From 3aa2fbbfa1213795335e8fb6f04ddf18e369b57a Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 14:07:05 -0400
Subject: [PATCH 01/30] Create the point_registration module, add a homography
 file for computing point transformations using the homography, and stub out
 its methods with docstrings.

---
 ChartExtractor/point_registration/__init__.py |  0
 .../point_registration/homography.py          | 56 +++++++++++++++++++
 2 files changed, 56 insertions(+)
 create mode 100644 ChartExtractor/point_registration/__init__.py
 create mode 100644 ChartExtractor/point_registration/homography.py

diff --git a/ChartExtractor/point_registration/__init__.py b/ChartExtractor/point_registration/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
new file mode 100644
index 0000000..df77e75
--- /dev/null
+++ b/ChartExtractor/point_registration/homography.py
@@ -0,0 +1,56 @@
+"""Module for remapping points using a homography transform.
+
+This module exposes a two functions, (1) find_homography, which is a thin wrapper around opencv's
+findHomography function so that cv2 doesn't have to be imported where this is used, and
+(2) transform_point, which takes a point and a homography matrix and transforms the point.
+
+Functions:
+    find_homography(source_points: List[Tuple[int, int]], destination_points: List[Tuple[int, int]])
+        -> np.ndarray:
+        Computes the homography transformation that maps the source_points array to the
+        destination_points array. A thin wrapper around opencv's findHomography function.
+    transform_point(point: Tuple[int, int], homography_matrix: np.ndarray) -> Tuple[int, int]:
+        Remaps a single point using the homography matrix.
+"""
+
+import cv2
+import numpy as np
+from typing import List, Tuple
+
+
+def find_homography(
+    source_points: List[Tuple[int, int]],
+    destination_points: List[Tuple[int, int]],
+) -> np.ndarray:
+    """A thin wrapper around opencv's findHomography function.
+    
+    Args:
+        source_points (List[Tuple[int, int]]):
+            The points to move to match to destination points.
+        destination_points (List[Tuple[int, int]]):
+            The points that the source points are moved to match.
+
+    Returns:
+        A numpy ndarray containing the homography matrix which can be used with transform_point
+        to transform points according to the transformation that remaps the source points to the
+        destination points.
+    """
+    pass
+
+
+def transform_point(
+    point: Tuple[int, int],
+    homography_matrix: np.ndarray,
+) -> Tuple[int, int]:
+    """Remaps a single point using the homography matrix.
+    
+    Args:
+        point (Tuple[int, int]):
+            The point to remap.
+        homography_matrix (np.ndarray):
+            A homography matrix.
+
+    Returns:
+        A point which has been transformed by the homography.
+    """
+    pass

From 518ab73b4d01396c2475eb31fbd0ba412367ec3b Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 14:23:10 -0400
Subject: [PATCH 02/30] Implemented find_homography.

---
 .../point_registration/homography.py          | 36 +++++++++++++++++--
 1 file changed, 34 insertions(+), 2 deletions(-)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index df77e75..fc1d036 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -1,7 +1,7 @@
 """Module for remapping points using a homography transform.
 
 This module exposes a two functions, (1) find_homography, which is a thin wrapper around opencv's
-findHomography function so that cv2 doesn't have to be imported where this is used, and
+findHomography function that provides more robust error messages for this libraries usage, and
 (2) transform_point, which takes a point and a homography matrix and transforms the point.
 
 Functions:
@@ -23,6 +23,8 @@ def find_homography(
     destination_points: List[Tuple[int, int]],
 ) -> np.ndarray:
     """A thin wrapper around opencv's findHomography function.
+
+    Provides some additional checks and more informative errors.
     
     Args:
         source_points (List[Tuple[int, int]]):
@@ -35,7 +37,37 @@ def find_homography(
         to transform points according to the transformation that remaps the source points to the
         destination points.
     """
-    pass
+    too_few_source_points: bool = len(source_points) < 4
+    too_few_destination_points: bool = len(destination_points) < 4
+    unequal_point_sets: bool = len(source_points) != len(destination_points)
+    inconsistent_source_point_dimenstions: bool = len(set([len(p) for p in source_points])) == 1
+    inconsistent_destination_point_dimenstions: bool = (
+        len(set([len(p) for p in destination_points])) == 1
+    )
+
+    if too_few_source_points:
+        raise ValueError(
+            f"Too few points in source set (need at least 4, had {len(source_points)})."
+        )
+    if too_few_destination_points:
+        raise ValueError(
+            f"Too few points in destination set (need at least 4, had {len(destination_points)})."
+        )
+    if unequal_point_sets:
+        err_msg: str = "Point sets were unequal in length. "
+        err_msg += f"(length of source: {len(source_points)}, "
+        err_msg += f"length of destination: {len(destination_points)})"
+        raise ValueError(err_msg)
+    if inconsistent_source_point_dimenstions:
+        err_msg: str = "Source point set had inconsistent dimensions. "
+        err_msg += f"(Included dimensions: {len(set([len(p) for p in source_points]))})"
+        raise ValueError(err_msg)
+    if inconsistent_destination_point_dimenstions:
+        err_msg: str = "Destination point set had inconsistent dimensions. "
+        err_msg += f"(Included dimensions: {len(set([len(p) for p in destination_points]))})"
+        raise ValueError(err_msg)
+    
+    return findHomography(source_points, destination_points)
 
 
 def transform_point(

From 436bcc425fc00baa92fd945c7bb1ce8f96a776df Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 14:27:27 -0400
Subject: [PATCH 03/30] Change some of the checks for find_homography.

---
 .../point_registration/homography.py           | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index fc1d036..439afcf 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -40,10 +40,8 @@ def find_homography(
     too_few_source_points: bool = len(source_points) < 4
     too_few_destination_points: bool = len(destination_points) < 4
     unequal_point_sets: bool = len(source_points) != len(destination_points)
-    inconsistent_source_point_dimenstions: bool = len(set([len(p) for p in source_points])) == 1
-    inconsistent_destination_point_dimenstions: bool = (
-        len(set([len(p) for p in destination_points])) == 1
-    )
+    source_points_not_two_dimensional: bool = set([len(p) for p in source_points]) == {2}
+    destination_points_not_two_dimensional: bool = set([len(p) for p in destination_points]) == {2}
 
     if too_few_source_points:
         raise ValueError(
@@ -58,13 +56,13 @@ def find_homography(
         err_msg += f"(length of source: {len(source_points)}, "
         err_msg += f"length of destination: {len(destination_points)})"
         raise ValueError(err_msg)
-    if inconsistent_source_point_dimenstions:
-        err_msg: str = "Source point set had inconsistent dimensions. "
-        err_msg += f"(Included dimensions: {len(set([len(p) for p in source_points]))})"
+    if source_points_not_two_dimensional:
+        err_msg: str = "Source point set contains non two dimensional points. "
+        err_msg += f"(Included dimensions: {set([len(p) for p in source_points])})"
         raise ValueError(err_msg)
-    if inconsistent_destination_point_dimenstions:
-        err_msg: str = "Destination point set had inconsistent dimensions. "
-        err_msg += f"(Included dimensions: {len(set([len(p) for p in destination_points]))})"
+    if destination_points_not_two_dimensional:
+        err_msg: str = "Destination point set contains non two dimensional points. "
+        err_msg += f"(Included dimensions: {set([len(p) for p in destination_points])})"
         raise ValueError(err_msg)
     
     return findHomography(source_points, destination_points)

From ddc1bf373d0365841364a4dc57e1e5d8219da789 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 14:29:16 -0400
Subject: [PATCH 04/30] Implement transform_point.

---
 ChartExtractor/point_registration/homography.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index 439afcf..2c4efa9 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -83,4 +83,9 @@ def transform_point(
     Returns:
         A point which has been transformed by the homography.
     """
-    pass
+    if len(point) != 2:
+        raise ValueError(f"Point is not two dimensional: {point}.")
+    
+    remapped_point = homography_matrix.dot(np.array([point[0], point[1], 1]))
+    remapped_point /= remapped_point[2]
+    return (remapped_point[0], remapped_point[1])

From 639088461b19679686b8a29910f5f144a3e6639d Mon Sep 17 00:00:00 2001
From: Ryan Folks <47860863+RyanDoesMath@users.noreply.github.com>
Date: Tue, 10 Jun 2025 16:35:50 -0400
Subject: [PATCH 05/30] update module docstring for
 point_registration/homography.py

---
 ChartExtractor/point_registration/homography.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index 2c4efa9..d2d9042 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -1,8 +1,9 @@
 """Module for remapping points using a homography transform.
 
-This module exposes a two functions, (1) find_homography, which is a thin wrapper around opencv's
-findHomography function that provides more robust error messages for this libraries usage, and
-(2) transform_point, which takes a point and a homography matrix and transforms the point.
+This module exposes two functions, (1) find_homography, which is a thin wrapper around opencv's
+findHomography function that restricts the original function's usage to only 2d points, and
+provides more robust error messages for this libraries usage, and (2) transform_point, which takes 
+a point and a homography matrix and transforms the point.
 
 Functions:
     find_homography(source_points: List[Tuple[int, int]], destination_points: List[Tuple[int, int]])

From f5733260d2852d078e77ccb8aa8d9403fc6766a5 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 17:05:44 -0400
Subject: [PATCH 06/30] Stub out two methods for serializing and deserializing
 detections to/from json.

---
 .../extraction/extraction_utilities.py          | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 4c97eaa..e1bf29d 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -5,14 +5,14 @@
 import json
 from pathlib import Path
 from PIL import Image
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 # External imports
 import numpy as np
 
 # Internal imports
 from ..object_detection_models.object_detection_model import ObjectDetectionModel
-from ..utilities.annotations import BoundingBox
+from ..utilities.annotations import BoundingBox, Keypoint
 from ..utilities.detections import Detection
 from ..utilities.detection_reassembly import (
     intersection_over_minimum,
@@ -205,3 +205,16 @@ def label_studio_to_bboxes(
         ]
         for sheet_data in json_data
     }
+
+
+def read_detections_from_json(
+    filepath: Path,
+    detection_type: Union[BoundingBox, Keypoint]
+) -> List[Detection]:
+    """Deserializes detections from a json file."""
+    pass
+
+
+def write_detections_to_json(filepath: Path):
+    """Serializes detections to a json file."""
+    pass

From d13b4ed4963ea01f71f8103e0c9d375760a0f89a Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 17:13:05 -0400
Subject: [PATCH 07/30] Implement the read fn.

---
 .../extraction/extraction_utilities.py        | 26 ++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index e1bf29d..67e9e24 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -5,7 +5,7 @@
 import json
 from pathlib import Path
 from PIL import Image
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 # External imports
 import numpy as np
@@ -211,8 +211,28 @@ def read_detections_from_json(
     filepath: Path,
     detection_type: Union[BoundingBox, Keypoint]
 ) -> List[Detection]:
-    """Deserializes detections from a json file."""
-    pass
+    """Deserializes detections from a json file.
+    
+    Args:
+        filepath (Path):
+            The filepath to the json detections.
+        detection_type (Union[BoundingBox, Keypoint]):
+            The type of detection that has been serialized.
+            Passed to Detection.from_dict directly.
+
+    Returns:
+        A list of Detection objects from the encoded data.
+    """
+    json_data: Dict[str, Any] = json.loads(open(str(filepath), 'r').read())
+    if not isinstance(json_data, list):
+        raise ValueError(f"Data at {filepath} is not a list of detections.")
+    
+    detections: List[Detection] = [
+        Detection.from_dict(det_dict, detection_type)
+        for det_dict in json_data
+    ]
+    return detections
+    
 
 
 def write_detections_to_json(filepath: Path):

From 19ac78f73b12e17742a2ffd275a580221040eaba Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Tue, 10 Jun 2025 17:17:15 -0400
Subject: [PATCH 08/30] Implement the write fn.

---
 .../extraction/extraction_utilities.py        | 27 ++++++++++++++++---
 1 file changed, 24 insertions(+), 3 deletions(-)

diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 67e9e24..4124008 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -235,6 +235,27 @@ def read_detections_from_json(
     
 
 
-def write_detections_to_json(filepath: Path):
-    """Serializes detections to a json file."""
-    pass
+def write_detections_to_json(
+    filepath: Path
+    detections: List[Detection]
+) -> bool:
+    """Serializes detections to a json file.
+    
+    Args:
+        filepath (Path):
+            The filepath to store the json detections at.
+        detections (List[Detection]):
+            The detections to serialize and save.
+
+    Returns:
+        True if the writing was a success, False otherwise.
+    """
+    detections_as_dicts: List[Dict[str, Any]] = [detection.to_dict() for detection in detections]
+    json_data: str = json.dumps(detections_as_dicts)
+    try:
+        with open(str(filepath), 'w') as f:
+            f.write(json_data)
+        return True
+    except Exception as e:
+        print(f"Writing detections to json generated the following error:\n{e}")
+        return False

From 7a6c5b5973b1cc2cf7e988511bea5993ce7defc9 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 09:04:16 -0400
Subject: [PATCH 09/30] Stub out a generalized fn for performing tiled
 detection on an image.

---
 .../extraction/extraction_utilities.py        | 59 ++++++++++++++++++-
 1 file changed, 58 insertions(+), 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 4124008..3a8c3bf 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -5,7 +5,7 @@
 import json
 from pathlib import Path
 from PIL import Image
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 # External imports
 import numpy as np
@@ -153,6 +153,63 @@ def detect_numbers(
     return detections
 
 
+def detect_objects_using_tiling(
+    image: Image.Image,
+    detection_model: ObjectDetectionModel,
+    slice_width: int,
+    slice_heigth: int,
+    horizontal_overlap_ratio: float,
+    vertical_overlap_ratio: float,
+    minimum_confidence: float,
+    nms_threshold: float,
+    overlap_comparator: Callable[[Detection, Detection], float] = intersection_over_minimum,
+    sorting_fn: Callable[[Detection], float] = lambda det: det.annotation.area * det.confidence,
+) -> List[Detection]:
+    """Detects objects, especially small ones, using image tiling.
+
+    Splits an image up into smaller tiles, runs the model on each tile, then untiles the detections
+    and performs non-maximum suppression on the result.
+
+    Args:
+        `image` (Image.Image):
+            The image to detect on.
+        `detection_model` (ObjectDetectionModel):
+            The detection model to use. Can be any object that implements the ObjectDetectionModel
+            protocol.
+        `slice_height` (int):
+            The height of each slice.
+        `slice_width` (int):
+            The width of each slice.
+        `horizontal_overlap_ratio` (float):
+            The amount of left-right overlap between slices.
+            (Ex: 0.2 results in 20% of a tile overlapping with the tile on the left and 20%
+            overlapping on the right.)
+        `vertical_overlap_ratio` (float):
+            The amount of top-bottom overlap between slices.
+            (Ex: 0.2 results in 20% of a tile overlapping with the tile on the top and 20%
+            overlapping on the bottom.)
+        `minimum_confidence` (float):
+            The minimum confidence level. Any detection with a confidence score below this will not
+            be added to the returned detections.
+        `nms_threshold` (float):
+            The threshold above which nms registers a 'match', and deletes all but the first
+            detection in a 'group'. A group is determined by the sorting_fn.
+        `overlap_comparator` (float):
+            The function that determines how much two detections overlap. Defaults to the
+            intersection of the detections divided by the minimum of the two detection's areas.
+            This default prevents partial detections from remaining inside the full detection.
+        `sorting_fn` (Callable[[Detection], float]):
+            The function that applies a 'score' to each detection to determine which has priority
+            when NMS deletes detections. Only the detection with the highest score in a group
+            remains. Defaults to the detection's confidence times its area.
+    
+    Returns:
+        A list of detections showing objects on the image that the object detection model was
+        trained to identify.
+    """
+    pass
+
+
 def get_detection_by_name(
     detections: List[Detection], name: str
 ) -> Optional[Detection]:

From afa2237969ebdc77345ce7339d7fc49a8cac7b96 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 09:07:25 -0400
Subject: [PATCH 10/30] implement detect_objects_with_tiling.

---
 .../extraction/extraction_utilities.py        | 34 ++++++++++++++++---
 1 file changed, 29 insertions(+), 5 deletions(-)

diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 3a8c3bf..9c67d47 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -160,8 +160,8 @@ def detect_objects_using_tiling(
     slice_heigth: int,
     horizontal_overlap_ratio: float,
     vertical_overlap_ratio: float,
-    minimum_confidence: float,
-    nms_threshold: float,
+    minimum_confidence: float = 0.5,
+    nms_threshold: float = 0.5,
     overlap_comparator: Callable[[Detection, Detection], float] = intersection_over_minimum,
     sorting_fn: Callable[[Detection], float] = lambda det: det.annotation.area * det.confidence,
 ) -> List[Detection]:
@@ -190,10 +190,10 @@ def detect_objects_using_tiling(
             overlapping on the bottom.)
         `minimum_confidence` (float):
             The minimum confidence level. Any detection with a confidence score below this will not
-            be added to the returned detections.
+            be added to the returned detections. Defaults to 0.5.
         `nms_threshold` (float):
             The threshold above which nms registers a 'match', and deletes all but the first
-            detection in a 'group'. A group is determined by the sorting_fn.
+            detection in a 'group'. A group is determined by the sorting_fn. Defaults to 0.5.
         `overlap_comparator` (float):
             The function that determines how much two detections overlap. Defaults to the
             intersection of the detections divided by the minimum of the two detection's areas.
@@ -207,7 +207,31 @@ def detect_objects_using_tiling(
         A list of detections showing objects on the image that the object detection model was
         trained to identify.
     """
-    pass
+    image_tiles: List[List[Image.Image]] = tile_image(
+        image,
+        slice_width,
+        slice_height,
+        horizontal_overlap_ratio,
+        vertical_overlap_ratio,
+    )
+    detections: List[List[List[Detection]]] = [
+        [detection_model(pil_to_cv2(tile), confidence=minimum_confidence)[0] for tile in row]
+        for row in image_tiles
+    ]
+    detections: List[Detection] = untile_detections(
+        detections,
+        slice_width,
+        slice_height,
+        horizontal_overlap_ratio,
+        vertical_overlap_ratio,
+    )
+    detections: List[Detection] = non_maximum_suppression(
+        detections=detections,
+        threshold=nms_threshold,
+        overlap_comparator=overlap_comparator,
+        sorting_fn=sorting_fn,
+    )
+    return detections
 
 
 def get_detection_by_name(

From 101d7ce6d4d5eb36a89d6621369f4c7b31a34c0d Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 10:22:24 -0400
Subject: [PATCH 11/30] Remove the detect_numbers() fn and replace with the
 generalized detect_objects_using_tiling() fn.

---
 ChartExtractor/extraction/extraction.py       |  4 +-
 .../extraction/extraction_utilities.py        | 55 -------------------
 2 files changed, 3 insertions(+), 56 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index f85050c..03013a7 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -1,6 +1,7 @@
 """Consolidates all the functionality for extracting data from charts into one function."""
 
 # Built-in imports
+from functools import partial
 import os
 from pathlib import Path
 from PIL import Image
@@ -18,6 +19,7 @@
 )
 from ..extraction.inhaled_volatile import extract_inhaled_volatile
 from ..extraction.intraoperative_digit_boxes import (
+    detect_objects_using_tiling,
     extract_drug_codes,
     extract_ett_size,
     extract_surgical_timing,
@@ -394,7 +396,7 @@ def make_digit_detections(
             image.size[1] * tile_size_proportion,
         )
     )
-    number_detections: List[Detection] = detect_numbers(
+    number_detections: List[Detection] = detect_objects_using_tiling(
         image,
         NUMBERS_MODEL,
         tile_size,
diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 9c67d47..7bcc722 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -98,61 +98,6 @@ def compute_digit_distances_to_centroids(
     return closest_boxes
 
 
-def detect_numbers(
-    image: Image.Image,
-    detection_model: ObjectDetectionModel,
-    slice_width: int,
-    slice_height: int,
-    horizontal_overlap_ratio: float,
-    vertical_overlap_ratio: float,
-    conf: float = 0.5,
-) -> List[Detection]:
-    """Detects handwritten digits on an image.
-
-    Args:
-        `image` (Image.Image):
-            The image to detect on.
-        `detection_model` (ObjectDetectionModel):
-            The digit detection model.
-        `slice_height` (int):
-            The height of each slice.
-        `slice_width` (int):
-            The width of each slice.
-        `horizontal_overlap_ratio` (float):
-            The amount of left-right overlap between slices.
-        `vertical_overlap_ratio` (float):
-            The amount of top-bottom overlap between slices.
-
-    Returns:
-        A list of handwritten digit detections on the image.
-    """
-    image_tiles: List[List[Image.Image]] = tile_image(
-        image,
-        slice_width,
-        slice_height,
-        horizontal_overlap_ratio,
-        vertical_overlap_ratio,
-    )
-    detections: List[List[List[Detection]]] = [
-        [detection_model(pil_to_cv2(tile), confidence=conf)[0] for tile in row]
-        for row in image_tiles
-    ]
-    detections: List[Detection] = untile_detections(
-        detections,
-        slice_width,
-        slice_height,
-        horizontal_overlap_ratio,
-        vertical_overlap_ratio,
-    )
-    detections: List[Detection] = non_maximum_suppression(
-        detections=detections,
-        threshold=0.5,
-        overlap_comparator=intersection_over_minimum,
-        sorting_fn=lambda det: det.annotation.area * det.confidence,
-    )
-    return detections
-
-
 def detect_objects_using_tiling(
     image: Image.Image,
     detection_model: ObjectDetectionModel,

From ecbe6855f09351fba9c5b108e497aed4ee2a2879 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 10:30:27 -0400
Subject: [PATCH 12/30] Remove the make_digit_detections fn in favor of
 slightly more verbose calls to detect_objects_using_tiling.

---
 ChartExtractor/extraction/extraction.py | 57 +++++++++++--------------
 1 file changed, 25 insertions(+), 32 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 03013a7..23bc36b 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -136,7 +136,19 @@ def digitize_intraop_record(image: Image.Image) -> Dict:
     document_landmark_detections: List[Detection] = make_document_landmark_detections(
         image, "intraop"
     )
-    digit_detections: List[Detection] = make_digit_detections(image)
+
+    digit_tile_size: int = int(min(
+        image.size[0]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
+        image.size[1]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
+    ))
+    digit_detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        NUMBERS_MODEL,
+        digit_tile_size,
+        digit_tile_size,
+        MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
+        MODEL_CONFIG["numbers"]["vertical_overlap_ratio"],
+    )
 
     # extract drug code and surgical timing
     codes: Dict = {"codes": extract_drug_codes(digit_detections, *image.size)}
@@ -212,7 +224,18 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
         image,
         make_document_landmark_detections(image, "preop_postop"),
     )
-    digit_detections: List[Detection] = make_digit_detections(image)
+    digit_tile_size: int = int(min(
+        image.size[0]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
+        image.size[1]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
+    ))
+    digit_detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        NUMBERS_MODEL,
+        digit_tile_size,
+        digit_tile_size,
+        MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
+        MODEL_CONFIG["numbers"]["vertical_overlap_ratio"],
+    )
     digit_data = extract_preop_postop_digit_data(digit_detections, *image.size)
     checkbox_data = {
         "preoperative_checkboxes": make_preop_postop_checkbox_detections(image)
@@ -377,36 +400,6 @@ def make_document_landmark_detections(
     return detections
 
 
-def make_digit_detections(
-    image: Image.Image,
-) -> List[Detection]:
-    """Runs the digit detection detection model to find handwritten digits.
-
-    Args:
-        `image` (Image.Image):
-            The image to detect on.
-
-    Returns:
-        A list of detections containing the locations of handwritten digits.
-    """
-    tile_size_proportion: float = MODEL_CONFIG["numbers"]["tile_size_proportion"]
-    tile_size = int(
-        min(
-            image.size[0] * tile_size_proportion,
-            image.size[1] * tile_size_proportion,
-        )
-    )
-    number_detections: List[Detection] = detect_objects_using_tiling(
-        image,
-        NUMBERS_MODEL,
-        tile_size,
-        tile_size,
-        MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
-        MODEL_CONFIG["numbers"]["vert_overlap_proportion"],
-    )
-    return number_detections
-
-
 def make_bp_and_hr_detections(
     image: Image.Image,
     time_clusters: List[Cluster],

From fe855ba745ab1cc29b0e6019d7942d6dae131be2 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 11:42:11 -0400
Subject: [PATCH 13/30] Fix some minor errors.

---
 ChartExtractor/extraction/extraction.py           | 7 +++----
 ChartExtractor/extraction/extraction_utilities.py | 4 ++--
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 23bc36b..f3fc8e0 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -14,12 +14,11 @@
 from ..extraction.checkboxes import extract_checkboxes
 from ..extraction.extraction_utilities import (
     combine_dictionaries,
-    detect_numbers,
+    detect_objects_using_tiling,
     label_studio_to_bboxes,
 )
 from ..extraction.inhaled_volatile import extract_inhaled_volatile
 from ..extraction.intraoperative_digit_boxes import (
-    detect_objects_using_tiling,
     extract_drug_codes,
     extract_ett_size,
     extract_surgical_timing,
@@ -147,7 +146,7 @@ def digitize_intraop_record(image: Image.Image) -> Dict:
         digit_tile_size,
         digit_tile_size,
         MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
-        MODEL_CONFIG["numbers"]["vertical_overlap_ratio"],
+        MODEL_CONFIG["numbers"]["vert_overlap_proportion"],
     )
 
     # extract drug code and surgical timing
@@ -234,7 +233,7 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
         digit_tile_size,
         digit_tile_size,
         MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
-        MODEL_CONFIG["numbers"]["vertical_overlap_ratio"],
+        MODEL_CONFIG["numbers"]["vert_overlap_proportion"],
     )
     digit_data = extract_preop_postop_digit_data(digit_detections, *image.size)
     checkbox_data = {
diff --git a/ChartExtractor/extraction/extraction_utilities.py b/ChartExtractor/extraction/extraction_utilities.py
index 7bcc722..7b95b30 100644
--- a/ChartExtractor/extraction/extraction_utilities.py
+++ b/ChartExtractor/extraction/extraction_utilities.py
@@ -102,7 +102,7 @@ def detect_objects_using_tiling(
     image: Image.Image,
     detection_model: ObjectDetectionModel,
     slice_width: int,
-    slice_heigth: int,
+    slice_height: int,
     horizontal_overlap_ratio: float,
     vertical_overlap_ratio: float,
     minimum_confidence: float = 0.5,
@@ -262,7 +262,7 @@ def read_detections_from_json(
 
 
 def write_detections_to_json(
-    filepath: Path
+    filepath: Path,
     detections: List[Detection]
 ) -> bool:
     """Serializes detections to a json file.

From 8d37c37f2a1ca04c44498119855918226f48138c Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 12:12:38 -0400
Subject: [PATCH 14/30] Move checkbox detection into extraction.py

---
 ChartExtractor/extraction/checkboxes.py | 119 ++++--------------------
 ChartExtractor/extraction/extraction.py |  28 +++++-
 2 files changed, 46 insertions(+), 101 deletions(-)

diff --git a/ChartExtractor/extraction/checkboxes.py b/ChartExtractor/extraction/checkboxes.py
index eceb515..da5479b 100644
--- a/ChartExtractor/extraction/checkboxes.py
+++ b/ChartExtractor/extraction/checkboxes.py
@@ -38,30 +38,22 @@
 
 
 def extract_checkboxes(
-    image: Image.Image,
-    detection_model: ObjectDetectionModel,
+    detections: List[Detection],
     side: Literal["intraoperative", "preoperative"],
-    slice_width: int,
-    slice_height: int,
-    horizontal_overlap_ratio: float = 0.5,
-    vertical_overlap_ratio: float = 0.5,
+    image_width: int,
+    image_height: int,
 ) -> Dict[str, str]:
     """Extracts checkbox data from an image of a chart.
 
     Args:
-        `image` (Image.Image):
-            The image to extract checkboxes from.
-        `detection_model` (ObjectDetectionModel):
-            An object that implements the ObjectDetectionModel interface.
-        `slice_height` (int):
-            The height of each slice.
-        `slice_width` (int):
-            The width of each slice.
-        `horizontal_overlap_ratio` (float):
-            The amount of left-right overlap between slices.
-        `vertical_overlap_ratio` (float):
-            The amount of top-bottom overlap between slices.
-
+        detections (List[Detection]):
+            The detected checkboxes.
+        side (Literal["intraoperative", "preoperative"]):
+            The side of the chart.
+        image_width (int):
+            The original image's width.
+        image_height (int):
+            The original image's height.
     Returns:
         A dictionary mapping the name of checkboxes to "checked" or "unchecked".
     """
@@ -74,76 +66,21 @@ def extract_checkboxes(
             f'Invalid selection for side. Must be one of ["intraoperative", "preoperative"], value supplied was {side}'
         )
 
-    checkbox_bboxes: List[BoundingBox] = detect_checkboxes(
-        image,
-        detection_model,
-        slice_width,
-        slice_height,
-        horizontal_overlap_ratio,
-        vertical_overlap_ratio,
+    checkbox_bboxes: List[BoundingBox] = [det.annotation for det in detections]
+    names: Dict[str, str] = find_checkbox_names(
+        checkbox_bboxes,
+        centroids,
+        image_width,
+        image_height
     )
-    names: Dict[str, str] = find_checkbox_names(checkbox_bboxes, centroids, image.size)
     return names
 
 
-def detect_checkboxes(
-    image: Image.Image,
-    detection_model: ObjectDetectionModel,
-    slice_width: int,
-    slice_height: int,
-    horizontal_overlap_ratio: float,
-    vertical_overlap_ratio: float,
-) -> List[BoundingBox]:
-    """Uses an object detector to detect checkboxes and their state on an image.
-
-    Args:
-        `image` (Image.Image):
-            The image to extract checkboxes from.
-        `detection_model` (ObjectDetectionModel):
-            An object that implements the ObjectDetectionModel interface.
-        `slice_height` (int):
-            The height of each slice.
-        `slice_width` (int):
-            The width of each slice.
-        `horizontal_overlap_ratio` (float):
-            The amount of left-right overlap between slices.
-        `vertical_overlap_ratio` (float):
-            The amount of top-bottom overlap between slices.
-
-    Returns:
-        A list of Detection objects encoding the location and state of checkboxes.
-    """
-    image_tiles: List[List[Image.Image]] = tile_image(
-        image,
-        slice_width,
-        slice_height,
-        horizontal_overlap_ratio,
-        vertical_overlap_ratio,
-    )
-    detections: List[List[List[Detection]]] = [
-        [detection_model(pil_to_cv2(tile))[0] for tile in row]
-        for row in image_tiles
-    ]
-    detections: List[Detection] = untile_detections(
-        detections,
-        slice_width,
-        slice_height,
-        horizontal_overlap_ratio,
-        vertical_overlap_ratio,
-    )
-    detections: List[Detection] = non_maximum_suppression(
-        detections=detections,
-        threshold=0.8,
-        overlap_comparator=intersection_over_minimum,
-        sorting_fn=lambda det: det.annotation.area * det.confidence,
-    )
-    return [det.annotation for det in detections]
-
-
 def find_checkbox_names(
     checkboxes: List[BoundingBox],
     centroids: Dict[str, Tuple[float, float]],
-    imsize: Tuple[int, int],
+    image_width: int,
+    image_height: int,
     threshold: float = 0.025,
 ) -> Dict[str, str]:
     """Finds the names of checkboxes.
@@ -175,7 +112,7 @@ def distance(p1: Tuple[float, float], p2: Tuple[float, float]) -> float:
 
     checkbox_values: Dict[str, str] = dict()
     for ckbx in checkboxes:
-        center = ckbx.center[0] / imsize[0], ckbx.center[1] / imsize[1]
+        center = ckbx.center[0] / image_width, ckbx.center[1] / image_height
         distance_to_all_centroids: Dict[str, float] = {
             name: distance(center, centroid) for (name, centroid) in centroids.items()
         }
@@ -188,19 +125,3 @@ def distance(p1: Tuple[float, float], p2: Tuple[float, float]) -> float:
         checkbox_values[closest_checkbox_centroid] = ckbx.category
 
     return checkbox_values
-
-
-def find_interaoperative_checkbox_names(
-    intraoperative_checkboxes: List[BoundingBox], threshold: float = 0.025
-) -> Dict[str, str]:
-    """Finds the names of intraoperative checkboxes."""
-    return find_checkbox_names(intraoperative_checkboxes, INTRAOP_CENTROIDS, threshold)
-
-
-def find_preoperative_checkbox_names(
-    preoperative_checkboxes: List[BoundingBox], threshold: float = 0.025
-) -> Dict[str, str]:
-    """Finds the names of preoperative checkboxes."""
-    return find_checkbox_names(
-        preoperative_checkboxes, PREOP_POSTOP_CENTROIDS, threshold
-    )
diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index f3fc8e0..ee998bf 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -536,8 +536,20 @@ def make_intraop_checkbox_detections(
             image.size[1] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
         )
     )
+    detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        CHECKBOXES_MODEL,
+        tile_size,
+        tile_size,
+        MODEL_CONFIG["checkboxes"]["horz_overlap_proportion"],
+        MODEL_CONFIG["checkboxes"]["vert_overlap_proportion"],
+        nms_threshold=0.8
+    )
     intraop_checkboxes = extract_checkboxes(
-        image, CHECKBOXES_MODEL, "intraoperative", tile_size, tile_size
+        detections,
+        "intraoperative",
+        image.size[0],
+        image.size[1],
     )
     return intraop_checkboxes
 
@@ -560,7 +572,19 @@ def make_preop_postop_checkbox_detections(
             image.size[1] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
         )
     )
+    detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        CHECKBOXES_MODEL,
+        tile_size,
+        tile_size,
+        MODEL_CONFIG["checkboxes"]["horz_overlap_proportion"],
+        MODEL_CONFIG["checkboxes"]["vert_overlap_proportion"],
+        nms_threshold=0.8
+    )
     preop_postop_checkboxes = extract_checkboxes(
-        image, CHECKBOXES_MODEL, "preoperative", tile_size, tile_size
+        detections,
+        "preoperative",
+        image.size[0],
+        image.size[1],
     )
     return preop_postop_checkboxes

From 8090e19e86bebd08349e0bf88159f6499b00ca7a Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 15:16:46 -0400
Subject: [PATCH 15/30] Add the compute_tile_size fn to reduce repetition.

---
 ChartExtractor/extraction/extraction.py | 89 +++++++++----------------
 1 file changed, 31 insertions(+), 58 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index ee998bf..0a7947e 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -51,12 +51,7 @@
 from ..utilities.tiling import tile_image
 
 
-CORNER_LANDMARK_NAMES: List[str] = [
-    "anesthesia_start",
-    "safety_checklist",
-    "lateral",
-    "units",
-]
+CORNER_LANDMARK_NAMES: List[str] = ["anesthesia_start", "safety_checklist", "lateral", "units"]
 PATH_TO_DATA: Path = (Path(os.path.dirname(__file__)) / ".." / ".." / "data").resolve()
 PATH_TO_MODELS: Path = PATH_TO_DATA / "models"
 PATH_TO_MODEL_METADATA = PATH_TO_DATA / "model_metadata"
@@ -136,10 +131,7 @@ def digitize_intraop_record(image: Image.Image) -> Dict:
         image, "intraop"
     )
 
-    digit_tile_size: int = int(min(
-        image.size[0]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
-        image.size[1]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
-    ))
+    digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], image.size)
     digit_detections: List[Detection] = detect_objects_using_tiling(
         image,
         NUMBERS_MODEL,
@@ -223,10 +215,7 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
         image,
         make_document_landmark_detections(image, "preop_postop"),
     )
-    digit_tile_size: int = int(min(
-        image.size[0]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
-        image.size[1]*MODEL_CONFIG["numbers"]["tile_size_proportion"],
-    ))
+    digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], image.size)
     digit_detections: List[Detection] = detect_objects_using_tiling(
         image,
         NUMBERS_MODEL,
@@ -344,6 +333,24 @@ def homography_preoperative_chart(
     )
 
 
+def compute_tile_size(model_config: Dict, image_size: Tuple[int, int]) -> int:
+    """Finds the tile size for a model based on how its training dataset was generated.
+    
+    Args:
+        model_config (Dict):
+            The model's config dictionary.
+        image_size (Tuple[int, int])
+    """
+    tile_size_proportion = model_config["tile_size_proportion"]
+    tile_size: int = int(
+        min(
+            image_size[0] * tile_size_proportion,
+            image_size[1] * tile_size_proportion,
+        )
+    )
+    return tile_size
+
+
 def make_document_landmark_detections(
     image: Image.Image,
     document_side: Literal["intraop", "preop_postop"],
@@ -367,14 +374,9 @@ def make_document_landmark_detections(
     document_model: UltralyticsYOLOv8 = (
         INTRAOP_DOC_MODEL if document_side == "intraop" else PREOP_POSTOP_DOC_MODEL
     )
-    tile_size_proportion: float = MODEL_CONFIG["intraoperative_document_landmarks"][
-        "tile_size_proportion"
-    ]
-    tile_size: int = int(
-        min(
-            image.size[0] * tile_size_proportion,
-            image.size[1] * tile_size_proportion,
-        )
+    tile_size: float = compute_tile_size(
+        MODEL_CONFIG["intraoperative_document_landmarks"],
+        image.size,
     )
     tiles: List[List[Image.Image]] = tile_image(
         image,
@@ -447,24 +449,9 @@ def tile_predict(
         )
         return detections
 
-    sys_tile_size = int(
-        min(
-            image.size[0] * MODEL_CONFIG["systolic"]["tile_size_proportion"],
-            image.size[1] * MODEL_CONFIG["systolic"]["tile_size_proportion"],
-        )
-    )
-    dia_tile_size = int(
-        min(
-            image.size[0] * MODEL_CONFIG["diastolic"]["tile_size_proportion"],
-            image.size[1] * MODEL_CONFIG["diastolic"]["tile_size_proportion"],
-        )
-    )
-    hr_tile_size = int(
-        min(
-            image.size[0] * MODEL_CONFIG["heart_rate"]["tile_size_proportion"],
-            image.size[1] * MODEL_CONFIG["heart_rate"]["tile_size_proportion"],
-        )
-    )
+    sys_tile_size: int = compute_tile_size(MODEL_CONFIG["systolic"], image.size)
+    dia_tile_size: int = compute_tile_size(MODEL_CONFIG["diastolic"], image.size)
+    hr_tile_size: int = compute_tile_size(MODEL_CONFIG["heart_rate"], image.size)
 
     sys_dets: List[Detection] = tile_predict(
         SYSTOLIC_MODEL,
@@ -518,9 +505,7 @@ def tile_predict(
     return bp_and_hr
 
 
-def make_intraop_checkbox_detections(
-    image: Image.Image,
-) -> Dict:
+def make_intraop_checkbox_detections(image: Image.Image) -> Dict:
     """Finds checkboxes on the intraoperative form, then associates a meaning to them.
 
     Args:
@@ -530,12 +515,7 @@ def make_intraop_checkbox_detections(
     Returns:
         A dictionary mapping names of checkboxes to a "checked" or "unchecked" state.
     """
-    tile_size: int = int(
-        min(
-            image.size[0] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
-            image.size[1] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
-        )
-    )
+    tile_size = compute_tile_size(MODEL_CONFIG["checkboxes"], image.size)
     detections: List[Detection] = detect_objects_using_tiling(
         image,
         CHECKBOXES_MODEL,
@@ -554,9 +534,7 @@ def make_intraop_checkbox_detections(
     return intraop_checkboxes
 
 
-def make_preop_postop_checkbox_detections(
-    image: Image.Image,
-):
+def make_preop_postop_checkbox_detections(image: Image.Image):
     """Finds checkboxes on the intraoperative form, then associates a meaning to them.
 
     Args:
@@ -566,12 +544,7 @@ def make_preop_postop_checkbox_detections(
     Returns:
         A dictionary mapping names of checkboxes to a "checked" or "unchecked" state.
     """
-    tile_size: int = int(
-        min(
-            image.size[0] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
-            image.size[1] * MODEL_CONFIG["checkboxes"]["tile_size_proportion"],
-        )
-    )
+    tile_size = compute_tile_size(MODEL_CONFIG["checkboxes"], image.size)
     detections: List[Detection] = detect_objects_using_tiling(
         image,
         CHECKBOXES_MODEL,

From 52606220dcf1cf3fab307135e2aa27cfd300581a Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 15:20:33 -0400
Subject: [PATCH 16/30] Remove unused constant.

---
 ChartExtractor/extraction/extraction.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 0a7947e..41b8c69 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -51,7 +51,6 @@
 from ..utilities.tiling import tile_image
 
 
-CORNER_LANDMARK_NAMES: List[str] = ["anesthesia_start", "safety_checklist", "lateral", "units"]
 PATH_TO_DATA: Path = (Path(os.path.dirname(__file__)) / ".." / ".." / "data").resolve()
 PATH_TO_MODELS: Path = PATH_TO_DATA / "models"
 PATH_TO_MODEL_METADATA = PATH_TO_DATA / "model_metadata"

From b92aaf525c288a84906f587c5d0fdf4f7b5fa474 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 15:34:48 -0400
Subject: [PATCH 17/30] Remove the make_document_landmark_detections fn and
 replace with calls to detect_objects_using_tiling.

---
 ChartExtractor/extraction/extraction.py | 93 ++++++++++---------------
 1 file changed, 36 insertions(+), 57 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 41b8c69..7cedec1 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -123,11 +123,29 @@ def digitize_intraop_record(image: Image.Image) -> Dict:
         A dictionary containing all the data from the intraoperative side of
         the paper anesthesia record.
     """
+    landmark_tile_size: int = compute_tile_size(
+        MODEL_CONFIG["intraoperative_document_landmarks"],
+        image.size
+    )
+    uncorrected_document_landmark_detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        INTRAOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
+    )
     image: Image.Image = homography_intraoperative_chart(
-        image, make_document_landmark_detections(image, "intraop")
+        image,
+        uncorrected_document_landmark_detections,
     )
-    document_landmark_detections: List[Detection] = make_document_landmark_detections(
-        image, "intraop"
+    document_landmark_detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        INTRAOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
     )
 
     digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], image.size)
@@ -210,10 +228,19 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
         A dictionary containing all the data from the preoperative/postoperative
         side of the paper anesthesia record.
     """
-    image: Image.Image = homography_preoperative_chart(
+    landmark_tile_size: int = compute_tile_size(
+        MODEL_CONFIG["preop_postop_document_landmarks"],
+        image.size,
+    )
+    document_landmark_detections: List[Detection] = detect_objects_using_tiling(
         image,
-        make_document_landmark_detections(image, "preop_postop"),
+        PREOP_POSTOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["preop_postop_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["preop_postop_document_landmarks"]["vert_overlap_proportion"],
     )
+    image: Image.Image = homography_preoperative_chart(image, document_landmark_detections)
     digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], image.size)
     digit_detections: List[Detection] = detect_objects_using_tiling(
         image,
@@ -231,7 +258,8 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
 
 
 def homography_intraoperative_chart(
-    image: Image.Image, intraop_document_detections: List[Detection]
+    image: Image.Image,
+    intraop_document_detections: List[Detection],
 ) -> Image.Image:
     """Performs a homography transformation on the intraoperative side of the chart.
 
@@ -282,7 +310,8 @@ def homography_intraoperative_chart(
 
 
 def homography_preoperative_chart(
-    image: Image.Image, preop_document_detections: List[Detection]
+    image: Image.Image,
+    preop_document_detections: List[Detection],
 ) -> Image.Image:
     """Performs a homography transformation on the preop/postop side of the chart.
 
@@ -350,56 +379,6 @@ def compute_tile_size(model_config: Dict, image_size: Tuple[int, int]) -> int:
     return tile_size
 
 
-def make_document_landmark_detections(
-    image: Image.Image,
-    document_side: Literal["intraop", "preop_postop"],
-) -> List[Detection]:
-    """Runs the document landmark detection model to find document landmarks.
-
-    Args:
-        `image` (Image.Image):
-            The image to detect on.
-        `document_side` (Path):
-            The side of the document to find landmarks on.
-
-    Returns:
-        A list of detections containing the locations of the document landmarks.
-    """
-    if document_side not in ["intraop", "preop_postop"]:
-        err_msg = f"Value for \"document_side\" is not in [\"intraop\", "
-        err_msg += f"\"preop_postop\"] (passed: {document_side})."
-        raise ValueError(err_msg)
-    
-    document_model: UltralyticsYOLOv8 = (
-        INTRAOP_DOC_MODEL if document_side == "intraop" else PREOP_POSTOP_DOC_MODEL
-    )
-    tile_size: float = compute_tile_size(
-        MODEL_CONFIG["intraoperative_document_landmarks"],
-        image.size,
-    )
-    tiles: List[List[Image.Image]] = tile_image(
-        image,
-        tile_size,
-        tile_size,
-        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
-        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
-    )
-    detections = [[document_model(pil_to_cv2(tile))[0] for tile in row] for row in tiles]
-    detections = untile_detections(
-        detections,
-        tile_size,
-        tile_size,
-        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
-        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
-    )
-    detections = non_maximum_suppression(
-        detections,
-        overlap_comparator=intersection_over_minimum,
-        sorting_fn=lambda det: det.annotation.area * det.confidence,
-    )
-    return detections
-
-
 def make_bp_and_hr_detections(
     image: Image.Image,
     time_clusters: List[Cluster],

From 382be32193c99eb82f61d7d40f53357059623996 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 15:49:12 -0400
Subject: [PATCH 18/30] Simplify the logic of the make_bp_and_hr_detections fn.

---
 ChartExtractor/extraction/extraction.py | 64 +++----------------------
 1 file changed, 7 insertions(+), 57 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 7cedec1..6fe3b24 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -397,89 +397,39 @@ def make_bp_and_hr_detections(
     Returns:
         A dictionary mapping timestamps to values for systolic, diastolic, and heart rate.
     """
-
-    def tile_predict(
-        model: ObjectDetectionModel,
-        image: Image.Image,
-        tile_width: int,
-        tile_height: int,
-        horizontal_overlap_ratio: float,
-        vertical_overlap_ratio: float,
-    ):
-        """Performs tiled prediction."""
-        tiles: List[List[Image.Image]] = tile_image(
-            image,
-            tile_width,
-            tile_height,
-            horizontal_overlap_ratio,
-            vertical_overlap_ratio,
-        )
-        tiled_detections: List[List[List[Detection]]] = [
-            [model(pil_to_cv2(tile), confidence=0.5)[0] for tile in row]
-            for row in tiles
-        ]
-        detections: List[Detection] = untile_detections(
-            tiled_detections,
-            tile_width,
-            tile_height,
-            horizontal_overlap_ratio,
-            vertical_overlap_ratio,
-        )
-        return detections
-
     sys_tile_size: int = compute_tile_size(MODEL_CONFIG["systolic"], image.size)
     dia_tile_size: int = compute_tile_size(MODEL_CONFIG["diastolic"], image.size)
     hr_tile_size: int = compute_tile_size(MODEL_CONFIG["heart_rate"], image.size)
-
-    sys_dets: List[Detection] = tile_predict(
-        SYSTOLIC_MODEL,
+    
+    sys_dets: List[Detection] = detect_objects_using_tiling(
         image.copy(),
+        SYSTOLIC_MODEL,
         sys_tile_size,
         sys_tile_size,
         MODEL_CONFIG["systolic"]["horz_overlap_proportion"],
         MODEL_CONFIG["systolic"]["vert_overlap_proportion"],
     )
-    dia_dets: List[Detection] = tile_predict(
-        DIASTOLIC_MODEL,
+    dia_dets: List[Detection] = detect_objects_using_tiling(
         image.copy(),
+        DIASTOLIC_MODEL,
         dia_tile_size,
         dia_tile_size,
         MODEL_CONFIG["diastolic"]["horz_overlap_proportion"],
         MODEL_CONFIG["diastolic"]["vert_overlap_proportion"],
     )
-    hr_dets: List[Detection] = tile_predict(
-        HEART_RATE_MODEL,
+    hr_dets: List[Detection] = detect_objects_using_tiling(
         image.copy(),
+        HEART_RATE_MODEL,
         hr_tile_size,
         hr_tile_size,
         MODEL_CONFIG["heart_rate"]["horz_overlap_proportion"],
         MODEL_CONFIG["heart_rate"]["vert_overlap_proportion"],
     )
 
-    sys_dets: List[Detection] = non_maximum_suppression(
-        sys_dets,
-        0.5,
-        intersection_over_minimum,
-        lambda det: det.annotation.area * det.confidence,
-    )
-    dia_dets: List[Detection] = non_maximum_suppression(
-        dia_dets,
-        0.5,
-        intersection_over_minimum,
-        lambda det: det.annotation.area * det.confidence,
-    )
-    hr_dets: List[Detection] = non_maximum_suppression(
-        hr_dets,
-        0.5,
-        intersection_over_minimum,
-        lambda det: det.annotation.area * det.confidence,
-    )
-
     dets: List[Detection] = sys_dets + dia_dets + hr_dets
     bp_and_hr = extract_heart_rate_and_blood_pressure(
         dets, time_clusters, mmhg_clusters
     )
-
     return bp_and_hr
 
 

From b683b4113eb955dbdee84fb44b9a4a174b564bf7 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 17:47:13 -0400
Subject: [PATCH 19/30] Stub out three functions that will run all of the yolo
 models in sequence.

---
 ChartExtractor/extraction/extraction.py | 84 +++++++++++++++++++++++++
 1 file changed, 84 insertions(+)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 6fe3b24..43a300d 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -111,6 +111,90 @@ def digitize_sheet(intraop_image: Image.Image, preop_postop_image: Image.Image)
     return data
 
 
+def run_models(
+    intraop_image: Image.Image,
+    preop_postop_image: Image.Image
+) -> Dict[str, List[Detection]]:
+    """Runs all the models and puts their output into a dictionary.
+    
+    Args:
+        `intraop_image` (Image.Image):
+            A smartphone photograph of the intraoperative side of the paper
+            anesthesia record.
+        `preop_postop_image` (Image.Image):
+            A smartphone photograph of the preoperative/postoperative side of the
+            paper anesthesia record.
+    
+    Returns:
+        A dictionary containing all the detections on both images. The structure of the dictionary
+        is set up as:
+        {
+            "intraoperative": {
+                "landmarks": [...],
+                "numbers": [...],
+                "checkboxes": [...],
+                "systolic": [...],
+                "diastoic": [...],
+                "heart_rate": [...],
+            },
+            "preoperative_postoperative": {
+                "landmarks": [...],
+                "numbers": [...],
+                "checkboxes": [...],
+            }
+        }
+    """
+    pass
+
+
+def run_intraoperative_models(intraop_image: Image.Image) -> Dict[str, List[Detection]]:
+    """Runs all the models on the preoperative/postoperative image and outputs to a dictionary.
+    
+    Args:
+        `intraop_image` (Image.Image):
+            A smartphone photograph of the intraoperative side of the paper anesthesia record.
+    
+    Returns:
+        A dictionary containing all of the detections on the intraoperative image.
+        The structure of the dictionary is set up as:
+        {
+            "intraoperative": {
+                "landmarks": [...],
+                "numbers": [...],
+                "checkboxes": [...],
+                "systolic": [...],
+                "diastoic": [...],
+                "heart_rate": [...],
+            }
+        }
+    """
+    pass
+
+
+def run_preoperative_postoperative_models(
+    preop_postop_image: Image.Image
+) -> Dict[str, List[Detection]]:
+    """Runs all the models on the preoperative/postoperative image and outputs to a dictionary.
+    
+    Args:
+        `preop_postop_image` (Image.Image):
+            A smartphone photograph of the preoperative/postoperative side of the
+            paper anesthesia record.
+    
+    Returns:
+        A dictionary containing all of the detections on the preoperative/postoperative image.
+        The structure of the dictionary is set up as:
+        {
+            "preoperative_postoperative": {
+                "landmarks": [...],
+                "numbers": [...],
+                "checkboxes": [...],
+            }
+        }
+    """
+    pass
+
+
 def digitize_intraop_record(image: Image.Image) -> Dict:
     """Digitizes the intraoperative side of a paper anesthesia record.
 

From d9dfc413a7e2584e63c5cebe805d4fef93284b93 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 17:50:29 -0400
Subject: [PATCH 20/30] Implement run_models.

Doesn't do anything yet since all it does is call the other two functions.
---
 ChartExtractor/extraction/extraction.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 43a300d..894e41d 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -144,7 +144,12 @@ def run_models(
             }
         }
     """
-    pass
+    detections_dict: Dict[str, List[Detection]] = dict()
+    detections_dict["intraoperative"] = run_intraoperative_models(intraop_image)
+    detections_dict["preoperative_postoperative"] = (
+        run_preoperative_postoperative_models(preop_postop_image)
+    )
+    return detections_dict
 
 
 def run_intraoperative_models(intraop_image: Image.Image) -> Dict[str, List[Detection]]:

From 224f7815dac3c90f6a34ce5a2d758d018056bfd5 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 18:02:14 -0400
Subject: [PATCH 21/30] Implement run_intraoperative_models.

---
 ChartExtractor/extraction/extraction.py | 96 +++++++++++++++++++++----
 1 file changed, 82 insertions(+), 14 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 894e41d..d1d8e53 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -163,17 +163,87 @@ def run_intraoperative_models(intraop_image: Image.Image) -> Dict[str, List[Dete
         A dictionary containing all of the detections on the intraoperative image.
         The structure of the dictionary is set up as:
         {
-            "intraoperative": {
-                "landmarks": [...],
-                "numbers": [...],
-                "checkboxes": [...],
-                "systolic": [...],
-                "diastoic": [...],
-                "heart_rate": [...],
-            }
+            "landmarks": [...],
+            "numbers": [...],
+            "checkboxes": [...],
+            "systolic": [...],
+            "diastoic": [...],
+            "heart_rate": [...],
         }
     """
-    pass
+    detections_dict: Dict[str, List[Detection]] = dict()
+
+    # landmarks
+    landmark_tile_size: int = compute_tile_size(
+        MODEL_CONFIG["intraoperative_document_landmarks"],
+        intraop_image.size
+    )
+    detections_dict["landmarks"] = detect_objects_using_tiling(
+        intraop_image,
+        INTRAOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
+    )
+
+    # numbers
+    digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], intraop_image.size)
+    detections_dict["numbers"] = detect_objects_using_tiling(
+        intraop_image,
+        NUMBERS_MODEL,
+        digit_tile_size,
+        digit_tile_size,
+        MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
+        MODEL_CONFIG["numbers"]["vert_overlap_proportion"],
+    )
+
+    # checkboxes
+    tile_size = compute_tile_size(MODEL_CONFIG["checkboxes"], intraop_image.size)
+    detections_dict["checkboxes"] = detect_objects_using_tiling(
+        intraop_image,
+        CHECKBOXES_MODEL,
+        tile_size,
+        tile_size,
+        MODEL_CONFIG["checkboxes"]["horz_overlap_proportion"],
+        MODEL_CONFIG["checkboxes"]["vert_overlap_proportion"],
+        nms_threshold=0.8
+    )
+
+    # systolic
+    sys_tile_size: int = compute_tile_size(MODEL_CONFIG["systolic"], intraop_image.size)
+    detections_dict["systolic"] = detect_objects_using_tiling(
+        intraop_image.copy(),
+        SYSTOLIC_MODEL,
+        sys_tile_size,
+        sys_tile_size,
+        MODEL_CONFIG["systolic"]["horz_overlap_proportion"],
+        MODEL_CONFIG["systolic"]["vert_overlap_proportion"],
+    )
+    
+    # diastolic
+    dia_tile_size: int = compute_tile_size(MODEL_CONFIG["diastolic"], intraop_image.size)
+    detections_dict["diastolic"] = detect_objects_using_tiling(
+        intraop_image.copy(),
+        DIASTOLIC_MODEL,
+        dia_tile_size,
+        dia_tile_size,
+        MODEL_CONFIG["diastolic"]["horz_overlap_proportion"],
+        MODEL_CONFIG["diastolic"]["vert_overlap_proportion"],
+    )
+    
+    # heart rate
+    hr_tile_size: int = compute_tile_size(MODEL_CONFIG["heart_rate"], intraop_image.size)
+    detections_dict["heart_rate"] = detect_objects_using_tiling(
+        intraop_image.copy(),
+        HEART_RATE_MODEL,
+        hr_tile_size,
+        hr_tile_size,
+        MODEL_CONFIG["heart_rate"]["horz_overlap_proportion"],
+        MODEL_CONFIG["heart_rate"]["vert_overlap_proportion"],
+    )
+
+    return detections_dict
 
 
 def run_preoperative_postoperative_models(
@@ -190,11 +260,9 @@ def run_preoperative_postoperative_models(
         A dictionary containing all of the detections on the preoperative/postoperative image.
         The structure of the dictionary is set up as:
         {
-            "preoperative_postoperative": {
-                "landmarks": [...],
-                "numbers": [...],
-                "checkboxes": [...],
-            }
+            "landmarks": [...],
+            "numbers": [...],
+            "checkboxes": [...],
         }
     """
     pass

From 330ebe47f8fd6a88f9ca8407092780c0b6060170 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Wed, 11 Jun 2025 18:31:51 -0400
Subject: [PATCH 22/30] implement run_preoperative_postoperative_models.

---
 ChartExtractor/extraction/extraction.py | 41 ++++++++++++++++++++++++-
 1 file changed, 40 insertions(+), 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index d1d8e53..66cfcda 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -265,7 +265,46 @@ def run_preoperative_postoperative_models(
             "checkboxes": [...],
         }
     """
-    pass
+    detections_dict: Dict[str, List[Detection]] = dict()
+    
+    # landmarks
+    landmark_tile_size: int = compute_tile_size(
+        MODEL_CONFIG["preop_postop_document_landmarks"],
+        preop_postop_image.size,
+    )
+    detections_dict["landmarks"] = detect_objects_using_tiling(
+        preop_postop_image,
+        PREOP_POSTOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["preop_postop_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["preop_postop_document_landmarks"]["vert_overlap_proportion"],
+    )
+
+    # numbers
+    digit_tile_size: int = compute_tile_size(MODEL_CONFIG["numbers"], preop_postop_image.size)
+    detections_dict["numbers"] = detect_objects_using_tiling(
+        preop_postop_image,
+        NUMBERS_MODEL,
+        digit_tile_size,
+        digit_tile_size,
+        MODEL_CONFIG["numbers"]["horz_overlap_proportion"],
+        MODEL_CONFIG["numbers"]["vert_overlap_proportion"],
+    )
+
+    # checkboxes
+    tile_size = compute_tile_size(MODEL_CONFIG["checkboxes"], preop_postop_image.size)
+    detections_dict["checkboxes"] = detect_objects_using_tiling(
+        preop_postop_image,
+        CHECKBOXES_MODEL,
+        tile_size,
+        tile_size,
+        MODEL_CONFIG["checkboxes"]["horz_overlap_proportion"],
+        MODEL_CONFIG["checkboxes"]["vert_overlap_proportion"],
+        nms_threshold=0.8
+    )
+
+    return detections_dict
 
 
 def digitize_intraop_record(image: Image.Image) -> Dict:

From 6ed9c24781abf384bea3ca83b2c5bc2cced41f57 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Thu, 12 Jun 2025 08:12:02 -0400
Subject: [PATCH 23/30] Stubbed out three functions that assign meaning to
 detections.

---
 ChartExtractor/extraction/extraction.py | 53 +++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 66cfcda..fa10f0a 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -40,6 +40,7 @@
 from ..object_detection_models.onnx_yolov11_detection import OnnxYolov11Detection
 from ..object_detection_models.onnx_yolov11_pose_single import OnnxYolov11PoseSingle
 from ..object_detection_models.object_detection_model import ObjectDetectionModel
+from ..point_registration.homography import find_homography, transform_point
 from ..utilities.detections import Detection
 from ..utilities.detection_reassembly import (
     untile_detections,
@@ -307,6 +308,58 @@ def run_preoperative_postoperative_models(
     return detections_dict
 
 
+def assign_meaning_to_detections(detections_dict: Dict[str, List[Detection]]) -> Dict[str, Any]:
+    """Imputes values to the detections to get the data encoded by the provider onto the chart.
+    
+    Examples of assigning meaning include getting mmHg and timestamp values for blood pressure
+    markers, assigning meaning to checked/unchecked checkbox detections, etc.
+
+    Args:
+        detections_dict (Dict[str, List[Detection]]):
+            The detections from all models on both sides of the chart. Dictionary must match the
+            template that is output by run_models.
+
+    Returns:
+        A dictionary with data that approximately matches the encoded meaning that the medical
+        provider wrote onto the chart.
+    """
+    pass
+
+
+def assign_meaning_to_intraoperative_detections(
+    intraop_detections_dict: Dict[str, List[Detection]]
+) -> Dict[str, Any]:
+    """Imputes values to the detections on the intraoperative side of the chart.
+    
+    Args:
+        intraop_detections_dict (Dict[str, List[Detection]]):
+            The detections from all models on the intraoperative side of the chart.
+            Must match the template that is output by run_intraoperative_models.
+
+    Returns:
+        A dictionary with data that approximately matches the encoded meaning that the medical
+        provider wrote onto the intraoperative side of the chart.
+    """
+    pass
+
+
+def assign_meaning_to_preoperative_postoperative_detections(
+    preop_postop_detections_dict: Dict[str, List[Detection]]
+) -> Dict[str, Any]:
+    """Imputes values to the detections on the preoperative/postoperative side of the chart.
+    
+    Args:
+        intraop_detections_dict (Dict[str, List[Detection]]):
+            The detections from all models on the preoperative/postoperative side of the chart.
+            Must match the template that is output by run_intraoperative_models.
+
+    Returns:
+        A dictionary with data that approximately matches the encoded meaning that the medical
+        provider wrote onto the preoperative/postoperative side of the chart.
+    """
+    pass
+
+
 def digitize_intraop_record(image: Image.Image) -> Dict:
     """Digitizes the intraoperative side of a paper anesthesia record.
 

From d7bbdd4f7814c91a659d0a8b890322d02a260f7f Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Thu, 12 Jun 2025 08:14:40 -0400
Subject: [PATCH 24/30] Implement assign_meaning_to_detections.

---
 ChartExtractor/extraction/extraction.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index fa10f0a..3832d43 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -5,7 +5,7 @@
 import os
 from pathlib import Path
 from PIL import Image
-from typing import Dict, List, Literal, Tuple
+from typing import Any, Dict, List, Literal, Tuple
 
 # Internal Imports
 from ..extraction.blood_pressure_and_heart_rate import (
@@ -323,7 +323,14 @@ def assign_meaning_to_detections(detections_dict: Dict[str, List[Detection]]) ->
         A dictionary with data that approximately matches the encoded meaning that the medical
         provider wrote onto the chart.
     """
-    pass
+    data: Dict[str, Any] = dict()
+    data.update(assign_meaning_to_intraoperative_detections(detections_dict["intraoperative"]))
+    data.update(
+        assign_meaning_to_preoperative_postoperative_detections(
+            data_dict["preoperative_postoperative"]
+        )
+    )
+    return data
 
 
 def assign_meaning_to_intraoperative_detections(

From 1e7ad34e2b029c91ef5305a337525d595c0f9179 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Thu, 12 Jun 2025 08:31:27 -0400
Subject: [PATCH 25/30] Add functions for finding the homography matrix for
 both sides of the chart.

---
 ChartExtractor/extraction/extraction.py | 105 +++++++++++++++++++++++-
 1 file changed, 104 insertions(+), 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 3832d43..ca15f51 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -51,6 +51,9 @@
 from ..utilities.read_config import read_config
 from ..utilities.tiling import tile_image
 
+# External Imports
+import numpy as np
+
 
 PATH_TO_DATA: Path = (Path(os.path.dirname(__file__)) / ".." / ".." / "data").resolve()
 PATH_TO_MODELS: Path = PATH_TO_DATA / "models"
@@ -347,7 +350,18 @@ def assign_meaning_to_intraoperative_detections(
         A dictionary with data that approximately matches the encoded meaning that the medical
         provider wrote onto the intraoperative side of the chart.
     """
-    pass
+    landmark_tile_size: int = compute_tile_size(
+        MODEL_CONFIG["intraoperative_document_landmarks"],
+        image.size
+    )
+    uncorrected_document_landmark_detections: List[Detection] = detect_objects_using_tiling(
+        image,
+        INTRAOP_DOC_MODEL,
+        landmark_tile_size,
+        landmark_tile_size,
+        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
+        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
+    )
 
 
 def assign_meaning_to_preoperative_postoperative_detections(
@@ -513,6 +527,95 @@ def digitize_preop_postop_record(image: Image.Image) -> Dict:
     return combine_dictionaries([digit_data, checkbox_data])
 
 
+def create_homography_matrix(
+    landmark_detections: List[Detection],
+    corner_landmark_names: List[str],
+    destination_landmarks: List[BoundingBox]
+) -> np.ndarray:
+    """Creates a homography matrix from the corner landmarks.
+    
+    Args:
+        landmark_detections (List[Detection]):
+            The list of detected landmarks.
+        corner_landmark_names (List[str]):
+            The list of names that match categories from the landmark detections.
+        destination_landmarks (List[BoundingBox]):
+            The landmark locations on the perfect, scanned image.
+
+    Returns:
+        A homography matrix that linearly transforms points from the original image to the
+        scanned, perfect image.
+    """
+    dest_points = [
+        bb.center
+        for bb in sorted(
+            list(filter(lambda x: x.category in corner_landmark_names, destination_landmarks)),
+            key=lambda bb: bb.category,
+        )
+    ]
+    src_points = [
+        bb.annotation.center
+        for bb in sorted(
+            list(
+                filter(
+                    lambda x: x.annotation.category in corner_landmark_names,
+                    landmark_detections,
+                )
+            ),
+            key=lambda bb: bb.annotation.category,
+        )
+    ]
+    return find_homography(src_points, dest_points)
+
+
+def create_intraoperative_homography_matrix(landmark_detections: List[Detection]) -> np.ndarray:
+    """Creates a homography matrix for the intraoperative side of the chart.
+    
+    Args:
+        landmark_detections (List[Detection]):
+            The list of detected landmarks.
+
+    Returns:
+        A homography matrix that linearly transforms points from the original image to the
+        scanned, perfect image.
+    """
+    corner_landmark_names: List[str] = [
+        "anesthesia_start",
+        "safety_checklist",
+        "lateral",
+        "units",
+    ]
+    dst_landmarks: List[BoundingBox] = label_studio_to_bboxes(
+        str(PATH_TO_DATA / "intraop_document_landmarks.json")
+    )["unified_intraoperative_preoperative_flowsheet_v1_1_front.png"]
+    return create_homography_matrix(landmark_detections, corner_landmark_names, dst_landmarks)
+
+
+def create_preoperative_postoperative_homography_matrix(
+    landmark_detections: List[Detection]
+) -> np.ndarray:
+    """Creates a homography matrix for the intraoperative side of the chart.
+    
+    Args:
+        landmark_detections (List[Detection]):
+            The list of detected landmarks.
+
+    Returns:
+        A homography matrix that linearly transforms points from the original image to the
+        scanned, perfect image.
+    """
+    corner_landmark_names: List[str] = [
+        "patient_profile",
+        "weight",
+        "signature",
+        "disposition",
+    ]
+    dst_landmarks: List[BoundingBox] = label_studio_to_bboxes(
+        str(PATH_TO_DATA / "preoperative_document_landmarks.json")
+    )["unified_intraoperative_preoperative_flowsheet_v1_1_back.png"]
+    return create_homography_matrix(landmark_detections, corner_landmark_names, dst_landmarks)
+
+
 def homography_intraoperative_chart(
     image: Image.Image,
     intraop_document_detections: List[Detection],

From baaacf27097fc80d560efafed0e542e03869747b Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Thu, 12 Jun 2025 08:43:02 -0400
Subject: [PATCH 26/30] Add a fn to transform boxes using the homography
 matrix.

---
 .../point_registration/homography.py          | 45 ++++++++++++++++---
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index d2d9042..13125c0 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -14,9 +14,15 @@
         Remaps a single point using the homography matrix.
 """
 
+# Built-in imports
+from typing import List, Tuple
+
+# Internal imports
+from ..utilities.annotations import BoundingBox
+
+# External imports
 import cv2
 import numpy as np
-from typing import List, Tuple
 
 
 def find_homography(
@@ -69,10 +75,7 @@ def find_homography(
     return findHomography(source_points, destination_points)
 
 
-def transform_point(
-    point: Tuple[int, int],
-    homography_matrix: np.ndarray,
-) -> Tuple[int, int]:
+def transform_point(point: Tuple[int, int], homography_matrix: np.ndarray) -> Tuple[int, int]:
     """Remaps a single point using the homography matrix.
     
     Args:
@@ -90,3 +93,35 @@ def transform_point(
     remapped_point = homography_matrix.dot(np.array([point[0], point[1], 1]))
     remapped_point /= remapped_point[2]
     return (remapped_point[0], remapped_point[1])
+
+
+def transform_box(box: BoundingBox, homography_matrix: np.ndarray) -> BoundingBox:
+    """Remaps a BoundingBox using the homography matrix.
+
+    Args:
+        box (BoundingBox):
+            The bounding box to remap.
+        homography_matrix (np.ndarray):
+            A homography matrix
+    """
+    remapped_top_left: Tuple[float, float] = transform_point((box.left, box.top), homography_matrix)
+    remapped_top_right: Tuple[float, float] = transform_point(
+        (box.right, box.top),
+        homography_matrix,
+    )
+    remapped_bottom_left: Tuple[float, float] = transform_point(
+        (box.left, box.bottom),
+        homography_matrix,
+    )
+    remapped_bottom_right: Tuple[float, float] = transform_point(
+        (box.right, box.bottom),
+        homography_matrix,
+    )
+    
+    left = min(remapped_top_left[0], remapped_bottom_left[0])
+    top = min(remapped_top_left[1], remapped_top_right[1])
+    right = max(remapped_top_right[0], remapped_bottom_right[0])
+    bottom = max(remapped_bottom_left[1], remapped_bottom_right[1])
+
+    return BoundingBox(box.category, left, top, right, bottom)
+    

From 8faac0d703d42cf39620debd80657bea3124111a Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Thu, 12 Jun 2025 08:52:02 -0400
Subject: [PATCH 27/30] Add a fn to remap Keypoints.

---
 .../point_registration/homography.py          | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index 13125c0..643d5ce 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -103,6 +103,9 @@ def transform_box(box: BoundingBox, homography_matrix: np.ndarray) -> BoundingBo
             The bounding box to remap.
         homography_matrix (np.ndarray):
             A homography matrix
+
+    Returns:
+        A BoundingBox that has been transformed by the homography.
     """
     remapped_top_left: Tuple[float, float] = transform_point((box.left, box.top), homography_matrix)
     remapped_top_right: Tuple[float, float] = transform_point(
@@ -124,4 +127,21 @@ def transform_box(box: BoundingBox, homography_matrix: np.ndarray) -> BoundingBo
     bottom = max(remapped_bottom_left[1], remapped_bottom_right[1])
 
     return BoundingBox(box.category, left, top, right, bottom)
+
+
+def transform_keypoint(keypoint: Keypoint, homography_matrix: np.ndarray) -> Keypoint:
+    """Remaps a Keypoint using the homography matrix.
     
+    Args:
+        keypoint (Keypoint):
+            The keypoint to remap.
+        homography_matrix (np.ndarray):
+            A homography matrix
+
+    Returns:
+        A Keypoint that has been transformed by the homography.
+    """
+    point = (keypoint.keypoint.x, keypoint.keypoint.y)
+    remapped_point = transform_point(point, homography_matrix)
+    remapped_box = transform_box(keypoint.bounding_box, homography_matrix)
+    return Keypoint(remapped_point, remapped_box, do_keypoint_validation=False)

From fb619a8572c0d6e2d858afb625ea86f3141c80c3 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Fri, 13 Jun 2025 12:02:02 -0400
Subject: [PATCH 28/30] Add a fn to interpret meaning from detections without
 running the models.

---
 ChartExtractor/extraction/extraction.py       | 109 +++++++++++++++---
 .../point_registration/homography.py          |  12 +-
 2 files changed, 102 insertions(+), 19 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index ca15f51..0766d49 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -1,7 +1,8 @@
 """Consolidates all the functionality for extracting data from charts into one function."""
 
 # Built-in imports
-from functools import partial
+from functools import partial, reduce
+from operator import concat
 import os
 from pathlib import Path
 from PIL import Image
@@ -40,7 +41,13 @@
 from ..object_detection_models.onnx_yolov11_detection import OnnxYolov11Detection
 from ..object_detection_models.onnx_yolov11_pose_single import OnnxYolov11PoseSingle
 from ..object_detection_models.object_detection_model import ObjectDetectionModel
-from ..point_registration.homography import find_homography, transform_point
+from ..point_registration.homography import (
+    find_homography,
+    transform_point,
+    transform_box,
+    transform_keypoint,
+)
+from ..utilities.annotations import BoundingBox, Keypoint
 from ..utilities.detections import Detection
 from ..utilities.detection_reassembly import (
     untile_detections,
@@ -337,7 +344,8 @@ def assign_meaning_to_detections(detections_dict: Dict[str, List[Detection]]) ->
 
 
 def assign_meaning_to_intraoperative_detections(
-    intraop_detections_dict: Dict[str, List[Detection]]
+    intraop_detections_dict: Dict[str, List[Detection]],
+    image_size: Tuple[int, int] = (3300, 2550)
 ) -> Dict[str, Any]:
     """Imputes values to the detections on the intraoperative side of the chart.
     
@@ -345,24 +353,98 @@ def assign_meaning_to_intraoperative_detections(
         intraop_detections_dict (Dict[str, List[Detection]]):
             The detections from all models on the intraoperative side of the chart.
             Must match the template that is output by run_intraoperative_models.
+        image_size (Tuple[int, int]):
+            The size of the image.
 
     Returns:
         A dictionary with data that approximately matches the encoded meaning that the medical
         provider wrote onto the intraoperative side of the chart.
     """
-    landmark_tile_size: int = compute_tile_size(
-        MODEL_CONFIG["intraoperative_document_landmarks"],
-        image.size
+    h = create_intraoperative_homography_matrix(intraop_detections_dict["landmarks"])
+    corrected_detections_dict: Dict[str, List[Detection]] = dict()
+    for (key, detections) in intraop_detections_dict.items():
+        if len(detections) == 0:
+            continue
+        remap_func = (
+            transform_box
+            if isinstance(detections[0].annotation, BoundingBox)
+            else transform_keypoint
+        )
+        corrected_detections_dict[key] = [
+            Detection(remap_func(det.annotation, h), det.confidence) for det in detections
+        ]
+    
+    extracted_data: Dict[str, Any] = dict()
+
+    # extract drug code and surgical timing
+    extracted_data["codes"] = extract_drug_codes(
+        corrected_detections_dict["numbers"],
+        *image_size
     )
-    uncorrected_document_landmark_detections: List[Detection] = detect_objects_using_tiling(
-        image,
-        INTRAOP_DOC_MODEL,
-        landmark_tile_size,
-        landmark_tile_size,
-        MODEL_CONFIG["intraoperative_document_landmarks"]["horz_overlap_proportion"],
-        MODEL_CONFIG["intraoperative_document_landmarks"]["vert_overlap_proportion"],
+    extracted_data["timing"] = extract_surgical_timing(
+        corrected_detections_dict["numbers"],
+        *image_size
+    )
+    extracted_data["ett_size"] = extract_ett_size(
+        corrected_detections_dict["numbers"],
+        *image_size
     )
 
+    # extract inhaled volatile drugs
+    time_boxes, mmhg_boxes = isolate_blood_pressure_legend_bounding_boxes(
+        [det.annotation for det in corrected_detections_dict["landmarks"]], *image_size
+    )
+    time_clusters: List[Cluster] = cluster_boxes(
+        time_boxes, cluster_kmeans, "mins", possible_nclusters=[40, 41, 42]
+    )
+    mmhg_clusters: List[Cluster] = cluster_boxes(
+        mmhg_boxes, cluster_kmeans, "mmhg", possible_nclusters=[18, 19, 20]
+    )
+
+    legend_locations: Dict[str, Tuple[float, float]] = find_legend_locations(
+        time_clusters + mmhg_clusters
+    )
+    extracted_data["inhaled_volatile"] = extract_inhaled_volatile(
+        corrected_detections_dict["numbers"],
+        legend_locations,
+        corrected_detections_dict["landmarks"]
+    )
+
+    # extract bp and hr
+    bp_and_hr_dets = reduce(
+        concat,
+        [
+            corrected_detections_dict["systolic"],
+            corrected_detections_dict["diastolic"],
+            corrected_detections_dict["heart_rate"],
+        ],
+        list()
+    )
+    
+    extracted_data["bp_and_hr"] = extract_heart_rate_and_blood_pressure(
+        bp_and_hr_dets,
+        time_clusters,
+        mmhg_clusters,
+    )
+
+    # extract physiological indicators
+    extracted_data["physiological_indicators"] = extract_physiological_indicators(
+        corrected_detections_dict["numbers"],
+        legend_locations,
+        corrected_detections_dict["landmarks"],
+        *image_size
+    )
+
+    # extract checkboxes
+    extracted_data["checkboxes"] = extract_checkboxes(
+        corrected_detections_dict["checkboxes"],
+        "intraoperative",
+        image_size[0],
+        image_size[1],
+    )
+
+    return extracted_data
+
 
 def assign_meaning_to_preoperative_postoperative_detections(
     preop_postop_detections_dict: Dict[str, List[Detection]]
@@ -447,6 +529,7 @@ def digitize_intraop_record(image: Image.Image) -> Dict:
     legend_locations: Dict[str, Tuple[float, float]] = find_legend_locations(
         time_clusters + mmhg_clusters
     )
+
     inhaled_volatile: Dict = {
         "inhaled_volatile": extract_inhaled_volatile(
             digit_detections, legend_locations, document_landmark_detections
diff --git a/ChartExtractor/point_registration/homography.py b/ChartExtractor/point_registration/homography.py
index 643d5ce..0f61df7 100644
--- a/ChartExtractor/point_registration/homography.py
+++ b/ChartExtractor/point_registration/homography.py
@@ -18,10 +18,10 @@
 from typing import List, Tuple
 
 # Internal imports
-from ..utilities.annotations import BoundingBox
+from ..utilities.annotations import BoundingBox, Keypoint, Point
 
 # External imports
-import cv2
+from cv2 import findHomography
 import numpy as np
 
 
@@ -47,8 +47,8 @@ def find_homography(
     too_few_source_points: bool = len(source_points) < 4
     too_few_destination_points: bool = len(destination_points) < 4
     unequal_point_sets: bool = len(source_points) != len(destination_points)
-    source_points_not_two_dimensional: bool = set([len(p) for p in source_points]) == {2}
-    destination_points_not_two_dimensional: bool = set([len(p) for p in destination_points]) == {2}
+    source_points_not_two_dimensional: bool = set([len(p) for p in source_points]) != {2}
+    destination_points_not_two_dimensional: bool = set([len(p) for p in destination_points]) != {2}
 
     if too_few_source_points:
         raise ValueError(
@@ -72,7 +72,7 @@ def find_homography(
         err_msg += f"(Included dimensions: {set([len(p) for p in destination_points])})"
         raise ValueError(err_msg)
     
-    return findHomography(source_points, destination_points)
+    return findHomography(np.array(source_points), np.array(destination_points))[0]
 
 
 def transform_point(point: Tuple[int, int], homography_matrix: np.ndarray) -> Tuple[int, int]:
@@ -144,4 +144,4 @@ def transform_keypoint(keypoint: Keypoint, homography_matrix: np.ndarray) -> Key
     point = (keypoint.keypoint.x, keypoint.keypoint.y)
     remapped_point = transform_point(point, homography_matrix)
     remapped_box = transform_box(keypoint.bounding_box, homography_matrix)
-    return Keypoint(remapped_point, remapped_box, do_keypoint_validation=False)
+    return Keypoint(Point(*remapped_point), remapped_box, do_keypoint_validation=False)

From eab6c174260fc298ae6679329e3bb06b2749bc3e Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Fri, 13 Jun 2025 12:41:43 -0400
Subject: [PATCH 29/30] Implement a fn to interpret meaning from preop postop
 detections.

---
 ChartExtractor/extraction/extraction.py | 44 +++++++++++++++++++++----
 1 file changed, 37 insertions(+), 7 deletions(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 0766d49..24dbf66 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -334,11 +334,11 @@ def assign_meaning_to_detections(detections_dict: Dict[str, List[Detection]]) ->
         provider wrote onto the chart.
     """
     data: Dict[str, Any] = dict()
-    data.update(assign_meaning_to_intraoperative_detections(detections_dict["intraoperative"]))
-    data.update(
-        assign_meaning_to_preoperative_postoperative_detections(
-            data_dict["preoperative_postoperative"]
-        )
+    data["intraoperative"] = assign_meaning_to_intraoperative_detections(
+        detections_dict["intraoperative"]
+    )
+    data["preoperative_postoperative"] = assign_meaning_to_preoperative_postoperative_detections(
+        detections_dict["preoperative_postoperative"]
     )
     return data
 
@@ -447,7 +447,8 @@ def assign_meaning_to_intraoperative_detections(
 
 
 def assign_meaning_to_preoperative_postoperative_detections(
-    preop_postop_detections_dict: Dict[str, List[Detection]]
+    preop_postop_detections_dict: Dict[str, List[Detection]],
+    image_size: Tuple[int, int] = (3300, 2550)
 ) -> Dict[str, Any]:
     """Imputes values to the detections on the preoperative/postoperative side of the chart.
     
@@ -460,7 +461,36 @@ def assign_meaning_to_preoperative_postoperative_detections(
         A dictionary with data that approximately matches the encoded meaning that the medical
         provider wrote onto the preoperative/postoperative side of the chart.
     """
-    pass
+    h = create_preoperative_postoperative_homography_matrix(
+        preop_postop_detections_dict["landmarks"]
+    )
+    corrected_detections_dict: Dict[str, List[Detection]] = dict()
+    for (key, detections) in preop_postop_detections_dict.items():
+        if len(detections) == 0:
+            continue
+        remap_func = (
+            transform_box
+            if isinstance(detections[0].annotation, BoundingBox)
+            else transform_keypoint
+        )
+        corrected_detections_dict[key] = [
+            Detection(remap_func(det.annotation, h), det.confidence) for det in detections
+        ]
+
+    extracted_data: Dict[str, Any] = dict()
+
+    extracted_data.update(
+        extract_preop_postop_digit_data(
+            corrected_detections_dict["numbers"], 
+            *image_size
+        )
+    )
+    extracted_data["preoperative_checkboxes"] = extract_checkboxes(
+        corrected_detections_dict["checkboxes"],
+        "preoperative",
+        *image_size
+    )
+    return extracted_data
 
 
 def digitize_intraop_record(image: Image.Image) -> Dict:

From 5b9a56235431c4868da199cca48fb6f78b6b1fd3 Mon Sep 17 00:00:00 2001
From: Ryan Folks <rfolks92@gmail.com>
Date: Fri, 13 Jun 2025 12:46:11 -0400
Subject: [PATCH 30/30] Change key name of preoperative checkboxes.

---
 ChartExtractor/extraction/extraction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ChartExtractor/extraction/extraction.py b/ChartExtractor/extraction/extraction.py
index 24dbf66..96b0a2c 100644
--- a/ChartExtractor/extraction/extraction.py
+++ b/ChartExtractor/extraction/extraction.py
@@ -485,7 +485,7 @@ def assign_meaning_to_preoperative_postoperative_detections(
             *image_size
         )
     )
-    extracted_data["preoperative_checkboxes"] = extract_checkboxes(
+    extracted_data["checkboxes"] = extract_checkboxes(
         corrected_detections_dict["checkboxes"],
         "preoperative",
         *image_size