523 lines
21 KiB
Python
523 lines
21 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import time
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import yaml
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import cv2
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import numpy as np
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from collections import defaultdict
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import paddle
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from benchmark_utils import PaddleInferBenchmark
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from preprocess import decode_image
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from utils import argsparser, Timer, get_current_memory_mb
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from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor
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# add python path
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import sys
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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sys.path.insert(0, parent_path)
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from pptracking.python.mot import JDETracker, DeepSORTTracker
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from pptracking.python.mot.utils import MOTTimer, write_mot_results, get_crops, clip_box
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from pptracking.python.mot.visualize import plot_tracking, plot_tracking_dict
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class SDE_Detector(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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tracker_config (str): tracker config path
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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output_dir (string): The path of output, default as 'output'
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threshold (float): Score threshold of the detected bbox, default as 0.5
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save_images (bool): Whether to save visualization image results, default as False
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save_mot_txts (bool): Whether to save tracking results (txt), default as False
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reid_model_dir (str): reid model dir, default None for ByteTrack, but set for DeepSORT
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"""
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def __init__(self,
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model_dir,
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tracker_config,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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output_dir='output',
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threshold=0.5,
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save_images=False,
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save_mot_txts=False,
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reid_model_dir=None):
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super(SDE_Detector, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn,
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output_dir=output_dir,
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threshold=threshold, )
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self.save_images = save_images
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self.save_mot_txts = save_mot_txts
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assert batch_size == 1, "MOT model only supports batch_size=1."
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self.det_times = Timer(with_tracker=True)
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self.num_classes = len(self.pred_config.labels)
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# reid config
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self.use_reid = False if reid_model_dir is None else True
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if self.use_reid:
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self.reid_pred_config = self.set_config(reid_model_dir)
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self.reid_predictor, self.config = load_predictor(
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reid_model_dir,
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run_mode=run_mode,
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batch_size=50, # reid_batch_size
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min_subgraph_size=self.reid_pred_config.min_subgraph_size,
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device=device,
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use_dynamic_shape=self.reid_pred_config.use_dynamic_shape,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn)
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else:
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self.reid_pred_config = None
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self.reid_predictor = None
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assert tracker_config is not None, 'Note that tracker_config should be set.'
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self.tracker_config = tracker_config
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tracker_cfg = yaml.safe_load(open(self.tracker_config))
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cfg = tracker_cfg[tracker_cfg['type']]
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# tracker config
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self.use_deepsort_tracker = True if tracker_cfg[
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'type'] == 'DeepSORTTracker' else False
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if self.use_deepsort_tracker:
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# use DeepSORTTracker
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if self.reid_pred_config is not None and hasattr(
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self.reid_pred_config, 'tracker'):
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cfg = self.reid_pred_config.tracker
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budget = cfg.get('budget', 100)
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max_age = cfg.get('max_age', 30)
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max_iou_distance = cfg.get('max_iou_distance', 0.7)
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matching_threshold = cfg.get('matching_threshold', 0.2)
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min_box_area = cfg.get('min_box_area', 0)
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vertical_ratio = cfg.get('vertical_ratio', 0)
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self.tracker = DeepSORTTracker(
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budget=budget,
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max_age=max_age,
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max_iou_distance=max_iou_distance,
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matching_threshold=matching_threshold,
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min_box_area=min_box_area,
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vertical_ratio=vertical_ratio, )
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else:
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# use ByteTracker
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use_byte = cfg.get('use_byte', False)
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det_thresh = cfg.get('det_thresh', 0.3)
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min_box_area = cfg.get('min_box_area', 0)
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vertical_ratio = cfg.get('vertical_ratio', 0)
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match_thres = cfg.get('match_thres', 0.9)
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conf_thres = cfg.get('conf_thres', 0.6)
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low_conf_thres = cfg.get('low_conf_thres', 0.1)
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self.tracker = JDETracker(
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use_byte=use_byte,
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det_thresh=det_thresh,
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num_classes=self.num_classes,
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min_box_area=min_box_area,
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vertical_ratio=vertical_ratio,
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match_thres=match_thres,
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conf_thres=conf_thres,
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low_conf_thres=low_conf_thres, )
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_boxes_num = result['boxes_num']
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if np_boxes_num[0] <= 0:
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print('[WARNNING] No object detected.')
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result = {'boxes': np.zeros([0, 6]), 'boxes_num': [0]}
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result = {k: v for k, v in result.items() if v is not None}
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return result
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def reidprocess(self, det_results, repeats=1):
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pred_dets = det_results['boxes']
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pred_xyxys = pred_dets[:, 2:6]
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ori_image = det_results['ori_image']
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ori_image_shape = ori_image.shape[:2]
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pred_xyxys, keep_idx = clip_box(pred_xyxys, ori_image_shape)
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if len(keep_idx[0]) == 0:
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det_results['boxes'] = np.zeros((1, 6), dtype=np.float32)
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det_results['embeddings'] = None
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return det_results
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pred_dets = pred_dets[keep_idx[0]]
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pred_xyxys = pred_dets[:, 2:6]
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w, h = self.tracker.input_size
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crops = get_crops(pred_xyxys, ori_image, w, h)
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# to keep fast speed, only use topk crops
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crops = crops[:50] # reid_batch_size
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det_results['crops'] = np.array(crops).astype('float32')
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det_results['boxes'] = pred_dets[:50]
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input_names = self.reid_predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.reid_predictor.get_input_handle(input_names[i])
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input_tensor.copy_from_cpu(det_results[input_names[i]])
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# model prediction
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for i in range(repeats):
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self.reid_predictor.run()
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output_names = self.reid_predictor.get_output_names()
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feature_tensor = self.reid_predictor.get_output_handle(output_names[
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0])
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pred_embs = feature_tensor.copy_to_cpu()
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det_results['embeddings'] = pred_embs
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return det_results
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def tracking(self, det_results):
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pred_dets = det_results['boxes'] # 'cls_id, score, x0, y0, x1, y1'
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pred_embs = det_results.get('embeddings', None)
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if self.use_deepsort_tracker:
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# use DeepSORTTracker, only support singe class
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self.tracker.predict()
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online_targets = self.tracker.update(pred_dets, pred_embs)
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online_tlwhs, online_scores, online_ids = [], [], []
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for t in online_targets:
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if not t.is_confirmed() or t.time_since_update > 1:
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continue
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tlwh = t.to_tlwh()
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tscore = t.score
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tid = t.track_id
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if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
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3] > self.tracker.vertical_ratio:
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continue
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online_tlwhs.append(tlwh)
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online_scores.append(tscore)
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online_ids.append(tid)
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tracking_outs = {
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'online_tlwhs': online_tlwhs,
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'online_scores': online_scores,
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'online_ids': online_ids,
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}
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return tracking_outs
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else:
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# use ByteTracker, support multiple class
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online_tlwhs = defaultdict(list)
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online_scores = defaultdict(list)
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online_ids = defaultdict(list)
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online_targets_dict = self.tracker.update(pred_dets, pred_embs)
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for cls_id in range(self.num_classes):
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online_targets = online_targets_dict[cls_id]
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for t in online_targets:
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tlwh = t.tlwh
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tid = t.track_id
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tscore = t.score
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if tlwh[2] * tlwh[3] <= self.tracker.min_box_area:
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continue
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if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
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3] > self.tracker.vertical_ratio:
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continue
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online_tlwhs[cls_id].append(tlwh)
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online_ids[cls_id].append(tid)
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online_scores[cls_id].append(tscore)
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tracking_outs = {
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'online_tlwhs': online_tlwhs,
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'online_scores': online_scores,
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'online_ids': online_ids,
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}
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return tracking_outs
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def predict_image(self,
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image_list,
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run_benchmark=False,
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repeats=1,
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visual=True,
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seq_name=None):
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num_classes = self.num_classes
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image_list.sort()
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ids2names = self.pred_config.labels
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mot_results = []
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for frame_id, img_file in enumerate(image_list):
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batch_image_list = [img_file] # bs=1 in MOT model
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frame, _ = decode_image(img_file, {})
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if run_benchmark:
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# preprocess
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inputs = self.preprocess(batch_image_list) # warmup
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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result_warmup = self.predict(repeats=repeats) # warmup
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self.det_times.inference_time_s.start()
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result = self.predict(repeats=repeats)
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self.det_times.inference_time_s.end(repeats=repeats)
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# postprocess
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result_warmup = self.postprocess(inputs, result) # warmup
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self.det_times.postprocess_time_s.start()
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det_result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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# tracking
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if self.use_reid:
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det_result['frame_id'] = frame_id
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det_result['seq_name'] = seq_name
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det_result['ori_image'] = frame
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det_result = self.reidprocess(det_result)
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result_warmup = self.tracking(det_result)
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self.det_times.tracking_time_s.start()
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if self.use_reid:
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det_result = self.reidprocess(det_result)
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tracking_outs = self.tracking(det_result)
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self.det_times.tracking_time_s.end()
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self.det_times.img_num += 1
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cm, gm, gu = get_current_memory_mb()
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self.cpu_mem += cm
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self.gpu_mem += gm
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self.gpu_util += gu
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else:
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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self.det_times.inference_time_s.start()
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result = self.predict()
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self.det_times.inference_time_s.end()
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self.det_times.postprocess_time_s.start()
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det_result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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# tracking process
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self.det_times.tracking_time_s.start()
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if self.use_reid:
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det_result['frame_id'] = frame_id
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det_result['seq_name'] = seq_name
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det_result['ori_image'] = frame
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det_result = self.reidprocess(det_result)
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tracking_outs = self.tracking(det_result)
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self.det_times.tracking_time_s.end()
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self.det_times.img_num += 1
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online_tlwhs = tracking_outs['online_tlwhs']
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online_scores = tracking_outs['online_scores']
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online_ids = tracking_outs['online_ids']
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mot_results.append([online_tlwhs, online_scores, online_ids])
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if visual:
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if len(image_list) > 1 and frame_id % 10 == 0:
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print('Tracking frame {}'.format(frame_id))
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frame, _ = decode_image(img_file, {})
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if isinstance(online_tlwhs, defaultdict):
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im = plot_tracking_dict(
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frame,
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num_classes,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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ids2names=ids2names)
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else:
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im = plot_tracking(
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frame,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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ids2names=ids2names)
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save_dir = os.path.join(self.output_dir, seq_name)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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cv2.imwrite(
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os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
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return mot_results
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def predict_video(self, video_file, camera_id):
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video_out_name = 'output.mp4'
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if camera_id != -1:
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capture = cv2.VideoCapture(camera_id)
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else:
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capture = cv2.VideoCapture(video_file)
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video_out_name = os.path.split(video_file)[-1]
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# Get Video info : resolution, fps, frame count
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width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = int(capture.get(cv2.CAP_PROP_FPS))
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frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
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print("fps: %d, frame_count: %d" % (fps, frame_count))
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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out_path = os.path.join(self.output_dir, video_out_name)
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video_format = 'mp4v'
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fourcc = cv2.VideoWriter_fourcc(*video_format)
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writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
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frame_id = 1
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timer = MOTTimer()
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results = defaultdict(list)
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num_classes = self.num_classes
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data_type = 'mcmot' if num_classes > 1 else 'mot'
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ids2names = self.pred_config.labels
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while (1):
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ret, frame = capture.read()
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if not ret:
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break
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if frame_id % 10 == 0:
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print('Tracking frame: %d' % (frame_id))
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frame_id += 1
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timer.tic()
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seq_name = video_out_name.split('.')[0]
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mot_results = self.predict_image(
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[frame[:, :, ::-1]], visual=False, seq_name=seq_name)
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timer.toc()
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# bs=1 in MOT model
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online_tlwhs, online_scores, online_ids = mot_results[0]
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fps = 1. / timer.duration
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if self.use_deepsort_tracker:
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# use DeepSORTTracker, only support singe class
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results[0].append(
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(frame_id + 1, online_tlwhs, online_scores, online_ids))
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im = plot_tracking(
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frame,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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fps=fps,
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ids2names=ids2names)
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else:
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# use ByteTracker, support multiple class
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for cls_id in range(num_classes):
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results[cls_id].append(
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(frame_id + 1, online_tlwhs[cls_id],
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online_scores[cls_id], online_ids[cls_id]))
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im = plot_tracking_dict(
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frame,
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num_classes,
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online_tlwhs,
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online_ids,
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online_scores,
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frame_id=frame_id,
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fps=fps,
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ids2names=ids2names)
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writer.write(im)
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if camera_id != -1:
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cv2.imshow('Mask Detection', im)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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if self.save_mot_txts:
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result_filename = os.path.join(
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self.output_dir, video_out_name.split('.')[-2] + '.txt')
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write_mot_results(result_filename, results)
|
|
|
|
writer.release()
|
|
|
|
|
|
def main():
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|
deploy_file = os.path.join(FLAGS.model_dir, 'infer_cfg.yml')
|
|
with open(deploy_file) as f:
|
|
yml_conf = yaml.safe_load(f)
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|
arch = yml_conf['arch']
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|
detector = SDE_Detector(
|
|
FLAGS.model_dir,
|
|
tracker_config=FLAGS.tracker_config,
|
|
device=FLAGS.device,
|
|
run_mode=FLAGS.run_mode,
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|
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,
|
|
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:
|
|
detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
|
|
else:
|
|
# predict from image
|
|
if FLAGS.image_dir is None and FLAGS.image_file is not None:
|
|
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)
|
|
|
|
if not FLAGS.run_benchmark:
|
|
detector.det_times.info(average=True)
|
|
else:
|
|
mode = FLAGS.run_mode
|
|
model_dir = FLAGS.model_dir
|
|
model_info = {
|
|
'model_name': model_dir.strip('/').split('/')[-1],
|
|
'precision': mode.split('_')[-1]
|
|
}
|
|
bench_log(detector, img_list, model_info, name='MOT')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
parser = argsparser()
|
|
FLAGS = parser.parse_args()
|
|
print_arguments(FLAGS)
|
|
FLAGS.device = FLAGS.device.upper()
|
|
assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
|
|
], "device should be CPU, GPU, NPU or XPU"
|
|
|
|
main()
|