# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys # add python path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) sys.path.insert(0, parent_path) # ignore warning log import warnings warnings.filterwarnings('ignore') import paddle from paddle.distributed import ParallelEnv from ppdet.core.workspace import load_config, merge_config from ppdet.engine import Tracker from ppdet.utils.check import check_gpu, check_version, check_config from ppdet.utils.cli import ArgsParser def parse_args(): parser = ArgsParser() parser.add_argument( "--det_results_dir", type=str, default='', help="Directory name for detection results.") parser.add_argument( '--output_dir', type=str, default='output', help='Directory name for output tracking results.') parser.add_argument( '--save_images', action='store_true', help='Save tracking results (image).') parser.add_argument( '--save_videos', action='store_true', help='Save tracking results (video).') parser.add_argument( '--show_image', action='store_true', help='Show tracking results (image).') parser.add_argument( '--scaled', type=bool, default=False, help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 " "True in general detector.") args = parser.parse_args() return args def run(FLAGS, cfg): dataset_dir = cfg['EvalMOTDataset'].dataset_dir data_root = cfg['EvalMOTDataset'].data_root data_root = '{}/{}'.format(dataset_dir, data_root) seqs = os.listdir(data_root) seqs.sort() # build Tracker tracker = Tracker(cfg, mode='eval') # load weights if cfg.architecture in ['DeepSORT', 'ByteTrack']: tracker.load_weights_sde(cfg.det_weights, cfg.reid_weights) else: tracker.load_weights_jde(cfg.weights) # inference tracker.mot_evaluate( data_root=data_root, seqs=seqs, data_type=cfg.metric.lower(), model_type=cfg.architecture, output_dir=FLAGS.output_dir, save_images=FLAGS.save_images, save_videos=FLAGS.save_videos, show_image=FLAGS.show_image, scaled=FLAGS.scaled, det_results_dir=FLAGS.det_results_dir) def main(): FLAGS = parse_args() cfg = load_config(FLAGS.config) merge_config(FLAGS.opt) check_config(cfg) check_gpu(cfg.use_gpu) check_version() place = 'gpu:{}'.format(ParallelEnv().dev_id) if cfg.use_gpu else 'cpu' place = paddle.set_device(place) run(FLAGS, cfg) if __name__ == '__main__': main()