修正车辆目标跟踪的路径bug

data/1.OpenCV初阶/6.视频分析/2.目标跟踪/obj_tracker_run.py

@@ -19,6 +19,8 @@ tracks = np.empty((0, 5))
 sort = Sort(5, 0, 0.2)
 
 # do track and render on image
+
+
 def track_and_render(detections, img):
     global total_frames
     global total_time
@@ -35,17 +37,18 @@ def track_and_render(detections, img):
     else:
         # if skip frame in detection process
         tracks = sort.update()
-    
+
     # get the fps, calculate avg speed in 100 frames
     cycle_time = time.time() - start_time
     total_time += cycle_time
     if total_frames % 100 == 0:
-        print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" % (total_time, total_frames, total_frames / total_time))
+        print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" %
+              (total_time, total_frames, total_frames / total_time))
         total_time = 0.0
         total_frames = 0
-    
+
     # render on frame image
-    for track in tracks: 
+    for track in tracks:
         # [left, top, right, bottom, id]
         pt1 = (int(track[0]), int(track[1]))
         pt2 = (int(track[2]), int(track[3]))
@@ -53,7 +56,7 @@ def track_and_render(detections, img):
 
         cv2.putText(img, str(int(track[4])),
                     (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
-                    (0, 255, 255), 1) 
+                    (0, 255, 255), 1)
 
     # return frame image to caller
     return img
@@ -61,34 +64,35 @@ def track_and_render(detections, img):
 
 # main
 if __name__ == '__main__':
-    
+
     # detection data, generated by object-detector algo model, including frame image
-    path = 'mot_benchmark\\MY_TEST\\www3'
+    path = 'mot_benchmark/MY_TEST/www3'
 
     # read test data from disk in a loop one frame by frame and send it to sort tracker
     for i in range(400000):
         # read frame image
-        bg = cv2.imread(path + '\\' + str(i % 800 + 1) + '.jpg')
+        bg = cv2.imread(path + '/' + str(i % 800 + 1) + '.jpg')
         # rescale
         bg = cv2.resize(bg, (int(1280 / scale), int(720 / scale)))
-        
+
         # read detection data in current frame
         dets = []
-        with open(path + '\\' + str(i % 800 + 1) + '.txt') as txt:
+        with open(path + '/' + str(i % 800 + 1) + '.txt') as txt:
             lines = txt.readlines()
             for line in lines:
                 items = line.split(' ')
                 left = float(items[1])
                 top = float(items[2])
                 right = float(items[3])
-                bottom = float(items[4]) 
-                
-                dets.append((left / scale, top / scale, right / scale, bottom / scale, int(items[0])))
-        
+                bottom = float(items[4])
+
+                dets.append((left / scale, top / scale, right /
+                            scale, bottom / scale, int(items[0])))
+
         # send to the tracker and draw the result on background image
         if len(dets) > 0:
             bg = track_and_render(dets, bg)
             cv2.imshow('tracker', bg)
             cv2.waitKey(40)
-    
+
     cv2.destroyAllWindows()