def test_interhand3d_demo():
# H36M demo
pose_model = init_pose_model(
'configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/'
'res50_interhand3d_all_256x256.py',
None,
device='cpu')
image_name = 'tests/data/interhand2.6m/image2017.jpg'
det_result = {
'image_name': image_name,
'bbox': [50, 50, 50, 50], # bbox format is 'xywh'
'camera_param': None,
'keypoints_3d_gt': None
}
det_results = [det_result]
dataset = pose_model.cfg.data['test']['type']
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
pose_results = inference_interhand_3d_model(pose_model,
image_name,
det_results,
dataset=dataset)
for res in pose_results:
res['title'] = 'title'
vis_3d_pose_result(
pose_model,
result=pose_results,
img=det_results[0]['image_name'],
dataset_info=dataset_info,
)
# test special cases
# Empty det results
_ = inference_interhand_3d_model(pose_model,
image_name, [],
dataset=dataset)
if torch.cuda.is_available():
_ = inference_interhand_3d_model(pose_model.cuda(),
image_name,
det_results,
dataset=dataset)
with pytest.raises(NotImplementedError):
_ = inference_interhand_3d_model(pose_model,
image_name,
det_results,
dataset='test')
def main():
parser = ArgumentParser()
parser.add_argument('pose_config', help='Config file for pose network')
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(
'--camera-param-file',
type=str,
default=None,
help='Camera parameter file for converting 3D pose predictions from '
' the pixel space to camera space. If None, keypoints in pixel space'
'will be visualized')
parser.add_argument(
'--gt-joints-file',
type=str,
default=None,
help='Optional arguement. Ground truth 3D keypoint parameter file. '
'If None, gt keypoints will not be shown and keypoints in pixel '
'space will be visualized.')
parser.add_argument(
'--rebase-keypoint-height',
action='store_true',
help='Rebase the predicted 3D pose so its lowest keypoint has a '
'height of 0 (landing on the ground). This is useful for '
'visualization when the model do not predict the global position '
'of the 3D pose.')
parser.add_argument(
'--show-ground-truth',
action='store_true',
help='If True, show ground truth keypoint if it is available.')
parser.add_argument('--show',
action='store_true',
default=False,
help='whether to show img')
parser.add_argument('--out-img-root',
type=str,
default=None,
help='Root of the output visualization images. '
'Default not saving the visualization images.')
parser.add_argument('--device',
default='cuda:0',
help='Device 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']
# load camera parameters
camera_params = None
if args.camera_param_file is not None:
camera_params = mmcv.load(args.camera_param_file)
# load ground truth joints parameters
gt_joint_params = None
if args.gt_joints_file is not None:
gt_joint_params = mmcv.load(args.gt_joints_file)
# load hand bounding boxes
det_results_list = []
for image_id, image in coco.imgs.items():
image_name = osp.join(args.img_root, image['file_name'])
ann_ids = coco.getAnnIds(image_id)
det_results = []
capture_key = str(image['capture'])
camera_key = image['camera']
frame_idx = image['frame_idx']
for ann_id in ann_ids:
ann = coco.anns[ann_id]
if camera_params is not None:
camera_param = {
key: camera_params[capture_key][key][camera_key]
for key in camera_params[capture_key].keys()
}
camera_param = _transform_interhand_camera_param(camera_param)
else:
camera_param = None
if gt_joint_params is not None:
joint_param = gt_joint_params[capture_key][str(frame_idx)]
gt_joint = np.concatenate([
np.array(joint_param['world_coord']),
np.array(joint_param['joint_valid'])
],
axis=-1)
else:
gt_joint = None
det_result = {
'image_name': image_name,
'bbox': ann['bbox'], # bbox format is 'xywh'
'camera_param': camera_param,
'keypoints_3d_gt': gt_joint
}
det_results.append(det_result)
det_results_list.append(det_results)
for i, det_results in enumerate(
mmcv.track_iter_progress(det_results_list)):
image_name = det_results[0]['image_name']
pose_results = inference_interhand_3d_model(pose_model,
image_name,
det_results,
dataset=dataset)
# Post processing
pose_results_vis = []
for idx, res in enumerate(pose_results):
keypoints_3d = res['keypoints_3d']
# normalize kpt score
if keypoints_3d[:, 3].max() > 1:
keypoints_3d[:, 3] /= 255
# get 2D keypoints in pixel space
res['keypoints'] = keypoints_3d[:, [0, 1, 3]]
# For model-predicted keypoints, channel 0 and 1 are coordinates
# in pixel space, and channel 2 is the depth (in mm) relative
# to root joints.
# If both camera parameter and absolute depth of root joints are
# provided, we can transform keypoint to camera space for better
# visualization.
camera_param = res['camera_param']
keypoints_3d_gt = res['keypoints_3d_gt']
if camera_param is not None and keypoints_3d_gt is not None:
# build camera model
camera = SimpleCamera(camera_param)
# transform gt joints from world space to camera space
keypoints_3d_gt[:, :3] = camera.world_to_camera(
keypoints_3d_gt[:, :3])
# transform relative depth to absolute depth
keypoints_3d[:21, 2] += keypoints_3d_gt[20, 2]
keypoints_3d[21:, 2] += keypoints_3d_gt[41, 2]
# transform keypoints from pixel space to camera space
keypoints_3d[:, :3] = camera.pixel_to_camera(
keypoints_3d[:, :3])
# rotate the keypoint to make z-axis correspondent to height
# for better visualization
vis_R = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])
keypoints_3d[:, :3] = keypoints_3d[:, :3] @ vis_R
if keypoints_3d_gt is not None:
keypoints_3d_gt[:, :3] = keypoints_3d_gt[:, :3] @ vis_R
# rebase height (z-axis)
if args.rebase_keypoint_height:
valid = keypoints_3d[..., 3] > 0
keypoints_3d[..., 2] -= np.min(keypoints_3d[valid, 2],
axis=-1,
keepdims=True)
res['keypoints_3d'] = keypoints_3d
res['keypoints_3d_gt'] = keypoints_3d_gt
# Add title
instance_id = res.get('track_id', idx)
res['title'] = f'Prediction ({instance_id})'
pose_results_vis.append(res)
# Add ground truth
if args.show_ground_truth:
if keypoints_3d_gt is None:
print('Fail to show ground truth. Please make sure that'
' gt-joints-file is provided.')
else:
gt = res.copy()
if args.rebase_keypoint_height:
valid = keypoints_3d_gt[..., 3] > 0
keypoints_3d_gt[...,
2] -= np.min(keypoints_3d_gt[valid, 2],
axis=-1,
keepdims=True)
gt['keypoints_3d'] = keypoints_3d_gt
gt['title'] = f'Ground truth ({instance_id})'
pose_results_vis.append(gt)
# Visualization
if args.out_img_root is None:
out_file = None
else:
os.makedirs(args.out_img_root, exist_ok=True)
out_file = osp.join(args.out_img_root, f'vis_{i}.jpg')
vis_3d_pose_result(
pose_model,
result=pose_results_vis,
img=det_results[0]['image_name'],
out_file=out_file,
dataset=dataset,
show=args.show,
kpt_score_thr=args.kpt_thr,
radius=args.radius,
thickness=args.thickness,
)