[pyproject,conda] Add scikit-image dependence

conda/meta.yaml

@@ -30,6 +30,7 @@ requirements:
     - pillow {{ pillow }}
     - psutil {{ psutil }}
     - pytorch {{ pytorch }}
+    - scikit-image {{ scikit_image }}
     - scikit-learn {{ scikit_learn }}
     - scipy {{ scipy }}
     - tabulate {{ tabulate }}
@@ -46,6 +47,7 @@ requirements:
     - {{ pin_compatible('pillow') }}
     - {{ pin_compatible('psutil') }}
     - {{ pin_compatible('pytorch') }}
+    - {{ pin_compatible('scikit-image') }}
     - {{ pin_compatible('scikit-learn') }}
     - {{ pin_compatible('scipy') }}
     - {{ pin_compatible('tabulate') }}

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
   "click",
   "numpy",
   "scipy",
+  "scikit-image",
   "scikit-learn",
   "tqdm",
   "psutil",

src/ptbench/engine/saliency/interpretability.py

@@ -6,10 +6,10 @@ import logging
 import pathlib
 import typing
 
-import cv2
 import lightning.pytorch
 import numpy
 import numpy.typing
+import skimage.measure
 import torch
 import torchvision.ops
 
@@ -69,14 +69,8 @@ def _ordered_connected_components(
     if not numpy.any(thresholded_mask):
         return []
 
-    # opencv implementation:
-    n, labelled = cv2.connectedComponents(thresholded_mask, connectivity=8)
-    retval = [labelled == k for k in range(1, n)]
-
-    # scikit-image implementation
-    # import skimage.measure
-    # labelled, n = skimage.measure.label(thresholded_mask, return_num=True)
-    # retval = [labelled == k for k in range(1, n+1)]
+    labelled, n = skimage.measure.label(thresholded_mask, return_num=True)  # type: ignore
+    retval = [labelled == k for k in range(1, n + 1)]
 
     return sorted(retval, key=lambda x: x.sum(), reverse=True)
 
@@ -138,7 +132,7 @@ def _compute_max_iou_and_ioda(
     return iou, ioda
 
 
-def get_largest_bounding_boxes(
+def _get_largest_bounding_boxes(
     saliency_map: typing.Sequence[typing.Sequence[float]]
     | numpy.typing.NDArray[numpy.double],
     n: int,
@@ -269,7 +263,7 @@ def _compute_proportional_energy(
 def _process_sample(
     gt_bboxes: BoundingBoxes,
     saliency_map: numpy.typing.NDArray[numpy.double],
-) -> tuple[float, float, float, float, tuple[int, int, int, int]]:
+) -> tuple[float, float]:
     """Calculates the metrics for a single sample.
 
     Parameters
@@ -289,13 +283,13 @@ def _process_sample(
         * 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)
+    # 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)
 
     # The binary_mask will be ON/True where the gt boxes are located
     binary_mask = numpy.zeros_like(saliency_map, dtype=numpy.bool_)
@@ -306,16 +300,16 @@ def _process_sample(
         ] = True
 
     return (
-        iou,
-        ioda,
+        # 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,
-        ),
+        # (
+        #     detected_box.xmin,
+        #     detected_box.ymin,
+        #     detected_box.width,
+        #     detected_box.height,
+        # ),
     )
 
 
@@ -348,11 +342,8 @@ def run(
 
         * Sample name (str)
         * Sample target class (int)
-        * IoU (float)
-        * IoDA (float)
         * Proportional energy (float)
         * Average saliency focus (float)
-        * Largest detected bounding box (x, y, width, height) (4 x int)
     """
 
     retval: dict[str, list[typing.Any]] = {}

src/ptbench/scripts/saliency/interpretability.py

@@ -94,7 +94,7 @@ def interpretability(
 
     .. note::
 
-       For obvious reasons, this evaluation is limited to databases that
+       For obvious reasons, this evaluation is limited to datasets that
        contain built-in annotations which corroborate classification.
 
 
@@ -102,11 +102,6 @@ def interpretability(
     that resembles the original datamodule, with added information containing
     the following measures, for each sample:
 
-    * IoU: The intersection of the (thresholded) saliency maps with
-      the annotation the most overlaps, over the union of both areas.
-    * IoDA: The intersection of the (thresholded) saliency maps with
-      the annotation that most overlaps, over area of (thresholded) saliency
-      maps.
     * Proportional Energy: A measure that compares (UNthresholed) saliency maps
       with annotations (based on [SCORECAM-2020]_). It estimates how much
       activation lies within the ground truth boxes compared to the total sum
@@ -115,21 +110,6 @@ def interpretability(
       boxes area is covered by the activations.  It is similar to the
       proportional energy measure in the sense it does not need explicit
       thresholding.
-
-    .. important::
-
-       The thresholding algorithm used to evaluate IoU and IoDA measures is
-       based on the process done by the original CAM paper [GRADCAM-2015]_. It
-       keeps all points from the saliency map that are above the 20% of its
-       maximum value.
-
-       It then calculates a **single** bounding box for largest connected
-       component.  This bounding box represents detected elements on the
-       original sample that corroborate the classification outcome.
-
-       IoU and IoDA are only evaluated for a single ground-truth bounding box
-       per sample (the one with the highest overlap).  Any other bounding box
-       marked on the sample is ignored in the present implementation.
     """
 
     import json