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[MOT] mot keypoint unite deploy (PaddlePaddle#3530)
* add mot keypoint unite python infer * fix mot pose fps
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| Original file line number | Diff line number | Diff line change |
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| # 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. | ||
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| import os | ||
| import cv2 | ||
| import math | ||
| import numpy as np | ||
| import paddle | ||
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| from mot_keypoint_unite_utils import argsparser | ||
| from keypoint_infer import KeyPoint_Detector, PredictConfig_KeyPoint | ||
| from keypoint_visualize import draw_pose | ||
| from benchmark_utils import PaddleInferBenchmark | ||
| from utils import Timer | ||
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| from tracker import JDETracker | ||
| from mot_preprocess import LetterBoxResize | ||
| from mot_infer import MOT_Detector, PredictConfig_MOT, write_mot_results | ||
| from infer import print_arguments | ||
| from ppdet.modeling.mot import visualization as mot_vis | ||
| from ppdet.modeling.mot.utils import Timer as FPSTimer | ||
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| def mot_keypoint_unite_predict_video(mot_model, keypoint_model, camera_id): | ||
| if camera_id != -1: | ||
| capture = cv2.VideoCapture(camera_id) | ||
| video_name = 'output.mp4' | ||
| else: | ||
| capture = cv2.VideoCapture(FLAGS.video_file) | ||
| video_name = os.path.split(FLAGS.video_file)[-1] | ||
| fps = 30 | ||
| frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) | ||
| print('frame_count', frame_count) | ||
| width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) | ||
| height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) | ||
| # yapf: disable | ||
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') | ||
| # yapf: enable | ||
| if not os.path.exists(FLAGS.output_dir): | ||
| os.makedirs(FLAGS.output_dir) | ||
| out_path = os.path.join(FLAGS.output_dir, video_name) | ||
| writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) | ||
| frame_id = 0 | ||
| timer_mot = FPSTimer() | ||
| timer_kp = FPSTimer() | ||
| timer_mot_kp = FPSTimer() | ||
| mot_results = [] | ||
| while (1): | ||
| ret, frame = capture.read() | ||
| if not ret: | ||
| break | ||
| timer_mot_kp.tic() | ||
| timer_mot.tic() | ||
| online_tlwhs, online_scores, online_ids = mot_model.predict( | ||
| frame, FLAGS.mot_threshold) | ||
| timer_mot.toc() | ||
|
|
||
| mot_results.append( | ||
| (frame_id + 1, online_tlwhs, online_scores, online_ids)) | ||
| mot_fps = 1. / timer_mot.average_time | ||
|
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||
| timer_kp.tic() | ||
| keypoint_results = keypoint_model.predict([frame], | ||
| FLAGS.keypoint_threshold) | ||
| timer_kp.toc() | ||
| timer_mot_kp.toc() | ||
| kp_fps = 1. / timer_kp.average_time | ||
| mot_kp_fps = 1. / timer_mot_kp.average_time | ||
|
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| im = draw_pose( | ||
| frame, | ||
| keypoint_results, | ||
| visual_thread=FLAGS.keypoint_threshold, | ||
| returnimg=True) | ||
|
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| online_im = mot_vis.plot_tracking( | ||
| im, | ||
| online_tlwhs, | ||
| online_ids, | ||
| online_scores, | ||
| frame_id=frame_id, | ||
| fps=mot_kp_fps) | ||
|
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| im = np.array(online_im) | ||
|
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| frame_id += 1 | ||
| print('detect frame:%d' % (frame_id)) | ||
|
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| if FLAGS.save_images: | ||
| save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2]) | ||
| if not os.path.exists(save_dir): | ||
| os.makedirs(save_dir) | ||
| cv2.imwrite( | ||
| os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im) | ||
|
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| writer.write(im) | ||
| if camera_id != -1: | ||
| cv2.imshow('Tracking and keypoint results', im) | ||
| if cv2.waitKey(1) & 0xFF == ord('q'): | ||
| break | ||
| if FLAGS.save_mot_txts: | ||
| result_filename = os.path.join(FLAGS.output_dir, | ||
| video_name.split('.')[-2] + '.txt') | ||
| write_mot_results(result_filename, mot_results) | ||
| writer.release() | ||
|
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| def main(): | ||
| pred_config = PredictConfig_MOT(FLAGS.mot_model_dir) | ||
| mot_model = MOT_Detector( | ||
| pred_config, | ||
| FLAGS.mot_model_dir, | ||
| device=FLAGS.device, | ||
| run_mode=FLAGS.run_mode, | ||
| trt_min_shape=FLAGS.trt_min_shape, | ||
| trt_max_shape=FLAGS.trt_max_shape, | ||
| trt_opt_shape=FLAGS.trt_opt_shape, | ||
| trt_calib_mode=FLAGS.trt_calib_mode, | ||
| cpu_threads=FLAGS.cpu_threads, | ||
| enable_mkldnn=FLAGS.enable_mkldnn) | ||
|
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| pred_config = PredictConfig_KeyPoint(FLAGS.keypoint_model_dir) | ||
| keypoint_model = KeyPoint_Detector( | ||
| pred_config, | ||
| FLAGS.keypoint_model_dir, | ||
| device=FLAGS.device, | ||
| run_mode=FLAGS.run_mode, | ||
| trt_min_shape=FLAGS.trt_min_shape, | ||
| trt_max_shape=FLAGS.trt_max_shape, | ||
| trt_opt_shape=FLAGS.trt_opt_shape, | ||
| trt_calib_mode=FLAGS.trt_calib_mode, | ||
| cpu_threads=FLAGS.cpu_threads, | ||
| enable_mkldnn=FLAGS.enable_mkldnn, | ||
| use_dark=FLAGS.use_dark) | ||
|
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| # predict from video file or camera video stream | ||
| if FLAGS.video_file is not None or FLAGS.camera_id != -1: | ||
| mot_keypoint_unite_predict_video(mot_model, keypoint_model, | ||
| FLAGS.camera_id) | ||
| else: | ||
| print('Do not support unite predict single image.') | ||
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| if __name__ == '__main__': | ||
| paddle.enable_static() | ||
| parser = argsparser() | ||
| FLAGS = parser.parse_args() | ||
| print_arguments(FLAGS) | ||
| FLAGS.device = FLAGS.device.upper() | ||
| assert FLAGS.device in ['CPU', 'GPU', 'XPU' | ||
| ], "device should be CPU, GPU or XPU" | ||
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| main() |
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