Debug: param extraction issue

yolov8.py

@@ -41,11 +41,7 @@ def generate_cam_image(model, target_layers, tensor, rgb_img, boxes):
     cam = EigenCAM(model, target_layers)
     grayscale_cam = cam(tensor)[0, :, :]
     img_float = np.float32(rgb_img) / 255
-
-    # Generate Grad-CAM
     cam_image = show_cam_on_image(img_float, grayscale_cam, use_rgb=True)
-
-    # Renormalize Grad-CAM inside bounding boxes
     renormalized_cam = np.zeros(grayscale_cam.shape, dtype=np.float32)
     for x1, y1, x2, y2 in boxes:
         renormalized_cam[y1:y2, x1:x2] = scale_cam_image(grayscale_cam[y1:y2, x1:x2].copy())
@@ -56,27 +52,19 @@ def generate_cam_image(model, target_layers, tensor, rgb_img, boxes):
 
 
 def xai_yolov8n(image):
-    # Load YOLOv8n model
     model = YOLO('yolov8n.pt')  # Load YOLOv8n pre-trained weights
     model.eval()
     model.cpu()
 
     target_layers = [model.model.model[-2]]  # Grad-CAM target layer
-
-    # Run YOLO detection
     results = model([image])
-    boxes, colors, names = parse_detections(results)
+    print(results)
+    boxes, colors, names = parse_detections(results[0])
     detections_img = draw_detections(boxes, colors, names, image.copy())
-
-    # Prepare input tensor for Grad-CAM
     img_float = np.float32(image) / 255
     transform = transforms.ToTensor()
     tensor = transform(img_float).unsqueeze(0)
-
-    # Grad-CAM visualization
     cam_image, renormalized_cam_image = generate_cam_image(model, target_layers, tensor, image, boxes)
-
-    # Combine results
     final_image = np.hstack((image, cam_image, renormalized_cam_image))
     caption = "Results using YOLOv8n"
     return Image.fromarray(final_image), caption