[MOT] update window timer for pptracking (#4527)

* update window timer for pptracking

* keep origin mp4v format

deploy/pptracking/python/mot/utils.py

@@ -17,6 +17,7 @@ import cv2
 import time
 import paddle
 import numpy as np
+import collections
 
 __all__ = [
     'MOTTimer', 'Detection', 'write_mot_results', 'load_det_results',
@@ -29,13 +30,11 @@ class MOTTimer(object):
     This class used to compute and print the current FPS while evaling.
     """
 
-    def __init__(self):
-        self.total_time = 0.
-        self.calls = 0
+    def __init__(self, window_size=20):
         self.start_time = 0.
         self.diff = 0.
-        self.average_time = 0.
         self.duration = 0.
+        self.deque = collections.deque(maxlen=window_size)
 
     def tic(self):
         # using time.time instead of time.clock because time time.clock
@@ -44,21 +43,16 @@ class MOTTimer(object):
 
     def toc(self, average=True):
         self.diff = time.time() - self.start_time
-        self.total_time += self.diff
-        self.calls += 1
-        self.average_time = self.total_time / self.calls
+        self.deque.append(self.diff)
         if average:
-            self.duration = self.average_time
+            self.duration = np.mean(self.deque)
         else:
-            self.duration = self.diff
+            self.duration = np.sum(self.deque)
         return self.duration
 
     def clear(self):
-        self.total_time = 0.
-        self.calls = 0
         self.start_time = 0.
         self.diff = 0.
-        self.average_time = 0.
         self.duration = 0.
 
 

deploy/pptracking/python/mot_jde_infer.py

@@ -211,7 +211,8 @@ def predict_video(detector, camera_id):
         os.makedirs(FLAGS.output_dir)
     out_path = os.path.join(FLAGS.output_dir, video_name)
     if not FLAGS.save_images:
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_format = 'mp4v'
+        fourcc = cv2.VideoWriter_fourcc(*video_format)
         writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
     frame_id = 0
     timer = MOTTimer()
@@ -243,6 +244,7 @@ def predict_video(detector, camera_id):
             results[cls_id].append((frame_id + 1, online_tlwhs[cls_id],
                                     online_scores[cls_id], online_ids[cls_id]))
 
+        fps = 1. / timer.duration
         # NOTE: just implement flow statistic for one class
         if num_classes == 1:
             result = (frame_id + 1, online_tlwhs[0], online_scores[0],
@@ -262,7 +264,6 @@ def predict_video(detector, camera_id):
             raise NotImplementedError(
                 'Multi-class flow counting is not implemented now!')
 
-        fps = 1. / timer.average_time
         im = plot_tracking_dict(
             frame,
             num_classes,
@@ -282,7 +283,7 @@ def predict_video(detector, camera_id):
             writer.write(im)
 
         frame_id += 1
-        print('detect frame: %d' % (frame_id))
+        print('detect frame: %d, fps: %f' % (frame_id, fps))
         if camera_id != -1:
             cv2.imshow('Tracking Detection', im)
             if cv2.waitKey(1) & 0xFF == ord('q'):

deploy/pptracking/python/mot_sde_infer.py

@@ -539,7 +539,8 @@ def predict_video(detector, reid_model, camera_id):
         os.makedirs(FLAGS.output_dir)
     out_path = os.path.join(FLAGS.output_dir, video_name)
     if not FLAGS.save_images:
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_format = 'mp4v'
+        fourcc = cv2.VideoWriter_fourcc(*video_format)
         writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
     frame_id = 0
     timer = MOTTimer()
@@ -566,7 +567,7 @@ def predict_video(detector, reid_model, camera_id):
                 (frame_id + 1, online_tlwhs, online_scores, online_ids))
             timer.toc()
 
-            fps = 1. / timer.average_time
+            fps = 1. / timer.duration
             im = plot_tracking(
                 frame,
                 online_tlwhs,
@@ -585,7 +586,7 @@ def predict_video(detector, reid_model, camera_id):
             writer.write(im)
 
         frame_id += 1
-        print('detect frame:%d' % (frame_id))
+        print('detect frame:%d, fps: %f' % (frame_id, fps))
 
         if camera_id != -1:
             cv2.imshow('Tracking Detection', im)