[doc] Cleaned up interpretability _process_sample()

src/mednet/engine/saliency/interpretability.py

@@ -19,12 +19,16 @@ from ...config.data.tbx11k.datamodule import BoundingBox, BoundingBoxes
 
 logger = logging.getLogger(__name__)
 
+SaliencyMap: typing.TypeAlias = (
+    typing.Sequence[typing.Sequence[float]] | numpy.typing.NDArray[numpy.double]
+)
+BinaryMask: typing.TypeAlias = numpy.typing.NDArray[numpy.bool_]
+
 
 def _ordered_connected_components(
-    saliency_map: typing.Sequence[typing.Sequence[float]]
-    | numpy.typing.NDArray[numpy.double],
+    saliency_map: SaliencyMap,
     threshold: float,
-) -> list[numpy.typing.NDArray[numpy.bool_]]:
+) -> list[BinaryMask]:
     """Calculates the largest connected components available on a saliency map
     and return those as individual masks.
 
@@ -76,7 +80,7 @@ def _ordered_connected_components(
 
 
 def _extract_bounding_box(
-    mask: numpy.typing.NDArray[numpy.bool_],
+    mask: BinaryMask,
 ) -> BoundingBox:
     """Defines a bounding box surrounding a connected component mask.
 
@@ -147,8 +151,7 @@ def _compute_max_iou_and_ioda(
 
 
 def _get_largest_bounding_boxes(
-    saliency_map: typing.Sequence[typing.Sequence[float]]
-    | numpy.typing.NDArray[numpy.double],
+    saliency_map: SaliencyMap,
     n: int,
     threshold: float = 0.2,
 ) -> list[BoundingBox]:
@@ -227,9 +230,37 @@ def _compute_simultaneous_iou_and_ioda(
     return float(iou), float(ioda)
 
 
+def _compute_iou_ioda_from_largest_bbox(
+    gt_bboxes: BoundingBoxes,
+    saliency_map: SaliencyMap,
+) -> tuple[float, float]:
+    """Calculates the metrics for a single sample.
+
+    Parameters
+    ----------
+    gt_bboxes
+        A list of ground-truth bounding boxes.
+    saliency_map
+        A real-valued saliency-map that conveys regions used for
+        classification in the original sample.
+
+
+    Returns
+    -------
+        A tuple containing the iou and ioda for the largest bounding box.
+    """
+
+    largest_bbox = _get_largest_bounding_boxes(saliency_map, n=1, threshold=0.2)
+    detected_box = (
+        largest_bbox[0] if largest_bbox else BoundingBox(-1, 0, 0, 0, 0)
+    )
+    iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_bboxes)
+    return (iou, ioda)
+
+
 def _compute_avg_saliency_focus(
-    saliency_map: numpy.typing.NDArray[numpy.double],
-    gt_mask: numpy.typing.NDArray[numpy.bool_],
+    saliency_map: SaliencyMap,
+    gt_mask: BinaryMask,
 ) -> float:
     """Integrates the saliency map over the ground-truth boxes and normalizes
     by total bounding-box area.
@@ -262,8 +293,8 @@ def _compute_avg_saliency_focus(
 
 
 def _compute_proportional_energy(
-    saliency_map: numpy.typing.NDArray[numpy.double],
-    gt_mask: numpy.typing.NDArray[numpy.bool_],
+    saliency_map: SaliencyMap,
+    gt_mask: BinaryMask,
 ) -> float:
     """Calculates how much activation lies within the ground truth boxes
     compared to the total sum of the activations (integral).
@@ -293,8 +324,8 @@ def _compute_proportional_energy(
 
 def _compute_binary_mask(
     gt_bboxes: BoundingBoxes,
-    saliency_map: numpy.typing.NDArray[numpy.double],
-) -> numpy.typing.NDArray[numpy.bool_]:
+    saliency_map: SaliencyMap,
+) -> BinaryMask:
     """Computes a binary mask for the saliency map using BoundingBoxes.
 
     The binary_mask will be ON/True where the gt boxes are located.
@@ -329,7 +360,7 @@ def _compute_binary_mask(
 
 def _process_sample(
     gt_bboxes: BoundingBoxes,
-    saliency_map: numpy.typing.NDArray[numpy.double],
+    saliency_map: SaliencyMap,
 ) -> tuple[float, float]:
     """Calculates the metrics for a single sample.
 
@@ -346,34 +377,15 @@ def _process_sample(
     -------
         A tuple containing the following values:
 
-        * IoU
-        * IoDA
         * Proportional energy
         * Average saliency focus
-        * Largest detected bounding box
     """
 
-    # largest_bbox = _get_largest_bounding_boxes(saliency_map, n=1, threshold=0.2)
-    # detected_box = (
-    #     largest_bbox[0] if largest_bbox else BoundingBox(-1, 0, 0, 0, 0)
-    # )
-    #
-    # # Calculate localization metrics
-    # iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_bboxes)
-
     binary_mask = _compute_binary_mask(gt_bboxes, saliency_map)
 
     return (
-        # iou,
-        # ioda,
         _compute_proportional_energy(saliency_map, binary_mask),
         _compute_avg_saliency_focus(saliency_map, binary_mask),
-        # (
-        #     detected_box.xmin,
-        #     detected_box.ymin,
-        #     detected_box.width,
-        #     detected_box.height,
-        # ),
     )