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.
129 lines
3.9 KiB
129 lines
3.9 KiB
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
import warnings
|
|
from argparse import ArgumentParser
|
|
|
|
from xtcocotools.coco import COCO
|
|
|
|
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.
|
|
|
|
Require the json_file containing boxes.
|
|
"""
|
|
parser = ArgumentParser()
|
|
parser.add_argument('pose_config', help='Config file for detection')
|
|
parser.add_argument('pose_checkpoint', help='Checkpoint file')
|
|
parser.add_argument('--img-root', type=str, default='', help='Image root')
|
|
parser.add_argument(
|
|
'--json-file',
|
|
type=str,
|
|
default='',
|
|
help='Json file containing image info.')
|
|
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(
|
|
'--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 != '')
|
|
|
|
coco = COCO(args.json_file)
|
|
# 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)
|
|
|
|
img_keys = list(coco.imgs.keys())
|
|
|
|
# optional
|
|
return_heatmap = False
|
|
|
|
# e.g. use ('backbone', ) to return backbone feature
|
|
output_layer_names = None
|
|
|
|
# process each image
|
|
for i in range(len(img_keys)):
|
|
# get bounding box annotations
|
|
image_id = img_keys[i]
|
|
image = coco.loadImgs(image_id)[0]
|
|
image_name = os.path.join(args.img_root, image['file_name'])
|
|
ann_ids = coco.getAnnIds(image_id)
|
|
|
|
# make person bounding boxes
|
|
person_results = []
|
|
for ann_id in ann_ids:
|
|
person = {}
|
|
ann = coco.anns[ann_id]
|
|
# bbox format is 'xywh'
|
|
person['bbox'] = ann['bbox']
|
|
person_results.append(person)
|
|
|
|
# test a single image, with a list of bboxes
|
|
pose_results, returned_outputs = inference_top_down_pose_model(
|
|
pose_model,
|
|
image_name,
|
|
person_results,
|
|
bbox_thr=None,
|
|
format='xywh',
|
|
dataset=dataset,
|
|
dataset_info=dataset_info,
|
|
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_{i}.jpg')
|
|
|
|
vis_pose_result(
|
|
pose_model,
|
|
image_name,
|
|
pose_results,
|
|
dataset=dataset,
|
|
dataset_info=dataset_info,
|
|
kpt_score_thr=args.kpt_thr,
|
|
radius=args.radius,
|
|
thickness=args.thickness,
|
|
show=args.show,
|
|
out_file=out_file)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|
|
|