Spaces:
Build error
Build error
File size: 4,072 Bytes
d7a991a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import cv2
from mmpose.apis import (inference_bottom_up_pose_model, init_pose_model,
vis_pose_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=0.3, help='Keypoint score threshold')
parser.add_argument(
'--pose-nms-thr',
type=float,
default=0.9,
help='OKS threshold for pose NMS')
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)
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
while (cap.isOpened()):
flag, img = cap.read()
if not flag:
break
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)
# show the results
vis_img = vis_pose_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()
|