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openvinotoolkit · Aug 21, 2024 · 0295382 · 0295382
1 parent 23714d4
commit 0295382
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Showing 5 changed files with 16 additions and 9 deletions.
diff --git a/examples/run_detection.py b/examples/run_detection.py
@@ -129,7 +129,7 @@ def explain_black_box(args):
     # Save saliency maps for visual inspection
     if args.output is not None:
         output = Path(args.output) / "detection_black_box"
-        explanation.save(output, Path(args.image_path).stem)
+        explanation.save(output, f"{Path(args.image_path).stem}_")
 
 
 def main(argv):

diff --git a/openvino_xai/explainer/visualizer.py b/openvino_xai/explainer/visualizer.py
@@ -171,10 +171,16 @@ def _apply_metadata(metadata: Dict[Task, Any], saliency_map_np: np.ndarray, indi
                     box, score, label_index = metadata[Task.DETECTION][target_index]
                     x1, y1, x2, y2 = box
                     cv2.rectangle(saliency_map, (int(x1), int(y1)), (int(x2), int(y2)), color=(255, 0, 0), thickness=2)
-                    box_name = f"{label_index}|{score:.2f}"
-                    org = int(x1), int(y1 - 5)
+                    box_label = f"{label_index}|{score:.2f}"
+                    box_label_loc = int(x1), int(y1 - 5)
                     cv2.putText(
-                        saliency_map, box_name, org=org, fontFace=1, fontScale=1, color=(255, 0, 0), thickness=2
+                        saliency_map,
+                        box_label,
+                        org=box_label_loc,
+                        fontFace=1,
+                        fontScale=1,
+                        color=(255, 0, 0),
+                        thickness=2,
                     )
         return saliency_map_np
 
@@ -241,14 +247,14 @@ def _apply_overlay(
     def _update_explanation_with_processed_sal_map(
         explanation: Explanation,
         saliency_map_np: np.ndarray,
-        indices: List,
+        target_indices: List,
     ) -> Explanation:
         dict_sal_map: Dict[int | str, np.ndarray] = {}
         if explanation.layout in ONE_MAP_LAYOUTS:
             dict_sal_map["per_image_map"] = saliency_map_np[0]
             saliency_map_np = dict_sal_map
         elif explanation.layout in MULTIPLE_MAP_LAYOUTS:
-            for index, sal_map in zip(indices, saliency_map_np):
+            for index, sal_map in zip(target_indices, saliency_map_np):
                 dict_sal_map[index] = sal_map
         else:
             raise ValueError

diff --git a/openvino_xai/methods/__init__.py b/openvino_xai/methods/__init__.py
@@ -3,7 +3,7 @@
 """
 XAI algorithms.
 """
-from openvino_xai.methods.black_box.aise import AISEClassification
+from openvino_xai.methods.black_box.aise import AISEClassification, AISEDetection
 from openvino_xai.methods.black_box.rise import RISE
 from openvino_xai.methods.white_box.activation_map import ActivationMap
 from openvino_xai.methods.white_box.base import WhiteBoxMethod
@@ -25,4 +25,5 @@
     "DetClassProbabilityMap",
     "RISE",
     "AISEClassification",
+    "AISEDetection",
 ]
diff --git a/openvino_xai/methods/black_box/aise.py b/openvino_xai/methods/black_box/aise.py
@@ -4,7 +4,7 @@
 import collections
 import math
 from abc import ABC, abstractmethod
-from typing import Callable, Dict, List, Mapping, Tuple
+from typing import Any, Callable, Dict, List, Mapping, Tuple
 
 import numpy as np
 import openvino.runtime as ov
@@ -371,6 +371,7 @@ def generate_saliency_map(  # type: ignore
         self._mask_generator = GaussianPerturbationMask(self.input_size)
 
         saliency_maps = {}
+        self.metadata: Dict[Task, Any] = collections.defaultdict(dict)
         for target in target_indices:
             self.target_box = boxes[target]
             self.target_label = labels[target]

diff --git a/tests/unit/methods/black_box/test_black_box_method.py b/tests/unit/methods/black_box/test_black_box_method.py
@@ -55,7 +55,6 @@ def preprocess_det_fn(x: np.ndarray) -> np.ndarray:
     @staticmethod
     def postprocess_det_fn(x) -> np.ndarray:
         """Returns boxes, scores, labels."""
-        # return x["boxes"][:, :4], x["boxes"][:, 4], x["labels"]
         return x["boxes"][0][:, :4], x["boxes"][0][:, 4], x["labels"][0]