def test_bottom_up_pose_tracking_demo():
# COCO demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
'coco/res50_coco_512x512.py',
None,
device='cpu')
image_name = 'tests/data/coco/000000000785.jpg'
pose_results, _ = inference_bottom_up_pose_model(pose_model, image_name)
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='BottomUpCocoDataset')
pose_results_last = pose_results
# oks
pose_results, next_id = get_track_id(pose_results,
pose_results_last,
next_id=next_id,
use_oks=True)
pose_results_last = pose_results
# one_euro
pose_results, next_id = get_track_id(pose_results,
pose_results_last,
next_id=next_id,
use_one_euro=True)
def test_bottom_up_pose_tracking_demo():
# COCO demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
'coco/res50_coco_512x512.py',
None,
device='cpu')
image_name = 'tests/data/coco/000000000785.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
pose_results, _ = inference_bottom_up_pose_model(pose_model,
image_name,
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
pose_results_last = pose_results
# oks
pose_results, next_id = get_track_id(pose_results,
pose_results_last,
next_id=next_id,
use_oks=True)
pose_results_last = pose_results
# one_euro (will be deprecated)
with pytest.deprecated_call():
pose_results, next_id = get_track_id(pose_results,
pose_results_last,
next_id=next_id,
use_one_euro=True)
def test_pose_tracking_demo():
# COCO demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/top_down/resnet/coco/res50_coco_256x192.py',
None,
device='cpu')
image_name = 'tests/data/coco/000000000785.jpg'
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
[[50, 50, 50, 100]],
format='xywh')
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model, image_name, pose_results)
pose_results_last = pose_results
# AIC demo
pose_model = init_pose_model(
'configs/top_down/resnet/aic/res50_aic_256x192.py', None, device='cpu')
image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg'
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(
pose_model,
image_name, [[50, 50, 50, 100]],
format='xywh',
dataset='TopDownAicDataset')
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='TopDownAicDataset')
# OneHand10K demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/hand/resnet/onehand10k/res50_onehand10k_256x256.py',
None,
device='cpu')
image_name = 'tests/data/onehand10k/9.jpg'
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(
pose_model,
image_name, [[10, 10, 30, 30]],
format='xywh',
dataset='OneHand10KDataset')
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='OneHand10KDataset')
# InterHand2D demo
pose_model = init_pose_model(
'configs/hand/resnet/interhand2d/res50_interhand2d_all_256x256.py',
None,
device='cpu')
image_name = 'tests/data/interhand2d/image2017.jpg'
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(
pose_model,
image_name, [[50, 50, 0, 0]],
format='xywh',
dataset='InterHand2DDataset')
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='InterHand2DDataset')
pose_results_last = pose_results
# MPII demo
pose_model = init_pose_model(
'configs/top_down/resnet/mpii/res50_mpii_256x256.py',
None,
device='cpu')
image_name = 'tests/data/mpii/004645041.jpg'
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(
pose_model,
image_name, [[50, 50, 0, 0]],
format='xywh',
dataset='TopDownMpiiDataset')
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='TopDownMpiiDataset')
with pytest.raises(NotImplementedError):
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='test')
def main():
"""Visualize the demo images.
Using mmdet to detect the human.
"""
parser = ArgumentParser()
parser.add_argument('det_config', help='Config file for detection')
parser.add_argument('det_checkpoint', help='Checkpoint file for detection')
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('--bbox-thr',
type=float,
default=0.3,
help='Bounding box score threshold')
parser.add_argument('--kpt-thr',
type=float,
default=0.3,
help='Keypoint score threshold')
parser.add_argument('--iou-thr',
type=float,
default=0.3,
help='IoU score threshold')
assert has_mmdet, 'Please install mmdet to run the demo.'
args = parser.parse_args()
assert args.show or (args.out_video_root != '')
assert args.det_config is not None
assert args.det_checkpoint is not None
det_model = init_detector(args.det_config,
args.det_checkpoint,
device=args.device.lower())
# 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']
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
next_id = 0
pose_results = []
while (cap.isOpened()):
pose_results_last = pose_results
flag, img = cap.read()
if not flag:
break
# test a single image, the resulting box is (x1, y1, x2, y2)
mmdet_results = inference_detector(det_model, img)
# keep the person class bounding boxes.
person_results = process_mmdet_results(mmdet_results)
# test a single image, with a list of bboxes.
pose_results, returned_outputs = inference_top_down_pose_model(
pose_model,
img,
person_results,
bbox_thr=args.bbox_thr,
format='xyxy',
dataset=dataset,
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,
iou_thr=args.iou_thr)
# show the results
vis_img = vis_pose_tracking_result(pose_model,
img,
pose_results,
dataset=dataset,
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 cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
if save_out_video:
videoWriter.release()
cv2.destroyAllWindows()
def test_top_down_pose_tracking_demo():
# COCO demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
'coco/res50_coco_256x192.py',
None,
device='cpu')
image_name = 'tests/data/coco/000000000785.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
person_result = [{'bbox': [50, 50, 50, 100]}]
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
person_result,
format='xywh',
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
pose_results_last = pose_results
# AIC demo
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
'aic/res50_aic_256x192.py',
None,
device='cpu')
image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
person_result,
format='xywh',
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
for pose_result in pose_results:
del pose_result['bbox']
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
# OneHand10K demo
# build the pose model from a config file and a checkpoint file
pose_model = init_pose_model(
'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'
'onehand10k/res50_onehand10k_256x256.py',
None,
device='cpu')
image_name = 'tests/data/onehand10k/9.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(
pose_model,
image_name, [{
'bbox': [10, 10, 30, 30]
}],
format='xywh',
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
# InterHand2D demo
pose_model = init_pose_model(
'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'
'interhand2d/res50_interhand2d_all_256x256.py',
None,
device='cpu')
image_name = 'tests/data/interhand2.6m/image2017.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name, [{
'bbox': [50, 50, 0, 0]
}],
format='xywh',
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
pose_results_last = pose_results
# MPII demo
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
'mpii/res50_mpii_256x256.py',
None,
device='cpu')
image_name = 'tests/data/mpii/004645041.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
# test a single image, with a list of bboxes.
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name, [{
'bbox': [50, 50, 0, 0]
}],
format='xywh',
dataset_info=dataset_info)
pose_results, next_id = get_track_id(pose_results, pose_results_last,
next_id)
# show the results
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset_info=dataset_info)
def main():
"""Visualize the demo images.
Using mmdet to detect the human.
"""
parser = ArgumentParser()
parser.add_argument('det_config', help='Config file for detection')
parser.add_argument('det_checkpoint', help='Checkpoint file for detection')
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('--det-cat-id',
type=int,
default=1,
help='Category id for bounding box detection model')
parser.add_argument('--bbox-thr',
type=float,
default=0.3,
help='Bounding box score threshold')
parser.add_argument('--kpt-thr',
type=float,
default=0.3,
help='Keypoint score threshold')
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')
assert has_mmdet, 'Please install mmdet to run the demo.'
args = parser.parse_args()
assert args.show or (args.out_video_root != '')
assert args.det_config is not None
assert args.det_checkpoint is not None
det_model = init_detector(args.det_config,
args.det_checkpoint,
device=args.device.lower())
# 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)
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()):
pose_results_last = pose_results
flag, img = cap.read()
if not flag:
break
# test a single image, the resulting box is (x1, y1, x2, y2)
mmdet_results = inference_detector(det_model, img)
# keep the person class bounding boxes.
person_results = process_mmdet_results(mmdet_results, args.det_cat_id)
# test a single image, with a list of bboxes.
pose_results, returned_outputs = inference_top_down_pose_model(
pose_model,
img,
person_results,
bbox_thr=args.bbox_thr,
format='xyxy',
dataset=dataset,
dataset_info=dataset_info,
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()
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']
assert (dataset == 'BottomUpCocoDataset')
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,
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,
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 cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
if save_out_video:
videoWriter.release()
cv2.destroyAllWindows()
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='(Deprecated, please use --smooth and --smooth-filter-cfg) '
'Using One_Euro_Filter for smoothing.')
parser.add_argument(
'--smooth',
action='store_true',
help='Apply a temporal filter to smooth the pose estimation results. '
'See also --smooth-filter-cfg.')
parser.add_argument(
'--smooth-filter-cfg',
type=str,
default='configs/_base_/filters/one_euro.py',
help='Config file of the filter to smooth the pose estimation '
'results. See also --smooth.')
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)
video = mmcv.VideoReader(args.video_path)
assert video.opened, 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 = video.fps
size = (video.width, video.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
# build pose smoother for temporal refinement
if args.euro:
warnings.warn(
'Argument --euro will be deprecated in the future. '
'Please use --smooth to enable temporal smoothing, and '
'--smooth-filter-cfg to set the filter config.',
DeprecationWarning)
smoother = Smoother(filter_cfg='configs/_base_/filters/one_euro.py',
keypoint_dim=2)
elif args.smooth:
smoother = Smoother(filter_cfg=args.smooth_filter_cfg, keypoint_dim=2)
else:
smoother = None
# e.g. use ('backbone', ) to return backbone feature
output_layer_names = None
next_id = 0
pose_results = []
for cur_frame in mmcv.track_iter_progress(video):
pose_results_last = pose_results
pose_results, _ = inference_bottom_up_pose_model(
pose_model,
cur_frame,
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)
# post-process the pose results with smoother
if smoother:
pose_results = smoother.smooth(pose_results)
# show the results
vis_frame = vis_pose_tracking_result(pose_model,
cur_frame,
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_frame)
if save_out_video:
videoWriter.write(vis_frame)
if args.show and cv2.waitKey(1) & 0xFF == ord('q'):
break
if save_out_video:
videoWriter.release()
if args.show:
cv2.destroyAllWindows()
def test_inference_without_dataset_info():
# Top down
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
'coco/res50_coco_256x192.py',
None,
device='cpu')
if 'dataset_info' in pose_model.cfg:
_ = pose_model.cfg.pop('dataset_info')
image_name = 'tests/data/coco/000000000785.jpg'
person_result = []
person_result.append({'bbox': [50, 50, 50, 100]})
with pytest.warns(DeprecationWarning):
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
person_result,
format='xywh')
with pytest.warns(DeprecationWarning):
vis_pose_result(pose_model, image_name, pose_results)
with pytest.raises(NotImplementedError):
with pytest.warns(DeprecationWarning):
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
person_result,
format='xywh',
dataset='test')
# Bottom up
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
'coco/res50_coco_512x512.py',
None,
device='cpu')
if 'dataset_info' in pose_model.cfg:
_ = pose_model.cfg.pop('dataset_info')
image_name = 'tests/data/coco/000000000785.jpg'
with pytest.warns(DeprecationWarning):
pose_results, _ = inference_bottom_up_pose_model(
pose_model, image_name)
with pytest.warns(DeprecationWarning):
vis_pose_result(pose_model, image_name, pose_results)
# Top down tracking
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
'coco/res50_coco_256x192.py',
None,
device='cpu')
if 'dataset_info' in pose_model.cfg:
_ = pose_model.cfg.pop('dataset_info')
image_name = 'tests/data/coco/000000000785.jpg'
person_result = [{'bbox': [50, 50, 50, 100]}]
with pytest.warns(DeprecationWarning):
pose_results, _ = inference_top_down_pose_model(pose_model,
image_name,
person_result,
format='xywh')
pose_results, _ = get_track_id(pose_results, [], next_id=0)
with pytest.warns(DeprecationWarning):
vis_pose_tracking_result(pose_model, image_name, pose_results)
with pytest.raises(NotImplementedError):
with pytest.warns(DeprecationWarning):
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='test')
# Bottom up tracking
pose_model = init_pose_model(
'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
'coco/res50_coco_512x512.py',
None,
device='cpu')
if 'dataset_info' in pose_model.cfg:
_ = pose_model.cfg.pop('dataset_info')
image_name = 'tests/data/coco/000000000785.jpg'
with pytest.warns(DeprecationWarning):
pose_results, _ = inference_bottom_up_pose_model(
pose_model, image_name)
pose_results, next_id = get_track_id(pose_results, [], next_id=0)
with pytest.warns(DeprecationWarning):
vis_pose_tracking_result(pose_model,
image_name,
pose_results,
dataset='BottomUpCocoDataset')
# Pose lifting
pose_model = init_pose_model(
'configs/body/3d_kpt_sview_rgb_img/pose_lift/'
'h36m/simplebaseline3d_h36m.py',
None,
device='cpu')
pose_det_result = {
'keypoints': np.zeros((17, 3)),
'bbox': [50, 50, 50, 50],
'track_id': 0,
'image_name': 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg',
}
if 'dataset_info' in pose_model.cfg:
_ = pose_model.cfg.pop('dataset_info')
pose_results_2d = [[pose_det_result]]
dataset = pose_model.cfg.data['test']['type']
pose_results_2d = extract_pose_sequence(pose_results_2d,
frame_idx=0,
causal=False,
seq_len=1,
step=1)
with pytest.warns(DeprecationWarning):
_ = inference_pose_lifter_model(pose_model,
pose_results_2d,
dataset,
with_track_id=False)
with pytest.warns(DeprecationWarning):
pose_lift_results = inference_pose_lifter_model(pose_model,
pose_results_2d,
dataset,
with_track_id=True)
for res in pose_lift_results:
res['title'] = 'title'
with pytest.warns(DeprecationWarning):
vis_3d_pose_result(pose_model,
pose_lift_results,
img=pose_results_2d[0][0]['image_name'],
dataset=dataset)
with pytest.raises(NotImplementedError):
with pytest.warns(DeprecationWarning):
_ = inference_pose_lifter_model(pose_model,
pose_results_2d,
dataset='test')
def main():
"""Visualize the demo images.
Using mmdet to detect the human.
"""
parser = ArgumentParser()
parser.add_argument('tracking_config', help='Config file for tracking')
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('--bbox-thr',
type=float,
default=0.3,
help='Bounding box score threshold')
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')
parser.add_argument(
'--smooth',
action='store_true',
help='Apply a temporal filter to smooth the pose estimation results. '
'See also --smooth-filter-cfg.')
parser.add_argument(
'--smooth-filter-cfg',
type=str,
default='configs/_base_/filters/one_euro.py',
help='Config file of the filter to smooth the pose estimation '
'results. See also --smooth.')
parser.add_argument(
'--use-multi-frames',
action='store_true',
default=False,
help='whether to use multi frames for inference in the pose'
'estimation stage. Default: False.')
parser.add_argument(
'--online',
action='store_true',
default=False,
help='inference mode. If set to True, can not use future frame'
'information when using multi frames for inference in the pose'
'estimation stage. Default: False.')
assert has_mmtrack, 'Please install mmtrack to run the demo.'
args = parser.parse_args()
assert args.show or (args.out_video_root != '')
assert args.tracking_config is not None
print('Initializing model...')
tracking_model = init_tracking_model(args.tracking_config,
None,
device=args.device.lower())
# 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)
# read video
video = mmcv.VideoReader(args.video_path)
assert video.opened, 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 = video.fps
size = (video.width, video.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)
# frame index offsets for inference, used in multi-frame inference setting
if args.use_multi_frames:
assert 'frame_indices_test' in pose_model.cfg.data.test.data_cfg
indices = pose_model.cfg.data.test.data_cfg['frame_indices_test']
# build pose smoother for temporal refinement
if args.smooth:
smoother = Smoother(filter_cfg=args.smooth_filter_cfg, keypoint_dim=2)
else:
smoother = None
# whether to return heatmap, optional
return_heatmap = False
# return the output of some desired layers,
# e.g. use ('backbone', ) to return backbone feature
output_layer_names = None
print('Running inference...')
for frame_id, cur_frame in enumerate(mmcv.track_iter_progress(video)):
if args.use_multi_frames:
frames = collect_multi_frames(video, frame_id, indices,
args.online)
mmtracking_results = inference_mot(tracking_model,
cur_frame,
frame_id=frame_id)
# keep the person class bounding boxes.
person_results = process_mmtracking_results(mmtracking_results)
# test a single image, with a list of bboxes.
pose_results, returned_outputs = inference_top_down_pose_model(
pose_model,
frames if args.use_multi_frames else cur_frame,
person_results,
bbox_thr=args.bbox_thr,
format='xyxy',
dataset=dataset,
dataset_info=dataset_info,
return_heatmap=return_heatmap,
outputs=output_layer_names)
if smoother:
pose_results = smoother.smooth(pose_results)
# show the results
vis_frame = vis_pose_tracking_result(pose_model,
cur_frame,
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('Frame', vis_frame)
if save_out_video:
videoWriter.write(vis_frame)
if args.show and cv2.waitKey(1) & 0xFF == ord('q'):
break
if save_out_video:
videoWriter.release()
if args.show:
cv2.destroyAllWindows()
