[cherry-pick][MOT] fix deploy python mot infer (#5630)

* fix deploy python mot infer, fix cfgs

* fix doc, test=document_fix

configs/mot/jde/_base_/jde_darknet53.yml

@@ -53,4 +53,4 @@ JDETracker:
   det_thresh: 0.3
   track_buffer: 30
   min_box_area: 200
-  motion: KalmanFilter
+  vertical_ratio: 1.6 # for pedestrian

deploy/pphuman/config/tracker_config.yml

@@ -11,8 +11,8 @@ JDETracker:
   conf_thres: 0.6
   low_conf_thres: 0.1
   match_thres: 0.9
-  min_box_area: 100
-  vertical_ratio: 1.6 # for pedestrian
+  min_box_area: 0
+  vertical_ratio: 0 # 1.6 for pedestrian
 
 DeepSORTTracker:
   input_size: [64, 192]

deploy/pptracking/python/mot/tracker/jde_tracker.py

@@ -38,7 +38,7 @@ class JDETracker(object):
         track_buffer (int): buffer for tracker
         min_box_area (int): min box area to filter out low quality boxes
         vertical_ratio (float): w/h, the vertical ratio of the bbox to filter
-            bad results. If set <0 means no need to filter bboxes，usually set
+            bad results. If set <= 0 means no need to filter bboxes，usually set
             1.6 for pedestrian tracking.
         tracked_thresh (float): linear assignment threshold of tracked 
             stracks and detections
@@ -64,8 +64,8 @@ class JDETracker(object):
                  num_classes=1,
                  det_thresh=0.3,
                  track_buffer=30,
-                 min_box_area=200,
-                 vertical_ratio=1.6,
+                 min_box_area=0,
+                 vertical_ratio=0,
                  tracked_thresh=0.7,
                  r_tracked_thresh=0.5,
                  unconfirmed_thresh=0.7,
@@ -161,9 +161,8 @@ class JDETracker(object):
                     detections = [
                         STrack(
                             STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id,
-                            30, temp_feat)
-                        for (tlbrs, temp_feat
-                             ) in zip(pred_dets_cls, pred_embs_cls)
+                            30, temp_feat) for (tlbrs, temp_feat) in
+                        zip(pred_dets_cls, pred_embs_cls)
                     ]
             else:
                 detections = []
@@ -238,15 +237,13 @@ class JDETracker(object):
                             for tlbrs in pred_dets_cls_second
                         ]
                     else:
-                        pred_embs_cls_second = pred_embs_dict[cls_id][inds_second]
+                        pred_embs_cls_second = pred_embs_dict[cls_id][
+                            inds_second]
                         detections_second = [
                             STrack(
-                                STrack.tlbr_to_tlwh(tlbrs[2:6]),
-                                tlbrs[1],
-                                cls_id,
-                                30,
-                                temp_feat)
-                            for (tlbrs, temp_feat) in zip(pred_dets_cls_second, pred_embs_cls_second)
+                                STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1],
+                                cls_id, 30, temp_feat) for (tlbrs, temp_feat) in
+                            zip(pred_dets_cls_second, pred_embs_cls_second)
                         ]
                 else:
                     detections_second = []

deploy/pptracking/python/mot_jde_infer.py

@@ -112,8 +112,8 @@ class JDE_Detector(Detector):
         # tracker config
         assert self.pred_config.tracker, "The exported JDE Detector model should have tracker."
         cfg = self.pred_config.tracker
-        min_box_area = cfg.get('min_box_area', 200)
-        vertical_ratio = cfg.get('vertical_ratio', 1.6)
+        min_box_area = cfg.get('min_box_area', 0.0)
+        vertical_ratio = cfg.get('vertical_ratio', 0.0)
         conf_thres = cfg.get('conf_thres', 0.0)
         tracked_thresh = cfg.get('tracked_thresh', 0.7)
         metric_type = cfg.get('metric_type', 'euclidean')
@@ -164,7 +164,7 @@ class JDE_Detector(Detector):
             repeats (int): repeats number for prediction
         Returns:
             result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
-                            matix element:[x_min, y_min, x_max, y_max, score, class]
+                            matix element:[class, score, x_min, y_min, x_max, y_max]
                             FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
                             shape: [N, 128]
         '''

deploy/pptracking/python/mot_sde_infer.py

@@ -165,8 +165,8 @@ class SDE_Detector(Detector):
             # use ByteTracker
             use_byte = cfg.get('use_byte', False)
             det_thresh = cfg.get('det_thresh', 0.3)
-            min_box_area = cfg.get('min_box_area', 200)
-            vertical_ratio = cfg.get('vertical_ratio', 1.6)
+            min_box_area = cfg.get('min_box_area', 0)
+            vertical_ratio = cfg.get('vertical_ratio', 0)
             match_thres = cfg.get('match_thres', 0.9)
             conf_thres = cfg.get('conf_thres', 0.6)
             low_conf_thres = cfg.get('low_conf_thres', 0.1)
@@ -194,7 +194,7 @@ class SDE_Detector(Detector):
         return result
 
     def reidprocess(self, det_results, repeats=1):
-        pred_dets = det_results['boxes']
+        pred_dets = det_results['boxes']  # cls_id, score, x0, y0, x1, y1
         pred_xyxys = pred_dets[:, 2:6]
 
         ori_image = det_results['ori_image']
@@ -234,7 +234,7 @@ class SDE_Detector(Detector):
         return det_results
 
     def tracking(self, det_results):
-        pred_dets = det_results['boxes']
+        pred_dets = det_results['boxes']  # cls_id, score, x0, y0, x1, y1
         pred_embs = det_results.get('embeddings', None)
 
         if self.use_deepsort_tracker:

deploy/pptracking/python/tracker_config.yml

@@ -11,8 +11,8 @@ JDETracker:
   conf_thres: 0.6
   low_conf_thres: 0.1
   match_thres: 0.9
-  min_box_area: 100
-  vertical_ratio: 1.6 # for pedestrian
+  min_box_area: 0
+  vertical_ratio: 0 # 1.6 for pedestrian
 
 DeepSORTTracker:
   input_size: [64, 192]

deploy/python/README.md

@@ -3,27 +3,76 @@
 在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 预测引擎使用了AnalysisPredictor，专门针对推理进行了优化，是基于[C++预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/native_infer.html)的Python接口，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。
 
 
-主要包含两个步骤：
-
+Python端预测部署主要包含两个步骤：
 - 导出预测模型
 - 基于Python进行预测
 
 ## 1. 导出预测模型
 
-PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/EXPORT_MODEL.md)
+PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](../deploy/EXPORT_MODEL.md)，例如
+
+```bash
+# 导出YOLOv3检测模型
+python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --output_dir=./inference_model \
+ -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
+
+# 导出HigherHRNet(bottom-up)关键点检测模型
+python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams
+
+# 导出HRNet(top-down)关键点检测模型
+python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_384x288.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_384x288.pdparams
+
+# 导出FairMOT多目标跟踪模型
+python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams
+
+# 导出ByteTrack多目标跟踪模型(相当于只导出检测器)
+python tools/export_model.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/ppyoloe_crn_l_36e_640x640_mot17half.pdparams
+```
 
 导出后目录下，包括`infer_cfg.yml`, `model.pdiparams`,  `model.pdiparams.info`, `model.pdmodel`四个文件。
 
-## 2. 基于Python的预测
 
+## 2. 基于Python的预测
 
+### 2.1 通用检测
+在终端输入以下命令进行预测：
+```bash
+python deploy/python/infer.py --model_dir=./output_inference/yolov3_darknet53_270e_coco --image_file=./demo/000000014439.jpg --device=GPU
+```
 
+### 2.2 关键点检测
 在终端输入以下命令进行预测：
+```bash
+# keypoint top-down(HRNet)/bottom-up(HigherHRNet)单独推理，该模式下top-down模型HRNet只支持单人截图预测
+python deploy/python/keypoint_infer.py --model_dir=output_inference/hrnet_w32_384x288/ --image_file=./demo/hrnet_demo.jpg --device=GPU --threshold=0.5
+python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=GPU --threshold=0.5
+
+# detector 检测 + keypoint top-down模型联合部署（联合推理只支持top-down关键点模型）
+python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/yolov3_darknet53_270e_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file={your video name}.mp4  --device=GPU
+```
+**注意:**
+ - 关键点检测模型导出和预测具体可参照[keypoint](../../configs/keypoint/README.md)，可分别在各个模型的文档中查找具体用法；
+ - 此目录下的关键点检测部署为基础前向功能，更多关键点检测功能可使用PP-Human项目，参照[pphuman](../pphuman/README.md)；
+
 
+### 2.3 多目标跟踪
+在终端输入以下命令进行预测：
 ```bash
-python deploy/python/infer.py --model_dir=./output_inference/yolov3_mobilenet_v1_roadsign --image_file=./demo/road554.png --device=GPU
+# FairMOT跟踪
+python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU
+
+# ByteTrack跟踪
+python deploy/python/mot_sde_infer.py --model_dir=output_inference/ppyoloe_crn_l_36e_640x640_mot17half/ --tracker_config=deploy/python/tracker_config.yml --video_file={your video name}.mp4 --device=GPU --scaled=True
+
+# FairMOT多目标跟踪联合HRNet关键点检测（联合推理只支持top-down关键点模型）
+python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file={your video name}.mp4 --device=GPU
 ```
 
+**注意:**
+ - 多目标跟踪模型导出和预测具体可参照[mot]](../../configs/mot/README.md)，可分别在各个模型的文档中查找具体用法；
+ - 此目录下的跟踪部署为基础前向功能以及联合关键点部署，更多跟踪功能可使用PP-Human项目，参照[pphuman](../pphuman/README.md)，或PP-Tracking项目(绘制轨迹、出入口流量计数)，参照[pptracking](../pptracking/README.md)；
+
+
 参数说明如下:
 
 | 参数 | 是否必须|含义 |

deploy/python/mot_jde_infer.py

@@ -32,7 +32,7 @@ sys.path.insert(0, parent_path)
 
 from pptracking.python.mot import JDETracker
 from pptracking.python.mot.utils import MOTTimer, write_mot_results
-from pptracking.python.visualize import plot_tracking, plot_tracking_dict
+from pptracking.python.mot.visualize import plot_tracking_dict
 
 # Global dictionary
 MOT_JDE_SUPPORT_MODELS = {
@@ -54,23 +54,30 @@ class JDE_Detector(Detector):
         trt_calib_mode (bool): If the model is produced by TRT offline quantitative
             calibration, trt_calib_mode need to set True
         cpu_threads (int): cpu threads
-        enable_mkldnn (bool): whether to open MKLDNN 
+        enable_mkldnn (bool): whether to open MKLDNN
+        output_dir (string): The path of output, default as 'output'
+        threshold (float): Score threshold of the detected bbox, default as 0.5
+        save_images (bool): Whether to save visualization image results, default as False
+        save_mot_txts (bool): Whether to save tracking results (txt), default as False
     """
 
-    def __init__(self,
-                 model_dir,
-                 tracker_config=None,
-                 device='CPU',
-                 run_mode='paddle',
-                 batch_size=1,
-                 trt_min_shape=1,
-                 trt_max_shape=1088,
-                 trt_opt_shape=608,
-                 trt_calib_mode=False,
-                 cpu_threads=1,
-                 enable_mkldnn=False,
-                 output_dir='output',
-                 threshold=0.5):
+    def __init__(
+            self,
+            model_dir,
+            tracker_config=None,
+            device='CPU',
+            run_mode='paddle',
+            batch_size=1,
+            trt_min_shape=1,
+            trt_max_shape=1088,
+            trt_opt_shape=608,
+            trt_calib_mode=False,
+            cpu_threads=1,
+            enable_mkldnn=False,
+            output_dir='output',
+            threshold=0.5,
+            save_images=False,
+            save_mot_txts=False, ):
         super(JDE_Detector, self).__init__(
             model_dir=model_dir,
             device=device,
@@ -84,6 +91,8 @@ class JDE_Detector(Detector):
             enable_mkldnn=enable_mkldnn,
             output_dir=output_dir,
             threshold=threshold, )
+        self.save_images = save_images
+        self.save_mot_txts = save_mot_txts
         assert batch_size == 1, "MOT model only supports batch_size=1."
         self.det_times = Timer(with_tracker=True)
         self.num_classes = len(self.pred_config.labels)
@@ -91,8 +100,8 @@ class JDE_Detector(Detector):
         # tracker config
         assert self.pred_config.tracker, "The exported JDE Detector model should have tracker."
         cfg = self.pred_config.tracker
-        min_box_area = cfg.get('min_box_area', 200)
-        vertical_ratio = cfg.get('vertical_ratio', 1.6)
+        min_box_area = cfg.get('min_box_area', 0.0)
+        vertical_ratio = cfg.get('vertical_ratio', 0.0)
         conf_thres = cfg.get('conf_thres', 0.0)
         tracked_thresh = cfg.get('tracked_thresh', 0.7)
         metric_type = cfg.get('metric_type', 'euclidean')
@@ -115,7 +124,7 @@ class JDE_Detector(Detector):
         return result
 
     def tracking(self, det_results):
-        pred_dets = det_results['pred_dets']  # 'cls_id, score, x0, y0, x1, y1'
+        pred_dets = det_results['pred_dets']  # cls_id, score, x0, y0, x1, y1
         pred_embs = det_results['pred_embs']
         online_targets_dict = self.tracker.update(pred_dets, pred_embs)
 
@@ -164,7 +173,8 @@ class JDE_Detector(Detector):
                       image_list,
                       run_benchmark=False,
                       repeats=1,
-                      visual=True):
+                      visual=True,
+                      seq_name=None):
         mot_results = []
         num_classes = self.num_classes
         image_list.sort()
@@ -225,7 +235,7 @@ class JDE_Detector(Detector):
                 self.det_times.img_num += 1
 
             if visual:
-                if frame_id % 10 == 0:
+                if len(image_list) > 1 and frame_id % 10 == 0:
                     print('Tracking frame {}'.format(frame_id))
                 frame, _ = decode_image(img_file, {})
 
@@ -237,7 +247,8 @@ class JDE_Detector(Detector):
                     online_scores,
                     frame_id=frame_id,
                     ids2names=ids2names)
-                seq_name = image_list[0].split('/')[-2]
+                if seq_name is None:
+                    seq_name = image_list[0].split('/')[-2]
                 save_dir = os.path.join(self.output_dir, seq_name)
                 if not os.path.exists(save_dir):
                     os.makedirs(save_dir)
@@ -264,7 +275,8 @@ class JDE_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')
+        video_format = 'mp4v'
+        fourcc = cv2.VideoWriter_fourcc(*video_format)
         writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
 
         frame_id = 1
@@ -282,7 +294,9 @@ class JDE_Detector(Detector):
             frame_id += 1
 
             timer.tic()
-            mot_results = self.predict_image([frame], visual=False)
+            seq_name = video_out_name.split('.')[0]
+            mot_results = self.predict_image(
+                [frame], visual=False, seq_name=seq_name)
             timer.toc()
 
             online_tlwhs, online_scores, online_ids = mot_results[0]
@@ -307,20 +321,33 @@ class JDE_Detector(Detector):
                 cv2.imshow('Mask Detection', im)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
+
+        if self.save_mot_txts:
+            result_filename = os.path.join(
+                self.output_dir, video_out_name.split('.')[-2] + '.txt')
+
+            write_mot_results(result_filename, results, data_type, num_classes)
+
         writer.release()
 
 
 def main():
     detector = JDE_Detector(
         FLAGS.model_dir,
+        tracker_config=None,
         device=FLAGS.device,
         run_mode=FLAGS.run_mode,
+        batch_size=1,
         trt_min_shape=FLAGS.trt_min_shape,
         trt_max_shape=FLAGS.trt_max_shape,
         trt_opt_shape=FLAGS.trt_opt_shape,
         trt_calib_mode=FLAGS.trt_calib_mode,
         cpu_threads=FLAGS.cpu_threads,
-        enable_mkldnn=FLAGS.enable_mkldnn)
+        enable_mkldnn=FLAGS.enable_mkldnn,
+        output_dir=FLAGS.output_dir,
+        threshold=FLAGS.threshold,
+        save_images=FLAGS.save_images,
+        save_mot_txts=FLAGS.save_mot_txts)
 
     # predict from video file or camera video stream
     if FLAGS.video_file is not None or FLAGS.camera_id != -1:

deploy/python/mot_keypoint_unite_infer.py

@@ -24,7 +24,7 @@ from collections import defaultdict
 
 from mot_keypoint_unite_utils import argsparser
 from preprocess import decode_image
-from infer import print_arguments, get_test_images
+from infer import print_arguments, get_test_images, bench_log
 from mot_sde_infer import SDE_Detector
 from mot_jde_infer import JDE_Detector, MOT_JDE_SUPPORT_MODELS
 from keypoint_infer import KeyPointDetector, KEYPOINT_SUPPORT_MODELS
@@ -39,7 +39,7 @@ import sys
 parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
 sys.path.insert(0, parent_path)
 
-from pptracking.python.visualize import plot_tracking, plot_tracking_dict
+from pptracking.python.mot.visualize import plot_tracking, plot_tracking_dict
 from pptracking.python.mot.utils import MOTTimer as FPSTimer
 
 
@@ -92,7 +92,7 @@ def mot_topdown_unite_predict(mot_detector,
 
         keypoint_res = predict_with_given_det(
             image, results, topdown_keypoint_detector, keypoint_batch_size,
-            FLAGS.mot_threshold, FLAGS.keypoint_threshold, FLAGS.run_benchmark)
+            FLAGS.run_benchmark)
 
         if save_res:
             store_res.append([
@@ -146,7 +146,7 @@ def mot_topdown_unite_predict_video(mot_detector,
     if not os.path.exists(FLAGS.output_dir):
         os.makedirs(FLAGS.output_dir)
     out_path = os.path.join(FLAGS.output_dir, video_name)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
     writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
     frame_id = 0
     timer_mot, timer_kp, timer_mot_kp = FPSTimer(), FPSTimer(), FPSTimer()
@@ -179,7 +179,7 @@ def mot_topdown_unite_predict_video(mot_detector,
         timer_kp.tic()
         keypoint_res = predict_with_given_det(
             frame, results, topdown_keypoint_detector, keypoint_batch_size,
-            FLAGS.mot_threshold, FLAGS.keypoint_threshold, FLAGS.run_benchmark)
+            FLAGS.run_benchmark)
         timer_kp.toc()
         timer_mot_kp.toc()
 

deploy/python/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
+  min_box_area: 0
+  vertical_ratio: 0 # 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

ppdet/modeling/mot/tracker/jde_tracker.py

@@ -44,7 +44,7 @@ class JDETracker(object):
         track_buffer (int): buffer for tracker
         min_box_area (int): min box area to filter out low quality boxes
         vertical_ratio (float): w/h, the vertical ratio of the bbox to filter
-            bad results. If set <0 means no need to filter bboxes，usually set
+            bad results. If set <= 0 means no need to filter bboxes，usually set
             1.6 for pedestrian tracking.
         tracked_thresh (float): linear assignment threshold of tracked 
             stracks and detections
@@ -70,8 +70,8 @@ class JDETracker(object):
                  num_classes=1,
                  det_thresh=0.3,
                  track_buffer=30,
-                 min_box_area=200,
-                 vertical_ratio=1.6,
+                 min_box_area=0,
+                 vertical_ratio=0,
                  tracked_thresh=0.7,
                  r_tracked_thresh=0.5,
                  unconfirmed_thresh=0.7,
@@ -167,9 +167,8 @@ class JDETracker(object):
                     detections = [
                         STrack(
                             STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1], cls_id,
-                            30, temp_feat)
-                        for (tlbrs, temp_feat
-                             ) in zip(pred_dets_cls, pred_embs_cls)
+                            30, temp_feat) for (tlbrs, temp_feat) in
+                        zip(pred_dets_cls, pred_embs_cls)
                     ]
             else:
                 detections = []
@@ -244,15 +243,13 @@ class JDETracker(object):
                             for tlbrs in pred_dets_cls_second
                         ]
                     else:
-                        pred_embs_cls_second = pred_embs_dict[cls_id][inds_second]
+                        pred_embs_cls_second = pred_embs_dict[cls_id][
+                            inds_second]
                         detections_second = [
                             STrack(
-                                STrack.tlbr_to_tlwh(tlbrs[2:6]),
-                                tlbrs[1],
-                                cls_id,
-                                30,
-                                temp_feat)
-                            for (tlbrs, temp_feat) in zip(pred_dets_cls_second, pred_embs_cls_second)
+                                STrack.tlbr_to_tlwh(tlbrs[2:6]), tlbrs[1],
+                                cls_id, 30, temp_feat) for (tlbrs, temp_feat) in
+                            zip(pred_dets_cls_second, pred_embs_cls_second)
                         ]
                 else:
                     detections_second = []