update pphuman for pptracking (#5419)

deploy/pphuman/config/tracker_config.yml

-# config of tracker for MOT SDE Detector, use ByteTracker as default.
-# The tracker of MOT JDE Detector is exported together with the model.
+# config of tracker for MOT SDE Detector, use 'JDETracker' as default.
+# The tracker of MOT JDE Detector (such as FairMOT) is exported together with the model.
 # Here 'min_box_area' and 'vertical_ratio' are set for pedestrian, you can modify for other objects tracking.
-tracker:
-  use_byte: true
+
+type: JDETracker # 'JDETracker' or 'DeepSORTTracker'
+
+# BYTETracker
+JDETracker:
+  use_byte: True
+  det_thresh: 0.3
   conf_thres: 0.6
   low_conf_thres: 0.1
   match_thres: 0.9
   min_box_area: 100
-  vertical_ratio: 1.6
+  vertical_ratio: 1.6 # for pedestrian
+
+DeepSORTTracker:
+  input_size: [64, 192]
+  min_box_area: 0
+  vertical_ratio: -1
+  budget: 100
+  max_age: 70
+  n_init: 3
+  metric_type: cosine
+  matching_threshold: 0.2
+  max_iou_distance: 0.9

deploy/pphuman/pipeline.py

@@ -28,7 +28,6 @@ parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
 sys.path.insert(0, parent_path)
 
 from python.infer import Detector, DetectorPicoDet
-from python.mot_sde_infer import SDE_Detector
 from python.attr_infer import AttrDetector
 from python.keypoint_infer import KeyPointDetector
 from python.keypoint_postprocess import translate_to_ori_images
@@ -39,6 +38,8 @@ from pipe_utils import argsparser, print_arguments, merge_cfg, PipeTimer
 from pipe_utils import get_test_images, crop_image_with_det, crop_image_with_mot, parse_mot_res, parse_mot_keypoint
 from python.preprocess import decode_image
 from python.visualize import visualize_box_mask, visualize_attr, visualize_pose, visualize_action
+
+from pptracking.python.mot_sde_infer import SDE_Detector
 from pptracking.python.visualize import plot_tracking
 
 
@@ -374,6 +375,8 @@ class PipePredictor(object):
             # det output format: class, score, xmin, ymin, xmax, ymax
             det_res = self.det_predictor.predict_image(
                 batch_input, visual=False)
+            det_res = self.det_predictor.filter_box(det_res,
+                                                    self.cfg['crop_thresh'])
             if i > self.warmup_frame:
                 self.pipe_timer.module_time['det'].end()
             self.pipeline_res.update(det_res, 'det')
@@ -563,6 +566,8 @@ class PipePredictor(object):
                     det_res_i,
                     labels=['person'],
                     threshold=self.cfg['crop_thresh'])
+                im = np.ascontiguousarray(np.copy(im))
+                im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
             if attr_res is not None:
                 attr_res_i = attr_res['output'][start_idx:start_idx +
                                                 boxes_num_i]
@@ -571,7 +576,7 @@ class PipePredictor(object):
             if not os.path.exists(self.output_dir):
                 os.makedirs(self.output_dir)
             out_path = os.path.join(self.output_dir, img_name)
-            im.save(out_path, quality=95)
+            cv2.imwrite(out_path, im)
             print("save result to: " + out_path)
             start_idx += boxes_num_i
 

deploy/pptracking/python/mot_sde_infer.py

@@ -24,8 +24,8 @@ import paddle
 
 from benchmark_utils import PaddleInferBenchmark
 from preprocess import decode_image
-from utils import argsparser, Timer, get_current_memory_mb, _is_valid_video, video2frames
-from det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor
+from .utils import argsparser, Timer, get_current_memory_mb, _is_valid_video, video2frames
+from .det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor
 
 # add python path
 import sys
@@ -34,7 +34,7 @@ sys.path.insert(0, parent_path)
 
 from mot.tracker import JDETracker, DeepSORTTracker
 from mot.utils import MOTTimer, write_mot_results, flow_statistic, get_crops, clip_box
-from visualize import plot_tracking, plot_tracking_dict
+from .visualize import plot_tracking, plot_tracking_dict
 
 from mot.mtmct.utils import parse_bias
 from mot.mtmct.postprocess import trajectory_fusion, sub_cluster, gen_res, print_mtmct_result
@@ -120,10 +120,12 @@ class SDE_Detector(Detector):
         cfg = tracker_cfg[tracker_cfg['type']]
 
         # tracker config
-        self.use_deepsort_tracker = True if tracker_cfg['type'] == 'DeepSORTTracker' else False
+        self.use_deepsort_tracker = True if tracker_cfg[
+            'type'] == 'DeepSORTTracker' else False
         if self.use_deepsort_tracker:
             # use DeepSORTTracker
-            if self.reid_pred_config is not None and hasattr(self.reid_pred_config, 'tracker'):
+            if self.reid_pred_config is not None and hasattr(
+                    self.reid_pred_config, 'tracker'):
                 cfg = self.reid_pred_config.tracker
             budget = cfg.get('budget', 100)
             max_age = cfg.get('max_age', 30)
@@ -138,8 +140,7 @@ class SDE_Detector(Detector):
                 max_iou_distance=max_iou_distance,
                 matching_threshold=matching_threshold,
                 min_box_area=min_box_area,
-                vertical_ratio=vertical_ratio,
-            )
+                vertical_ratio=vertical_ratio, )
         else:
             # use ByteTracker
             use_byte = cfg.get('use_byte', False)
@@ -158,8 +159,7 @@ class SDE_Detector(Detector):
                 vertical_ratio=vertical_ratio,
                 match_thres=match_thres,
                 conf_thres=conf_thres,
-                low_conf_thres=low_conf_thres,
-            )
+                low_conf_thres=low_conf_thres, )
 
         self.do_mtmct = False if mtmct_dir is None else True
         self.mtmct_dir = mtmct_dir
@@ -206,7 +206,8 @@ class SDE_Detector(Detector):
         for i in range(repeats):
             self.reid_predictor.run()
             output_names = self.reid_predictor.get_output_names()
-            feature_tensor = self.reid_predictor.get_output_handle(output_names[0])
+            feature_tensor = self.reid_predictor.get_output_handle(output_names[
+                0])
             pred_embs = feature_tensor.copy_to_cpu()
 
         det_results['embeddings'] = pred_embs
@@ -249,7 +250,8 @@ class SDE_Detector(Detector):
                 frame_id = det_results['frame_id']
 
                 tracking_outs['feat_data'] = {}
-                for _tlbr, _id, _feat in zip(online_tlbrs, online_ids, online_feats):
+                for _tlbr, _id, _feat in zip(online_tlbrs, online_ids,
+                                             online_feats):
                     feat_data = {}
                     feat_data['bbox'] = _tlbr
                     feat_data['frame'] = f"{frame_id:06d}"
@@ -265,7 +267,8 @@ class SDE_Detector(Detector):
             online_scores = defaultdict(list)
             online_ids = defaultdict(list)
             if self.do_mtmct:
-                online_tlbrs, online_feats = defaultdict(list), defaultdict(list)
+                online_tlbrs, online_feats = defaultdict(list), defaultdict(
+                    list)
             online_targets_dict = self.tracker.update(pred_dets, pred_embs)
             for cls_id in range(self.num_classes):
                 online_targets = online_targets_dict[cls_id]
@@ -295,7 +298,8 @@ class SDE_Detector(Detector):
                 seq_name = det_results['seq_name']
                 frame_id = det_results['frame_id']
                 tracking_outs['feat_data'] = {}
-                for _tlbr, _id, _feat in zip(online_tlbrs[0], online_ids[0], online_feats[0]):
+                for _tlbr, _id, _feat in zip(online_tlbrs[0], online_ids[0],
+                                             online_feats[0]):
                     feat_data = {}
                     feat_data['bbox'] = _tlbr
                     feat_data['frame'] = f"{frame_id:06d}"
@@ -452,7 +456,7 @@ class SDE_Detector(Detector):
         if not os.path.exists(self.output_dir):
             os.makedirs(self.output_dir)
         out_path = os.path.join(self.output_dir, video_out_name)
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
         writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
 
         frame_id = 1
@@ -469,14 +473,17 @@ class SDE_Detector(Detector):
 
             timer.tic()
             seq_name = video_out_name.split('.')[0]
-            mot_results = self.predict_image([frame], visual=False, seq_name=seq_name)
+            mot_results = self.predict_image(
+                [frame], visual=False, seq_name=seq_name)
             timer.toc()
 
-            online_tlwhs, online_scores, online_ids = mot_results[0] # bs=1 in MOT model
+            online_tlwhs, online_scores, online_ids = mot_results[
+                0]  # bs=1 in MOT model
             fps = 1. / timer.duration
             if num_classes == 1 and self.use_reid:
                 # use DeepSORTTracker, only support singe class
-                results[0].append((frame_id + 1, online_tlwhs, online_scores, online_ids))
+                results[0].append(
+                    (frame_id + 1, online_tlwhs, online_scores, online_ids))
                 im = plot_tracking(
                     frame,
                     online_tlwhs,
@@ -488,8 +495,8 @@ class SDE_Detector(Detector):
                 # use ByteTracker, support multiple class
                 for cls_id in range(num_classes):
                     results[cls_id].append(
-                        (frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id],
-                        online_ids[cls_id]))
+                        (frame_id + 1, online_tlwhs[cls_id],
+                         online_scores[cls_id], online_ids[cls_id]))
                 im = plot_tracking_dict(
                     frame,
                     num_classes,
@@ -549,13 +556,15 @@ class SDE_Detector(Detector):
                 continue
             if os.path.exists(os.path.join(fpath, 'img1')):
                 fpath = os.path.join(fpath, 'img1')
-            assert os.path.isdir(fpath), '{} should be a directory'.format(fpath)
+            assert os.path.isdir(fpath), '{} should be a directory'.format(
+                fpath)
             image_list = glob.glob(os.path.join(fpath, '*.jpg'))
             image_list.sort()
             assert len(image_list) > 0, '{} has no images.'.format(fpath)
             print('start tracking seq: {}'.format(seq))
 
-            mot_features_dict = self.predict_image(image_list, visual=False, seq_name=seq)
+            mot_features_dict = self.predict_image(
+                image_list, visual=False, seq_name=seq)
 
             cid = int(re.sub('[a-z,A-Z]', "", seq))
             tid_data, mot_list_break = trajectory_fusion(
@@ -627,8 +636,7 @@ def main():
         threshold=FLAGS.threshold,
         output_dir=FLAGS.output_dir,
         reid_model_dir=FLAGS.reid_model_dir,
-        mtmct_dir=FLAGS.mtmct_dir,
-    )
+        mtmct_dir=FLAGS.mtmct_dir, )
 
     # predict from video file or camera video stream
     if FLAGS.video_file is not None or FLAGS.camera_id != -1:
@@ -643,7 +651,8 @@ def main():
             assert FLAGS.batch_size == 1, "--batch_size should be 1 in MOT models."
         img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
         seq_name = FLAGS.image_dir.split('/')[-1]
-        detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10, seq_name=seq_name)
+        detector.predict_image(
+            img_list, FLAGS.run_benchmark, repeats=10, seq_name=seq_name)
 
         if not FLAGS.run_benchmark:
             detector.det_times.info(average=True)

deploy/python/infer.py

@@ -150,6 +150,25 @@ class Detector(object):
         result = {k: v for k, v in result.items() if v is not None}
         return result
 
+    def filter_box(self, result, threshold):
+        np_boxes_num = result['boxes_num']
+        boxes = result['boxes']
+        start_idx = 0
+        filter_boxes = []
+        filter_num = []
+        for i in range(len(np_boxes_num)):
+            boxes_num = np_boxes_num[i]
+            boxes_i = boxes[start_idx:start_idx + boxes_num, :]
+            idx = boxes_i[:, 1] > threshold
+            filter_boxes_i = boxes_i[idx, :]
+            filter_boxes.append(filter_boxes_i)
+            filter_num.append(filter_boxes_i.shape[0])
+            start_idx += boxes_num
+        boxes = np.concatenate(filter_boxes)
+        filter_num = np.array(filter_num)
+        filter_res = {'boxes': boxes, 'boxes_num': filter_num}
+        return filter_res
+
     def predict(self, repeats=1):
         '''
         Args:
@@ -736,7 +755,8 @@ def main():
     elif arch == 'PicoDet':
         detector_func = 'DetectorPicoDet'
 
-    detector = eval(detector_func)(FLAGS.model_dir,
+    detector = eval(detector_func)(
+        FLAGS.model_dir,
         device=FLAGS.device,
         run_mode=FLAGS.run_mode,
         batch_size=FLAGS.batch_size,
@@ -781,6 +801,8 @@ if __name__ == '__main__':
                             ], "device should be CPU, GPU or XPU"
     assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
 
-    assert not (FLAGS.enable_mkldnn==False and FLAGS.enable_mkldnn_bfloat16==True), 'To enable mkldnn bfloat, please turn on both enable_mkldnn and enable_mkldnn_bfloat16'
+    assert not (
+        FLAGS.enable_mkldnn == False and FLAGS.enable_mkldnn_bfloat16 == True
+    ), 'To enable mkldnn bfloat, please turn on both enable_mkldnn and enable_mkldnn_bfloat16'
 
     main()