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# Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import cv2
from mmpose.apis import (get_track_id, inference_bottom_up_pose_model,
init_pose_model, vis_pose_tracking_result)
from mmpose.datasets import DatasetInfo
def main():
"""Visualize the demo images."""
parser = ArgumentParser()
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(
'--kpt-thr', type=float, default=0.5, help='Keypoint score threshold')
parser.add_argument(
'--pose-nms-thr',
type=float,
default=0.9,
help='OKS threshold for pose NMS')
parser.add_argument(
'--use-oks-tracking', action='store_true', help='Using OKS tracking')
parser.add_argument(
'--tracking-thr', type=float, default=0.3, help='Tracking threshold')
parser.add_argument(
'--euro',
action='store_true',
help='Using One_Euro_Filter for smoothing')
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')
args = parser.parse_args()
assert args.show or (args.out_video_root != '')
# 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']
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)
assert (dataset == 'BottomUpCocoDataset')
else:
dataset_info = DatasetInfo(dataset_info)
cap = cv2.VideoCapture(args.video_path)
fps = None
assert cap.isOpened(), 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 = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_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)
# optional
return_heatmap = False
# e.g. use ('backbone', ) to return backbone feature
output_layer_names = None
next_id = 0
pose_results = []
while (cap.isOpened()):
flag, img = cap.read()
if not flag:
break
pose_results_last = pose_results
pose_results, returned_outputs = inference_bottom_up_pose_model(
pose_model,
img,
dataset=dataset,
dataset_info=dataset_info,
pose_nms_thr=args.pose_nms_thr,
return_heatmap=return_heatmap,
outputs=output_layer_names)
# get track id for each person instance
pose_results, next_id = get_track_id(
pose_results,
pose_results_last,
next_id,
use_oks=args.use_oks_tracking,
tracking_thr=args.tracking_thr,
use_one_euro=args.euro,
fps=fps)
# show the results
vis_img = vis_pose_tracking_result(
pose_model,
img,
pose_results,
radius=args.radius,
thickness=args.thickness,
dataset=dataset,
dataset_info=dataset_info,
kpt_score_thr=args.kpt_thr,
show=False)
if args.show:
cv2.imshow('Image', vis_img)
if save_out_video:
videoWriter.write(vis_img)
if args.show and cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
if save_out_video:
videoWriter.release()
if args.show:
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
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