update: dff_nmf for no attribute 'xywh'

yolov8.py

@@ -161,37 +161,37 @@ class DeepFeatureFactorization:
             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
     std = [0.229, 0.224, 0.225]   # Standard deviation for RGB channels
     img = cv2.resize(image, (640, 640))
     rgb_img_float = np.float32(img) / 255.0
-    input_tensor = torch.from_numpy(rgb_img_float).permute(2, 0, 1).unsqueeze(0).to(device) 
+    input_tensor = torch.from_numpy(rgb_img_float).permute(2, 0, 1).unsqueeze(0).to(device)
 
     model = YOLO('yolov8s.pt')  # Ensure the model is loaded correctly
-    dff = DeepFeatureFactorization(model=model,    
-                                   target_layer=model.model.model[int(target_lyr)],    
+    dff = DeepFeatureFactorization(model=model,
+                                   target_layer=model.model.model[int(target_lyr)],
                                    computation_on_concepts=None)
-    
+
     concepts, batch_explanations, explanations = dff(input_tensor, model, n_components)
 
     # Getting predictions directly from YOLO
     with torch.no_grad():
         results = model(input_tensor)
 
-    # Post-processing to extract detections
-    boxes, scores, classes = results.xywh[0][:, :4], results.xywh[0][:, 4], results.xywh[0][:, 5]
-    boxes = boxes.cpu().numpy()
-    scores = scores.cpu().numpy()
-    classes = classes.cpu().numpy()
+    # Assuming results is a list, extract the first element
+    detections = results[0]  # The first element should contain the detection data
+
+    # Access detection results
+    boxes = detections.boxes.xyxy.cpu().numpy()  # Bounding box coordinates (xyxy)
+    scores = detections.scores.cpu().numpy()    # Confidence scores
+    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]
-    
-    # Use the processed detections for visualization and further tasks
+
     # Example visualization and output processing
     fig, ax = plt.subplots(1, figsize=(8, 8))
     ax.axis("off")
@@ -209,11 +209,11 @@ def dff_nmf(image, target_lyr, n_components):
     image_array = np.array(fig.canvas.renderer.buffer_rgba())
     image_resized = cv2.resize(image_array, (640, 640))
     rgba_channels = cv2.split(image_resized)
-    alpha_channel = rgba_channels[3] 
+    alpha_channel = rgba_channels[3]
     rgb_channels = np.stack(rgba_channels[:3], axis=-1)
-    
+
     visualization = show_factorization_on_image(rgb_img_float, np.transpose(rgb_channels, (2, 0, 1)), image_weight=0.3)
-    
+
     return rgb_img_float, batch_explanations, visualization