1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos,
dim=0,
keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
if args.render:
print('Rendering...')
input_keypoints = keypoints[args.viz_subject][args.viz_action][
args.viz_camera].copy()
ground_truth = None
if args.viz_subject in dataset.subjects() and args.viz_action in dataset[
args.viz_subject]:
if 'positions_3d' in dataset[args.viz_subject][args.viz_action]:
ground_truth = dataset[args.viz_subject][
args.viz_action]['positions_3d'][args.viz_camera].copy()
if ground_truth is None:
print(
'INFO: this action is unlabeled. Ground truth will not be rendered.'
)
gen = UnchunkedGenerator(None,
None, [input_keypoints],
pad=pad,
causal_shift=causal_shift,
augment=args.test_time_augmentation,
kps_left=kps_left,
'Unable to create checkpoint directory:', args.checkpoint)
print('ARGS EVAL:::::', args.evaluate)
print('Loading dataset...')
dataset_path = 'data/data_3d_' + args.dataset + '.npz'
dataset = CustomDataset('data/data_2d_' + args.dataset +
'_' + args.keypoints + '.npz')
# print(dataset.subjects())
print(dataset['009_FL_R.MTS'])
dataset_poses = np.load('data/data_2d_' + args.dataset + '_' +
args.keypoints + '.npz', allow_pickle=True)
print('Preparing data...')
for subject in dataset.subjects():
for action in dataset[subject].keys():
anim = dataset[subject][action]
print(anim)
if 'positions' in anim:
positions_3d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(
anim['positions'], R=cam['orientation'], t=cam['translation'])
# Remove global offset, but keep trajectory in first position
pos_3d[:, 1:] -= pos_3d[:, :1]
positions_3d.append(pos_3d)
anim['positions_3d'] = positions_3d
print('Loading 2D detections...')
#don't use ellipsis to truncate arrays when printing
#np.set_printoptions(threshold=sys.maxsize)
#dataset init
from common.custom_dataset import CustomDataset
#check path of npz
print('PATH: outs/data_2d_' + args.dataset + '_' + args.keypoints + '.npz')
#create new CustomDataset object
dataset = CustomDataset('data/data_2d_' + args.dataset + '_' + args.keypoints +
'.npz') #NOTE CHANGE
print('Preparing data...')
print(dataset.subjects()) #looks like dict_keys(['../vids/output.mp4'])
for subject in dataset.subjects(
): #should have just one subject, which will be '../vids/output.mp4'
print(dataset[subject])
'''looks like {'custom': {'cameras': {'id': '../vids/output.mp4', 'res_w': 1080, 'res_h': 1920,
'azimuth': 70, 'orientation': array([ 0.14070565, -0.15007018, -0.7552408 , 0.62232804], dtype=float32),
'translation': array([1.841107 , 4.9552846, 1.5634454], dtype=float32)}}}'''
print(dataset[subject].keys()) #something like dict_keys(['custom'])
#should just be one key 'custom'
for action in dataset[subject].keys():
anim = dataset[subject][action]
print(anim)
'''anim looks like {'cameras': {'id': '../vids/output.mp4', 'res_w': 1080, 'res_h': 1920, 'azimuth': 70,
viz_fps = 30
resume = ''
evaluate = 'pretrained_h36m_detectron_coco.bin'
checkpoint = 'checkpoint'
dataset = CustomDataset(myvideos_path)
# print(dataset)
# print(dataset.cameras())
# print(dataset.fps())
# print(dataset.skeleton())
# print(dataset.subjects())
print('Preparing data...')
for subject in dataset.subjects():
for action in dataset[subject].keys():
anim = dataset[subject][action]
if 'positions' in anim:
positions_3d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(anim['positions'],
R=cam['orientation'],
t=cam['translation'])
pos_3d[:,
1:] -= pos_3d[:, :
1] # Remove global offset, but keep trajectory in first position
positions_3d.append(pos_3d)
anim['positions_3d'] = positions_3d