update: dff_nmf depreciation of 'scores'

yolov8.py

@@ -160,7 +160,6 @@ class DeepFeatureFactorization:
                 f"An exception occurred in ActivationSummary with block: {exc_type}. Message: {exc_value}")
             return True
 
-
 def dff_nmf(image, target_lyr, n_components):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     mean = [0.485, 0.456, 0.406]  # Mean for RGB channels
@@ -185,24 +184,26 @@ def dff_nmf(image, target_lyr, n_components):
 
     # Access detection results
     boxes = detections.boxes.xyxy.cpu().numpy()  # Bounding box coordinates (xyxy)
-    scores = detections.scores.cpu().numpy()    # Confidence scores
+    probs = detections.probs.cpu().numpy()      # Confidence scores (probabilities)
     classes = detections.classes.cpu().numpy()  # Class IDs
 
     # Filter detections with confidence score > threshold (e.g., 0.5)
-    high_conf_boxes = boxes[scores > 0.5]
-    high_conf_classes = classes[scores > 0.5]
+    high_conf_indices = probs > 0.5
+    high_conf_boxes = boxes[high_conf_indices]
+    high_conf_classes = classes[high_conf_indices]
+    high_conf_probs = probs[high_conf_indices]
 
     # Example visualization and output processing
     fig, ax = plt.subplots(1, figsize=(8, 8))
     ax.axis("off")
     ax.imshow(rgb_img_float)
 
-    for box, cls in zip(high_conf_boxes, high_conf_classes):
+    for box, cls, prob in zip(high_conf_boxes, high_conf_classes, high_conf_probs):
         x1, y1, x2, y2 = box
         rect = patches.Rectangle((x1, y1), x2 - x1, y2 - y1,
                                  linewidth=2, edgecolor='r', facecolor='none')
         ax.add_patch(rect)
-        ax.text(x1, y1, f"Class {cls}", color='r', fontsize=12, verticalalignment='top')
+        ax.text(x1, y1, f"Class {cls}, Prob {prob:.2f}", color='r', fontsize=12, verticalalignment='top')
 
     plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
     fig.canvas.draw()
@@ -217,7 +218,6 @@ def dff_nmf(image, target_lyr, n_components):
     return rgb_img_float, batch_explanations, visualization
 
 
-
 def visualize_batch_explanations(rgb_img_float, batch_explanations, image_weight=0.7):
     for i, explanation in enumerate(batch_explanations):
         # Create visualization for each explanation