mmpose-0.29.0/demo/bottom_up_img_demo.py

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

import mmcv

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 detection')
 parser.add_argument('pose_checkpoint', help='Checkpoint file')
 parser.add_argument(
 '--img-path',
 type=str,
 help='Path to an image file or a image folder.')
 parser.add_argument(
 '--show',
 action='store_true',
 default=False,
 help='whether to show img')
 parser.add_argument(
 '--out-img-root',
 type=str,
 default='',
 help='Root of the output img file. '
 'Default not saving the visualization images.')
 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_img_root != '')

 # prepare image list
 if osp.isfile(args.img_path):
 image_list = [args.img_path]
 elif osp.isdir(args.img_path):
 image_list = [
 osp.join(args.img_path, fn) for fn in os.listdir(args.img_path)
 if fn.lower().endswith(('.png', '.jpg', '.jpeg', '.tiff', '.bmp'))
 ]
 else:
 raise ValueError('Image path should be an image or image folder.'
 f'Got invalid image path: {args.img_path}')

 # 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)

 # optional
 return_heatmap = False

 # e.g. use ('backbone', ) to return backbone feature
 output_layer_names = None

 # process each image
 for image_name in mmcv.track_iter_progress(image_list):

 # test a single image, with a list of bboxes.
 pose_results, returned_outputs = inference_bottom_up_pose_model(
 pose_model,
 image_name,
 dataset=dataset,
 dataset_info=dataset_info,
 pose_nms_thr=args.pose_nms_thr,
 return_heatmap=return_heatmap,
 outputs=output_layer_names)

 if args.out_img_root == '':
 out_file = None
 else:
 os.makedirs(args.out_img_root, exist_ok=True)
 out_file = os.path.join(
 args.out_img_root,
 f'vis_{osp.splitext(osp.basename(image_name))[0]}.jpg')

 # show the results
 vis_pose_result(
 pose_model,
 image_name,
 pose_results,
 radius=args.radius,
 thickness=args.thickness,
 dataset=dataset,
 dataset_info=dataset_info,
 kpt_score_thr=args.kpt_thr,
 show=args.show,
 out_file=out_file)


if __name__ == '__main__':
 main()