更换文档检测模型
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389
paddle_detection/deploy/pptracking/python/mot_utils.py
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389
paddle_detection/deploy/pptracking/python/mot_utils.py
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# 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 time
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import os
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import sys
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import ast
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import argparse
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def argsparser():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--model_dir",
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type=str,
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default=None,
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help=("Directory include:'model.pdiparams', 'model.pdmodel', "
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"'infer_cfg.yml', created by tools/export_model.py."),
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required=True)
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parser.add_argument(
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"--image_file", type=str, default=None, help="Path of image file.")
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parser.add_argument(
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"--image_dir",
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type=str,
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default=None,
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help="Dir of image file, `image_file` has a higher priority.")
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parser.add_argument(
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"--batch_size", type=int, default=1, help="batch_size for inference.")
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parser.add_argument(
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"--video_file",
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type=str,
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default=None,
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help="Path of video file, `video_file` or `camera_id` has a highest priority."
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)
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parser.add_argument(
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"--camera_id",
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type=int,
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default=-1,
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help="device id of camera to predict.")
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parser.add_argument(
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"--threshold", type=float, default=0.5, help="Threshold of score.")
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parser.add_argument(
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"--output_dir",
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type=str,
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default="output",
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help="Directory of output visualization files.")
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parser.add_argument(
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"--run_mode",
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type=str,
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default='paddle',
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help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU."
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)
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parser.add_argument(
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"--use_gpu",
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type=ast.literal_eval,
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default=False,
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help="Deprecated, please use `--device`.")
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parser.add_argument(
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"--run_benchmark",
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type=ast.literal_eval,
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default=False,
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help="Whether to predict a image_file repeatedly for benchmark")
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parser.add_argument(
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"--enable_mkldnn",
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type=ast.literal_eval,
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default=False,
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help="Whether use mkldnn with CPU.")
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parser.add_argument(
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"--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
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parser.add_argument(
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"--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
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parser.add_argument(
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"--trt_max_shape",
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type=int,
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default=1280,
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help="max_shape for TensorRT.")
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parser.add_argument(
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"--trt_opt_shape",
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type=int,
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default=640,
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help="opt_shape for TensorRT.")
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parser.add_argument(
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"--trt_calib_mode",
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type=bool,
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default=False,
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help="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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parser.add_argument(
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'--save_images',
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action='store_true',
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help='Save visualization image results.')
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parser.add_argument(
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'--save_mot_txts',
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action='store_true',
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help='Save tracking results (txt).')
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parser.add_argument(
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'--save_mot_txt_per_img',
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action='store_true',
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help='Save tracking results (txt) for each image.')
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parser.add_argument(
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'--scaled',
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type=bool,
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default=False,
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help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
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"True in general detector.")
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parser.add_argument(
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"--tracker_config", type=str, default=None, help=("tracker donfig"))
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parser.add_argument(
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"--reid_model_dir",
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type=str,
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default=None,
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help=("Directory include:'model.pdiparams', 'model.pdmodel', "
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"'infer_cfg.yml', created by tools/export_model.py."))
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parser.add_argument(
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"--reid_batch_size",
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type=int,
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default=50,
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help="max batch_size for reid model inference.")
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parser.add_argument(
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'--use_dark',
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type=ast.literal_eval,
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default=True,
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help='whether to use darkpose to get better keypoint position predict ')
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parser.add_argument(
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'--skip_frame_num',
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type=int,
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default=-1,
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help='Skip frames to speed up the process of getting mot results.')
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parser.add_argument(
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'--warmup_frame',
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type=int,
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default=50,
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help='Warmup frames to test speed of the process of getting mot results.'
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)
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parser.add_argument(
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"--do_entrance_counting",
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action='store_true',
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help="Whether counting the numbers of identifiers entering "
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"or getting out from the entrance. Note that only support single-class MOT."
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)
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parser.add_argument(
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"--do_break_in_counting",
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action='store_true',
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help="Whether counting the numbers of identifiers break in "
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"the area. Note that only support single-class MOT and "
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"the video should be taken by a static camera.")
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parser.add_argument(
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"--region_type",
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type=str,
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default='horizontal',
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help="Area type for entrance counting or break in counting, 'horizontal' and "
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"'vertical' used when do entrance counting. 'custom' used when do break in counting. "
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"Note that only support single-class MOT, and the video should be taken by a static camera."
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)
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parser.add_argument(
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'--region_polygon',
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nargs='+',
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type=int,
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default=[],
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help="Clockwise point coords (x0,y0,x1,y1...) of polygon of area when "
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"do_break_in_counting. Note that only support single-class MOT and "
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"the video should be taken by a static camera.")
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parser.add_argument(
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"--secs_interval",
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type=int,
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default=2,
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help="The seconds interval to count after tracking")
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parser.add_argument(
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"--draw_center_traj",
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action='store_true',
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help="Whether drawing the trajectory of center")
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parser.add_argument(
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"--mtmct_dir",
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type=str,
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default=None,
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help="The MTMCT scene video folder.")
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parser.add_argument(
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"--mtmct_cfg", type=str, default=None, help="The MTMCT config.")
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return parser
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class Times(object):
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def __init__(self):
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self.time = 0.
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# start time
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self.st = 0.
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# end time
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self.et = 0.
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def start(self):
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self.st = time.time()
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def end(self, repeats=1, accumulative=True):
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self.et = time.time()
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if accumulative:
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self.time += (self.et - self.st) / repeats
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else:
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self.time = (self.et - self.st) / repeats
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def reset(self):
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self.time = 0.
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self.st = 0.
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self.et = 0.
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def value(self):
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return round(self.time, 4)
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class Timer(Times):
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def __init__(self, with_tracker=False):
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super(Timer, self).__init__()
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self.with_tracker = with_tracker
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self.preprocess_time_s = Times()
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self.inference_time_s = Times()
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self.postprocess_time_s = Times()
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self.tracking_time_s = Times()
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self.img_num = 0
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def info(self, average=False):
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pre_time = self.preprocess_time_s.value()
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infer_time = self.inference_time_s.value()
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post_time = self.postprocess_time_s.value()
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track_time = self.tracking_time_s.value()
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total_time = pre_time + infer_time + post_time
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if self.with_tracker:
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total_time = total_time + track_time
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total_time = round(total_time, 4)
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print("------------------ Inference Time Info ----------------------")
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print("total_time(ms): {}, img_num: {}".format(total_time * 1000,
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self.img_num))
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preprocess_time = round(pre_time / max(1, self.img_num),
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4) if average else pre_time
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postprocess_time = round(post_time / max(1, self.img_num),
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4) if average else post_time
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inference_time = round(infer_time / max(1, self.img_num),
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4) if average else infer_time
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tracking_time = round(track_time / max(1, self.img_num),
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4) if average else track_time
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average_latency = total_time / max(1, self.img_num)
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qps = 0
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if total_time > 0:
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qps = 1 / average_latency
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print("average latency time(ms): {:.2f}, QPS: {:2f}".format(
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average_latency * 1000, qps))
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if self.with_tracker:
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print(
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"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}".
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format(preprocess_time * 1000, inference_time * 1000,
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postprocess_time * 1000, tracking_time * 1000))
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else:
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print(
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"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".
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format(preprocess_time * 1000, inference_time * 1000,
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postprocess_time * 1000))
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def tracking_info(self, average=True):
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pre_time = self.preprocess_time_s.value()
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infer_time = self.inference_time_s.value()
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post_time = self.postprocess_time_s.value()
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track_time = self.tracking_time_s.value()
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total_time = pre_time + infer_time + post_time
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if self.with_tracker:
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total_time = total_time + track_time
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total_time = round(total_time, 4)
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print(
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"------------------ Tracking Module Time Info ----------------------"
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)
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preprocess_time = round(pre_time / max(1, self.img_num),
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4) if average else pre_time
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postprocess_time = round(post_time / max(1, self.img_num),
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4) if average else post_time
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inference_time = round(infer_time / max(1, self.img_num),
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4) if average else infer_time
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tracking_time = round(track_time / max(1, self.img_num),
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4) if average else track_time
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if self.with_tracker:
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print(
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"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}".
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format(preprocess_time * 1000, inference_time * 1000,
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postprocess_time * 1000, tracking_time * 1000))
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else:
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print(
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"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".
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format(preprocess_time * 1000, inference_time * 1000,
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postprocess_time * 1000))
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def report(self, average=False):
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dic = {}
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pre_time = self.preprocess_time_s.value()
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infer_time = self.inference_time_s.value()
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post_time = self.postprocess_time_s.value()
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track_time = self.tracking_time_s.value()
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dic['preprocess_time_s'] = round(pre_time / max(1, self.img_num),
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4) if average else pre_time
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dic['inference_time_s'] = round(infer_time / max(1, self.img_num),
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4) if average else infer_time
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dic['postprocess_time_s'] = round(post_time / max(1, self.img_num),
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4) if average else post_time
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dic['img_num'] = self.img_num
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total_time = pre_time + infer_time + post_time
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if self.with_tracker:
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dic['tracking_time_s'] = round(track_time / max(1, self.img_num),
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4) if average else track_time
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total_time = total_time + track_time
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dic['total_time_s'] = round(total_time, 4)
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return dic
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def get_current_memory_mb():
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"""
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It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
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And this function Current program is time-consuming.
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"""
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import pynvml
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import psutil
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import GPUtil
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gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
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pid = os.getpid()
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p = psutil.Process(pid)
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info = p.memory_full_info()
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cpu_mem = info.uss / 1024. / 1024.
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gpu_mem = 0
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gpu_percent = 0
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gpus = GPUtil.getGPUs()
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if gpu_id is not None and len(gpus) > 0:
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gpu_percent = gpus[gpu_id].load
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pynvml.nvmlInit()
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
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gpu_mem = meminfo.used / 1024. / 1024.
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return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)
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def video2frames(video_path, outpath, frame_rate=25, **kargs):
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def _dict2str(kargs):
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cmd_str = ''
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for k, v in kargs.items():
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cmd_str += (' ' + str(k) + ' ' + str(v))
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return cmd_str
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ffmpeg = ['ffmpeg ', ' -y -loglevel ', ' error ']
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vid_name = os.path.basename(video_path).split('.')[0]
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out_full_path = os.path.join(outpath, vid_name)
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if not os.path.exists(out_full_path):
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os.makedirs(out_full_path)
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# video file name
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outformat = os.path.join(out_full_path, '%05d.jpg')
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cmd = ffmpeg
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cmd = ffmpeg + [
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' -i ', video_path, ' -r ', str(frame_rate), ' -f image2 ', outformat
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]
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cmd = ''.join(cmd) + _dict2str(kargs)
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if os.system(cmd) != 0:
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raise RuntimeError('ffmpeg process video: {} error'.format(video_path))
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sys.exit(-1)
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sys.stdout.flush()
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return out_full_path
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def _is_valid_video(f, extensions=('.mp4', '.avi', '.mov', '.rmvb', '.flv')):
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return f.lower().endswith(extensions)
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