382 lines
14 KiB
Python
382 lines
14 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
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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
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from pptracking.python.mot.utils import MOTTimer, write_mot_results
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from pptracking.python.mot.visualize import plot_tracking_dict
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# Global dictionary
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MOT_JDE_SUPPORT_MODELS = {
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'JDE',
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'FairMOT',
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}
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class JDE_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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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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"""
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def __init__(
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self,
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model_dir,
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tracker_config=None,
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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=1088,
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trt_opt_shape=608,
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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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super(JDE_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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# tracker config
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assert self.pred_config.tracker, "The exported JDE Detector model should have tracker."
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cfg = self.pred_config.tracker
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min_box_area = cfg.get('min_box_area', 0.0)
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vertical_ratio = cfg.get('vertical_ratio', 0.0)
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conf_thres = cfg.get('conf_thres', 0.0)
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tracked_thresh = cfg.get('tracked_thresh', 0.7)
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metric_type = cfg.get('metric_type', 'euclidean')
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self.tracker = JDETracker(
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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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conf_thres=conf_thres,
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tracked_thresh=tracked_thresh,
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metric_type=metric_type)
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_boxes = result['pred_dets']
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if np_boxes.shape[0] <= 0:
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print('[WARNNING] No object detected.')
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result = {'pred_dets': np.zeros([0, 6]), 'pred_embs': None}
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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 tracking(self, det_results):
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pred_dets = det_results['pred_dets'] # cls_id, score, x0, y0, x1, y1
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pred_embs = det_results['pred_embs']
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online_targets_dict = self.tracker.update(pred_dets, pred_embs)
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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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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: 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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return online_tlwhs, online_scores, online_ids
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def predict(self, repeats=1):
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'''
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Args:
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repeats (int): repeats number for prediction
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Returns:
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result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
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shape: [N, 128]
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'''
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# model prediction
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np_pred_dets, np_pred_embs = None, None
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_pred_dets = boxes_tensor.copy_to_cpu()
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embs_tensor = self.predictor.get_output_handle(output_names[1])
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np_pred_embs = embs_tensor.copy_to_cpu()
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result = dict(pred_dets=np_pred_dets, pred_embs=np_pred_embs)
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return result
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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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mot_results = []
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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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data_type = 'mcmot' if num_classes > 1 else 'mot'
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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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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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result_warmup = self.tracking(det_result)
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self.det_times.tracking_time_s.start()
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online_tlwhs, online_scores, online_ids = self.tracking(
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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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online_tlwhs, online_scores, online_ids = self.tracking(
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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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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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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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if seq_name is None:
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seq_name = image_list[0].split('/')[-2]
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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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mot_results.append([online_tlwhs, online_scores, online_ids])
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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 = 'mot_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) # support single class and multi classes
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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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online_tlwhs, online_scores, online_ids = mot_results[0]
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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], online_scores[cls_id],
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online_ids[cls_id]))
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fps = 1. / timer.duration
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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, data_type, num_classes)
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writer.release()
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def main():
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detector = JDE_Detector(
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FLAGS.model_dir,
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tracker_config=None,
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device=FLAGS.device,
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run_mode=FLAGS.run_mode,
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batch_size=1,
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trt_min_shape=FLAGS.trt_min_shape,
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trt_max_shape=FLAGS.trt_max_shape,
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trt_opt_shape=FLAGS.trt_opt_shape,
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trt_calib_mode=FLAGS.trt_calib_mode,
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cpu_threads=FLAGS.cpu_threads,
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enable_mkldnn=FLAGS.enable_mkldnn,
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output_dir=FLAGS.output_dir,
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threshold=FLAGS.threshold,
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save_images=FLAGS.save_images,
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save_mot_txts=FLAGS.save_mot_txts)
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# predict from video file or camera video stream
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if FLAGS.video_file is not None or FLAGS.camera_id != -1:
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detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
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else:
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# predict from image
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
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if not FLAGS.run_benchmark:
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detector.det_times.info(average=True)
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else:
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mode = FLAGS.run_mode
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model_dir = FLAGS.model_dir
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model_info = {
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'model_name': model_dir.strip('/').split('/')[-1],
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'precision': mode.split('_')[-1]
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}
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bench_log(detector, img_list, model_info, name='MOT')
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if __name__ == '__main__':
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paddle.enable_static()
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parser = argsparser()
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FLAGS = parser.parse_args()
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print_arguments(FLAGS)
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FLAGS.device = FLAGS.device.upper()
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assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
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], "device should be CPU, GPU, NPU or XPU"
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main()
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