mmpose-0.29.0/demo/top_down_video_demo_with_mmdet.py

# Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser

import cv2
import mmcv

from mmpose.apis import (collect_multi_frames, inference_top_down_pose_model,
 init_pose_model, process_mmdet_results,
 vis_pose_result)
from mmpose.datasets import DatasetInfo

try:
 from mmdet.apis import inference_detector, init_detector
 has_mmdet = True
except (ImportError, ModuleNotFoundError):
 has_mmdet = False


def main():
 """Visualize the demo video (support both single-frame and multi-frame).

 Using mmdet to detect the human.
 """
 parser = ArgumentParser()
 parser.add_argument('det_config', help='Config file for detection')
 parser.add_argument('det_checkpoint', help='Checkpoint file for detection')
 parser.add_argument('pose_config', help='Config file for pose')
 parser.add_argument('pose_checkpoint', help='Checkpoint file for pose')
 parser.add_argument('--video-path', type=str, help='Video path')
 parser.add_argument(
 '--show',
 action='store_true',
 default=False,
 help='whether to show visualizations.')
 parser.add_argument(
 '--out-video-root',
 default='',
 help='Root of the output video file. '
 'Default not saving the visualization video.')
 parser.add_argument(
 '--device', default='cuda:0', help='Device used for inference')
 parser.add_argument(
 '--det-cat-id',
 type=int,
 default=1,
 help='Category id for bounding box detection model')
 parser.add_argument(
 '--bbox-thr',
 type=float,
 default=0.3,
 help='Bounding box score threshold')
 parser.add_argument(
 '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
 parser.add_argument(
 '--radius',
 type=int,
 default=4,
 help='Keypoint radius for visualization')
 parser.add_argument(
 '--thickness',
 type=int,
 default=1,
 help='Link thickness for visualization')

 parser.add_argument(
 '--use-multi-frames',
 action='store_true',
 default=False,
 help='whether to use multi frames for inference in the pose'
 'estimation stage. Default: False.')
 parser.add_argument(
 '--online',
 action='store_true',
 default=False,
 help='inference mode. If set to True, can not use future frame'
 'information when using multi frames for inference in the pose'
 'estimation stage. Default: False.')

 assert has_mmdet, 'Please install mmdet to run the demo.'

 args = parser.parse_args()

 assert args.show or (args.out_video_root != '')
 assert args.det_config is not None
 assert args.det_checkpoint is not None

 print('Initializing model...')
 # build the detection model from a config file and a checkpoint file
 det_model = init_detector(
 args.det_config, args.det_checkpoint, device=args.device.lower())

 # build the pose model from a config file and a checkpoint file
 pose_model = init_pose_model(
 args.pose_config, args.pose_checkpoint, device=args.device.lower())

 dataset = pose_model.cfg.data['test']['type']
 # get datasetinfo
 dataset_info = pose_model.cfg.data['test'].get('dataset_info', None)
 if dataset_info is None:
 warnings.warn(
 'Please set `dataset_info` in the config.'
 'Check https://github.com/open-mmlab/mmpose/pull/663 for details.',
 DeprecationWarning)
 else:
 dataset_info = DatasetInfo(dataset_info)

 # read video
 video = mmcv.VideoReader(args.video_path)
 assert video.opened, f'Faild to load video file {args.video_path}'

 if args.out_video_root == '':
 save_out_video = False
 else:
 os.makedirs(args.out_video_root, exist_ok=True)
 save_out_video = True

 if save_out_video:
 fps = video.fps
 size = (video.width, video.height)
 fourcc = cv2.VideoWriter_fourcc(*'mp4v')
 videoWriter = cv2.VideoWriter(
 os.path.join(args.out_video_root,
 f'vis_{os.path.basename(args.video_path)}'), fourcc,
 fps, size)

 # frame index offsets for inference, used in multi-frame inference setting
 if args.use_multi_frames:
 assert 'frame_indices_test' in pose_model.cfg.data.test.data_cfg
 indices = pose_model.cfg.data.test.data_cfg['frame_indices_test']

 # whether to return heatmap, optional
 return_heatmap = False

 # return the output of some desired layers,
 # e.g. use ('backbone', ) to return backbone feature
 output_layer_names = None

 print('Running inference...')
 for frame_id, cur_frame in enumerate(mmcv.track_iter_progress(video)):
 # get the detection results of current frame
 # the resulting box is (x1, y1, x2, y2)
 mmdet_results = inference_detector(det_model, cur_frame)

 # keep the person class bounding boxes.
 person_results = process_mmdet_results(mmdet_results, args.det_cat_id)

 if args.use_multi_frames:
 frames = collect_multi_frames(video, frame_id, indices,
 args.online)

 # test a single image, with a list of bboxes.
 pose_results, returned_outputs = inference_top_down_pose_model(
 pose_model,
 frames if args.use_multi_frames else cur_frame,
 person_results,
 bbox_thr=args.bbox_thr,
 format='xyxy',
 dataset=dataset,
 dataset_info=dataset_info,
 return_heatmap=return_heatmap,
 outputs=output_layer_names)

 # show the results
 vis_frame = vis_pose_result(
 pose_model,
 cur_frame,
 pose_results,
 dataset=dataset,
 dataset_info=dataset_info,
 kpt_score_thr=args.kpt_thr,
 radius=args.radius,
 thickness=args.thickness,
 show=False)

 if args.show:
 cv2.imshow('Frame', vis_frame)

 if save_out_video:
 videoWriter.write(vis_frame)

 if args.show and cv2.waitKey(1) & 0xFF == ord('q'):
 break

 if save_out_video:
 videoWriter.release()
 if args.show:
 cv2.destroyAllWindows()


if __name__ == '__main__':
 main()