Update gradio_utils.py

gradio_utils.py

@@ -71,8 +71,8 @@ def infer_transform(IMAGE_SIZE=config.INFERENCE_IMAGE_SIZE):
             A.PadIfNeeded(
                 min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
             ),
-            A.Normalize(mean=[0,0,0], 
-                        std=[1,1,1], 
+            A.Normalize(mean=[0.45484068, 0.43406072, 0.40103856], 
+                        std=[0.23936155, 0.23471538, 0.23876129], 
                         max_pixel_value=255,),
             ToTensorV2(),
         ]
@@ -195,12 +195,11 @@ def upload_image_inference(img, transparency):
                         config.PASCAL_CLASSES[class_prediction]])
 
 
+  new_bboxes = [a[0] for a in annotations]
   objs = [b[1] for b in nms_boxes_output[0]]
   bbox_coord = [b[2:] for b in nms_boxes_output[0]]
   targets = [FasterRCNNBoxScoreTarget(objs, bbox_coord)]
 
-  new_bboxes = [a[0] for a in annotations]
-
   cam = EigenCAM(model=model, 
                 target_layers=[model.model], 
                 reshape_transform=yolov3_reshape_transform)
@@ -213,9 +212,8 @@ def upload_image_inference(img, transparency):
   for x1, y1, x2, y2 in new_bboxes:
     renormalized_cam[y1:y2, x1:x2] = scale_cam_image(grayscale_cam[y1:y2, x1:x2].copy())  
   
-  img_copy= np.float32(img_copy) / 255
   renormalized_cam = scale_cam_image(renormalized_cam)
-  eigencam_image_renormalized = show_cam_on_image(img_copy, renormalized_cam, use_rgb=True)
+  eigencam_image_renormalized = show_cam_on_image(np.float32(img_copy)/255, renormalized_cam, use_rgb=True)
 
   return([[img_copy, annotations],
           [grayscale_cam, eigencam_image_renormalized]])