fix score threshold in mot_infer (PaddlePaddle#3444)

Author: jerrywgz

deploy/python/mot_infer.py

@@ -93,14 +93,15 @@ def preprocess(self, im):
         inputs = create_inputs(im, im_info)
         return inputs
 
-    def postprocess(self, pred_dets, pred_embs):
+    def postprocess(self, pred_dets, pred_embs, threshold):
         online_targets = self.tracker.update(pred_dets, pred_embs)
         online_tlwhs, online_ids = [], []
         online_scores = []
         for t in online_targets:
             tlwh = t.tlwh
             tid = t.track_id
             tscore = t.score
+            if tscore < threshold: continue
             vertical = tlwh[2] / tlwh[3] > 1.6
             if tlwh[2] * tlwh[3] > self.tracker.min_box_area and not vertical:
                 online_tlwhs.append(tlwh)
@@ -137,8 +138,8 @@ def predict(self, image, threshold=0.5, repeats=1):
         self.det_times.inference_time_s.end(repeats=repeats)
 
         self.det_times.postprocess_time_s.start()
-        online_tlwhs, online_scores, online_ids = self.postprocess(pred_dets,
-                                                                   pred_embs)
+        online_tlwhs, online_scores, online_ids = self.postprocess(
+            pred_dets, pred_embs, threshold)
         self.det_times.postprocess_time_s.end()
         self.det_times.img_num += 1
         return online_tlwhs, online_scores, online_ids
@@ -363,7 +364,8 @@ def predict_video(detector, camera_id):
             online_ids,
             online_scores,
             frame_id=frame_id,
-            fps=fps)
+            fps=fps,
+            threhold=FLAGS.threshold)
         if FLAGS.save_images:
             save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
             if not os.path.exists(save_dir):

ppdet/engine/tracker.py

@@ -112,7 +112,8 @@ def _eval_seq_jde(self,
                       dataloader,
                       save_dir=None,
                       show_image=False,
-                      frame_rate=30):
+                      frame_rate=30,
+                      draw_threshold=0):
         if save_dir:
             if not os.path.exists(save_dir): os.makedirs(save_dir)
         tracker = self.model.tracker
@@ -140,6 +141,7 @@ def _eval_seq_jde(self,
                 tlwh = t.tlwh
                 tid = t.track_id
                 tscore = t.score
+                if tscore < draw_threshold: continue
                 vertical = tlwh[2] / tlwh[3] > 1.6
                 if tlwh[2] * tlwh[3] > tracker.min_box_area and not vertical:
                     online_tlwhs.append(tlwh)
@@ -162,7 +164,8 @@ def _eval_seq_sde(self,
                       save_dir=None,
                       show_image=False,
                       frame_rate=30,
-                      det_file=''):
+                      det_file='',
+                      draw_threshold=0):
         if save_dir:
             if not os.path.exists(save_dir): os.makedirs(save_dir)
         tracker = self.model.tracker
@@ -191,6 +194,7 @@ def _eval_seq_sde(self,
                 dets = dets_list[frame_id]
                 bbox_tlwh = paddle.to_tensor(dets['bbox'], dtype='float32')
                 pred_scores = paddle.to_tensor(dets['score'], dtype='float32')
+                if pred_scores < draw_threshold: continue
                 if bbox_tlwh.shape[0] > 0:
                     pred_bboxes = paddle.concat(
                         (bbox_tlwh[:, 0:2],
@@ -343,7 +347,8 @@ def mot_predict(self,
                     save_images=False,
                     save_videos=True,
                     show_image=False,
-                    det_results_dir=''):
+                    det_results_dir='',
+                    draw_threshold=0.5):
         if not os.path.exists(output_dir): os.makedirs(output_dir)
         result_root = os.path.join(output_dir, 'mot_results')
         if not os.path.exists(result_root): os.makedirs(result_root)
@@ -369,15 +374,17 @@ def mot_predict(self,
                     dataloader,
                     save_dir=save_dir,
                     show_image=show_image,
-                    frame_rate=frame_rate)
+                    frame_rate=frame_rate,
+                    draw_threshold=draw_threshold)
             elif model_type in ['DeepSORT']:
                 results, nf, ta, tc = self._eval_seq_sde(
                     dataloader,
                     save_dir=save_dir,
                     show_image=show_image,
                     frame_rate=frame_rate,
                     det_file=os.path.join(det_results_dir,
-                                          '{}.txt'.format(seq)))
+                                          '{}.txt'.format(seq)),
+                    draw_threshold=draw_threshold)
             else:
                 raise ValueError(model_type)
 

tools/infer_mot.py

@@ -68,6 +68,11 @@ def parse_args():
         '--show_image',
         action='store_true',
         help='Show tracking results (image).')
+    parser.add_argument(
+        "--draw_threshold",
+        type=float,
+        default=0.5,
+        help="Threshold to reserve the result for visualization.")
     args = parser.parse_args()
     return args
 
@@ -94,7 +99,8 @@ def run(FLAGS, cfg):
         save_images=FLAGS.save_images,
         save_videos=FLAGS.save_videos,
         show_image=FLAGS.show_image,
-        det_results_dir=FLAGS.det_results_dir)
+        det_results_dir=FLAGS.det_results_dir,
+        draw_threshold=FLAGS.draw_threshold)
 
 
 def main():