path_suffix = clip_path[len(prefix):]
# This is same number used to identify pose clip (not sequential!)
tmp2_id = video_entry['video_id']
new_name = 'vid%d' % tmp2_id
meta_dict[new_name] = {'path_suffix': path_suffix, 'tmp2_id': tmp2_id}
meta = meta_dict[vid_name]
path_suffix = meta['path_suffix']
tmp2_id = meta['tmp2_id']
tmp2_id = int(re.match(r'^vid(\d+)$', vid_name).groups()[0])
all_frame_fns = load_sorted_paths(os.path.join(FRAME_DIR, path_suffix))
# for some reason there is one video directory with a subdirectory that has
# a numeric name
frame_paths = [f for f in all_frame_fns if f.endswith('.jpg')]
pose_seqs = poses[None, ...]
seq_names = ['Full sequence']
action_labels = action_names[actions]
assert len(action_labels) == len(poses)
print('Action names: ' + ', '.join(action_names))
print('Actions: ' + ', '.join(action_labels))
anims = draw_poses('Original training poses in %s' % H5_PATH,
parents,
pose_seqs,
frame_paths=[frame_paths] * 3,
subplot_titles=seq_names,
fps=50,
action_labels=action_labels)
plt.show()
# a numeric name
all_frame_fns = [f for f in all_frame_fns if f.endswith('.jpg')]
frame_paths = [all_frame_fns[i] for i in d['frame_inds']]
pose_seqs = np.stack(
(d['true_poses'], d['prior_poses'], d['posterior_poses']), axis=0)
seq_names = ['True poses', 'Prior prediction', 'Posterior prediction']
pose_mat_path = os.path.join(POSE_DIR, 'pose_clip_%d.mat' % tmp2_id)
pose_mat = loadmat(pose_mat_path, squeeze_me=True)
# important not to let return value be gc'd (anims won't run otherwise!)
anim = draw_poses('Completed poses in %s' % args.completion_path,
d['parents'],
pose_seqs,
frame_paths=[frame_paths] * 3,
subplot_titles=seq_names,
fps=50 / 9.0,
crossover=d['crossover_time'])
if args.vid_dir is not None:
# save video
print('Saving video')
try:
os.makedirs(args.vid_dir)
except FileExistsError:
pass
bn = os.path.basename(args.completion_path).rsplit('.')[0]
key = d['vid_name'] + '-' + bn
anim.save(
os.path.join(args.vid_dir, key + '.mp4'),
action_name_map = np.asarray(
json.loads(fp['/action_names'].value.tostring().decode('utf8')))
parents = fp['/parents'].value
pose_seqs = poses[None, ...]
action_labels = action_name_map[action_ids]
frame_paths = load_sorted_paths(os.path.join(FRAME_DIR, vid_name))
seq_names = ['True poses']
all_mat_pose_paths = load_sorted_paths(os.path.join(POSE_DIR, vid_name))
mat_fst_pose_path = all_mat_pose_paths[0]
with h5py.File(mat_fst_pose_path) as fp:
# gives us 2*14
ref_pose = fp['pose'].value[:, :8].astype('float')
alpha, beta = alignment_constant(pose_seqs[0, 0], ref_pose)
pose_seqs = pose_seqs * alpha + beta[None, None, :, None]
assert len(action_labels) == len(poses)
print('Action names: ' + ', '.join(action_name_map))
print('Actions: ' + ', '.join(action_labels))
# important not to let return value be gc'd (anims won't run otherwise!)
anims = draw_poses('Poses in %s' % vid_name,
parents,
pose_seqs,
frame_paths=[frame_paths] * 3,
subplot_titles=seq_names,
fps=50 / 9.0,
action_labels=action_labels)
plt.show()