run reid every 10 frames

deploy/pphuman/datacollector.py

@@ -35,6 +35,9 @@ class Result(object):
             return self.res_dict[name]
         return None
 
+    def clear(self, name):
+        self.res_dict[name].clear()
+
 
 class DataCollector(object):
     """
@@ -80,7 +83,6 @@ class DataCollector(object):
             ids = int(mot_item[0])
             if ids not in self.collector:
                 self.collector[ids] = copy.deepcopy(self.mots)
-
             self.collector[ids]["frames"].append(frameid)
             self.collector[ids]["rects"].append([mot_item[2:]])
             if attr_res:

deploy/pphuman/mtmct.py

@@ -297,10 +297,9 @@ def distill_idfeat(mot_res):
         feature_new = feature_list
 
     #if available frames number is more than 200, take one frame data per 20 frames
-    if len(qualities_new) > 200:
-        skipf = 20
-    else:
-        skipf = max(10, len(qualities_new) // 10)
+    skipf = 1
+    if len(qualities_new) > 20:
+        skipf = 2
     quality_skip = np.array(qualities_new[::skipf])
     feature_skip = np.array(feature_new[::skipf])
 

deploy/pphuman/pipeline.py

@@ -587,7 +587,7 @@ class PipePredictor(object):
                 if self.cfg['visual']:
                     self.action_visual_helper.update(action_res)
 
-            if self.with_mtmct:
+            if self.with_mtmct and frame_id % 10 == 0:
                 crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot(
                     frame, mot_res)
                 if frame_id > self.warmup_frame:
@@ -603,6 +603,8 @@ class PipePredictor(object):
                     "rects": rects
                 }
                 self.pipeline_res.update(reid_res_dict, 'reid')
+            else:
+                self.pipeline_res.clear('reid')
 
             self.collector.append(frame_id, self.pipeline_res)