update: xai_yolov5 added support for dff

yolov5.py

@@ -76,8 +76,12 @@ def xai_yolov5(image):
     # Grad-CAM visualization
     cam_image, renormalized_cam_image = generate_cam_image(model, target_layers, tensor, image, boxes)
 
+    rgb_img_float, batch_explanations = dff_nmf(image, target_lyr = -5, n_components = 8)
+    visualize_batch_explanations(rgb_img_float, batch_explanations)
+    im = cv2.imread("test_w.png")
+    
     # Combine results
-    final_image = np.hstack((image, detections_img, renormalized_cam_image))
+    final_image = np.hstack((image, detections_img, renormalized_cam_image, im))
     caption = "Results using YOLOv5"
     return Image.fromarray(final_image), caption
 
@@ -244,4 +248,3 @@ def visualize_batch_explanations(rgb_img_float, batch_explanations, image_weight
         plt.axis('off')  # Hide axes
         plt.show()  # Show the plot
     plt.savefig("test_w.png")
-