")[0] for box in relevant_bounding_boxes]\n", + "print_classes_and_values(labels, relevant_numbers)\n" + ] } ], "metadata": { diff --git a/poetry.lock b/poetry.lock index 6cfdbd2..1f75547 100644 --- a/poetry.lock +++ b/poetry.lock @@ -665,6 +665,17 @@ MarkupSafe = ">=2.0" [package.extras] i18n = ["Babel (>=2.7)"] +[[package]] +name = "joblib" +version = "1.4.2" +description = "Lightweight pipelining with Python functions" +optional = false +python-versions = ">=3.8" +files = [ + {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, + {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, +] + [[package]] name = "jupyter-client" version = "8.6.3" @@ -1880,6 +1891,56 @@ files = [ {file = "ruff-0.6.9.tar.gz", hash = "sha256:b076ef717a8e5bc819514ee1d602bbdca5b4420ae13a9cf61a0c0a4f53a2baa2"}, ] +[[package]] +name = "scikit-learn" 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(>=0.5.0)", "sphinx-design (>=0.6.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"] +examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"] +install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"] +maintenance = ["conda-lock (==2.5.6)"] +tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"] + [[package]] name = "scipy" version = "1.14.1" @@ -2018,6 +2079,17 @@ mpmath = ">=1.1.0,<1.4" [package.extras] dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] +[[package]] +name = "threadpoolctl" +version = "3.5.0" +description = "threadpoolctl" +optional = false +python-versions = ">=3.8" +files = [ + {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"}, + {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"}, +] + [[package]] name = "torch" version = "2.4.1" @@ -2293,4 +2365,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "bd53340dfbd0ed7b82d38efcf05090103be9715485c5f8e94d7b0fd81ff85bbe" +content-hash = "f1b0f00a1156e4fd0f24f2b828b1ae2333f64929b5d3342e9af1a5058686e2e6" diff --git a/pyproject.toml b/pyproject.toml index cb893f2..b9a7e51 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ ultralytics = "^8.3.15" opencv-python = "^4.10.0.84" pandas = "^2.2.3" tqdm = "^4.66.5" +scikit-learn = "^1.5.2" [tool.poetry.group.dev.dependencies] From 92d7545c88338abb336827a01eb95e944cc4ad01 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Fri, 18 Oct 2024 21:17:09 -0400 Subject: [PATCH 14/55] Clustering time and mmHg/bpm seperately --- experiments/clustering/clustering.ipynb | 746 +++++++++++------------- 1 file changed, 351 insertions(+), 395 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index f7e972f..1c8a477 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -25,12 +25,12 @@ "source": [ "#### Install Packages\n", "\n", - "This will be added to as I develop." + "These are the necessary packages to run the functions and scripts below." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ "import json\n", "import random\n", "from pathlib import Path\n", - "from typing import List\n", + "from typing import List, Tuple, Dict\n", "\n", "import cv2\n", "import numpy as np\n", @@ -57,12 +57,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images." + "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -81,6 +81,7 @@ "with open(PATH_TO_YOLO_DATA) as json_file:\n", " yolo_data = json.load(json_file)\n", "\n", + "# See how many intraoperative images are registered\n", "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" ] }, @@ -95,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -124,62 +125,112 @@ " y_max = int((float(y_center) * image_height) + (height * image_height) / 2)\n", " return x_min, y_min, x_max, y_max\n", "\n", - "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path) -> List[str]:\n", - " \"\"\"\n", - " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", - " Return the bounding boxes that are within the selected region.\n", - "\n", - " Args:\n", - " sheet_data: List of bounding boxes in YOLO format.\n", - " path_to_image: Path to the image file.\n", - "\n", - " Returns:\n", - " Bounding boxes that are within the selected region, in YOLO format.\n", - " \"\"\"\n", - " # Load the image\n", - " image = cv2.imread(path_to_image)\n", - "\n", - " # Display the image and allow the user to select a ROI\n", - " resized_image = cv2.resize(image, (800, 600))\n", - " roi = cv2.selectROI(\"Select ROI\", resized_image)\n", - " print(f\"ROI selected: {roi}\")\n", - "\n", - " # The function returns a tuple (x, y, width, height)\n", - " x, y, w, h = roi\n", - " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", - "\n", - " # Draw a rectangle around the selected region\n", - " cv2.rectangle(img=resized_image, pt1=(x, y), pt2=(x + w, y + h), color=(0, 255, 0), thickness=1)\n", - "\n", - " # Close all OpenCV windows\n", - " cv2.destroyAllWindows()\n", - "\n", - " # List of bounding boxes that are within the selected region\n", - " bounding_boxes_within_region = []\n", + "# Function to determine whether a point is above or below the diagonal line\n", + "def is_point_in_above(x_center, y_center, m, b):\n", + " \"\"\"\n", + " Determine if a point is above or below the diagonal line y = mx + b.\n", + " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", "\n", - " # Identify bounding boxes that are within the selected region\n", - " for bounding_box in sheet_data:\n", - " # Bounding boxes are in YOLO format, let's convert to pixels and see if they are within the selected region\n", - " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ')\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", - " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", + " Args:\n", + " x_center: float, x coordinate of the point\n", + " y_center: float, y coordinate of the point\n", + " m: float, slope of the diagonal line\n", + " b: float, intercept of the diagonal line\n", + " \n", + " Returns:\n", + " bool, True if the point is above the line, False otherwise\n", + " \"\"\"\n", + " # Calculate the y value on the line for the given x_center\n", + " y_line = m * x_center + b\n", + " return y_center > y_line\n", "\n", - " # Generate a random color for the bounding box in (0, 255, 0) scalar format\n", - " generate_color = lambda: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n", "\n", - " # Draw a rectangle around the bounding box\n", - " cv2.rectangle(img=resized_image, pt1=(x_min, y_min), pt2=(x_max, y_max), color=generate_color(), thickness=1)\n", "\n", - " # Append the bounding box to the list of bounding boxes within the selected region\n", - " bounding_boxes_within_region.append(bounding_box)\n", + "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path, show_images: bool = False) -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", + " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", + " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", "\n", - " # Display the image with the selected region and bounding boxes\n", - " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", - " resized_image = Image.fromarray(resized_image)\n", - " resized_image.show()\n", + " Args:\n", + " sheet_data: List of bounding boxes in YOLO format.\n", + " path_to_image: Path to the image file.\n", "\n", - " return bounding_boxes_within_region\n" + " Returns:\n", + " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", + " The first list contains bounding boxes in the top-right region -- representing time labels.\n", + " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", + " (bounding_boxes_time, bounding_boxes_numbers)\n", + " \"\"\"\n", + " # Load the image\n", + " image = cv2.imread(path_to_image)\n", + "\n", + " # Display the image and allow the user to select a ROI\n", + " resized_image = cv2.resize(image, (800, 600))\n", + " roi = cv2.selectROI(\"Select Region of Interest\", resized_image)\n", + " print(f\"ROI selected: {roi}\")\n", + "\n", + " # Unpack ROI\n", + " x, y, w, h = roi\n", + " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", + "\n", + " # Calculate the coordinates of the top-left and bottom-right corners of the selected region\n", + " x_top_left = x\n", + " y_top_left = y\n", + " x_bottom_right = x + w\n", + " y_bottom_right = y + h\n", + "\n", + " # Draw the diagonal line of the selected region from top-left to bottom-right\n", + " cv2.line(resized_image, (x_top_left, y_top_left), (x_bottom_right, y_bottom_right), (0, 255, 0), 1)\n", + " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", + " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", + " # Top-right region is where time labels are located\n", + " # Bottom-left region is where numerical values for mmHg and bpm are located\n", + " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", + " b = y_top_left - m * x_top_left\n", + "\n", + " # List of bounding boxes in the top-right and bottom-left regions\n", + " bounding_boxes_time = []\n", + " bounding_boxes_numbers = []\n", + "\n", + " # Process the bounding boxes\n", + " for bounding_box in sheet_data:\n", + " # Bounding boxes are in YOLO format; convert them to pixels\n", + " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ') # Identifier is the value in the bounding box, we don't need that here\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", + " \n", + " # Check if the bounding box is within the selected region\n", + " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", + " # Calculate the center of the bounding box\n", + " x_center_bb = (x_min + x_max) / 2\n", + " y_center_bb = (y_min + y_max) / 2\n", + " \n", + " # If we want to generalize this function we can add the option to disregard the diagonal line\n", + "\n", + " # Determine if the bounding box center is in the top-right region\n", + " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", + " # Bounding box is in the top-right region\n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1)\n", + " bounding_boxes_numbers.append(bounding_box)\n", + " else:\n", + " # Bounding box is in the bottom-left region\n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1)\n", + " bounding_boxes_time.append(bounding_box)\n", + "\n", + " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", + " # You can also manually quit out with ESC key.\n", + " cv2.destroyAllWindows()\n", + "\n", + " # If we are showing the images, display the image with the selected region and bounding boxes\n", + " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", + " if show_images:\n", + " # Display the image with the selected region and bounding boxes\n", + " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + " resized_image = Image.fromarray(resized_image)\n", + " resized_image.show()\n", + "\n", + " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", + " return (bounding_boxes_time, bounding_boxes_numbers)\n" ] }, { @@ -191,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -216,7 +267,55 @@ " # Get cluster labels\n", " labels = kmeans.predict(data)\n", "\n", - " return labels" + " return labels\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to create a result dictionary that we can save as a JSON file to analyze performance." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def create_result_dictionary(labels: List[str], bounding_boxes: List[str]) -> Dict[int, int]:\n", + " \"\"\"\n", + " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", + "\n", + " Args:\n", + " labels: List of cluster labels.\n", + " bounding_boxes: List of bounding boxes in YOLO format.\n", + "\n", + " Returns:\n", + " Dictionary with cluster labels as keys and bounding box values as values.\n", + " \"\"\"\n", + " # Create a dictionary to store labelled elements\n", + " label_dict = {}\n", + "\n", + " # Iterate over both lists\n", + " for label, element in zip(labels, bounding_boxes):\n", + " label = int(label)\n", + " element = str(element)\n", + " if label not in label_dict:\n", + " # Create a new list for this label if it doesn't exist\n", + " label_dict[label] = []\n", + " # Append the element to the corresponding label list\n", + " label_dict[label].append(element)\n", + "\n", + " # Sort the lists in the dictionary by x_center\n", + " for key in label_dict:\n", + " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(' ')[1]))\n", + " label_dict[key] = [element.split(' ')[0] for element in label_dict[key]]\n", + " # Turn list of strings into a string\n", + " label_dict[key] = int(''.join(label_dict[key]))\n", + " \n", + " return label_dict" ] }, { @@ -228,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -237,161 +336,145 @@ "text": [ "Sheet: RC_0001_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "ROI selected: (102, 221, 634, 203)\n", + "Selected region: x=102, y=221, w=634, h=203\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "ROI selected: (102, 220, 635, 204)\n", + "Selected region: x=102, y=220, w=635, h=204\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "ROI selected: (102, 220, 634, 204)\n", + "Selected region: x=102, y=220, w=634, h=204\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "ROI selected: (104, 222, 631, 201)\n", "Selected region: x=104, y=222, w=631, h=201\n", - "Cluster 28: 5 0.9098491136955492 0.38141891180300247 0.0047951438210227515 0.010082098268995088\n", - "Cluster 10: 2 0.13794031316583807 0.39902471804151346 0.00507585005326705 0.010464657054227944\n", - "Cluster 10: 2 0.1434342216722893 0.39900106991038603 0.005159921357125952 0.010430668849571112\n", - "Cluster 10: 0 0.14874451145981296 0.3989715576171875 0.004878808223839959 0.010312284581801445\n", - "Cluster 39: 2 0.13839600996537643 0.41458115521599265 0.005050483472419515 0.010243135340073484\n", - "Cluster 39: 1 0.14340712576201467 0.4147241689644608 0.004516906738281257 0.010044136795343106\n", - "Cluster 39: 0 0.1484095810398911 0.4144839298023897 0.004963064482717799 0.010348642386642182\n", - "Cluster 19: 2 0.13811845259232955 0.4301294424019608 0.0047374933416193254 0.010339403339460762\n", - "Cluster 19: 0 0.14345488114790483 0.4301910041360294 0.005082036798650574 0.009986979166666687\n", - "Cluster 19: 0 0.14887493711529357 0.4302002192478554 0.004909926905776518 0.010090140548406845\n", - "Cluster 37: 1 0.1376401912804806 0.4458321126302084 0.004371883507930857 0.010252517999387256\n", - "Cluster 37: 9 0.14284934303977273 0.4458483408011642 0.004968835079308731 0.009987410003063746\n", - "Cluster 37: 0 0.14849560824307528 0.4458433143765319 0.005082073789654362 0.00991033815870096\n", - "Cluster 1: 1 0.13771269364790484 0.4615478994332108 0.004487739331794499 0.010035807291666643\n", - "Cluster 1: 8 0.14295229825106534 0.4613467467064951 0.004853219696969724 0.010126282935049025\n", - "Cluster 1: 0 0.1485103260387074 0.4615417959175858 0.0049125347715435475 0.009873764935661777\n", - "Cluster 29: 1 0.1379021708170573 0.47700669232536763 0.004599766586766113 0.010043370863970613\n", - "Cluster 29: 7 0.14299120353929923 0.47685860428155635 0.0051032603870738436 0.009644464231004901\n", - "Cluster 29: 0 0.14867162068684897 0.47691504384957106 0.00507972486091382 0.009978697533701009\n", - "Cluster 33: 1 0.1378735536517519 0.49277197744332113 0.004180575284090909 0.009765864353553921\n", - "Cluster 33: 6 0.14319538925633285 0.49268571442248776 0.004992666533499057 0.010155053232230371\n", - "Cluster 33: 0 0.14871603763464725 0.49253202550551467 0.00507732044566761 0.010084826899509791\n", - "Cluster 11: 1 0.13820145578095405 0.5083780445772059 0.004347099535393001 0.009914790134803897\n", - "Cluster 11: 5 0.14322742808948863 0.508258846507353 0.004736642548532205 0.01018832337622555\n", - "Cluster 11: 0 0.14874616680723246 0.5081815353094363 0.00521840413411459 0.010050359987745172\n", - "Cluster 38: 1 0.13802489309599908 0.523800048828125 0.00428908839370265 0.010019291896446125\n", - "Cluster 38: 4 0.14306269327799478 0.5237577071844363 0.004656455300071027 0.009921683517156943\n", - "Cluster 38: 0 0.14873589717980587 0.5236627077588848 0.004765218098958357 0.010106033624387223\n", - "Cluster 21: 1 0.13793073711973247 0.5392588895909927 0.004460033069957375 0.009839680989583321\n", - "Cluster 21: 3 0.14305358424331202 0.5392010857077206 0.005218737053148681 0.010009191176470589\n", - "Cluster 21: 0 0.14886316472833808 0.5393448893229167 0.005275989879261367 0.009938055300245052\n", - "Cluster 36: 1 0.1380303446451823 0.555029177198223 0.004406285141453581 0.009690515854779425\n", - "Cluster 36: 2 0.14305828672466858 0.5549668016620711 0.005177131421638254 0.010077694163602935\n", - "Cluster 36: 0 0.1487662529222893 0.5549629480698529 0.005158053311434679 0.010149260876225474\n", - "Cluster 20: 1 0.13802087032433713 0.5705203067555147 0.00423336144649622 0.009863855698529433\n", - "Cluster 20: 1 0.1427608975497159 0.5705034562653186 0.00451282848011364 0.009982000612745123\n", - "Cluster 20: 0 0.14811153989849668 0.570541752833946 0.005231387976444124 0.009821633731617596\n", - "Cluster 3: 1 0.13790817723129734 0.5862586885340073 0.0043769605232007736 0.009684579886642175\n", - "Cluster 3: 0 0.14295925255977748 0.5861784572227329 0.004822036280776515 0.010025227864583375\n", - "Cluster 3: 0 0.14861735488429212 0.5860258453967524 0.0050772279681581545 0.009886690027573475\n", - "Cluster 40: 9 0.14040775645862924 0.6018566415824143 0.004934387207031238 0.01002618527879895\n", - "Cluster 40: 0 0.14577925710967093 0.6019493432138481 0.0050696448123816185 0.010319776348039267\n", - "Cluster 46: 8 0.140310234301018 0.6175523226868873 0.0050445371685606255 0.010135569852941173\n", - "Cluster 46: 0 0.14575131965406013 0.6176652736289829 0.004873851429332388 0.010166159237132377\n", - "Cluster 17: 7 0.14023251157818417 0.6328832289751838 0.005249772505326683 0.009667442172181406\n", - "Cluster 17: 0 0.1457973410866477 0.6331068809359681 0.004873953154592797 0.010127383961397118\n", - "Cluster 44: 6 0.1402722352923769 0.6487937777650122 0.00507311271898675 0.010058737362132364\n", - "Cluster 44: 0 0.14590905391808712 0.6488928462009804 0.004736217151988631 0.009903301164215672\n", - "Cluster 25: 5 0.1405757834694602 0.6640935441559437 0.004903878876657192 0.010014792049632404\n", - "Cluster 25: 0 0.14603031042850378 0.6641141285615808 0.004874729965672342 0.010013547411151902\n", - "Cluster 42: 4 0.14035383744673297 0.6796077952665441 0.005218505859375 0.009727807138480316\n", - "Cluster 42: 0 0.14601876923532198 0.6796538947610294 0.004878577030066278 0.009756625306372557\n", - "Cluster 7: 3 0.14052866155450994 0.69531494140625 0.004854838053385407 0.010225183823529327\n", - "Cluster 7: 0 0.14593774044152463 0.6951335114123774 0.004789058800899609 0.01004193474264703\n", - "Cluster 60: 0 0.16587118437795928 0.3818634033203125 0.004725952148437518 0.010094520718443634\n", - "Cluster 34: 5 0.18457039572975853 0.3817957141352635 0.005019956646543561 0.010063165402879881\n", - "Cluster 13: 1 0.19968412457090434 0.38190079034543506 0.004037623549952657 0.009629887599571063\n", - "Cluster 13: 0 0.2048508615204782 0.3818134023628983 0.00521956010298294 0.009812993068321063\n", - "Cluster 54: 1 0.2179007050485322 0.3819432756012561 0.004511496803977277 0.009861916934742698\n", - "Cluster 54: 5 0.22321859648733428 0.3819884535845588 0.005096158114346605 0.010281048943014681\n", - "Cluster 27: 2 0.2363887255119555 0.3818835209865196 0.004943126331676123 0.010016659007352935\n", - "Cluster 27: 0 0.24191182454427085 0.38182753619025733 0.005009617660984872 0.010204407935049009\n", - "Cluster 9: 2 0.2546861775716146 0.3819860720166973 0.0053478449041193254 0.010141864851409332\n", - "Cluster 9: 5 0.2601831609552557 0.3821086689070159 0.004950764973958299 0.010237606272977928\n", - "Cluster 43: 3 0.27290805701053505 0.3821193321078431 0.0050712631687973575 0.010458505667892137\n", - "Cluster 43: 0 0.2786404511422822 0.38203175264246325 0.005030369614109853 0.010347876455269633\n", - "Cluster 55: 3 0.29124179724491006 0.3820396513097426 0.004749552408854163 0.010192823223039216\n", - "Cluster 55: 5 0.2966511396928267 0.3821407781862745 0.005036565607244303 0.010056487438725448\n", - "Cluster 4: 4 0.30929721716678504 0.38233169854856003 0.0047427460641571995 0.009682114545036757\n", - "Cluster 4: 0 0.31502907492897725 0.3821165915096507 0.004972330729166641 0.010198495902267124\n", - "Cluster 48: 4 0.3276321688565341 0.3824337828393076 0.005286532315340875 0.009880059934129937\n", - "Cluster 48: 5 0.33330375902580495 0.38239826277190564 0.0047296327533143945 0.010225686465992645\n", - "Cluster 15: 5 0.34590320933948865 0.38236894196155025 0.004852627840909118 0.010301417930453471\n", - "Cluster 15: 0 0.35132405598958333 0.3824903181487439 0.004711840080492413 0.010260201248468104\n", - "Cluster 51: 5 0.36408240116003787 0.38248385560278797 0.0049909002130681945 0.010304673138786746\n", - "Cluster 51: 5 0.3695796157374527 0.3825164794921875 0.004559770063920443 0.010000119676776997\n", - "Cluster 26: 0 0.3845553496389678 0.38274670170802694 0.004613185073390147 0.010073433670343135\n", - "Cluster 56: 5 0.40287364612926135 0.38273882697610295 0.004986239346590926 0.010265634574142146\n", - "Cluster 8: 1 0.4178893488103693 0.38271556181066174 0.0043999689275567855 0.009955623851102935\n", - "Cluster 8: 0 0.422972412109375 0.3825348379097733 0.0045865885416666585 0.010432823031556349\n", - "Cluster 45: 1 0.43551524769176136 0.3828317200903799 0.00453901811079549 0.009922688802083357\n", - "Cluster 45: 5 0.4408444121389678 0.3829491230085784 0.004507908676609884 0.010030110677083315\n", - "Cluster 14: 2 0.4537920587713068 0.3825503958907782 0.005120220762310612 0.010302997663909352\n", - "Cluster 14: 0 0.4591027647076231 0.38279470406326593 0.004502064098011349 0.010210080614276973\n", - "Cluster 50: 2 0.4715062921697443 0.3827597704120711 0.004608450224905303 0.010430261948529418\n", - "Cluster 50: 5 0.4769446540601326 0.3828994571461397 0.004414802320075739 0.010206083409926459\n", - "Cluster 22: 3 0.4893110240589489 0.38275467218137255 0.004593875769412892 0.010356110217524472\n", - "Cluster 22: 0 0.4946734804095644 0.3827644856770833 0.004666933001893914 0.010176882276348054\n", - "Cluster 41: 3 0.5070133463541666 0.3827826167087929 0.00477250532670459 0.010433205997242623\n", - "Cluster 41: 5 0.5126307077118845 0.38296329273897056 0.004330573804450788 0.009986835554534335\n", - "Cluster 2: 4 0.5249030095880682 0.38268938849954043 0.0051524029356061485 0.009537162032781876\n", - "Cluster 2: 0 0.5305247173887311 0.3826352347579657 0.004638819839015151 0.009919960171568598\n", - "Cluster 30: 4 0.5427307683771307 0.3827019545611213 0.0048346132220643545 0.0098486567478554\n", - "Cluster 30: 5 0.5481516150272254 0.3827708524816177 0.004657796223958344 0.01028181487438723\n", - "Cluster 49: 5 0.5604993045691288 0.38262589996936275 0.004909889914772814 0.010216710707720567\n", - "Cluster 49: 0 0.565989472360322 0.38267627192478554 0.004707179214015089 0.010464513442095535\n", - "Cluster 12: 5 0.5785705381451232 0.38266199448529414 0.004601495916193188 0.01006065219056368\n", - "Cluster 12: 5 0.583848359079072 0.3826736869064032 0.004487970525568152 0.010092893114276968\n", - "Cluster 61: 0 0.5987218683416193 0.3823198744829963 0.004796364524147778 0.010029464422487755\n", - "Cluster 59: 5 0.617369902639678 0.38252314548866423 0.004818004261363584 0.01017716950061276\n", - "Cluster 23: 1 0.6322139855587121 0.38256816789215686 0.004319069602272796 0.00979784198835787\n", - "Cluster 23: 0 0.6372403231534092 0.38235124176623775 0.004644442471590904 0.010348067938112715\n", - "Cluster 31: 1 0.6502870131983902 0.3823184622970282 0.004205063328598491 0.009785419538909323\n", - "Cluster 31: 5 0.6552334502249053 0.38218898399203427 0.004599831321022707 0.010305989583333341\n", - "Cluster 5: 2 0.6682829145951705 0.3821959013097427 0.00478641394412882 0.010060987285539225\n", - "Cluster 5: 0 0.6739927719578598 0.38205502977558214 0.004979654947916745 0.010139327703737766\n", - "Cluster 53: 2 0.6866014145359849 0.3818985404220282 0.005006806344696968 0.010106488396139701\n", - "Cluster 53: 5 0.6921630859375 0.3820809757008272 0.004916548295454515 0.010389547909007368\n", - "Cluster 16: 3 0.7047801994554924 0.38196439855238973 0.004862097537878807 0.010205939797794106\n", - "Cluster 16: 0 0.7103043249881629 0.3818713737936581 0.0047473514441287445 0.010310465494791643\n", - "Cluster 57: 3 0.7228476784446023 0.38184248381969976 0.004995930989583286 0.010390074486825995\n", - "Cluster 57: 5 0.728689667672822 0.38188597436044736 0.00474779533617431 0.010164507697610292\n", - "Cluster 24: 4 0.7413370768229166 0.3817519962086397 0.005275065104166754 0.00967304304534311\n", - "Cluster 24: 0 0.7469070342092803 0.38166058708639705 0.004968187736742458 0.010206705729166654\n", - "Cluster 47: 4 0.7595242956912879 0.3817976888020833 0.005004586884469697 0.009735514322916694\n", - "Cluster 47: 5 0.7650375458688448 0.38170449649586397 0.004754305752840859 0.010270972158394565\n", - "Cluster 0: 5 0.7778333999171402 0.3815218457988664 0.004798103101325779 0.010090643190870108\n", - "Cluster 0: 0 0.783281434955019 0.3814627853094363 0.004878151633522676 0.010273820465686256\n", - "Cluster 32: 5 0.7958795720880683 0.38156327789905026 0.004940222537878847 0.009875943053002434\n", - "Cluster 32: 5 0.8014029947916667 0.3815045285692402 0.004948656486742475 0.010133463541666665\n", - "Cluster 58: 0 0.8166511304450758 0.38151131424249385 0.004765920928030409 0.0100871486289828\n", - "Cluster 18: 5 0.8354467033617424 0.38141405292585784 0.004737511837121122 0.009976495481004877\n", - "Cluster 35: 1 0.8502276056463068 0.3816766237745098 0.004263065222537943 0.009814405254289227\n", - "Cluster 35: 0 0.855240996389678 0.3814403578814338 0.004703554095643936 0.010118815104166679\n", - "Cluster 52: 1 0.8682825816761364 0.38148327397365195 0.0044398082386363225 0.009928624770220607\n", - "Cluster 52: 5 0.8734008419152461 0.38144994399126836 0.00466108842329549 0.010043921377144605\n", - "Cluster 6: 2 0.8865554717092803 0.3812796678730086 0.0048915423768939315 0.010303715724571061\n", - "Cluster 6: 0 0.8920348011363637 0.38133043476179534 0.004733220880681732 0.009827067057291639\n", - "Cluster 28: 2 0.9045032108191289 0.38140860763250617 0.004780421401515134 0.010118120978860279\n" + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "ROI selected: (103, 221, 631, 203)\n", + "Selected region: x=103, y=221, w=631, h=203\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "ROI selected: (103, 221, 633, 203)\n", + "Selected region: x=103, y=221, w=633, h=203\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "ROI selected: (102, 220, 635, 205)\n", + "Selected region: x=102, y=220, w=635, h=205\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "ROI selected: (103, 220, 632, 203)\n", + "Selected region: x=103, y=220, w=632, h=203\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "ROI selected: (103, 221, 632, 204)\n", + "Selected region: x=103, y=221, w=632, h=204\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "ROI selected: (101, 219, 633, 204)\n", + "Selected region: x=101, y=219, w=633, h=204\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "ROI selected: (102, 219, 633, 205)\n", + "Selected region: x=102, y=219, w=633, h=205\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "ROI selected: (103, 220, 630, 205)\n", + "Selected region: x=103, y=220, w=630, h=205\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "ROI selected: (102, 220, 634, 202)\n", + "Selected region: x=102, y=220, w=634, h=202\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "ROI selected: (101, 221, 634, 204)\n", + "Selected region: x=101, y=221, w=634, h=204\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "ROI selected: (104, 221, 628, 204)\n", + "Selected region: x=104, y=221, w=628, h=204\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "ROI selected: (104, 222, 630, 201)\n", + "Selected region: x=104, y=222, w=630, h=201\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "ROI selected: (104, 221, 630, 200)\n", + "Selected region: x=104, y=221, w=630, h=200\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "ROI selected: (102, 221, 633, 204)\n", + "Selected region: x=102, y=221, w=633, h=204\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "ROI selected: (102, 220, 633, 204)\n", + "Selected region: x=102, y=220, w=633, h=204\n" ] } ], "source": [ - "# Iterate over all images\n", + "\n", + "# Iterate over all images and their bounding boxes\n", "for sheet, bounding_boxes in yolo_data.items():\n", " print(f\"Sheet: {sheet}\")\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " print(f\"Full image path: {full_image_path}\")\n", "\n", " # Call the analyze_sheet function with data from the loop\n", - " relevant_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", "\n", " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", - " labels = cluster_kmeans(relevant_bounding_boxes, 62)\n", + " time_labels = cluster_kmeans(time_bounding_boxes, 42)\n", + " number_labels = cluster_kmeans(number_bounding_boxes, 20)\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(time_labels):\n", + "\n", + " # Get the bounding box\n", + " bounding_box = time_bounding_boxes[i]\n", + " value, x_center, y_center, width, height = bounding_box.split(' ')\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", + "\n", + " # Draw bounding boxes on the image\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " box = [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ]\n", + " draw.rectangle(box, outline=label_color_map[label], width=3)\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f'../../data/kmeans_clustered_images/time/{sheet}')\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(f'../../data/kmeans_clustered_images/results/time/{sheet}.json', 'w') as f:\n", + " json.dump(create_result_dictionary(time_labels, time_bounding_boxes), f)\n", "\n", + " # Create an image object\n", " image: Image = Image.open(full_image_path)\n", " image_width, image_height = image.size\n", "\n", " label_color_map = {}\n", - " for i, label in enumerate(labels):\n", + " for i, label in enumerate(number_labels):\n", "\n", " # Get the bounding box\n", - " bounding_box = relevant_bounding_boxes[i]\n", + " bounding_box = number_bounding_boxes[i]\n", " value, x_center, y_center, width, height = bounding_box.split(' ')\n", " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", "\n", @@ -414,217 +497,90 @@ " ]\n", " draw.rectangle(box, outline=label_color_map[label], width=3)\n", "\n", - " # Display the image\n", - " image.show()\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f'../../data/kmeans_clustered_images/number/{sheet}')\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(f'../../data/kmeans_clustered_images/results/number/{sheet}.json', 'w') as f:\n", + " json.dump(create_result_dictionary(number_labels, number_bounding_boxes), f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create Cluster Mapping\n", + "Contains a cluster number that maps list of numbers that belong to that cluster *in-order*\n", + "\n", + "This will allow us to easily impute meanings of the labels since we have them split by time and number (mmHg and HR)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[27], line 18\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# For each cluster in time cluster, sort by left most to right most\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cluster \u001b[38;5;129;01min\u001b[39;00m time_clusters:\n\u001b[1;32m---> 18\u001b[0m cluster \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcluster\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 19\u001b[0m final_time_clusters[sheet] \u001b[38;5;241m=\u001b[39m time_clusters\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(NUMBER_JSON, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m'\u001b[39m)) \u001b[38;5;28;01mas\u001b[39;00m f:\n", + "Cell \u001b[1;32mIn[27], line 18\u001b[0m, in \u001b[0;36m\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# For each cluster in time cluster, sort by left most to right most\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cluster \u001b[38;5;129;01min\u001b[39;00m time_clusters:\n\u001b[1;32m---> 18\u001b[0m cluster \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(cluster, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m 19\u001b[0m final_time_clusters[sheet] \u001b[38;5;241m=\u001b[39m time_clusters\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(NUMBER_JSON, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m'\u001b[39m)) \u001b[38;5;28;01mas\u001b[39;00m f:\n", + "\u001b[1;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", + "\n", + "# Paths to the JSON files\n", + "PATH_TO_KMEANS_RESULTS = '../../data/kmeans_clustered_images/results'\n", + "TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'time')\n", + "NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'number')\n", "\n", - " # Print numbers for each cluster\n", - " for i, label in enumerate(labels):\n", - " print(f\"Cluster {label}: {relevant_bounding_boxes[i]}\")\n", + "final_time_clusters = {}\n", + "final_number_clusters = {}\n", "\n", - " # Break after the first image\n", - " break" + "# Iterate over all images and their bounding boxes\n", + "for sheet, bounding_boxes in yolo_data.items():\n", + " # Load JSON\n", + " with open(os.path.join(TIME_JSON, f'{sheet}.json')) as f:\n", + " time_clusters = json.load(f)\n", + " \n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " \n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analyze accuracy\n", + "\n", + "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Class 28:\n", - " Index 0: 5\n", - " Index 128: 2\n", - "Class 10:\n", - " Index 1: 2\n", - " Index 2: 2\n", - " Index 3: 0\n", - "Class 39:\n", - " Index 4: 2\n", - " Index 5: 1\n", - " Index 6: 0\n", - "Class 19:\n", - " Index 7: 2\n", - " Index 8: 0\n", - " Index 9: 0\n", - "Class 37:\n", - " Index 10: 1\n", - " Index 11: 9\n", - " Index 12: 0\n", - "Class 1:\n", - " Index 13: 1\n", - " Index 14: 8\n", - " Index 15: 0\n", - "Class 29:\n", - " Index 16: 1\n", - " Index 17: 7\n", - " Index 18: 0\n", - "Class 33:\n", - " Index 19: 1\n", - " Index 20: 6\n", - " Index 21: 0\n", - "Class 11:\n", - " Index 22: 1\n", - " Index 23: 5\n", - " Index 24: 0\n", - "Class 38:\n", - " Index 25: 1\n", - " Index 26: 4\n", - " Index 27: 0\n", - "Class 21:\n", - " Index 28: 1\n", - 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" Index 65: 0\n", - "Class 55:\n", - " Index 66: 3\n", - " Index 67: 5\n", - "Class 4:\n", - " Index 68: 4\n", - " Index 69: 0\n", - "Class 48:\n", - " Index 70: 4\n", - " Index 71: 5\n", - "Class 15:\n", - " Index 72: 5\n", - " Index 73: 0\n", - "Class 51:\n", - " Index 74: 5\n", - " Index 75: 5\n", - "Class 26:\n", - " Index 76: 0\n", - "Class 56:\n", - " Index 77: 5\n", - "Class 8:\n", - " Index 78: 1\n", - " Index 79: 0\n", - "Class 45:\n", - " Index 80: 1\n", - " Index 81: 5\n", - "Class 14:\n", - " Index 82: 2\n", - " Index 83: 0\n", - "Class 50:\n", - " Index 84: 2\n", - " Index 85: 5\n", - "Class 22:\n", - " Index 86: 3\n", - " Index 87: 0\n", - "Class 41:\n", - " Index 88: 3\n", - " Index 89: 5\n", - "Class 2:\n", - " Index 90: 4\n", - " Index 91: 0\n", - "Class 30:\n", - " Index 92: 4\n", - " Index 93: 5\n", - "Class 49:\n", - " Index 94: 5\n", - " Index 95: 0\n", - "Class 12:\n", - " Index 96: 5\n", - " Index 97: 5\n", - "Class 61:\n", - " Index 98: 0\n", - "Class 59:\n", - " Index 99: 5\n", - "Class 23:\n", - " Index 100: 1\n", - " Index 101: 0\n", - "Class 31:\n", - " Index 102: 1\n", - " Index 103: 5\n", - "Class 5:\n", - " Index 104: 2\n", - " Index 105: 0\n", - "Class 53:\n", - " Index 106: 2\n", - " Index 107: 5\n", - "Class 16:\n", - " Index 108: 3\n", - " Index 109: 0\n", - "Class 57:\n", - " Index 110: 3\n", - " Index 111: 5\n", - "Class 24:\n", - " Index 112: 4\n", - " Index 113: 0\n", - "Class 47:\n", - " Index 114: 4\n", - " Index 115: 5\n", - "Class 0:\n", - " Index 116: 5\n", - " Index 117: 0\n", - "Class 32:\n", - " Index 118: 5\n", - " Index 119: 5\n", - "Class 58:\n", - " Index 120: 0\n", - "Class 18:\n", - " Index 121: 5\n", - "Class 35:\n", - " Index 122: 1\n", - " Index 123: 0\n", - "Class 52:\n", - " Index 124: 1\n", - " Index 125: 5\n", - "Class 6:\n", - " Index 126: 2\n", - " Index 127: 0\n" + "ename": "NameError", + "evalue": "name 'relevant_bounding_boxes' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 23\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, value \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 23\u001b[0m relevant_numbers \u001b[38;5;241m=\u001b[39m [box\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m box \u001b[38;5;129;01min\u001b[39;00m \u001b[43mrelevant_bounding_boxes\u001b[49m]\n\u001b[0;32m 24\u001b[0m print_classes_and_values(labels, relevant_numbers)\n", + "\u001b[1;31mNameError\u001b[0m: name 'relevant_bounding_boxes' is not defined" ] } ], From 9b5584fb37051ecadcbbb851c1c2713ef26503da Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Fri, 18 Oct 2024 21:51:53 -0400 Subject: [PATCH 15/55] Accuracy among time stamps in 99.75%. Among number labels it is found to be 100%. --- experiments/clustering/clustering.ipynb | 111 +++++++++--------------- 1 file changed, 42 insertions(+), 69 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 1c8a477..01c8c66 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -509,27 +509,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Create Cluster Mapping\n", - "Contains a cluster number that maps list of numbers that belong to that cluster *in-order*\n", + "#### Analyze accuracy\n", "\n", - "This will allow us to easily impute meanings of the labels since we have them split by time and number (mmHg and HR)" + "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "metadata": {}, "outputs": [ { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[27], line 18\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# For each cluster in time cluster, sort by left most to right most\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cluster \u001b[38;5;129;01min\u001b[39;00m time_clusters:\n\u001b[1;32m---> 18\u001b[0m cluster \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcluster\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 19\u001b[0m final_time_clusters[sheet] \u001b[38;5;241m=\u001b[39m time_clusters\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(NUMBER_JSON, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m'\u001b[39m)) \u001b[38;5;28;01mas\u001b[39;00m f:\n", - "Cell \u001b[1;32mIn[27], line 18\u001b[0m, in \u001b[0;36m\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# For each cluster in time cluster, sort by left most to right most\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cluster \u001b[38;5;129;01min\u001b[39;00m time_clusters:\n\u001b[1;32m---> 18\u001b[0m cluster \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(cluster, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m 19\u001b[0m final_time_clusters[sheet] \u001b[38;5;241m=\u001b[39m time_clusters\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(NUMBER_JSON, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m'\u001b[39m)) \u001b[38;5;28;01mas\u001b[39;00m f:\n", - "\u001b[1;31mIndexError\u001b[0m: list index out of range" + "name": "stdout", + "output_type": "stream", + "text": [ + "Time labels: 796 correct clusters, 2 incorrect clusters. The accuracy is 99.75%\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n" ] } ], @@ -541,11 +536,15 @@ "TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'time')\n", "NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'number')\n", "\n", - "final_time_clusters = {}\n", - "final_number_clusters = {}\n", - "\n", + "time_wrong_clusters_count = 0\n", + "time_correct_clusters_count = 0\n", + "number_wrong_clusters_count = 0\n", + "number_correct_clusters_count = 0\n", "# Iterate over all images and their bounding boxes\n", "for sheet, bounding_boxes in yolo_data.items():\n", + " expected_time_values = [0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25]\n", + " expected_number_values = [30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220]\n", + "\n", " # Load JSON\n", " with open(os.path.join(TIME_JSON, f'{sheet}.json')) as f:\n", " time_clusters = json.load(f)\n", @@ -553,62 +552,36 @@ " # Each cluster contains the number (integer) that the cluster represents\n", " # We know what integers should be represented in the time labels, lets check that they are all there.\n", " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in time_clusters.items():\n", + " if value not in expected_time_values:\n", + " # We have an erroneous cluster\n", + " time_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_time_values.remove(value)\n", + " time_correct_clusters_count += 1\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(NUMBER_JSON, f'{sheet}.json')) as f:\n", + " number_clusters = json.load(f)\n", " \n", "\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Analyze accuracy\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in number_clusters.items():\n", + " if value not in expected_number_values:\n", + " # We have an erroneous cluster\n", + " number_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_number_values.remove(value)\n", + " number_correct_clusters_count += 1\n", + " \n", "\n", - "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'relevant_bounding_boxes' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[7], line 23\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, value \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 23\u001b[0m relevant_numbers \u001b[38;5;241m=\u001b[39m [box\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m box \u001b[38;5;129;01min\u001b[39;00m \u001b[43mrelevant_bounding_boxes\u001b[49m]\n\u001b[0;32m 24\u001b[0m print_classes_and_values(labels, relevant_numbers)\n", - "\u001b[1;31mNameError\u001b[0m: name 'relevant_bounding_boxes' is not defined" - ] - } - ], - "source": [ - "def print_classes_and_values(classes, values):\n", - " \"\"\"Prints the class number followed by all the indices and values belonging to that class.\n", - "\n", - " Args:\n", - " classes: A list of class labels for each value.\n", - " values: A list of values.\n", - " \"\"\"\n", - "\n", - " # Create a dictionary to store indices and values by class\n", - " class_data = {}\n", - " for i, class_label in enumerate(classes):\n", - " if class_label not in class_data:\n", - " class_data[class_label] = []\n", - " class_data[class_label].append((i, values[i]))\n", - "\n", - " # Print the class number followed by the indices and values\n", - " for class_label, data in class_data.items():\n", - " print(f\"Class {class_label}:\")\n", - " for index, value in data:\n", - " print(f\" Index {index}: {value}\")\n", - "\n", - "\n", - "relevant_numbers = [box.split(\" \")[0] for box in relevant_bounding_boxes]\n", - "print_classes_and_values(labels, relevant_numbers)\n" + "\n", + "print(f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\")\n", + "print(f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\")\n" ] } ], From b8319d68ca882700461de0cce4a7f603bfca742a Mon Sep 17 00:00:00 2001 From: hvalenty Date: Wed, 23 Oct 2024 15:17:07 -0400 Subject: [PATCH 16/55] Density based clustering experiement --- .../clustering/density_clustering.ipynb | 638 ++++++++++++++++++ 1 file changed, 638 insertions(+) create mode 100644 experiments/clustering/density_clustering.ipynb diff --git a/experiments/clustering/density_clustering.ipynb b/experiments/clustering/density_clustering.ipynb new file mode 100644 index 0000000..f0e8fd5 --- /dev/null +++ b/experiments/clustering/density_clustering.ipynb @@ -0,0 +1,638 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Density-Based Clustering\n", + "### Hannah Valenty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Charbel's comment on the process of predetermining clusters and that method's lack of flexibility piqued my interest to look into other input options. This pointed me in the direction of agglomerative clustering and more specifically, Ward hierarchical clustering using linkage distance. The input parameter of distance is a shift from the number of clusters used in kmeans and other common clustering approaches.\n", + "\n", + "However, the Ward clustering was not performing well, so I pivoted to DBSCAN clustering. Density-Based Spatial Clustering of Applications with Noise clusters together those points that are close to each other based on any distance metric and a minimum number of points." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preface\n", + "The methods to translate images, load yolo data, and select bounding boxes are from Charbel's `clustering.ipynb` document. This pipeline was immensely helpful for allowing me to smoothly create and train a supplemental clustering algorithm. The sections taken from this document will be labelled as such." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register Images to Start (`clustering.ipynb`)\n", + "\n", + "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install packages\n", + "Import libraries for analysis, with change in sklearn.cluster from KMeans to AgglomerativeClustering." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import random\n", + "from pathlib import Path\n", + "from typing import List, Tuple, Dict\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "from PIL import Image, ImageDraw\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "from sklearn.cluster import DBSCAN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start by loading YOLO data (`clustering.ipynb`)\n", + "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 19 sheets in yolo_data.json\n" + ] + } + ], + "source": [ + "# Load yolo_data.json\n", + "PATH_TO_YOLO_DATA = '../../data/yolo_data.json'\n", + "PATH_TO_REGISTERED_IMAGES = '../../data/registered_images'\n", + "UNIFIED_IMAGE_PATH = '../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png'\n", + "with open(PATH_TO_YOLO_DATA) as json_file:\n", + " yolo_data = json.load(json_file)\n", + "\n", + "# See how many intraoperative images are registered\n", + "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's select relevant bounding boxes from the blood pressure and HR zone. \n", + "\n", + "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n", + "\n", + "Also identifies which boxes are for timestamps (top-right) or numeric values of mmHg and bpm (bottom-left)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def YOLO_to_pixels(x_center, y_center, width, height, image_width, image_height):\n", + " \"\"\"\n", + " Convert YOLO bounding box format to pixel coordinates\n", + "\n", + " Args:\n", + " x_center: float, x center of the bounding box\n", + " y_center: float, y center of the bounding box\n", + " width: float, width of the bounding box\n", + " height: float, height of the bounding box\n", + " image_width: int, width of the image\n", + " image_height: int, height of the image\n", + "\n", + " Returns:\n", + " A single tuple with the following values:\n", + " x_min: int, minimum x coordinate of the bounding box in pixels\n", + " y_min: int, minimum y coordinate of the bounding box in pixels\n", + " x_max: int, maximum x coordinate of the bounding box in pixels\n", + " y_max: int, maximum y coordinate of the bounding box in pixels\n", + " \"\"\"\n", + " x_min = int((float(x_center) * image_width) - (width * image_width) / 2)\n", + " y_min = int((float(y_center) * image_height) - (height * image_height) / 2)\n", + " x_max = int((float(x_center) * image_width) + (width * image_width) / 2)\n", + " y_max = int((float(y_center) * image_height) + (height * image_height) / 2)\n", + " return x_min, y_min, x_max, y_max\n", + "\n", + "# Function to determine whether a point is above or below the diagonal line\n", + "def is_point_in_above(x_center, y_center, m, b):\n", + " \"\"\"\n", + " Determine if a point is above or below the diagonal line y = mx + b.\n", + " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", + "\n", + " Args:\n", + " x_center: float, x coordinate of the point\n", + " y_center: float, y coordinate of the point\n", + " m: float, slope of the diagonal line\n", + " b: float, intercept of the diagonal line\n", + " \n", + " Returns:\n", + " bool, True if the point is above the line, False otherwise\n", + " \"\"\"\n", + " # Calculate the y value on the line for the given x_center\n", + " y_line = m * x_center + b\n", + " return y_center > y_line\n", + "\n", + "\n", + "\n", + "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path, show_images: bool = False) -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", + " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", + " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", + "\n", + " Args:\n", + " sheet_data: List of bounding boxes in YOLO format.\n", + " path_to_image: Path to the image file.\n", + "\n", + " Returns:\n", + " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", + " The first list contains bounding boxes in the top-right region -- representing time labels.\n", + " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", + " (bounding_boxes_time, bounding_boxes_numbers)\n", + " \"\"\"\n", + " # Load the image\n", + " image = cv2.imread(path_to_image)\n", + "\n", + " # Display the image and allow the user to select a ROI\n", + " resized_image = cv2.resize(image, (800, 600))\n", + " roi = cv2.selectROI(\"Select Region of Interest\", resized_image)\n", + " print(f\"ROI selected: {roi}\")\n", + "\n", + " # Unpack ROI\n", + " x, y, w, h = roi\n", + " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", + "\n", + " # Calculate the coordinates of the top-left and bottom-right corners of the selected region\n", + " x_top_left = x\n", + " y_top_left = y\n", + " x_bottom_right = x + w\n", + " y_bottom_right = y + h\n", + "\n", + " # Draw the diagonal line of the selected region from top-left to bottom-right\n", + " cv2.line(resized_image, (x_top_left, y_top_left), (x_bottom_right, y_bottom_right), (0, 255, 0), 1)\n", + " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", + " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", + " # Top-right region is where time labels are located\n", + " # Bottom-left region is where numerical values for mmHg and bpm are located\n", + " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", + " b = y_top_left - m * x_top_left\n", + "\n", + " # List of bounding boxes in the top-right and bottom-left regions\n", + " bounding_boxes_time = []\n", + " bounding_boxes_numbers = []\n", + "\n", + " # Process the bounding boxes\n", + " for bounding_box in sheet_data:\n", + " # Bounding boxes are in YOLO format; convert them to pixels\n", + " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ') # Identifier is the value in the bounding box, we don't need that here\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", + " \n", + " # Check if the bounding box is within the selected region\n", + " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", + " # Calculate the center of the bounding box\n", + " x_center_bb = (x_min + x_max) / 2\n", + " y_center_bb = (y_min + y_max) / 2\n", + " \n", + " # If we want to generalize this function we can add the option to disregard the diagonal line\n", + "\n", + " # Determine if the bounding box center is in the top-right region\n", + " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", + " # Bounding box is in the top-right region\n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1)\n", + " bounding_boxes_numbers.append(bounding_box)\n", + " else:\n", + " # Bounding box is in the bottom-left region\n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1)\n", + " bounding_boxes_time.append(bounding_box)\n", + "\n", + " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", + " # You can also manually quit out with ESC key.\n", + " cv2.destroyAllWindows()\n", + "\n", + " # If we are showing the images, display the image with the selected region and bounding boxes\n", + " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", + " if show_images:\n", + " # Display the image with the selected region and bounding boxes\n", + " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + " resized_image = Image.fromarray(resized_image)\n", + " resized_image.show()\n", + "\n", + " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", + " return (bounding_boxes_time, bounding_boxes_numbers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implementing a function for Ward hierarchical clustering using linkage distance" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def dbscan_clustering(bounding_boxes: List[str], defined_eps: float, min_samples: int) -> List[int]:\n", + " \"\"\"\n", + " Cluster bounding boxes using Ward hierarchical clustering algorithm with linkage distance.\n", + "\n", + " Args:\n", + " bounding_boxes: List of bounding boxes in YOLO format.\n", + " defined_eps: The maximum distance between two samples for one to be considered as in the neighborhood of the other.\n", + " min_samples: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.\n", + "\n", + " Returns:\n", + " List of cluster labels.\n", + " \"\"\"\n", + " # Convert to a NumPy array (using only x_center and y_center)\n", + " data = np.array([[float(box.split(' ')[1]), float(box.split(' ')[2])] for box in bounding_boxes])\n", + "\n", + " # DBSCAN\n", + " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", + " labels = scan.fit_predict(data)\n", + "\n", + " return labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to create a result dictionary that we can save as a JSON file to analyze performance. (`clustering.ipynb`)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def create_result_dictionary(labels: List[str], bounding_boxes: List[str]) -> Dict[int, int]:\n", + " \"\"\"\n", + " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", + "\n", + " Args:\n", + " labels: List of cluster labels.\n", + " bounding_boxes: List of bounding boxes in YOLO format.\n", + "\n", + " Returns:\n", + " Dictionary with cluster labels as keys and bounding box values as values.\n", + " \"\"\"\n", + " # Create a dictionary to store labelled elements\n", + " label_dict = {}\n", + "\n", + " # Iterate over both lists\n", + " for label, element in zip(labels, bounding_boxes):\n", + " label = int(label)\n", + " element = str(element)\n", + " if label not in label_dict:\n", + " # Create a new list for this label if it doesn't exist\n", + " label_dict[label] = []\n", + " # Append the element to the corresponding label list\n", + " label_dict[label].append(element)\n", + "\n", + " # Sort the lists in the dictionary by x_center\n", + " for key in label_dict:\n", + " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(' ')[1]))\n", + " label_dict[key] = [element.split(' ')[0] for element in label_dict[key]]\n", + " # Turn list of strings into a string\n", + " label_dict[key] = int(''.join(label_dict[key]))\n", + " \n", + " return label_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets use these functions to get the relevant bounding boxes for clustering. (`clustering.ipynb`)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "ROI selected: (103, 221, 635, 205)\n", + "Selected region: x=103, y=221, w=635, h=205\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "ROI selected: (104, 221, 634, 205)\n", + "Selected region: x=104, y=221, w=634, h=205\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "ROI selected: (101, 221, 636, 204)\n", + "Selected region: x=101, y=221, w=636, h=204\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "ROI selected: (101, 219, 636, 206)\n", + "Selected region: x=101, y=219, w=636, h=206\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "ROI selected: (101, 219, 640, 207)\n", + "Selected region: x=101, y=219, w=640, h=207\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "ROI selected: (101, 219, 640, 207)\n", + "Selected region: x=101, y=219, w=640, h=207\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "ROI selected: (104, 217, 633, 207)\n", + "Selected region: x=104, y=217, w=633, h=207\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "ROI selected: (105, 223, 628, 201)\n", + "Selected region: x=105, y=223, w=628, h=201\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "ROI selected: (103, 221, 633, 205)\n", + "Selected region: x=103, y=221, w=633, h=205\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "ROI selected: (103, 221, 635, 205)\n", + "Selected region: x=103, y=221, w=635, h=205\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "ROI selected: (102, 218, 636, 208)\n", + "Selected region: x=102, y=218, w=636, h=208\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "ROI selected: (102, 218, 632, 205)\n", + "Selected region: x=102, y=218, w=632, h=205\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "ROI selected: (101, 219, 633, 204)\n", + "Selected region: x=101, y=219, w=633, h=204\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "ROI selected: (101, 219, 637, 207)\n", + "Selected region: x=101, y=219, w=637, h=207\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "ROI selected: (104, 221, 634, 205)\n", + "Selected region: x=104, y=221, w=634, h=205\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "ROI selected: (104, 221, 631, 207)\n", + "Selected region: x=104, y=221, w=631, h=207\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "ROI selected: (105, 221, 630, 203)\n", + "Selected region: x=105, y=221, w=630, h=203\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "ROI selected: (104, 220, 631, 204)\n", + "Selected region: x=104, y=220, w=631, h=204\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "ROI selected: (104, 220, 632, 204)\n", + "Selected region: x=104, y=220, w=632, h=204\n" + ] + } + ], + "source": [ + "# Iterate over all images and their bounding boxes\n", + "for sheet, bounding_boxes in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " print(f\"Full image path: {full_image_path}\")\n", + "\n", + " # Call the analyze_sheet function with data from the loop\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", + "\n", + " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", + " # Eps chosen based on width units of bounding boxes -- on average 0.0042-0.0045 wide\n", + " time_labels = dbscan_clustering(time_bounding_boxes, defined_eps=0.01, min_samples=1)\n", + " number_labels = dbscan_clustering(number_bounding_boxes, defined_eps=0.01, min_samples=2)\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(time_labels):\n", + "\n", + " # Get the bounding box\n", + " bounding_box = time_bounding_boxes[i]\n", + " value, x_center, y_center, width, height = bounding_box.split(' ')\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", + "\n", + " # Draw bounding boxes on the image\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " box = [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ]\n", + " draw.rectangle(box, outline=label_color_map[label], width=3)\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f'../../data/kmeans_clustered_images/time/{sheet}')\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(f'../../data/kmeans_clustered_images/results/time/{sheet}.json', 'w') as f:\n", + " json.dump(create_result_dictionary(time_labels, time_bounding_boxes), f)\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(number_labels):\n", + "\n", + " # Get the bounding box\n", + " bounding_box = number_bounding_boxes[i]\n", + " value, x_center, y_center, width, height = bounding_box.split(' ')\n", + " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", + "\n", + " # Draw bounding boxes on the image\n", + " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " box = [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ]\n", + " draw.rectangle(box, outline=label_color_map[label], width=3)\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f'../../data/kmeans_clustered_images/number/{sheet}')\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(f'../../data/kmeans_clustered_images/results/number/{sheet}.json', 'w') as f:\n", + " json.dump(create_result_dictionary(number_labels, number_bounding_boxes), f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analyze accuracy (`clustering.ipynb`)\n", + "\n", + "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time labels: 794 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Number labels: 380 correct clusters, 3 incorrect clusters. The accuracy is 99.22%\n" + ] + } + ], + "source": [ + "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", + "\n", + "# Paths to the JSON files\n", + "PATH_TO_KMEANS_RESULTS = '../../data/kmeans_clustered_images/results'\n", + "TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'time')\n", + "NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'number')\n", + "\n", + "time_wrong_clusters_count = 0\n", + "time_correct_clusters_count = 0\n", + "number_wrong_clusters_count = 0\n", + "number_correct_clusters_count = 0\n", + "# Iterate over all images and their bounding boxes\n", + "for sheet, bounding_boxes in yolo_data.items():\n", + " expected_time_values = [0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25]\n", + " expected_number_values = [30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220]\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(TIME_JSON, f'{sheet}.json')) as f:\n", + " time_clusters = json.load(f)\n", + " \n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in time_clusters.items():\n", + " if value not in expected_time_values:\n", + " # We have an erroneous cluster\n", + " time_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_time_values.remove(value)\n", + " time_correct_clusters_count += 1\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(NUMBER_JSON, f'{sheet}.json')) as f:\n", + " number_clusters = json.load(f)\n", + " \n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in number_clusters.items():\n", + " if value not in expected_number_values:\n", + " # We have an erroneous cluster\n", + " number_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_number_values.remove(value)\n", + " number_correct_clusters_count += 1\n", + " \n", + "\n", + "\n", + "print(f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\")\n", + "print(f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Previous Attempts\n", + "First tried AgglomerativeClustering using a Ward hierarchical clustering algorithm with linkage distance.\n", + "* Misjudged distance_threshold input, only takes meaningful values form 0-1\n", + "* Best accuracy with distance_threshold = 0, 10.55% accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ward linkage clustering fit and prediction\n", + "# distance_threshold shows the limit at which to cut the dendrogram tree\n", + "ward = AgglomerativeClustering(n_clusters=None, metric='euclidean', linkage='single', compute_full_tree=True, distance_threshold=nonmerge_threshold)\n", + "labels = ward.fit_predict(data)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 280b9eaf372811f23528eaa18575aae96a57a35b Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Wed, 23 Oct 2024 22:52:42 -0400 Subject: [PATCH 17/55] Added all two methods to the same notebook. ROI selected via an edited version of Ryan's selected method and datapoints are split by a diagonal line. Converted to using BoundingBox class instead of handling coordinates directly. Added labels to output. --- experiments/clustering/clustering.ipynb | 2311 +++++++++++++++-- .../clustering/density_clustering.ipynb | 638 ----- experiments/clustering/utils/annotations.py | 434 ++++ .../apply_homography_to_labels.ipynb | 4 +- 4 files changed, 2476 insertions(+), 911 deletions(-) delete mode 100644 experiments/clustering/density_clustering.ipynb create mode 100644 experiments/clustering/utils/annotations.py diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 01c8c66..434e477 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -4,10 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Clustering Experiment \n", - "#### By: Charbel Marche\n", + "# Clustering Experiment\n", "\n", - "We decided to individually tackle the problem using 1 method, and by the time we are all done we will be able to merge our techniques and select the optimal techinque. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." + "#### By: Hannah Valenty and Charbel Marche\n", + "\n", + "We decided to individually tackle the problem using 1 method, and by the time we are all done we will be able to merge our techniques and select the optimal techinque. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers.\n" ] }, { @@ -16,7 +17,7 @@ "source": [ "### Register Images to Start\n", "\n", - "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook. " + "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook.\n" ] }, { @@ -25,44 +26,50 @@ "source": [ "#### Install Packages\n", "\n", - "These are the necessary packages to run the functions and scripts below." + "These are the necessary packages to run the functions and scripts below.\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ + "# Standard libraries\n", "import os\n", "import json\n", "import random\n", "from pathlib import Path\n", - "from typing import List, Tuple, Dict\n", + "from typing import List, Tuple, Literal, Dict\n", "\n", + "# Third-party libraries\n", "import cv2\n", "import numpy as np\n", "from PIL import Image, ImageDraw\n", - "from sklearn.cluster import KMeans" + "from sklearn.cluster import KMeans, DBSCAN\n", + "from sklearn.metrics import silhouette_score\n", + "\n", + "# Local libraries\n", + "from utils.annotations import BoundingBox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Start By Loading YOLO Data" + "#### Start By Loading YOLO Data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data." + "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data.\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -75,9 +82,11 @@ ], "source": [ "# Load yolo_data.json\n", - "PATH_TO_YOLO_DATA = '../../data/yolo_data.json'\n", - "PATH_TO_REGISTERED_IMAGES = '../../data/registered_images'\n", - "UNIFIED_IMAGE_PATH = '../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png'\n", + "PATH_TO_YOLO_DATA = \"../../data/yolo_data.json\"\n", + "PATH_TO_REGISTERED_IMAGES = \"../../data/registered_images\"\n", + "UNIFIED_IMAGE_PATH = (\n", + " \"../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png\"\n", + ")\n", "with open(PATH_TO_YOLO_DATA) as json_file:\n", " yolo_data = json.load(json_file)\n", "\n", @@ -89,44 +98,69 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's select relevant bounding boxes from the blood pressure and HR zone. \n", + "Now let's select relevant bounding boxes from the blood pressure and HR zone.\n", "\n", - "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI." + "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ - "def YOLO_to_pixels(x_center, y_center, width, height, image_width, image_height):\n", - " \"\"\"\n", - " Convert YOLO bounding box format to pixel coordinates\n", + "def get_bp_section_coordinates(\n", + " image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", + ") -> List[int]:\n", + " \"\"\"Crops the blood pressure section out of an image of a chart.\n", "\n", " Args:\n", - " x_center: float, x center of the bounding box\n", - " y_center: float, y center of the bounding box\n", - " width: float, width of the bounding box\n", - " height: float, height of the bounding box\n", - " image_width: int, width of the image\n", - " image_height: int, height of the image\n", + " image_height (int):\n", + " The height of the image in pixels.\n", + " bboxes (List[BoundingBox]):\n", + " List of BoundingBoxes within this image.\n", + " buffer_pixels (int):\n", + " An optional integer that specifies the number of pixels around the digit detections to\n", + " 'zoom out' by. Defaults to 5 pixels.\n", "\n", " Returns:\n", - " A single tuple with the following values:\n", - " x_min: int, minimum x coordinate of the bounding box in pixels\n", - " y_min: int, minimum y coordinate of the bounding box in pixels\n", - " x_max: int, maximum x coordinate of the bounding box in pixels\n", - " y_max: int, maximum y coordinate of the bounding box in pixels\n", + " Coordinates of the bounding box that contains the blood pressure section.\n", " \"\"\"\n", - " x_min = int((float(x_center) * image_width) - (width * image_width) / 2)\n", - " y_min = int((float(y_center) * image_height) - (height * image_height) / 2)\n", - " x_max = int((float(x_center) * image_width) + (width * image_width) / 2)\n", - " y_max = int((float(y_center) * image_height) + (height * image_height) / 2)\n", - " return x_min, y_min, x_max, y_max\n", - "\n", - "# Function to determine whether a point is above or below the diagonal line\n", - "def is_point_in_above(x_center, y_center, m, b):\n", + " # Get bounding boxes from detections and filter non bounding boxes out.\n", + " bboxes: List[BoundingBox] = list(\n", + " filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", + " )\n", + "\n", + " digit_categories: List[str] = [str(i) for i in range(10)]\n", + "\n", + " # Filter bounding boxes to those which are within the approximate region and are digits.\n", + " bp_legend_digits: List[BoundingBox] = list(\n", + " filter(\n", + " lambda bb: all(\n", + " [\n", + " bb.top / image_height > 0.2,\n", + " bb.top / image_height < 0.8,\n", + " bb.category in digit_categories,\n", + " ]\n", + " ),\n", + " bboxes,\n", + " )\n", + " )\n", + " bp_legend_coordinates: List[int] = list(\n", + " map(\n", + " int,\n", + " [\n", + " min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", + " min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", + " max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", + " max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", + " ],\n", + " )\n", + " )\n", + " return bp_legend_coordinates\n", + "\n", + "\n", + "def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", " \"\"\"\n", " Determine if a point is above or below the diagonal line y = mx + b.\n", " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", @@ -136,7 +170,7 @@ " y_center: float, y coordinate of the point\n", " m: float, slope of the diagonal line\n", " b: float, intercept of the diagonal line\n", - " \n", + "\n", " Returns:\n", " bool, True if the point is above the line, False otherwise\n", " \"\"\"\n", @@ -145,11 +179,16 @@ " return y_center > y_line\n", "\n", "\n", - "\n", - "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path, show_images: bool = False) -> Tuple[List[str], List[str]]:\n", + "def select_relevant_bounding_boxes(\n", + " sheet_data: List[str],\n", + " path_to_image: Path,\n", + " show_images: bool = False,\n", + " desired_img_width: int = 800,\n", + " desired_img_height: int = 600,\n", + ") -> Tuple[List[str], List[str]]:\n", " \"\"\"\n", " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", + " Identify bounding boxes that are within the selected region and draw rectangles around them.\n", " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", "\n", " Args:\n", @@ -162,26 +201,38 @@ " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", " (bounding_boxes_time, bounding_boxes_numbers)\n", " \"\"\"\n", + "\n", " # Load the image\n", " image = cv2.imread(path_to_image)\n", "\n", " # Display the image and allow the user to select a ROI\n", - " resized_image = cv2.resize(image, (800, 600))\n", - " roi = cv2.selectROI(\"Select Region of Interest\", resized_image)\n", - " print(f\"ROI selected: {roi}\")\n", - "\n", - " # Unpack ROI\n", - " x, y, w, h = roi\n", - " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", - "\n", - " # Calculate the coordinates of the top-left and bottom-right corners of the selected region\n", - " x_top_left = x\n", - " y_top_left = y\n", - " x_bottom_right = x + w\n", - " y_bottom_right = y + h\n", + " resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", + "\n", + " x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", + " image_height=desired_img_height,\n", + " bboxes=[\n", + " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", + " for yolo_bb in sheet_data\n", + " ],\n", + " buffer_pixels=2,\n", + " )\n", + "\n", + " cv2.rectangle(\n", + " resized_image,\n", + " (x_top_left, y_top_left),\n", + " (x_bottom_right, y_bottom_right),\n", + " (255, 255, 0),\n", + " 1,\n", + " )\n", "\n", " # Draw the diagonal line of the selected region from top-left to bottom-right\n", - " cv2.line(resized_image, (x_top_left, y_top_left), (x_bottom_right, y_bottom_right), (0, 255, 0), 1)\n", + " cv2.line(\n", + " resized_image,\n", + " (x_top_left, y_top_left),\n", + " (x_bottom_right, y_bottom_right),\n", + " (0, 255, 0),\n", + " 1,\n", + " )\n", " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", " # Top-right region is where time labels are located\n", @@ -196,26 +247,55 @@ " # Process the bounding boxes\n", " for bounding_box in sheet_data:\n", " # Bounding boxes are in YOLO format; convert them to pixels\n", - " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ') # Identifier is the value in the bounding box, we don't need that here\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", - " \n", + " x_min, y_min, x_max, y_max = list(\n", + " map(\n", + " int,\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " ).box,\n", + " )\n", + " )\n", + "\n", " # Check if the bounding box is within the selected region\n", - " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", + " if (\n", + " x_min >= x_top_left\n", + " and y_min >= y_top_left\n", + " and x_max <= x_bottom_right\n", + " and y_max <= y_bottom_right\n", + " ):\n", " # Calculate the center of the bounding box\n", " x_center_bb = (x_min + x_max) / 2\n", " y_center_bb = (y_min + y_max) / 2\n", - " \n", + "\n", " # If we want to generalize this function we can add the option to disregard the diagonal line\n", "\n", " # Determine if the bounding box center is in the top-right region\n", " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", " # Bounding box is in the top-right region\n", - " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1)\n", - " bounding_boxes_numbers.append(bounding_box)\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", + " )\n", + " bounding_boxes_numbers.append(\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " )\n", + " )\n", " else:\n", " # Bounding box is in the bottom-left region\n", - " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1)\n", - " bounding_boxes_time.append(bounding_box)\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", + " )\n", + " bounding_boxes_time.append(\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " )\n", + " )\n", "\n", " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", " # You can also manually quit out with ESC key.\n", @@ -230,67 +310,136 @@ " resized_image.show()\n", "\n", " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", - " return (bounding_boxes_time, bounding_boxes_numbers)\n" + " return (bounding_boxes_time, bounding_boxes_numbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Create a function for K-means clustering" + "Create a function for K-means clustering, dbscan clustering\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ - "def cluster_kmeans(bounding_boxes: List[str], number_of_clusters: int) -> List[int]:\n", + "def cluster_kmeans(\n", + " bounding_boxes: List[BoundingBox], possible_nclusters: List[int]\n", + ") -> List[int]:\n", " \"\"\"\n", " Cluster bounding boxes using K-Means clustering algorithm.\n", "\n", " Args:\n", " bounding_boxes: List of bounding boxes in YOLO format.\n", - " number_of_clusters: Number of clusters to use in K-Means clustering.\n", + " possible_nclusters: List of possible number of clusters to try.\n", "\n", " Returns:\n", " List of cluster labels.\n", " \"\"\"\n", " # Convert to a NumPy array (using only x_center and y_center)\n", - " data = np.array([[float(box.split(' ')[1]), float(box.split(' ')[2])] for box in bounding_boxes])\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " cluster_performance_map = {}\n", + " for number_of_clusters in possible_nclusters:\n", + " if number_of_clusters > len(data):\n", + " raise (\n", + " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", + " )\n", + " if number_of_clusters < 1:\n", + " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", + " # Apply K-Means\n", + " kmeans = KMeans(\n", + " n_clusters=number_of_clusters,\n", + " init=\"k-means++\",\n", + " n_init=20,\n", + " max_iter=500,\n", + " tol=1e-8,\n", + " random_state=42,\n", + " )\n", + " kmeans.fit(data)\n", + "\n", + " # Get cluster labels\n", + " labels = kmeans.predict(data)\n", + " silhouette_avg = silhouette_score(data, labels)\n", + "\n", + " # print(\n", + " # f\"Number of clusters: {number_of_clusters}, Silhouette score: {silhouette_avg}\"\n", + " # )\n", + "\n", + " cluster_performance_map[number_of_clusters] = {\n", + " \"score\": silhouette_avg,\n", + " \"labels\": labels,\n", + " }\n", + "\n", + " # Evaluate the performance of each number of clusters and select the one with the highest silhouette score\n", + " # if it is 0.003 greater than what should be the number of clusters otherwise go with proper_nclusters\n", + " n_clusters_max_silhouette = max(\n", + " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", + " )\n", + " best_n_clusters = (\n", + " n_clusters_max_silhouette\n", + " if (\n", + " (\n", + " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", + " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", + " )\n", + " >= 0.003\n", + " )\n", + " else max(possible_nclusters)\n", + " )\n", + " return cluster_performance_map[best_n_clusters][\"labels\"]\n", + "\n", + "\n", + "def dbscan_clustering(\n", + " bounding_boxes: List[BoundingBox], defined_eps: float, min_samples: int\n", + ") -> List[int]:\n", + " \"\"\"\n", + " Cluster bounding boxes using Ward hierarchical clustering algorithm with linkage distance.\n", "\n", - " # Apply K-Means\n", - " kmeans = KMeans(n_clusters=number_of_clusters, init='k-means++', n_init=10, max_iter=300, tol=1e-8, random_state=42)\n", - " kmeans.fit(data)\n", + " Args:\n", + " bounding_boxes: List of bounding boxes.\n", + " defined_eps: The maximum distance between two samples for one to be considered as in the neighborhood of the other.\n", + " min_samples: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.\n", "\n", - " # Get cluster labels\n", - " labels = kmeans.predict(data)\n", + " Returns:\n", + " List of cluster labels.\n", + " \"\"\"\n", + " # Convert to a NumPy array (using only x_center and y_center)\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " # DBSCAN\n", + " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", + " labels = scan.fit_predict(data)\n", "\n", - " return labels\n", - "\n" + " return labels\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Function to create a result dictionary that we can save as a JSON file to analyze performance." + "Function to create a result dictionary that we can save as a JSON file to analyze performance.\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ - "def create_result_dictionary(labels: List[str], bounding_boxes: List[str]) -> Dict[int, int]:\n", + "def create_result_dictionary(\n", + " labels: List[str], bounding_boxes: List[BoundingBox], unit: Literal[\"mmHg\", \"mins\"]\n", + ") -> Dict[int, int]:\n", " \"\"\"\n", " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", "\n", " Args:\n", " labels: List of cluster labels.\n", - " bounding_boxes: List of bounding boxes in YOLO format.\n", + " bounding_boxes: List of bounding boxes.\n", + " suffix: Suffix to append to the category of the bounding box. One of [\"mmHg\", \"mins\"].\n", "\n", " Returns:\n", " Dictionary with cluster labels as keys and bounding box values as values.\n", @@ -301,20 +450,19 @@ " # Iterate over both lists\n", " for label, element in zip(labels, bounding_boxes):\n", " label = int(label)\n", - " element = str(element)\n", " if label not in label_dict:\n", " # Create a new list for this label if it doesn't exist\n", " label_dict[label] = []\n", " # Append the element to the corresponding label list\n", - " label_dict[label].append(element)\n", + " label_dict[label].append(f\"{element.category} {element.center[0]}\")\n", "\n", " # Sort the lists in the dictionary by x_center\n", " for key in label_dict:\n", - " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(' ')[1]))\n", - " label_dict[key] = [element.split(' ')[0] for element in label_dict[key]]\n", + " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(\" \")[1]))\n", + " label_dict[key] = [element.split(\" \")[0] for element in label_dict[key]]\n", " # Turn list of strings into a string\n", - " label_dict[key] = int(''.join(label_dict[key]))\n", - " \n", + " label_dict[key] = f\"{''.join(label_dict[key])}_{unit}\"\n", + "\n", " return label_dict" ] }, @@ -322,12 +470,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now lets use these functions to get the relevant bounding boxes for clustering." + "Function to generate colors!\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Draw bounding boxes on the image\n", + "def generate_color():\n", + " return \"#%06x\" % random.randint(0, 0xFFFFFF)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets use these functions to get the relevant bounding boxes for clustering.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -336,173 +502,368 @@ "text": [ "Sheet: RC_0001_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "ROI selected: (102, 221, 634, 203)\n", - "Selected region: x=102, y=221, w=634, h=203\n", + "Number of clusters: 40, Silhouette score: 0.5673304019641547\n", + "Number of clusters: 41, Silhouette score: 0.573554537323082\n", + "Number of clusters: 42, Silhouette score: 0.5794580334870207\n", + "Number of clusters: 18, Silhouette score: 0.4495336665724898\n", + "Number of clusters: 19, Silhouette score: 0.4565570559900914\n", + "Number of clusters: 20, Silhouette score: 0.48217162440948763\n", "Sheet: RC_0002_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "ROI selected: (102, 220, 635, 204)\n", - "Selected region: x=102, y=220, w=635, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5757864617809657\n", + "Number of clusters: 41, Silhouette score: 0.5745338744362858\n", + "Number of clusters: 42, Silhouette score: 0.5807009923903196\n", + "Number of clusters: 18, Silhouette score: 0.4645679790516578\n", + "Number of clusters: 19, Silhouette score: 0.45606688292674913\n", + "Number of clusters: 20, Silhouette score: 0.4821769375394214\n", "Sheet: RC_0003_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "ROI selected: (102, 220, 634, 204)\n", - "Selected region: x=102, y=220, w=634, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5695007328244894\n", + "Number of clusters: 41, Silhouette score: 0.5753095048764951\n", + "Number of clusters: 42, Silhouette score: 0.5812476240545377\n", + "Number of clusters: 18, Silhouette score: 0.45190589435844714\n", + "Number of clusters: 19, Silhouette score: 0.47435914138918883\n", + "Number of clusters: 20, Silhouette score: 0.4874026792412265\n", "Sheet: RC_0004_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "ROI selected: (104, 222, 631, 201)\n", - "Selected region: x=104, y=222, w=631, h=201\n", + "Number of clusters: 40, Silhouette score: 0.5685485285425728\n", + "Number of clusters: 41, Silhouette score: 0.5748442881410089\n", + "Number of clusters: 42, Silhouette score: 0.5809760766497314\n", + "Number of clusters: 18, Silhouette score: 0.465702160794159\n", + "Number of clusters: 19, Silhouette score: 0.45617952207011625\n", + "Number of clusters: 20, Silhouette score: 0.4829684242152072\n", "Sheet: RC_0005_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "ROI selected: (103, 221, 631, 203)\n", - "Selected region: x=103, y=221, w=631, h=203\n", + "Number of clusters: 40, Silhouette score: 0.5836836598572558\n", + "Number of clusters: 41, Silhouette score: 0.5898427313766192\n", + "Number of clusters: 42, Silhouette score: 0.5710713840389365\n", + "Number of clusters: 18, Silhouette score: 0.46876985682305644\n", + "Number of clusters: 19, Silhouette score: 0.459272521022717\n", + "Number of clusters: 20, Silhouette score: 0.4862658077720203\n", "Sheet: RC_0006_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "ROI selected: (103, 221, 633, 203)\n", - "Selected region: x=103, y=221, w=633, h=203\n", + "Number of clusters: 40, Silhouette score: 0.5679322504891184\n", + "Number of clusters: 41, Silhouette score: 0.5744495397276003\n", + "Number of clusters: 42, Silhouette score: 0.5803539076672589\n", + "Number of clusters: 18, Silhouette score: 0.46770777943959585\n", + "Number of clusters: 19, Silhouette score: 0.45831315379093635\n", + "Number of clusters: 20, Silhouette score: 0.4847740597509014\n", "Sheet: RC_0007_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "ROI selected: (102, 220, 635, 205)\n", - "Selected region: x=102, y=220, w=635, h=205\n", + "Number of clusters: 40, Silhouette score: 0.5771510682113896\n", + "Number of clusters: 41, Silhouette score: 0.5740151526073118\n", + "Number of clusters: 42, Silhouette score: 0.5799274191057299\n", + "Number of clusters: 18, Silhouette score: 0.4671043898386821\n", + "Number of clusters: 19, Silhouette score: 0.4573182608442791\n", + "Number of clusters: 20, Silhouette score: 0.48381162499364594\n", "Sheet: RC_0008_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "ROI selected: (103, 220, 632, 203)\n", - "Selected region: x=103, y=220, w=632, h=203\n", + "Number of clusters: 40, Silhouette score: 0.5783042263718302\n", + "Number of clusters: 41, Silhouette score: 0.5777095024613623\n", + "Number of clusters: 42, Silhouette score: 0.5836200275266411\n", + "Number of clusters: 18, Silhouette score: 0.4423450398671474\n", + "Number of clusters: 19, Silhouette score: 0.4602179063948201\n", + "Number of clusters: 20, Silhouette score: 0.48670337274658315\n", "Sheet: RC_0009_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "ROI selected: (103, 221, 632, 204)\n", - "Selected region: x=103, y=221, w=632, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5705953671094\n", + "Number of clusters: 41, Silhouette score: 0.5843332987625905\n", + "Number of clusters: 42, Silhouette score: 0.5832591565174813\n", + "Number of clusters: 18, Silhouette score: 0.449004063369966\n", + "Number of clusters: 19, Silhouette score: 0.4754651504869549\n", + "Number of clusters: 20, Silhouette score: 0.48863334728235436\n", "Sheet: RC_0010_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "ROI selected: (101, 219, 633, 204)\n", - "Selected region: x=101, y=219, w=633, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5696512766258542\n", + "Number of clusters: 41, Silhouette score: 0.5758294080776938\n", + "Number of clusters: 42, Silhouette score: 0.5821005635322705\n", + "Number of clusters: 18, Silhouette score: 0.46886747023890174\n", + "Number of clusters: 19, Silhouette score: 0.47416661573185115\n", + "Number of clusters: 20, Silhouette score: 0.4869059593276823\n", "Sheet: RC_0011_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "ROI selected: (102, 219, 633, 205)\n", - "Selected region: x=102, y=219, w=633, h=205\n", + "Number of clusters: 40, Silhouette score: 0.5704800351423258\n", + "Number of clusters: 41, Silhouette score: 0.5840189162171326\n", + "Number of clusters: 42, Silhouette score: 0.5827157912879839\n", + "Number of clusters: 18, Silhouette score: 0.4431856593199326\n", + "Number of clusters: 19, Silhouette score: 0.46045874525827857\n", + "Number of clusters: 20, Silhouette score: 0.4870806675902949\n", "Sheet: RC_0012_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "ROI selected: (103, 220, 630, 205)\n", - "Selected region: x=103, y=220, w=630, h=205\n", + "Number of clusters: 40, Silhouette score: 0.5688121078791504\n", + "Number of clusters: 41, Silhouette score: 0.5751690051189831\n", + "Number of clusters: 42, Silhouette score: 0.5810578374862433\n", + "Number of clusters: 18, Silhouette score: 0.45239813991162\n", + "Number of clusters: 19, Silhouette score: 0.4749655361633029\n", + "Number of clusters: 20, Silhouette score: 0.48736300502824453\n", "Sheet: RC_0013_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "ROI selected: (102, 220, 634, 202)\n", - "Selected region: x=102, y=220, w=634, h=202\n", + "Number of clusters: 40, Silhouette score: 0.5692629552118674\n", + "Number of clusters: 41, Silhouette score: 0.5753891647892224\n", + "Number of clusters: 42, Silhouette score: 0.5817129565418376\n", + "Number of clusters: 18, Silhouette score: 0.4706847310839202\n", + "Number of clusters: 19, Silhouette score: 0.475257123252011\n", + "Number of clusters: 20, Silhouette score: 0.4875528062560131\n", "Sheet: RC_0014_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "ROI selected: (101, 221, 634, 204)\n", - "Selected region: x=101, y=221, w=634, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5688868783614993\n", + "Number of clusters: 41, Silhouette score: 0.5754140844152069\n", + "Number of clusters: 42, Silhouette score: 0.5816092749418681\n", + "Number of clusters: 18, Silhouette score: 0.4687266938745312\n", + "Number of clusters: 19, Silhouette score: 0.4738489650797876\n", + "Number of clusters: 20, Silhouette score: 0.4870337163251255\n", "Sheet: RC_0015_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "ROI selected: (104, 221, 628, 204)\n", - "Selected region: x=104, y=221, w=628, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5782371272793899\n", + "Number of clusters: 41, Silhouette score: 0.5750034132529208\n", + "Number of clusters: 42, Silhouette score: 0.5810348685765858\n", + "Number of clusters: 18, Silhouette score: 0.4672520452537935\n", + "Number of clusters: 19, Silhouette score: 0.4718915122644036\n", + "Number of clusters: 20, Silhouette score: 0.4842497675839784\n", "Sheet: RC_0016_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "ROI selected: (104, 222, 630, 201)\n", - "Selected region: x=104, y=222, w=630, h=201\n", + "Number of clusters: 40, Silhouette score: 0.5676632616806152\n", + "Number of clusters: 41, Silhouette score: 0.5736362329571111\n", + "Number of clusters: 42, Silhouette score: 0.5802480507716713\n", + "Number of clusters: 18, Silhouette score: 0.44191611332733033\n", + "Number of clusters: 19, Silhouette score: 0.45434202908298355\n", + "Number of clusters: 20, Silhouette score: 0.4814815759616046\n", "Sheet: RC_0017_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "ROI selected: (104, 221, 630, 200)\n", - "Selected region: x=104, y=221, w=630, h=200\n", + "Number of clusters: 40, Silhouette score: 0.5815327713337762\n", + "Number of clusters: 41, Silhouette score: 0.5751821885994145\n", + "Number of clusters: 42, Silhouette score: 0.5812544814861884\n", + "Number of clusters: 18, Silhouette score: 0.4483463455207681\n", + "Number of clusters: 19, Silhouette score: 0.45607265099900907\n", + "Number of clusters: 20, Silhouette score: 0.48279582576378477\n", "Sheet: RC_0018_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "ROI selected: (102, 221, 633, 204)\n", - "Selected region: x=102, y=221, w=633, h=204\n", + "Number of clusters: 40, Silhouette score: 0.5770352445932048\n", + "Number of clusters: 41, Silhouette score: 0.576158726619254\n", + "Number of clusters: 42, Silhouette score: 0.5821804848012874\n", + "Number of clusters: 18, Silhouette score: 0.46939443438692574\n", + "Number of clusters: 19, Silhouette score: 0.4591096371818493\n", + "Number of clusters: 20, Silhouette score: 0.4858105308263671\n", "Sheet: RC_0019_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", - "ROI selected: (102, 220, 633, 204)\n", - "Selected region: x=102, y=220, w=633, h=204\n" + "Number of clusters: 40, Silhouette score: 0.5674806892582026\n", + "Number of clusters: 41, Silhouette score: 0.5737909263825772\n", + "Number of clusters: 42, Silhouette score: 0.5798289275740274\n", + "Number of clusters: 18, Silhouette score: 0.4700425696501259\n", + "Number of clusters: 19, Silhouette score: 0.46077114989977574\n", + "Number of clusters: 20, Silhouette score: 0.48793240108374225\n", + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" ] } ], "source": [ + "def test_clustering_methods() -> None:\n", + " \"\"\"\n", + " Test the clustering methods on the YOLO data.\n", + " Saves the clustered images and the clustered bounding boxes to JSON files.\n", "\n", - "# Iterate over all images and their bounding boxes\n", - "for sheet, bounding_boxes in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " print(f\"Full image path: {full_image_path}\")\n", - "\n", - " # Call the analyze_sheet function with data from the loop\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", - "\n", - " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", - " time_labels = cluster_kmeans(time_bounding_boxes, 42)\n", - " number_labels = cluster_kmeans(number_bounding_boxes, 20)\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - "\n", - " label_color_map = {}\n", - " for i, label in enumerate(time_labels):\n", - "\n", - " # Get the bounding box\n", - " bounding_box = time_bounding_boxes[i]\n", - " value, x_center, y_center, width, height = bounding_box.split(' ')\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", - "\n", - " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - "\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " box = [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ]\n", - " draw.rectangle(box, outline=label_color_map[label], width=3)\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f'../../data/kmeans_clustered_images/time/{sheet}')\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(f'../../data/kmeans_clustered_images/results/time/{sheet}.json', 'w') as f:\n", - " json.dump(create_result_dictionary(time_labels, time_bounding_boxes), f)\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - "\n", - " label_color_map = {}\n", - " for i, label in enumerate(number_labels):\n", - "\n", - " # Get the bounding box\n", - " bounding_box = number_bounding_boxes[i]\n", - " value, x_center, y_center, width, height = bounding_box.split(' ')\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", - "\n", - " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - "\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " box = [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ]\n", - " draw.rectangle(box, outline=label_color_map[label], width=3)\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f'../../data/kmeans_clustered_images/number/{sheet}')\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(f'../../data/kmeans_clustered_images/results/number/{sheet}.json', 'w') as f:\n", - " json.dump(create_result_dictionary(number_labels, number_bounding_boxes), f)" + " Returns:\n", + " None\n", + " \"\"\"\n", + " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " # Iterate over all images and their bounding boxes\n", + " for sheet, yolo_bbs in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " print(f\"Full image path: {full_image_path}\")\n", + "\n", + " # Call the analyze_sheet function with data from the loop\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bbs, full_image_path\n", + " )\n", + "\n", + " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", + " if method == \"kmeans\":\n", + " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", + " number_labels = cluster_kmeans(number_bounding_boxes, [18, 19, 20])\n", + " elif method == \"dbscan\":\n", + " time_labels = dbscan_clustering(\n", + " time_bounding_boxes, defined_eps=0.01, min_samples=1\n", + " )\n", + " number_labels = dbscan_clustering(\n", + " number_bounding_boxes, defined_eps=0.01, min_samples=2\n", + " )\n", + " elif method == \"agglomerative\":\n", + " continue\n", + " else:\n", + " raise ValueError(f\"Invalid clustering method: {method}\")\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(time_labels):\n", + " # Get the bounding box\n", + " bounding_box = time_bounding_boxes[i]\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(bounding_box.box)\n", + " ]\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=label_color_map[label],\n", + " width=3,\n", + " )\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f\"../../data/{method}_clustered_images/time/{sheet}\")\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(\n", + " f\"../../data/{method}_clustered_images/results/time/{sheet.split('.')[0]}.json\",\n", + " \"w\",\n", + " ) as f:\n", + " json.dump(\n", + " create_result_dictionary(time_labels, time_bounding_boxes, \"mins\"),\n", + " f,\n", + " )\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + " label_color_map = {}\n", + " for i, label in enumerate(number_labels):\n", + " # Get the bounding box\n", + " bounding_box = number_bounding_boxes[i]\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(bounding_box.box)\n", + " ]\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=label_color_map[label],\n", + " width=3,\n", + " )\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f\"../../data/{method}_clustered_images/number/{sheet}\")\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(\n", + " f\"../../data/{method}_clustered_images/results/number/{sheet.split('.')[0]}.json\",\n", + " \"w\",\n", + " ) as f:\n", + " json.dump(\n", + " create_result_dictionary(\n", + " number_labels, number_bounding_boxes, \"mmHg\"\n", + " ),\n", + " f,\n", + " )\n", + "\n", + "\n", + "# Test the clustering methods\n", + "test_clustering_methods()" ] }, { @@ -511,78 +872,1484 @@ "source": [ "#### Analyze accuracy\n", "\n", - "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." + "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters.\n" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time labels: 796 correct clusters, 2 incorrect clusters. The accuracy is 99.75%\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n" + "Method: kmeans\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Method: dbscan\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 11111121112127684935198073246512000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 11111111121229847563190563247810200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 11111112212114978563798153246102000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 11111111212124956378715946832010200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 11111111121224589763132459078610200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 11121111121214973568149785206310200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 11112111111224895763127035498612000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 11111121121219875643190742583610200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 11111111121229458763130245678912000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 11112111221114598673978456131200200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 11111111121224563879102345786912000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 11111111112224536879154936278010200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 11111112221114569783978514261300200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 21121211111114953786143975120286000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 11111112111224356798120345768912000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 12111111112124563897109458372162000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 11211111211214536987791483560212000000000000000000000_mmHg\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 11111111112224896573143958072610200000000000000000000_mmHg\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 11111111121226549378120547968310200000000000000000000_mmHg\n", + "Time labels: 152 correct clusters, 1291 incorrect clusters. The accuracy is 10.53%\n", + "Number labels: 0 correct clusters, 19 incorrect clusters. The accuracy is 0.00%\n" ] } ], "source": [ "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", + "for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " if method == \"agglomerative\":\n", + " continue\n", + " print(f\"Method: {method}\")\n", + " # Paths to the JSON files\n", + " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", + " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", + " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", + "\n", + " time_wrong_clusters_count = 0\n", + " time_correct_clusters_count = 0\n", + " number_wrong_clusters_count = 0\n", + " number_correct_clusters_count = 0\n", + "\n", + " # Iterate over all images and their bounding boxes\n", + " for sheet, yolo_bb in yolo_data.items():\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bb, full_image_path\n", + " )\n", + " # Convert the bounding boxes to a list of strings with proper suffixes\n", + " expected_time_values = [\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " ]\n", "\n", - "# Paths to the JSON files\n", - "PATH_TO_KMEANS_RESULTS = '../../data/kmeans_clustered_images/results'\n", - "TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'time')\n", - "NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'number')\n", - "\n", - "time_wrong_clusters_count = 0\n", - "time_correct_clusters_count = 0\n", - "number_wrong_clusters_count = 0\n", - "number_correct_clusters_count = 0\n", - "# Iterate over all images and their bounding boxes\n", - "for sheet, bounding_boxes in yolo_data.items():\n", - " expected_time_values = [0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25]\n", - " expected_number_values = [30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220]\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(TIME_JSON, f'{sheet}.json')) as f:\n", - " time_clusters = json.load(f)\n", - " \n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in time_clusters.items():\n", - " if value not in expected_time_values:\n", - " # We have an erroneous cluster\n", - " time_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_time_values.remove(value)\n", - " time_correct_clusters_count += 1\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(NUMBER_JSON, f'{sheet}.json')) as f:\n", - " number_clusters = json.load(f)\n", - " \n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in number_clusters.items():\n", - " if value not in expected_number_values:\n", - " # We have an erroneous cluster\n", - " number_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_number_values.remove(value)\n", - " number_correct_clusters_count += 1\n", - " \n", - "\n", - "\n", - "print(f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\")\n", - "print(f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\")\n" + " expected_number_values = [\n", + " \"30_mmHg\",\n", + " \"40_mmHg\",\n", + " \"50_mmHg\",\n", + " \"60_mmHg\",\n", + " \"70_mmHg\",\n", + " \"80_mmHg\",\n", + " \"90_mmHg\",\n", + " \"100_mmHg\",\n", + " \"110_mmHg\",\n", + " \"120_mmHg\",\n", + " \"130_mmHg\",\n", + " \"140_mmHg\",\n", + " \"150_mmHg\",\n", + " \"160_mmHg\",\n", + " \"170_mmHg\",\n", + " \"180_mmHg\",\n", + " \"190_mmHg\",\n", + " \"200_mmHg\",\n", + " \"210_mmHg\",\n", + " \"220_mmHg\",\n", + " ]\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(TIME_JSON, f\"{sheet.split(\".\")[0]}.json\")) as f:\n", + " time_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in time_clusters.items():\n", + " if value not in expected_time_values:\n", + " # Print the sheet, value that is not in the expected values\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " time_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_time_values.remove(value)\n", + " time_correct_clusters_count += 1\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(NUMBER_JSON, f\"{sheet.split(\".\")[0]}.json\")) as f:\n", + " number_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in number_clusters.items():\n", + " if value not in expected_number_values:\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " number_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_number_values.remove(value)\n", + " number_correct_clusters_count += 1\n", + "\n", + " print(\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\"\n", + " )\n", + " print(\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\"\n", + " )\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/experiments/clustering/density_clustering.ipynb b/experiments/clustering/density_clustering.ipynb deleted file mode 100644 index f0e8fd5..0000000 --- a/experiments/clustering/density_clustering.ipynb +++ /dev/null @@ -1,638 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Density-Based Clustering\n", - "### Hannah Valenty" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Charbel's comment on the process of predetermining clusters and that method's lack of flexibility piqued my interest to look into other input options. This pointed me in the direction of agglomerative clustering and more specifically, Ward hierarchical clustering using linkage distance. The input parameter of distance is a shift from the number of clusters used in kmeans and other common clustering approaches.\n", - "\n", - "However, the Ward clustering was not performing well, so I pivoted to DBSCAN clustering. Density-Based Spatial Clustering of Applications with Noise clusters together those points that are close to each other based on any distance metric and a minimum number of points." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preface\n", - "The methods to translate images, load yolo data, and select bounding boxes are from Charbel's `clustering.ipynb` document. This pipeline was immensely helpful for allowing me to smoothly create and train a supplemental clustering algorithm. The sections taken from this document will be labelled as such." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register Images to Start (`clustering.ipynb`)\n", - "\n", - "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install packages\n", - "Import libraries for analysis, with change in sklearn.cluster from KMeans to AgglomerativeClustering." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import random\n", - "from pathlib import Path\n", - "from typing import List, Tuple, Dict\n", - "\n", - "import cv2\n", - "import numpy as np\n", - "from PIL import Image, ImageDraw\n", - "from sklearn.cluster import AgglomerativeClustering\n", - "from sklearn.cluster import DBSCAN" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start by loading YOLO data (`clustering.ipynb`)\n", - "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 19 sheets in yolo_data.json\n" - ] - } - ], - "source": [ - "# Load yolo_data.json\n", - "PATH_TO_YOLO_DATA = '../../data/yolo_data.json'\n", - "PATH_TO_REGISTERED_IMAGES = '../../data/registered_images'\n", - "UNIFIED_IMAGE_PATH = '../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png'\n", - "with open(PATH_TO_YOLO_DATA) as json_file:\n", - " yolo_data = json.load(json_file)\n", - "\n", - "# See how many intraoperative images are registered\n", - "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's select relevant bounding boxes from the blood pressure and HR zone. \n", - "\n", - "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n", - "\n", - "Also identifies which boxes are for timestamps (top-right) or numeric values of mmHg and bpm (bottom-left)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def YOLO_to_pixels(x_center, y_center, width, height, image_width, image_height):\n", - " \"\"\"\n", - " Convert YOLO bounding box format to pixel coordinates\n", - "\n", - " Args:\n", - " x_center: float, x center of the bounding box\n", - " y_center: float, y center of the bounding box\n", - " width: float, width of the bounding box\n", - " height: float, height of the bounding box\n", - " image_width: int, width of the image\n", - " image_height: int, height of the image\n", - "\n", - " Returns:\n", - " A single tuple with the following values:\n", - " x_min: int, minimum x coordinate of the bounding box in pixels\n", - " y_min: int, minimum y coordinate of the bounding box in pixels\n", - " x_max: int, maximum x coordinate of the bounding box in pixels\n", - " y_max: int, maximum y coordinate of the bounding box in pixels\n", - " \"\"\"\n", - " x_min = int((float(x_center) * image_width) - (width * image_width) / 2)\n", - " y_min = int((float(y_center) * image_height) - (height * image_height) / 2)\n", - " x_max = int((float(x_center) * image_width) + (width * image_width) / 2)\n", - " y_max = int((float(y_center) * image_height) + (height * image_height) / 2)\n", - " return x_min, y_min, x_max, y_max\n", - "\n", - "# Function to determine whether a point is above or below the diagonal line\n", - "def is_point_in_above(x_center, y_center, m, b):\n", - " \"\"\"\n", - " Determine if a point is above or below the diagonal line y = mx + b.\n", - " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", - "\n", - " Args:\n", - " x_center: float, x coordinate of the point\n", - " y_center: float, y coordinate of the point\n", - " m: float, slope of the diagonal line\n", - " b: float, intercept of the diagonal line\n", - " \n", - " Returns:\n", - " bool, True if the point is above the line, False otherwise\n", - " \"\"\"\n", - " # Calculate the y value on the line for the given x_center\n", - " y_line = m * x_center + b\n", - " return y_center > y_line\n", - "\n", - "\n", - "\n", - "def select_relevant_bounding_boxes(sheet_data: List[str], path_to_image: Path, show_images: bool = False) -> Tuple[List[str], List[str]]:\n", - " \"\"\"\n", - " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - " Identify bounding boxes that are within the selected region and draw rectangles around them. \n", - " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", - "\n", - " Args:\n", - " sheet_data: List of bounding boxes in YOLO format.\n", - " path_to_image: Path to the image file.\n", - "\n", - " Returns:\n", - " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", - " The first list contains bounding boxes in the top-right region -- representing time labels.\n", - " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", - " (bounding_boxes_time, bounding_boxes_numbers)\n", - " \"\"\"\n", - " # Load the image\n", - " image = cv2.imread(path_to_image)\n", - "\n", - " # Display the image and allow the user to select a ROI\n", - " resized_image = cv2.resize(image, (800, 600))\n", - " roi = cv2.selectROI(\"Select Region of Interest\", resized_image)\n", - " print(f\"ROI selected: {roi}\")\n", - "\n", - " # Unpack ROI\n", - " x, y, w, h = roi\n", - " print(f\"Selected region: x={x}, y={y}, w={w}, h={h}\")\n", - "\n", - " # Calculate the coordinates of the top-left and bottom-right corners of the selected region\n", - " x_top_left = x\n", - " y_top_left = y\n", - " x_bottom_right = x + w\n", - " y_bottom_right = y + h\n", - "\n", - " # Draw the diagonal line of the selected region from top-left to bottom-right\n", - " cv2.line(resized_image, (x_top_left, y_top_left), (x_bottom_right, y_bottom_right), (0, 255, 0), 1)\n", - " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", - " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", - " # Top-right region is where time labels are located\n", - " # Bottom-left region is where numerical values for mmHg and bpm are located\n", - " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", - " b = y_top_left - m * x_top_left\n", - "\n", - " # List of bounding boxes in the top-right and bottom-left regions\n", - " bounding_boxes_time = []\n", - " bounding_boxes_numbers = []\n", - "\n", - " # Process the bounding boxes\n", - " for bounding_box in sheet_data:\n", - " # Bounding boxes are in YOLO format; convert them to pixels\n", - " identifier, x_center, y_center, bb_width, bb_height = bounding_box.split(' ') # Identifier is the value in the bounding box, we don't need that here\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(bb_width), float(bb_height), 800, 600)\n", - " \n", - " # Check if the bounding box is within the selected region\n", - " if x_min >= x and y_min >= y and x_max <= x + w and y_max <= y + h:\n", - " # Calculate the center of the bounding box\n", - " x_center_bb = (x_min + x_max) / 2\n", - " y_center_bb = (y_min + y_max) / 2\n", - " \n", - " # If we want to generalize this function we can add the option to disregard the diagonal line\n", - "\n", - " # Determine if the bounding box center is in the top-right region\n", - " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", - " # Bounding box is in the top-right region\n", - " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1)\n", - " bounding_boxes_numbers.append(bounding_box)\n", - " else:\n", - " # Bounding box is in the bottom-left region\n", - " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1)\n", - " bounding_boxes_time.append(bounding_box)\n", - "\n", - " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", - " # You can also manually quit out with ESC key.\n", - " cv2.destroyAllWindows()\n", - "\n", - " # If we are showing the images, display the image with the selected region and bounding boxes\n", - " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", - " if show_images:\n", - " # Display the image with the selected region and bounding boxes\n", - " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", - " resized_image = Image.fromarray(resized_image)\n", - " resized_image.show()\n", - "\n", - " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", - " return (bounding_boxes_time, bounding_boxes_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implementing a function for Ward hierarchical clustering using linkage distance" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def dbscan_clustering(bounding_boxes: List[str], defined_eps: float, min_samples: int) -> List[int]:\n", - " \"\"\"\n", - " Cluster bounding boxes using Ward hierarchical clustering algorithm with linkage distance.\n", - "\n", - " Args:\n", - " bounding_boxes: List of bounding boxes in YOLO format.\n", - " defined_eps: The maximum distance between two samples for one to be considered as in the neighborhood of the other.\n", - " min_samples: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.\n", - "\n", - " Returns:\n", - " List of cluster labels.\n", - " \"\"\"\n", - " # Convert to a NumPy array (using only x_center and y_center)\n", - " data = np.array([[float(box.split(' ')[1]), float(box.split(' ')[2])] for box in bounding_boxes])\n", - "\n", - " # DBSCAN\n", - " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", - " labels = scan.fit_predict(data)\n", - "\n", - " return labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function to create a result dictionary that we can save as a JSON file to analyze performance. (`clustering.ipynb`)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def create_result_dictionary(labels: List[str], bounding_boxes: List[str]) -> Dict[int, int]:\n", - " \"\"\"\n", - " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", - "\n", - " Args:\n", - " labels: List of cluster labels.\n", - " bounding_boxes: List of bounding boxes in YOLO format.\n", - "\n", - " Returns:\n", - " Dictionary with cluster labels as keys and bounding box values as values.\n", - " \"\"\"\n", - " # Create a dictionary to store labelled elements\n", - " label_dict = {}\n", - "\n", - " # Iterate over both lists\n", - " for label, element in zip(labels, bounding_boxes):\n", - " label = int(label)\n", - " element = str(element)\n", - " if label not in label_dict:\n", - " # Create a new list for this label if it doesn't exist\n", - " label_dict[label] = []\n", - " # Append the element to the corresponding label list\n", - " label_dict[label].append(element)\n", - "\n", - " # Sort the lists in the dictionary by x_center\n", - " for key in label_dict:\n", - " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(' ')[1]))\n", - " label_dict[key] = [element.split(' ')[0] for element in label_dict[key]]\n", - " # Turn list of strings into a string\n", - " label_dict[key] = int(''.join(label_dict[key]))\n", - " \n", - " return label_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets use these functions to get the relevant bounding boxes for clustering. (`clustering.ipynb`)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "ROI selected: (103, 221, 635, 205)\n", - "Selected region: x=103, y=221, w=635, h=205\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "ROI selected: (104, 221, 634, 205)\n", - "Selected region: x=104, y=221, w=634, h=205\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "ROI selected: (101, 221, 636, 204)\n", - "Selected region: x=101, y=221, w=636, h=204\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "ROI selected: (101, 219, 636, 206)\n", - "Selected region: x=101, y=219, w=636, h=206\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "ROI selected: (101, 219, 640, 207)\n", - "Selected region: x=101, y=219, w=640, h=207\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "ROI selected: (101, 219, 640, 207)\n", - "Selected region: x=101, y=219, w=640, h=207\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "ROI selected: (104, 217, 633, 207)\n", - "Selected region: x=104, y=217, w=633, h=207\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "ROI selected: (105, 223, 628, 201)\n", - "Selected region: x=105, y=223, w=628, h=201\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "ROI selected: (103, 221, 633, 205)\n", - "Selected region: x=103, y=221, w=633, h=205\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "ROI selected: (103, 221, 635, 205)\n", - "Selected region: x=103, y=221, w=635, h=205\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "ROI selected: (102, 218, 636, 208)\n", - "Selected region: x=102, y=218, w=636, h=208\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "ROI selected: (102, 218, 632, 205)\n", - "Selected region: x=102, y=218, w=632, h=205\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "ROI selected: (101, 219, 633, 204)\n", - "Selected region: x=101, y=219, w=633, h=204\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "ROI selected: (101, 219, 637, 207)\n", - "Selected region: x=101, y=219, w=637, h=207\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "ROI selected: (104, 221, 634, 205)\n", - "Selected region: x=104, y=221, w=634, h=205\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "ROI selected: (104, 221, 631, 207)\n", - "Selected region: x=104, y=221, w=631, h=207\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "ROI selected: (105, 221, 630, 203)\n", - "Selected region: x=105, y=221, w=630, h=203\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "ROI selected: (104, 220, 631, 204)\n", - "Selected region: x=104, y=220, w=631, h=204\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", - "ROI selected: (104, 220, 632, 204)\n", - "Selected region: x=104, y=220, w=632, h=204\n" - ] - } - ], - "source": [ - "# Iterate over all images and their bounding boxes\n", - "for sheet, bounding_boxes in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " print(f\"Full image path: {full_image_path}\")\n", - "\n", - " # Call the analyze_sheet function with data from the loop\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(bounding_boxes, full_image_path)\n", - "\n", - " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", - " # Eps chosen based on width units of bounding boxes -- on average 0.0042-0.0045 wide\n", - " time_labels = dbscan_clustering(time_bounding_boxes, defined_eps=0.01, min_samples=1)\n", - " number_labels = dbscan_clustering(number_bounding_boxes, defined_eps=0.01, min_samples=2)\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - "\n", - " label_color_map = {}\n", - " for i, label in enumerate(time_labels):\n", - "\n", - " # Get the bounding box\n", - " bounding_box = time_bounding_boxes[i]\n", - " value, x_center, y_center, width, height = bounding_box.split(' ')\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", - "\n", - " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " box = [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ]\n", - " draw.rectangle(box, outline=label_color_map[label], width=3)\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f'../../data/kmeans_clustered_images/time/{sheet}')\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(f'../../data/kmeans_clustered_images/results/time/{sheet}.json', 'w') as f:\n", - " json.dump(create_result_dictionary(time_labels, time_bounding_boxes), f)\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - "\n", - " label_color_map = {}\n", - " for i, label in enumerate(number_labels):\n", - "\n", - " # Get the bounding box\n", - " bounding_box = number_bounding_boxes[i]\n", - " value, x_center, y_center, width, height = bounding_box.split(' ')\n", - " x_min, y_min, x_max, y_max = YOLO_to_pixels(float(x_center), float(y_center), float(width), float(height), image_width, image_height)\n", - "\n", - " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", - "\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " box = [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ]\n", - " draw.rectangle(box, outline=label_color_map[label], width=3)\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f'../../data/kmeans_clustered_images/number/{sheet}')\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(f'../../data/kmeans_clustered_images/results/number/{sheet}.json', 'w') as f:\n", - " json.dump(create_result_dictionary(number_labels, number_bounding_boxes), f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyze accuracy (`clustering.ipynb`)\n", - "\n", - "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time labels: 794 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", - "Number labels: 380 correct clusters, 3 incorrect clusters. The accuracy is 99.22%\n" - ] - } - ], - "source": [ - "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", - "\n", - "# Paths to the JSON files\n", - "PATH_TO_KMEANS_RESULTS = '../../data/kmeans_clustered_images/results'\n", - "TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'time')\n", - "NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, 'number')\n", - "\n", - "time_wrong_clusters_count = 0\n", - "time_correct_clusters_count = 0\n", - "number_wrong_clusters_count = 0\n", - "number_correct_clusters_count = 0\n", - "# Iterate over all images and their bounding boxes\n", - "for sheet, bounding_boxes in yolo_data.items():\n", - " expected_time_values = [0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25,30,35,40,45,50,55,0,5,10,15,20,25]\n", - " expected_number_values = [30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220]\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(TIME_JSON, f'{sheet}.json')) as f:\n", - " time_clusters = json.load(f)\n", - " \n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in time_clusters.items():\n", - " if value not in expected_time_values:\n", - " # We have an erroneous cluster\n", - " time_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_time_values.remove(value)\n", - " time_correct_clusters_count += 1\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(NUMBER_JSON, f'{sheet}.json')) as f:\n", - " number_clusters = json.load(f)\n", - " \n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in number_clusters.items():\n", - " if value not in expected_number_values:\n", - " # We have an erroneous cluster\n", - " number_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_number_values.remove(value)\n", - " number_correct_clusters_count += 1\n", - " \n", - "\n", - "\n", - "print(f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\")\n", - "print(f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Previous Attempts\n", - "First tried AgglomerativeClustering using a Ward hierarchical clustering algorithm with linkage distance.\n", - "* Misjudged distance_threshold input, only takes meaningful values form 0-1\n", - "* Best accuracy with distance_threshold = 0, 10.55% accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Ward linkage clustering fit and prediction\n", - "# distance_threshold shows the limit at which to cut the dendrogram tree\n", - "ward = AgglomerativeClustering(n_clusters=None, metric='euclidean', linkage='single', compute_full_tree=True, distance_threshold=nonmerge_threshold)\n", - "labels = ward.fit_predict(data)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/experiments/clustering/utils/annotations.py b/experiments/clustering/utils/annotations.py new file mode 100644 index 0000000..6d4f99c --- /dev/null +++ b/experiments/clustering/utils/annotations.py @@ -0,0 +1,434 @@ +"""This module defines classes for representing bounding boxes and keypoints associated with objects in images. + +It also provides helper functions for constructing these objects from YOLO formatted labels. +""" + +from dataclasses import dataclass +from typing import Dict, List, Tuple +import warnings + + +class Point: + """The `Point` class is a struct which contains an x and y value for a point. + + Attributes : + `x` (float): + The x coordinate for the point. + `y` (float): + The y coordinate for the point. + """ + + def __init__(self, x: float, y: float): + """inits this point.""" + self.x = x + self.y = y + + def __eq__(self, other): + """Determines if two points are the same.""" + return self.x == other.x and self.y == other.y + + +@dataclass +class BoundingBox: + """The `BoundingBox` class represents a bounding box around an object in an image. + + + Attributes : + `category` (str): + The category of the object within the bounding box. + `left` (float): + The x-coordinate of the top-left corner of the bounding box. + `top` (float): + The y-coordinate of the top-left corner of the bounding box. + `right` (float): + The x-coordinate of the bottom-right corner of the bounding box. + `bottom` (float): + The y-coordinate of the bottom-right corner of the bounding box. + + + Constructors : + `from_yolo(yolo_line: str, image_width: int, image_height: int, int_to_category: Dict[int, str])`: + Constructs a `BoundingBox` from a line in a YOLO formatted labels file. It requires the original image dimensions and a dictionary mapping category IDs to category names. + + `from_coco(coco_annotation: Dict, categories: List[Dict])`: + Constructs a `BoundingBox` from an annotation in a COCO data JSON file. It requires the annotation dictionary and a list of category dictionaries. + + + Properties : + `center` (Tuple[int]): + A tuple containing the (x, y) coordinates of the bounding box's center. + `box` (List[int]): + A list containing the bounding box coordinates as [left, top, right, bottom]. + + + Methods : + `to_yolo(image_width: int, image_height: int, category_to_int: Dict[str, int]) -> str`: + Writes a yolo formatted string using this bounding box's data. + `validate_box_values(cls, left: float, top: float, right: float, bottom: float) -> None`: + Validates the box parameters and throws a value error if left > right or top > bottom. + Also issues a warning for the case when left == right or top == bottom letting the user + know that they are constructing a degenerate rectangle. + """ + + category: str + left: float + top: float + right: float + bottom: float + + def __init__( + self, category: str, left: float, top: float, right: float, bottom: float + ): + """Overrides the default constructor from dataclass to validate the parameters before constructing.""" + BoundingBox.validate_box_values(left, top, right, bottom) + self.category = category + self.left = left + self.top = top + self.right = right + self.bottom = bottom + + @staticmethod + def from_yolo( + yolo_line: str, + image_width: int, + image_height: int, + ): + """Constructs a `BoundingBox` from a line in a yolo formatted labels file. + + Because the yolo format stores data in normalized xywh format (from 0 to 1), this method + requires the original image's width and height. + + Args : + `yolo_line` (str): + A string in the yolo label format (c x y w h). + `image_width` (int): + The original image's width. + `image_height` (int): + The original image's height. + + Returns: + A `BoundingBox` object containing the yolo_line's data. + """ + data = yolo_line.split() + x, y, w, h = float(data[1]), float(data[2]), float(data[3]), float(data[4]) + x, y, w, h = ( + x * image_width, + y * image_height, + w * image_width, + h * image_height, + ) + left, top, right, bottom = ( + x - (1 / 2) * w, + y - (1 / 2) * h, + x + (1 / 2) * w, + y + (1 / 2) * h, + ) + return BoundingBox(data[0], left, top, right, bottom) + + @staticmethod + def from_coco(coco_annotation: Dict, categories: List[Dict]): + """Constructs a `BoundingBox` from an annotation in a coco data json file. + + Args : + `coco_annotation` (Dict): A bounding box annotation from the 'annotations' section. + `categories` (List[Dict]): A list of dictionaries containing their numeric ids and categories. + + Returns: + A `BoundingBox` object containing the coco annotation's data. + """ + left, top, w, h = coco_annotation["bbox"] + right, bottom = left + w, top + h + try: + category = list( + filter(lambda c: c["id"] == coco_annotation["category_id"], categories) + )[0].get("name") + except IndexError: + raise ValueError( + f"Category {int(coco_annotation['category_id'])} not found in the categories list." + ) + return BoundingBox(category, left, top, right, bottom) + + @classmethod + def validate_box_values( + cls, left: float, top: float, right: float, bottom: float + ) -> None: + """Validates the coordinates of a rectangle (bounding box). + + This classmethod ensures that the left coordinate is less than the right coordinate, and + the top coordinate is less than the bottom coordinate. It raises a `ValueError` if these + conditions are not met, indicating an invalid box configuration. If the left coordinate + is equal to the right coordinate or if the top coordinate is equal to the bottom + coordinate, this method issues a warning. + + Args: + `left` (float): + The left x-coordinate of the box. + `top` (float): + The top y-coordinate of the box. + `right` (float): + The right x-coordinate of the box. + `bottom` (float): + The bottom y-coordinate of the box. + + Raises: + ValueError: If `left > right` or `top > bottom`. + """ + if left > right: + raise ValueError( + f"Box's left side greater than its right side (Left:{left} > Right:{right})." + ) + if top > bottom: + raise ValueError( + f"Box's top side greater than its bottom side (Top:{top} > Bottom:{bottom})." + ) + if left == right and bottom == top: + warnings.warn( + f"Degenerate rectangle detected. All of the box's parameters are equal (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + elif left == right: + warnings.warn( + f"Degenerate rectangle detected. The box's left side equals its right side (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + elif top == bottom: + warnings.warn( + f"Degenerate rectangle detected. The box's top side equals its bottom side (Left:{left}, Top:{top}, Right:{right}, Bottom:{bottom}).", + UserWarning, + ) + + @property + def center(self) -> Tuple[float]: + """This `BoundingBox`'s center.""" + return ( + self.left + (1 / 2) * (self.right - self.left), + self.top + (1 / 2) * (self.bottom - self.top), + ) + + @property + def box(self) -> List[int]: + """A list containing this `BoundingBox`'s [left, top, right, bottom].""" + return [self.left, self.top, self.right, self.bottom] + + def set_box(self, new_left: int, new_top: int, new_right: int, new_bottom: int): + """Sets this BoundingBox's values for left, top, right, bottom. + + Args : + new_left (int): + The new left side for the box. + new_top (int): + The new top side for the box. + new_right (int): + The new right side for the box. + new_bottom (int): + The new bottom side for the box. + """ + self.validate_box_values(new_left, new_top, new_right, new_bottom) + return BoundingBox( + category=self.category, + left=new_left, + top=new_top, + right=new_right, + bottom=new_bottom, + ) + + def to_yolo( + self, image_width: int, image_height: int, category_to_id: Dict[str, int] + ) -> str: + """Writes the data from this `BoundingBox` into a yolo formatted string. + + Args : + `image_width` (int): + The image's width that this boundingbox belongs to. + `image_height` (int): + The image's height that this boundingbox belongs to. + `category_to_id` (Dict[str, int]): + A dictionary that maps the category string to an id (integer). + + Returns: + A string that encodes this `BoundingBox`'s data for a single line in a yolo label file. + """ + c = category_to_id[self.category] + x, y = self.center + x /= image_width + y /= image_height + w = (self.right - self.left) / image_width + h = (self.bottom - self.top) / image_height + return f"{c} {x} {y} {w} {h}" + + +@dataclass +class Keypoint: + """The `Keypoint` class represents a keypoint associated with an object in an image. + + Attributes : + `keypoint` (Tuple[float]): + A tuple containing the (x, y) coordinates of the keypoint relative to the top-left corner of the image. + `bounding_box` (BoundingBox): + A `BoundingBox` object that defines the bounding box around the object containing the keypoint. + + + Constructors : + `from_yolo(yolo_line: str, image_width: int, image_height: int)`: + Constructs a Keypoint from a line in a YOLO formatted labels file. It requires the original image dimensions and a dictionary mapping category IDs to category names. + **Note:** This method ignores the "visibility" information (denoted by 'v') in the YOLO format. + + + Properties : + `category` (str): + The category of the object the keypoint belongs to (inherited from the `bounding_box`). + `center` (Tuple[float]): + The (x, y) coordinates of the bounding box's center (inherited from the `bounding_box`). + `box` (Tuple[float]): + A list containing the bounding box coordinates as [left, top, right, bottom] (inherited from the `bounding_box`). + + + Methods : + `to_yolo(self, image_width: int, image_height: int, category_to_id: Dict[str, int]) -> str`: + Generates a YOLO formatted string representation of this `Keypoint` object. It requires the image dimensions and a dictionary mapping category strings to integer labels. + `validate_keypoint(cls, bounding_box: BoundingBox, keypoint: Point) -> None`: + Validates that a keypoint lies within the specified bounding box. Raises a ValueError if the keypoint is outside the bounding box. + """ + + keypoint: Point + bounding_box: BoundingBox + + def __init__(self, keypoint: Point, bounding_box: BoundingBox): + """Overrides the default constructor from dataclass to validate the parameters before constructing.""" + Keypoint.validate_keypoint(bounding_box, keypoint) + self.keypoint = keypoint + self.bounding_box = bounding_box + + @staticmethod + def from_yolo( + yolo_line: str, + image_width: int, + image_height: int, + ): + """Constructs a `Keypoint` from a line in a yolo formatted labels file. + + Because the yolo format stores data in normalized xywh format (from 0 to 1), this method + requires the original image's width and height. The 'visible' data is optional, and is not + read to create the object. + + Args : + `yolo_line` (str): + A string in the yolo label format (c x y w h kpx kpy v). + `image_width` (int): + The original image's width. + `image_height` (int): + The original image's height. + `id_to_category` (Dict): + A dictionary that maps the id number in the label to the category. + + Returns: + A `BoundingBox` object containing the yolo_line's data. + """ + bounding_box = BoundingBox.from_yolo(yolo_line, image_width, image_height) + keypoint_x = float(yolo_line.split()[5]) + keypoint_y = float(yolo_line.split()[6]) + keypoint = Point(keypoint_x * image_width, keypoint_y * image_height) + return Keypoint(keypoint, bounding_box) + + @classmethod + def validate_keypoint(cls, bounding_box: BoundingBox, keypoint: Point) -> None: + """Validates that a keypoint lies within the specified bounding box. + + This classmethod ensures that the `keypoint` (represented by a `Point` object) + falls within the confines of the provided `bounding_box` (represented by a + `BoundingBox` object). It checks both the x and y coordinates of the keypoint + against the left, top, right, and bottom boundaries of the bounding box. + + Args: + bounding_box: + The `BoundingBox` object representing the enclosing region. + keypoint: + The `Point` object representing the keypoint to be validated. + + Raises: + ValueError: If the keypoint's coordinates are not within the bounding box. + """ + in_bounds_x: bool = bounding_box.left <= keypoint.x <= bounding_box.right + in_bounds_y: bool = bounding_box.top <= keypoint.y <= bounding_box.bottom + in_bounds: bool = in_bounds_x and in_bounds_y + if not in_bounds: + raise ValueError( + f"Keypoint is not in the bounding box intended to enclose it (Keypoint:{(keypoint.x, keypoint.y)}, BoundingBox:{str(bounding_box)})" + ) + + @property + def category(self) -> str: + """This `Keypoint`'s category.""" + return self.bounding_box.category + + @property + def center(self) -> Tuple[float]: + """This `Keypoint`'s boundingbox center.""" + return self.bounding_box.center + + @property + def box(self) -> Tuple[float]: + """This keypoints boundingbox's [left, top, right, bottom].""" + return self.bounding_box.box + + def set_box( + self, new_left: int, new_top: int, new_right: int, new_bottom: int + ) -> BoundingBox: + """Sets this Keypoints's BoundingBox's values for left, top, right, bottom. + + Args: + new_left (int): + The new left side for the box. + new_top (int): + The new top side for the box. + new_right (int): + The new right side for the box. + new_bottom (int): + The new bottom side for the box. + + Returns: A new Keypoint with a new bounding box. + """ + return Keypoint( + point=self.point, + bounding_box=self.bounding_box.set_box( + new_left, new_top, new_right, new_bottom + ), + ) + + def set_keypoint(self, new_x: int, new_y: int) -> "Keypoint": + """Sets this Keypoint's Keypoint to a new point. + + Args: + new_x (int): + The new x value for the Keypoint. + new_y (int): + The new y value for the Keypoint. + + Returns: A new Keypoint with a new Point as its keypoint. + """ + self.validate_keypoint(self.bounding_box, Point(new_x, new_y)) + return Keypoint(Point(new_x, new_y), self.bounding_box) + + def to_yolo( + self, image_width: int, image_height: int, category_to_id: Dict[str, int] + ) -> str: + """Writes the data from this `Keypoint` into a yolo formatted string. + + Args : + `image_width` (int): + The image's width that this `Keypoint` belongs to. + `image_height` (int): + The image's height that this `Keypoint` belongs to. + `category_to_id` (Dict[str, int]): + A dictionary that maps the category string to an id (int). + + Returns: + A string that encodes this `Keypoint`'s data for a single line in a yolo label file. + """ + yolo_line = self.bounding_box.to_yolo(image_width, image_height, category_to_id) + keypoint_x, keypoint_y = ( + self.keypoint.x / image_width, + self.keypoint.y / image_height, + ) + yolo_line += f" {keypoint_x} {keypoint_y}" + return yolo_line diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 917e269..14f2776 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -354,7 +354,9 @@ " # You only need to do this drawing if we are intentionally being visual\n", " if show_images:\n", " # Draw bounding boxes on the image\n", - " generate_color = lambda: \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + " def generate_color():\n", + " return \"#%06x\" % random.randint(0, 0xFFFFFF)\n", + "\n", " draw = ImageDraw.Draw(pil_img)\n", "\n", " for bounding_box in remapped_locations:\n", From f359fb03466f3fcfe3be87a7f3630aa3d78e43ad Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Thu, 24 Oct 2024 22:59:10 -0400 Subject: [PATCH 18/55] Add agglomerative clustering method --- experiments/clustering/clustering.ipynb | 1615 ++--------------------- 1 file changed, 119 insertions(+), 1496 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 434e477..177b270 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ "import cv2\n", "import numpy as np\n", "from PIL import Image, ImageDraw\n", - "from sklearn.cluster import KMeans, DBSCAN\n", + "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", "\n", "# Local libraries\n", @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +414,52 @@ " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", " labels = scan.fit_predict(data)\n", "\n", - " return labels\n" + " return labels\n", + "\n", + "def agglomerative_clustering(bounding_boxes: List[BoundingBox], possible_nclusters: List[int]) -> List[int]:\n", + " \n", + " # make the bonding box data into a Numpy array\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " # follow suit of the cluster_kmeans algorithm to measure accuracy through silhoutte scores\n", + " cluster_performance_map = {}\n", + " for number_of_clusters in possible_nclusters:\n", + " if number_of_clusters > len(data):\n", + " raise (\n", + " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", + " )\n", + " if number_of_clusters < 1:\n", + " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", + " # use agglomerative clustering\n", + " agg = AgglomerativeClustering(n_clusters=number_of_clusters, linkage='single')\n", + " # get labels\n", + " labels = agg.fit_predict(data)\n", + " # compute the silhoutte scores\n", + " silhouette_avg = silhouette_score(data, labels)\n", + "\n", + " cluster_performance_map[number_of_clusters] = {\n", + " \"score\": silhouette_avg,\n", + " \"labels\": labels,\n", + " }\n", + "\n", + " # get the number of clusters with the best silhoutte score\n", + " n_clusters_max_silhouette = max(\n", + " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", + " )\n", + "\n", + "\n", + " best_n_clusters = (\n", + " n_clusters_max_silhouette\n", + " if (\n", + " (\n", + " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", + " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", + " )\n", + " >= 0.003\n", + " )\n", + " else max(possible_nclusters)\n", + " )\n", + " return cluster_performance_map[best_n_clusters][\"labels\"]\n" ] }, { @@ -426,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -475,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -501,233 +546,119 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5673304019641547\n", - "Number of clusters: 41, Silhouette score: 0.573554537323082\n", - "Number of clusters: 42, Silhouette score: 0.5794580334870207\n", - "Number of clusters: 18, Silhouette score: 0.4495336665724898\n", - "Number of clusters: 19, Silhouette score: 0.4565570559900914\n", - "Number of clusters: 20, Silhouette score: 0.48217162440948763\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5757864617809657\n", - "Number of clusters: 41, Silhouette score: 0.5745338744362858\n", - "Number of clusters: 42, Silhouette score: 0.5807009923903196\n", - "Number of clusters: 18, Silhouette score: 0.4645679790516578\n", - "Number of clusters: 19, Silhouette score: 0.45606688292674913\n", - "Number of clusters: 20, Silhouette score: 0.4821769375394214\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - 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"Number of clusters: 20, Silhouette score: 0.4875528062560131\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5688868783614993\n", - "Number of clusters: 41, Silhouette score: 0.5754140844152069\n", - "Number of clusters: 42, Silhouette score: 0.5816092749418681\n", - "Number of clusters: 18, Silhouette score: 0.4687266938745312\n", - "Number of clusters: 19, Silhouette score: 0.4738489650797876\n", - "Number of clusters: 20, Silhouette score: 0.4870337163251255\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5782371272793899\n", - "Number of clusters: 41, Silhouette score: 0.5750034132529208\n", - "Number of clusters: 42, Silhouette score: 0.5810348685765858\n", - "Number of clusters: 18, Silhouette score: 0.4672520452537935\n", - "Number of clusters: 19, Silhouette score: 0.4718915122644036\n", - "Number of clusters: 20, Silhouette score: 0.4842497675839784\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5676632616806152\n", - "Number of clusters: 41, Silhouette score: 0.5736362329571111\n", - "Number of clusters: 42, Silhouette score: 0.5802480507716713\n", - "Number of clusters: 18, Silhouette score: 0.44191611332733033\n", - "Number of clusters: 19, Silhouette score: 0.45434202908298355\n", - "Number of clusters: 20, Silhouette score: 0.4814815759616046\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5815327713337762\n", - "Number of clusters: 41, Silhouette score: 0.5751821885994145\n", - "Number of clusters: 42, Silhouette score: 0.5812544814861884\n", - "Number of clusters: 18, Silhouette score: 0.4483463455207681\n", - "Number of clusters: 19, Silhouette score: 0.45607265099900907\n", - "Number of clusters: 20, Silhouette score: 0.48279582576378477\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5770352445932048\n", - "Number of clusters: 41, Silhouette score: 0.576158726619254\n", - "Number of clusters: 42, Silhouette score: 0.5821804848012874\n", - "Number of clusters: 18, Silhouette score: 0.46939443438692574\n", - "Number of clusters: 19, Silhouette score: 0.4591096371818493\n", - "Number of clusters: 20, Silhouette score: 0.4858105308263671\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", - "Number of clusters: 40, Silhouette score: 0.5674806892582026\n", - "Number of clusters: 41, Silhouette score: 0.5737909263825772\n", - "Number of clusters: 42, Silhouette score: 0.5798289275740274\n", - "Number of clusters: 18, Silhouette score: 0.4700425696501259\n", - "Number of clusters: 19, Silhouette score: 0.46077114989977574\n", - "Number of clusters: 20, Silhouette score: 0.48793240108374225\n", + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" ] } ], @@ -764,7 +695,8 @@ " number_bounding_boxes, defined_eps=0.01, min_samples=2\n", " )\n", " elif method == \"agglomerative\":\n", - " continue\n", + " time_labels = agglomerative_clustering(time_bounding_boxes, [40,41,42])\n", + " number_labels = agglomerative_clustering(number_bounding_boxes, [18,19,20])\n", " else:\n", " raise ValueError(f\"Invalid clustering method: {method}\")\n", "\n", @@ -877,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -888,1326 +820,17 @@ "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Method: dbscan\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: 11111121112127684935198073246512000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: 11111111121229847563190563247810200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0003_intraoperative.JPG, Value: 11111112212114978563798153246102000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0004_intraoperative.JPG, Value: 11111111212124956378715946832010200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: 11111111121224589763132459078610200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: 11121111121214973568149785206310200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: 11112111111224895763127035498612000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: 11111121121219875643190742583610200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: 11111111121229458763130245678912000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: 11112111221114598673978456131200200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: 11111111121224563879102345786912000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0012_intraoperative.JPG, Value: 11111111112224536879154936278010200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: 11111112221114569783978514261300200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0014_intraoperative.JPG, Value: 21121211111114953786143975120286000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0015_intraoperative.JPG, Value: 11111112111224356798120345768912000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: 12111111112124563897109458372162000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: 11211111211214536987791483560212000000000000000000000_mmHg\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: 11111111112224896573143958072610200000000000000000000_mmHg\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 0_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 1_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 2_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 3_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 4_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 5_mins\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: 11111111121226549378120547968310200000000000000000000_mmHg\n", "Time labels: 152 correct clusters, 1291 incorrect clusters. The accuracy is 10.53%\n", - "Number labels: 0 correct clusters, 19 incorrect clusters. The accuracy is 0.00%\n" + "Number labels: 0 correct clusters, 19 incorrect clusters. The accuracy is 0.00%\n", + "Method: agglomerative\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n" ] } ], "source": [ "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", "for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " if method == \"agglomerative\":\n", - " continue\n", " print(f\"Method: {method}\")\n", " # Paths to the JSON files\n", " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", @@ -2354,7 +977,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "ChartExtractor", "language": "python", "name": "python3" }, From 2358727cbe127ee91255afbe411b2d14e80bb03a Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Fri, 25 Oct 2024 00:32:27 -0400 Subject: [PATCH 19/55] Updated first cell of notebook --- experiments/clustering/clustering.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 177b270..5ee5427 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -6,9 +6,10 @@ "source": [ "# Clustering Experiment\n", "\n", - "#### By: Hannah Valenty and Charbel Marche\n", + "We have put all of our methods together for direct comparison in a single notebook.\n", "\n", - "We decided to individually tackle the problem using 1 method, and by the time we are all done we will be able to merge our techniques and select the optimal techinque. Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers.\n" + "Problem: Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers.\n", + "\n" ] }, { From 2022f85056855c2e7532049ade6983938e2ee249 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Sun, 27 Oct 2024 23:48:34 -0400 Subject: [PATCH 20/55] Updated dbscan parameters --- experiments/clustering/clustering.ipynb | 146 ++++++++++++------------ 1 file changed, 73 insertions(+), 73 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 5ee5427..a25c323 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -398,12 +398,12 @@ " bounding_boxes: List[BoundingBox], defined_eps: float, min_samples: int\n", ") -> List[int]:\n", " \"\"\"\n", - " Cluster bounding boxes using Ward hierarchical clustering algorithm with linkage distance.\n", + " Cluster bounding boxes density based spatial clustering algorithm.\n", "\n", " Args:\n", " bounding_boxes: List of bounding boxes.\n", - " defined_eps: The maximum distance between two samples for one to be considered as in the neighborhood of the other.\n", - " min_samples: The number of samples (or total weight) in a neighborhood for a point to be considered as a core point.\n", + " defined_eps: Maximum distance between two samples to be in the neighborhood of one another (center of BB).\n", + " min_samples: The number of samples (or total weight) for a point to be considered as core\n", "\n", " Returns:\n", " List of cluster labels.\n", @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -539,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -547,119 +547,119 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" ] } ], @@ -690,10 +690,10 @@ " number_labels = cluster_kmeans(number_bounding_boxes, [18, 19, 20])\n", " elif method == \"dbscan\":\n", " time_labels = dbscan_clustering(\n", - " time_bounding_boxes, defined_eps=0.01, min_samples=1\n", + " time_bounding_boxes, defined_eps=5, min_samples=1\n", " )\n", " number_labels = dbscan_clustering(\n", - " number_bounding_boxes, defined_eps=0.01, min_samples=2\n", + " number_bounding_boxes, defined_eps=5, min_samples=2\n", " )\n", " elif method == \"agglomerative\":\n", " time_labels = agglomerative_clustering(time_bounding_boxes, [40,41,42])\n", @@ -810,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -821,8 +821,8 @@ "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Method: dbscan\n", - "Time labels: 152 correct clusters, 1291 incorrect clusters. The accuracy is 10.53%\n", - "Number labels: 0 correct clusters, 19 incorrect clusters. The accuracy is 0.00%\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Method: agglomerative\n", "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n" @@ -919,7 +919,7 @@ " ]\n", "\n", " # Load JSON\n", - " with open(os.path.join(TIME_JSON, f\"{sheet.split(\".\")[0]}.json\")) as f:\n", + " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", " time_clusters = json.load(f)\n", "\n", " # Each cluster contains the number (integer) that the cluster represents\n", @@ -937,7 +937,7 @@ " time_correct_clusters_count += 1\n", "\n", " # Load JSON\n", - " with open(os.path.join(NUMBER_JSON, f\"{sheet.split(\".\")[0]}.json\")) as f:\n", + " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", " number_clusters = json.load(f)\n", "\n", " # Each cluster contains the number (integer) that the cluster represents\n", @@ -978,7 +978,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ChartExtractor", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -992,7 +992,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.9.5" } }, "nbformat": 4, From 08d089ce0810cea6ee84b501ae8f71db6a788331 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Mon, 28 Oct 2024 16:43:42 -0400 Subject: [PATCH 21/55] 5% erroneous bounding boxes for cluster testing --- .../clustering/clustering_erroneous.ipynb | 1085 +++++++++++++++++ 1 file changed, 1085 insertions(+) create mode 100644 experiments/clustering/clustering_erroneous.ipynb diff --git a/experiments/clustering/clustering_erroneous.ipynb b/experiments/clustering/clustering_erroneous.ipynb new file mode 100644 index 0000000..f3aa153 --- /dev/null +++ b/experiments/clustering/clustering_erroneous.ipynb @@ -0,0 +1,1085 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clustering Experiment with Erroneous BB\n", + "\n", + "We have put all of our methods together for direct comparison in a single notebook.\n", + "\n", + "Problem: Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Changes from `clustering.ipynb`**\n", + "* Added `random_BB_generate` and `erroneous_bounding_boxes` functions.\n", + "\n", + "* `erroneous_bounding_boxes` is implemented within `test_clustering_methods`.\n", + "\n", + "* The two lists, time_bounding_boxes & number_bounding_boxes have 5% bounding boxes removed and 5% erroneous added.\n", + "\n", + "* By commenting out the `erroneous_bounding_boxes` within `test_clustering_methods` the notebook can be run with no erroneous boxes as before.\n", + "\n", + "* Also imported random library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register Images to Start\n", + "\n", + "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Install Packages\n", + "\n", + "These are the necessary packages to run the functions and scripts below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard libraries\n", + "import os\n", + "import json\n", + "import random\n", + "from pathlib import Path\n", + "from typing import List, Tuple, Literal, Dict\n", + "\n", + "# Third-party libraries\n", + "import cv2\n", + "import numpy as np\n", + "from PIL import Image, ImageDraw\n", + "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", + "from sklearn.metrics import silhouette_score\n", + "import random\n", + "\n", + "# Local libraries\n", + "from utils.annotations import BoundingBox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start By Loading YOLO Data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 19 sheets in yolo_data.json\n" + ] + } + ], + "source": [ + "# Load yolo_data.json\n", + "PATH_TO_YOLO_DATA = \"../../data/yolo_data.json\"\n", + "PATH_TO_REGISTERED_IMAGES = \"../../data/registered_images\"\n", + "UNIFIED_IMAGE_PATH = (\n", + " \"../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png\"\n", + ")\n", + "with open(PATH_TO_YOLO_DATA) as json_file:\n", + " yolo_data = json.load(json_file)\n", + "\n", + "# See how many intraoperative images are registered\n", + "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's select relevant bounding boxes from the blood pressure and HR zone.\n", + "\n", + "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_bp_section_coordinates(\n", + " image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", + ") -> List[int]:\n", + " \"\"\"Crops the blood pressure section out of an image of a chart.\n", + "\n", + " Args:\n", + " image_height (int):\n", + " The height of the image in pixels.\n", + " bboxes (List[BoundingBox]):\n", + " List of BoundingBoxes within this image.\n", + " buffer_pixels (int):\n", + " An optional integer that specifies the number of pixels around the digit detections to\n", + " 'zoom out' by. Defaults to 5 pixels.\n", + "\n", + " Returns:\n", + " Coordinates of the bounding box that contains the blood pressure section.\n", + " \"\"\"\n", + " # Get bounding boxes from detections and filter non bounding boxes out.\n", + " bboxes: List[BoundingBox] = list(\n", + " filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", + " )\n", + "\n", + " digit_categories: List[str] = [str(i) for i in range(10)]\n", + "\n", + " # Filter bounding boxes to those which are within the approximate region and are digits.\n", + " bp_legend_digits: List[BoundingBox] = list(\n", + " filter(\n", + " lambda bb: all(\n", + " [\n", + " bb.top / image_height > 0.2,\n", + " bb.top / image_height < 0.8,\n", + " bb.category in digit_categories,\n", + " ]\n", + " ),\n", + " bboxes,\n", + " )\n", + " )\n", + " bp_legend_coordinates: List[int] = list(\n", + " map(\n", + " int,\n", + " [\n", + " min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", + " min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", + " max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", + " max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", + " ],\n", + " )\n", + " )\n", + " return bp_legend_coordinates\n", + "\n", + "\n", + "def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", + " \"\"\"\n", + " Determine if a point is above or below the diagonal line y = mx + b.\n", + " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", + "\n", + " Args:\n", + " x_center: float, x coordinate of the point\n", + " y_center: float, y coordinate of the point\n", + " m: float, slope of the diagonal line\n", + " b: float, intercept of the diagonal line\n", + "\n", + " Returns:\n", + " bool, True if the point is above the line, False otherwise\n", + " \"\"\"\n", + " # Calculate the y value on the line for the given x_center\n", + " y_line = m * x_center + b\n", + " return y_center > y_line\n", + "\n", + "\n", + "def select_relevant_bounding_boxes(\n", + " sheet_data: List[str],\n", + " path_to_image: Path,\n", + " show_images: bool = False,\n", + " desired_img_width: int = 800,\n", + " desired_img_height: int = 600,\n", + ") -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", + " Identify bounding boxes that are within the selected region and draw rectangles around them.\n", + " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", + "\n", + " Args:\n", + " sheet_data: List of bounding boxes in YOLO format.\n", + " path_to_image: Path to the image file.\n", + "\n", + " Returns:\n", + " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", + " The first list contains bounding boxes in the top-right region -- representing time labels.\n", + " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", + " (bounding_boxes_time, bounding_boxes_numbers)\n", + " \"\"\"\n", + "\n", + " # Load the image\n", + " image = cv2.imread(path_to_image)\n", + "\n", + " # Display the image and allow the user to select a ROI\n", + " resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", + "\n", + " x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", + " image_height=desired_img_height,\n", + " bboxes=[\n", + " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", + " for yolo_bb in sheet_data\n", + " ],\n", + " buffer_pixels=2,\n", + " )\n", + "\n", + " cv2.rectangle(\n", + " resized_image,\n", + " (x_top_left, y_top_left),\n", + " (x_bottom_right, y_bottom_right),\n", + " (255, 255, 0),\n", + " 1,\n", + " )\n", + "\n", + " # Draw the diagonal line of the selected region from top-left to bottom-right\n", + " cv2.line(\n", + " resized_image,\n", + " (x_top_left, y_top_left),\n", + " (x_bottom_right, y_bottom_right),\n", + " (0, 255, 0),\n", + " 1,\n", + " )\n", + " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", + " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", + " # Top-right region is where time labels are located\n", + " # Bottom-left region is where numerical values for mmHg and bpm are located\n", + " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", + " b = y_top_left - m * x_top_left\n", + "\n", + " # List of bounding boxes in the top-right and bottom-left regions\n", + " bounding_boxes_time = []\n", + " bounding_boxes_numbers = []\n", + "\n", + " # Process the bounding boxes\n", + " for bounding_box in sheet_data:\n", + " # Bounding boxes are in YOLO format; convert them to pixels\n", + " x_min, y_min, x_max, y_max = list(\n", + " map(\n", + " int,\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " ).box,\n", + " )\n", + " )\n", + "\n", + " # Check if the bounding box is within the selected region\n", + " if (\n", + " x_min >= x_top_left\n", + " and y_min >= y_top_left\n", + " and x_max <= x_bottom_right\n", + " and y_max <= y_bottom_right\n", + " ):\n", + " # Calculate the center of the bounding box\n", + " x_center_bb = (x_min + x_max) / 2\n", + " y_center_bb = (y_min + y_max) / 2\n", + "\n", + " # If we want to generalize this function we can add the option to disregard the diagonal line\n", + "\n", + " # Determine if the bounding box center is in the top-right region\n", + " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", + " # Bounding box is in the top-right region\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", + " )\n", + " bounding_boxes_numbers.append(\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " )\n", + " )\n", + " else:\n", + " # Bounding box is in the bottom-left region\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", + " )\n", + " bounding_boxes_time.append(\n", + " BoundingBox.from_yolo(\n", + " yolo_line=bounding_box,\n", + " image_width=desired_img_width,\n", + " image_height=desired_img_height,\n", + " )\n", + " )\n", + "\n", + " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", + " # You can also manually quit out with ESC key.\n", + " cv2.destroyAllWindows()\n", + "\n", + " # If we are showing the images, display the image with the selected region and bounding boxes\n", + " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", + " if show_images:\n", + " # Display the image with the selected region and bounding boxes\n", + " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + " resized_image = Image.fromarray(resized_image)\n", + " resized_image.show()\n", + "\n", + " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", + " return (bounding_boxes_time, bounding_boxes_numbers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a function for K-means clustering, dbscan clustering\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def cluster_kmeans(\n", + " bounding_boxes: List[BoundingBox], possible_nclusters: List[int]\n", + ") -> List[int]:\n", + " \"\"\"\n", + " Cluster bounding boxes using K-Means clustering algorithm.\n", + "\n", + " Args:\n", + " bounding_boxes: List of bounding boxes in YOLO format.\n", + " possible_nclusters: List of possible number of clusters to try.\n", + "\n", + " Returns:\n", + " List of cluster labels.\n", + " \"\"\"\n", + " # Convert to a NumPy array (using only x_center and y_center)\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " cluster_performance_map = {}\n", + " for number_of_clusters in possible_nclusters:\n", + " if number_of_clusters > len(data):\n", + " raise (\n", + " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", + " )\n", + " if number_of_clusters < 1:\n", + " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", + " # Apply K-Means\n", + " kmeans = KMeans(\n", + " n_clusters=number_of_clusters,\n", + " init=\"k-means++\",\n", + " n_init=20,\n", + " max_iter=500,\n", + " tol=1e-8,\n", + " random_state=42,\n", + " )\n", + " kmeans.fit(data)\n", + "\n", + " # Get cluster labels\n", + " labels = kmeans.predict(data)\n", + " silhouette_avg = silhouette_score(data, labels)\n", + "\n", + " # print(\n", + " # f\"Number of clusters: {number_of_clusters}, Silhouette score: {silhouette_avg}\"\n", + " # )\n", + "\n", + " cluster_performance_map[number_of_clusters] = {\n", + " \"score\": silhouette_avg,\n", + " \"labels\": labels,\n", + " }\n", + "\n", + " # Evaluate the performance of each number of clusters and select the one with the highest silhouette score\n", + " # if it is 0.003 greater than what should be the number of clusters otherwise go with proper_nclusters\n", + " n_clusters_max_silhouette = max(\n", + " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", + " )\n", + " best_n_clusters = (\n", + " n_clusters_max_silhouette\n", + " if (\n", + " (\n", + " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", + " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", + " )\n", + " >= 0.003\n", + " )\n", + " else max(possible_nclusters)\n", + " )\n", + " return cluster_performance_map[best_n_clusters][\"labels\"]\n", + "\n", + "\n", + "def dbscan_clustering(\n", + " bounding_boxes: List[BoundingBox], defined_eps: float, min_samples: int\n", + ") -> List[int]:\n", + " \"\"\"\n", + " Cluster bounding boxes density based spatial clustering algorithm.\n", + "\n", + " Args:\n", + " bounding_boxes: List of bounding boxes.\n", + " defined_eps: Maximum distance between two samples to be in the neighborhood of one another (center of BB).\n", + " min_samples: The number of samples (or total weight) for a point to be considered as core\n", + "\n", + " Returns:\n", + " List of cluster labels.\n", + " \"\"\"\n", + " # Convert to a NumPy array (using only x_center and y_center)\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " # DBSCAN\n", + " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", + " labels = scan.fit_predict(data)\n", + "\n", + " return labels\n", + "\n", + "def agglomerative_clustering(bounding_boxes: List[BoundingBox], possible_nclusters: List[int]) -> List[int]:\n", + " \n", + " # make the bonding box data into a Numpy array\n", + " data = np.array([box.center for box in bounding_boxes])\n", + "\n", + " # follow suit of the cluster_kmeans algorithm to measure accuracy through silhoutte scores\n", + " cluster_performance_map = {}\n", + " for number_of_clusters in possible_nclusters:\n", + " if number_of_clusters > len(data):\n", + " raise (\n", + " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", + " )\n", + " if number_of_clusters < 1:\n", + " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", + " # use agglomerative clustering\n", + " agg = AgglomerativeClustering(n_clusters=number_of_clusters, linkage='single')\n", + " # get labels\n", + " labels = agg.fit_predict(data)\n", + " # compute the silhoutte scores\n", + " silhouette_avg = silhouette_score(data, labels)\n", + "\n", + " cluster_performance_map[number_of_clusters] = {\n", + " \"score\": silhouette_avg,\n", + " \"labels\": labels,\n", + " }\n", + "\n", + " # get the number of clusters with the best silhoutte score\n", + " n_clusters_max_silhouette = max(\n", + " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", + " )\n", + "\n", + "\n", + " best_n_clusters = (\n", + " n_clusters_max_silhouette\n", + " if (\n", + " (\n", + " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", + " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", + " )\n", + " >= 0.003\n", + " )\n", + " else max(possible_nclusters)\n", + " )\n", + " return cluster_performance_map[best_n_clusters][\"labels\"]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to create a result dictionary that we can save as a JSON file to analyze performance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def create_result_dictionary(\n", + " labels: List[str], bounding_boxes: List[BoundingBox], unit: Literal[\"mmHg\", \"mins\"]\n", + ") -> Dict[int, int]:\n", + " \"\"\"\n", + " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", + "\n", + " Args:\n", + " labels: List of cluster labels.\n", + " bounding_boxes: List of bounding boxes.\n", + " suffix: Suffix to append to the category of the bounding box. One of [\"mmHg\", \"mins\"].\n", + "\n", + " Returns:\n", + " Dictionary with cluster labels as keys and bounding box values as values.\n", + " \"\"\"\n", + " # Create a dictionary to store labelled elements\n", + " label_dict = {}\n", + "\n", + " # Iterate over both lists\n", + " for label, element in zip(labels, bounding_boxes):\n", + " label = int(label)\n", + " if label not in label_dict:\n", + " # Create a new list for this label if it doesn't exist\n", + " label_dict[label] = []\n", + " # Append the element to the corresponding label list\n", + " label_dict[label].append(f\"{element.category} {element.center[0]}\")\n", + "\n", + " # Sort the lists in the dictionary by x_center\n", + " for key in label_dict:\n", + " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(\" \")[1]))\n", + " label_dict[key] = [element.split(\" \")[0] for element in label_dict[key]]\n", + " # Turn list of strings into a string\n", + " label_dict[key] = f\"{''.join(label_dict[key])}_{unit}\"\n", + "\n", + " return label_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to generate colors!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Draw bounding boxes on the image\n", + "def generate_color():\n", + " return \"#%06x\" % random.randint(0, 0xFFFFFF)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to generate random Bounding Box formatted occurances" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def random_BB_generate(x):\n", + " # function paired with map to generate erroneous bounding boxes\n", + " category_int = random.randint(0, 9)\n", + " left_int = random.randint(20, 781)\n", + " top_int = random.randint(20, 581)\n", + "\n", + " # input generated integers to bounding box \n", + " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " return box" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to create 5% erroneous BB and remove 5% existing BB." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def erroneous_bounding_boxes(time_BB: List[str], number_BB: List[str]) -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", + "\n", + " Args:\n", + " time_BB: list of time bounding boxes in BoundingBox format\n", + " number_BB: list of number bounding boxes in BoundingBox format\n", + "\n", + " Returns:\n", + " Tuple: lists of time and number bounding boxes in BoundingBox format\n", + " \"\"\"\n", + " # make copies of input bounding box lists to avoid unwanted manipulation\n", + " time_BB_copy = time_BB.copy()\n", + " number_BB_copy = number_BB.copy()\n", + " \n", + " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", + " ## sample\n", + " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", + " number_BB_sample = list(random.sample(number_BB_copy, 4))\n", + " ## remove\n", + " time_BB_removal = [time_BB_copy.remove(line) for line in time_BB_sample]\n", + " number_BB_removal = [number_BB_copy.remove(line) for line in number_BB_sample]\n", + "\n", + " # use random bounding box generation to refill removed BBs with erroneous boxes\n", + " time_BB_generate = list(map(random_BB_generate, range(len(time_BB_sample))))\n", + " number_BB_generate = list(map(random_BB_generate, range(len(number_BB_sample))))\n", + "\n", + " # append BB generated list back to copy with 5% removal\n", + " time_BB_erroneous = time_BB_copy + time_BB_generate\n", + " number_BB_erroneous = number_BB_copy + number_BB_generate\n", + "\n", + " return (time_BB_erroneous, number_BB_erroneous)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets use these functions to get the relevant bounding boxes for clustering.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Sheet: RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" + ] + } + ], + "source": [ + "def test_clustering_methods() -> None:\n", + " \"\"\"\n", + " Test the clustering methods on the YOLO data.\n", + " Saves the clustered images and the clustered bounding boxes to JSON files.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " # Iterate over all images and their bounding boxes\n", + " for sheet, yolo_bbs in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " print(f\"Full image path: {full_image_path}\")\n", + "\n", + " # Call the analyze_sheet function with data from the loop\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bbs, full_image_path\n", + " )\n", + "\n", + " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", + " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", + " time_bounding_boxes, number_bounding_boxes\n", + " )\n", + "\n", + " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", + " if method == \"kmeans\":\n", + " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", + " number_labels = cluster_kmeans(number_bounding_boxes, [18, 19, 20])\n", + " elif method == \"dbscan\":\n", + " time_labels = dbscan_clustering(\n", + " time_bounding_boxes, defined_eps=5, min_samples=1\n", + " )\n", + " number_labels = dbscan_clustering(\n", + " number_bounding_boxes, defined_eps=5, min_samples=2\n", + " )\n", + " elif method == \"agglomerative\":\n", + " time_labels = agglomerative_clustering(time_bounding_boxes, [40,41,42])\n", + " number_labels = agglomerative_clustering(number_bounding_boxes, [18,19,20])\n", + " else:\n", + " raise ValueError(f\"Invalid clustering method: {method}\")\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + "\n", + " label_color_map = {}\n", + " for i, label in enumerate(time_labels):\n", + " # Get the bounding box\n", + " bounding_box = time_bounding_boxes[i]\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(bounding_box.box)\n", + " ]\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=label_color_map[label],\n", + " width=3,\n", + " )\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f\"../../data/{method}_clustered_images/time/{sheet}\")\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(\n", + " f\"../../data/{method}_clustered_images/results/time/{sheet.split('.')[0]}.json\",\n", + " \"w\",\n", + " ) as f:\n", + " json.dump(\n", + " create_result_dictionary(time_labels, time_bounding_boxes, \"mins\"),\n", + " f,\n", + " )\n", + "\n", + " # Create an image object\n", + " image: Image = Image.open(full_image_path)\n", + " image_width, image_height = image.size\n", + " label_color_map = {}\n", + " for i, label in enumerate(number_labels):\n", + " # Get the bounding box\n", + " bounding_box = number_bounding_boxes[i]\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(bounding_box.box)\n", + " ]\n", + "\n", + " # If the label is not in the color map, generate a new color\n", + " if label not in label_color_map:\n", + " label_color_map[label] = generate_color()\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=label_color_map[label],\n", + " width=3,\n", + " )\n", + "\n", + " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", + " image.save(f\"../../data/{method}_clustered_images/number/{sheet}\")\n", + "\n", + " # Save the clustered bounding boxes to a JSON file\n", + " with open(\n", + " f\"../../data/{method}_clustered_images/results/number/{sheet.split('.')[0]}.json\",\n", + " \"w\",\n", + " ) as f:\n", + " json.dump(\n", + " create_result_dictionary(\n", + " number_labels, number_bounding_boxes, \"mmHg\"\n", + " ),\n", + " f,\n", + " )\n", + "\n", + "\n", + "# Test the clustering methods\n", + "test_clustering_methods()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analyze accuracy\n", + "\n", + "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time labels: 645 correct clusters, 144 incorrect clusters. The accuracy is 81.75%\n", + "Number labels: 189 correct clusters, 187 incorrect clusters. The accuracy is 50.27%\n", + "Method: dbscan\n", + "Time labels: 730 correct clusters, 132 incorrect clusters. The accuracy is 84.69%\n", + "Number labels: 310 correct clusters, 53 incorrect clusters. The accuracy is 85.40%\n", + "Method: agglomerative\n", + "Time labels: 634 correct clusters, 158 incorrect clusters. The accuracy is 80.05%\n", + "Number labels: 186 correct clusters, 190 incorrect clusters. The accuracy is 49.47%\n" + ] + } + ], + "source": [ + "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", + "for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " print(f\"Method: {method}\")\n", + " # Paths to the JSON files\n", + " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", + " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", + " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", + "\n", + " time_wrong_clusters_count = 0\n", + " time_correct_clusters_count = 0\n", + " number_wrong_clusters_count = 0\n", + " number_correct_clusters_count = 0\n", + "\n", + " # Iterate over all images and their bounding boxes\n", + " for sheet, yolo_bb in yolo_data.items():\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bb, full_image_path\n", + " )\n", + " # Convert the bounding boxes to a list of strings with proper suffixes\n", + " expected_time_values = [\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " ]\n", + "\n", + " expected_number_values = [\n", + " \"30_mmHg\",\n", + " \"40_mmHg\",\n", + " \"50_mmHg\",\n", + " \"60_mmHg\",\n", + " \"70_mmHg\",\n", + " \"80_mmHg\",\n", + " \"90_mmHg\",\n", + " \"100_mmHg\",\n", + " \"110_mmHg\",\n", + " \"120_mmHg\",\n", + " \"130_mmHg\",\n", + " \"140_mmHg\",\n", + " \"150_mmHg\",\n", + " \"160_mmHg\",\n", + " \"170_mmHg\",\n", + " \"180_mmHg\",\n", + " \"190_mmHg\",\n", + " \"200_mmHg\",\n", + " \"210_mmHg\",\n", + " \"220_mmHg\",\n", + " ]\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", + " time_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in time_clusters.items():\n", + " if value not in expected_time_values:\n", + " # Print the sheet, value that is not in the expected values\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " time_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_time_values.remove(value)\n", + " time_correct_clusters_count += 1\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", + " number_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in number_clusters.items():\n", + " if value not in expected_number_values:\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " number_wrong_clusters_count += 1\n", + " else:\n", + " # We have a correct cluster\n", + " expected_number_values.remove(value)\n", + " number_correct_clusters_count += 1\n", + "\n", + " print(\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\"\n", + " )\n", + " print(\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\"\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0589cdc84a6f04d825a9f551d8223641ec28124d Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Mon, 28 Oct 2024 20:40:37 -0400 Subject: [PATCH 22/55] Add density approach to select relevant bounding boxes --- experiments/clustering/clustering.ipynb | 274 ++++++++++++++++++------ 1 file changed, 207 insertions(+), 67 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index a25c323..c52e9d2 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -49,6 +49,7 @@ "from PIL import Image, ImageDraw\n", "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", + "from scipy.stats import gaussian_kde\n", "\n", "# Local libraries\n", "from utils.annotations import BoundingBox" @@ -70,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -106,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -314,6 +315,145 @@ " return (bounding_boxes_time, bounding_boxes_numbers)" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def find_density_max(values: List[int], search_area: int) -> int:\n", + " \"\"\"\n", + " Given a list of values and a search area, find the index of where the highest density is.\n", + " The list of values correspond to identifying points for the bounding boxes and the search area corresponds to the images height or width.\n", + "\n", + " Args:\n", + " values: List of identifying points for the bounding boxes\n", + " search_area: height/width of the image dependent on whether x or y axis is being search.\n", + "\n", + " Returns:\n", + " The axis value that has the highest density of bounding boxes.\n", + " \"\"\"\n", + " kde = gaussian_kde(values, bw_method=0.2)\n", + "\n", + " x_values = np.linspace(0, search_area, 10000)\n", + "\n", + " kde_vals = kde(x_values)\n", + "\n", + " max_index=np.argmax(kde_vals)\n", + " return x_values[max_index]\n", + "\n", + "def select_relevant_bounding_boxes(\n", + " sheet_data: List[str],\n", + " path_to_image: Path,\n", + " show_images: bool = False,\n", + " desired_img_width: int = 800,\n", + " desired_img_height: int = 600,\n", + ") -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Given sheet data for bounding boxes in YOLO format, find the bounding boxes corresponding to the number and time on the BP chart.\n", + " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", + "\n", + " Args:\n", + " sheet_data: List of bounding boxes in YOLO format.\n", + " path_to_image: Path to the image file.\n", + "\n", + " Returns:\n", + " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", + " The first list contains bounding boxes in the top-right region -- representing time labels.\n", + " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", + " (bounding_boxes_time, bounding_boxes_numbers)\n", + " \"\"\"\n", + "\n", + " # Load the image\n", + " image = cv2.imread(path_to_image)\n", + "\n", + " # Display the image and allow the user to select a ROI\n", + " resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", + "\n", + " # convert the YOLO data to Bounding Boxes\n", + " bboxes: List[BoundingBox] = [\n", + " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", + " for yolo_bb in sheet_data\n", + " ]\n", + "\n", + " # generate a list of the digit categories\n", + " digit_categories: List[str] = [str(i) for i in range(10)]\n", + " \n", + " # filter out non bounding boxes and those whose category is not a digit\n", + " bboxes: List[BoundingBox] = list(\n", + " filter(\n", + " lambda bb:\n", + " isinstance(bb, BoundingBox) and\n", + " bb.category in digit_categories\n", + " ,\n", + " bboxes,\n", + " )\n", + " )\n", + "\n", + " # find the point with the maximum density of bounding boxes\n", + " bboxes_right: List[int] = [bb.right for bb in bboxes]\n", + " numbers_loc: int = find_density_max(bboxes_right, desired_img_width)\n", + "\n", + " bboxes_bottom: List[int] = [bb.bottom for bb in bboxes]\n", + " time_loc: int = find_density_max(bboxes_bottom, desired_img_height)\n", + "\n", + " bounding_boxes_time = []\n", + " bounding_boxes_numbers = []\n", + "\n", + " # Process the bounding boxes\n", + " for bounding_box in bboxes:\n", + " # get the pixels for plotting\n", + " x_min = int(bounding_box.left)\n", + " x_max = int(bounding_box.right)\n", + " y_min = int(bounding_box.top)\n", + " y_max = int(bounding_box.bottom)\n", + " \n", + " # get the center point of the bounding box for comparison\n", + " x_center_bb, y_center_bb = bounding_box.center\n", + "\n", + " # check if the bounding box is a number on the BP chart by comparing to the KDE index + a threshold\n", + " if (x_center_bb > numbers_loc-15 and x_center_bb < numbers_loc+2):\n", + " # Bounding box is in the top-right region\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", + " )\n", + " bounding_boxes_numbers.append(\n", + " bounding_box\n", + " )\n", + " # check if the bounding box is a time on the BP chart by comparing to the KDE index + a threshold\n", + " elif (y_center_bb > time_loc-10 and y_max < time_loc+2):\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", + " )\n", + " bounding_boxes_time.append(\n", + " bounding_box\n", + " )\n", + " # plot the lines of the KDE index found for debugging\n", + " # numbers_start = (int(numbers_loc), 0)\n", + " # numbers_end = (int(numbers_loc), desired_img_height)\n", + "\n", + " # time_start = (0, int(time_loc))\n", + " # time_end = (desired_img_width, int(time_loc))\n", + "\n", + " # cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", + " # cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", + "\n", + " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", + " # You can also manually quit out with ESC key.\n", + " cv2.destroyAllWindows()\n", + "\n", + " # If we are showing the images, display the image with the selected region and bounding boxes\n", + " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", + " if show_images:\n", + " # Display the image with the selected region and bounding boxes\n", + " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + " resized_image = Image.fromarray(resized_image)\n", + " resized_image.show()\n", + "\n", + " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", + " return (bounding_boxes_time, bounding_boxes_numbers)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -323,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -521,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -539,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -547,119 +687,119 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: 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../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" ] } ], @@ -810,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -978,7 +1118,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ChartExtractor", "language": "python", "name": "python3" }, @@ -992,7 +1132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.12.7" } }, "nbformat": 4, From 8b67e2db841c56d2ebda5077e238215a13980575 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 28 Oct 2024 21:03:33 -0400 Subject: [PATCH 23/55] Commented out old code --- experiments/clustering/clustering.ipynb | 549 ++++++++++++------------ 1 file changed, 263 insertions(+), 286 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index c52e9d2..7d413a8 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -107,217 +107,217 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "def get_bp_section_coordinates(\n", - " image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", - ") -> List[int]:\n", - " \"\"\"Crops the blood pressure section out of an image of a chart.\n", - "\n", - " Args:\n", - " image_height (int):\n", - " The height of the image in pixels.\n", - " bboxes (List[BoundingBox]):\n", - " List of BoundingBoxes within this image.\n", - " buffer_pixels (int):\n", - " An optional integer that specifies the number of pixels around the digit detections to\n", - " 'zoom out' by. Defaults to 5 pixels.\n", - "\n", - " Returns:\n", - " Coordinates of the bounding box that contains the blood pressure section.\n", - " \"\"\"\n", - " # Get bounding boxes from detections and filter non bounding boxes out.\n", - " bboxes: List[BoundingBox] = list(\n", - " filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", - " )\n", - "\n", - " digit_categories: List[str] = [str(i) for i in range(10)]\n", - "\n", - " # Filter bounding boxes to those which are within the approximate region and are digits.\n", - " bp_legend_digits: List[BoundingBox] = list(\n", - " filter(\n", - " lambda bb: all(\n", - " [\n", - " bb.top / image_height > 0.2,\n", - " bb.top / image_height < 0.8,\n", - " bb.category in digit_categories,\n", - " ]\n", - " ),\n", - " bboxes,\n", - " )\n", - " )\n", - " bp_legend_coordinates: List[int] = list(\n", - " map(\n", - " int,\n", - " [\n", - " min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", - " min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", - " max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", - " max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", - " ],\n", - " )\n", - " )\n", - " return bp_legend_coordinates\n", - "\n", - "\n", - "def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", - " \"\"\"\n", - " Determine if a point is above or below the diagonal line y = mx + b.\n", - " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", - "\n", - " Args:\n", - " x_center: float, x coordinate of the point\n", - " y_center: float, y coordinate of the point\n", - " m: float, slope of the diagonal line\n", - " b: float, intercept of the diagonal line\n", - "\n", - " Returns:\n", - " bool, True if the point is above the line, False otherwise\n", - " \"\"\"\n", - " # Calculate the y value on the line for the given x_center\n", - " y_line = m * x_center + b\n", - " return y_center > y_line\n", - "\n", - "\n", - "def select_relevant_bounding_boxes(\n", - " sheet_data: List[str],\n", - " path_to_image: Path,\n", - " show_images: bool = False,\n", - " desired_img_width: int = 800,\n", - " desired_img_height: int = 600,\n", - ") -> Tuple[List[str], List[str]]:\n", - " \"\"\"\n", - " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - " Identify bounding boxes that are within the selected region and draw rectangles around them.\n", - " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", - "\n", - " Args:\n", - " sheet_data: List of bounding boxes in YOLO format.\n", - " path_to_image: Path to the image file.\n", - "\n", - " Returns:\n", - " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", - " The first list contains bounding boxes in the top-right region -- representing time labels.\n", - " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", - " (bounding_boxes_time, bounding_boxes_numbers)\n", - " \"\"\"\n", - "\n", - " # Load the image\n", - " image = cv2.imread(path_to_image)\n", - "\n", - " # Display the image and allow the user to select a ROI\n", - " resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", - "\n", - " x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", - " image_height=desired_img_height,\n", - " bboxes=[\n", - " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", - " for yolo_bb in sheet_data\n", - " ],\n", - " buffer_pixels=2,\n", - " )\n", - "\n", - " cv2.rectangle(\n", - " resized_image,\n", - " (x_top_left, y_top_left),\n", - " (x_bottom_right, y_bottom_right),\n", - " (255, 255, 0),\n", - " 1,\n", - " )\n", - "\n", - " # Draw the diagonal line of the selected region from top-left to bottom-right\n", - " cv2.line(\n", - " resized_image,\n", - " (x_top_left, y_top_left),\n", - " (x_bottom_right, y_bottom_right),\n", - " (0, 255, 0),\n", - " 1,\n", - " )\n", - " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", - " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", - " # Top-right region is where time labels are located\n", - " # Bottom-left region is where numerical values for mmHg and bpm are located\n", - " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", - " b = y_top_left - m * x_top_left\n", - "\n", - " # List of bounding boxes in the top-right and bottom-left regions\n", - " bounding_boxes_time = []\n", - " bounding_boxes_numbers = []\n", - "\n", - " # Process the bounding boxes\n", - " for bounding_box in sheet_data:\n", - " # Bounding boxes are in YOLO format; convert them to pixels\n", - " x_min, y_min, x_max, y_max = list(\n", - " map(\n", - " int,\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " ).box,\n", - " )\n", - " )\n", - "\n", - " # Check if the bounding box is within the selected region\n", - " if (\n", - " x_min >= x_top_left\n", - " and y_min >= y_top_left\n", - " and x_max <= x_bottom_right\n", - " and y_max <= y_bottom_right\n", - " ):\n", - " # Calculate the center of the bounding box\n", - " x_center_bb = (x_min + x_max) / 2\n", - " y_center_bb = (y_min + y_max) / 2\n", - "\n", - " # If we want to generalize this function we can add the option to disregard the diagonal line\n", - "\n", - " # Determine if the bounding box center is in the top-right region\n", - " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", - " # Bounding box is in the top-right region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", - " )\n", - " bounding_boxes_numbers.append(\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " )\n", - " )\n", - " else:\n", - " # Bounding box is in the bottom-left region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", - " )\n", - " bounding_boxes_time.append(\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " )\n", - " )\n", - "\n", - " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", - " # You can also manually quit out with ESC key.\n", - " cv2.destroyAllWindows()\n", - "\n", - " # If we are showing the images, display the image with the selected region and bounding boxes\n", - " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", - " if show_images:\n", - " # Display the image with the selected region and bounding boxes\n", - " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", - " resized_image = Image.fromarray(resized_image)\n", - " resized_image.show()\n", - "\n", - " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", - " return (bounding_boxes_time, bounding_boxes_numbers)" + "# def get_bp_section_coordinates(\n", + "# image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", + "# ) -> List[int]:\n", + "# \"\"\"Crops the blood pressure section out of an image of a chart.\n", + "\n", + "# Args:\n", + "# image_height (int):\n", + "# The height of the image in pixels.\n", + "# bboxes (List[BoundingBox]):\n", + "# List of BoundingBoxes within this image.\n", + "# buffer_pixels (int):\n", + "# An optional integer that specifies the number of pixels around the digit detections to\n", + "# 'zoom out' by. Defaults to 5 pixels.\n", + "\n", + "# Returns:\n", + "# Coordinates of the bounding box that contains the blood pressure section.\n", + "# \"\"\"\n", + "# # Get bounding boxes from detections and filter non bounding boxes out.\n", + "# bboxes: List[BoundingBox] = list(\n", + "# filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", + "# )\n", + "\n", + "# digit_categories: List[str] = [str(i) for i in range(10)]\n", + "\n", + "# # Filter bounding boxes to those which are within the approximate region and are digits.\n", + "# bp_legend_digits: List[BoundingBox] = list(\n", + "# filter(\n", + "# lambda bb: all(\n", + "# [\n", + "# bb.top / image_height > 0.2,\n", + "# bb.top / image_height < 0.8,\n", + "# bb.category in digit_categories,\n", + "# ]\n", + "# ),\n", + "# bboxes,\n", + "# )\n", + "# )\n", + "# bp_legend_coordinates: List[int] = list(\n", + "# map(\n", + "# int,\n", + "# [\n", + "# min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", + "# min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", + "# max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", + "# max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", + "# ],\n", + "# )\n", + "# )\n", + "# return bp_legend_coordinates\n", + "\n", + "\n", + "# def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", + "# \"\"\"\n", + "# Determine if a point is above or below the diagonal line y = mx + b.\n", + "# For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", + "\n", + "# Args:\n", + "# x_center: float, x coordinate of the point\n", + "# y_center: float, y coordinate of the point\n", + "# m: float, slope of the diagonal line\n", + "# b: float, intercept of the diagonal line\n", + "\n", + "# Returns:\n", + "# bool, True if the point is above the line, False otherwise\n", + "# \"\"\"\n", + "# # Calculate the y value on the line for the given x_center\n", + "# y_line = m * x_center + b\n", + "# return y_center > y_line\n", + "\n", + "\n", + "# def select_relevant_bounding_boxes(\n", + "# sheet_data: List[str],\n", + "# path_to_image: Path,\n", + "# show_images: bool = False,\n", + "# desired_img_width: int = 800,\n", + "# desired_img_height: int = 600,\n", + "# ) -> Tuple[List[str], List[str]]:\n", + "# \"\"\"\n", + "# Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", + "# Identify bounding boxes that are within the selected region and draw rectangles around them.\n", + "# Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", + "\n", + "# Args:\n", + "# sheet_data: List of bounding boxes in YOLO format.\n", + "# path_to_image: Path to the image file.\n", + "\n", + "# Returns:\n", + "# Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", + "# The first list contains bounding boxes in the top-right region -- representing time labels.\n", + "# The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", + "# (bounding_boxes_time, bounding_boxes_numbers)\n", + "# \"\"\"\n", + "\n", + "# # Load the image\n", + "# image = cv2.imread(path_to_image)\n", + "\n", + "# # Display the image and allow the user to select a ROI\n", + "# resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", + "\n", + "# x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", + "# image_height=desired_img_height,\n", + "# bboxes=[\n", + "# BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", + "# for yolo_bb in sheet_data\n", + "# ],\n", + "# buffer_pixels=2,\n", + "# )\n", + "\n", + "# cv2.rectangle(\n", + "# resized_image,\n", + "# (x_top_left, y_top_left),\n", + "# (x_bottom_right, y_bottom_right),\n", + "# (255, 255, 0),\n", + "# 1,\n", + "# )\n", + "\n", + "# # Draw the diagonal line of the selected region from top-left to bottom-right\n", + "# cv2.line(\n", + "# resized_image,\n", + "# (x_top_left, y_top_left),\n", + "# (x_bottom_right, y_bottom_right),\n", + "# (0, 255, 0),\n", + "# 1,\n", + "# )\n", + "# # Calculate the slope (m) and intercept (b) of the diagonal line.\n", + "# # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", + "# # Top-right region is where time labels are located\n", + "# # Bottom-left region is where numerical values for mmHg and bpm are located\n", + "# m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", + "# b = y_top_left - m * x_top_left\n", + "\n", + "# # List of bounding boxes in the top-right and bottom-left regions\n", + "# bounding_boxes_time = []\n", + "# bounding_boxes_numbers = []\n", + "\n", + "# # Process the bounding boxes\n", + "# for bounding_box in sheet_data:\n", + "# # Bounding boxes are in YOLO format; convert them to pixels\n", + "# x_min, y_min, x_max, y_max = list(\n", + "# map(\n", + "# int,\n", + "# BoundingBox.from_yolo(\n", + "# yolo_line=bounding_box,\n", + "# image_width=desired_img_width,\n", + "# image_height=desired_img_height,\n", + "# ).box,\n", + "# )\n", + "# )\n", + "\n", + "# # Check if the bounding box is within the selected region\n", + "# if (\n", + "# x_min >= x_top_left\n", + "# and y_min >= y_top_left\n", + "# and x_max <= x_bottom_right\n", + "# and y_max <= y_bottom_right\n", + "# ):\n", + "# # Calculate the center of the bounding box\n", + "# x_center_bb = (x_min + x_max) / 2\n", + "# y_center_bb = (y_min + y_max) / 2\n", + "\n", + "# # If we want to generalize this function we can add the option to disregard the diagonal line\n", + "\n", + "# # Determine if the bounding box center is in the top-right region\n", + "# if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", + "# # Bounding box is in the top-right region\n", + "# cv2.rectangle(\n", + "# resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", + "# )\n", + "# bounding_boxes_numbers.append(\n", + "# BoundingBox.from_yolo(\n", + "# yolo_line=bounding_box,\n", + "# image_width=desired_img_width,\n", + "# image_height=desired_img_height,\n", + "# )\n", + "# )\n", + "# else:\n", + "# # Bounding box is in the bottom-left region\n", + "# cv2.rectangle(\n", + "# resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", + "# )\n", + "# bounding_boxes_time.append(\n", + "# BoundingBox.from_yolo(\n", + "# yolo_line=bounding_box,\n", + "# image_width=desired_img_width,\n", + "# image_height=desired_img_height,\n", + "# )\n", + "# )\n", + "\n", + "# # Close all OpenCV windows, always do this or it will annoyingly not go away\n", + "# # You can also manually quit out with ESC key.\n", + "# cv2.destroyAllWindows()\n", + "\n", + "# # If we are showing the images, display the image with the selected region and bounding boxes\n", + "# # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", + "# if show_images:\n", + "# # Display the image with the selected region and bounding boxes\n", + "# resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", + "# resized_image = Image.fromarray(resized_image)\n", + "# resized_image.show()\n", + "\n", + "# # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", + "# return (bounding_boxes_time, bounding_boxes_numbers)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -679,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -687,119 +687,96 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\time\\\\RC_0001_intraoperative.JPG'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[8], line 133\u001b[0m\n\u001b[0;32m 124\u001b[0m json\u001b[38;5;241m.\u001b[39mdump(\n\u001b[0;32m 125\u001b[0m create_result_dictionary(\n\u001b[0;32m 126\u001b[0m number_labels, number_bounding_boxes, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmmHg\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 127\u001b[0m ),\n\u001b[0;32m 128\u001b[0m f,\n\u001b[0;32m 129\u001b[0m )\n\u001b[0;32m 132\u001b[0m \u001b[38;5;66;03m# Test the clustering methods\u001b[39;00m\n\u001b[1;32m--> 133\u001b[0m \u001b[43mtest_clustering_methods\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[8], line 72\u001b[0m, in \u001b[0;36mtest_clustering_methods\u001b[1;34m()\u001b[0m\n\u001b[0;32m 60\u001b[0m draw\u001b[38;5;241m.\u001b[39mrectangle(\n\u001b[0;32m 61\u001b[0m [\n\u001b[0;32m 62\u001b[0m x_min,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 68\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m 69\u001b[0m )\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# Save the image with the bounding boxes to the kmeans_clustered_images folder\u001b[39;00m\n\u001b[1;32m---> 72\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../../data/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmethod\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_clustered_images/time/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msheet\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 74\u001b[0m \u001b[38;5;66;03m# Save the clustered bounding boxes to a JSON file\u001b[39;00m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 76\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../data/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_clustered_images/results/time/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 77\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 78\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m f:\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2600\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2598\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 2599\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2600\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2601\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\time\\\\RC_0001_intraoperative.JPG'" ] } ], @@ -1118,7 +1095,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ChartExtractor", + "display_name": ".venv", "language": "python", "name": "python3" }, From ae22aa5ba157ec12f4b96e3b7161ec636fd33319 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Mon, 28 Oct 2024 22:42:11 -0400 Subject: [PATCH 24/55] Added 5% erroneous test with ROI bounds --- experiments/clustering/clustering.ipynb | 176 +++++++++++++++--- .../clustering/clustering_erroneous.ipynb | 40 ++-- 2 files changed, 174 insertions(+), 42 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 7d413a8..7ba60e3 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -50,6 +50,7 @@ "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", "from scipy.stats import gaussian_kde\n", + "import random\n", "\n", "# Local libraries\n", "from utils.annotations import BoundingBox" @@ -71,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -317,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -612,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -661,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -670,6 +671,93 @@ " return \"#%06x\" % random.randint(0, 0xFFFFFF)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to generate random Bounding Box formatted occurances." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def random_time_generate(x):\n", + " # erroneous bounding boxes for time ROI\n", + " category_int = random.randint(0, 9)\n", + " left_int = random.randint(105, 720)\n", + "\n", + " # slope and random points ABOVE the line (but coordinates flipped)\n", + " slope, intercept = 1/3, 225\n", + " top_int = random.uniform(225, slope*left_int + intercept)\n", + "\n", + " # input generated integers to bounding box \n", + " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " return box\n", + "\n", + "def random_number_generate(x):\n", + " # erroneous bounding boxes for number ROI\n", + " category_int = random.randint(0, 9)\n", + " left_int = random.randint(105, 720)\n", + " \n", + " # slope and random points BELOW the line (but coordinates flipped)\n", + " slope, intercept = 1/3, 225\n", + " top_int = random.uniform(slope*left_int + intercept, 430)\n", + "\n", + " # input generated integers to bounding box \n", + " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " return box" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to remove 5% bounding boxes and create 5% erroneous." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def erroneous_bounding_boxes(time_BB: List[str], number_BB: List[str]) -> Tuple[List[str], List[str]]:\n", + " \"\"\"\n", + " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", + "\n", + " Args:\n", + " time_BB: list of time bounding boxes in BoundingBox format\n", + " number_BB: list of number bounding boxes in BoundingBox format\n", + "\n", + " Returns:\n", + " Tuple: lists of time and number bounding boxes in BoundingBox format\n", + " \"\"\"\n", + " # make copies of input bounding box lists to avoid unwanted manipulation\n", + " time_BB_copy = time_BB.copy()\n", + " number_BB_copy = number_BB.copy()\n", + " \n", + " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", + " ## sample\n", + " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", + " number_BB_sample = list(random.sample(number_BB_copy, 4))\n", + " ## remove\n", + " time_BB_removal = [time_BB_copy.remove(line) for line in time_BB_sample]\n", + " number_BB_removal = [number_BB_copy.remove(line) for line in number_BB_sample]\n", + "\n", + " # use random bounding box generation to refill removed BBs with erroneous boxes\n", + " time_BB_generate = list(map(random_time_generate, range(len(time_BB_sample))))\n", + " number_BB_generate = list(map(random_number_generate, range(len(number_BB_sample))))\n", + "\n", + " # append BB generated list back to copy with 5% removal\n", + " time_BB_erroneous = time_BB_copy + time_BB_generate\n", + " number_BB_erroneous = number_BB_copy + number_BB_generate\n", + "\n", + " return (time_BB_erroneous, number_BB_erroneous)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -679,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -763,20 +851,43 @@ "Sheet: RC_0019_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n" - ] - }, - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\time\\\\RC_0001_intraoperative.JPG'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 133\u001b[0m\n\u001b[0;32m 124\u001b[0m json\u001b[38;5;241m.\u001b[39mdump(\n\u001b[0;32m 125\u001b[0m create_result_dictionary(\n\u001b[0;32m 126\u001b[0m number_labels, number_bounding_boxes, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmmHg\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 127\u001b[0m ),\n\u001b[0;32m 128\u001b[0m f,\n\u001b[0;32m 129\u001b[0m )\n\u001b[0;32m 132\u001b[0m \u001b[38;5;66;03m# Test the clustering methods\u001b[39;00m\n\u001b[1;32m--> 133\u001b[0m \u001b[43mtest_clustering_methods\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[8], line 72\u001b[0m, in \u001b[0;36mtest_clustering_methods\u001b[1;34m()\u001b[0m\n\u001b[0;32m 60\u001b[0m draw\u001b[38;5;241m.\u001b[39mrectangle(\n\u001b[0;32m 61\u001b[0m [\n\u001b[0;32m 62\u001b[0m x_min,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 68\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m 69\u001b[0m )\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# Save the image with the bounding boxes to the kmeans_clustered_images folder\u001b[39;00m\n\u001b[1;32m---> 72\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../../data/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmethod\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_clustered_images/time/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msheet\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 74\u001b[0m \u001b[38;5;66;03m# Save the clustered bounding boxes to a JSON file\u001b[39;00m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 76\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../data/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_clustered_images/results/time/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.json\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 77\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 78\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m f:\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\PIL\\Image.py:2600\u001b[0m, in \u001b[0;36mImage.save\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m 2598\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 2599\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2600\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2601\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 2602\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\time\\\\RC_0001_intraoperative.JPG'" + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Sheet: RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Sheet: RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Sheet: RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Sheet: RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Sheet: RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Sheet: RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Sheet: RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Sheet: RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Sheet: RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Sheet: RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Sheet: RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Sheet: RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Sheet: RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Sheet: RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Sheet: RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Sheet: RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Sheet: RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Sheet: RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" ] } ], @@ -801,6 +912,11 @@ " yolo_bbs, full_image_path\n", " )\n", "\n", + " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", + " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", + " time_bounding_boxes, number_bounding_boxes\n", + " )\n", + "\n", " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", " if method == \"kmeans\":\n", " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", @@ -927,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -935,14 +1051,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Time labels: 652 correct clusters, 138 incorrect clusters. The accuracy is 82.53%\n", + "Number labels: 196 correct clusters, 179 incorrect clusters. The accuracy is 52.27%\n", "Method: dbscan\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", + "Time labels: 728 correct clusters, 136 incorrect clusters. The accuracy is 84.26%\n", + "Number labels: 308 correct clusters, 56 incorrect clusters. The accuracy is 84.62%\n", "Method: agglomerative\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. The accuracy is 100.00%\n" + "Time labels: 645 correct clusters, 149 incorrect clusters. The accuracy is 81.23%\n", + "Number labels: 185 correct clusters, 188 incorrect clusters. The accuracy is 49.60%\n" ] } ], @@ -1095,7 +1211,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1109,7 +1225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.9.5" } }, "nbformat": 4, diff --git a/experiments/clustering/clustering_erroneous.ipynb b/experiments/clustering/clustering_erroneous.ipynb index f3aa153..683bf12 100644 --- a/experiments/clustering/clustering_erroneous.ipynb +++ b/experiments/clustering/clustering_erroneous.ipynb @@ -559,11 +559,27 @@ "metadata": {}, "outputs": [], "source": [ - "def random_BB_generate(x):\n", - " # function paired with map to generate erroneous bounding boxes\n", + "def random_time_generate(x):\n", + " # erroneous bounding boxes for time ROI\n", " category_int = random.randint(0, 9)\n", - " left_int = random.randint(20, 781)\n", - " top_int = random.randint(20, 581)\n", + " left_int = random.randint(105, 720)\n", + "\n", + " # slope and random points ABOVE the line (but coordinates flipped)\n", + " slope, intercept = 1/3, 225\n", + " top_int = random.uniform(225, slope*left_int + intercept)\n", + "\n", + " # input generated integers to bounding box \n", + " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " return box\n", + "\n", + "def random_number_generate(x):\n", + " # erroneous bounding boxes for number ROI\n", + " category_int = random.randint(0, 9)\n", + " left_int = random.randint(105, 720)\n", + " \n", + " # slope and random points BELOW the line (but coordinates flipped)\n", + " slope, intercept = 1/3, 225\n", + " top_int = random.uniform(slope*left_int + intercept, 430)\n", "\n", " # input generated integers to bounding box \n", " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", @@ -607,8 +623,8 @@ " number_BB_removal = [number_BB_copy.remove(line) for line in number_BB_sample]\n", "\n", " # use random bounding box generation to refill removed BBs with erroneous boxes\n", - " time_BB_generate = list(map(random_BB_generate, range(len(time_BB_sample))))\n", - " number_BB_generate = list(map(random_BB_generate, range(len(number_BB_sample))))\n", + " time_BB_generate = list(map(random_time_generate, range(len(time_BB_sample))))\n", + " number_BB_generate = list(map(random_number_generate, range(len(number_BB_sample))))\n", "\n", " # append BB generated list back to copy with 5% removal\n", " time_BB_erroneous = time_BB_copy + time_BB_generate\n", @@ -910,14 +926,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 645 correct clusters, 144 incorrect clusters. The accuracy is 81.75%\n", - "Number labels: 189 correct clusters, 187 incorrect clusters. The accuracy is 50.27%\n", + "Time labels: 657 correct clusters, 138 incorrect clusters. The accuracy is 82.64%\n", + "Number labels: 190 correct clusters, 181 incorrect clusters. The accuracy is 51.21%\n", "Method: dbscan\n", - "Time labels: 730 correct clusters, 132 incorrect clusters. The accuracy is 84.69%\n", - "Number labels: 310 correct clusters, 53 incorrect clusters. The accuracy is 85.40%\n", + "Time labels: 728 correct clusters, 129 incorrect clusters. The accuracy is 84.95%\n", + "Number labels: 307 correct clusters, 55 incorrect clusters. The accuracy is 84.81%\n", "Method: agglomerative\n", - "Time labels: 634 correct clusters, 158 incorrect clusters. The accuracy is 80.05%\n", - "Number labels: 186 correct clusters, 190 incorrect clusters. The accuracy is 49.47%\n" + "Time labels: 645 correct clusters, 152 incorrect clusters. The accuracy is 80.93%\n", + "Number labels: 177 correct clusters, 195 incorrect clusters. The accuracy is 47.58%\n" ] } ], From eedd5865cb406b7db89616f8b6aafddb185b9a00 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 28 Oct 2024 22:56:03 -0400 Subject: [PATCH 25/55] Formatted files, ready for PR --- experiments/clustering/clustering.ipynb | 127 +- .../clustering/clustering_erroneous.ipynb | 1101 ----------------- .../apply_homography_to_labels.ipynb | 143 ++- 3 files changed, 157 insertions(+), 1214 deletions(-) delete mode 100644 experiments/clustering/clustering_erroneous.ipynb diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 7ba60e3..bb68e9e 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -8,8 +8,7 @@ "\n", "We have put all of our methods together for direct comparison in a single notebook.\n", "\n", - "Problem: Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers.\n", - "\n" + "Problem: Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers.\n" ] }, { @@ -32,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -318,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -340,9 +339,10 @@ "\n", " kde_vals = kde(x_values)\n", "\n", - " max_index=np.argmax(kde_vals)\n", + " max_index = np.argmax(kde_vals)\n", " return x_values[max_index]\n", "\n", + "\n", "def select_relevant_bounding_boxes(\n", " sheet_data: List[str],\n", " path_to_image: Path,\n", @@ -375,18 +375,15 @@ " bboxes: List[BoundingBox] = [\n", " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", " for yolo_bb in sheet_data\n", - " ]\n", + " ]\n", "\n", " # generate a list of the digit categories\n", " digit_categories: List[str] = [str(i) for i in range(10)]\n", - " \n", + "\n", " # filter out non bounding boxes and those whose category is not a digit\n", " bboxes: List[BoundingBox] = list(\n", " filter(\n", - " lambda bb:\n", - " isinstance(bb, BoundingBox) and\n", - " bb.category in digit_categories\n", - " ,\n", + " lambda bb: isinstance(bb, BoundingBox) and bb.category in digit_categories,\n", " bboxes,\n", " )\n", " )\n", @@ -408,27 +405,23 @@ " x_max = int(bounding_box.right)\n", " y_min = int(bounding_box.top)\n", " y_max = int(bounding_box.bottom)\n", - " \n", + "\n", " # get the center point of the bounding box for comparison\n", " x_center_bb, y_center_bb = bounding_box.center\n", "\n", " # check if the bounding box is a number on the BP chart by comparing to the KDE index + a threshold\n", - " if (x_center_bb > numbers_loc-15 and x_center_bb < numbers_loc+2):\n", + " if x_center_bb > numbers_loc - 15 and x_center_bb < numbers_loc + 2:\n", " # Bounding box is in the top-right region\n", " cv2.rectangle(\n", " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", " )\n", - " bounding_boxes_numbers.append(\n", - " bounding_box\n", - " )\n", + " bounding_boxes_numbers.append(bounding_box)\n", " # check if the bounding box is a time on the BP chart by comparing to the KDE index + a threshold\n", - " elif (y_center_bb > time_loc-10 and y_max < time_loc+2):\n", + " elif y_center_bb > time_loc - 10 and y_max < time_loc + 2:\n", " cv2.rectangle(\n", " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", " )\n", - " bounding_boxes_time.append(\n", - " bounding_box\n", - " )\n", + " bounding_boxes_time.append(bounding_box)\n", " # plot the lines of the KDE index found for debugging\n", " # numbers_start = (int(numbers_loc), 0)\n", " # numbers_end = (int(numbers_loc), desired_img_height)\n", @@ -464,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -558,8 +551,10 @@ "\n", " return labels\n", "\n", - "def agglomerative_clustering(bounding_boxes: List[BoundingBox], possible_nclusters: List[int]) -> List[int]:\n", - " \n", + "\n", + "def agglomerative_clustering(\n", + " bounding_boxes: List[BoundingBox], possible_nclusters: List[int]\n", + ") -> List[int]:\n", " # make the bonding box data into a Numpy array\n", " data = np.array([box.center for box in bounding_boxes])\n", "\n", @@ -573,7 +568,7 @@ " if number_of_clusters < 1:\n", " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", " # use agglomerative clustering\n", - " agg = AgglomerativeClustering(n_clusters=number_of_clusters, linkage='single')\n", + " agg = AgglomerativeClustering(n_clusters=number_of_clusters, linkage=\"single\")\n", " # get labels\n", " labels = agg.fit_predict(data)\n", " # compute the silhoutte scores\n", @@ -589,7 +584,6 @@ " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", " )\n", "\n", - "\n", " best_n_clusters = (\n", " n_clusters_max_silhouette\n", " if (\n", @@ -613,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -662,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -675,12 +669,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Function to generate random Bounding Box formatted occurances." + "Function to generate random Bounding Box formatted occurances.\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -690,24 +684,37 @@ " left_int = random.randint(105, 720)\n", "\n", " # slope and random points ABOVE the line (but coordinates flipped)\n", - " slope, intercept = 1/3, 225\n", - " top_int = random.uniform(225, slope*left_int + intercept)\n", - "\n", - " # input generated integers to bounding box \n", - " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " slope, intercept = 1 / 3, 225\n", + " top_int = random.uniform(225, slope * left_int + intercept)\n", + "\n", + " # input generated integers to bounding box\n", + " box = BoundingBox(\n", + " category=f\"{category_int}\",\n", + " left=left_int,\n", + " top=top_int,\n", + " right=left_int + 4,\n", + " bottom=top_int + 6,\n", + " )\n", " return box\n", "\n", + "\n", "def random_number_generate(x):\n", " # erroneous bounding boxes for number ROI\n", " category_int = random.randint(0, 9)\n", " left_int = random.randint(105, 720)\n", - " \n", - " # slope and random points BELOW the line (but coordinates flipped)\n", - " slope, intercept = 1/3, 225\n", - " top_int = random.uniform(slope*left_int + intercept, 430)\n", "\n", - " # input generated integers to bounding box \n", - " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", + " # slope and random points BELOW the line (but coordinates flipped)\n", + " slope, intercept = 1 / 3, 225\n", + " top_int = random.uniform(slope * left_int + intercept, 430)\n", + "\n", + " # input generated integers to bounding box\n", + " box = BoundingBox(\n", + " category=f\"{category_int}\",\n", + " left=left_int,\n", + " top=top_int,\n", + " right=left_int + 4,\n", + " bottom=top_int + 6,\n", + " )\n", " return box" ] }, @@ -715,16 +722,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Function to remove 5% bounding boxes and create 5% erroneous." + "Function to remove 5% bounding boxes and create 5% erroneous.\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "def erroneous_bounding_boxes(time_BB: List[str], number_BB: List[str]) -> Tuple[List[str], List[str]]:\n", + "def erroneous_bounding_boxes(\n", + " time_BB: List[str], number_BB: List[str]\n", + ") -> Tuple[List[str], List[str]]:\n", " \"\"\"\n", " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", "\n", @@ -738,7 +747,7 @@ " # make copies of input bounding box lists to avoid unwanted manipulation\n", " time_BB_copy = time_BB.copy()\n", " number_BB_copy = number_BB.copy()\n", - " \n", + "\n", " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", " ## sample\n", " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", @@ -767,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -929,8 +938,12 @@ " number_bounding_boxes, defined_eps=5, min_samples=2\n", " )\n", " elif method == \"agglomerative\":\n", - " time_labels = agglomerative_clustering(time_bounding_boxes, [40,41,42])\n", - " number_labels = agglomerative_clustering(number_bounding_boxes, [18,19,20])\n", + " time_labels = agglomerative_clustering(\n", + " time_bounding_boxes, [40, 41, 42]\n", + " )\n", + " number_labels = agglomerative_clustering(\n", + " number_bounding_boxes, [18, 19, 20]\n", + " )\n", " else:\n", " raise ValueError(f\"Invalid clustering method: {method}\")\n", "\n", @@ -1043,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1051,14 +1064,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 652 correct clusters, 138 incorrect clusters. The accuracy is 82.53%\n", - "Number labels: 196 correct clusters, 179 incorrect clusters. The accuracy is 52.27%\n", + "Time labels: 653 correct clusters, 137 incorrect clusters. The accuracy is 82.66%\n", + "Number labels: 192 correct clusters, 183 incorrect clusters. The accuracy is 51.20%\n", "Method: dbscan\n", - "Time labels: 728 correct clusters, 136 incorrect clusters. The accuracy is 84.26%\n", - "Number labels: 308 correct clusters, 56 incorrect clusters. The accuracy is 84.62%\n", + "Time labels: 729 correct clusters, 127 incorrect clusters. The accuracy is 85.16%\n", + "Number labels: 306 correct clusters, 51 incorrect clusters. The accuracy is 85.71%\n", "Method: agglomerative\n", - "Time labels: 645 correct clusters, 149 incorrect clusters. The accuracy is 81.23%\n", - "Number labels: 185 correct clusters, 188 incorrect clusters. The accuracy is 49.60%\n" + "Time labels: 654 correct clusters, 139 incorrect clusters. The accuracy is 82.47%\n", + "Number labels: 186 correct clusters, 189 incorrect clusters. The accuracy is 49.60%\n" ] } ], @@ -1211,7 +1224,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1225,7 +1238,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/experiments/clustering/clustering_erroneous.ipynb b/experiments/clustering/clustering_erroneous.ipynb deleted file mode 100644 index 683bf12..0000000 --- a/experiments/clustering/clustering_erroneous.ipynb +++ /dev/null @@ -1,1101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Clustering Experiment with Erroneous BB\n", - "\n", - "We have put all of our methods together for direct comparison in a single notebook.\n", - "\n", - "Problem: Currently we are determining only the indiviudal digits, but we need to recognize these as coherent numbers and be able to assign entries to numbers." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Changes from `clustering.ipynb`**\n", - "* Added `random_BB_generate` and `erroneous_bounding_boxes` functions.\n", - "\n", - "* `erroneous_bounding_boxes` is implemented within `test_clustering_methods`.\n", - "\n", - "* The two lists, time_bounding_boxes & number_bounding_boxes have 5% bounding boxes removed and 5% erroneous added.\n", - "\n", - "* By commenting out the `erroneous_bounding_boxes` within `test_clustering_methods` the notebook can be run with no erroneous boxes as before.\n", - "\n", - "* Also imported random library" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register Images to Start\n", - "\n", - "To start, we need to register images using the `utilities/conversion/apply_homography_to_labels.ipynb` notebook. This should be run before running this notebook. This notebook is built on the assumption that the `data/registered_images` directory has been created and populated. Additionally it assumes that the `data/yolo_data.json` file is created. Both of these are created in the referenced notebook.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Install Packages\n", - "\n", - "These are the necessary packages to run the functions and scripts below.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Standard libraries\n", - "import os\n", - "import json\n", - "import random\n", - "from pathlib import Path\n", - "from typing import List, Tuple, Literal, Dict\n", - "\n", - "# Third-party libraries\n", - "import cv2\n", - "import numpy as np\n", - "from PIL import Image, ImageDraw\n", - "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", - "from sklearn.metrics import silhouette_score\n", - "import random\n", - "\n", - "# Local libraries\n", - "from utils.annotations import BoundingBox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Start By Loading YOLO Data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To start I want to bring in the YOLO formatted data for each sheet and I can additionally load the respective images. As mentioned above you must have ran the `utilities/conversion/apply_homography_to_labels.ipynb` notebook to generate this YOLO data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 19 sheets in yolo_data.json\n" - ] - } - ], - "source": [ - "# Load yolo_data.json\n", - "PATH_TO_YOLO_DATA = \"../../data/yolo_data.json\"\n", - "PATH_TO_REGISTERED_IMAGES = \"../../data/registered_images\"\n", - "UNIFIED_IMAGE_PATH = (\n", - " \"../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png\"\n", - ")\n", - "with open(PATH_TO_YOLO_DATA) as json_file:\n", - " yolo_data = json.load(json_file)\n", - "\n", - "# See how many intraoperative images are registered\n", - "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's select relevant bounding boxes from the blood pressure and HR zone.\n", - "\n", - "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def get_bp_section_coordinates(\n", - " image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", - ") -> List[int]:\n", - " \"\"\"Crops the blood pressure section out of an image of a chart.\n", - "\n", - " Args:\n", - " image_height (int):\n", - " The height of the image in pixels.\n", - " bboxes (List[BoundingBox]):\n", - " List of BoundingBoxes within this image.\n", - " buffer_pixels (int):\n", - " An optional integer that specifies the number of pixels around the digit detections to\n", - " 'zoom out' by. Defaults to 5 pixels.\n", - "\n", - " Returns:\n", - " Coordinates of the bounding box that contains the blood pressure section.\n", - " \"\"\"\n", - " # Get bounding boxes from detections and filter non bounding boxes out.\n", - " bboxes: List[BoundingBox] = list(\n", - " filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", - " )\n", - "\n", - " digit_categories: List[str] = [str(i) for i in range(10)]\n", - "\n", - " # Filter bounding boxes to those which are within the approximate region and are digits.\n", - " bp_legend_digits: List[BoundingBox] = list(\n", - " filter(\n", - " lambda bb: all(\n", - " [\n", - " bb.top / image_height > 0.2,\n", - " bb.top / image_height < 0.8,\n", - " bb.category in digit_categories,\n", - " ]\n", - " ),\n", - " bboxes,\n", - " )\n", - " )\n", - " bp_legend_coordinates: List[int] = list(\n", - " map(\n", - " int,\n", - " [\n", - " min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", - " min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", - " max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", - " max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", - " ],\n", - " )\n", - " )\n", - " return bp_legend_coordinates\n", - "\n", - "\n", - "def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", - " \"\"\"\n", - " Determine if a point is above or below the diagonal line y = mx + b.\n", - " For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", - "\n", - " Args:\n", - " x_center: float, x coordinate of the point\n", - " y_center: float, y coordinate of the point\n", - " m: float, slope of the diagonal line\n", - " b: float, intercept of the diagonal line\n", - "\n", - " Returns:\n", - " bool, True if the point is above the line, False otherwise\n", - " \"\"\"\n", - " # Calculate the y value on the line for the given x_center\n", - " y_line = m * x_center + b\n", - " return y_center > y_line\n", - "\n", - "\n", - "def select_relevant_bounding_boxes(\n", - " sheet_data: List[str],\n", - " path_to_image: Path,\n", - " show_images: bool = False,\n", - " desired_img_width: int = 800,\n", - " desired_img_height: int = 600,\n", - ") -> Tuple[List[str], List[str]]:\n", - " \"\"\"\n", - " Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - " Identify bounding boxes that are within the selected region and draw rectangles around them.\n", - " Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", - "\n", - " Args:\n", - " sheet_data: List of bounding boxes in YOLO format.\n", - " path_to_image: Path to the image file.\n", - "\n", - " Returns:\n", - " Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", - " The first list contains bounding boxes in the top-right region -- representing time labels.\n", - " The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", - " (bounding_boxes_time, bounding_boxes_numbers)\n", - " \"\"\"\n", - "\n", - " # Load the image\n", - " image = cv2.imread(path_to_image)\n", - "\n", - " # Display the image and allow the user to select a ROI\n", - " resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", - "\n", - " x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", - " image_height=desired_img_height,\n", - " bboxes=[\n", - " BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", - " for yolo_bb in sheet_data\n", - " ],\n", - " buffer_pixels=2,\n", - " )\n", - "\n", - " cv2.rectangle(\n", - " resized_image,\n", - " (x_top_left, y_top_left),\n", - " (x_bottom_right, y_bottom_right),\n", - " (255, 255, 0),\n", - " 1,\n", - " )\n", - "\n", - " # Draw the diagonal line of the selected region from top-left to bottom-right\n", - " cv2.line(\n", - " resized_image,\n", - " (x_top_left, y_top_left),\n", - " (x_bottom_right, y_bottom_right),\n", - " (0, 255, 0),\n", - " 1,\n", - " )\n", - " # Calculate the slope (m) and intercept (b) of the diagonal line.\n", - " # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", - " # Top-right region is where time labels are located\n", - " # Bottom-left region is where numerical values for mmHg and bpm are located\n", - " m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", - " b = y_top_left - m * x_top_left\n", - "\n", - " # List of bounding boxes in the top-right and bottom-left regions\n", - " bounding_boxes_time = []\n", - " bounding_boxes_numbers = []\n", - "\n", - " # Process the bounding boxes\n", - " for bounding_box in sheet_data:\n", - " # Bounding boxes are in YOLO format; convert them to pixels\n", - " x_min, y_min, x_max, y_max = list(\n", - " map(\n", - " int,\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " ).box,\n", - " )\n", - " )\n", - "\n", - " # Check if the bounding box is within the selected region\n", - " if (\n", - " x_min >= x_top_left\n", - " and y_min >= y_top_left\n", - " and x_max <= x_bottom_right\n", - " and y_max <= y_bottom_right\n", - " ):\n", - " # Calculate the center of the bounding box\n", - " x_center_bb = (x_min + x_max) / 2\n", - " y_center_bb = (y_min + y_max) / 2\n", - "\n", - " # If we want to generalize this function we can add the option to disregard the diagonal line\n", - "\n", - " # Determine if the bounding box center is in the top-right region\n", - " if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", - " # Bounding box is in the top-right region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", - " )\n", - " bounding_boxes_numbers.append(\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " )\n", - " )\n", - " else:\n", - " # Bounding box is in the bottom-left region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", - " )\n", - " bounding_boxes_time.append(\n", - " BoundingBox.from_yolo(\n", - " yolo_line=bounding_box,\n", - " image_width=desired_img_width,\n", - " image_height=desired_img_height,\n", - " )\n", - " )\n", - "\n", - " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", - " # You can also manually quit out with ESC key.\n", - " cv2.destroyAllWindows()\n", - "\n", - " # If we are showing the images, display the image with the selected region and bounding boxes\n", - " # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", - " if show_images:\n", - " # Display the image with the selected region and bounding boxes\n", - " resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", - " resized_image = Image.fromarray(resized_image)\n", - " resized_image.show()\n", - "\n", - " # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", - " return (bounding_boxes_time, bounding_boxes_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a function for K-means clustering, dbscan clustering\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def cluster_kmeans(\n", - " bounding_boxes: List[BoundingBox], possible_nclusters: List[int]\n", - ") -> List[int]:\n", - " \"\"\"\n", - " Cluster bounding boxes using K-Means clustering algorithm.\n", - "\n", - " Args:\n", - " bounding_boxes: List of bounding boxes in YOLO format.\n", - " possible_nclusters: List of possible number of clusters to try.\n", - "\n", - " Returns:\n", - " List of cluster labels.\n", - " \"\"\"\n", - " # Convert to a NumPy array (using only x_center and y_center)\n", - " data = np.array([box.center for box in bounding_boxes])\n", - "\n", - " cluster_performance_map = {}\n", - " for number_of_clusters in possible_nclusters:\n", - " if number_of_clusters > len(data):\n", - " raise (\n", - " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", - " )\n", - " if number_of_clusters < 1:\n", - " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", - " # Apply K-Means\n", - " kmeans = KMeans(\n", - " n_clusters=number_of_clusters,\n", - " init=\"k-means++\",\n", - " n_init=20,\n", - " max_iter=500,\n", - " tol=1e-8,\n", - " random_state=42,\n", - " )\n", - " kmeans.fit(data)\n", - "\n", - " # Get cluster labels\n", - " labels = kmeans.predict(data)\n", - " silhouette_avg = silhouette_score(data, labels)\n", - "\n", - " # print(\n", - " # f\"Number of clusters: {number_of_clusters}, Silhouette score: {silhouette_avg}\"\n", - " # )\n", - "\n", - " cluster_performance_map[number_of_clusters] = {\n", - " \"score\": silhouette_avg,\n", - " \"labels\": labels,\n", - " }\n", - "\n", - " # Evaluate the performance of each number of clusters and select the one with the highest silhouette score\n", - " # if it is 0.003 greater than what should be the number of clusters otherwise go with proper_nclusters\n", - " n_clusters_max_silhouette = max(\n", - " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", - " )\n", - " best_n_clusters = (\n", - " n_clusters_max_silhouette\n", - " if (\n", - " (\n", - " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", - " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", - " )\n", - " >= 0.003\n", - " )\n", - " else max(possible_nclusters)\n", - " )\n", - " return cluster_performance_map[best_n_clusters][\"labels\"]\n", - "\n", - "\n", - "def dbscan_clustering(\n", - " bounding_boxes: List[BoundingBox], defined_eps: float, min_samples: int\n", - ") -> List[int]:\n", - " \"\"\"\n", - " Cluster bounding boxes density based spatial clustering algorithm.\n", - "\n", - " Args:\n", - " bounding_boxes: List of bounding boxes.\n", - " defined_eps: Maximum distance between two samples to be in the neighborhood of one another (center of BB).\n", - " min_samples: The number of samples (or total weight) for a point to be considered as core\n", - "\n", - " Returns:\n", - " List of cluster labels.\n", - " \"\"\"\n", - " # Convert to a NumPy array (using only x_center and y_center)\n", - " data = np.array([box.center for box in bounding_boxes])\n", - "\n", - " # DBSCAN\n", - " scan = DBSCAN(eps=defined_eps, min_samples=min_samples)\n", - " labels = scan.fit_predict(data)\n", - "\n", - " return labels\n", - "\n", - "def agglomerative_clustering(bounding_boxes: List[BoundingBox], possible_nclusters: List[int]) -> List[int]:\n", - " \n", - " # make the bonding box data into a Numpy array\n", - " data = np.array([box.center for box in bounding_boxes])\n", - "\n", - " # follow suit of the cluster_kmeans algorithm to measure accuracy through silhoutte scores\n", - " cluster_performance_map = {}\n", - " for number_of_clusters in possible_nclusters:\n", - " if number_of_clusters > len(data):\n", - " raise (\n", - " f\"Number of clusters {number_of_clusters} is greater than number of bounding boxes {len(data)}.\"\n", - " )\n", - " if number_of_clusters < 1:\n", - " raise (f\"Number of clusters {number_of_clusters} must be greater than 0.\")\n", - " # use agglomerative clustering\n", - " agg = AgglomerativeClustering(n_clusters=number_of_clusters, linkage='single')\n", - " # get labels\n", - " labels = agg.fit_predict(data)\n", - " # compute the silhoutte scores\n", - " silhouette_avg = silhouette_score(data, labels)\n", - "\n", - " cluster_performance_map[number_of_clusters] = {\n", - " \"score\": silhouette_avg,\n", - " \"labels\": labels,\n", - " }\n", - "\n", - " # get the number of clusters with the best silhoutte score\n", - " n_clusters_max_silhouette = max(\n", - " cluster_performance_map, key=lambda x: cluster_performance_map[x][\"score\"]\n", - " )\n", - "\n", - "\n", - " best_n_clusters = (\n", - " n_clusters_max_silhouette\n", - " if (\n", - " (\n", - " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", - " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", - " )\n", - " >= 0.003\n", - " )\n", - " else max(possible_nclusters)\n", - " )\n", - " return cluster_performance_map[best_n_clusters][\"labels\"]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function to create a result dictionary that we can save as a JSON file to analyze performance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def create_result_dictionary(\n", - " labels: List[str], bounding_boxes: List[BoundingBox], unit: Literal[\"mmHg\", \"mins\"]\n", - ") -> Dict[int, int]:\n", - " \"\"\"\n", - " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", - "\n", - " Args:\n", - " labels: List of cluster labels.\n", - " bounding_boxes: List of bounding boxes.\n", - " suffix: Suffix to append to the category of the bounding box. One of [\"mmHg\", \"mins\"].\n", - "\n", - " Returns:\n", - " Dictionary with cluster labels as keys and bounding box values as values.\n", - " \"\"\"\n", - " # Create a dictionary to store labelled elements\n", - " label_dict = {}\n", - "\n", - " # Iterate over both lists\n", - " for label, element in zip(labels, bounding_boxes):\n", - " label = int(label)\n", - " if label not in label_dict:\n", - " # Create a new list for this label if it doesn't exist\n", - " label_dict[label] = []\n", - " # Append the element to the corresponding label list\n", - " label_dict[label].append(f\"{element.category} {element.center[0]}\")\n", - "\n", - " # Sort the lists in the dictionary by x_center\n", - " for key in label_dict:\n", - " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(\" \")[1]))\n", - " label_dict[key] = [element.split(\" \")[0] for element in label_dict[key]]\n", - " # Turn list of strings into a string\n", - " label_dict[key] = f\"{''.join(label_dict[key])}_{unit}\"\n", - "\n", - " return label_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function to generate colors!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Draw bounding boxes on the image\n", - "def generate_color():\n", - " return \"#%06x\" % random.randint(0, 0xFFFFFF)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function to generate random Bounding Box formatted occurances" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "def random_time_generate(x):\n", - " # erroneous bounding boxes for time ROI\n", - " category_int = random.randint(0, 9)\n", - " left_int = random.randint(105, 720)\n", - "\n", - " # slope and random points ABOVE the line (but coordinates flipped)\n", - " slope, intercept = 1/3, 225\n", - " top_int = random.uniform(225, slope*left_int + intercept)\n", - "\n", - " # input generated integers to bounding box \n", - " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", - " return box\n", - "\n", - "def random_number_generate(x):\n", - " # erroneous bounding boxes for number ROI\n", - " category_int = random.randint(0, 9)\n", - " left_int = random.randint(105, 720)\n", - " \n", - " # slope and random points BELOW the line (but coordinates flipped)\n", - " slope, intercept = 1/3, 225\n", - " top_int = random.uniform(slope*left_int + intercept, 430)\n", - "\n", - " # input generated integers to bounding box \n", - " box = BoundingBox(category=f'{category_int}', left=left_int, top=top_int, right=left_int+4, bottom=top_int+6)\n", - " return box" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function to create 5% erroneous BB and remove 5% existing BB." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "def erroneous_bounding_boxes(time_BB: List[str], number_BB: List[str]) -> Tuple[List[str], List[str]]:\n", - " \"\"\"\n", - " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", - "\n", - " Args:\n", - " time_BB: list of time bounding boxes in BoundingBox format\n", - " number_BB: list of number bounding boxes in BoundingBox format\n", - "\n", - " Returns:\n", - " Tuple: lists of time and number bounding boxes in BoundingBox format\n", - " \"\"\"\n", - " # make copies of input bounding box lists to avoid unwanted manipulation\n", - " time_BB_copy = time_BB.copy()\n", - " number_BB_copy = number_BB.copy()\n", - " \n", - " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", - " ## sample\n", - " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", - " number_BB_sample = list(random.sample(number_BB_copy, 4))\n", - " ## remove\n", - " time_BB_removal = [time_BB_copy.remove(line) for line in time_BB_sample]\n", - " number_BB_removal = [number_BB_copy.remove(line) for line in number_BB_sample]\n", - "\n", - " # use random bounding box generation to refill removed BBs with erroneous boxes\n", - " time_BB_generate = list(map(random_time_generate, range(len(time_BB_sample))))\n", - " number_BB_generate = list(map(random_number_generate, range(len(number_BB_sample))))\n", - "\n", - " # append BB generated list back to copy with 5% removal\n", - " time_BB_erroneous = time_BB_copy + time_BB_generate\n", - " number_BB_erroneous = number_BB_copy + number_BB_generate\n", - "\n", - " return (time_BB_erroneous, number_BB_erroneous)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets use these functions to get the relevant bounding boxes for clustering.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - 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"Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", - "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" - ] - } - ], - "source": [ - "def test_clustering_methods() -> None:\n", - " \"\"\"\n", - " Test the clustering methods on the YOLO data.\n", - " Saves the clustered images and the clustered bounding boxes to JSON files.\n", - "\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " # Iterate over all images and their bounding boxes\n", - " for sheet, yolo_bbs in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " print(f\"Full image path: {full_image_path}\")\n", - "\n", - " # Call the analyze_sheet function with data from the loop\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bbs, full_image_path\n", - " )\n", - "\n", - " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", - " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", - " time_bounding_boxes, number_bounding_boxes\n", - " )\n", - "\n", - " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", - " if method == \"kmeans\":\n", - " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", - " number_labels = cluster_kmeans(number_bounding_boxes, [18, 19, 20])\n", - " elif method == \"dbscan\":\n", - " time_labels = dbscan_clustering(\n", - " time_bounding_boxes, defined_eps=5, min_samples=1\n", - " )\n", - " number_labels = dbscan_clustering(\n", - " number_bounding_boxes, defined_eps=5, min_samples=2\n", - " )\n", - " elif method == \"agglomerative\":\n", - " time_labels = agglomerative_clustering(time_bounding_boxes, [40,41,42])\n", - " number_labels = agglomerative_clustering(number_bounding_boxes, [18,19,20])\n", - " else:\n", - " raise ValueError(f\"Invalid clustering method: {method}\")\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - "\n", - " label_color_map = {}\n", - " for i, label in enumerate(time_labels):\n", - " # Get the bounding box\n", - " bounding_box = time_bounding_boxes[i]\n", - " x_min, y_min, x_max, y_max = [\n", - " (coor / 800) * image_width\n", - " if i % 2 == 0\n", - " else (coor / 600) * image_height\n", - " for i, coor in enumerate(bounding_box.box)\n", - " ]\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " draw.rectangle(\n", - " [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ],\n", - " outline=label_color_map[label],\n", - " width=3,\n", - " )\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f\"../../data/{method}_clustered_images/time/{sheet}\")\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(\n", - " f\"../../data/{method}_clustered_images/results/time/{sheet.split('.')[0]}.json\",\n", - " \"w\",\n", - " ) as f:\n", - " json.dump(\n", - " create_result_dictionary(time_labels, time_bounding_boxes, \"mins\"),\n", - " f,\n", - " )\n", - "\n", - " # Create an image object\n", - " image: Image = Image.open(full_image_path)\n", - " image_width, image_height = image.size\n", - " label_color_map = {}\n", - " for i, label in enumerate(number_labels):\n", - " # Get the bounding box\n", - " bounding_box = number_bounding_boxes[i]\n", - " x_min, y_min, x_max, y_max = [\n", - " (coor / 800) * image_width\n", - " if i % 2 == 0\n", - " else (coor / 600) * image_height\n", - " for i, coor in enumerate(bounding_box.box)\n", - " ]\n", - "\n", - " # If the label is not in the color map, generate a new color\n", - " if label not in label_color_map:\n", - " label_color_map[label] = generate_color()\n", - "\n", - " # Open the image\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " draw.rectangle(\n", - " [\n", - " x_min,\n", - " y_min,\n", - " x_max,\n", - " y_max,\n", - " ],\n", - " outline=label_color_map[label],\n", - " width=3,\n", - " )\n", - "\n", - " # Save the image with the bounding boxes to the kmeans_clustered_images folder\n", - " image.save(f\"../../data/{method}_clustered_images/number/{sheet}\")\n", - "\n", - " # Save the clustered bounding boxes to a JSON file\n", - " with open(\n", - " f\"../../data/{method}_clustered_images/results/number/{sheet.split('.')[0]}.json\",\n", - " \"w\",\n", - " ) as f:\n", - " json.dump(\n", - " create_result_dictionary(\n", - " number_labels, number_bounding_boxes, \"mmHg\"\n", - " ),\n", - " f,\n", - " )\n", - "\n", - "\n", - "# Test the clustering methods\n", - "test_clustering_methods()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Analyze accuracy\n", - "\n", - "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method: kmeans\n", - "Time labels: 657 correct clusters, 138 incorrect clusters. The accuracy is 82.64%\n", - "Number labels: 190 correct clusters, 181 incorrect clusters. The accuracy is 51.21%\n", - "Method: dbscan\n", - "Time labels: 728 correct clusters, 129 incorrect clusters. The accuracy is 84.95%\n", - "Number labels: 307 correct clusters, 55 incorrect clusters. The accuracy is 84.81%\n", - "Method: agglomerative\n", - "Time labels: 645 correct clusters, 152 incorrect clusters. The accuracy is 80.93%\n", - "Number labels: 177 correct clusters, 195 incorrect clusters. The accuracy is 47.58%\n" - ] - } - ], - "source": [ - "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", - "for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " print(f\"Method: {method}\")\n", - " # Paths to the JSON files\n", - " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", - " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", - " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", - "\n", - " time_wrong_clusters_count = 0\n", - " time_correct_clusters_count = 0\n", - " number_wrong_clusters_count = 0\n", - " number_correct_clusters_count = 0\n", - "\n", - " # Iterate over all images and their bounding boxes\n", - " for sheet, yolo_bb in yolo_data.items():\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb, full_image_path\n", - " )\n", - " # Convert the bounding boxes to a list of strings with proper suffixes\n", - " expected_time_values = [\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " ]\n", - "\n", - " expected_number_values = [\n", - " \"30_mmHg\",\n", - " \"40_mmHg\",\n", - " \"50_mmHg\",\n", - " \"60_mmHg\",\n", - " \"70_mmHg\",\n", - " \"80_mmHg\",\n", - " \"90_mmHg\",\n", - " \"100_mmHg\",\n", - " \"110_mmHg\",\n", - " \"120_mmHg\",\n", - " \"130_mmHg\",\n", - " \"140_mmHg\",\n", - " \"150_mmHg\",\n", - " \"160_mmHg\",\n", - " \"170_mmHg\",\n", - " \"180_mmHg\",\n", - " \"190_mmHg\",\n", - " \"200_mmHg\",\n", - " \"210_mmHg\",\n", - " \"220_mmHg\",\n", - " ]\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", - " time_clusters = json.load(f)\n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in time_clusters.items():\n", - " if value not in expected_time_values:\n", - " # Print the sheet, value that is not in the expected values\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", - " # We have an erroneous cluster\n", - " time_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_time_values.remove(value)\n", - " time_correct_clusters_count += 1\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", - " number_clusters = json.load(f)\n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in number_clusters.items():\n", - " if value not in expected_number_values:\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", - " # We have an erroneous cluster\n", - " number_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_number_values.remove(value)\n", - " number_correct_clusters_count += 1\n", - "\n", - " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\"\n", - " )\n", - " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\"\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/utilities/conversion/apply_homography_to_labels.ipynb b/utilities/conversion/apply_homography_to_labels.ipynb index 14f2776..fac2fc8 100644 --- a/utilities/conversion/apply_homography_to_labels.ipynb +++ b/utilities/conversion/apply_homography_to_labels.ipynb @@ -7,7 +7,7 @@ "source": [ "# Apply Homography to Labels\n", "\n", - "This script applies homography to the labels from anesthesia data flowsheets and maps the bounding boxes into the unified space." + "This script applies homography to the labels from anesthesia data flowsheets and maps the bounding boxes into the unified space.\n" ] }, { @@ -19,6 +19,7 @@ "source": [ "import sys\n", "import os\n", + "\n", "sys.path.append(os.path.join(\"..\", \"..\", \"..\", \"ChartExtractor\", \"src\"))" ] }, @@ -35,6 +36,7 @@ "from pathlib import Path\n", "from typing import Dict, List, Tuple, Optional\n", "from tqdm import tqdm\n", + "\n", "# Created a folder utils in the conversion folder and moved these files into there so we can call their functions\n", "# There should be a better way to do this perhaps, if this is something we will use across various microservices\n", "# Perhaps they can be a part of a package.\n", @@ -72,7 +74,7 @@ "id": "ddfd5339-e298-4223-a19f-94203044e543", "metadata": {}, "source": [ - "---" + "---\n" ] }, { @@ -80,7 +82,7 @@ "id": "3dd0d783-7093-4e21-9907-fa112f6deb57", "metadata": {}, "source": [ - "## Load Data" + "## Load Data\n" ] }, { @@ -95,30 +97,34 @@ " Loads data from LabelStudio's json format into BoundingBoxes.\n", "\n", " Args:\n", - " path_to_json_data (Path) \n", + " path_to_json_data (Path)\n", " A path to the json file containing the data.\n", " Returns:\n", " A list of BoundingBoxes.\n", " \"\"\"\n", " json_data: List[Dict] = json.loads(open(str(path_to_json_data)).read())\n", " return {\n", - " sheet_data['data']['image'].split(\"-\")[-1]:[\n", + " sheet_data[\"data\"][\"image\"].split(\"-\")[-1]: [\n", " BoundingBox(\n", - " category=label['value']['rectanglelabels'][0],\n", - " left=label['value']['x']/100,\n", - " top=label['value']['y']/100,\n", - " right=label['value']['x']/100+label['value']['width']/100,\n", - " bottom=label['value']['y']/100+label['value']['height']/100,\n", + " category=label[\"value\"][\"rectanglelabels\"][0],\n", + " left=label[\"value\"][\"x\"] / 100,\n", + " top=label[\"value\"][\"y\"] / 100,\n", + " right=label[\"value\"][\"x\"] / 100 + label[\"value\"][\"width\"] / 100,\n", + " bottom=label[\"value\"][\"y\"] / 100 + label[\"value\"][\"height\"] / 100,\n", " )\n", - " for label in sheet_data['annotations'][0]['result']\n", + " for label in sheet_data[\"annotations\"][0][\"result\"]\n", " ]\n", " for sheet_data in json_data\n", " }\n", "\n", "\n", - "data_path: Path = Path(\"..\")/\"..\"/\"data\"\n", - "landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(data_path/\"intraop_document_landmarks.json\")\n", - "checkbox_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(data_path/\"intraop_checkbox_names.json\")" + "data_path: Path = Path(\"..\") / \"..\" / \"data\"\n", + "landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(\n", + " data_path / \"intraop_document_landmarks.json\"\n", + ")\n", + "checkbox_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(\n", + " data_path / \"intraop_checkbox_names.json\"\n", + ")" ] }, { @@ -126,7 +132,7 @@ "id": "c169a1f4-dc7f-4242-b8a4-bb5062fa6cdc", "metadata": {}, "source": [ - "This is a slightly different version of the homography function from the main program. The only thing it changes is to return the homography matrix along with the image." + "This is a slightly different version of the homography function from the main program. The only thing it changes is to return the homography matrix along with the image.\n" ] }, { @@ -195,18 +201,20 @@ "source": [ "def get_corresponding_points(bboxes, imsize) -> List[Tuple[float, float]]:\n", " \"\"\"Gets and sorts the points used for the homography from all the bounding boxes that are labeled.\"\"\"\n", - " categories_to_get = ['anesthesia_start', 'lateral', 'safety_checklist', 'units']\n", + " categories_to_get = [\"anesthesia_start\", \"lateral\", \"safety_checklist\", \"units\"]\n", " if not all([c in [bb.category for bb in bboxes] for c in categories_to_get]):\n", " raise ValueError(f\"Necessary labels not found: {categories_to_get}\")\n", - " \n", - " points = list(map(\n", - " attrgetter('center'),\n", - " sorted(\n", - " list(filter(lambda bb: bb.category in categories_to_get, bboxes)), \n", - " key=lambda bb: bb.category\n", + "\n", + " points = list(\n", + " map(\n", + " attrgetter(\"center\"),\n", + " sorted(\n", + " list(filter(lambda bb: bb.category in categories_to_get, bboxes)),\n", + " key=lambda bb: bb.category,\n", + " ),\n", " )\n", - " ))\n", - " return [(p[0]*imsize[0], p[1]*imsize[1]) for p in points]" + " )\n", + " return [(p[0] * imsize[0], p[1] * imsize[1]) for p in points]" ] }, { @@ -216,25 +224,38 @@ "metadata": {}, "outputs": [], "source": [ - "remap_point = lambda p, h: cv2.perspectiveTransform(np.array(p, dtype=np.float32).reshape(-1, 1, 2), h).tolist()[0][0]\n", + "remap_point = lambda p, h: cv2.perspectiveTransform(\n", + " np.array(p, dtype=np.float32).reshape(-1, 1, 2), h\n", + ").tolist()[0][0]\n", "\n", "\n", "def remap_bbox(\n", - " bbox: BoundingBox, \n", - " homography_matrix: List[List[float]], \n", - " original_width:int=4032, \n", - " original_height:int=3024,\n", - " new_width:int=3300,\n", - " new_height:int=2550,\n", + " bbox: BoundingBox,\n", + " homography_matrix: List[List[float]],\n", + " original_width: int = 4032,\n", + " original_height: int = 3024,\n", + " new_width: int = 3300,\n", + " new_height: int = 2550,\n", ") -> BoundingBox:\n", " \"\"\"Maps boundingboxes to a new space using the homography matrix.\n", - " \n", - " Given a bounding box, homography matrix, and the image sizes of the original \n", + "\n", + " Given a bounding box, homography matrix, and the image sizes of the original\n", " and destination (new) image, this function returns a remapped bounding box.\n", " \"\"\"\n", - " new_left, new_top = remap_point((bbox.left*original_width, bbox.top*original_height), homography_matrix)\n", - " new_right, new_bottom = remap_point((bbox.right*original_width, bbox.bottom*original_height), homography_matrix)\n", - " return BoundingBox(bbox.category, new_left/new_width, new_top/new_height, new_right/new_width, new_bottom/new_height)\n", + " new_left, new_top = remap_point(\n", + " (bbox.left * original_width, bbox.top * original_height), homography_matrix\n", + " )\n", + " new_right, new_bottom = remap_point(\n", + " (bbox.right * original_width, bbox.bottom * original_height), homography_matrix\n", + " )\n", + " return BoundingBox(\n", + " bbox.category,\n", + " new_left / new_width,\n", + " new_top / new_height,\n", + " new_right / new_width,\n", + " new_bottom / new_height,\n", + " )\n", + "\n", "\n", "# Remap all bounding boxes\n", "remap_all_bboxes = lambda bboxes, h: [remap_bbox(bb, h) for bb in bboxes]" @@ -245,7 +266,7 @@ "id": "1c2c1fd7", "metadata": {}, "source": [ - "### Functions To Quickly Grab the Remapped Bounding Boxes In YOLO Format" + "### Functions To Quickly Grab the Remapped Bounding Boxes In YOLO Format\n" ] }, { @@ -260,6 +281,7 @@ "To be called from a for loop to complete for all documents, then exported to YOLO format.\n", "\"\"\"\n", "\n", + "\n", "def __get_image_size(path: Path) -> Tuple[int, int]:\n", " \"\"\"\n", " Returns the size of the image based on path\n", @@ -269,7 +291,10 @@ " Returns:\n", " Tuple[int, int]: The width and height of the image\n", " \"\"\"\n", - " return Image.open(data_path/\"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\").size\n", + " return Image.open(\n", + " data_path / \"unified_intraoperative_preoperative_flowsheet_v1_1_front.png\"\n", + " ).size\n", + "\n", "\n", "# Function to convert bounding boxes to YOLO format\n", "def convert_to_yolo_format(bbox_list):\n", @@ -284,7 +309,7 @@ "\n", "\n", "def complete_homography_and_get_bounding_boxes(\n", - " path_to_sheet: Path, \n", + " path_to_sheet: Path,\n", " path_to_landmarks: Path,\n", " path_to_registered: Path,\n", " intraoperative: bool = True,\n", @@ -303,25 +328,29 @@ " Optional[str]: The bounding boxes for the sheet in YOLO format. If None then the image could not be opened.\n", " \"\"\"\n", " # Get the landmark location data\n", - " data_path: Path = Path(\"..\")/\"..\"/\"data\"\n", - " landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(path_to_landmarks)\n", + " data_path: Path = Path(\"..\") / \"..\" / \"data\"\n", + " landmark_location_data: Dict[str, List[BoundingBox]] = label_studio_to_bboxes(\n", + " path_to_landmarks\n", + " )\n", " # Check if the sheet is in the landmark data\n", " if path_to_sheet.name not in landmark_location_data:\n", " print(f\"Sheet {path_to_sheet.name} not found in landmark data.\")\n", " return None\n", " # Get locations of landmarks for this sheet by getting the file name from the path\n", " locations = landmark_location_data[path_to_sheet.name]\n", - " \n", + "\n", " # Get the unified front/back image\n", " unified_file = f\"unified_intraoperative_preoperative_flowsheet_v1_1_{'front' if intraoperative else 'back'}.png\"\n", " locations_unified = landmark_location_data[unified_file]\n", - " unified_width, unified_height = __get_image_size(data_path/unified_file)\n", + " unified_width, unified_height = __get_image_size(data_path / unified_file)\n", "\n", " # Get the image\n", " try:\n", " image = Image.open(path_to_sheet)\n", " except:\n", - " print(f\"Unable to obtain image for sheet {path_to_sheet}. Likely in main directory and png format.\")\n", + " print(\n", + " f\"Unable to obtain image for sheet {path_to_sheet}. Likely in main directory and png format.\"\n", + " )\n", " return None\n", "\n", " # Get image dimensions\n", @@ -335,7 +364,9 @@ " h, pil_img = homography_transform(\n", " src_image=image,\n", " src_points=get_corresponding_points(locations, (width, height)),\n", - " dest_points=get_corresponding_points(locations_unified, (unified_width, unified_height)),\n", + " dest_points=get_corresponding_points(\n", + " locations_unified, (unified_width, unified_height)\n", + " ),\n", " original_image_size=(unified_width, unified_height),\n", " )\n", "\n", @@ -349,7 +380,7 @@ " # Save original image wih bounding boxes\n", " # Make a copy of the image\n", " pil_img_no_boxes = pil_img.copy()\n", - " #pil_img_no_boxes = pil_img.resize((800, 600))\n", + " # pil_img_no_boxes = pil_img.resize((800, 600))\n", "\n", " # You only need to do this drawing if we are intentionally being visual\n", " if show_images:\n", @@ -361,17 +392,17 @@ "\n", " for bounding_box in remapped_locations:\n", " box = [\n", - " bounding_box.left*unified_width,\n", - " bounding_box.top*unified_height,\n", - " bounding_box.right*unified_width,\n", - " bounding_box.bottom*unified_height,\n", + " bounding_box.left * unified_width,\n", + " bounding_box.top * unified_height,\n", + " bounding_box.right * unified_width,\n", + " bounding_box.bottom * unified_height,\n", " ]\n", " draw.rectangle(box, outline=generate_color(), width=3)\n", " pil_img.resize((800, 600))\n", "\n", " pil_img.show()\n", "\n", - " pil_img_no_boxes.save(path_to_registered/path_to_sheet.name)\n", + " pil_img_no_boxes.save(path_to_registered / path_to_sheet.name)\n", "\n", " return remapped_locations" ] @@ -383,7 +414,7 @@ "source": [ "### Iterate Over All Sheets, Get Bounding Boxes in YOLO For Registered Images\n", "\n", - "For each sheet, get the bounding box data in YOLO format. Make sure to create the `registered_images` directory." + "For each sheet, get the bounding box data in YOLO format. Make sure to create the `registered_images` directory.\n" ] }, { @@ -405,9 +436,9 @@ "yolo_dict = {}\n", "for sheet in landmark_location_data:\n", " bounding_boxes = complete_homography_and_get_bounding_boxes(\n", - " data_path/f\"chart_images/{sheet}\", \n", - " data_path/\"intraop_document_landmarks.json\", \n", - " data_path/\"registered_images\",\n", + " data_path / f\"chart_images/{sheet}\",\n", + " data_path / \"intraop_document_landmarks.json\",\n", + " data_path / \"registered_images\",\n", " show_images=False,\n", " )\n", " if bounding_boxes is None:\n", @@ -417,7 +448,7 @@ "\n", "print(yolo_dict)\n", "# Save the yolo_dict to a json file\n", - "with open(data_path/\"yolo_data.json\", \"w\") as f:\n", + "with open(data_path / \"yolo_data.json\", \"w\") as f:\n", " json.dump(yolo_dict, f, indent=4)" ] } From a5f2dee1f97bd8fbb4eec35d3f1bdff642dca64e Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Thu, 31 Oct 2024 15:09:19 -0400 Subject: [PATCH 26/55] Stricter selection of bounding boxes (preprocessing) --- experiments/clustering/clustering.ipynb | 183 +++++++++++++----------- 1 file changed, 99 insertions(+), 84 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index bb68e9e..2dd3421 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -390,10 +390,17 @@ "\n", " # find the point with the maximum density of bounding boxes\n", " bboxes_right: List[int] = [bb.right for bb in bboxes]\n", - " numbers_loc: int = find_density_max(bboxes_right, desired_img_width)\n", + " # x_loc is the vertical line to the left of the time axis and right of the numbers axis\n", + " x_loc: int = find_density_max(bboxes_right, desired_img_width)\n", "\n", " bboxes_bottom: List[int] = [bb.bottom for bb in bboxes]\n", - " time_loc: int = find_density_max(bboxes_bottom, desired_img_height)\n", + " # y_loc is the horizontal line undert the time axis and above the number axis\n", + " y_loc: int = find_density_max(bboxes_bottom, desired_img_height)\n", + "\n", + " # x_loc_right is the vertical line at the end of the time axis\n", + " x_loc_right: int = x_loc + 610\n", + " # y_loc_bottom is the horizontal line at the bottom of the number axis\n", + " y_loc_bottom: int = y_loc + 185\n", "\n", " bounding_boxes_time = []\n", " bounding_boxes_numbers = []\n", @@ -405,32 +412,40 @@ " x_max = int(bounding_box.right)\n", " y_min = int(bounding_box.top)\n", " y_max = int(bounding_box.bottom)\n", - "\n", + " \n", " # get the center point of the bounding box for comparison\n", " x_center_bb, y_center_bb = bounding_box.center\n", "\n", " # check if the bounding box is a number on the BP chart by comparing to the KDE index + a threshold\n", - " if x_center_bb > numbers_loc - 15 and x_center_bb < numbers_loc + 2:\n", + " if ((x_center_bb > x_loc-15 and x_center_bb < x_loc+2) and (y_center_bb > y_loc-2 and y_center_bb < y_loc_bottom+2)):\n", " # Bounding box is in the top-right region\n", " cv2.rectangle(\n", " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", " )\n", " bounding_boxes_numbers.append(bounding_box)\n", " # check if the bounding box is a time on the BP chart by comparing to the KDE index + a threshold\n", - " elif y_center_bb > time_loc - 10 and y_max < time_loc + 2:\n", + " elif ((y_center_bb > y_loc-10 and y_max < y_loc+2) and (x_center_bb > x_loc-2 and x_center_bb < x_loc_right+2)):\n", " cv2.rectangle(\n", " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", " )\n", " bounding_boxes_time.append(bounding_box)\n", " # plot the lines of the KDE index found for debugging\n", - " # numbers_start = (int(numbers_loc), 0)\n", - " # numbers_end = (int(numbers_loc), desired_img_height)\n", + " # numbers_start = (int(x_loc), 0)\n", + " # numbers_end = (int(x_loc), desired_img_height)\n", + "\n", + " # numbers2_start = (int(x_loc_right), 0)\n", + " # numbers2_end = (int(x_loc_right), desired_img_height)\n", + "\n", + " # time_start = (0, int(y_loc))\n", + " # time_end = (desired_img_width, int(y_loc))\n", "\n", - " # time_start = (0, int(time_loc))\n", - " # time_end = (desired_img_width, int(time_loc))\n", + " # time2_start = (0, int(y_loc_bottom))\n", + " # time2_end = (desired_img_width, int(y_loc_bottom))\n", "\n", " # cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", + " # cv2.line(resized_image, numbers2_start, numbers2_end, (255,255,0), 1)\n", " # cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", + " # cv2.line(resized_image, time2_start, time2_end, (255,0,255), 1)\n", "\n", " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", " # You can also manually quit out with ESC key.\n", @@ -457,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -656,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -674,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -776,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -784,119 +799,119 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - 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"Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" ] } ], @@ -1056,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1064,14 +1079,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 653 correct clusters, 137 incorrect clusters. The accuracy is 82.66%\n", - "Number labels: 192 correct clusters, 183 incorrect clusters. The accuracy is 51.20%\n", + "Time labels: 651 correct clusters, 138 incorrect clusters. The accuracy is 82.51%\n", + "Number labels: 192 correct clusters, 181 incorrect clusters. The accuracy is 51.47%\n", "Method: dbscan\n", - "Time labels: 729 correct clusters, 127 incorrect clusters. The accuracy is 85.16%\n", - "Number labels: 306 correct clusters, 51 incorrect clusters. The accuracy is 85.71%\n", + "Time labels: 729 correct clusters, 130 incorrect clusters. The accuracy is 84.87%\n", + "Number labels: 304 correct clusters, 57 incorrect clusters. The accuracy is 84.21%\n", "Method: agglomerative\n", - "Time labels: 654 correct clusters, 139 incorrect clusters. The accuracy is 82.47%\n", - "Number labels: 186 correct clusters, 189 incorrect clusters. The accuracy is 49.60%\n" + "Time labels: 628 correct clusters, 165 incorrect clusters. The accuracy is 79.19%\n", + "Number labels: 185 correct clusters, 189 incorrect clusters. The accuracy is 49.47%\n" ] } ], @@ -1093,7 +1108,7 @@ " for sheet, yolo_bb in yolo_data.items():\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb, full_image_path\n", + " yolo_bb, full_image_path, show_images=True\n", " )\n", " # Convert the bounding boxes to a list of strings with proper suffixes\n", " expected_time_values = [\n", From f167bdd38abe4f1aa07b3b4d2214cd3dadf0b94e Mon Sep 17 00:00:00 2001 From: hvalenty Date: Sat, 2 Nov 2024 16:19:48 -0400 Subject: [PATCH 27/55] Constrained erroneous bounding boxes to time and number axes --- experiments/clustering/clustering.ipynb | 164 ++++++++++++------------ 1 file changed, 81 insertions(+), 83 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 2dd3421..7ae7612 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -622,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -671,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -689,18 +689,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def random_time_generate(x):\n", " # erroneous bounding boxes for time ROI\n", " category_int = random.randint(0, 9)\n", - " left_int = random.randint(105, 720)\n", + " left_int = random.randint(127, 715)\n", "\n", - " # slope and random points ABOVE the line (but coordinates flipped)\n", - " slope, intercept = 1 / 3, 225\n", - " top_int = random.uniform(225, slope * left_int + intercept)\n", + " # slope and random points constrained to time (x) axis\n", + " top_int = random.uniform(222, 234)\n", "\n", " # input generated integers to bounding box\n", " box = BoundingBox(\n", @@ -716,11 +715,10 @@ "def random_number_generate(x):\n", " # erroneous bounding boxes for number ROI\n", " category_int = random.randint(0, 9)\n", - " left_int = random.randint(105, 720)\n", + " left_int = random.randint(108, 117)\n", "\n", - " # slope and random points BELOW the line (but coordinates flipped)\n", - " slope, intercept = 1 / 3, 225\n", - " top_int = random.uniform(slope * left_int + intercept, 430)\n", + " # slope and random points constrained to number (y) axis\n", + " top_int = random.uniform(235, 411)\n", "\n", " # input generated integers to bounding box\n", " box = BoundingBox(\n", @@ -742,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -791,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -799,119 +797,119 @@ "output_type": "stream", "text": [ "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0001_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0002_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0003_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0004_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0005_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0006_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0007_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0008_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0009_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0010_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0011_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0012_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0013_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0014_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0015_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0016_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0017_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0018_intraoperative.JPG\n", + "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images/RC_0019_intraoperative.JPG\n" + "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" ] } ], @@ -1071,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1079,14 +1077,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 651 correct clusters, 138 incorrect clusters. The accuracy is 82.51%\n", - "Number labels: 192 correct clusters, 181 incorrect clusters. The accuracy is 51.47%\n", + "Time labels: 663 correct clusters, 118 incorrect clusters. The accuracy is 84.89%\n", + "Number labels: 253 correct clusters, 117 incorrect clusters. The accuracy is 68.38%\n", "Method: dbscan\n", - "Time labels: 729 correct clusters, 130 incorrect clusters. The accuracy is 84.87%\n", - "Number labels: 304 correct clusters, 57 incorrect clusters. The accuracy is 84.21%\n", + "Time labels: 691 correct clusters, 127 incorrect clusters. The accuracy is 84.47%\n", + "Number labels: 254 correct clusters, 103 incorrect clusters. The accuracy is 71.15%\n", "Method: agglomerative\n", - "Time labels: 628 correct clusters, 165 incorrect clusters. The accuracy is 79.19%\n", - "Number labels: 185 correct clusters, 189 incorrect clusters. The accuracy is 49.47%\n" + "Time labels: 671 correct clusters, 113 incorrect clusters. The accuracy is 85.59%\n", + "Number labels: 245 correct clusters, 128 incorrect clusters. The accuracy is 65.68%\n" ] } ], @@ -1239,7 +1237,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1253,7 +1251,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.9.5" } }, "nbformat": 4, From 9299d67e8d9c57ce7ac077b3ffe920642a5444c8 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 4 Nov 2024 18:46:23 -0500 Subject: [PATCH 28/55] Imputing meaning via expected locations. Will need to get MSE of distance of center of found cluster to expected cluster center. --- experiments/clustering/clustering.ipynb | 451 ++++++++---------------- 1 file changed, 144 insertions(+), 307 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 7ae7612..cfe0ff9 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,6 @@ "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", "from scipy.stats import gaussian_kde\n", - "import random\n", "\n", "# Local libraries\n", "from utils.annotations import BoundingBox" @@ -71,14 +70,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 241, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 19 sheets in yolo_data.json\n" + "Found 19 sheets in yolo_data.json\n", + "Found 19 items in bp_and_hr_cluster_locations.json\n" ] } ], @@ -89,235 +89,50 @@ "UNIFIED_IMAGE_PATH = (\n", " \"../../data/unified_intraoperative_preoperative_flowsheet_v1_1_front.png\"\n", ")\n", + "\n", + "# Load yolo_data.json\n", "with open(PATH_TO_YOLO_DATA) as json_file:\n", " yolo_data = json.load(json_file)\n", "\n", "# See how many intraoperative images are registered\n", - "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")" + "print(f\"Found {len(yolo_data)} sheets in yolo_data.json\")\n", + "\n", + "# Load the json for bp and hr cluster locations\n", + "PATH_TO_CLUSTER_LOCATIONS = \"../../data/bp_and_hr_cluster_locations.json\"\n", + "with open(PATH_TO_CLUSTER_LOCATIONS) as json_file:\n", + " bp_hr_cluster_locations = json.load(json_file)\n", + " print(f\"Found {len(bp_hr_cluster_locations)} items in bp_and_hr_cluster_locations.json\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's select relevant bounding boxes from the blood pressure and HR zone.\n", - "\n", - "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n" + "#### Define Constants Used In Notebook" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 242, "metadata": {}, "outputs": [], "source": [ - "# def get_bp_section_coordinates(\n", - "# image_height: int, bboxes: List[BoundingBox], buffer_pixels: int = 5\n", - "# ) -> List[int]:\n", - "# \"\"\"Crops the blood pressure section out of an image of a chart.\n", - "\n", - "# Args:\n", - "# image_height (int):\n", - "# The height of the image in pixels.\n", - "# bboxes (List[BoundingBox]):\n", - "# List of BoundingBoxes within this image.\n", - "# buffer_pixels (int):\n", - "# An optional integer that specifies the number of pixels around the digit detections to\n", - "# 'zoom out' by. Defaults to 5 pixels.\n", - "\n", - "# Returns:\n", - "# Coordinates of the bounding box that contains the blood pressure section.\n", - "# \"\"\"\n", - "# # Get bounding boxes from detections and filter non bounding boxes out.\n", - "# bboxes: List[BoundingBox] = list(\n", - "# filter(lambda ann: isinstance(ann, BoundingBox), bboxes)\n", - "# )\n", - "\n", - "# digit_categories: List[str] = [str(i) for i in range(10)]\n", - "\n", - "# # Filter bounding boxes to those which are within the approximate region and are digits.\n", - "# bp_legend_digits: List[BoundingBox] = list(\n", - "# filter(\n", - "# lambda bb: all(\n", - "# [\n", - "# bb.top / image_height > 0.2,\n", - "# bb.top / image_height < 0.8,\n", - "# bb.category in digit_categories,\n", - "# ]\n", - "# ),\n", - "# bboxes,\n", - "# )\n", - "# )\n", - "# bp_legend_coordinates: List[int] = list(\n", - "# map(\n", - "# int,\n", - "# [\n", - "# min([digit.left for digit in bp_legend_digits]) - buffer_pixels,\n", - "# min([digit.top for digit in bp_legend_digits]) - buffer_pixels,\n", - "# max([digit.right for digit in bp_legend_digits]) + buffer_pixels,\n", - "# max([digit.bottom for digit in bp_legend_digits]) + buffer_pixels,\n", - "# ],\n", - "# )\n", - "# )\n", - "# return bp_legend_coordinates\n", - "\n", - "\n", - "# def is_point_in_above(x_center: float, y_center: float, m: float, b: float) -> bool:\n", - "# \"\"\"\n", - "# Determine if a point is above or below the diagonal line y = mx + b.\n", - "# For our purposes we use it to check if a bounding box is in the top-right region -- meaning time labels.\n", - "\n", - "# Args:\n", - "# x_center: float, x coordinate of the point\n", - "# y_center: float, y coordinate of the point\n", - "# m: float, slope of the diagonal line\n", - "# b: float, intercept of the diagonal line\n", - "\n", - "# Returns:\n", - "# bool, True if the point is above the line, False otherwise\n", - "# \"\"\"\n", - "# # Calculate the y value on the line for the given x_center\n", - "# y_line = m * x_center + b\n", - "# return y_center > y_line\n", - "\n", - "\n", - "# def select_relevant_bounding_boxes(\n", - "# sheet_data: List[str],\n", - "# path_to_image: Path,\n", - "# show_images: bool = False,\n", - "# desired_img_width: int = 800,\n", - "# desired_img_height: int = 600,\n", - "# ) -> Tuple[List[str], List[str]]:\n", - "# \"\"\"\n", - "# Given sheet data for bounding boxes in YOLO format, display the image and allow the user to select a region of interest (ROI).\n", - "# Identify bounding boxes that are within the selected region and draw rectangles around them.\n", - "# Return the bounding boxes that are within the selected region split into two lists: time labels and numerical values.\n", - "\n", - "# Args:\n", - "# sheet_data: List of bounding boxes in YOLO format.\n", - "# path_to_image: Path to the image file.\n", - "\n", - "# Returns:\n", - "# Tuple of Lists of string representations of bounding boxes that are within the selected region, in YOLO format.\n", - "# The first list contains bounding boxes in the top-right region -- representing time labels.\n", - "# The second list contains bounding boxes in the bottom-left region -- representing numerical values for mmHg and bpm.\n", - "# (bounding_boxes_time, bounding_boxes_numbers)\n", - "# \"\"\"\n", - "\n", - "# # Load the image\n", - "# image = cv2.imread(path_to_image)\n", - "\n", - "# # Display the image and allow the user to select a ROI\n", - "# resized_image = cv2.resize(image, (desired_img_width, desired_img_height))\n", - "\n", - "# x_top_left, y_top_left, x_bottom_right, y_bottom_right = get_bp_section_coordinates(\n", - "# image_height=desired_img_height,\n", - "# bboxes=[\n", - "# BoundingBox.from_yolo(yolo_bb, desired_img_width, desired_img_height)\n", - "# for yolo_bb in sheet_data\n", - "# ],\n", - "# buffer_pixels=2,\n", - "# )\n", - "\n", - "# cv2.rectangle(\n", - "# resized_image,\n", - "# (x_top_left, y_top_left),\n", - "# (x_bottom_right, y_bottom_right),\n", - "# (255, 255, 0),\n", - "# 1,\n", - "# )\n", - "\n", - "# # Draw the diagonal line of the selected region from top-left to bottom-right\n", - "# cv2.line(\n", - "# resized_image,\n", - "# (x_top_left, y_top_left),\n", - "# (x_bottom_right, y_bottom_right),\n", - "# (0, 255, 0),\n", - "# 1,\n", - "# )\n", - "# # Calculate the slope (m) and intercept (b) of the diagonal line.\n", - "# # This will allow us to determine if a bounding box is in the top-right region or bottom-left region\n", - "# # Top-right region is where time labels are located\n", - "# # Bottom-left region is where numerical values for mmHg and bpm are located\n", - "# m = (y_bottom_right - y_top_left) / (x_bottom_right - x_top_left)\n", - "# b = y_top_left - m * x_top_left\n", - "\n", - "# # List of bounding boxes in the top-right and bottom-left regions\n", - "# bounding_boxes_time = []\n", - "# bounding_boxes_numbers = []\n", - "\n", - "# # Process the bounding boxes\n", - "# for bounding_box in sheet_data:\n", - "# # Bounding boxes are in YOLO format; convert them to pixels\n", - "# x_min, y_min, x_max, y_max = list(\n", - "# map(\n", - "# int,\n", - "# BoundingBox.from_yolo(\n", - "# yolo_line=bounding_box,\n", - "# image_width=desired_img_width,\n", - "# image_height=desired_img_height,\n", - "# ).box,\n", - "# )\n", - "# )\n", - "\n", - "# # Check if the bounding box is within the selected region\n", - "# if (\n", - "# x_min >= x_top_left\n", - "# and y_min >= y_top_left\n", - "# and x_max <= x_bottom_right\n", - "# and y_max <= y_bottom_right\n", - "# ):\n", - "# # Calculate the center of the bounding box\n", - "# x_center_bb = (x_min + x_max) / 2\n", - "# y_center_bb = (y_min + y_max) / 2\n", - "\n", - "# # If we want to generalize this function we can add the option to disregard the diagonal line\n", - "\n", - "# # Determine if the bounding box center is in the top-right region\n", - "# if is_point_in_above(x_center_bb, y_center_bb, m, b):\n", - "# # Bounding box is in the top-right region\n", - "# cv2.rectangle(\n", - "# resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", - "# )\n", - "# bounding_boxes_numbers.append(\n", - "# BoundingBox.from_yolo(\n", - "# yolo_line=bounding_box,\n", - "# image_width=desired_img_width,\n", - "# image_height=desired_img_height,\n", - "# )\n", - "# )\n", - "# else:\n", - "# # Bounding box is in the bottom-left region\n", - "# cv2.rectangle(\n", - "# resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", - "# )\n", - "# bounding_boxes_time.append(\n", - "# BoundingBox.from_yolo(\n", - "# yolo_line=bounding_box,\n", - "# image_width=desired_img_width,\n", - "# image_height=desired_img_height,\n", - "# )\n", - "# )\n", - "\n", - "# # Close all OpenCV windows, always do this or it will annoyingly not go away\n", - "# # You can also manually quit out with ESC key.\n", - "# cv2.destroyAllWindows()\n", - "\n", - "# # If we are showing the images, display the image with the selected region and bounding boxes\n", - "# # Bounding boxes in the top-right region (time) are in one color while those in the bottom left (numerical) are in another\n", - "# if show_images:\n", - "# # Display the image with the selected region and bounding boxes\n", - "# resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)\n", - "# resized_image = Image.fromarray(resized_image)\n", - "# resized_image.show()\n", - "\n", - "# # Return a tuple of bounding boxes in the top-right and bottom-left regions\n", - "# return (bounding_boxes_time, bounding_boxes_numbers)" + "DESIRED_IMAGE_WIDTH = 800\n", + "DESIRED_IMAGE_HEIGHT = 600" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's select relevant bounding boxes from the blood pressure and HR zone.\n", + "\n", + "Start by defining functions to convert YOLO bounding box format to pixels (to see if the bounding box is within region of interest). Then create a function that allows you to select ROI and returns a list of bounding boxes within this ROI.\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 243, "metadata": {}, "outputs": [], "source": [ @@ -347,8 +162,8 @@ " sheet_data: List[str],\n", " path_to_image: Path,\n", " show_images: bool = False,\n", - " desired_img_width: int = 800,\n", - " desired_img_height: int = 600,\n", + " desired_img_width: int = DESIRED_IMAGE_WIDTH,\n", + " desired_img_height: int = DESIRED_IMAGE_HEIGHT,\n", ") -> Tuple[List[str], List[str]]:\n", " \"\"\"\n", " Given sheet data for bounding boxes in YOLO format, find the bounding boxes corresponding to the number and time on the BP chart.\n", @@ -429,23 +244,24 @@ " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", " )\n", " bounding_boxes_time.append(bounding_box)\n", + " \n", " # plot the lines of the KDE index found for debugging\n", - " # numbers_start = (int(x_loc), 0)\n", - " # numbers_end = (int(x_loc), desired_img_height)\n", + " numbers_start = (int(x_loc), 0)\n", + " numbers_end = (int(x_loc), desired_img_height)\n", "\n", - " # numbers2_start = (int(x_loc_right), 0)\n", - " # numbers2_end = (int(x_loc_right), desired_img_height)\n", + " numbers2_start = (int(x_loc_right), 0)\n", + " numbers2_end = (int(x_loc_right), desired_img_height)\n", "\n", - " # time_start = (0, int(y_loc))\n", - " # time_end = (desired_img_width, int(y_loc))\n", + " time_start = (0, int(y_loc))\n", + " time_end = (desired_img_width, int(y_loc))\n", "\n", - " # time2_start = (0, int(y_loc_bottom))\n", - " # time2_end = (desired_img_width, int(y_loc_bottom))\n", + " time2_start = (0, int(y_loc_bottom))\n", + " time2_end = (desired_img_width, int(y_loc_bottom))\n", "\n", - " # cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", - " # cv2.line(resized_image, numbers2_start, numbers2_end, (255,255,0), 1)\n", - " # cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", - " # cv2.line(resized_image, time2_start, time2_end, (255,0,255), 1)\n", + " cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", + " cv2.line(resized_image, numbers2_start, numbers2_end, (255,255,0), 1)\n", + " cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", + " cv2.line(resized_image, time2_start, time2_end, (255,0,255), 1)\n", "\n", " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", " # You can also manually quit out with ESC key.\n", @@ -467,12 +283,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create a function for K-means clustering, dbscan clustering\n" + "Create functions for K-means clustering, dbscan clustering, and agglomerative clustering\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 244, "metadata": {}, "outputs": [], "source": [ @@ -622,12 +438,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 245, "metadata": {}, "outputs": [], "source": [ "def create_result_dictionary(\n", - " labels: List[str], bounding_boxes: List[BoundingBox], unit: Literal[\"mmHg\", \"mins\"]\n", + " labels: List[str], bounding_boxes: List[BoundingBox], expected_clusters: dict\n", ") -> Dict[int, int]:\n", " \"\"\"\n", " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", @@ -635,7 +451,8 @@ " Args:\n", " labels: List of cluster labels.\n", " bounding_boxes: List of bounding boxes.\n", - " suffix: Suffix to append to the category of the bounding box. One of [\"mmHg\", \"mins\"].\n", + " expected_clusters: dictionary containing expected clustered\n", + " - This should be passed as specific for the sheet we are working with. It can be obtained from bp_hr_cluster_locations.json.\n", "\n", " Returns:\n", " Dictionary with cluster labels as keys and bounding box values as values.\n", @@ -643,21 +460,37 @@ " # Create a dictionary to store labelled elements\n", " label_dict = {}\n", "\n", - " # Iterate over both lists\n", - " for label, element in zip(labels, bounding_boxes):\n", + " # Iterate over both lists for labels and the bounding boxes found\n", + " for label, box in zip(labels, bounding_boxes):\n", " label = int(label)\n", " if label not in label_dict:\n", " # Create a new list for this label if it doesn't exist\n", " label_dict[label] = []\n", " # Append the element to the corresponding label list\n", - " label_dict[label].append(f\"{element.category} {element.center[0]}\")\n", + " label_dict[label].append(box)\n", + "\n", + " # So now we have a dictionary with the clusters as keys and a list of bounding box objects as strings as values\n", "\n", - " # Sort the lists in the dictionary by x_center\n", + " # Now we want to impute meaning of these clusters based on how close they are to their expected cluster locations\n", " for key in label_dict:\n", - " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.split(\" \")[1]))\n", - " label_dict[key] = [element.split(\" \")[0] for element in label_dict[key]]\n", - " # Turn list of strings into a string\n", - " label_dict[key] = f\"{''.join(label_dict[key])}_{unit}\"\n", + " # For each key get the centers of the bounding boxes, and compute the middle point\n", + " x_centers, y_centers = [element.center[0] for element in label_dict[key]], [element.center[1] for element in label_dict[key]]\n", + " x_found, y_found = sum(x_centers) / len(x_centers), sum(y_centers) / len(y_centers)\n", + " # Now we have the center point of the cluster\n", + " # We will use the euclidean distance to determine the closest expected cluster location and use that as the label\n", + " distances = [] # List that contains the distances for each expected cluster from our found cluster\n", + " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", + " x_expected_perc, y_expected_perc = cluster['value']['x'], cluster['value']['y'] # Get the expected cluster location (percent x and y in the original image space)\n", + " x_expected, y_expected = (x_expected_perc/100) * DESIRED_IMAGE_WIDTH, (y_expected_perc/100) * DESIRED_IMAGE_HEIGHT # Convert the expected cluster location to pixel space\n", + " #print(f\"Cluster location: {x}, {y}\")\n", + " distance = np.sqrt((x_expected - x_found) ** 2 + (y_expected - y_found) ** 2)\n", + " #print(f\"Distance: {distance}\")\n", + " distances.append(distance)\n", + " # Get the index of the minimum distance\n", + " min_distance_index = distances.index(min(distances))\n", + "\n", + " # Get the label of the cluster\n", + " label_dict[key] = list(expected_clusters[\"annotations\"][0][\"result\"])[min_distance_index]['value']['rectanglelabels'][0]\n", "\n", " return label_dict" ] @@ -671,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 246, "metadata": {}, "outputs": [], "source": [ @@ -689,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 247, "metadata": {}, "outputs": [], "source": [ @@ -740,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 248, "metadata": {}, "outputs": [], "source": [ @@ -766,8 +599,8 @@ " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", " number_BB_sample = list(random.sample(number_BB_copy, 4))\n", " ## remove\n", - " time_BB_removal = [time_BB_copy.remove(line) for line in time_BB_sample]\n", - " number_BB_removal = [number_BB_copy.remove(line) for line in number_BB_sample]\n", + " _ = [time_BB_copy.remove(line) for line in time_BB_sample]\n", + " _ = [number_BB_copy.remove(line) for line in number_BB_sample]\n", "\n", " # use random bounding box generation to refill removed BBs with erroneous boxes\n", " time_BB_generate = list(map(random_time_generate, range(len(time_BB_sample))))\n", @@ -789,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 249, "metadata": {}, "outputs": [ { @@ -923,6 +756,7 @@ " None\n", " \"\"\"\n", " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " sheet_num = 0\n", " # Iterate over all images and their bounding boxes\n", " for sheet, yolo_bbs in yolo_data.items():\n", " print(f\"Sheet: {sheet}\")\n", @@ -1002,7 +836,7 @@ " \"w\",\n", " ) as f:\n", " json.dump(\n", - " create_result_dictionary(time_labels, time_bounding_boxes, \"mins\"),\n", + " create_result_dictionary(time_labels, time_bounding_boxes, bp_hr_cluster_locations[sheet_num]),\n", " f,\n", " )\n", "\n", @@ -1048,11 +882,14 @@ " ) as f:\n", " json.dump(\n", " create_result_dictionary(\n", - " number_labels, number_bounding_boxes, \"mmHg\"\n", + " number_labels, number_bounding_boxes, bp_hr_cluster_locations[sheet_num]\n", " ),\n", " f,\n", " )\n", "\n", + " sheet_num += 1\n", + " \n", + "\n", "\n", "# Test the clustering methods\n", "test_clustering_methods()" @@ -1069,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 250, "metadata": {}, "outputs": [ { @@ -1077,14 +914,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 663 correct clusters, 118 incorrect clusters. The accuracy is 84.89%\n", - "Number labels: 253 correct clusters, 117 incorrect clusters. The accuracy is 68.38%\n", + "Time labels: 775 correct clusters, 4 incorrect clusters. The accuracy is 99.49%\n", + "Number labels: 371 correct clusters, 3 incorrect clusters. The accuracy is 99.20%\n", "Method: dbscan\n", - "Time labels: 691 correct clusters, 127 incorrect clusters. The accuracy is 84.47%\n", - "Number labels: 254 correct clusters, 103 incorrect clusters. The accuracy is 71.15%\n", + "Time labels: 782 correct clusters, 44 incorrect clusters. The accuracy is 94.67%\n", + "Number labels: 342 correct clusters, 12 incorrect clusters. The accuracy is 96.61%\n", "Method: agglomerative\n", - "Time labels: 671 correct clusters, 113 incorrect clusters. The accuracy is 85.59%\n", - "Number labels: 245 correct clusters, 128 incorrect clusters. The accuracy is 65.68%\n" + "Time labels: 779 correct clusters, 7 incorrect clusters. The accuracy is 99.11%\n", + "Number labels: 366 correct clusters, 11 incorrect clusters. The accuracy is 97.08%\n" ] } ], @@ -1106,7 +943,7 @@ " for sheet, yolo_bb in yolo_data.items():\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb, full_image_path, show_images=True\n", + " yolo_bb, full_image_path,\n", " )\n", " # Convert the bounding boxes to a list of strings with proper suffixes\n", " expected_time_values = [\n", @@ -1122,59 +959,59 @@ " \"45_mins\",\n", " \"50_mins\",\n", " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", + " \"60_mins\",\n", + " \"65_mins\",\n", + " \"70_mins\",\n", + " \"75_mins\",\n", + " \"80_mins\",\n", + " \"85_mins\",\n", + " \"90_mins\",\n", + " \"95_mins\",\n", + " \"100_mins\",\n", + " \"105_mins\",\n", + " \"110_mins\",\n", + " \"115_mins\",\n", + " \"120_mins\",\n", + " \"125_mins\",\n", + " \"130_mins\",\n", + " \"135_mins\",\n", + " \"140_mins\",\n", + " \"145_mins\",\n", + " \"150_mins\",\n", + " \"155_mins\",\n", + " \"160_mins\",\n", + " \"165_mins\",\n", + " \"170_mins\",\n", + " \"175_mins\",\n", + " \"180_mins\",\n", + " \"185_mins\",\n", + " \"190_mins\",\n", + " \"195_mins\",\n", + " \"200_mins\",\n", + " \"205_mins\",\n", " ]\n", "\n", " expected_number_values = [\n", - " \"30_mmHg\",\n", - " \"40_mmHg\",\n", - " \"50_mmHg\",\n", - " \"60_mmHg\",\n", - " \"70_mmHg\",\n", - " \"80_mmHg\",\n", - " \"90_mmHg\",\n", - " \"100_mmHg\",\n", - " \"110_mmHg\",\n", - " \"120_mmHg\",\n", - " \"130_mmHg\",\n", - " \"140_mmHg\",\n", - " \"150_mmHg\",\n", - " \"160_mmHg\",\n", - " \"170_mmHg\",\n", - " \"180_mmHg\",\n", - " \"190_mmHg\",\n", - " \"200_mmHg\",\n", - " \"210_mmHg\",\n", - " \"220_mmHg\",\n", + " \"30_mmhg\",\n", + " \"40_mmhg\",\n", + " \"50_mmhg\",\n", + " \"60_mmhg\",\n", + " \"70_mmhg\",\n", + " \"80_mmhg\",\n", + " \"90_mmhg\",\n", + " \"100_mmhg\",\n", + " \"110_mmhg\",\n", + " \"120_mmhg\",\n", + " \"130_mmhg\",\n", + " \"140_mmhg\",\n", + " \"150_mmhg\",\n", + " \"160_mmhg\",\n", + " \"170_mmhg\",\n", + " \"180_mmhg\",\n", + " \"190_mmhg\",\n", + " \"200_mmhg\",\n", + " \"210_mmhg\",\n", + " \"220_mmhg\",\n", " ]\n", "\n", " # Load JSON\n", @@ -1237,7 +1074,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1251,7 +1088,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.12.7" } }, "nbformat": 4, From 67431858f8cac67351193d57303d36580b24634f Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 4 Nov 2024 21:03:11 -0500 Subject: [PATCH 29/55] Added average distance of clusters from the proposed cluster label. --- experiments/clustering/clustering.ipynb | 57 +++++++++++++++---------- 1 file changed, 34 insertions(+), 23 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index cfe0ff9..755b5fd 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 251, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 252, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 253, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 254, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 255, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 256, "metadata": {}, "outputs": [], "source": [ @@ -492,6 +492,9 @@ " # Get the label of the cluster\n", " label_dict[key] = list(expected_clusters[\"annotations\"][0][\"result\"])[min_distance_index]['value']['rectanglelabels'][0]\n", "\n", + " # Add the distance to the dictionary\n", + " label_dict[key] = {\"label\": label_dict[key], \"distance\": min(distances)}\n", + "\n", " return label_dict" ] }, @@ -504,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 257, "metadata": {}, "outputs": [], "source": [ @@ -522,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 258, "metadata": {}, "outputs": [], "source": [ @@ -573,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 259, "metadata": {}, "outputs": [], "source": [ @@ -622,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 260, "metadata": {}, "outputs": [ { @@ -906,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 266, "metadata": {}, "outputs": [ { @@ -914,14 +917,14 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 775 correct clusters, 4 incorrect clusters. The accuracy is 99.49%\n", - "Number labels: 371 correct clusters, 3 incorrect clusters. The accuracy is 99.20%\n", + "Time labels: 774 correct clusters, 7 incorrect clusters. The accuracy is 99.10%. Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", + "Number labels: 369 correct clusters, 1 incorrect clusters. The accuracy is 99.73%. Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", "Method: dbscan\n", - "Time labels: 782 correct clusters, 44 incorrect clusters. The accuracy is 94.67%\n", - "Number labels: 342 correct clusters, 12 incorrect clusters. The accuracy is 96.61%\n", + "Time labels: 785 correct clusters, 28 incorrect clusters. The accuracy is 96.56%. Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", + "Number labels: 355 correct clusters, 11 incorrect clusters. The accuracy is 96.99%. Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", "Method: agglomerative\n", - "Time labels: 779 correct clusters, 7 incorrect clusters. The accuracy is 99.11%\n", - "Number labels: 366 correct clusters, 11 incorrect clusters. The accuracy is 97.08%\n" + "Time labels: 776 correct clusters, 8 incorrect clusters. The accuracy is 98.98%. Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", + "Number labels: 363 correct clusters, 11 incorrect clusters. The accuracy is 97.06%. Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n" ] } ], @@ -940,6 +943,10 @@ " number_correct_clusters_count = 0\n", "\n", " # Iterate over all images and their bounding boxes\n", + " number_distance_values = []\n", + " time_distance_values = []\n", + " number_distance_values_erroneous = []\n", + " time_distance_values_erroneous = []\n", " for sheet, yolo_bb in yolo_data.items():\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", @@ -1022,15 +1029,17 @@ " # We know what integers should be represented in the time labels, lets check that they are all there.\n", " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", " for cluster, value in time_clusters.items():\n", - " if value not in expected_time_values:\n", + " if value['label'] not in expected_time_values:\n", " # Print the sheet, value that is not in the expected values\n", " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", " # We have an erroneous cluster\n", " time_wrong_clusters_count += 1\n", + " time_distance_values_erroneous.append(value['distance'])\n", " else:\n", " # We have a correct cluster\n", - " expected_time_values.remove(value)\n", + " expected_time_values.remove(value['label'])\n", " time_correct_clusters_count += 1\n", + " time_distance_values.append(value['distance'])\n", "\n", " # Load JSON\n", " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", @@ -1040,20 +1049,22 @@ " # We know what integers should be represented in the time labels, lets check that they are all there.\n", " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", " for cluster, value in number_clusters.items():\n", - " if value not in expected_number_values:\n", + " if value['label'] not in expected_number_values:\n", " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", " # We have an erroneous cluster\n", " number_wrong_clusters_count += 1\n", + " number_distance_values_erroneous.append(value['distance'])\n", " else:\n", " # We have a correct cluster\n", - " expected_number_values.remove(value)\n", + " expected_number_values.remove(value['label'])\n", " number_correct_clusters_count += 1\n", + " number_distance_values.append(value['distance'])\n", "\n", " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%\"\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%. Average distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", " )\n", " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%\"\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%. Average distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", " )\n" ] }, From bf1597a0278b4f6590f3a3b7555a80a7a355294f Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 4 Nov 2024 21:24:29 -0500 Subject: [PATCH 30/55] Improved method of calculating accuracy to include undetected clusters not just incorrect clusters. --- experiments/clustering/clustering.ipynb | 57 +++++++++++++++++-------- 1 file changed, 40 insertions(+), 17 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 755b5fd..47ee0f2 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -909,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -917,14 +917,35 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 774 correct clusters, 7 incorrect clusters. The accuracy is 99.10%. Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", - "Number labels: 369 correct clusters, 1 incorrect clusters. The accuracy is 99.73%. Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", + "Time labels: 774 correct clusters, 7 incorrect clusters. There were 24 undetected clusters. The accuracy is 63.67%.\n", + "Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", + "\n", + "\n", + "Number labels: 369 correct clusters, 1 incorrect clusters. There were 11 undetected clusters. The accuracy is 30.39%.\n", + "Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", + "\n", + "\n", + "\n", "Method: dbscan\n", - "Time labels: 785 correct clusters, 28 incorrect clusters. The accuracy is 96.56%. Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", - "Number labels: 355 correct clusters, 11 incorrect clusters. The accuracy is 96.99%. Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", + "Time labels: 785 correct clusters, 28 incorrect clusters. There were 13 undetected clusters. The accuracy is 65.53%.\n", + "Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", + "\n", + "\n", + "Number labels: 355 correct clusters, 11 incorrect clusters. There were 25 undetected clusters. The accuracy is 28.01%.\n", + "Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", + "\n", + "\n", + "\n", "Method: agglomerative\n", - "Time labels: 776 correct clusters, 8 incorrect clusters. The accuracy is 98.98%. Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", - "Number labels: 363 correct clusters, 11 incorrect clusters. The accuracy is 97.06%. Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n" + "Time labels: 776 correct clusters, 8 incorrect clusters. There were 22 undetected clusters. The accuracy is 64.01%.\n", + "Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", + "\n", + "\n", + "Number labels: 363 correct clusters, 11 incorrect clusters. There were 17 undetected clusters. The accuracy is 29.37%.\n", + "Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n", + "\n", + "\n", + "\n" ] } ], @@ -947,6 +968,9 @@ " time_distance_values = []\n", " number_distance_values_erroneous = []\n", " time_distance_values_erroneous = []\n", + " # Undetected clusters\n", + " undetected_time_clusters = []\n", + " undetected_number_clusters = []\n", " for sheet, yolo_bb in yolo_data.items():\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", @@ -1040,6 +1064,8 @@ " expected_time_values.remove(value['label'])\n", " time_correct_clusters_count += 1\n", " time_distance_values.append(value['distance'])\n", + " \n", + " undetected_time_clusters += expected_time_values\n", "\n", " # Load JSON\n", " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", @@ -1060,21 +1086,18 @@ " number_correct_clusters_count += 1\n", " number_distance_values.append(value['distance'])\n", "\n", + " undetected_number_clusters += expected_number_values\n", + "\n", " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. The accuracy is {time_correct_clusters_count / (time_correct_clusters_count + time_wrong_clusters_count) * 100:.2f}%. Average distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - len(undetected_time_clusters)) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", " )\n", + " print(\"\\n\")\n", " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. The accuracy is {number_correct_clusters_count / (number_correct_clusters_count + number_wrong_clusters_count) * 100:.2f}%. Average distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", - " )\n" + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - len(undetected_number_clusters)) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", + " )\n", + " print(\"\\n\\n\")\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, From a80a82111422cb8f7d1c0b5c6e33e968fbfc472d Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 4 Nov 2024 21:27:08 -0500 Subject: [PATCH 31/55] Increased threshold for kmeans --- experiments/clustering/clustering.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 47ee0f2..3725feb 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +352,7 @@ " cluster_performance_map[n_clusters_max_silhouette][\"score\"]\n", " - cluster_performance_map[max(possible_nclusters)][\"score\"]\n", " )\n", - " >= 0.003\n", + " >= 0.005\n", " )\n", " else max(possible_nclusters)\n", " )\n", @@ -909,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 274, "metadata": {}, "outputs": [ { @@ -917,31 +917,31 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 774 correct clusters, 7 incorrect clusters. There were 24 undetected clusters. The accuracy is 63.67%.\n", + "Time labels: 774 correct clusters, 7 incorrect clusters. There were 24 undetected clusters. The accuracy is 93.98%.\n", "Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", "\n", "\n", - "Number labels: 369 correct clusters, 1 incorrect clusters. There were 11 undetected clusters. The accuracy is 30.39%.\n", + "Number labels: 369 correct clusters, 1 incorrect clusters. There were 11 undetected clusters. The accuracy is 94.21%.\n", "Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 785 correct clusters, 28 incorrect clusters. There were 13 undetected clusters. The accuracy is 65.53%.\n", + "Time labels: 785 correct clusters, 28 incorrect clusters. There were 13 undetected clusters. The accuracy is 96.74%.\n", "Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", "\n", "\n", - "Number labels: 355 correct clusters, 11 incorrect clusters. There were 25 undetected clusters. The accuracy is 28.01%.\n", + "Number labels: 355 correct clusters, 11 incorrect clusters. There were 25 undetected clusters. The accuracy is 86.84%.\n", "Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 776 correct clusters, 8 incorrect clusters. There were 22 undetected clusters. The accuracy is 64.01%.\n", + "Time labels: 776 correct clusters, 8 incorrect clusters. There were 22 undetected clusters. The accuracy is 94.49%.\n", "Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", "\n", "\n", - "Number labels: 363 correct clusters, 11 incorrect clusters. There were 17 undetected clusters. The accuracy is 29.37%.\n", + "Number labels: 363 correct clusters, 11 incorrect clusters. There were 17 undetected clusters. The accuracy is 91.05%.\n", "Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n", "\n", "\n", From 550286c32e8fe8bf1521806769696f3414601729 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Tue, 5 Nov 2024 10:45:18 -0500 Subject: [PATCH 32/55] Normalized erroneous bounding box method. --- experiments/clustering/clustering.ipynb | 158 ++++++------------------ 1 file changed, 41 insertions(+), 117 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 3725feb..872afb4 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -625,89 +625,13 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", - "Sheet: RC_0001_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n", "Sheet: RC_0001_intraoperative.JPG\n", "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", "Sheet: RC_0002_intraoperative.JPG\n", @@ -758,24 +682,24 @@ " Returns:\n", " None\n", " \"\"\"\n", - " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " sheet_num = 0\n", - " # Iterate over all images and their bounding boxes\n", - " for sheet, yolo_bbs in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " print(f\"Full image path: {full_image_path}\")\n", - "\n", - " # Call the analyze_sheet function with data from the loop\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bbs, full_image_path\n", - " )\n", + " # Iterate over all images and their bounding boxes\n", + " for sheet, yolo_bbs in yolo_data.items():\n", + " print(f\"Sheet: {sheet}\")\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " print(f\"Full image path: {full_image_path}\")\n", "\n", - " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", - " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", - " time_bounding_boxes, number_bounding_boxes\n", - " )\n", + " # Call the analyze_sheet function with data from the loop\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bbs, full_image_path\n", + " )\n", + "\n", + " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", + " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", + " time_bounding_boxes, number_bounding_boxes\n", + " )\n", "\n", + " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " sheet_num = 0\n", " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", " if method == \"kmeans\":\n", " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", @@ -909,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -917,32 +841,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 774 correct clusters, 7 incorrect clusters. There were 24 undetected clusters. The accuracy is 93.98%.\n", - "Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", + "Time labels: 762 correct clusters, 21 incorrect clusters. There were 36 undetected clusters. The accuracy is 90.98%.\n", + "Average distance when correct: 4.90px, incorrect: 4.41px, and overall: 4.89px\n", "\n", "\n", - "Number labels: 369 correct clusters, 1 incorrect clusters. There were 11 undetected clusters. The accuracy is 94.21%.\n", - "Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", + "Number labels: 371 correct clusters, 2 incorrect clusters. There were 9 undetected clusters. The accuracy is 95.26%.\n", + "Average distance when correct: 6.55px, incorrect: 6.78px, and overall: 6.55px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 785 correct clusters, 28 incorrect clusters. There were 13 undetected clusters. The accuracy is 96.74%.\n", - "Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", + "Time labels: 768 correct clusters, 63 incorrect clusters. There were 30 undetected clusters. The accuracy is 92.48%.\n", + "Average distance when correct: 4.84px, incorrect: 6.84px, and overall: 5.00px\n", "\n", "\n", - "Number labels: 355 correct clusters, 11 incorrect clusters. There were 25 undetected clusters. The accuracy is 86.84%.\n", - "Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", + "Number labels: 353 correct clusters, 10 incorrect clusters. There were 27 undetected clusters. The accuracy is 85.79%.\n", + "Average distance when correct: 6.56px, incorrect: 6.30px, and overall: 6.56px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 776 correct clusters, 8 incorrect clusters. There were 22 undetected clusters. The accuracy is 94.49%.\n", - "Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", + "Time labels: 760 correct clusters, 24 incorrect clusters. There were 38 undetected clusters. The accuracy is 90.48%.\n", + "Average distance when correct: 4.90px, incorrect: 5.12px, and overall: 4.91px\n", "\n", "\n", - "Number labels: 363 correct clusters, 11 incorrect clusters. There were 17 undetected clusters. The accuracy is 91.05%.\n", - "Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n", + "Number labels: 363 correct clusters, 6 incorrect clusters. There were 17 undetected clusters. The accuracy is 91.05%.\n", + "Average distance when correct: 6.58px, incorrect: 4.68px, and overall: 6.55px\n", "\n", "\n", "\n" @@ -1108,7 +1032,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1122,7 +1046,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.9.5" } }, "nbformat": 4, From 07c05ecb970b089fe29ce0e0e5234239b15fa550 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Tue, 5 Nov 2024 11:07:18 -0500 Subject: [PATCH 33/55] Improved kmeans results --- experiments/clustering/clustering.ipynb | 46 ++++++++++++------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 3725feb..4913050 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 275, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 276, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 277, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 278, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 279, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 280, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 281, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 282, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 283, "metadata": {}, "outputs": [], "source": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -909,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 285, "metadata": {}, "outputs": [ { @@ -917,32 +917,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 774 correct clusters, 7 incorrect clusters. There were 24 undetected clusters. The accuracy is 93.98%.\n", - "Average distance when correct: 4.93px, incorrect: 7.61px, and overall: 4.95px\n", + "Time labels: 774 correct clusters, 6 incorrect clusters. There were 24 undetected clusters. The accuracy is 93.98%.\n", + "Average distance when correct: 4.89px, incorrect: 6.83px, and overall: 4.90px\n", "\n", "\n", - "Number labels: 369 correct clusters, 1 incorrect clusters. There were 11 undetected clusters. The accuracy is 94.21%.\n", - "Average distance when correct: 6.15px, incorrect: 4.56px, and overall: 6.15px\n", + "Number labels: 374 correct clusters, 4 incorrect clusters. There were 6 undetected clusters. The accuracy is 96.84%.\n", + "Average distance when correct: 6.17px, incorrect: 6.54px, and overall: 6.17px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 785 correct clusters, 28 incorrect clusters. There were 13 undetected clusters. The accuracy is 96.74%.\n", - "Average distance when correct: 4.85px, incorrect: 7.44px, and overall: 4.94px\n", + "Time labels: 786 correct clusters, 28 incorrect clusters. There were 12 undetected clusters. The accuracy is 96.99%.\n", + "Average distance when correct: 4.84px, incorrect: 8.09px, and overall: 4.95px\n", "\n", "\n", - "Number labels: 355 correct clusters, 11 incorrect clusters. There were 25 undetected clusters. The accuracy is 86.84%.\n", - "Average distance when correct: 6.13px, incorrect: 5.46px, and overall: 6.11px\n", + "Number labels: 343 correct clusters, 17 incorrect clusters. There were 37 undetected clusters. The accuracy is 80.53%.\n", + "Average distance when correct: 6.17px, incorrect: 6.31px, and overall: 6.17px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 776 correct clusters, 8 incorrect clusters. There were 22 undetected clusters. The accuracy is 94.49%.\n", - "Average distance when correct: 4.86px, incorrect: 8.04px, and overall: 4.89px\n", + "Time labels: 776 correct clusters, 9 incorrect clusters. There were 22 undetected clusters. The accuracy is 94.49%.\n", + "Average distance when correct: 4.86px, incorrect: 7.08px, and overall: 4.89px\n", "\n", "\n", - "Number labels: 363 correct clusters, 11 incorrect clusters. There were 17 undetected clusters. The accuracy is 91.05%.\n", - "Average distance when correct: 6.21px, incorrect: 4.88px, and overall: 6.17px\n", + "Number labels: 364 correct clusters, 10 incorrect clusters. There were 16 undetected clusters. The accuracy is 91.58%.\n", + "Average distance when correct: 6.22px, incorrect: 5.26px, and overall: 6.19px\n", "\n", "\n", "\n" From b3d9fab54178e3e5789cd5a3c199badcbf55cb57 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Tue, 5 Nov 2024 11:35:35 -0500 Subject: [PATCH 34/55] Adjusted accuracy calculation. --- experiments/clustering/clustering.ipynb | 495 +++++++++++++----------- 1 file changed, 259 insertions(+), 236 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index f19dc6e..9b87a1f 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -620,90 +620,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Function that tests the clustering methods with our without erroneous boxes\n", + "\n", "Now lets use these functions to get the relevant bounding boxes for clustering.\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sheet: RC_0001_intraoperative.JPG" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Full image path: ../../data/registered_images\\RC_0001_intraoperative.JPG\n", - "Sheet: RC_0002_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0002_intraoperative.JPG\n", - "Sheet: RC_0003_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0003_intraoperative.JPG\n", - "Sheet: RC_0004_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0004_intraoperative.JPG\n", - "Sheet: RC_0005_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0005_intraoperative.JPG\n", - "Sheet: RC_0006_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0006_intraoperative.JPG\n", - "Sheet: RC_0007_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0007_intraoperative.JPG\n", - "Sheet: RC_0008_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0008_intraoperative.JPG\n", - "Sheet: RC_0009_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0009_intraoperative.JPG\n", - "Sheet: RC_0010_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0010_intraoperative.JPG\n", - "Sheet: RC_0011_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0011_intraoperative.JPG\n", - "Sheet: RC_0012_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0012_intraoperative.JPG\n", - "Sheet: RC_0013_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0013_intraoperative.JPG\n", - "Sheet: RC_0014_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0014_intraoperative.JPG\n", - "Sheet: RC_0015_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0015_intraoperative.JPG\n", - "Sheet: RC_0016_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0016_intraoperative.JPG\n", - "Sheet: RC_0017_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0017_intraoperative.JPG\n", - "Sheet: RC_0018_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0018_intraoperative.JPG\n", - "Sheet: RC_0019_intraoperative.JPG\n", - "Full image path: ../../data/registered_images\\RC_0019_intraoperative.JPG\n" - ] - } - ], + "outputs": [], "source": [ - "def test_clustering_methods() -> None:\n", + "def test_clustering_methods(add_erroneous = True) -> None:\n", " \"\"\"\n", " Test the clustering methods on the YOLO data.\n", " Saves the clustered images and the clustered bounding boxes to JSON files.\n", "\n", + " Args:\n", + " add_erroneous: Boolean flag to add erroneous bounding boxes to the data.\n", + "\n", " Returns:\n", " None\n", " \"\"\"\n", " # Iterate over all images and their bounding boxes\n", " for sheet, yolo_bbs in yolo_data.items():\n", - " print(f\"Sheet: {sheet}\")\n", + " #print(f\"Sheet: {sheet}\")\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " print(f\"Full image path: {full_image_path}\")\n", + " #print(f\"Full image path: {full_image_path}\")\n", "\n", " # Call the analyze_sheet function with data from the loop\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", " yolo_bbs, full_image_path\n", " )\n", "\n", - " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", - " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", - " time_bounding_boxes, number_bounding_boxes\n", - " )\n", + " if add_erroneous:\n", + " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", + " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", + " time_bounding_boxes, number_bounding_boxes\n", + " )\n", "\n", " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", " sheet_num = 0\n", @@ -821,12 +775,7 @@ " f,\n", " )\n", "\n", - " sheet_num += 1\n", - " \n", - "\n", - "\n", - "# Test the clustering methods\n", - "test_clustering_methods()" + " sheet_num += 1" ] }, { @@ -835,12 +784,174 @@ "source": [ "#### Analyze accuracy\n", "\n", - "Below we use assumptions on what we know the labels should represent in both the time and number groups. We check that these values are present within clusters.\n" + "Below we write a function that analyzes the accuracy of our clustering methods.\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_accuracy():\n", + " # Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", + " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " print(f\"Method: {method}\")\n", + " # Paths to the JSON files\n", + " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", + " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", + " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", + "\n", + " time_wrong_clusters_count = 0\n", + " time_correct_clusters_count = 0\n", + " number_wrong_clusters_count = 0\n", + " number_correct_clusters_count = 0\n", + "\n", + " # Iterate over all images and their bounding boxes\n", + " number_distance_values = []\n", + " time_distance_values = []\n", + " number_distance_values_erroneous = []\n", + " time_distance_values_erroneous = []\n", + " # Undetected clusters\n", + " undetected_time_clusters = []\n", + " undetected_number_clusters = []\n", + " for sheet, yolo_bb in yolo_data.items():\n", + " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", + " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", + " yolo_bb, full_image_path,\n", + " )\n", + " # Convert the bounding boxes to a list of strings with proper suffixes\n", + " expected_time_values = [\n", + " \"0_mins\",\n", + " \"5_mins\",\n", + " \"10_mins\",\n", + " \"15_mins\",\n", + " \"20_mins\",\n", + " \"25_mins\",\n", + " \"30_mins\",\n", + " \"35_mins\",\n", + " \"40_mins\",\n", + " \"45_mins\",\n", + " \"50_mins\",\n", + " \"55_mins\",\n", + " \"60_mins\",\n", + " \"65_mins\",\n", + " \"70_mins\",\n", + " \"75_mins\",\n", + " \"80_mins\",\n", + " \"85_mins\",\n", + " \"90_mins\",\n", + " \"95_mins\",\n", + " \"100_mins\",\n", + " \"105_mins\",\n", + " \"110_mins\",\n", + " \"115_mins\",\n", + " \"120_mins\",\n", + " \"125_mins\",\n", + " \"130_mins\",\n", + " \"135_mins\",\n", + " \"140_mins\",\n", + " \"145_mins\",\n", + " \"150_mins\",\n", + " \"155_mins\",\n", + " \"160_mins\",\n", + " \"165_mins\",\n", + " \"170_mins\",\n", + " \"175_mins\",\n", + " \"180_mins\",\n", + " \"185_mins\",\n", + " \"190_mins\",\n", + " \"195_mins\",\n", + " \"200_mins\",\n", + " \"205_mins\",\n", + " ]\n", + "\n", + " expected_number_values = [\n", + " \"30_mmhg\",\n", + " \"40_mmhg\",\n", + " \"50_mmhg\",\n", + " \"60_mmhg\",\n", + " \"70_mmhg\",\n", + " \"80_mmhg\",\n", + " \"90_mmhg\",\n", + " \"100_mmhg\",\n", + " \"110_mmhg\",\n", + " \"120_mmhg\",\n", + " \"130_mmhg\",\n", + " \"140_mmhg\",\n", + " \"150_mmhg\",\n", + " \"160_mmhg\",\n", + " \"170_mmhg\",\n", + " \"180_mmhg\",\n", + " \"190_mmhg\",\n", + " \"200_mmhg\",\n", + " \"210_mmhg\",\n", + " \"220_mmhg\",\n", + " ]\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", + " time_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in time_clusters.items():\n", + " if value['label'] not in expected_time_values:\n", + " # Print the sheet, value that is not in the expected values\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " time_wrong_clusters_count += 1\n", + " time_distance_values_erroneous.append(value['distance'])\n", + " else:\n", + " # We have a correct cluster\n", + " expected_time_values.remove(value['label'])\n", + " time_correct_clusters_count += 1\n", + " time_distance_values.append(value['distance'])\n", + " \n", + " undetected_time_clusters += expected_time_values\n", + "\n", + " # Load JSON\n", + " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", + " number_clusters = json.load(f)\n", + "\n", + " # Each cluster contains the number (integer) that the cluster represents\n", + " # We know what integers should be represented in the time labels, lets check that they are all there.\n", + " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", + " for cluster, value in number_clusters.items():\n", + " if value['label'] not in expected_number_values:\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " # We have an erroneous cluster\n", + " number_wrong_clusters_count += 1\n", + " number_distance_values_erroneous.append(value['distance'])\n", + " else:\n", + " # We have a correct cluster\n", + " expected_number_values.remove(value['label'])\n", + " number_correct_clusters_count += 1\n", + " number_distance_values.append(value['distance'])\n", + "\n", + " undetected_number_clusters += expected_number_values\n", + "\n", + " print(\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / 1 if len(time_distance_values_erroneous) == 0 else len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", + " )\n", + " print(\"\\n\")\n", + " print(\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / 1 if len(number_distance_values_erroneous) == 0 else len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", + " )\n", + " print(\"\\n\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test without Erroneous bounding boxes" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -848,32 +959,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 757 correct clusters, 23 incorrect clusters. There were 41 undetected clusters. The accuracy is 89.72%.\n", - "Average distance when correct: 4.91px, incorrect: 4.90px, and overall: 4.91px\n", + "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", + "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", "\n", "\n", - "Number labels: 368 correct clusters, 4 incorrect clusters. There were 12 undetected clusters. The accuracy is 93.68%.\n", - "Average distance when correct: 6.70px, incorrect: 5.82px, and overall: 6.69px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 766 correct clusters, 52 incorrect clusters. There were 32 undetected clusters. The accuracy is 91.98%.\n", - "Average distance when correct: 4.84px, incorrect: 6.97px, and overall: 4.98px\n", + "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", + "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", "\n", "\n", - "Number labels: 352 correct clusters, 9 incorrect clusters. There were 28 undetected clusters. The accuracy is 85.26%.\n", - "Average distance when correct: 6.68px, incorrect: 6.81px, and overall: 6.68px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 760 correct clusters, 25 incorrect clusters. There were 38 undetected clusters. The accuracy is 90.48%.\n", - "Average distance when correct: 4.91px, incorrect: 5.04px, and overall: 4.92px\n", + "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", + "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", "\n", "\n", - "Number labels: 356 correct clusters, 14 incorrect clusters. There were 24 undetected clusters. The accuracy is 87.37%.\n", - "Average distance when correct: 6.72px, incorrect: 5.31px, and overall: 6.67px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", "\n", "\n", "\n" @@ -881,152 +992,64 @@ } ], "source": [ - "# Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", - "for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " print(f\"Method: {method}\")\n", - " # Paths to the JSON files\n", - " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", - " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", - " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", - "\n", - " time_wrong_clusters_count = 0\n", - " time_correct_clusters_count = 0\n", - " number_wrong_clusters_count = 0\n", - " number_correct_clusters_count = 0\n", - "\n", - " # Iterate over all images and their bounding boxes\n", - " number_distance_values = []\n", - " time_distance_values = []\n", - " number_distance_values_erroneous = []\n", - " time_distance_values_erroneous = []\n", - " # Undetected clusters\n", - " undetected_time_clusters = []\n", - " undetected_number_clusters = []\n", - " for sheet, yolo_bb in yolo_data.items():\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb, full_image_path,\n", - " )\n", - " # Convert the bounding boxes to a list of strings with proper suffixes\n", - " expected_time_values = [\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"60_mins\",\n", - " \"65_mins\",\n", - " \"70_mins\",\n", - " \"75_mins\",\n", - " \"80_mins\",\n", - " \"85_mins\",\n", - " \"90_mins\",\n", - " \"95_mins\",\n", - " \"100_mins\",\n", - " \"105_mins\",\n", - " \"110_mins\",\n", - " \"115_mins\",\n", - " \"120_mins\",\n", - " \"125_mins\",\n", - " \"130_mins\",\n", - " \"135_mins\",\n", - " \"140_mins\",\n", - " \"145_mins\",\n", - " \"150_mins\",\n", - " \"155_mins\",\n", - " \"160_mins\",\n", - " \"165_mins\",\n", - " \"170_mins\",\n", - " \"175_mins\",\n", - " \"180_mins\",\n", - " \"185_mins\",\n", - " \"190_mins\",\n", - " \"195_mins\",\n", - " \"200_mins\",\n", - " \"205_mins\",\n", - " ]\n", - "\n", - " expected_number_values = [\n", - " \"30_mmhg\",\n", - " \"40_mmhg\",\n", - " \"50_mmhg\",\n", - " \"60_mmhg\",\n", - " \"70_mmhg\",\n", - " \"80_mmhg\",\n", - " \"90_mmhg\",\n", - " \"100_mmhg\",\n", - " \"110_mmhg\",\n", - " \"120_mmhg\",\n", - " \"130_mmhg\",\n", - " \"140_mmhg\",\n", - " \"150_mmhg\",\n", - " \"160_mmhg\",\n", - " \"170_mmhg\",\n", - " \"180_mmhg\",\n", - " \"190_mmhg\",\n", - " \"200_mmhg\",\n", - " \"210_mmhg\",\n", - " \"220_mmhg\",\n", - " ]\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", - " time_clusters = json.load(f)\n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in time_clusters.items():\n", - " if value['label'] not in expected_time_values:\n", - " # Print the sheet, value that is not in the expected values\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", - " # We have an erroneous cluster\n", - " time_wrong_clusters_count += 1\n", - " time_distance_values_erroneous.append(value['distance'])\n", - " else:\n", - " # We have a correct cluster\n", - " expected_time_values.remove(value['label'])\n", - " time_correct_clusters_count += 1\n", - " time_distance_values.append(value['distance'])\n", - " \n", - " undetected_time_clusters += expected_time_values\n", - "\n", - " # Load JSON\n", - " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", - " number_clusters = json.load(f)\n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in number_clusters.items():\n", - " if value['label'] not in expected_number_values:\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", - " # We have an erroneous cluster\n", - " number_wrong_clusters_count += 1\n", - " number_distance_values_erroneous.append(value['distance'])\n", - " else:\n", - " # We have a correct cluster\n", - " expected_number_values.remove(value['label'])\n", - " number_correct_clusters_count += 1\n", - " number_distance_values.append(value['distance'])\n", - "\n", - " undetected_number_clusters += expected_number_values\n", - "\n", - " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - len(undetected_time_clusters)) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", - " )\n", - " print(\"\\n\")\n", - " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - len(undetected_number_clusters)) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", - " )\n", - " print(\"\\n\\n\")\n" + "# Test the clustering methods with errouneous bounding boxes\n", + "test_clustering_methods(add_erroneous=False)\n", + "analyze_accuracy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test with Erroneous bounding boxes" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time labels: 759 correct clusters, 21 incorrect clusters. There were 39 undetected clusters. The accuracy is 95.11%.\n", + "Average distance when correct: 4.87px, incorrect: 4.52px, and overall: 4.86px\n", + "\n", + "\n", + "Number labels: 374 correct clusters, 2 incorrect clusters. There were 6 undetected clusters. The accuracy is 98.42%.\n", + "Average distance when correct: 6.68px, incorrect: 2.00px, and overall: 6.68px\n", + "\n", + "\n", + "\n", + "Method: dbscan\n", + "Time labels: 770 correct clusters, 55 incorrect clusters. There were 28 undetected clusters. The accuracy is 96.49%.\n", + "Average distance when correct: 4.81px, incorrect: 6.55px, and overall: 4.92px\n", + "\n", + "\n", + "Number labels: 347 correct clusters, 14 incorrect clusters. There were 33 undetected clusters. The accuracy is 91.32%.\n", + "Average distance when correct: 6.65px, incorrect: 14.00px, and overall: 6.65px\n", + "\n", + "\n", + "\n", + "Method: agglomerative\n", + "Time labels: 763 correct clusters, 24 incorrect clusters. There were 35 undetected clusters. The accuracy is 95.61%.\n", + "Average distance when correct: 4.86px, incorrect: 5.14px, and overall: 4.87px\n", + "\n", + "\n", + "Number labels: 364 correct clusters, 11 incorrect clusters. There were 16 undetected clusters. The accuracy is 95.79%.\n", + "Average distance when correct: 6.72px, incorrect: 11.00px, and overall: 6.68px\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Test the clustering methods with errouneous bounding boxes\n", + "test_clustering_methods(add_erroneous=True)\n", + "analyze_accuracy()" ] }, { From 09f009119e01ec482c52ed8d844b32c9ad318c98 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Tue, 5 Nov 2024 13:37:17 -0500 Subject: [PATCH 35/55] Clustering accuracy is now (correct - (incorrect + undetected)) / number expected clusters --- experiments/clustering/clustering.ipynb | 84 ++++++++++++------------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 9b87a1f..9df86c3 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -642,6 +642,7 @@ " Returns:\n", " None\n", " \"\"\"\n", + " sheet_num = 0\n", " # Iterate over all images and their bounding boxes\n", " for sheet, yolo_bbs in yolo_data.items():\n", " #print(f\"Sheet: {sheet}\")\n", @@ -660,7 +661,6 @@ " )\n", "\n", " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", - " sheet_num = 0\n", " # Now we need to cluster the bounding boxes that pertain to the same multi-digit number\n", " if method == \"kmeans\":\n", " time_labels = cluster_kmeans(time_bounding_boxes, [40, 41, 42])\n", @@ -775,7 +775,7 @@ " f,\n", " )\n", "\n", - " sheet_num += 1" + " sheet_num += 1" ] }, { @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -899,7 +899,7 @@ " for cluster, value in time_clusters.items():\n", " if value['label'] not in expected_time_values:\n", " # Print the sheet, value that is not in the expected values\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " #print(f\"Time -> Sheet: {sheet}, Value: {value[\"label\"]}.\")\n", " # We have an erroneous cluster\n", " time_wrong_clusters_count += 1\n", " time_distance_values_erroneous.append(value['distance'])\n", @@ -933,11 +933,11 @@ " undetected_number_clusters += expected_number_values\n", "\n", " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / 1 if len(time_distance_values_erroneous) == 0 else len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - (time_wrong_clusters_count + len(undetected_time_clusters))) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / 1 if len(time_distance_values_erroneous) == 0 else len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", " )\n", " print(\"\\n\")\n", " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / 1 if len(number_distance_values_erroneous) == 0 else len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - (number_wrong_clusters_count + len(undetected_number_clusters))) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / 1 if len(number_distance_values_erroneous) == 0 else len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", " )\n", " print(\"\\n\\n\")\n" ] @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -959,32 +959,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", - "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", + "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", + "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", + "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", + "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", - "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", + "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", + "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", + "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", + "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 778 correct clusters, 19 incorrect clusters. There were 20 undetected clusters. The accuracy is 97.49%.\n", - "Average distance when correct: 4.81px, incorrect: 3.94px, and overall: 4.79px\n", + "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", + "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.60px, incorrect: 0.00px, and overall: 6.60px\n", + "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", + "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", "\n", "\n", "\n" @@ -1006,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1014,32 +1014,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 759 correct clusters, 21 incorrect clusters. There were 39 undetected clusters. The accuracy is 95.11%.\n", - "Average distance when correct: 4.87px, incorrect: 4.52px, and overall: 4.86px\n", + "Time labels: 765 correct clusters, 8 incorrect clusters. There were 33 undetected clusters. The accuracy is 90.73%.\n", + "Average distance when correct: 4.91px, incorrect: 8.00px, and overall: 4.93px\n", "\n", "\n", - "Number labels: 374 correct clusters, 2 incorrect clusters. There were 6 undetected clusters. The accuracy is 98.42%.\n", - "Average distance when correct: 6.68px, incorrect: 2.00px, and overall: 6.68px\n", + "Number labels: 376 correct clusters, 2 incorrect clusters. There were 4 undetected clusters. The accuracy is 97.37%.\n", + "Average distance when correct: 6.15px, incorrect: 2.00px, and overall: 6.15px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 770 correct clusters, 55 incorrect clusters. There were 28 undetected clusters. The accuracy is 96.49%.\n", - "Average distance when correct: 4.81px, incorrect: 6.55px, and overall: 4.92px\n", + "Time labels: 782 correct clusters, 37 incorrect clusters. There were 16 undetected clusters. The accuracy is 91.35%.\n", + "Average distance when correct: 4.87px, incorrect: 37.00px, and overall: 5.02px\n", "\n", "\n", - "Number labels: 347 correct clusters, 14 incorrect clusters. There were 33 undetected clusters. The accuracy is 91.32%.\n", - "Average distance when correct: 6.65px, incorrect: 14.00px, and overall: 6.65px\n", + "Number labels: 351 correct clusters, 14 incorrect clusters. There were 29 undetected clusters. The accuracy is 81.05%.\n", + "Average distance when correct: 6.18px, incorrect: 14.00px, and overall: 6.15px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 763 correct clusters, 24 incorrect clusters. There were 35 undetected clusters. The accuracy is 95.61%.\n", - "Average distance when correct: 4.86px, incorrect: 5.14px, and overall: 4.87px\n", + "Time labels: 778 correct clusters, 10 incorrect clusters. There were 20 undetected clusters. The accuracy is 93.73%.\n", + "Average distance when correct: 4.90px, incorrect: 10.00px, and overall: 4.94px\n", "\n", "\n", - "Number labels: 364 correct clusters, 11 incorrect clusters. There were 16 undetected clusters. The accuracy is 95.79%.\n", - "Average distance when correct: 6.72px, incorrect: 11.00px, and overall: 6.68px\n", + "Number labels: 361 correct clusters, 14 incorrect clusters. There were 19 undetected clusters. The accuracy is 86.32%.\n", + "Average distance when correct: 6.23px, incorrect: 14.00px, and overall: 6.19px\n", "\n", "\n", "\n" From 3e77659a8641738beb33e9ac45bf54ae7bd5bb79 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Tue, 5 Nov 2024 13:50:48 -0500 Subject: [PATCH 36/55] Re-ran whole notebook --- experiments/clustering/clustering.ipynb | 62 ++++++++++++++++++------- 1 file changed, 46 insertions(+), 16 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 9df86c3..24383c2 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -920,7 +920,7 @@ " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", " for cluster, value in number_clusters.items():\n", " if value['label'] not in expected_number_values:\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value}\")\n", + " #print(f\"Number -> Sheet: {sheet}, Value: {value}\")\n", " # We have an erroneous cluster\n", " number_wrong_clusters_count += 1\n", " number_distance_values_erroneous.append(value['distance'])\n", @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -959,6 +959,7 @@ "output_type": "stream", "text": [ "Method: kmeans\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -969,6 +970,7 @@ "\n", "\n", "Method: dbscan\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -979,6 +981,7 @@ "\n", "\n", "Method: agglomerative\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -1006,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1014,32 +1017,59 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 765 correct clusters, 8 incorrect clusters. There were 33 undetected clusters. The accuracy is 90.73%.\n", - "Average distance when correct: 4.91px, incorrect: 8.00px, and overall: 4.93px\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '40_mmhg', 'distance': 5.183788043583087}\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 6.1316966112142905}\n", + "Time labels: 771 correct clusters, 9 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.11%.\n", + "Average distance when correct: 4.90px, incorrect: 9.00px, and overall: 4.92px\n", "\n", "\n", - "Number labels: 376 correct clusters, 2 incorrect clusters. There were 4 undetected clusters. The accuracy is 97.37%.\n", - "Average distance when correct: 6.15px, incorrect: 2.00px, and overall: 6.15px\n", + "Number labels: 375 correct clusters, 2 incorrect clusters. There were 5 undetected clusters. The accuracy is 96.84%.\n", + "Average distance when correct: 6.10px, incorrect: 2.00px, and overall: 6.10px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 782 correct clusters, 37 incorrect clusters. There were 16 undetected clusters. The accuracy is 91.35%.\n", - "Average distance when correct: 4.87px, incorrect: 37.00px, and overall: 5.02px\n", + "Time -> Sheet: RC_0001_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 5.180344603918557}\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 6.007774871034995}\n", + "Time -> Sheet: RC_0005_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 5.154766705109225}\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '40_mmhg', 'distance': 5.183788043583087}\n", + "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 5.066812625083303}\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 5.994486889373393}\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '170_mmhg', 'distance': 6.795881051183203}\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: {'label': '100_mmhg', 'distance': 4.7953322880555165}\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 5.102439879048022}\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 3.062043241211753}\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 6.666556359082255}\n", + "Time -> Sheet: RC_0016_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 6.683502203641104}\n", + "Time -> Sheet: RC_0017_intraoperative.JPG, Value: {'label': '60_mmhg', 'distance': 8.268570745370752}\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 6.844576419938307}\n", + "Time labels: 784 correct clusters, 34 incorrect clusters. There were 14 undetected clusters. The accuracy is 92.23%.\n", + "Average distance when correct: 4.84px, incorrect: 34.00px, and overall: 4.98px\n", "\n", "\n", - "Number labels: 351 correct clusters, 14 incorrect clusters. There were 29 undetected clusters. The accuracy is 81.05%.\n", - "Average distance when correct: 6.18px, incorrect: 14.00px, and overall: 6.15px\n", + "Number labels: 354 correct clusters, 14 incorrect clusters. There were 26 undetected clusters. The accuracy is 82.63%.\n", + "Average distance when correct: 6.12px, incorrect: 14.00px, and overall: 6.11px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 778 correct clusters, 10 incorrect clusters. There were 20 undetected clusters. The accuracy is 93.73%.\n", - "Average distance when correct: 4.90px, incorrect: 10.00px, and overall: 4.94px\n", + "Time -> Sheet: RC_0002_intraoperative.JPG, Value: {'label': '200_mmhg', 'distance': 3.354495506784342}\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '120_mmhg', 'distance': 3.318892100399529}\n", + "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '210_mmhg', 'distance': 3.3901530203134507}\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 3.76910779112449}\n", + "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '190_mmhg', 'distance': 3.7830823077202314}\n", + "Time -> Sheet: RC_0009_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 3.764961287135599}\n", + "Time -> Sheet: RC_0010_intraoperative.JPG, Value: {'label': '210_mmhg', 'distance': 3.2372488004611957}\n", + "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 6.1316966112142905}\n", + "Time -> Sheet: RC_0013_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 10.697378890833223}\n", + "Time -> Sheet: RC_0018_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 3.5671566928598124}\n", + "Time -> Sheet: RC_0019_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 3.4219013467424286}\n", + "Time labels: 774 correct clusters, 11 incorrect clusters. There were 24 undetected clusters. The accuracy is 92.61%.\n", + "Average distance when correct: 4.89px, incorrect: 11.00px, and overall: 4.93px\n", "\n", "\n", - "Number labels: 361 correct clusters, 14 incorrect clusters. There were 19 undetected clusters. The accuracy is 86.32%.\n", - "Average distance when correct: 6.23px, incorrect: 14.00px, and overall: 6.19px\n", + "Number labels: 365 correct clusters, 11 incorrect clusters. There were 15 undetected clusters. The accuracy is 89.21%.\n", + "Average distance when correct: 6.14px, incorrect: 11.00px, and overall: 6.09px\n", "\n", "\n", "\n" From d064e248e1e2d3cee74df6e4671e75da9dfee473 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Tue, 5 Nov 2024 13:51:04 -0500 Subject: [PATCH 37/55] Re-ran notebook --- experiments/clustering/clustering.ipynb | 60 +++++++------------------ 1 file changed, 15 insertions(+), 45 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 24383c2..486dbe9 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -951,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -959,7 +959,6 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -970,7 +969,6 @@ "\n", "\n", "Method: dbscan\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -981,7 +979,6 @@ "\n", "\n", "Method: agglomerative\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 4.809698530392025}\n", "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", "\n", @@ -1009,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1017,59 +1014,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '40_mmhg', 'distance': 5.183788043583087}\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 6.1316966112142905}\n", - "Time labels: 771 correct clusters, 9 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.11%.\n", - "Average distance when correct: 4.90px, incorrect: 9.00px, and overall: 4.92px\n", + "Time labels: 771 correct clusters, 4 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.73%.\n", + "Average distance when correct: 4.90px, incorrect: 4.00px, and overall: 4.91px\n", "\n", "\n", - "Number labels: 375 correct clusters, 2 incorrect clusters. There were 5 undetected clusters. The accuracy is 96.84%.\n", - "Average distance when correct: 6.10px, incorrect: 2.00px, and overall: 6.10px\n", + "Number labels: 371 correct clusters, 3 incorrect clusters. There were 9 undetected clusters. The accuracy is 94.47%.\n", + "Average distance when correct: 6.11px, incorrect: 3.00px, and overall: 6.10px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time -> Sheet: RC_0001_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 5.180344603918557}\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 6.007774871034995}\n", - "Time -> Sheet: RC_0005_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 5.154766705109225}\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '40_mmhg', 'distance': 5.183788043583087}\n", - "Time -> Sheet: RC_0006_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 5.066812625083303}\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 5.994486889373393}\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '170_mmhg', 'distance': 6.795881051183203}\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: {'label': '100_mmhg', 'distance': 4.7953322880555165}\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 5.102439879048022}\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 3.062043241211753}\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 6.666556359082255}\n", - "Time -> Sheet: RC_0016_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 6.683502203641104}\n", - "Time -> Sheet: RC_0017_intraoperative.JPG, Value: {'label': '60_mmhg', 'distance': 8.268570745370752}\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: {'label': '130_mmhg', 'distance': 6.844576419938307}\n", - "Time labels: 784 correct clusters, 34 incorrect clusters. There were 14 undetected clusters. The accuracy is 92.23%.\n", - "Average distance when correct: 4.84px, incorrect: 34.00px, and overall: 4.98px\n", + "Time labels: 786 correct clusters, 33 incorrect clusters. There were 12 undetected clusters. The accuracy is 92.86%.\n", + "Average distance when correct: 4.83px, incorrect: 33.00px, and overall: 4.98px\n", "\n", "\n", - "Number labels: 354 correct clusters, 14 incorrect clusters. There were 26 undetected clusters. The accuracy is 82.63%.\n", - "Average distance when correct: 6.12px, incorrect: 14.00px, and overall: 6.11px\n", + "Number labels: 347 correct clusters, 11 incorrect clusters. There were 33 undetected clusters. The accuracy is 79.74%.\n", + "Average distance when correct: 6.12px, incorrect: 11.00px, and overall: 6.11px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time -> Sheet: RC_0002_intraoperative.JPG, Value: {'label': '200_mmhg', 'distance': 3.354495506784342}\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '120_mmhg', 'distance': 3.318892100399529}\n", - "Time -> Sheet: RC_0007_intraoperative.JPG, Value: {'label': '210_mmhg', 'distance': 3.3901530203134507}\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 3.76910779112449}\n", - "Time -> Sheet: RC_0008_intraoperative.JPG, Value: {'label': '190_mmhg', 'distance': 3.7830823077202314}\n", - "Time -> Sheet: RC_0009_intraoperative.JPG, Value: {'label': '110_mmhg', 'distance': 3.764961287135599}\n", - "Time -> Sheet: RC_0010_intraoperative.JPG, Value: {'label': '210_mmhg', 'distance': 3.2372488004611957}\n", - "Time -> Sheet: RC_0011_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 6.1316966112142905}\n", - "Time -> Sheet: RC_0013_intraoperative.JPG, Value: {'label': '90_mmhg', 'distance': 10.697378890833223}\n", - "Time -> Sheet: RC_0018_intraoperative.JPG, Value: {'label': '160_mmhg', 'distance': 3.5671566928598124}\n", - "Time -> Sheet: RC_0019_intraoperative.JPG, Value: {'label': '140_mmhg', 'distance': 3.4219013467424286}\n", - "Time labels: 774 correct clusters, 11 incorrect clusters. There were 24 undetected clusters. The accuracy is 92.61%.\n", - "Average distance when correct: 4.89px, incorrect: 11.00px, and overall: 4.93px\n", + "Time labels: 781 correct clusters, 7 incorrect clusters. There were 17 undetected clusters. The accuracy is 94.86%.\n", + "Average distance when correct: 4.89px, incorrect: 7.00px, and overall: 4.92px\n", "\n", "\n", - "Number labels: 365 correct clusters, 11 incorrect clusters. There were 15 undetected clusters. The accuracy is 89.21%.\n", - "Average distance when correct: 6.14px, incorrect: 11.00px, and overall: 6.09px\n", + "Number labels: 354 correct clusters, 17 incorrect clusters. There were 26 undetected clusters. The accuracy is 81.84%.\n", + "Average distance when correct: 6.19px, incorrect: 17.00px, and overall: 6.16px\n", "\n", "\n", "\n" From 112245fe40eac66e6b2f32fc6cade173a81817df Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Tue, 5 Nov 2024 14:14:36 -0500 Subject: [PATCH 38/55] edit preprocessing --- experiments/clustering/clustering.ipynb | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 486dbe9..6900653 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -203,6 +203,11 @@ " )\n", " )\n", "\n", + " # TODO: Perform the single axis filtering first, then filter the adjacent axis by removing outliers\n", + " # ? Possibilities of removing outliers based off of distance vs. location\n", + " # ? Distance will result in the possibility of boxes being included if there are two together\n", + " # ? location will result in the possibility of tihe boxes being included if there are close\n", + "\n", " # find the point with the maximum density of bounding boxes\n", " bboxes_right: List[int] = [bb.right for bb in bboxes]\n", " # x_loc is the vertical line to the left of the time axis and right of the numbers axis\n", From 7be99a82ae3bbe52c8df3ae663dd5bdb61175fb5 Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Tue, 5 Nov 2024 15:01:20 -0500 Subject: [PATCH 39/55] Add remove outliers to preprocessing --- experiments/clustering/clustering.ipynb | 161 ++++++++++++++---------- 1 file changed, 97 insertions(+), 64 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 6900653..5c36ac6 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,6 +157,45 @@ " max_index = np.argmax(kde_vals)\n", " return x_values[max_index]\n", "\n", + "def remove_bb_outliers(boxes: List[BoundingBox]) -> List[BoundingBox]:\n", + " \"\"\"\n", + " Given a list of bounding boxes, remove the outliers from the x axis, then remove the outliers from the y axis\n", + "\n", + " Args:\n", + " boxes: List of Bounding Boxes to filter\n", + "\n", + " Returns:\n", + " Filtered list of Bounding Boxes\n", + " \"\"\"\n", + " x_vals = [bb.left for bb in boxes]\n", + " # find the 25th percentile\n", + " x_Q1 = np.percentile(x_vals, 25)\n", + " # find the 75th percentile\n", + " x_Q3 = np.percentile(x_vals, 75)\n", + " # find the IQR\n", + " x_IQR = x_Q3 - x_Q1\n", + " # determine lower and upper bounds\n", + " x_lower = x_Q1 - 1.5*x_IQR\n", + " x_upper = x_Q3 + 1.5*x_IQR\n", + " # remove outliers via the x axis\n", + " x_filtered = [bb for bb in boxes if x_lower <= bb.left <= x_upper]\n", + "\n", + " y_vals = [bb.top for bb in x_filtered]\n", + " # find the 25th percentile\n", + " y_Q1 = np.percentile(y_vals, 25)\n", + " # find the 75th percentile\n", + " y_Q3 = np.percentile(y_vals, 75)\n", + " # find the IQR\n", + " y_IQR = y_Q3 - y_Q1\n", + " # determine the lower and upper bounds\n", + " y_lower = y_Q1 - 1.5*y_IQR\n", + " y_upper = y_Q3 + 1.5*y_IQR\n", + " # remove outliers via the y axis\n", + " filtered = [bb for bb in x_filtered if y_lower <= bb.top <= y_upper]\n", + "\n", + " return x_filtered\n", + "\n", + "\n", "\n", "def select_relevant_bounding_boxes(\n", " sheet_data: List[str],\n", @@ -203,11 +242,6 @@ " )\n", " )\n", "\n", - " # TODO: Perform the single axis filtering first, then filter the adjacent axis by removing outliers\n", - " # ? Possibilities of removing outliers based off of distance vs. location\n", - " # ? Distance will result in the possibility of boxes being included if there are two together\n", - " # ? location will result in the possibility of tihe boxes being included if there are close\n", - "\n", " # find the point with the maximum density of bounding boxes\n", " bboxes_right: List[int] = [bb.right for bb in bboxes]\n", " # x_loc is the vertical line to the left of the time axis and right of the numbers axis\n", @@ -217,56 +251,55 @@ " # y_loc is the horizontal line undert the time axis and above the number axis\n", " y_loc: int = find_density_max(bboxes_bottom, desired_img_height)\n", "\n", - " # x_loc_right is the vertical line at the end of the time axis\n", - " x_loc_right: int = x_loc + 610\n", - " # y_loc_bottom is the horizontal line at the bottom of the number axis\n", - " y_loc_bottom: int = y_loc + 185\n", "\n", " bounding_boxes_time = []\n", " bounding_boxes_numbers = []\n", "\n", " # Process the bounding boxes\n", " for bounding_box in bboxes:\n", - " # get the pixels for plotting\n", - " x_min = int(bounding_box.left)\n", - " x_max = int(bounding_box.right)\n", - " y_min = int(bounding_box.top)\n", - " y_max = int(bounding_box.bottom)\n", - " \n", " # get the center point of the bounding box for comparison\n", " x_center_bb, y_center_bb = bounding_box.center\n", "\n", " # check if the bounding box is a number on the BP chart by comparing to the KDE index + a threshold\n", - " if ((x_center_bb > x_loc-15 and x_center_bb < x_loc+2) and (y_center_bb > y_loc-2 and y_center_bb < y_loc_bottom+2)):\n", - " # Bounding box is in the top-right region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", - " )\n", + " if (x_center_bb > x_loc-15 and x_center_bb < x_loc+2):\n", " bounding_boxes_numbers.append(bounding_box)\n", " # check if the bounding box is a time on the BP chart by comparing to the KDE index + a threshold\n", - " elif ((y_center_bb > y_loc-10 and y_max < y_loc+2) and (x_center_bb > x_loc-2 and x_center_bb < x_loc_right+2)):\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", - " )\n", + " elif (y_center_bb > y_loc-10 and y_center_bb < y_loc+2):\n", " bounding_boxes_time.append(bounding_box)\n", - " \n", - " # plot the lines of the KDE index found for debugging\n", - " numbers_start = (int(x_loc), 0)\n", - " numbers_end = (int(x_loc), desired_img_height)\n", + " \n", + " bounding_boxes_numbers = remove_bb_outliers(bounding_boxes_numbers)\n", + " bounding_boxes_time = remove_bb_outliers(bounding_boxes_time)\n", "\n", - " numbers2_start = (int(x_loc_right), 0)\n", - " numbers2_end = (int(x_loc_right), desired_img_height)\n", + " for bounding_box in bounding_boxes_numbers:\n", + " x_min = int(bounding_box.left)\n", + " x_max = int(bounding_box.right)\n", + " y_min = int(bounding_box.top)\n", + " y_max = int(bounding_box.bottom)\n", "\n", - " time_start = (0, int(y_loc))\n", - " time_end = (desired_img_width, int(y_loc))\n", + " # Bounding box is in the top-right region\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", + " )\n", + " \n", + " for bounding_box in bounding_boxes_time:\n", + " x_min = int(bounding_box.left)\n", + " x_max = int(bounding_box.right)\n", + " y_min = int(bounding_box.top)\n", + " y_max = int(bounding_box.bottom)\n", + "\n", + " cv2.rectangle(\n", + " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", + " )\n", + " \n", + " # plot the lines of the KDE index found for debugging\n", + " # numbers_start = (int(x_loc), 0)\n", + " # numbers_end = (int(x_loc), desired_img_height)\n", "\n", - " time2_start = (0, int(y_loc_bottom))\n", - " time2_end = (desired_img_width, int(y_loc_bottom))\n", + " # time_start = (0, int(y_loc))\n", + " # time_end = (desired_img_width, int(y_loc))\n", "\n", - " cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", - " cv2.line(resized_image, numbers2_start, numbers2_end, (255,255,0), 1)\n", - " cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", - " cv2.line(resized_image, time2_start, time2_end, (255,0,255), 1)\n", + " # cv2.line(resized_image, numbers_start, numbers_end, (255,255,0), 1)\n", + " # cv2.line(resized_image, time_start, time_end, (255,0,255), 1)\n", "\n", " # Close all OpenCV windows, always do this or it will annoyingly not go away\n", " # You can also manually quit out with ESC key.\n", @@ -293,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -512,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -530,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -581,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -794,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -956,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1011,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1019,32 +1052,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 771 correct clusters, 4 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.73%.\n", - "Average distance when correct: 4.90px, incorrect: 4.00px, and overall: 4.91px\n", + "Time labels: 771 correct clusters, 5 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.61%.\n", + "Average distance when correct: 4.92px, incorrect: 5.00px, and overall: 4.92px\n", "\n", "\n", - "Number labels: 371 correct clusters, 3 incorrect clusters. There were 9 undetected clusters. The accuracy is 94.47%.\n", - "Average distance when correct: 6.11px, incorrect: 3.00px, and overall: 6.10px\n", + "Number labels: 366 correct clusters, 8 incorrect clusters. There were 14 undetected clusters. The accuracy is 90.53%.\n", + "Average distance when correct: 6.19px, incorrect: 8.00px, and overall: 6.19px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 786 correct clusters, 33 incorrect clusters. There were 12 undetected clusters. The accuracy is 92.86%.\n", - "Average distance when correct: 4.83px, incorrect: 33.00px, and overall: 4.98px\n", + "Time labels: 780 correct clusters, 38 incorrect clusters. There were 18 undetected clusters. The accuracy is 90.73%.\n", + "Average distance when correct: 4.87px, incorrect: 38.00px, and overall: 5.00px\n", "\n", "\n", - "Number labels: 347 correct clusters, 11 incorrect clusters. There were 33 undetected clusters. The accuracy is 79.74%.\n", - "Average distance when correct: 6.12px, incorrect: 11.00px, and overall: 6.11px\n", + "Number labels: 350 correct clusters, 14 incorrect clusters. There were 30 undetected clusters. The accuracy is 80.53%.\n", + "Average distance when correct: 6.17px, incorrect: 14.00px, and overall: 6.18px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 781 correct clusters, 7 incorrect clusters. There were 17 undetected clusters. The accuracy is 94.86%.\n", - "Average distance when correct: 4.89px, incorrect: 7.00px, and overall: 4.92px\n", + "Time labels: 774 correct clusters, 10 incorrect clusters. There were 24 undetected clusters. The accuracy is 92.73%.\n", + "Average distance when correct: 4.91px, incorrect: 10.00px, and overall: 4.94px\n", "\n", "\n", - "Number labels: 354 correct clusters, 17 incorrect clusters. There were 26 undetected clusters. The accuracy is 81.84%.\n", - "Average distance when correct: 6.19px, incorrect: 17.00px, and overall: 6.16px\n", + "Number labels: 364 correct clusters, 9 incorrect clusters. There were 16 undetected clusters. The accuracy is 89.21%.\n", + "Average distance when correct: 6.22px, incorrect: 9.00px, and overall: 6.19px\n", "\n", "\n", "\n" @@ -1067,7 +1100,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "ChartExtractor", "language": "python", "name": "python3" }, From 697a60c6cdcd83b5db19f27382b3056fe4ca7100 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Wed, 6 Nov 2024 20:47:36 -0500 Subject: [PATCH 40/55] Generalized the process of selecting nearest expected cluster --- experiments/clustering/clustering.ipynb | 2005 +++++++++++++++++++++-- 1 file changed, 1911 insertions(+), 94 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 5c36ac6..48b8511 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ "import json\n", "import random\n", "from pathlib import Path\n", - "from typing import List, Tuple, Literal, Dict\n", + "from typing import List, Tuple, Dict\n", "\n", "# Third-party libraries\n", "import cv2\n", @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -467,6 +467,38 @@ " return cluster_performance_map[best_n_clusters][\"labels\"]\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Code block to get the average X and Y of the expected clusters. We get the average of the location of each label across sheets." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "cluster_locations_dict = {} # Dictionary containing the cluster_name as the key to another dictionary with 'x' and 'y' as keys for lists of x and y coordinates\n", + "for sheet_num, data in enumerate(yolo_data.items()):\n", + " # For each sheet, get the cluster center X and Y coordinates and add them to the cluster_locations_dict dictionary\n", + " expected_clusters = bp_hr_cluster_locations[sheet_num]\n", + " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", + " x_expected_perc, y_expected_perc = cluster['value']['x'], cluster['value']['y'] # Get the expected cluster location (percent x and y in the original image space)\n", + " x_expected, y_expected = (x_expected_perc/100) * DESIRED_IMAGE_WIDTH, (y_expected_perc/100) * DESIRED_IMAGE_HEIGHT # Convert the expected cluster location to pixel space\n", + " cluster_name = cluster['value']['rectanglelabels'][0]\n", + " if cluster_name not in cluster_locations_dict:\n", + " cluster_locations_dict[cluster_name] = {'x': [], 'y': []}\n", + " cluster_locations_dict[cluster_name]['x'].append(x_expected)\n", + " cluster_locations_dict[cluster_name]['y'].append(y_expected)\n", + "\n", + "# Average the cluster locations\n", + "for cluster_name, cluster_data in cluster_locations_dict.items():\n", + " cluster_locations_dict[cluster_name]['x'] = float(np.mean(cluster_data['x']))\n", + " cluster_locations_dict[cluster_name]['y'] = float(np.mean(cluster_data['y']))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -476,12 +508,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def create_result_dictionary(\n", - " labels: List[str], bounding_boxes: List[BoundingBox], expected_clusters: dict\n", + " labels: List[str], bounding_boxes: List[BoundingBox]\n", ") -> Dict[int, int]:\n", " \"\"\"\n", " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", @@ -517,9 +549,8 @@ " # Now we have the center point of the cluster\n", " # We will use the euclidean distance to determine the closest expected cluster location and use that as the label\n", " distances = [] # List that contains the distances for each expected cluster from our found cluster\n", - " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", - " x_expected_perc, y_expected_perc = cluster['value']['x'], cluster['value']['y'] # Get the expected cluster location (percent x and y in the original image space)\n", - " x_expected, y_expected = (x_expected_perc/100) * DESIRED_IMAGE_WIDTH, (y_expected_perc/100) * DESIRED_IMAGE_HEIGHT # Convert the expected cluster location to pixel space\n", + " for cluster in cluster_locations_dict:\n", + " x_expected, y_expected = cluster_locations_dict[cluster]['x'], cluster_locations_dict[cluster]['y']\n", " #print(f\"Cluster location: {x}, {y}\")\n", " distance = np.sqrt((x_expected - x_found) ** 2 + (y_expected - y_found) ** 2)\n", " #print(f\"Distance: {distance}\")\n", @@ -528,7 +559,7 @@ " min_distance_index = distances.index(min(distances))\n", "\n", " # Get the label of the cluster\n", - " label_dict[key] = list(expected_clusters[\"annotations\"][0][\"result\"])[min_distance_index]['value']['rectanglelabels'][0]\n", + " label_dict[key] = list(cluster_locations_dict.keys())[min_distance_index]\n", "\n", " # Add the distance to the dictionary\n", " label_dict[key] = {\"label\": label_dict[key], \"distance\": min(distances)}\n", @@ -545,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -563,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -614,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -762,7 +793,7 @@ " \"w\",\n", " ) as f:\n", " json.dump(\n", - " create_result_dictionary(time_labels, time_bounding_boxes, bp_hr_cluster_locations[sheet_num]),\n", + " create_result_dictionary(time_labels, time_bounding_boxes),\n", " f,\n", " )\n", "\n", @@ -808,7 +839,7 @@ " ) as f:\n", " json.dump(\n", " create_result_dictionary(\n", - " number_labels, number_bounding_boxes, bp_hr_cluster_locations[sheet_num]\n", + " number_labels, number_bounding_boxes\n", " ),\n", " f,\n", " )\n", @@ -827,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -836,9 +867,9 @@ " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", " print(f\"Method: {method}\")\n", " # Paths to the JSON files\n", - " PATH_TO_KMEANS_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", - " TIME_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"time\")\n", - " NUMBER_JSON = os.path.join(PATH_TO_KMEANS_RESULTS, \"number\")\n", + " PATH_TO_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", + " TIME_JSON = os.path.join(PATH_TO_RESULTS, \"time\")\n", + " NUMBER_JSON = os.path.join(PATH_TO_RESULTS, \"number\")\n", "\n", " time_wrong_clusters_count = 0\n", " time_correct_clusters_count = 0\n", @@ -937,7 +968,7 @@ " for cluster, value in time_clusters.items():\n", " if value['label'] not in expected_time_values:\n", " # Print the sheet, value that is not in the expected values\n", - " #print(f\"Time -> Sheet: {sheet}, Value: {value[\"label\"]}.\")\n", + " # print(f\"Time -> Sheet: {sheet}, Value: {value[\"label\"]}.\")\n", " # We have an erroneous cluster\n", " time_wrong_clusters_count += 1\n", " time_distance_values_erroneous.append(value['distance'])\n", @@ -989,46 +1020,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method: kmeans\n", - "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", - "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", - "\n", - "\n", - "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", - "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", - "\n", - "\n", - "\n", - "Method: dbscan\n", - "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", - "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", - "\n", - "\n", - "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", - "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 796 correct clusters, 1 incorrect clusters. There were 2 undetected clusters. The accuracy is 99.37%.\n", - "Average distance when correct: 4.83px, incorrect: 1.00px, and overall: 4.83px\n", - "\n", - "\n", - "Number labels: 379 correct clusters, 1 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.21%.\n", - "Average distance when correct: 6.16px, incorrect: 1.00px, and overall: 6.16px\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(add_erroneous=False)\n", @@ -1044,43 +1038,1866 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Method: kmeans\n", - "Time labels: 771 correct clusters, 5 incorrect clusters. There were 27 undetected clusters. The accuracy is 92.61%.\n", - "Average distance when correct: 4.92px, incorrect: 5.00px, and overall: 4.92px\n", - "\n", - "\n", - "Number labels: 366 correct clusters, 8 incorrect clusters. There were 14 undetected clusters. The accuracy is 90.53%.\n", - "Average distance when correct: 6.19px, incorrect: 8.00px, and overall: 6.19px\n", - "\n", - "\n", - "\n", - "Method: dbscan\n", - "Time labels: 780 correct clusters, 38 incorrect clusters. There were 18 undetected clusters. The accuracy is 90.73%.\n", - "Average distance when correct: 4.87px, incorrect: 38.00px, and overall: 5.00px\n", - "\n", - "\n", - "Number labels: 350 correct clusters, 14 incorrect clusters. There were 30 undetected clusters. The accuracy is 80.53%.\n", - "Average distance when correct: 6.17px, incorrect: 14.00px, and overall: 6.18px\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 774 correct clusters, 10 incorrect clusters. There were 24 undetected clusters. The accuracy is 92.73%.\n", - "Average distance when correct: 4.91px, incorrect: 10.00px, and overall: 4.94px\n", - "\n", - "\n", - "Number labels: 364 correct clusters, 9 incorrect clusters. There were 16 undetected clusters. The accuracy is 89.21%.\n", - "Average distance when correct: 6.22px, incorrect: 9.00px, and overall: 6.19px\n", - "\n", - "\n", - "\n" + "5.128868424650202\n", + "3.597980694608979\n", + "3.6395575952532355\n", + "4.804956720638203\n", + "5.085709814009798\n", + "5.329439138658003\n", + "5.468683100585791\n", + "5.3653974835934015\n", + "5.359311360003873\n", + "5.51992258407943\n", + "4.03895502689332\n", + "5.384104017654249\n", + "5.47355122243425\n", + "3.7985818772779707\n", + "3.7220368158846755\n", + "5.091528957051915\n", + "5.0637997966833606\n", + "5.3084852757707885\n", + "5.172937368260436\n", + "5.074207897853961\n", + "5.172965966723361\n", + "5.310827116685281\n", + "5.149645919955512\n", + "3.346670391093665\n", + "3.765323134093238\n", + "3.2225625389035732\n", + "3.296491342258328\n", + "3.655187371229006\n", + "6.163149911474913\n", + "7.0523907056064\n", + "5.0474097355117395\n", + "4.987380699121186\n", + "5.160089775698283\n", + "2.756527305864094\n", + 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\u001b[43mtest_clustering_methods\u001b[49m\u001b[43m(\u001b[49m\u001b[43madd_erroneous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m analyze_accuracy()\n", + "Cell \u001b[1;32mIn[39], line 33\u001b[0m, in \u001b[0;36mtest_clustering_methods\u001b[1;34m(add_erroneous)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magglomerative\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 31\u001b[0m \u001b[38;5;66;03m# Now we need to cluster the bounding boxes that pertain to the same multi-digit number\u001b[39;00m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 33\u001b[0m time_labels \u001b[38;5;241m=\u001b[39m \u001b[43mcluster_kmeans\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime_bounding_boxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m41\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 34\u001b[0m number_labels \u001b[38;5;241m=\u001b[39m cluster_kmeans(number_bounding_boxes, [\u001b[38;5;241m18\u001b[39m, \u001b[38;5;241m19\u001b[39m, \u001b[38;5;241m20\u001b[39m])\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "Cell \u001b[1;32mIn[33], line 34\u001b[0m, in \u001b[0;36mcluster_kmeans\u001b[1;34m(bounding_boxes, possible_nclusters)\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[38;5;66;03m# Apply K-Means\u001b[39;00m\n\u001b[0;32m 26\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(\n\u001b[0;32m 27\u001b[0m n_clusters\u001b[38;5;241m=\u001b[39mnumber_of_clusters,\n\u001b[0;32m 28\u001b[0m init\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 32\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,\n\u001b[0;32m 33\u001b[0m )\n\u001b[1;32m---> 34\u001b[0m \u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;66;03m# Get cluster labels\u001b[39;00m\n\u001b[0;32m 37\u001b[0m labels \u001b[38;5;241m=\u001b[39m kmeans\u001b[38;5;241m.\u001b[39mpredict(data)\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1519\u001b[0m, in \u001b[0;36mKMeans.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialization complete\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;66;03m# run a k-means once\u001b[39;00m\n\u001b[1;32m-> 1519\u001b[0m labels, inertia, centers, n_iter_ \u001b[38;5;241m=\u001b[39m \u001b[43mkmeans_single\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1520\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1521\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1522\u001b[0m \u001b[43m \u001b[49m\u001b[43mcenters_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1523\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1524\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1525\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1526\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_n_threads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;66;03m# determine if these results are the best so far\u001b[39;00m\n\u001b[0;32m 1530\u001b[0m \u001b[38;5;66;03m# we chose a new run if it has a better inertia and the clustering is\u001b[39;00m\n\u001b[0;32m 1531\u001b[0m \u001b[38;5;66;03m# different from the best so far (it's possible that the inertia is\u001b[39;00m\n\u001b[0;32m 1532\u001b[0m \u001b[38;5;66;03m# slightly better even if the clustering is the same with potentially\u001b[39;00m\n\u001b[0;32m 1533\u001b[0m \u001b[38;5;66;03m# permuted labels, due to rounding errors)\u001b[39;00m\n\u001b[0;32m 1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m best_inertia \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 1535\u001b[0m inertia \u001b[38;5;241m<\u001b[39m best_inertia\n\u001b[0;32m 1536\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_same_clustering(labels, best_labels, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_clusters)\n\u001b[0;32m 1537\u001b[0m ):\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:161\u001b[0m, in \u001b[0;36m_threadpool_controller_decorator..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 160\u001b[0m controller \u001b[38;5;241m=\u001b[39m _get_threadpool_controller()\n\u001b[1;32m--> 161\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mcontroller\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlimits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_api\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_api\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:921\u001b[0m, in \u001b[0;36mThreadpoolController.limit\u001b[1;34m(self, limits, user_api)\u001b[0m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;129m@_format_docstring\u001b[39m(\n\u001b[0;32m 871\u001b[0m USER_APIS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(api) \u001b[38;5;28;01mfor\u001b[39;00m api \u001b[38;5;129;01min\u001b[39;00m _ALL_USER_APIS),\n\u001b[0;32m 872\u001b[0m BLAS_LIBS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_ALL_BLAS_LIBRARIES),\n\u001b[0;32m 873\u001b[0m OPENMP_LIBS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_ALL_OPENMP_LIBRARIES),\n\u001b[0;32m 874\u001b[0m )\n\u001b[0;32m 875\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlimit\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, limits\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, user_api\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 876\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Change the maximal number of threads that can be used in thread pools.\u001b[39;00m\n\u001b[0;32m 877\u001b[0m \n\u001b[0;32m 878\u001b[0m \u001b[38;5;124;03m This function returns an object that can be used either as a callable (the\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[38;5;124;03m - If None, this function will apply to all supported libraries.\u001b[39;00m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ThreadpoolLimiter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_api\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_api\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:587\u001b[0m, in \u001b[0;36m_ThreadpoolLimiter.__init__\u001b[1;34m(self, controller, limits, user_api)\u001b[0m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_limits, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_user_api, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prefixes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params(\n\u001b[0;32m 584\u001b[0m limits, user_api\n\u001b[0;32m 585\u001b[0m )\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_controller\u001b[38;5;241m.\u001b[39minfo()\n\u001b[1;32m--> 587\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_threadpool_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:720\u001b[0m, in \u001b[0;36m_ThreadpoolLimiter._set_threadpool_limits\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 717\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_threads \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 720\u001b[0m \u001b[43mlib_controller\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_num_threads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_threads\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:199\u001b[0m, in \u001b[0;36mOpenBLASController.set_num_threads\u001b[1;34m(self, num_threads)\u001b[0m\n\u001b[0;32m 197\u001b[0m set_num_threads_func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_symbol(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopenblas_set_num_threads\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 198\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m set_num_threads_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mset_num_threads_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -1100,7 +2917,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ChartExtractor", + "display_name": ".venv", "language": "python", "name": "python3" }, From e1df718838a8d092b6a8853084cace8fc7378049 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Wed, 6 Nov 2024 21:16:50 -0500 Subject: [PATCH 41/55] Pushing results --- experiments/clustering/clustering.ipynb | 1926 +---------------------- 1 file changed, 70 insertions(+), 1856 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 48b8511..335bcaf 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -1020,9 +1020,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", + "\n", + "\n", + "\n", + "Method: dbscan\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", + "\n", + "\n", + "\n", + "Method: agglomerative\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(add_erroneous=False)\n", @@ -1038,1866 +1075,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5.128868424650202\n", - "3.597980694608979\n", - "3.6395575952532355\n", - "4.804956720638203\n", - "5.085709814009798\n", - "5.329439138658003\n", - "5.468683100585791\n", - "5.3653974835934015\n", - "5.359311360003873\n", - "5.51992258407943\n", - "4.03895502689332\n", - "5.384104017654249\n", - "5.47355122243425\n", - "3.7985818772779707\n", - "3.7220368158846755\n", - "5.091528957051915\n", - "5.0637997966833606\n", - "5.3084852757707885\n", - "5.172937368260436\n", - "5.074207897853961\n", - "5.172965966723361\n", - "5.310827116685281\n", - 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"6.061632077201744\n", - "4.1024381894531095\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[42], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Test the clustering methods with errouneous bounding boxes\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mtest_clustering_methods\u001b[49m\u001b[43m(\u001b[49m\u001b[43madd_erroneous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m analyze_accuracy()\n", - "Cell \u001b[1;32mIn[39], line 33\u001b[0m, in \u001b[0;36mtest_clustering_methods\u001b[1;34m(add_erroneous)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magglomerative\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 31\u001b[0m \u001b[38;5;66;03m# Now we need to cluster the bounding boxes that pertain to the same multi-digit number\u001b[39;00m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 33\u001b[0m time_labels \u001b[38;5;241m=\u001b[39m \u001b[43mcluster_kmeans\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime_bounding_boxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m41\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 34\u001b[0m number_labels \u001b[38;5;241m=\u001b[39m cluster_kmeans(number_bounding_boxes, [\u001b[38;5;241m18\u001b[39m, \u001b[38;5;241m19\u001b[39m, \u001b[38;5;241m20\u001b[39m])\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "Cell \u001b[1;32mIn[33], line 34\u001b[0m, in \u001b[0;36mcluster_kmeans\u001b[1;34m(bounding_boxes, possible_nclusters)\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[38;5;66;03m# Apply K-Means\u001b[39;00m\n\u001b[0;32m 26\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(\n\u001b[0;32m 27\u001b[0m n_clusters\u001b[38;5;241m=\u001b[39mnumber_of_clusters,\n\u001b[0;32m 28\u001b[0m init\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 32\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,\n\u001b[0;32m 33\u001b[0m )\n\u001b[1;32m---> 34\u001b[0m \u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;66;03m# Get cluster labels\u001b[39;00m\n\u001b[0;32m 37\u001b[0m labels \u001b[38;5;241m=\u001b[39m kmeans\u001b[38;5;241m.\u001b[39mpredict(data)\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1519\u001b[0m, in \u001b[0;36mKMeans.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialization complete\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1518\u001b[0m \u001b[38;5;66;03m# run a k-means once\u001b[39;00m\n\u001b[1;32m-> 1519\u001b[0m labels, inertia, centers, n_iter_ \u001b[38;5;241m=\u001b[39m \u001b[43mkmeans_single\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1520\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1521\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1522\u001b[0m \u001b[43m \u001b[49m\u001b[43mcenters_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1523\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1524\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1525\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1526\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_n_threads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;66;03m# determine if these results are the best so far\u001b[39;00m\n\u001b[0;32m 1530\u001b[0m \u001b[38;5;66;03m# we chose a new run if it has a better inertia and the clustering is\u001b[39;00m\n\u001b[0;32m 1531\u001b[0m \u001b[38;5;66;03m# different from the best so far (it's possible that the inertia is\u001b[39;00m\n\u001b[0;32m 1532\u001b[0m \u001b[38;5;66;03m# slightly better even if the clustering is the same with potentially\u001b[39;00m\n\u001b[0;32m 1533\u001b[0m \u001b[38;5;66;03m# permuted labels, due to rounding errors)\u001b[39;00m\n\u001b[0;32m 1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m best_inertia \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 1535\u001b[0m inertia \u001b[38;5;241m<\u001b[39m best_inertia\n\u001b[0;32m 1536\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_same_clustering(labels, best_labels, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_clusters)\n\u001b[0;32m 1537\u001b[0m ):\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:161\u001b[0m, in \u001b[0;36m_threadpool_controller_decorator..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 160\u001b[0m controller \u001b[38;5;241m=\u001b[39m _get_threadpool_controller()\n\u001b[1;32m--> 161\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mcontroller\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlimits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_api\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_api\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:921\u001b[0m, in \u001b[0;36mThreadpoolController.limit\u001b[1;34m(self, limits, user_api)\u001b[0m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;129m@_format_docstring\u001b[39m(\n\u001b[0;32m 871\u001b[0m USER_APIS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(api) \u001b[38;5;28;01mfor\u001b[39;00m api \u001b[38;5;129;01min\u001b[39;00m _ALL_USER_APIS),\n\u001b[0;32m 872\u001b[0m BLAS_LIBS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_ALL_BLAS_LIBRARIES),\n\u001b[0;32m 873\u001b[0m OPENMP_LIBS\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_ALL_OPENMP_LIBRARIES),\n\u001b[0;32m 874\u001b[0m )\n\u001b[0;32m 875\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlimit\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, limits\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, user_api\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 876\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Change the maximal number of threads that can be used in thread pools.\u001b[39;00m\n\u001b[0;32m 877\u001b[0m \n\u001b[0;32m 878\u001b[0m \u001b[38;5;124;03m This function returns an object that can be used either as a callable (the\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[38;5;124;03m - If None, this function will apply to all supported libraries.\u001b[39;00m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ThreadpoolLimiter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlimits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_api\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muser_api\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:587\u001b[0m, in \u001b[0;36m_ThreadpoolLimiter.__init__\u001b[1;34m(self, controller, limits, user_api)\u001b[0m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_limits, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_user_api, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prefixes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params(\n\u001b[0;32m 584\u001b[0m limits, user_api\n\u001b[0;32m 585\u001b[0m )\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_controller\u001b[38;5;241m.\u001b[39minfo()\n\u001b[1;32m--> 587\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_threadpool_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:720\u001b[0m, in \u001b[0;36m_ThreadpoolLimiter._set_threadpool_limits\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 717\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_threads \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 720\u001b[0m \u001b[43mlib_controller\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_num_threads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_threads\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\.venv\\Lib\\site-packages\\threadpoolctl.py:199\u001b[0m, in \u001b[0;36mOpenBLASController.set_num_threads\u001b[1;34m(self, num_threads)\u001b[0m\n\u001b[0;32m 197\u001b[0m set_num_threads_func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_symbol(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopenblas_set_num_threads\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 198\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m set_num_threads_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mset_num_threads_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Method: kmeans\n", + "Time labels: 772 correct clusters, 2 incorrect clusters. There were 26 undetected clusters. The accuracy is 93.23%.\n", + "Average distance when correct: 4.89px, incorrect: 2.00px, and overall: 4.90px\n", + "\n", + "\n", + "Number labels: 372 correct clusters, 2 incorrect clusters. There were 8 undetected clusters. The accuracy is 95.26%.\n", + "Average distance when correct: 6.15px, incorrect: 2.00px, and overall: 6.15px\n", + "\n", + "\n", + "\n", + "Method: dbscan\n", + "Time labels: 789 correct clusters, 36 incorrect clusters. There were 9 undetected clusters. The accuracy is 93.23%.\n", + "Average distance when correct: 4.81px, incorrect: 36.00px, and overall: 4.98px\n", + "\n", + "\n", + "Number labels: 346 correct clusters, 17 incorrect clusters. There were 34 undetected clusters. The accuracy is 77.63%.\n", + "Average distance when correct: 6.16px, incorrect: 17.00px, and overall: 6.16px\n", + "\n", + "\n", + "\n", + "Method: agglomerative\n", + "Time labels: 779 correct clusters, 10 incorrect clusters. There were 19 undetected clusters. The accuracy is 93.98%.\n", + "Average distance when correct: 4.85px, incorrect: 10.00px, and overall: 4.91px\n", + "\n", + "\n", + "Number labels: 357 correct clusters, 12 incorrect clusters. There were 23 undetected clusters. The accuracy is 84.74%.\n", + "Average distance when correct: 6.16px, incorrect: 12.00px, and overall: 6.14px\n", + "\n", + "\n", + "\n" ] } ], From e88f2f69750032286ba115217048e465ee8083a6 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Wed, 6 Nov 2024 23:41:26 -0500 Subject: [PATCH 42/55] Preprocessing testing method --- experiments/clustering/clustering.ipynb | 127 +++++++++++++++++++----- 1 file changed, 104 insertions(+), 23 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 872afb4..617c832 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -49,6 +49,7 @@ "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", "from scipy.stats import gaussian_kde\n", + "from itertools import compress\n", "\n", "# Local libraries\n", "from utils.annotations import BoundingBox" @@ -70,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -113,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -616,6 +617,86 @@ " return (time_BB_erroneous, number_BB_erroneous)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to generate random yolo data" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_random_yolo(x):\n", + " x_rand = random.uniform(0, 1)\n", + " y_rand = random.uniform(0, 1)\n", + " return f'0 {x_rand} {y_rand} 0.0048989405776515005 0.009852199180453436'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to test preprocessing effectiveness" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def test_preprocess(yolo_data_sheet, percent_erroneous) -> Dict:\n", + " total_yolo = {}\n", + " # iterate through sheets in yolo json file\n", + " for sheet in range(1, len(yolo_data)+1):\n", + " if sheet < 10:\n", + " # for ease of replacement, select bounding boxes with 0 label\n", + " select_sheet = yolo_data_sheet[f'RC_000{sheet}_intraoperative.JPG']\n", + " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_000{sheet}_intraoperative.JPG']]\n", + " zero_list = list(compress(select_sheet, boolean_list))\n", + " # replace percent of the 64 possible zeros in the sheet\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " # subset and remove % of yolo lines from json file\n", + " lines_remove = list(random.sample(zero_list, count_remove))\n", + " _ = [select_sheet.remove(line) for line in lines_remove]\n", + " # use random yolo generation to refill removed lines\n", + " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # append yolo generated list back to copy \n", + " yolo_shuffle = select_sheet + lines_gen\n", + " total_yolo[f'RC_000{sheet}_intraoperative.JPG'] = yolo_shuffle\n", + " \n", + " else:\n", + " select_sheet = yolo_data_sheet[f'RC_00{sheet}_intraoperative.JPG']\n", + " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_00{sheet}_intraoperative.JPG']]\n", + " zero_list = list(compress(select_sheet, boolean_list))\n", + " # replace percent of the 64 possible zeros in the sheet\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " # subset and remove % of yolo lines from json file\n", + " lines_remove = list(random.sample(zero_list, count_remove))\n", + " _ = [select_sheet.remove(line) for line in lines_remove]\n", + " # use random yolo generation to refill removed lines\n", + " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # append yolo generated list back to copy \n", + " yolo_shuffle = select_sheet + lines_gen\n", + " total_yolo[f'RC_00{sheet}_intraoperative.JPG'] = yolo_shuffle\n", + "\n", + " return total_yolo" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# preprocessing test by manipulating yolo data\n", + "yolo_data = test_preprocess(yolo_data, 0.1)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -625,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -833,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -841,32 +922,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 762 correct clusters, 21 incorrect clusters. There were 36 undetected clusters. The accuracy is 90.98%.\n", - "Average distance when correct: 4.90px, incorrect: 4.41px, and overall: 4.89px\n", + "Time labels: 738 correct clusters, 25 incorrect clusters. There were 60 undetected clusters. The accuracy is 84.96%.\n", + "Average distance when correct: 4.88px, incorrect: 4.87px, and overall: 4.88px\n", "\n", "\n", - "Number labels: 371 correct clusters, 2 incorrect clusters. There were 9 undetected clusters. The accuracy is 95.26%.\n", - "Average distance when correct: 6.55px, incorrect: 6.78px, and overall: 6.55px\n", + "Number labels: 373 correct clusters, 2 incorrect clusters. There were 7 undetected clusters. The accuracy is 96.32%.\n", + "Average distance when correct: 6.44px, incorrect: 7.04px, and overall: 6.44px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 768 correct clusters, 63 incorrect clusters. There were 30 undetected clusters. The accuracy is 92.48%.\n", - "Average distance when correct: 4.84px, incorrect: 6.84px, and overall: 5.00px\n", + "Time labels: 755 correct clusters, 46 incorrect clusters. There were 43 undetected clusters. The accuracy is 89.22%.\n", + "Average distance when correct: 4.79px, incorrect: 6.88px, and overall: 4.91px\n", "\n", "\n", - "Number labels: 353 correct clusters, 10 incorrect clusters. There were 27 undetected clusters. The accuracy is 85.79%.\n", - "Average distance when correct: 6.56px, incorrect: 6.30px, and overall: 6.56px\n", + "Number labels: 337 correct clusters, 17 incorrect clusters. There were 43 undetected clusters. The accuracy is 77.37%.\n", + "Average distance when correct: 6.46px, incorrect: 6.78px, and overall: 6.47px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 760 correct clusters, 24 incorrect clusters. There were 38 undetected clusters. The accuracy is 90.48%.\n", - "Average distance when correct: 4.90px, incorrect: 5.12px, and overall: 4.91px\n", + "Time labels: 755 correct clusters, 28 incorrect clusters. There were 43 undetected clusters. The accuracy is 89.22%.\n", + "Average distance when correct: 4.83px, incorrect: 5.46px, and overall: 4.86px\n", "\n", "\n", - "Number labels: 363 correct clusters, 6 incorrect clusters. There were 17 undetected clusters. The accuracy is 91.05%.\n", - "Average distance when correct: 6.58px, incorrect: 4.68px, and overall: 6.55px\n", + "Number labels: 358 correct clusters, 14 incorrect clusters. There were 22 undetected clusters. The accuracy is 88.42%.\n", + "Average distance when correct: 6.52px, incorrect: 4.83px, and overall: 6.45px\n", "\n", "\n", "\n" From a362f4161dac2ea455fe026dd7e06593824c3d22 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Thu, 7 Nov 2024 00:01:27 -0500 Subject: [PATCH 43/55] Test for preprocessing effectiveness --- experiments/clustering/clustering.ipynb | 83 ++++++++++++++++++++++++- 1 file changed, 80 insertions(+), 3 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 81b78bf..b994ad4 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -327,7 +327,6 @@ }, { "cell_type": "code", - "execution_count": 81, "execution_count": 47, "metadata": {}, "outputs": [], @@ -687,6 +686,86 @@ " return (time_BB_erroneous, number_BB_erroneous)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to generate random yolo data" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_random_yolo(x):\n", + " x_rand = random.uniform(0, 1)\n", + " y_rand = random.uniform(0, 1)\n", + " return f'0 {x_rand} {y_rand} 0.0048989405776515005 0.009852199180453436'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to test preprocessing effectiveness" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "def test_preprocess(yolo_data_sheet, percent_erroneous) -> Dict:\n", + " total_yolo = {}\n", + " # iterate through sheets in yolo json file\n", + " for sheet in range(1, len(yolo_data)+1):\n", + " if sheet < 10:\n", + " # for ease of replacement, select bounding boxes with 0 label\n", + " select_sheet = yolo_data_sheet[f'RC_000{sheet}_intraoperative.JPG']\n", + " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_000{sheet}_intraoperative.JPG']]\n", + " zero_list = list(compress(select_sheet, boolean_list))\n", + " # replace percent of the 64 possible zeros in the sheet\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " # subset and remove % of yolo lines from json file\n", + " lines_remove = list(random.sample(zero_list, count_remove))\n", + " _ = [select_sheet.remove(line) for line in lines_remove]\n", + " # use random yolo generation to refill removed lines\n", + " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # append yolo generated list back to copy \n", + " yolo_shuffle = select_sheet + lines_gen\n", + " total_yolo[f'RC_000{sheet}_intraoperative.JPG'] = yolo_shuffle\n", + " \n", + " else:\n", + " select_sheet = yolo_data_sheet[f'RC_00{sheet}_intraoperative.JPG']\n", + " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_00{sheet}_intraoperative.JPG']]\n", + " zero_list = list(compress(select_sheet, boolean_list))\n", + " # replace percent of the 64 possible zeros in the sheet\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " # subset and remove % of yolo lines from json file\n", + " lines_remove = list(random.sample(zero_list, count_remove))\n", + " _ = [select_sheet.remove(line) for line in lines_remove]\n", + " # use random yolo generation to refill removed lines\n", + " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # append yolo generated list back to copy \n", + " yolo_shuffle = select_sheet + lines_gen\n", + " total_yolo[f'RC_00{sheet}_intraoperative.JPG'] = yolo_shuffle\n", + "\n", + " return total_yolo" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# preprocessing test by manipulating yolo data\n", + "# yolo_data = test_preprocess(yolo_data, 0.1)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -698,7 +777,6 @@ }, { "cell_type": "code", - "execution_count": 90, "execution_count": 53, "metadata": {}, "outputs": [], @@ -861,7 +939,6 @@ }, { "cell_type": "code", - "execution_count": 91, "execution_count": 54, "metadata": {}, "outputs": [], From ac94d59943cfc345fdbbb3b0ba7b572e526e2e31 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Sun, 10 Nov 2024 12:56:44 -0500 Subject: [PATCH 44/55] Updated clustering test for varying percent of erroneous bounding boxes --- experiments/clustering/clustering.ipynb | 77 ++++++++++++++----------- 1 file changed, 42 insertions(+), 35 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index b994ad4..34e9f51 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -477,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -577,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -646,12 +646,13 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def erroneous_bounding_boxes(\n", - " time_BB: List[str], number_BB: List[str]\n", + " time_BB: List[str], number_BB: List[str],\n", + " percent_erroneous: float\n", ") -> Tuple[List[str], List[str]]:\n", " \"\"\"\n", " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", @@ -659,6 +660,7 @@ " Args:\n", " time_BB: list of time bounding boxes in BoundingBox format\n", " number_BB: list of number bounding boxes in BoundingBox format\n", + " percent_erroneous: number between 0 and 1 for percent of erroneous Bounding Boxes\n", "\n", " Returns:\n", " Tuple: lists of time and number bounding boxes in BoundingBox format\n", @@ -667,10 +669,15 @@ " time_BB_copy = time_BB.copy()\n", " number_BB_copy = number_BB.copy()\n", "\n", + " # convert percent input\n", + " time_BB_count = round(percent_erroneous * 76) # 76 time bounding boxes\n", + " number_BB_count = round(percent_erroneous * 53) # 53 number bounding boxes\n", + "\n", " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", " ## sample\n", - " time_BB_sample = list(random.sample(time_BB_copy, 4))\n", - " number_BB_sample = list(random.sample(number_BB_copy, 4))\n", + " time_BB_sample = list(random.sample(time_BB_copy, time_BB_count))\n", + " number_BB_sample = list(random.sample(number_BB_copy, number_BB_count))\n", + "\n", " ## remove\n", " _ = [time_BB_copy.remove(line) for line in time_BB_sample]\n", " _ = [number_BB_copy.remove(line) for line in number_BB_sample]\n", @@ -695,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -777,11 +784,11 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "def test_clustering_methods(add_erroneous = True) -> None:\n", + "def test_clustering_methods(percent_erroneous_BB: float, add_erroneous = True) -> None:\n", " \"\"\"\n", " Test the clustering methods on the YOLO data.\n", " Saves the clustered images and the clustered bounding boxes to JSON files.\n", @@ -807,7 +814,7 @@ " if add_erroneous:\n", " # make erroneous bounding boxes -- simultaneously add and remove %5 of boxes\n", " time_bounding_boxes, number_bounding_boxes = erroneous_bounding_boxes(\n", - " time_bounding_boxes, number_bounding_boxes\n", + " time_bounding_boxes, number_bounding_boxes, percent_erroneous_BB\n", " )\n", "\n", " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", @@ -939,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1101,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1109,32 +1116,32 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 738 correct clusters, 25 incorrect clusters. There were 60 undetected clusters. The accuracy is 84.96%.\n", - "Average distance when correct: 4.88px, incorrect: 4.87px, and overall: 4.88px\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", "\n", "\n", - "Number labels: 373 correct clusters, 2 incorrect clusters. There were 7 undetected clusters. The accuracy is 96.32%.\n", - "Average distance when correct: 6.44px, incorrect: 7.04px, and overall: 6.44px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 755 correct clusters, 46 incorrect clusters. There were 43 undetected clusters. The accuracy is 89.22%.\n", - "Average distance when correct: 4.79px, incorrect: 6.88px, and overall: 4.91px\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", "\n", "\n", - "Number labels: 337 correct clusters, 17 incorrect clusters. There were 43 undetected clusters. The accuracy is 77.37%.\n", - "Average distance when correct: 6.46px, incorrect: 6.78px, and overall: 6.47px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 755 correct clusters, 28 incorrect clusters. There were 43 undetected clusters. The accuracy is 89.22%.\n", - "Average distance when correct: 4.83px, incorrect: 5.46px, and overall: 4.86px\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", "\n", "\n", - "Number labels: 358 correct clusters, 14 incorrect clusters. There were 22 undetected clusters. The accuracy is 88.42%.\n", - "Average distance when correct: 6.52px, incorrect: 4.83px, and overall: 6.45px\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", "\n", "\n", "\n" @@ -1143,7 +1150,7 @@ ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", - "test_clustering_methods(add_erroneous=False)\n", + "test_clustering_methods(0.05, add_erroneous=False)\n", "analyze_accuracy()" ] }, @@ -1212,7 +1219,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1226,7 +1233,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.9.5" } }, "nbformat": 4, From 0fdeee3b408e4063346da6ff3d9b71a5e12ce94b Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Sun, 10 Nov 2024 23:42:41 -0500 Subject: [PATCH 45/55] Add function to find cluster bounding box --- experiments/clustering/clustering.ipynb | 129 +++++++++++++----------- 1 file changed, 72 insertions(+), 57 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 34e9f51..e1b4b0c 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -500,6 +500,55 @@ " cluster_locations_dict[cluster_name]['y'] = float(np.mean(cluster_data['y']))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cluster_bbs(labels: List[str], bounding_boxes: List[BoundingBox]) -> Dict[str, BoundingBox]:\n", + " \"\"\"\n", + " Create a dictionary with cluster labels as keys and a BoundingBox for the cluster.\n", + "\n", + " Args:\n", + " labels: List of cluster labels.\n", + " bounding_boxes: List of bounding boxes.\n", + "\n", + " Returns:\n", + " Dictionary with cluster labels as keys and a bounding box value as values.\n", + " \"\"\"\n", + " # Create a dictionary to store labelled elements\n", + " label_dict = {}\n", + "\n", + " # Iterate over both lists for labels and the bounding boxes found\n", + " for label, box in zip(labels, bounding_boxes):\n", + " label = int(label)\n", + " if label not in label_dict:\n", + " # Create a new list for this label if it doesn't exist\n", + " label_dict[label] = []\n", + " # Append the element to the corresponding label list\n", + " label_dict[label].append(box)\n", + "\n", + " # Create dictionary that will hold the cluster label and bounding box\n", + " cluster_dict = {}\n", + " # Iterate over the label_dict to find the overall coordinates for the cluster\n", + " for key in label_dict:\n", + " # calculate the coordinates of the cluster bounding box\n", + " x_left = min([bb.left for bb in label_dict[key]])\n", + " x_right = max([bb.right for bb in label_dict[key]])\n", + " y_top = min([bb.top for bb in label_dict[key]])\n", + " y_bottom = max([bb.bottom for bb in label_dict[key]])\n", + " # store the bounding box into the dictionary\n", + " cluster_dict[key] = BoundingBox(\n", + " category = key,\n", + " left = x_left,\n", + " right = x_right,\n", + " top = y_top,\n", + " bottom = y_bottom\n", + " )\n", + " return cluster_dict\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -509,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -577,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -595,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -646,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -702,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -721,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -765,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -784,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -843,10 +892,11 @@ " image: Image = Image.open(full_image_path)\n", " image_width, image_height = image.size\n", "\n", + " # get time clusters\n", + " time_cluster_bbs = get_cluster_bbs(time_labels, time_bounding_boxes)\n", + "\n", " label_color_map = {}\n", - " for i, label in enumerate(time_labels):\n", - " # Get the bounding box\n", - " bounding_box = time_bounding_boxes[i]\n", + " for label, bounding_box in time_cluster_bbs.items():\n", " x_min, y_min, x_max, y_max = [\n", " (coor / 800) * image_width\n", " if i % 2 == 0\n", @@ -888,10 +938,12 @@ " # Create an image object\n", " image: Image = Image.open(full_image_path)\n", " image_width, image_height = image.size\n", + "\n", + " # get number clusters\n", + " number_cluster_bbs = get_cluster_bbs(number_labels, number_bounding_boxes)\n", + "\n", " label_color_map = {}\n", - " for i, label in enumerate(number_labels):\n", - " # Get the bounding box\n", - " bounding_box = number_bounding_boxes[i]\n", + " for label, bounding_box in number_cluster_bbs.items():\n", " x_min, y_min, x_max, y_max = [\n", " (coor / 800) * image_width\n", " if i % 2 == 0\n", @@ -946,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1108,50 +1160,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method: kmeans\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", - "\n", - "\n", - "\n", - "Method: dbscan\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "Average distance when correct: 4.76px, incorrect: 0.00px, and overall: 4.76px\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "Average distance when correct: 6.11px, incorrect: 0.00px, and overall: 6.11px\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(0.05, add_erroneous=False)\n", - "analyze_accuracy()" + "# analyze_accuracy()" ] }, { @@ -1219,7 +1234,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ChartExtractor", "language": "python", "name": "python3" }, @@ -1233,7 +1248,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.12.7" } }, "nbformat": 4, From ef07e5d648e3fa04f35e21f1b442122adc3aeaee Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 11 Nov 2024 16:34:45 -0500 Subject: [PATCH 46/55] Ready for spacing function and accuracy metrics --- experiments/clustering/clustering.ipynb | 341 +++++++++++--------- experiments/clustering/utils/annotations.py | 8 +- 2 files changed, 199 insertions(+), 150 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index e1b4b0c..ad0cc87 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,16 +31,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 245, "metadata": {}, "outputs": [], "source": [ "# Standard libraries\n", "import os\n", + "import re\n", "import json\n", "import random\n", "from pathlib import Path\n", - "from typing import List, Tuple, Dict\n", + "from typing import List, Tuple, Dict, Literal\n", "\n", "# Third-party libraries\n", "import cv2\n", @@ -71,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 246, "metadata": {}, "outputs": [ { @@ -102,19 +103,21 @@ "PATH_TO_CLUSTER_LOCATIONS = \"../../data/bp_and_hr_cluster_locations.json\"\n", "with open(PATH_TO_CLUSTER_LOCATIONS) as json_file:\n", " bp_hr_cluster_locations = json.load(json_file)\n", - " print(f\"Found {len(bp_hr_cluster_locations)} items in bp_and_hr_cluster_locations.json\")" + " print(\n", + " f\"Found {len(bp_hr_cluster_locations)} items in bp_and_hr_cluster_locations.json\"\n", + " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Define Constants Used In Notebook" + "#### Define Constants Used In Notebook\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 247, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 248, "metadata": {}, "outputs": [], "source": [ @@ -158,6 +161,7 @@ " max_index = np.argmax(kde_vals)\n", " return x_values[max_index]\n", "\n", + "\n", "def remove_bb_outliers(boxes: List[BoundingBox]) -> List[BoundingBox]:\n", " \"\"\"\n", " Given a list of bounding boxes, remove the outliers from the x axis, then remove the outliers from the y axis\n", @@ -176,8 +180,8 @@ " # find the IQR\n", " x_IQR = x_Q3 - x_Q1\n", " # determine lower and upper bounds\n", - " x_lower = x_Q1 - 1.5*x_IQR\n", - " x_upper = x_Q3 + 1.5*x_IQR\n", + " x_lower = x_Q1 - 1.5 * x_IQR\n", + " x_upper = x_Q3 + 1.5 * x_IQR\n", " # remove outliers via the x axis\n", " x_filtered = [bb for bb in boxes if x_lower <= bb.left <= x_upper]\n", "\n", @@ -189,15 +193,14 @@ " # find the IQR\n", " y_IQR = y_Q3 - y_Q1\n", " # determine the lower and upper bounds\n", - " y_lower = y_Q1 - 1.5*y_IQR\n", - " y_upper = y_Q3 + 1.5*y_IQR\n", + " y_lower = y_Q1 - 1.5 * y_IQR\n", + " y_upper = y_Q3 + 1.5 * y_IQR\n", " # remove outliers via the y axis\n", " filtered = [bb for bb in x_filtered if y_lower <= bb.top <= y_upper]\n", "\n", " return x_filtered\n", "\n", "\n", - "\n", "def select_relevant_bounding_boxes(\n", " sheet_data: List[str],\n", " path_to_image: Path,\n", @@ -252,7 +255,6 @@ " # y_loc is the horizontal line undert the time axis and above the number axis\n", " y_loc: int = find_density_max(bboxes_bottom, desired_img_height)\n", "\n", - "\n", " bounding_boxes_time = []\n", " bounding_boxes_numbers = []\n", "\n", @@ -262,12 +264,12 @@ " x_center_bb, y_center_bb = bounding_box.center\n", "\n", " # check if the bounding box is a number on the BP chart by comparing to the KDE index + a threshold\n", - " if (x_center_bb > x_loc-15 and x_center_bb < x_loc+2):\n", + " if x_center_bb > x_loc - 15 and x_center_bb < x_loc + 2:\n", " bounding_boxes_numbers.append(bounding_box)\n", " # check if the bounding box is a time on the BP chart by comparing to the KDE index + a threshold\n", - " elif (y_center_bb > y_loc-10 and y_center_bb < y_loc+2):\n", + " elif y_center_bb > y_loc - 10 and y_center_bb < y_loc + 2:\n", " bounding_boxes_time.append(bounding_box)\n", - " \n", + "\n", " bounding_boxes_numbers = remove_bb_outliers(bounding_boxes_numbers)\n", " bounding_boxes_time = remove_bb_outliers(bounding_boxes_time)\n", "\n", @@ -278,20 +280,16 @@ " y_max = int(bounding_box.bottom)\n", "\n", " # Bounding box is in the top-right region\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1\n", - " )\n", - " \n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 255, 0), 1)\n", + "\n", " for bounding_box in bounding_boxes_time:\n", " x_min = int(bounding_box.left)\n", " x_max = int(bounding_box.right)\n", " y_min = int(bounding_box.top)\n", " y_max = int(bounding_box.bottom)\n", "\n", - " cv2.rectangle(\n", - " resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1\n", - " )\n", - " \n", + " cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), (255, 0, 255), 1)\n", + "\n", " # plot the lines of the KDE index found for debugging\n", " # numbers_start = (int(x_loc), 0)\n", " # numbers_end = (int(x_loc), desired_img_height)\n", @@ -327,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 249, "metadata": {}, "outputs": [], "source": [ @@ -472,41 +470,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Code block to get the average X and Y of the expected clusters. We get the average of the location of each label across sheets." + "Code block to get the average X and Y of the expected clusters. We get the average of the location of each label across sheets.\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ - "cluster_locations_dict = {} # Dictionary containing the cluster_name as the key to another dictionary with 'x' and 'y' as keys for lists of x and y coordinates\n", + "cluster_locations_dict = {} # Dictionary containing the cluster_name as the key to another dictionary with 'x' and 'y' as keys for lists of x and y coordinates\n", "for sheet_num, data in enumerate(yolo_data.items()):\n", " # For each sheet, get the cluster center X and Y coordinates and add them to the cluster_locations_dict dictionary\n", " expected_clusters = bp_hr_cluster_locations[sheet_num]\n", " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", - " x_expected_perc, y_expected_perc = cluster['value']['x'], cluster['value']['y'] # Get the expected cluster location (percent x and y in the original image space)\n", - " x_expected, y_expected = (x_expected_perc/100) * DESIRED_IMAGE_WIDTH, (y_expected_perc/100) * DESIRED_IMAGE_HEIGHT # Convert the expected cluster location to pixel space\n", - " cluster_name = cluster['value']['rectanglelabels'][0]\n", - " if cluster_name not in cluster_locations_dict:\n", - " cluster_locations_dict[cluster_name] = {'x': [], 'y': []}\n", - " cluster_locations_dict[cluster_name]['x'].append(x_expected)\n", - " cluster_locations_dict[cluster_name]['y'].append(y_expected)\n", + " x_expected_perc, y_expected_perc = (\n", + " cluster[\"value\"][\"x\"],\n", + " cluster[\"value\"][\"y\"],\n", + " ) # Get the expected cluster location (percent x and y in the original image space)\n", + " x_expected, y_expected = (\n", + " (x_expected_perc / 100) * DESIRED_IMAGE_WIDTH,\n", + " (y_expected_perc / 100) * DESIRED_IMAGE_HEIGHT,\n", + " ) # Convert the expected cluster location to pixel space\n", + " cluster_name = cluster[\"value\"][\"rectanglelabels\"][0]\n", + " if cluster_name not in cluster_locations_dict:\n", + " cluster_locations_dict[cluster_name] = {\"x\": [], \"y\": []}\n", + " cluster_locations_dict[cluster_name][\"x\"].append(x_expected)\n", + " cluster_locations_dict[cluster_name][\"y\"].append(y_expected)\n", "\n", "# Average the cluster locations\n", "for cluster_name, cluster_data in cluster_locations_dict.items():\n", - " cluster_locations_dict[cluster_name]['x'] = float(np.mean(cluster_data['x']))\n", - " cluster_locations_dict[cluster_name]['y'] = float(np.mean(cluster_data['y']))" + " cluster_locations_dict[cluster_name][\"x\"] = float(np.mean(cluster_data[\"x\"]))\n", + " cluster_locations_dict[cluster_name][\"y\"] = float(np.mean(cluster_data[\"y\"]))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 251, "metadata": {}, "outputs": [], "source": [ - "def get_cluster_bbs(labels: List[str], bounding_boxes: List[BoundingBox]) -> Dict[str, BoundingBox]:\n", + "def get_cluster_bbs(\n", + " labels: List[str], bounding_boxes: List[BoundingBox]\n", + ") -> Dict[str, BoundingBox]:\n", " \"\"\"\n", " Create a dictionary with cluster labels as keys and a BoundingBox for the cluster.\n", "\n", @@ -540,11 +546,7 @@ " y_bottom = max([bb.bottom for bb in label_dict[key]])\n", " # store the bounding box into the dictionary\n", " cluster_dict[key] = BoundingBox(\n", - " category = key,\n", - " left = x_left,\n", - " right = x_right,\n", - " top = y_top,\n", - " bottom = y_bottom\n", + " category=key, left=x_left, right=x_right, top=y_top, bottom=y_bottom\n", " )\n", " return cluster_dict\n" ] @@ -558,24 +560,27 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 252, "metadata": {}, "outputs": [], "source": [ "def create_result_dictionary(\n", - " labels: List[str], bounding_boxes: List[BoundingBox]\n", + " labels: List[str],\n", + " bounding_boxes: List[BoundingBox],\n", + " cluster_bbs: Dict[str, BoundingBox],\n", + " unit: Literal[\"mmhg\", \"mins\"],\n", ") -> Dict[int, int]:\n", " \"\"\"\n", - " Create a dictionary with cluster labels as keys and lists of bounding boxes as values.\n", + " Create a dictionary with cluster labels as keys and cluster bounding boxes as values.\n", "\n", " Args:\n", " labels: List of cluster labels.\n", " bounding_boxes: List of bounding boxes.\n", - " expected_clusters: dictionary containing expected clustered\n", - " - This should be passed as specific for the sheet we are working with. It can be obtained from bp_hr_cluster_locations.json.\n", + " cluster_bbs: Dictionary with cluster labels as keys and a bounding box value as values.\n", + " unit: Suffix to add to the cluster label.\n", "\n", " Returns:\n", - " Dictionary with cluster labels as keys and bounding box values as values.\n", + " Dictionary with cluster labels as keys and cluster bounding box values as value.\n", " \"\"\"\n", " # Create a dictionary to store labelled elements\n", " label_dict = {}\n", @@ -589,32 +594,54 @@ " # Append the element to the corresponding label list\n", " label_dict[label].append(box)\n", "\n", + " # Create dictionary that will hold the cluster label and bounding box\n", + " results = []\n", + "\n", " # So now we have a dictionary with the clusters as keys and a list of bounding box objects as strings as values\n", "\n", - " # Now we want to impute meaning of these clusters based on how close they are to their expected cluster locations\n", + " # Sort the lists in the dictionary by x_center\n", " for key in label_dict:\n", - " # For each key get the centers of the bounding boxes, and compute the middle point\n", - " x_centers, y_centers = [element.center[0] for element in label_dict[key]], [element.center[1] for element in label_dict[key]]\n", - " x_found, y_found = sum(x_centers) / len(x_centers), sum(y_centers) / len(y_centers)\n", - " # Now we have the center point of the cluster\n", - " # We will use the euclidean distance to determine the closest expected cluster location and use that as the label\n", - " distances = [] # List that contains the distances for each expected cluster from our found cluster\n", - " for cluster in cluster_locations_dict:\n", - " x_expected, y_expected = cluster_locations_dict[cluster]['x'], cluster_locations_dict[cluster]['y']\n", - " #print(f\"Cluster location: {x}, {y}\")\n", - " distance = np.sqrt((x_expected - x_found) ** 2 + (y_expected - y_found) ** 2)\n", - " #print(f\"Distance: {distance}\")\n", - " distances.append(distance)\n", - " # Get the index of the minimum distance\n", - " min_distance_index = distances.index(min(distances))\n", - "\n", - " # Get the label of the cluster\n", - " label_dict[key] = list(cluster_locations_dict.keys())[min_distance_index]\n", - "\n", - " # Add the distance to the dictionary\n", - " label_dict[key] = {\"label\": label_dict[key], \"distance\": min(distances)}\n", - "\n", - " return label_dict" + " label_dict[key] = sorted(label_dict[key], key=lambda x: float(x.left))\n", + " label_dict[key] = [element.category for element in label_dict[key]]\n", + " # Turn list of strings into a string\n", + " label_dict[key] = f\"{''.join(label_dict[key])}_{unit}\"\n", + " # Get the bounding box for the cluster\n", + " cluster_bb = cluster_bbs[key]\n", + " # Add the cluster label and bounding box to the result dictionary\n", + " results.append((label_dict[key], cluster_bb.to_yolo(\n", + " DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT\n", + " )))\n", + "\n", + " results_dict = {}\n", + " # Now if unit is mins, turn repeats into a new value depending on it's X position\n", + " # Meaning if you have two \"0's\", or 5's etc on is truly 0 and the other is 60\n", + " # Since our axis goes 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55\n", + " # We can determine which one is 60 by looking at it's X position\n", + " if unit == \"mins\":\n", + " # See if any repeats and identify them\n", + " count_dict = {}\n", + " for label, bb in results:\n", + " if label in count_dict:\n", + " count_dict[label].append(bb)\n", + " else:\n", + " count_dict[label] = [bb]\n", + " # Now iterate over the dictionary and find the labels with many bounding boxes. Lets change the labels of these.\n", + " for label, bbs in count_dict.items():\n", + " if len(bbs) > 1:\n", + " # Sort by x\n", + " sorted_bbs = sorted(bbs, key=lambda x: float(x.split(\" \")[1]))\n", + " # The one furthest to the left is the true one for the label.\n", + " # For the rest add 60 to them depending on their index.\n", + " for i, bb in enumerate(sorted_bbs):\n", + " results_dict[f\"{str(int(re.findall(r'\\d+', label)[0]) + (i * 60))}_{unit}\"] = bb\n", + " else:\n", + " # Add the label to the results dictionary\n", + " results_dict[label] = bbs[0]\n", + " else:\n", + " # Add the label to the results dictionary\n", + " results_dict = {label: bb for label, bb in results}\n", + "\n", + " return results_dict" ] }, { @@ -626,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 253, "metadata": {}, "outputs": [], "source": [ @@ -644,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 254, "metadata": {}, "outputs": [], "source": [ @@ -695,13 +722,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 255, "metadata": {}, "outputs": [], "source": [ "def erroneous_bounding_boxes(\n", - " time_BB: List[str], number_BB: List[str],\n", - " percent_erroneous: float\n", + " time_BB: List[str], number_BB: List[str], percent_erroneous: float\n", ") -> Tuple[List[str], List[str]]:\n", " \"\"\"\n", " Create 5% erroneous bounding boxes by simultaneously removing and generating time and number BB.\n", @@ -719,8 +745,8 @@ " number_BB_copy = number_BB.copy()\n", "\n", " # convert percent input\n", - " time_BB_count = round(percent_erroneous * 76) # 76 time bounding boxes\n", - " number_BB_count = round(percent_erroneous * 53) # 53 number bounding boxes\n", + " time_BB_count = round(percent_erroneous * 76) # 76 time bounding boxes\n", + " number_BB_count = round(percent_erroneous * 53) # 53 number bounding boxes\n", "\n", " # subset and remove 5% of bounding boxes from time/number_bounding_boxes lists\n", " ## sample\n", @@ -746,75 +772,80 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Function to generate random yolo data" + "Function to generate random yolo data\n" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 256, "metadata": {}, "outputs": [], "source": [ "def generate_random_yolo(x):\n", " x_rand = random.uniform(0, 1)\n", " y_rand = random.uniform(0, 1)\n", - " return f'0 {x_rand} {y_rand} 0.0048989405776515005 0.009852199180453436'" + " return f\"0 {x_rand} {y_rand} 0.0048989405776515005 0.009852199180453436\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Function to test preprocessing effectiveness" + "Function to test preprocessing effectiveness\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 257, "metadata": {}, "outputs": [], "source": [ "def test_preprocess(yolo_data_sheet, percent_erroneous) -> Dict:\n", " total_yolo = {}\n", " # iterate through sheets in yolo json file\n", - " for sheet in range(1, len(yolo_data)+1):\n", + " for sheet in range(1, len(yolo_data) + 1):\n", " if sheet < 10:\n", " # for ease of replacement, select bounding boxes with 0 label\n", - " select_sheet = yolo_data_sheet[f'RC_000{sheet}_intraoperative.JPG']\n", - " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_000{sheet}_intraoperative.JPG']]\n", + " select_sheet = yolo_data_sheet[f\"RC_000{sheet}_intraoperative.JPG\"]\n", + " boolean_list = [\n", + " x.startswith(\"0\")\n", + " for x in yolo_data[f\"RC_000{sheet}_intraoperative.JPG\"]\n", + " ]\n", " zero_list = list(compress(select_sheet, boolean_list))\n", " # replace percent of the 64 possible zeros in the sheet\n", - " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", " # subset and remove % of yolo lines from json file\n", " lines_remove = list(random.sample(zero_list, count_remove))\n", " _ = [select_sheet.remove(line) for line in lines_remove]\n", " # use random yolo generation to refill removed lines\n", " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", - " # append yolo generated list back to copy \n", + " # append yolo generated list back to copy\n", " yolo_shuffle = select_sheet + lines_gen\n", - " total_yolo[f'RC_000{sheet}_intraoperative.JPG'] = yolo_shuffle\n", - " \n", + " total_yolo[f\"RC_000{sheet}_intraoperative.JPG\"] = yolo_shuffle\n", + "\n", " else:\n", - " select_sheet = yolo_data_sheet[f'RC_00{sheet}_intraoperative.JPG']\n", - " boolean_list = [x.startswith('0') for x in yolo_data[f'RC_00{sheet}_intraoperative.JPG']]\n", + " select_sheet = yolo_data_sheet[f\"RC_00{sheet}_intraoperative.JPG\"]\n", + " boolean_list = [\n", + " x.startswith(\"0\") for x in yolo_data[f\"RC_00{sheet}_intraoperative.JPG\"]\n", + " ]\n", " zero_list = list(compress(select_sheet, boolean_list))\n", " # replace percent of the 64 possible zeros in the sheet\n", - " count_remove = round(percent_erroneous * 64) # 64 zeros\n", + " count_remove = round(percent_erroneous * 64) # 64 zeros\n", " # subset and remove % of yolo lines from json file\n", " lines_remove = list(random.sample(zero_list, count_remove))\n", " _ = [select_sheet.remove(line) for line in lines_remove]\n", " # use random yolo generation to refill removed lines\n", " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", - " # append yolo generated list back to copy \n", + " # append yolo generated list back to copy\n", " yolo_shuffle = select_sheet + lines_gen\n", - " total_yolo[f'RC_00{sheet}_intraoperative.JPG'] = yolo_shuffle\n", + " total_yolo[f\"RC_00{sheet}_intraoperative.JPG\"] = yolo_shuffle\n", "\n", " return total_yolo" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 258, "metadata": {}, "outputs": [], "source": [ @@ -833,11 +864,11 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 259, "metadata": {}, "outputs": [], "source": [ - "def test_clustering_methods(percent_erroneous_BB: float, add_erroneous = True) -> None:\n", + "def test_clustering_methods(percent_erroneous_BB: float, add_erroneous=True) -> None:\n", " \"\"\"\n", " Test the clustering methods on the YOLO data.\n", " Saves the clustered images and the clustered bounding boxes to JSON files.\n", @@ -851,9 +882,9 @@ " sheet_num = 0\n", " # Iterate over all images and their bounding boxes\n", " for sheet, yolo_bbs in yolo_data.items():\n", - " #print(f\"Sheet: {sheet}\")\n", + " # print(f\"Sheet: {sheet}\")\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " #print(f\"Full image path: {full_image_path}\")\n", + " # print(f\"Full image path: {full_image_path}\")\n", "\n", " # Call the analyze_sheet function with data from the loop\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", @@ -931,7 +962,9 @@ " \"w\",\n", " ) as f:\n", " json.dump(\n", - " create_result_dictionary(time_labels, time_bounding_boxes),\n", + " create_result_dictionary(\n", + " time_labels, time_bounding_boxes, time_cluster_bbs, \"mins\"\n", + " ),\n", " f,\n", " )\n", "\n", @@ -979,7 +1012,7 @@ " ) as f:\n", " json.dump(\n", " create_result_dictionary(\n", - " number_labels, number_bounding_boxes\n", + " number_labels, number_bounding_boxes, number_cluster_bbs, \"mmhg\"\n", " ),\n", " f,\n", " )\n", @@ -998,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 260, "metadata": {}, "outputs": [], "source": [ @@ -1016,18 +1049,14 @@ " number_wrong_clusters_count = 0\n", " number_correct_clusters_count = 0\n", "\n", - " # Iterate over all images and their bounding boxes\n", - " number_distance_values = []\n", - " time_distance_values = []\n", - " number_distance_values_erroneous = []\n", - " time_distance_values_erroneous = []\n", " # Undetected clusters\n", " undetected_time_clusters = []\n", " undetected_number_clusters = []\n", " for sheet, yolo_bb in yolo_data.items():\n", " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb, full_image_path,\n", + " yolo_bb,\n", + " full_image_path,\n", " )\n", " # Convert the bounding boxes to a list of strings with proper suffixes\n", " expected_time_values = [\n", @@ -1105,19 +1134,17 @@ " # Each cluster contains the number (integer) that the cluster represents\n", " # We know what integers should be represented in the time labels, lets check that they are all there.\n", " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in time_clusters.items():\n", - " if value['label'] not in expected_time_values:\n", + " for cluster, bounding_box in time_clusters.items():\n", + " if cluster not in expected_time_values:\n", " # Print the sheet, value that is not in the expected values\n", - " # print(f\"Time -> Sheet: {sheet}, Value: {value[\"label\"]}.\")\n", + " #print(f\"Time -> Sheet: {sheet}, Value: {cluster}.\")\n", " # We have an erroneous cluster\n", " time_wrong_clusters_count += 1\n", - " time_distance_values_erroneous.append(value['distance'])\n", " else:\n", " # We have a correct cluster\n", - " expected_time_values.remove(value['label'])\n", + " expected_time_values.remove(cluster)\n", " time_correct_clusters_count += 1\n", - " time_distance_values.append(value['distance'])\n", - " \n", + "\n", " undetected_time_clusters += expected_time_values\n", "\n", " # Load JSON\n", @@ -1127,26 +1154,25 @@ " # Each cluster contains the number (integer) that the cluster represents\n", " # We know what integers should be represented in the time labels, lets check that they are all there.\n", " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, value in number_clusters.items():\n", - " if value['label'] not in expected_number_values:\n", - " #print(f\"Number -> Sheet: {sheet}, Value: {value}\")\n", + " for cluster, bounding_box in number_clusters.items():\n", + " if cluster not in expected_number_values:\n", + " \n", + " # print(f\"Number -> Sheet: {sheet}, Value: {value}\")\n", " # We have an erroneous cluster\n", " number_wrong_clusters_count += 1\n", - " number_distance_values_erroneous.append(value['distance'])\n", " else:\n", " # We have a correct cluster\n", - " expected_number_values.remove(value['label'])\n", + " expected_number_values.remove(cluster)\n", " number_correct_clusters_count += 1\n", - " number_distance_values.append(value['distance'])\n", "\n", " undetected_number_clusters += expected_number_values\n", "\n", " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - (time_wrong_clusters_count + len(undetected_time_clusters))) / (42 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(time_distance_values) / len(time_distance_values):.2f}px, incorrect: {sum(time_distance_values_erroneous) / 1 if len(time_distance_values_erroneous) == 0 else len(time_distance_values_erroneous):.2f}px, and overall: {sum(time_distance_values + time_distance_values_erroneous) / (len(time_distance_values) + len(time_distance_values_erroneous)):.2f}px\"\n", + " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - (time_wrong_clusters_count + len(undetected_time_clusters))) / (42 * 19) * 100:.2f}%.\"\n", " )\n", " print(\"\\n\")\n", " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - (number_wrong_clusters_count + len(undetected_number_clusters))) / (20 * 19) * 100:.2f}%.\\nAverage distance when correct: {sum(number_distance_values) / len(number_distance_values):.2f}px, incorrect: {sum(number_distance_values_erroneous) / 1 if len(number_distance_values_erroneous) == 0 else len(number_distance_values_erroneous):.2f}px, and overall: {sum(number_distance_values + number_distance_values_erroneous) / (len(number_distance_values) + len(number_distance_values_erroneous)):.2f}px\"\n", + " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - (number_wrong_clusters_count + len(undetected_number_clusters))) / (20 * 19) * 100:.2f}%.\"\n", " )\n", " print(\"\\n\\n\")\n" ] @@ -1155,30 +1181,61 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Test without Erroneous bounding boxes" + "### Test without Erroneous bounding boxes\n" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 261, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "\n", + "\n", + "\n", + "Method: dbscan\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "\n", + "\n", + "\n", + "Method: agglomerative\n", + "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", + "\n", + "\n", + "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(0.05, add_erroneous=False)\n", - "# analyze_accuracy()" + "analyze_accuracy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Test with Erroneous bounding boxes" + "### Test with Erroneous bounding boxes\n" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 262, "metadata": {}, "outputs": [ { @@ -1186,32 +1243,26 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 772 correct clusters, 2 incorrect clusters. There were 26 undetected clusters. The accuracy is 93.23%.\n", - "Average distance when correct: 4.89px, incorrect: 2.00px, and overall: 4.90px\n", + "Time labels: 668 correct clusters, 98 incorrect clusters. There were 130 undetected clusters. The accuracy is 55.14%.\n", "\n", "\n", - "Number labels: 372 correct clusters, 2 incorrect clusters. There were 8 undetected clusters. The accuracy is 95.26%.\n", - "Average distance when correct: 6.15px, incorrect: 2.00px, and overall: 6.15px\n", + "Number labels: 274 correct clusters, 96 incorrect clusters. There were 106 undetected clusters. The accuracy is 18.95%.\n", "\n", "\n", "\n", "Method: dbscan\n", - "Time labels: 789 correct clusters, 36 incorrect clusters. There were 9 undetected clusters. The accuracy is 93.23%.\n", - "Average distance when correct: 4.81px, incorrect: 36.00px, and overall: 4.98px\n", + "Time labels: 697 correct clusters, 115 incorrect clusters. There were 101 undetected clusters. The accuracy is 60.28%.\n", "\n", "\n", - "Number labels: 346 correct clusters, 17 incorrect clusters. There were 34 undetected clusters. The accuracy is 77.63%.\n", - "Average distance when correct: 6.16px, incorrect: 17.00px, and overall: 6.16px\n", + "Number labels: 281 correct clusters, 83 incorrect clusters. There were 99 undetected clusters. The accuracy is 26.05%.\n", "\n", "\n", "\n", "Method: agglomerative\n", - "Time labels: 779 correct clusters, 10 incorrect clusters. There were 19 undetected clusters. The accuracy is 93.98%.\n", - "Average distance when correct: 4.85px, incorrect: 10.00px, and overall: 4.91px\n", + "Time labels: 676 correct clusters, 98 incorrect clusters. There were 122 undetected clusters. The accuracy is 57.14%.\n", "\n", "\n", - "Number labels: 357 correct clusters, 12 incorrect clusters. There were 23 undetected clusters. The accuracy is 84.74%.\n", - "Average distance when correct: 6.16px, incorrect: 12.00px, and overall: 6.14px\n", + "Number labels: 271 correct clusters, 94 incorrect clusters. There were 109 undetected clusters. The accuracy is 17.89%.\n", "\n", "\n", "\n" @@ -1220,7 +1271,7 @@ ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", - "test_clustering_methods(add_erroneous=True)\n", + "test_clustering_methods(0.05, add_erroneous=True)\n", "analyze_accuracy()" ] }, diff --git a/experiments/clustering/utils/annotations.py b/experiments/clustering/utils/annotations.py index 6d4f99c..ed236a0 100644 --- a/experiments/clustering/utils/annotations.py +++ b/experiments/clustering/utils/annotations.py @@ -232,9 +232,7 @@ def set_box(self, new_left: int, new_top: int, new_right: int, new_bottom: int): bottom=new_bottom, ) - def to_yolo( - self, image_width: int, image_height: int, category_to_id: Dict[str, int] - ) -> str: + def to_yolo(self, image_width: int, image_height: int) -> str: """Writes the data from this `BoundingBox` into a yolo formatted string. Args : @@ -243,12 +241,12 @@ def to_yolo( `image_height` (int): The image's height that this boundingbox belongs to. `category_to_id` (Dict[str, int]): - A dictionary that maps the category string to an id (integer). + A dictionary that maps the category string to an id (integer). Removed at this moment. Returns: A string that encodes this `BoundingBox`'s data for a single line in a yolo label file. """ - c = category_to_id[self.category] + c = self.category x, y = self.center x /= image_width y /= image_height From e6e4e9c0ac4e7e802d022719ff0e70a5f5c09f1c Mon Sep 17 00:00:00 2001 From: Matt Beck Date: Mon, 11 Nov 2024 21:18:59 -0500 Subject: [PATCH 47/55] Add categories to clusters --- experiments/clustering/clustering.ipynb | 105 ++++++++++++++---------- 1 file changed, 62 insertions(+), 43 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index ad0cc87..92ae87e 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -506,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -544,9 +544,14 @@ " x_right = max([bb.right for bb in label_dict[key]])\n", " y_top = min([bb.top for bb in label_dict[key]])\n", " y_bottom = max([bb.bottom for bb in label_dict[key]])\n", + " # get the category based off of digit detections\n", + " sorted_boxes = sorted(label_dict[key], key=lambda x: float(x.left))\n", + " sorted_categories = [bb.category for bb in sorted_boxes]\n", + " # Turn list of strings into a string\n", + " cluster_category = f\"{''.join(sorted_categories)}\"\n", " # store the bounding box into the dictionary\n", " cluster_dict[key] = BoundingBox(\n", - " category=key, left=x_left, right=x_right, top=y_top, bottom=y_bottom\n", + " category=cluster_category, left=x_left, right=x_right, top=y_top, bottom=y_bottom\n", " )\n", " return cluster_dict\n" ] @@ -560,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -653,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -671,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -722,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -777,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -845,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -864,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1031,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1186,37 +1191,51 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Method: kmeans\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n", - "Method: dbscan\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n" + "{6: BoundingBox(category='25', left=721.6904000946971, top=225.8097282858456, right=729.7973484848485, bottom=231.88060087316177), 2: BoundingBox(category='0', left=130.80656664299244, top=226.08968577665442, right=134.58732836174244, bottom=232.14639820772058), 26: BoundingBox(category='5', left=145.64833392518938, top=226.05847886029412, right=149.66429924242425, bottom=232.09637810202204), 33: BoundingBox(category='10', left=158.1322502367424, top=226.14414349724265, right=165.96851325757575, bottom=232.03193933823528), 12: BoundingBox(category='15', left=172.51596531723484, top=226.10875746783088, right=180.61334043560606, bottom=232.27738683363967), 7: BoundingBox(category='20', left=187.13372987689397, top=226.0351993336397, right=195.53330669981062, bottom=232.1578440946691), 37: BoundingBox(category='25', left=201.60980409564397, top=226.14908375459558, right=210.12683475378785, bottom=232.33648322610293), 14: 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33\u001b[0m, in \u001b[0;36mtest_clustering_methods\u001b[0;34m(percent_erroneous_BB, add_erroneous)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magglomerative\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# Now we need to cluster the bounding boxes that pertain to the same multi-digit number\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkmeans\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 33\u001b[0m time_labels \u001b[38;5;241m=\u001b[39m \u001b[43mcluster_kmeans\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime_bounding_boxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m41\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m number_labels \u001b[38;5;241m=\u001b[39m cluster_kmeans(number_bounding_boxes, [\u001b[38;5;241m18\u001b[39m, \u001b[38;5;241m19\u001b[39m, \u001b[38;5;241m20\u001b[39m])\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdbscan\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "Cell \u001b[0;32mIn[5], line 34\u001b[0m, in \u001b[0;36mcluster_kmeans\u001b[0;34m(bounding_boxes, possible_nclusters)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Apply K-Means\u001b[39;00m\n\u001b[1;32m 26\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(\n\u001b[1;32m 27\u001b[0m n_clusters\u001b[38;5;241m=\u001b[39mnumber_of_clusters,\n\u001b[1;32m 28\u001b[0m init\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,\n\u001b[1;32m 33\u001b[0m )\n\u001b[0;32m---> 34\u001b[0m \u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;66;03m# Get cluster labels\u001b[39;00m\n\u001b[1;32m 37\u001b[0m labels \u001b[38;5;241m=\u001b[39m kmeans\u001b[38;5;241m.\u001b[39mpredict(data)\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/sklearn/base.py:1473\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1471\u001b[0m )\n\u001b[1;32m 1472\u001b[0m ):\n\u001b[0;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1508\u001b[0m, in \u001b[0;36mKMeans.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 1504\u001b[0m best_inertia, best_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1506\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_init):\n\u001b[1;32m 1507\u001b[0m \u001b[38;5;66;03m# Initialize centers\u001b[39;00m\n\u001b[0;32m-> 1508\u001b[0m centers_init \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_centroids\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1509\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1510\u001b[0m \u001b[43m \u001b[49m\u001b[43mx_squared_norms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx_squared_norms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1511\u001b[0m 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\u001b[0;36m_BaseKMeans._init_centroids\u001b[0;34m(self, X, x_squared_norms, init, random_state, sample_weight, init_size, n_centroids)\u001b[0m\n\u001b[1;32m 1017\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m sample_weight[init_indices]\n\u001b[1;32m 1019\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1020\u001b[0m centers, _ \u001b[38;5;241m=\u001b[39m \u001b[43m_kmeans_plusplus\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1021\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1022\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_clusters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1023\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1024\u001b[0m \u001b[43m \u001b[49m\u001b[43mx_squared_norms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx_squared_norms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1025\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1026\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1027\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1028\u001b[0m seeds \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39mchoice(\n\u001b[1;32m 1029\u001b[0m n_samples,\n\u001b[1;32m 1030\u001b[0m size\u001b[38;5;241m=\u001b[39mn_clusters,\n\u001b[1;32m 1031\u001b[0m replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 1032\u001b[0m p\u001b[38;5;241m=\u001b[39msample_weight \u001b[38;5;241m/\u001b[39m sample_weight\u001b[38;5;241m.\u001b[39msum(),\n\u001b[1;32m 1033\u001b[0m )\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:252\u001b[0m, in \u001b[0;36m_kmeans_plusplus\u001b[0;34m(X, n_clusters, x_squared_norms, sample_weight, random_state, n_local_trials)\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, n_clusters):\n\u001b[1;32m 248\u001b[0m \u001b[38;5;66;03m# Choose center candidates by sampling with probability proportional\u001b[39;00m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# to the squared distance to the closest existing center\u001b[39;00m\n\u001b[1;32m 250\u001b[0m rand_vals \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39muniform(size\u001b[38;5;241m=\u001b[39mn_local_trials) \u001b[38;5;241m*\u001b[39m current_pot\n\u001b[1;32m 251\u001b[0m candidate_ids \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msearchsorted(\n\u001b[0;32m--> 252\u001b[0m \u001b[43mstable_cumsum\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mclosest_dist_sq\u001b[49m\u001b[43m)\u001b[49m, rand_vals\n\u001b[1;32m 253\u001b[0m )\n\u001b[1;32m 254\u001b[0m \u001b[38;5;66;03m# XXX: numerical imprecision can result in a candidate_id out of range\u001b[39;00m\n\u001b[1;32m 255\u001b[0m np\u001b[38;5;241m.\u001b[39mclip(candidate_ids, \u001b[38;5;28;01mNone\u001b[39;00m, closest_dist_sq\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m, out\u001b[38;5;241m=\u001b[39mcandidate_ids)\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/sklearn/utils/extmath.py:1228\u001b[0m, in \u001b[0;36mstable_cumsum\u001b[0;34m(arr, axis, rtol, atol)\u001b[0m\n\u001b[1;32m 1205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstable_cumsum\u001b[39m(arr, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, rtol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-05\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-08\u001b[39m):\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Use high precision for cumsum and check that final value matches sum.\u001b[39;00m\n\u001b[1;32m 1207\u001b[0m \n\u001b[1;32m 1208\u001b[0m \u001b[38;5;124;03m Warns if the final cumulative sum does not match the sum (up to the chosen\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;124;03m Array with the cumulative sums along the chosen axis.\u001b[39;00m\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1228\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcumsum\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1229\u001b[0m expected \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(arr, axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m 1230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(\n\u001b[1;32m 1231\u001b[0m out\u001b[38;5;241m.\u001b[39mtake(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, axis\u001b[38;5;241m=\u001b[39maxis), expected, rtol\u001b[38;5;241m=\u001b[39mrtol, atol\u001b[38;5;241m=\u001b[39matol, equal_nan\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1232\u001b[0m ):\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:2980\u001b[0m, in \u001b[0;36mcumsum\u001b[0;34m(a, axis, dtype, out)\u001b[0m\n\u001b[1;32m 2904\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_cumsum_dispatcher)\n\u001b[1;32m 2905\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcumsum\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 2906\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2907\u001b[0m \u001b[38;5;124;03m Return the cumulative sum of the elements along a given axis.\u001b[39;00m\n\u001b[1;32m 2908\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2978\u001b[0m \n\u001b[1;32m 2979\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2980\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcumsum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/ChartExtractor/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:57\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -1235,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": null, "metadata": {}, "outputs": [ { From 9718985fd90264adbf67d8968952a10d09d27c77 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 11 Nov 2024 23:28:39 -0500 Subject: [PATCH 48/55] Added mAP as accuracy metric. Need spacing/post-processing to improve current methods. --- experiments/clustering/clustering.ipynb | 510 ++++++++++-------- .../clustering/utils/detection_reassembly.py | 90 ++++ 2 files changed, 363 insertions(+), 237 deletions(-) create mode 100644 experiments/clustering/utils/detection_reassembly.py diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index ad0cc87..966be35 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,8 @@ "from itertools import compress\n", "\n", "# Local libraries\n", - "from utils.annotations import BoundingBox" + "from utils.annotations import BoundingBox\n", + "from utils.detection_reassembly import intersection_over_union, Detection" ] }, { @@ -72,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -117,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -125,6 +126,62 @@ "DESIRED_IMAGE_HEIGHT = 600" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Clean Ground Truth Labels\n", + "\n", + "Need it to contain a dictionary with labels: yolo\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sheet RC_0001_intraoperative.JPG already has a cluster named 190_mins in the ground_truth_clusters dictionary\n" + ] + } + ], + "source": [ + "ground_truth_clusters = {} \n", + "for sheet_num, data in enumerate(yolo_data.items()):\n", + " # For each sheet, get the cluster center X and Y coordinates and add them to the cluster_locations_dict dictionary\n", + " expected_clusters = bp_hr_cluster_locations[sheet_num]\n", + " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", + " x_expected_perc, y_expected_perc = (\n", + " cluster[\"value\"][\"x\"],\n", + " cluster[\"value\"][\"y\"],\n", + " ) # Get the expected cluster location (percent x and y in the original image space)\n", + " x_expected_frac, y_expected_frac = (\n", + " (x_expected_perc / 100),\n", + " (y_expected_perc / 100),\n", + " ) # Convert the expected cluster location to pixel space\n", + " height_perc, width_perc = (\n", + " cluster[\"value\"][\"height\"],\n", + " cluster[\"value\"][\"width\"],\n", + " )\n", + " height_frac, width_frac = (\n", + " (height_perc / 100),\n", + " (width_perc / 100),\n", + " )\n", + " x_expected_frac = x_expected_frac + (width_frac / 2)\n", + " y_expected_frac = y_expected_frac + (height_frac / 2)\n", + " cluster_name = cluster[\"value\"][\"rectanglelabels\"][0]\n", + " if data[0] not in ground_truth_clusters:\n", + " ground_truth_clusters[data[0]] = {cluster_name: BoundingBox.from_yolo(f\"{cluster_name} {x_expected_frac} {y_expected_frac} {width_frac} {height_frac}\", DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)}\n", + " elif cluster_name not in ground_truth_clusters[data[0]]:\n", + " ground_truth_clusters[data[0]][cluster_name] = BoundingBox.from_yolo(f\"{cluster_name} {x_expected_frac} {y_expected_frac} {width_frac} {height_frac}\", DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)\n", + " else:\n", + " print(f\"Sheet {data[0]} already has a cluster named {cluster_name} in the ground_truth_clusters dictionary\")\n", + " continue" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -136,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 248, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -325,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -470,43 +527,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Code block to get the average X and Y of the expected clusters. We get the average of the location of each label across sheets.\n" + "#### This takes our clusters of bounding boxes and makes new bounding boxes for the cluster.\n" ] }, { "cell_type": "code", - "execution_count": 250, - "metadata": {}, - "outputs": [], - "source": [ - "cluster_locations_dict = {} # Dictionary containing the cluster_name as the key to another dictionary with 'x' and 'y' as keys for lists of x and y coordinates\n", - "for sheet_num, data in enumerate(yolo_data.items()):\n", - " # For each sheet, get the cluster center X and Y coordinates and add them to the cluster_locations_dict dictionary\n", - " expected_clusters = bp_hr_cluster_locations[sheet_num]\n", - " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", - " x_expected_perc, y_expected_perc = (\n", - " cluster[\"value\"][\"x\"],\n", - " cluster[\"value\"][\"y\"],\n", - " ) # Get the expected cluster location (percent x and y in the original image space)\n", - " x_expected, y_expected = (\n", - " (x_expected_perc / 100) * DESIRED_IMAGE_WIDTH,\n", - " (y_expected_perc / 100) * DESIRED_IMAGE_HEIGHT,\n", - " ) # Convert the expected cluster location to pixel space\n", - " cluster_name = cluster[\"value\"][\"rectanglelabels\"][0]\n", - " if cluster_name not in cluster_locations_dict:\n", - " cluster_locations_dict[cluster_name] = {\"x\": [], \"y\": []}\n", - " cluster_locations_dict[cluster_name][\"x\"].append(x_expected)\n", - " cluster_locations_dict[cluster_name][\"y\"].append(y_expected)\n", - "\n", - "# Average the cluster locations\n", - "for cluster_name, cluster_data in cluster_locations_dict.items():\n", - " cluster_locations_dict[cluster_name][\"x\"] = float(np.mean(cluster_data[\"x\"]))\n", - " cluster_locations_dict[cluster_name][\"y\"] = float(np.mean(cluster_data[\"y\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 251, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -560,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -633,13 +659,14 @@ " # The one furthest to the left is the true one for the label.\n", " # For the rest add 60 to them depending on their index.\n", " for i, bb in enumerate(sorted_bbs):\n", - " results_dict[f\"{str(int(re.findall(r'\\d+', label)[0]) + (i * 60))}_{unit}\"] = bb\n", + " correct_label = f\"{str(int(re.findall(r'\\d+', label)[0]) + (i * 60))}_{unit}\"\n", + " results_dict[correct_label] = f\"{correct_label} {\" \".join(bb.split(\" \")[1:5])}\"\n", " else:\n", " # Add the label to the results dictionary\n", - " results_dict[label] = bbs[0]\n", + " results_dict[label] = f\"{label} {\" \".join(bbs[0].split(\" \")[1:5])}\"\n", " else:\n", " # Add the label to the results dictionary\n", - " results_dict = {label: bb for label, bb in results}\n", + " results_dict = {label: f\"{label} {\" \".join(bb.split(\" \")[1:5])}\" for label, bb in results}\n", "\n", " return results_dict" ] @@ -653,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -671,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -722,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -777,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -845,12 +872,12 @@ }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "# preprocessing test by manipulating yolo data\n", - "# yolo_data = test_preprocess(yolo_data, 0.1)" + "#yolo_data = test_preprocess(yolo_data, 0.1)" ] }, { @@ -864,11 +891,11 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "def test_clustering_methods(percent_erroneous_BB: float, add_erroneous=True) -> None:\n", + "def test_clustering_methods(add_erroneous: bool = True, percent_erroneous_BB: float = 0) -> None:\n", " \"\"\"\n", " Test the clustering methods on the YOLO data.\n", " Saves the clustered images and the clustered bounding boxes to JSON files.\n", @@ -888,7 +915,7 @@ "\n", " # Call the analyze_sheet function with data from the loop\n", " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bbs, full_image_path\n", + " yolo_bbs, full_image_path, show_images=False\n", " )\n", "\n", " if add_erroneous:\n", @@ -1026,155 +1053,212 @@ "source": [ "#### Analyze accuracy\n", "\n", - "Below we write a function that analyzes the accuracy of our clustering methods.\n" + "Below we write a function that analyzes the accuracy of our clustering methods. " ] }, { "cell_type": "code", - "execution_count": 260, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# This function needs to be redone.\n", + "# We instead want to use bp_hr_cluster_locations to calculate intersection over union for each.\n", + "# Then add these stats to get mean average precision.\n", + "\n", + "def calculate_map(ious):\n", + " \"\"\"Calculates Mean Average Precision (mAP) from a list of IoUs.\n", + "\n", + " Args:\n", + " ious: A list of IoUs for each prediction-ground truth pair.\n", + "\n", + " Returns:\n", + " The calculated mAP.\n", + " \"\"\"\n", + "\n", + " # Sort IoUs in descending order\n", + " ious.sort(reverse=True)\n", + "\n", + " # Calculate precision and recall\n", + " tp = np.cumsum([1 if iou >= 0.5 else 0 for iou in ious])\n", + " fp = np.cumsum([1 if iou < 0.5 else 0 for iou in ious])\n", + " precision = tp / (tp + fp)\n", + " recall = tp / len(ious)\n", + "\n", + " # Calculate Average Precision (AP)\n", + " ap = calculate_ap(precision, recall)\n", + "\n", + " return ap\n", + "\n", + "def calculate_ap(precision, recall):\n", + " \"\"\"Calculates Average Precision (AP) from precision and recall curves.\n", + "\n", + " Args:\n", + " precision: A list of precision values.\n", + " recall: A list of recall values.\n", + "\n", + " Returns:\n", + " The calculated AP.\n", + " \"\"\"\n", + "\n", + " # Approximate the area under the curve using the trapezoidal rule\n", + " mrec = np.concatenate(([0.], recall, [1.]))\n", + " mpre = np.concatenate(([0.], precision, [0.]))\n", + " for i in range(mpre.size - 1)[::-1]:\n", + " mpre[i] = max(mpre[i], mpre[i+1])\n", + " index = np.where(mrec[1:] != mrec[:-1])[0]\n", + " ap = np.sum((mrec[index + 1] - mrec[index]) * mpre[index + 1])\n", + " return ap \n", + "\n", "def analyze_accuracy():\n", - " # Since this work is done above, we can simply read in from the JSON files created in the previous step and work from there.\n", " for method in [\"kmeans\", \"dbscan\", \"agglomerative\"]:\n", + " time_ious = []\n", + " number_ious = []\n", " print(f\"Method: {method}\")\n", " # Paths to the JSON files\n", " PATH_TO_RESULTS = f\"../../data/{method}_clustered_images/results\"\n", " TIME_JSON = os.path.join(PATH_TO_RESULTS, \"time\")\n", " NUMBER_JSON = os.path.join(PATH_TO_RESULTS, \"number\")\n", "\n", - " time_wrong_clusters_count = 0\n", - " time_correct_clusters_count = 0\n", - " number_wrong_clusters_count = 0\n", - " number_correct_clusters_count = 0\n", - "\n", - " # Undetected clusters\n", - " undetected_time_clusters = []\n", - " undetected_number_clusters = []\n", " for sheet, yolo_bb in yolo_data.items():\n", - " full_image_path = os.path.join(PATH_TO_REGISTERED_IMAGES, sheet)\n", - " time_bounding_boxes, number_bounding_boxes = select_relevant_bounding_boxes(\n", - " yolo_bb,\n", - " full_image_path,\n", - " )\n", - " # Convert the bounding boxes to a list of strings with proper suffixes\n", - " expected_time_values = [\n", - " \"0_mins\",\n", - " \"5_mins\",\n", - " \"10_mins\",\n", - " \"15_mins\",\n", - " \"20_mins\",\n", - " \"25_mins\",\n", - " \"30_mins\",\n", - " \"35_mins\",\n", - " \"40_mins\",\n", - " \"45_mins\",\n", - " \"50_mins\",\n", - " \"55_mins\",\n", - " \"60_mins\",\n", - " \"65_mins\",\n", - " \"70_mins\",\n", - " \"75_mins\",\n", - " \"80_mins\",\n", - " \"85_mins\",\n", - " \"90_mins\",\n", - " \"95_mins\",\n", - " \"100_mins\",\n", - " \"105_mins\",\n", - " \"110_mins\",\n", - " \"115_mins\",\n", - " \"120_mins\",\n", - " \"125_mins\",\n", - " \"130_mins\",\n", - " \"135_mins\",\n", - " \"140_mins\",\n", - " \"145_mins\",\n", - " \"150_mins\",\n", - " \"155_mins\",\n", - " \"160_mins\",\n", - " \"165_mins\",\n", - " \"170_mins\",\n", - " \"175_mins\",\n", - " \"180_mins\",\n", - " \"185_mins\",\n", - " \"190_mins\",\n", - " \"195_mins\",\n", - " \"200_mins\",\n", - " \"205_mins\",\n", - " ]\n", - "\n", - " expected_number_values = [\n", - " \"30_mmhg\",\n", - " \"40_mmhg\",\n", - " \"50_mmhg\",\n", - " \"60_mmhg\",\n", - " \"70_mmhg\",\n", - " \"80_mmhg\",\n", - " \"90_mmhg\",\n", - " \"100_mmhg\",\n", - " \"110_mmhg\",\n", - " \"120_mmhg\",\n", - " \"130_mmhg\",\n", - " \"140_mmhg\",\n", - " \"150_mmhg\",\n", - " \"160_mmhg\",\n", - " \"170_mmhg\",\n", - " \"180_mmhg\",\n", - " \"190_mmhg\",\n", - " \"200_mmhg\",\n", - " \"210_mmhg\",\n", - " \"220_mmhg\",\n", - " ]\n", - "\n", " # Load JSON\n", " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", " time_clusters = json.load(f)\n", - "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, bounding_box in time_clusters.items():\n", - " if cluster not in expected_time_values:\n", - " # Print the sheet, value that is not in the expected values\n", - " #print(f\"Time -> Sheet: {sheet}, Value: {cluster}.\")\n", - " # We have an erroneous cluster\n", - " time_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_time_values.remove(cluster)\n", - " time_correct_clusters_count += 1\n", - "\n", - " undetected_time_clusters += expected_time_values\n", - "\n", + " \n", " # Load JSON\n", " with open(os.path.join(NUMBER_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", " number_clusters = json.load(f)\n", + " \n", + " # Calculate the intersection over union for each cluster\n", + " # Take each cluster in ground_truth_clusters and compare it to the clusters in time_clusters and number_clusters\n", + " ground_truth_clusters_sheet = ground_truth_clusters[sheet] \n", + " for cluster, yolo_bb in number_clusters.items():\n", + " # Get the bounding box for the cluster\n", + " cluster_bb = BoundingBox.from_yolo(yolo_bb, DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)\n", + " try:\n", + " image: Image = Image.open(f\"../../data/{method}_clustered_images/accuracy/{sheet}\")\n", + " except Exception as e: \n", + " # Create an image object\n", + " print(f\"{e}: {f\"../../data/{method}_clustered_images/accuracy/{sheet}\"}\")\n", + " image: Image = Image.open(os.path.join(PATH_TO_REGISTERED_IMAGES, sheet))\n", + " image_width, image_height = image.size\n", + " # Show the found cluster in an image in RED\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(cluster_bb.box)\n", + " ]\n", "\n", - " # Each cluster contains the number (integer) that the cluster represents\n", - " # We know what integers should be represented in the time labels, lets check that they are all there.\n", - " # Keep track of any false positives (new clusters that don't exist) or negatives (missing clusters)\n", - " for cluster, bounding_box in number_clusters.items():\n", - " if cluster not in expected_number_values:\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=\"red\",\n", + " width=3,\n", + " )\n", + " if cluster in ground_truth_clusters_sheet:\n", + " ground_truth_cluster = ground_truth_clusters_sheet[cluster]\n", + " iou = intersection_over_union(Detection(cluster_bb, 1.0), Detection(ground_truth_cluster, 1.0))\n", + " number_ious.append(iou)\n", + " #print(f\"Cluster: {cluster}, IOU: {iou}\")\n", + " \n", + " # Draw the ground truth clusters in blue\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(ground_truth_cluster.box)\n", + " ]\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=\"blue\",\n", + " width=3,\n", + " )\n", + " else: \n", + " #print(f\"Cluster: {cluster}, IOU: 0\")\n", + " number_ious.append(iou)\n", + " # Save the image with the bounding boxes to the test folder\n", + " image.save(f\"../../data/{method}_clustered_images/accuracy/{sheet}\")\n", " \n", - " # print(f\"Number -> Sheet: {sheet}, Value: {value}\")\n", - " # We have an erroneous cluster\n", - " number_wrong_clusters_count += 1\n", - " else:\n", - " # We have a correct cluster\n", - " expected_number_values.remove(cluster)\n", - " number_correct_clusters_count += 1\n", - "\n", - " undetected_number_clusters += expected_number_values\n", - "\n", - " print(\n", - " f\"Time labels: {time_correct_clusters_count} correct clusters, {time_wrong_clusters_count} incorrect clusters. There were {len(undetected_time_clusters)} undetected clusters. The accuracy is {(time_correct_clusters_count - (time_wrong_clusters_count + len(undetected_time_clusters))) / (42 * 19) * 100:.2f}%.\"\n", - " )\n", - " print(\"\\n\")\n", - " print(\n", - " f\"Number labels: {number_correct_clusters_count} correct clusters, {number_wrong_clusters_count} incorrect clusters. There were {len(undetected_number_clusters)} undetected clusters. The accuracy is {(number_correct_clusters_count - (number_wrong_clusters_count + len(undetected_number_clusters))) / (20 * 19) * 100:.2f}%.\"\n", - " )\n", - " print(\"\\n\\n\")\n" + "\n", + " for cluster, yolo_bb in time_clusters.items():\n", + " # Get the bounding box for the cluster\n", + " cluster_bb = BoundingBox.from_yolo(yolo_bb, DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)\n", + " try:\n", + " image: Image = Image.open(f\"../../data/{method}_clustered_images/accuracy/{sheet}\")\n", + " except Exception as e: \n", + " # Create an image object\n", + " #print(f\"{e}: {f\"../../data/{method}_clustered_images/accuracy/{sheet}\"}\")\n", + " image: Image = Image.open(os.path.join(PATH_TO_REGISTERED_IMAGES, sheet))\n", + " image_width, image_height = image.size\n", + " # Show the found cluster in an image in RED\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(cluster_bb.box)\n", + " ]\n", + "\n", + " # Open the image\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=\"red\",\n", + " width=3,\n", + " )\n", + " if cluster in ground_truth_clusters_sheet:\n", + " ground_truth_cluster = ground_truth_clusters_sheet[cluster]\n", + " iou = intersection_over_union(Detection(cluster_bb, 1.0), Detection(ground_truth_cluster, 1.0))\n", + " time_ious.append(iou)\n", + " # print(f\"Cluster: {cluster}, IOU: {iou}\")\n", + " \n", + " # Draw the ground truth clusters in blue\n", + " x_min, y_min, x_max, y_max = [\n", + " (coor / 800) * image_width\n", + " if i % 2 == 0\n", + " else (coor / 600) * image_height\n", + " for i, coor in enumerate(ground_truth_cluster.box)\n", + " ]\n", + " draw.rectangle(\n", + " [\n", + " x_min,\n", + " y_min,\n", + " x_max,\n", + " y_max,\n", + " ],\n", + " outline=\"blue\",\n", + " width=3,\n", + " )\n", + " else: \n", + " #print(f\"Cluster: {cluster}, IOU: 0\")\n", + " time_ious.append(iou)\n", + "\n", + " # Save the image with the bounding boxes to the test folder\n", + " image.save(f\"../../data/{method}_clustered_images/accuracy/{sheet}\")\n", + "\n", + " # Calculate the Mean Average Precision (mAP) for the time and number clusters\n", + " time_map = calculate_map(time_ious)\n", + " number_map = calculate_map(number_ious)\n", + " print(f\"Time mAP: {time_map}\")\n", + " print(f\"Number mAP: {number_map}\")" ] }, { @@ -1186,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1194,35 +1278,18 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n", + "Time mAP: 0.8419071518193224\n", + "Number mAP: 0.9131578947368421\n", "Method: dbscan\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 797 correct clusters, 0 incorrect clusters. There were 1 undetected clusters. The accuracy is 99.75%.\n", - "\n", - "\n", - "Number labels: 380 correct clusters, 0 incorrect clusters. There were 0 undetected clusters. The accuracy is 100.00%.\n", - "\n", - "\n", - "\n" + "Time mAP: 0.8419071518193224\n", + "Number mAP: 0.9131578947368421\n", + "Method: agglomerative\n" ] } ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", - "test_clustering_methods(0.05, add_erroneous=False)\n", + "test_clustering_methods(add_erroneous=False)\n", "analyze_accuracy()" ] }, @@ -1235,43 +1302,12 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method: kmeans\n", - "Time labels: 668 correct clusters, 98 incorrect clusters. There were 130 undetected clusters. The accuracy is 55.14%.\n", - "\n", - "\n", - "Number labels: 274 correct clusters, 96 incorrect clusters. There were 106 undetected clusters. The accuracy is 18.95%.\n", - "\n", - "\n", - "\n", - "Method: dbscan\n", - "Time labels: 697 correct clusters, 115 incorrect clusters. There were 101 undetected clusters. The accuracy is 60.28%.\n", - "\n", - "\n", - "Number labels: 281 correct clusters, 83 incorrect clusters. There were 99 undetected clusters. The accuracy is 26.05%.\n", - "\n", - "\n", - "\n", - "Method: agglomerative\n", - "Time labels: 676 correct clusters, 98 incorrect clusters. There were 122 undetected clusters. The accuracy is 57.14%.\n", - "\n", - "\n", - "Number labels: 271 correct clusters, 94 incorrect clusters. There were 109 undetected clusters. The accuracy is 17.89%.\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", - "test_clustering_methods(0.05, add_erroneous=True)\n", + "test_clustering_methods(True, 0.05)\n", "analyze_accuracy()" ] }, @@ -1285,7 +1321,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ChartExtractor", + "display_name": ".venv", "language": "python", "name": "python3" }, diff --git a/experiments/clustering/utils/detection_reassembly.py b/experiments/clustering/utils/detection_reassembly.py new file mode 100644 index 0000000..e3ea4c7 --- /dev/null +++ b/experiments/clustering/utils/detection_reassembly.py @@ -0,0 +1,90 @@ +"""This module defines functions for reassembling tiled detections.""" + +from dataclasses import dataclass +from typing import Tuple, Union +from .annotations import BoundingBox, Keypoint + +"""This module defines the Detection class representing a single object detection result. + +This class is used to store the output of an object detection model, including: + +* The predicted location of the object, represented by either a BoundingBox or a Keypoint instance (depending on the model's output format). +* The confidence score assigned by the model to this detection (a float between 0.0 and 1.0). +""" + + +@dataclass +class Detection: + """Represents a single detection result from an object detection model. + + Attributes: + annotation: + An instance of either BoundingBox or Keypoint class, depending on the + type of annotation used for localization (bounding box or keypoints). + confidence: + A float value between 0.0 and 1.0 representing the confidence score + assigned by the object detection model to this detection. + """ + + annotation: Union[BoundingBox, Keypoint] + confidence: float + + +def compute_area(box: Tuple[float, float, float, float]): + """Computes the area of a rectangle. + + Args: + `box` (Tuple[float, float, float, float]): + A tuple of four floats that define the (left, top, right, bottom) of a rectangle. + + Returns: + The area of the rectangle. + """ + return (box[2] - box[0]) * (box[3] - box[1]) + + +def compute_intersection_area( + box_1: Tuple[float, float, float, float], box_2: Tuple[float, float, float, float] +) -> float: + """Computes the area of the intersection of two rectangle. + + Args: + `box_1` (Tuple[float, float, float, float]): + A tuple of four floats that define the (left, top, right, bottom) of the first rectangle. + `box_2` (Tuple[float, float, float, float]): + A tuple of four floats that define the (left, top, right, bottom) of the second rectangle. + + Returns: + The area of the intersection of the two rectangles box_1 and box_2. + """ + intersection_left = max(box_1[0], box_2[0]) + intersection_top = max(box_1[1], box_2[1]) + intersection_right = min(box_1[2], box_2[2]) + intersection_bottom = min(box_1[3], box_2[3]) + if intersection_right < intersection_left or intersection_bottom < intersection_top: + return 0 + intersection_area = compute_area( + [intersection_left, intersection_top, intersection_right, intersection_bottom] + ) + return intersection_area + + +def intersection_over_union(detection_1: Detection, detection_2: Detection) -> float: + """Calculates the Intersection over Union (IoU) between two detections. + + This function calculates the area of overlap between the bounding boxes of two + detection objects and divides it by the total area covered by their bounding boxes. + + Args: + `detection_1` (Detection): + A Detection object representing the first detection. + `detection_2` (Detection): + A Detection object representing the second detection. + + Returns: + A float value between 0.0 and 1.0 representing the IoU between the two detections. + """ + box_1, box_2 = detection_1.annotation.box, detection_2.annotation.box + intersection_area = compute_intersection_area(box_1, box_2) + union_area = compute_area(box_1) + compute_area(box_2) - intersection_area + return intersection_area / union_area From 40d357ebaeb60782de27f2bd3b3a3d768a6f7830 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 11 Nov 2024 23:44:10 -0500 Subject: [PATCH 49/55] Added results --- experiments/clustering/clustering.ipynb | 55 ++++++++----------------- 1 file changed, 17 insertions(+), 38 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 286b5b1..872b649 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -591,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -703,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -828,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -896,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1063,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1277,21 +1277,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method: kmeans\n", - "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n", - "Method: dbscan\n", - "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n", - "Method: agglomerative\n" - ] - } - ], + "outputs": [], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(add_erroneous=False)\n", @@ -1315,13 +1301,6 @@ "test_clustering_methods(True, 0.05)\n", "analyze_accuracy()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From c3093b9db24258407ec6ed8499dc39fec9a9411f Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 11 Nov 2024 23:44:22 -0500 Subject: [PATCH 50/55] Added results --- experiments/clustering/clustering.ipynb | 40 ++++++++++++++++++++++--- 1 file changed, 36 insertions(+), 4 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 872b649..f14e63c 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -1275,9 +1275,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time mAP: 0.8419071518193224\n", + "Number mAP: 0.9131578947368421\n", + "Method: dbscan\n", + "Time mAP: 0.8419071518193224\n", + "Number mAP: 0.9131578947368421\n", + "Method: agglomerative\n", + "Time mAP: 0.8419071518193224\n", + "Number mAP: 0.9131578947368421\n" + ] + } + ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(add_erroneous=False)\n", @@ -1293,9 +1309,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method: kmeans\n", + "Time mAP: 0.6080729166666666\n", + "Number mAP: 0.8793565683646114\n", + "Method: dbscan\n", + "Time mAP: 0.6146220570012392\n", + "Number mAP: 0.8767123287671232\n", + "Method: agglomerative\n", + "Time mAP: 0.5878552971576227\n", + "Number mAP: 0.875\n" + ] + } + ], "source": [ "# Test the clustering methods with errouneous bounding boxes\n", "test_clustering_methods(True, 0.05)\n", From e7c94788c859d6488881bbae08f1fbde4c133496 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Sun, 17 Nov 2024 21:34:54 -0500 Subject: [PATCH 51/55] Fixed filtering typo --- experiments/clustering/clustering.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index f14e63c..175e129 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +255,7 @@ " # remove outliers via the y axis\n", " filtered = [bb for bb in x_filtered if y_lower <= bb.top <= y_upper]\n", "\n", - " return x_filtered\n", + " return filtered\n", "\n", "\n", "def select_relevant_bounding_boxes(\n", From c5101d714798c76b39835e2b6255356960d721c7 Mon Sep 17 00:00:00 2001 From: hvalenty Date: Mon, 18 Nov 2024 14:48:13 -0500 Subject: [PATCH 52/55] Updated preprocessing test to only add new bounding boxes and constrain to outside ROI --- experiments/clustering/clustering.ipynb | 61 +++++++++++-------------- 1 file changed, 27 insertions(+), 34 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 175e129..aa0ab64 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -814,8 +814,10 @@ "outputs": [], "source": [ "def generate_random_yolo(x):\n", + " # x values across whole picture\n", " x_rand = random.uniform(0, 1)\n", - " y_rand = random.uniform(0, 1)\n", + " # y values from top of picture to time axis\n", + " y_rand = random.uniform(0, 0.36)\n", " return f\"0 {x_rand} {y_rand} 0.0048989405776515005 0.009852199180453436\"" ] }, @@ -828,46 +830,37 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "def test_preprocess(yolo_data_sheet, percent_erroneous) -> Dict:\n", + "def test_preprocess(yolo_data_sheet, number_erroneous: int) -> Dict:\n", + " \"\"\"Generate random bounding boxes to fall outside the region of interest,\n", + " testing the effectiveness of the preprocessing steps\n", + "\n", + " Args:\n", + " yolo_data_sheet (dict): yolo data to add erroneous boxes\n", + " number_erroneous (int): amount of erroneous boxes to add \n", + "\n", + " Returns:\n", + " Dict: _description_\n", + " \"\"\"\n", " total_yolo = {}\n", " # iterate through sheets in yolo json file\n", " for sheet in range(1, len(yolo_data) + 1):\n", " if sheet < 10:\n", " # for ease of replacement, select bounding boxes with 0 label\n", " select_sheet = yolo_data_sheet[f\"RC_000{sheet}_intraoperative.JPG\"]\n", - " boolean_list = [\n", - " x.startswith(\"0\")\n", - " for x in yolo_data[f\"RC_000{sheet}_intraoperative.JPG\"]\n", - " ]\n", - " zero_list = list(compress(select_sheet, boolean_list))\n", - " # replace percent of the 64 possible zeros in the sheet\n", - " count_remove = round(percent_erroneous * 64) # 64 zeros\n", - " # subset and remove % of yolo lines from json file\n", - " lines_remove = list(random.sample(zero_list, count_remove))\n", - " _ = [select_sheet.remove(line) for line in lines_remove]\n", - " # use random yolo generation to refill removed lines\n", - " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # add specified number of erroneous boxes outside the region of interest\n", + " lines_gen = list(map(generate_random_yolo, range(number_erroneous)))\n", " # append yolo generated list back to copy\n", " yolo_shuffle = select_sheet + lines_gen\n", " total_yolo[f\"RC_000{sheet}_intraoperative.JPG\"] = yolo_shuffle\n", "\n", " else:\n", " select_sheet = yolo_data_sheet[f\"RC_00{sheet}_intraoperative.JPG\"]\n", - " boolean_list = [\n", - " x.startswith(\"0\") for x in yolo_data[f\"RC_00{sheet}_intraoperative.JPG\"]\n", - " ]\n", - " zero_list = list(compress(select_sheet, boolean_list))\n", - " # replace percent of the 64 possible zeros in the sheet\n", - " count_remove = round(percent_erroneous * 64) # 64 zeros\n", - " # subset and remove % of yolo lines from json file\n", - " lines_remove = list(random.sample(zero_list, count_remove))\n", - " _ = [select_sheet.remove(line) for line in lines_remove]\n", - " # use random yolo generation to refill removed lines\n", - " lines_gen = list(map(generate_random_yolo, range(len(lines_remove))))\n", + " # add specified number of erroneous boxes outside the region of interest\n", + " lines_gen = list(map(generate_random_yolo, range(number_erroneous)))\n", " # append yolo generated list back to copy\n", " yolo_shuffle = select_sheet + lines_gen\n", " total_yolo[f\"RC_00{sheet}_intraoperative.JPG\"] = yolo_shuffle\n", @@ -877,12 +870,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# preprocessing test by manipulating yolo data\n", - "#yolo_data = test_preprocess(yolo_data, 0.1)" + "# yolo_data = test_preprocess(yolo_data, 10)" ] }, { @@ -896,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1063,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1275,7 +1268,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1337,7 +1330,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1351,7 +1344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.13.0" } }, "nbformat": 4, From de7c23c3f21131e1433fa5d2b254f0a4281bb904 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Mon, 18 Nov 2024 21:54:08 -0500 Subject: [PATCH 53/55] Added mAP challeges --- experiments/clustering/clustering.ipynb | 98 ++++++++++++++++++------- 1 file changed, 70 insertions(+), 28 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index aa0ab64..499139d 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -591,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -685,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -703,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -830,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1056,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1088,6 +1088,32 @@ "\n", " return ap\n", "\n", + "def calculate_map_challenge(ious):\n", + " \"\"\"Calculates Mean Average Precision (mAP) from a list of IoUs.\n", + " Challenge version: AP is calculated only for IoUs >= 0.5 and average is returned.\n", + "\n", + " Args:\n", + " ious: A list of IoUs for each prediction-ground truth pair.\n", + "\n", + " Returns:\n", + " The calculated mAP.\n", + " \"\"\"\n", + "\n", + " # Sort IoUs in descending order\n", + " ious.sort(reverse=True)\n", + " aps = []\n", + " for limit in [i / 100 for i in range(50, 100, 5)]:\n", + " # Calculate precision and recall\n", + " tp = np.cumsum([1 if iou >= limit else 0 for iou in ious])\n", + " fp = np.cumsum([1 if iou < limit else 0 for iou in ious])\n", + " precision = tp / (tp + fp)\n", + " recall = tp / len(ious)\n", + "\n", + " # Calculate Average Precision (AP)\n", + " aps.append(calculate_ap(precision, recall))\n", + "\n", + " return np.mean(aps)\n", + "\n", "def calculate_ap(precision, recall):\n", " \"\"\"Calculates Average Precision (AP) from precision and recall curves.\n", "\n", @@ -1254,9 +1280,13 @@ "\n", " # Calculate the Mean Average Precision (mAP) for the time and number clusters\n", " time_map = calculate_map(time_ious)\n", + " time_challenge_map = calculate_map_challenge(time_ious)\n", " number_map = calculate_map(number_ious)\n", + " number_challenge_map = calculate_map_challenge(number_ious)\n", " print(f\"Time mAP: {time_map}\")\n", - " print(f\"Number mAP: {number_map}\")" + " print(f\"Number mAP: {number_map}\")\n", + " print(f\"Time Challenge mAP: {time_challenge_map}\")\n", + " print(f\"Number Challenge mAP: {number_challenge_map}\")" ] }, { @@ -1268,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1278,12 +1308,18 @@ "Method: kmeans\n", "Time mAP: 0.8419071518193224\n", "Number mAP: 0.9131578947368421\n", + "Time Challenge mAP: 0.5555834378920954\n", + "Number Challenge mAP: 0.58\n", "Method: dbscan\n", "Time mAP: 0.8419071518193224\n", "Number mAP: 0.9131578947368421\n", + "Time Challenge mAP: 0.5555834378920954\n", + "Number Challenge mAP: 0.58\n", "Method: agglomerative\n", "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n" + "Number mAP: 0.9131578947368421\n", + "Time Challenge mAP: 0.5555834378920954\n", + "Number Challenge mAP: 0.58\n" ] } ], @@ -1302,7 +1338,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1310,14 +1346,20 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time mAP: 0.6080729166666666\n", - "Number mAP: 0.8793565683646114\n", + "Time mAP: 0.5764248704663213\n", + "Number mAP: 0.8831521739130435\n", + "Time Challenge mAP: 0.3765544041450778\n", + "Number Challenge mAP: 0.5638586956521738\n", "Method: dbscan\n", - "Time mAP: 0.6146220570012392\n", - "Number mAP: 0.8767123287671232\n", + "Time mAP: 0.5963190184049079\n", + "Number mAP: 0.8823529411764706\n", + "Time Challenge mAP: 0.3890797546012269\n", + "Number Challenge mAP: 0.5649859943977591\n", "Method: agglomerative\n", - "Time mAP: 0.5878552971576227\n", - "Number mAP: 0.875\n" + "Time mAP: 0.5862068965517241\n", + "Number mAP: 0.8989071038251366\n", + "Time Challenge mAP: 0.3828863346104725\n", + "Number Challenge mAP: 0.5658469945355191\n" ] } ], @@ -1330,7 +1372,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1344,7 +1386,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.12.7" } }, "nbformat": 4, From 04703ee12cf9a4829e383ddbd2cdce89e1c7eb41 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Sun, 6 Apr 2025 21:42:44 -0400 Subject: [PATCH 54/55] Used new data for ground-truth label. Significant improvement in mAP --- experiments/clustering/clustering.ipynb | 253 ++++++++++++++++-------- 1 file changed, 175 insertions(+), 78 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index 499139d..bd2bf3a 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,6 @@ "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n", "from sklearn.metrics import silhouette_score\n", "from scipy.stats import gaussian_kde\n", - "from itertools import compress\n", "\n", "# Local libraries\n", "from utils.annotations import BoundingBox\n", @@ -73,14 +72,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 19 sheets in yolo_data.json\n", + "Found 22 sheets in yolo_data.json\n", "Found 19 items in bp_and_hr_cluster_locations.json\n" ] } @@ -118,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -137,49 +136,84 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sheet RC_0001_intraoperative.JPG already has a cluster named 190_mins in the ground_truth_clusters dictionary\n" + "Looking for files in: c:\\Users\\15406\\Coding-Projects\\Paper Chart Extraction\\Supplements\\data\\cluster_bp_and_hr_yolo\n", + "Classes path exists: True\n", + "Notes path exists: True\n", + "Labels dir exists: True\n", + "RC_0007_intraoperative.JPG already has a cluster named 190_mins in the ground_truth_clusters dictionary\n", + "{'RC_0014_intraoperative.JPG': {'220_mmhg': BoundingBox(category='220_mmhg', left=108.20330255681814, top=236.03159179687498, right=120.50542288115527, bottom=242.00917968750002), '200_mmhg': BoundingBox(category='200_mmhg', left=108.23109019886363, top=254.89550781249997, right=120.78500828598482, bottom=260.7752278645833), '130_mins': BoundingBox(category='130_mins', left=504.20519649621224, top=226.55922851562497, right=511.77651515151524, bottom=232.3546223958333), '50_mins': BoundingBox(category='50_mins', left=274.1754853219697, top=226.37864583333342, right=282.28370620265156, bottom=232.17477213541676), '70_mins': BoundingBox(category='70_mins', left=332.0161872632576, top=226.51027018229175, right=339.6029829545455, bottom=232.3799641927084), '160_mmhg': BoundingBox(category='160_mmhg', left=108.77360765861744, top=292.4086263020833, right=120.67647668087119, bottom=298.1953450520834), '120_mins': BoundingBox(category='120_mins', left=477.2423946496211, top=226.61767578125, right=480.9184422348483, bottom=232.34724934895837), '90_mmhg': BoundingBox(category='90_mmhg', left=110.50431315104167, top=358.0300130208333, right=118.51432291666664, bottom=363.78655598958335), '190_mins': BoundingBox(category='190_mins', left=678.6657788825758, top=226.30149739583337, right=686.271425189394, bottom=231.91943359374997), '60_mins': BoundingBox(category='60_mins', left=305.2239879261364, top=226.44375000000005, right=308.8409386837121, bottom=232.14038085937503), '110_mins': BoundingBox(category='110_mins', left=446.409031723485, top=226.80152994791672, right=454.5483546401516, bottom=232.48081054687503), '60_mmhg': BoundingBox(category='60_mmhg', left=110.52849786931817, top=386.17861328124997, right=118.47484611742424, bottom=391.8661783854166), '120_mmhg': BoundingBox(category='120_mmhg', left=108.61570046164776, top=329.9754557291667, right=120.56190074573867, bottom=335.5901041666666), '210_mmhg': BoundingBox(category='210_mmhg', left=108.23086085464013, top=245.40953776041667, right=120.40776663115528, bottom=251.29464518229165), '150_mmhg': BoundingBox(category='150_mmhg', left=108.66103663589013, top=301.84547526041666, right=120.64718720407197, bottom=307.55947265624997), '150_mins': BoundingBox(category='150_mins', left=562.1354166666666, top=226.46170247395835, right=570.1735321969696, bottom=232.1731119791667), '180_mins': BoundingBox(category='180_mins', left=651.6473721590909, top=226.30987955729165, right=655.2782315340909, bottom=231.93141276041666), '80_mins': BoundingBox(category='80_mins', left=360.5474964488637, top=226.60595703125, right=368.62701231060606, bottom=232.45468749999998), '170_mmhg': BoundingBox(category='170_mmhg', left=108.63955226089017, top=282.98225911458337, right=120.84086470170455, bottom=288.7316731770833), '110_mmhg': BoundingBox(category='110_mmhg', left=108.603241891572, top=339.23102213541665, right=120.29055693655303, bottom=344.99176432291665), '80_mmhg': BoundingBox(category='80_mmhg', left=110.64500473484848, top=367.3858398437499, right=118.46949721827654, bottom=373.1163085937499), '140_mins': BoundingBox(category='140_mins', left=533.0011245265152, top=226.4511881510417, right=541.2515980113637, bottom=232.23201497395834), '100_mmhg': BoundingBox(category='100_mmhg', left=108.46555397727273, top=348.6044921875, right=120.73099402225377, bottom=354.3450846354167), '160_mins': BoundingBox(category='160_mins', left=591.1482599431818, top=226.3121256510417, right=599.3804450757575, bottom=232.12973632812498), '20_mins': BoundingBox(category='20_mins', left=186.66077769886363, top=225.87213541666665, right=194.77660392992425, bottom=231.61484375000003), '40_mmhg': BoundingBox(category='40_mmhg', left=110.16344105113637, top=404.81119791666674, right=118.41941879734847, bottom=410.50166015625007), '30_mins': BoundingBox(category='30_mins', left=215.781767874053, top=226.00447591145837, right=223.94692530776518, bottom=231.85029296875), '30_mmhg': BoundingBox(category='30_mmhg', left=110.5681670217803, top=414.12532552083337, right=118.43162582859848, bottom=419.81998697916674), '40_mins': BoundingBox(category='40_mins', left=244.75381747159093, top=226.2870117187499, right=253.16589725378788, bottom=231.94454752604162), '180_mmhg': BoundingBox(category='180_mmhg', left=108.70724579782195, top=273.5790364583333, right=120.70433830492424, bottom=279.44736328125003), '200_mins': BoundingBox(category='200_mins', left=707.2281013257576, top=226.32154947916663, right=715.4076704545454, bottom=231.99780273437503), '0_mins': BoundingBox(category='0_mins', left=130.58031486742425, top=225.92954101562495, right=134.23293974905306, bottom=231.5670084635416), '130_mmhg': BoundingBox(category='130_mmhg', left=108.59060576467805, top=320.4333333333334, right=120.60273141571967, bottom=326.25966796875), '190_mmhg': BoundingBox(category='190_mmhg', left=108.59366861979167, top=264.3543619791667, right=120.68414861505683, bottom=270.1235026041667), '70_mmhg': BoundingBox(category='70_mmhg', left=110.41419566761361, top=376.8795572916667, right=118.56438654119314, bottom=382.5185872395834), '100_mins': BoundingBox(category='100_mins', left=417.626923532197, top=226.76486002604167, right=426.04950875946963, bottom=232.486572265625), '50_mmhg': BoundingBox(category='50_mmhg', left=110.52755089962122, top=395.4709635416667, right=118.474365234375, bottom=401.0985026041668), '10_mins': BoundingBox(category='10_mins', left=157.68831380208331, top=225.96450195312502, right=165.25544507575756, bottom=231.64412434895837), '90_mins': BoundingBox(category='90_mins', left=389.36455374053025, top=226.79780273437507, right=397.3462062026515, bottom=232.48291015625), '140_mmhg': BoundingBox(category='140_mmhg', left=108.54822887073861, top=311.14186197916666, right=120.6161443536932, bottom=316.8866536458334), '170_mins': BoundingBox(category='170_mins', left=620.4614109848484, top=226.27932942708333, right=628.4475615530304, bottom=232.10569661458334), '195_mins': BoundingBox(category='195_mins', left=693.0930397727273, top=226.27027994791666, right=700.6276041666667, bottom=232.01316731770834), '15_mins': BoundingBox(category='15_mins', left=172.05081084280303, top=225.8993815104167, right=179.84256628787878, bottom=231.6600423177084), '75_mins': BoundingBox(category='75_mins', left=346.1603042140151, top=226.71775716145837, right=354.0250059185606, bottom=232.449609375), '135_mins': BoundingBox(category='135_mins', left=518.8355823863635, top=226.54235026041664, right=526.3052793560605, bottom=232.28435872395835), '25_mins': BoundingBox(category='25_mins', left=201.08863044507578, top=225.947705078125, right=209.22440222537878, bottom=231.82599283854165), '205_mins': BoundingBox(category='205_mins', left=721.5680042613636, top=226.30079752604166, right=729.6763139204546, bottom=232.06106770833338), '85_mins': BoundingBox(category='85_mins', left=374.9710582386364, top=226.76020507812504, right=382.881806344697, bottom=232.46049804687496), '145_mins': BoundingBox(category='145_mins', left=547.4910629734848, top=226.44926757812505, right=555.7602391098485, bottom=232.22446289062498), '155_mins': BoundingBox(category='155_mins', left=576.5523200757575, top=226.31889648437496, right=584.9044744318182, bottom=232.20628255208337), '95_mins': BoundingBox(category='95_mins', left=403.5797230113636, top=226.78149414062503, right=411.6639145359849, bottom=232.54886067708335), '35_mins': BoundingBox(category='35_mins', left=230.41296756628788, top=226.08165690104164, right=238.59255149147737, bottom=231.84153645833328), '105_mins': BoundingBox(category='105_mins', left=432.0698686079546, top=226.79601236979164, right=440.30391808712125, bottom=232.55208333333331), '165_mins': BoundingBox(category='165_mins', left=605.6865530303029, top=226.36708984375, right=613.9043560606059, bottom=232.1150227864583), '45_mins': BoundingBox(category='45_mins', left=258.9847597064394, top=226.2623209635417, right=267.7017341382576, bottom=232.02091471354169), '55_mins': BoundingBox(category='55_mins', left=288.58863044507575, top=226.50906575520827, right=296.7612156723484, bottom=232.2332194010416), '5_mins': BoundingBox(category='5_mins', left=145.3302556818182, top=225.90221354166664, right=149.16434363162875, bottom=231.4552571614583), '115_mins': BoundingBox(category='115_mins', left=461.1174242424242, top=226.74921874999995, right=468.82673413825756, bottom=232.5426106770833), '65_mins': BoundingBox(category='65_mins', left=319.862275094697, top=226.6657877604167, right=323.7256747159091, bottom=232.38151041666666), '125_mins': BoundingBox(category='125_mins', left=492.1725852272728, top=226.6923990885417, right=495.8272372159092, bottom=232.35976562500005), '175_mins': BoundingBox(category='175_mins', left=635.0211884469696, top=226.29259440104167, right=643.1335819128786, bottom=232.05916341145834), '185_mins': BoundingBox(category='185_mins', left=666.5147372159091, top=226.26630859374995, 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right=134.6209013967803, bottom=232.0752115885417)}, 'RC_0015_intraoperative.JPG': {'70_mins': BoundingBox(category='70_mins', left=332.41722892992425, top=227.2618977864583, right=340.0404829545455, bottom=233.00976562499994), '90_mmhg': BoundingBox(category='90_mmhg', left=112.00941790956439, top=356.96227213541664, right=120.00832297585227, bottom=362.72972005208334), '10_mins': BoundingBox(category='10_mins', left=159.2322443181818, top=225.94794921875, right=166.76605409564394, bottom=231.40068359375005), '0_mins': BoundingBox(category='0_mins', left=132.53924005681816, top=225.78450520833334, right=135.81687973484847, bottom=231.40688476562505), '220_mmhg': BoundingBox(category='220_mmhg', left=110.18991921164775, top=235.85638020833335, right=122.34948360558712, bottom=241.68365885416665), '160_mmhg': BoundingBox(category='160_mmhg', left=110.33717299952652, top=291.73763020833337, right=122.27917850378786, bottom=297.4213541666667), '50_mins': BoundingBox(category='50_mins', 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BoundingBox(category='110_mins', left=445.8016394412878, top=227.96723632812504, right=453.74553148674244, bottom=233.59928385416666), '130_mmhg': BoundingBox(category='130_mmhg', left=110.09581409801139, top=319.6064778645834, right=122.23729728929924, bottom=325.26660156250006), '80_mmhg': BoundingBox(category='80_mmhg', left=112.06065784801133, top=366.35621744791655, right=119.83558978456436, bottom=372.0891927083333), '100_mmhg': BoundingBox(category='100_mmhg', left=109.96279444839014, top=347.59091796875, right=122.25489021070076, bottom=353.24687500000005), '125_mins': BoundingBox(category='125_mins', left=490.9077060416827, top=227.8029947916639, right=494.9856849909837, bottom=233.92098088640287), '180_mmhg': BoundingBox(category='180_mmhg', left=110.4025361032197, top=273.1941080729167, right=122.36702473958336, bottom=278.78815104166677), '150_mmhg': BoundingBox(category='150_mmhg', left=110.50008138020833, top=301.09999999999997, right=122.32950106534092, bottom=306.7393554687499), '140_mins': BoundingBox(category='140_mins', left=531.5102391098486, top=227.7055013020833, right=539.7502367424244, bottom=233.48183593749997), '30_mmhg': BoundingBox(category='30_mmhg', left=111.6529356060606, top=413.0656901041667, right=119.61837121212122, bottom=418.7525390625), '60_mins': BoundingBox(category='60_mins', left=305.87591737689394, top=227.06144205729166, right=309.39997632575756, bottom=232.56531575520827), '120_mins': BoundingBox(category='120_mins', left=476.2650035511363, top=227.89241536458331, right=479.79450757575745, bottom=233.52210286458336), '170_mmhg': BoundingBox(category='170_mmhg', left=110.3457919034091, top=282.4329752604167, right=122.25832297585225, bottom=287.9576497395833), '50_mmhg': BoundingBox(category='50_mmhg', left=111.81838156960227, top=394.4505859375, right=119.77316376657195, bottom=400.07412109375), '140_mmhg': BoundingBox(category='140_mmhg', left=110.25577799479169, top=310.3781901041666, right=122.16803533380684, bottom=315.991796875), '180_mins': BoundingBox(category='180_mins', left=649.5641571969697, top=227.77324218750002, right=653.169034090909, bottom=233.4483723958334), '110_mmhg': BoundingBox(category='110_mmhg', left=110.04906486742424, top=338.3596354166667, right=121.79822147253786, bottom=343.87639973958335), '150_mins': BoundingBox(category='150_mins', left=560.5223721590909, top=227.71178385416675, right=568.4886955492424, bottom=233.5211100260417), '160_mins': BoundingBox(category='160_mins', left=589.4883996212121, top=227.74877929687503, right=597.4955610795454, bottom=233.4146484375), '80_mins': BoundingBox(category='80_mins', left=360.7965198863637, top=227.5074544270834, right=368.71478456439394, bottom=233.3053873697917), '70_mmhg': BoundingBox(category='70_mmhg', left=111.83430249763256, top=375.7521809895834, right=119.88445490056819, bottom=381.3885416666667), '40_mins': BoundingBox(category='40_mins', left=245.8513553503788, top=226.53339843749998, right=254.16243489583331, bottom=232.16337890624996), '190_mins': BoundingBox(category='190_mins', left=676.575106534091, top=227.82270507812495, right=684.169034090909, bottom=233.36653645833331), '90_mins': BoundingBox(category='90_mins', left=389.2461825284091, top=227.728662109375, right=397.0911754261364, bottom=233.41365559895834), '210_mmhg': BoundingBox(category='210_mmhg', left=110.34915068655302, top=245.10024414062502, right=122.18093779592805, bottom=250.89921875000005), '40_mmhg': BoundingBox(category='40_mmhg', left=111.42668383049244, top=403.85149739583335, right=119.73165246212122, bottom=409.43587239583326), '30_mins': BoundingBox(category='30_mins', left=217.22487571022725, top=226.16453450520834, right=225.08359966856062, bottom=231.88211263020833), '100_mins': BoundingBox(category='100_mins', left=417.30400686553037, top=227.90901692708334, right=425.50813802083337, bottom=233.47353515625), '75_mins': BoundingBox(category='75_mins', left=346.601592092803, top=227.48688151041665, right=354.273467092803, bottom=233.141357421875), '15_mins': BoundingBox(category='15_mins', left=173.33463541666669, top=225.91139322916672, right=181.18202533143943, bottom=231.50843098958336), '195_mins': BoundingBox(category='195_mins', left=691.0368134469697, top=227.81134440104162, right=698.6361268939395, bottom=233.51494140625002), '135_mins': BoundingBox(category='135_mins', left=517.4178503787879, top=227.86409505208337, right=524.7962239583334, bottom=233.59353841145833), '190_mmhg': BoundingBox(category='190_mmhg', left=110.42045454545435, top=263.7809474303032, right=122.67433169675836, bottom=269.6471708945952), '205_mins': BoundingBox(category='205_mins', left=719.9209280303031, top=227.8224283854167, right=728.1335819128789, bottom=233.50089518229166), '85_mins': BoundingBox(category='85_mins', left=375.0280243844697, top=227.68681640625002, right=382.9563210227272, bottom=233.3990234375), '145_mins': BoundingBox(category='145_mins', left=545.8624526515152, top=227.69327799479169, right=554.0813802083335, bottom=233.48740234375003), '25_mins': BoundingBox(category='25_mins', left=202.35205078125, top=226.15553385416666, right=210.5466530539773, bottom=231.89475911458334), '155_mins': BoundingBox(category='155_mins', left=574.8246922348485, top=227.69881184895831, right=583.0134351325758, bottom=233.49703776041667), '95_mins': BoundingBox(category='95_mins', left=403.38873106060606, top=227.76419270833333, right=411.3021129261363, bottom=233.490771484375), '35_mins': BoundingBox(category='35_mins', left=231.5133315577651, top=226.33357747395834, right=239.6768465909091, bottom=232.01678059895832), '165_mins': BoundingBox(category='165_mins', left=603.7967566287878, top=227.79402669270831, right=611.9096827651515, bottom=233.46852213541666), '45_mins': BoundingBox(category='45_mins', left=260.2354699337121, top=226.69435221354175, right=268.555604876894, bottom=232.33709309895832), '105_mins': BoundingBox(category='105_mins', left=431.5520241477273, top=227.8904296875, right=439.57620146780306, bottom=233.48561197916666), '175_mins': BoundingBox(category='175_mins', left=632.9540127840909, top=227.81010742187493, right=641.0240293560606, bottom=233.48333333333335), '115_mins': BoundingBox(category='115_mins', left=460.28145714962125, top=227.94643554687497, right=467.88503196022725, bottom=233.60195312499997), '5_mins': BoundingBox(category='5_mins', left=146.9136925899621, top=225.8774088541667, right=150.64547821969697, bottom=231.32506510416667), '55_mins': BoundingBox(category='55_mins', left=289.4452237215909, top=226.96914062500002, right=297.3813328598485, bottom=232.6203125), '185_mins': BoundingBox(category='185_mins', left=664.412997159091, top=227.76831054687497, right=668.1344105113637, bottom=233.45056966145833), '65_mins': BoundingBox(category='65_mins', left=320.33072916666674, top=227.31246744791665, right=324.0789831912879, bottom=232.8297526041666), '130_mins': BoundingBox(category='130_mins', left=502.77972741993244, top=227.72977433736935, right=510.55665226074575, bottom=233.88284626706917)}}\n" ] } ], "source": [ - "ground_truth_clusters = {} \n", - "for sheet_num, data in enumerate(yolo_data.items()):\n", - " # For each sheet, get the cluster center X and Y coordinates and add them to the cluster_locations_dict dictionary\n", - " expected_clusters = bp_hr_cluster_locations[sheet_num]\n", - " for cluster in expected_clusters[\"annotations\"][0][\"result\"]:\n", - " x_expected_perc, y_expected_perc = (\n", - " cluster[\"value\"][\"x\"],\n", - " cluster[\"value\"][\"y\"],\n", - " ) # Get the expected cluster location (percent x and y in the original image space)\n", - " x_expected_frac, y_expected_frac = (\n", - " (x_expected_perc / 100),\n", - " (y_expected_perc / 100),\n", - " ) # Convert the expected cluster location to pixel space\n", - " height_perc, width_perc = (\n", - " cluster[\"value\"][\"height\"],\n", - " cluster[\"value\"][\"width\"],\n", - " )\n", - " height_frac, width_frac = (\n", - " (height_perc / 100),\n", - " (width_perc / 100),\n", - " )\n", - " x_expected_frac = x_expected_frac + (width_frac / 2)\n", - " y_expected_frac = y_expected_frac + (height_frac / 2)\n", - " cluster_name = cluster[\"value\"][\"rectanglelabels\"][0]\n", - " if data[0] not in ground_truth_clusters:\n", - " ground_truth_clusters[data[0]] = {cluster_name: BoundingBox.from_yolo(f\"{cluster_name} {x_expected_frac} {y_expected_frac} {width_frac} {height_frac}\", DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)}\n", - " elif cluster_name not in ground_truth_clusters[data[0]]:\n", - " ground_truth_clusters[data[0]][cluster_name] = BoundingBox.from_yolo(f\"{cluster_name} {x_expected_frac} {y_expected_frac} {width_frac} {height_frac}\", DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT)\n", + "# Paths\n", + "base_path = \"../../data/cluster_bp_and_hr_yolo\"\n", + "labels_path = os.path.join(base_path, \"labels\")\n", + "classes_path = os.path.join(base_path, \"classes.txt\")\n", + "notes_path = os.path.join(base_path, \"notes.json\")\n", + "\n", + "# Debug: print full paths and check existence\n", + "print(\"Looking for files in:\", os.path.abspath(base_path))\n", + "print(\"Classes path exists:\", os.path.exists(classes_path))\n", + "print(\"Notes path exists:\", os.path.exists(notes_path))\n", + "print(\"Labels dir exists:\", os.path.exists(labels_path))\n", + "\n", + "# Load class names\n", + "with open(classes_path, \"r\") as f:\n", + " class_names = [line.strip() for line in f.readlines()]\n", + "\n", + "# Optional: Load categories from notes.json (if needed for mapping)\n", + "with open(notes_path, \"r\") as f:\n", + " notes_data = json.load(f)\n", + "id_to_category = {str(item[\"id\"]): item[\"name\"] for item in notes_data.get(\"categories\", [])}\n", + "\n", + "# Function to build bounding boxes from YOLO-format .txt files\n", + "ground_truth_clusters = {}\n", + "\n", + "for filename in os.listdir(labels_path):\n", + " if not filename.endswith(\".txt\"):\n", + " continue\n", + "\n", + " filepath = os.path.join(labels_path, filename)\n", + " sheet_id = re.sub(r\"^[^-]+-remapped_\", \"\", filename.replace(\".txt\", \".JPG\"))\n", + "\n", + " \n", + "\n", + " with open(filepath, \"r\") as f:\n", + " lines = f.readlines()\n", + "\n", + " for line in lines:\n", + " parts = line.strip().split()\n", + " if len(parts) != 5:\n", + " continue\n", + "\n", + " class_id, x_center, y_center, width, height = map(float, parts)\n", + " class_id = int(class_id)\n", + " class_name = class_names[class_id] if class_id < len(class_names) else str(class_id)\n", + "\n", + " yolo_string = f\"{class_name} {x_center} {y_center} {width} {height}\"\n", + "\n", + " if sheet_id not in ground_truth_clusters:\n", + " ground_truth_clusters[sheet_id] = {\n", + " class_name: BoundingBox.from_yolo(\n", + " yolo_string, DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT\n", + " )\n", + " }\n", + " elif class_name not in ground_truth_clusters[sheet_id]:\n", + " ground_truth_clusters[sheet_id][class_name] = BoundingBox.from_yolo(\n", + " yolo_string, DESIRED_IMAGE_WIDTH, DESIRED_IMAGE_HEIGHT\n", + " )\n", " else:\n", - " print(f\"Sheet {data[0]} already has a cluster named {cluster_name} in the ground_truth_clusters dictionary\")\n", - " continue" + " print(f\"{sheet_id} already has a cluster named {class_name} in the ground_truth_clusters dictionary\")\n", + "\n", + "print(ground_truth_clusters)\n" ] }, { @@ -193,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -532,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -591,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -685,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -703,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -754,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -830,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -870,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -889,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1056,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1145,6 +1179,9 @@ " NUMBER_JSON = os.path.join(PATH_TO_RESULTS, \"number\")\n", "\n", " for sheet, yolo_bb in yolo_data.items():\n", + " # If above sheet 19, skip it\n", + " if int(sheet.split(\"_\")[1].split(\".\")[0]) > 19:\n", + " continue\n", " # Load JSON\n", " with open(os.path.join(TIME_JSON, f\"{sheet.split('.')[0]}.json\")) as f:\n", " time_clusters = json.load(f)\n", @@ -1224,7 +1261,7 @@ " image: Image = Image.open(f\"../../data/{method}_clustered_images/accuracy/{sheet}\")\n", " except Exception as e: \n", " # Create an image object\n", - " #print(f\"{e}: {f\"../../data/{method}_clustered_images/accuracy/{sheet}\"}\")\n", + " print(f\"{e}: {f\"../../data/{method}_clustered_images/accuracy/{sheet}\"}\")\n", " image: Image = Image.open(os.path.join(PATH_TO_REGISTERED_IMAGES, sheet))\n", " image_width, image_height = image.size\n", " # Show the found cluster in an image in RED\n", @@ -1298,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1306,20 +1343,77 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n", - "Time Challenge mAP: 0.5555834378920954\n", - "Number Challenge mAP: 0.58\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0001_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0001_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0002_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0002_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0003_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0003_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0004_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0004_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0005_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0005_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0006_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0006_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0007_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0007_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0008_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0008_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0009_intraoperative.JPG': 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Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0013_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0013_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0014_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0014_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0015_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0015_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0016_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0016_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0017_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0017_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0018_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0018_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\kmeans_clustered_images\\\\accuracy\\\\RC_0019_intraoperative.JPG': ../../data/kmeans_clustered_images/accuracy/RC_0019_intraoperative.JPG\n", + "Time mAP: 1.0\n", + "Number mAP: 1.0\n", + "Time Challenge mAP: 0.9989974937343359\n", + "Number Challenge mAP: 0.9994736842105263\n", "Method: dbscan\n", - "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n", - "Time Challenge mAP: 0.5555834378920954\n", - "Number Challenge mAP: 0.58\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0001_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0001_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0002_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0002_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0003_intraoperative.JPG': 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../../data/dbscan_clustered_images/accuracy/RC_0010_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0011_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0011_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0012_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0012_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0013_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0013_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0014_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0014_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0015_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0015_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0016_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0016_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0017_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0017_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0018_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0018_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\dbscan_clustered_images\\\\accuracy\\\\RC_0019_intraoperative.JPG': ../../data/dbscan_clustered_images/accuracy/RC_0019_intraoperative.JPG\n", + "Time mAP: 1.0\n", + "Number mAP: 1.0\n", + "Time Challenge mAP: 0.9989974937343359\n", + "Number Challenge mAP: 0.9994736842105263\n", "Method: agglomerative\n", - "Time mAP: 0.8419071518193224\n", - "Number mAP: 0.9131578947368421\n", - "Time Challenge mAP: 0.5555834378920954\n", - "Number Challenge mAP: 0.58\n" + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0001_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0001_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0002_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0002_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0003_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0003_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart 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'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0011_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0011_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0012_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0012_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0013_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0013_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0014_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0014_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0015_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0015_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0016_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0016_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0017_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0017_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0018_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0018_intraoperative.JPG\n", + "[Errno 2] No such file or directory: 'C:\\\\Users\\\\15406\\\\Coding-Projects\\\\Paper Chart Extraction\\\\Supplements\\\\data\\\\agglomerative_clustered_images\\\\accuracy\\\\RC_0019_intraoperative.JPG': ../../data/agglomerative_clustered_images/accuracy/RC_0019_intraoperative.JPG\n", + "Time mAP: 1.0\n", + "Number mAP: 1.0\n", + "Time Challenge mAP: 0.9989974937343359\n", + "Number Challenge mAP: 0.9994736842105263\n" ] } ], @@ -1338,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1346,20 +1440,16 @@ "output_type": "stream", "text": [ "Method: kmeans\n", - "Time mAP: 0.5764248704663213\n", - "Number mAP: 0.8831521739130435\n", - "Time Challenge mAP: 0.3765544041450778\n", - "Number Challenge mAP: 0.5638586956521738\n", + "Time mAP: 0.6688144329896907\n", + "Number mAP: 0.9756756756756757\n", + "Time Challenge mAP: 0.6684278350515463\n", + "Number Challenge mAP: 0.9732432432432432\n", "Method: dbscan\n", - "Time mAP: 0.5963190184049079\n", - "Number mAP: 0.8823529411764706\n", - "Time Challenge mAP: 0.3890797546012269\n", - "Number Challenge mAP: 0.5649859943977591\n", - "Method: agglomerative\n", - "Time mAP: 0.5862068965517241\n", - "Number mAP: 0.8989071038251366\n", - "Time Challenge mAP: 0.3828863346104725\n", - "Number Challenge mAP: 0.5658469945355191\n" + "Time mAP: 0.6901234567901234\n", + "Number mAP: 0.9750692520775623\n", + "Time Challenge mAP: 0.6895061728395062\n", + "Number Challenge mAP: 0.9745152354570636\n", + "Method: agglomerative\n" ] } ], @@ -1368,6 +1458,13 @@ "test_clustering_methods(True, 0.05)\n", "analyze_accuracy()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1386,7 +1483,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.9" } }, "nbformat": 4, From 302d77496d2f51035daf04c552dc1286ff3383e7 Mon Sep 17 00:00:00 2001 From: charbelmarche33 Date: Sun, 6 Apr 2025 21:43:09 -0400 Subject: [PATCH 55/55] Significant mAP improvement with appropriate ground-truth labels. --- experiments/clustering/clustering.ipynb | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/experiments/clustering/clustering.ipynb b/experiments/clustering/clustering.ipynb index bd2bf3a..1ddfbe5 100644 --- a/experiments/clustering/clustering.ipynb +++ b/experiments/clustering/clustering.ipynb @@ -1432,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1449,7 +1449,11 @@ "Number mAP: 0.9750692520775623\n", "Time Challenge mAP: 0.6895061728395062\n", "Number Challenge mAP: 0.9745152354570636\n", - "Method: agglomerative\n" + "Method: agglomerative\n", + "Time mAP: 0.6726114649681527\n", + "Number mAP: 0.977961432506887\n", + "Time Challenge mAP: 0.6719745222929935\n", + "Number Challenge mAP: 0.9771349862258955\n" ] } ],