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
@@ -40,7 +42,8 @@ def track_and_render(detections, img):
     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
 
@@ -63,18 +66,18 @@ def track_and_render(detections, img):
 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(' ')
@@ -83,7 +86,8 @@ if __name__ == '__main__':
                 right = float(items[3])
                 bottom = float(items[4])
 
-                dets.append((left / scale, top / scale, right / scale, bottom / scale, int(items[0])))
+                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: