552 lines
17 KiB
Python
552 lines
17 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 time
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import os
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import ast
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import argparse
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import numpy as np
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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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"--enable_mkldnn_bfloat16",
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type=ast.literal_eval,
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default=False,
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help="Whether use mkldnn bfloat16 inference 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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type=ast.literal_eval,
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default=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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"--action_file",
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type=str,
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default=None,
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help="Path of input file for action recognition.")
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parser.add_argument(
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"--window_size",
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type=int,
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default=50,
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help="Temporal size of skeleton feature for action recognition.")
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parser.add_argument(
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"--random_pad",
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type=ast.literal_eval,
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default=False,
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help="Whether do random padding for action recognition.")
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parser.add_argument(
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"--save_results",
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action='store_true',
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default=False,
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help="Whether save detection result to file using coco format")
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parser.add_argument(
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'--use_coco_category',
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action='store_true',
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default=False,
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help='Whether to use the coco format dictionary `clsid2catid`')
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parser.add_argument(
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"--slice_infer",
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action='store_true',
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help="Whether to slice the image and merge the inference results for small object detection."
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)
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parser.add_argument(
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'--slice_size',
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nargs='+',
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type=int,
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default=[640, 640],
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help="Height of the sliced image.")
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parser.add_argument(
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"--overlap_ratio",
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nargs='+',
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type=float,
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default=[0.25, 0.25],
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help="Overlap height ratio of the sliced image.")
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parser.add_argument(
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"--combine_method",
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type=str,
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default='nms',
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help="Combine method of the sliced images' detection results, choose in ['nms', 'nmm', 'concat']."
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)
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parser.add_argument(
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"--match_threshold",
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type=float,
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default=0.6,
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help="Combine method matching threshold.")
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parser.add_argument(
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"--match_metric",
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type=str,
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default='ios',
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help="Combine method matching metric, choose in ['iou', 'ios'].")
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parser.add_argument(
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"--collect_trt_shape_info",
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action='store_true',
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default=False,
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help="Whether to collect dynamic shape before using tensorrt.")
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parser.add_argument(
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"--tuned_trt_shape_file",
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type=str,
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default="shape_range_info.pbtxt",
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help="Path of a dynamic shape file for tensorrt.")
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parser.add_argument("--use_fd_format", action="store_true")
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parser.add_argument(
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"--task_type",
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type=str,
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default='Detection',
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help="How to save the coco result, it only work with save_results==True. Optional inputs are Rotate or Detection, default is Detection."
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)
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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 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 multiclass_nms(bboxs, num_classes, match_threshold=0.6, match_metric='iou'):
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final_boxes = []
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for c in range(num_classes):
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idxs = bboxs[:, 0] == c
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if np.count_nonzero(idxs) == 0: continue
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r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
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final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1))
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return final_boxes
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def nms(dets, match_threshold=0.6, match_metric='iou'):
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""" Apply NMS to avoid detecting too many overlapping bounding boxes.
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Args:
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dets: shape [N, 5], [score, x1, y1, x2, y2]
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match_metric: 'iou' or 'ios'
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match_threshold: overlap thresh for match metric.
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"""
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if dets.shape[0] == 0:
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return dets[[], :]
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scores = dets[:, 0]
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x1 = dets[:, 1]
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y1 = dets[:, 2]
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x2 = dets[:, 3]
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y2 = dets[:, 4]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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ndets = dets.shape[0]
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suppressed = np.zeros((ndets), dtype=np.int32)
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for _i in range(ndets):
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i = order[_i]
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if suppressed[i] == 1:
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continue
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ix1 = x1[i]
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iy1 = y1[i]
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ix2 = x2[i]
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iy2 = y2[i]
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iarea = areas[i]
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for _j in range(_i + 1, ndets):
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j = order[_j]
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if suppressed[j] == 1:
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continue
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xx1 = max(ix1, x1[j])
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yy1 = max(iy1, y1[j])
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xx2 = min(ix2, x2[j])
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yy2 = min(iy2, y2[j])
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w = max(0.0, xx2 - xx1 + 1)
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h = max(0.0, yy2 - yy1 + 1)
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inter = w * h
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if match_metric == 'iou':
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union = iarea + areas[j] - inter
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match_value = inter / union
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elif match_metric == 'ios':
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smaller = min(iarea, areas[j])
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match_value = inter / smaller
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else:
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raise ValueError()
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if match_value >= match_threshold:
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suppressed[j] = 1
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keep = np.where(suppressed == 0)[0]
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dets = dets[keep, :]
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return dets
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coco_clsid2catid = {
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0: 1,
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1: 2,
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2: 3,
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3: 4,
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4: 5,
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5: 6,
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6: 7,
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7: 8,
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8: 9,
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9: 10,
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10: 11,
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11: 13,
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12: 14,
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13: 15,
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14: 16,
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15: 17,
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16: 18,
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17: 19,
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18: 20,
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19: 21,
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20: 22,
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21: 23,
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22: 24,
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23: 25,
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24: 27,
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25: 28,
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26: 31,
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27: 32,
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28: 33,
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29: 34,
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30: 35,
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31: 36,
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32: 37,
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33: 38,
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34: 39,
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35: 40,
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36: 41,
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37: 42,
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38: 43,
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39: 44,
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40: 46,
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41: 47,
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42: 48,
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43: 49,
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44: 50,
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45: 51,
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46: 52,
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47: 53,
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48: 54,
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49: 55,
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50: 56,
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51: 57,
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52: 58,
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53: 59,
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54: 60,
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55: 61,
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56: 62,
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57: 63,
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58: 64,
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59: 65,
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60: 67,
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61: 70,
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62: 72,
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63: 73,
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64: 74,
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65: 75,
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66: 76,
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67: 77,
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68: 78,
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69: 79,
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70: 80,
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71: 81,
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72: 82,
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73: 84,
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74: 85,
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75: 86,
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76: 87,
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77: 88,
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78: 89,
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79: 90
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}
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def gaussian_radius(bbox_size, min_overlap):
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height, width = bbox_size
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a1 = 1
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b1 = (height + width)
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c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
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sq1 = np.sqrt(b1**2 - 4 * a1 * c1)
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radius1 = (b1 + sq1) / (2 * a1)
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a2 = 4
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b2 = 2 * (height + width)
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c2 = (1 - min_overlap) * width * height
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sq2 = np.sqrt(b2**2 - 4 * a2 * c2)
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radius2 = (b2 + sq2) / 2
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a3 = 4 * min_overlap
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b3 = -2 * min_overlap * (height + width)
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c3 = (min_overlap - 1) * width * height
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sq3 = np.sqrt(b3**2 - 4 * a3 * c3)
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radius3 = (b3 + sq3) / 2
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return min(radius1, radius2, radius3)
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|
|
|
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def gaussian2D(shape, sigma_x=1, sigma_y=1):
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m, n = [(ss - 1.) / 2. for ss in shape]
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y, x = np.ogrid[-m:m + 1, -n:n + 1]
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|
|
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h = np.exp(-(x * x / (2 * sigma_x * sigma_x) + y * y / (2 * sigma_y *
|
|
sigma_y)))
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h[h < np.finfo(h.dtype).eps * h.max()] = 0
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return h
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|
|
|
|
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def draw_umich_gaussian(heatmap, center, radius, k=1):
|
|
"""
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draw_umich_gaussian, refer to https://github.com/xingyizhou/CenterNet/blob/master/src/lib/utils/image.py#L126
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"""
|
|
diameter = 2 * radius + 1
|
|
gaussian = gaussian2D(
|
|
(diameter, diameter), sigma_x=diameter / 6, sigma_y=diameter / 6)
|
|
|
|
x, y = int(center[0]), int(center[1])
|
|
|
|
height, width = heatmap.shape[0:2]
|
|
|
|
left, right = min(x, radius), min(width - x, radius + 1)
|
|
top, bottom = min(y, radius), min(height - y, radius + 1)
|
|
|
|
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
|
|
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:
|
|
radius + right]
|
|
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
|
|
np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
|
|
return heatmap
|