You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
139 lines
4.1 KiB
139 lines
4.1 KiB
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
import warnings
|
|
from argparse import ArgumentParser
|
|
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
|
|
vis_pose_result)
|
|
from mmpose.datasets import DatasetInfo
|
|
|
|
|
|
def main():
|
|
"""Visualize the demo images.
|
|
|
|
Using mmdet to detect the human.
|
|
"""
|
|
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(
|
|
'--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)
|
|
else:
|
|
dataset_info = DatasetInfo(dataset_info)
|
|
|
|
cap = cv2.VideoCapture(args.video_path)
|
|
assert cap.isOpened(), f'Faild to load video file {args.video_path}'
|
|
|
|
fps = cap.get(cv2.CAP_PROP_FPS)
|
|
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
|
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
|
|
|
|
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:
|
|
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
|
|
|
|
# keep the person class bounding boxes.
|
|
person_results = [{'bbox': np.array([0, 0, size[0], size[1]])}]
|
|
|
|
# test a single image, with a list of bboxes.
|
|
pose_results, returned_outputs = inference_top_down_pose_model(
|
|
pose_model,
|
|
img,
|
|
person_results,
|
|
format='xyxy',
|
|
dataset=dataset,
|
|
dataset_info=dataset_info,
|
|
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()
|
|
|