diff --git a/deep_sort/deep/feature_extractor.py b/deep_sort/deep/feature_extractor.py
index 5b54525..c7e48ec 100644
--- a/deep_sort/deep/feature_extractor.py
+++ b/deep_sort/deep/feature_extractor.py
@@ -52,8 +52,9 @@ class MyExtractor(object):
     def __init__(self, model_path, use_cuda=True):
         self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
         self.net = RestNet18()
-        self.net.load_state_dict(torch.load(model_path))
-        self.net = self.net.to(self.device)
+        self.net.load_state_dict(torch.load(model_path,map_location=torch.device(self.device)))
+        if self.device=="cuda":
+            self.net = self.net.to(self.device)
         logger = logging.getLogger("root.tracker")
         logger.info("Loading weights from {}... Done!".format(model_path))
         self.raw_transformer = transforms.Compose([
diff --git a/example_reid.py b/example_reid.py
index 6ca6d8a..75806d2 100644
--- a/example_reid.py
+++ b/example_reid.py
@@ -30,6 +30,20 @@ def opencv_box_plot(img:cv2.Mat,pred_img:np.array):
     cv2.imshow("test",img)
     cv2.waitKey(0)
     cv2.imwrite("result.png",img)
+    
+def opencv_match_pointer_plot(img:cv2.Mat,start_base:tuple,text:str,color:tuple):
+    SIZE = 50
+    brush = [0,0]
+    brush[0]=start_base[0]
+    brush[1]=start_base[1]
+    brush_push = [brush[0]+SIZE,brush[1]-SIZE]
+    cv2.line(img,brush,brush_push,color,1)
+    # brush=brush_push
+    # brush_push[0]+=SIZE*2
+    # brush_push[1]+=0
+    # cv2.line(img,brush,brush_push,color,1)    
+    cv2.putText(img,text,brush_push, cv2.FONT_HERSHEY_PLAIN, 1, color, 2)
+    return img
 
 routine = {}
     
@@ -49,8 +63,6 @@ def opencv_sort_plot(img:cv2.Mat,pred_yolo:np.array,pred_sort:np.array):
                 cv2.line(img,routine[id][i-1],routine[id][i],color,1)
             cv2.line(img,routine[id][-1],cxy,color,1)
             routine[id].append(cxy)
-    cv2.imshow("test",img)
-    cv2.waitKey(1)
     return img
 
 def extractidfeature(id_path:str,extractor):
@@ -115,6 +127,7 @@ if __name__ == '__main__':
             dpsort, features = deepsort.update(bbox_xywh=np.array(bbox_xywhs),confidences=np.array(confs),ori_img=np.array(frame))
             
             img_drawing = np.array(frame)
+            img_drawing = opencv_sort_plot(img_drawing,detections,dpsort)
             
             # ReID: compute features
             for i in range(len(dpsort)):
@@ -139,17 +152,21 @@ if __name__ == '__main__':
                                 if term[0] == MOT_id:
                                     count+=1
                             if count > MAX_BUFFLEN//3:
-                                cx = int((dpsort[i][0]+dpsort[i][2])/2)
-                                cy = int((dpsort[i][1]+dpsort[i][3])/2)
+                                cx = int((track[0]+track[2])/2)
+                                cy = int((track[1]+track[3])/2)
                                 color = (int(index_d%2*100),int(index_d%3*75),int(index_d%4*50))
-                                cv2.putText(img_drawing,f"{paths[index_d]}",(cx-10,cy-10), cv2.FONT_HERSHEY_PLAIN, 1, color, 2)
+                                
+                                # img_drawing=opencv_match_pointer_plot(img_drawing,(cx,track[1]-10),f"{paths[index_d]}",color)
+                                # cv2.rectangle(img_drawing,(track[i][0],track[i][1]),(track[i][2],track[i][3]),color,1)                
+                                cv2.putText(img_drawing,f"q_{paths[index_d]}".split(".")[0],(track[0],track[1]), cv2.FONT_HERSHEY_PLAIN, 1, color, 2)
                             break
                     else:
                         break
             
             # Final plot and show video
-            frame_t = opencv_sort_plot(img_drawing,detections,dpsort)
-            target.write(frame_t)
+            cv2.imshow("test",img_drawing)
+            cv2.waitKey(1)
+            target.write(img_drawing)
             frame_counter += 1
         
         # save the video and end the program