def gendata(data_path,
out_path,
ignored_sample_path=None,
benchmark='xview',
part='eval'):
if ignored_sample_path != None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [
line.strip() + '.skeleton' for line in f.readlines()
]
else:
ignored_samples = []
sample_name = []
sample_label = []
for filename in os.listdir(data_path):
if filename in ignored_samples:
continue
action_class = int(filename[filename.find('A') + 1:filename.find('A') +
4])
subject_id = int(filename[filename.find('P') + 1:filename.find('P') +
4])
camera_id = int(filename[filename.find('C') + 1:filename.find('C') +
4])
if benchmark == 'xview':
istraining = (camera_id in training_cameras)
elif benchmark == 'xsub':
istraining = (subject_id in training_subjects)
else:
raise ValueError(
'Invalid benchmark provided: {}'.format(benchmark))
if part == 'train':
issample = istraining
elif part == 'val':
issample = not (istraining)
else:
raise ValueError('Invalid data part provided: {}'.format(part))
if issample:
sample_name.append(filename)
sample_label.append(action_class - 1)
with open('{}/{}_label.pkl'.format(out_path, part), 'wb') as f:
pickle.dump((sample_name, list(sample_label)), f)
# Create data tensor with shape (# examples (N), C, T, V, M)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true),
dtype=np.float32)
# Fill in the data tensor `fp` one training example a time
for i, s in enumerate(tqdm(sample_name)):
data = read_xyz(os.path.join(data_path, s),
max_body=max_body_kinect,
num_joint=num_joint)
fp[i, :, :data.shape[1], :, :] = data
fp = pre_normalization(fp)
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)
def gendata(file_list, out_path, ignored_sample_path, benchmark, part):
ignored_samples = []
if ignored_sample_path is not None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [
line.strip() + '.skeleton' for line in f.readlines()
]
sample_name = []
sample_label = []
sample_paths = []
for folder, filename in sorted(file_list):
if filename in ignored_samples:
continue
path = os.path.join(folder, filename)
setup_loc = filename.find('S')
subject_loc = filename.find('P')
action_loc = filename.find('A')
setup_id = int(filename[(setup_loc + 1):(setup_loc + 4)])
subject_id = int(filename[(subject_loc + 1):(subject_loc + 4)])
action_class = int(filename[(action_loc + 1):(action_loc + 4)])
if benchmark == 'xsub':
istraining = (subject_id in training_subjects)
elif benchmark == 'xset':
istraining = (setup_id in training_setups)
else:
raise ValueError(f'Unsupported benchmark: {benchmark}')
if part == 'train':
issample = istraining
elif part == 'val':
issample = not (istraining)
else:
raise ValueError(f'Unsupported dataset part: {part}')
if issample:
sample_paths.append(path)
sample_label.append(action_class - 1) # to 0-indexed
# Save labels
with open(f'{out_path}/{part}_label.pkl', 'wb') as f:
pickle.dump((sample_paths, list(sample_label)), f)
# Create data tensor (N,C,T,V,M)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true),
dtype=np.float32)
# Fill in the data tensor `fp` one training example a time
for i, s in enumerate(tqdm(sample_paths, dynamic_ncols=True)):
data = read_xyz(s, max_body=max_body_kinect, num_joint=num_joint)
# Fill (C,T,V,M) to data tensor (N,C,T,V,M)
fp[i, :, 0:data.shape[1], :, :] = data
# Perform preprocessing on data tensor
fp = pre_normalization(fp)
# Save input data (train/val)
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)
def gendata(data_path,
out_path,
ignored_sample_path=None,
benchmark='xview',
part='eval'):
if ignored_sample_path != None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [
line.strip() + '.skeleton' for line in f.readlines()
]
else:
ignored_samples = []
sample_name = []
sample_label = []
for filename in os.listdir(data_path):
if filename in ignored_samples:
continue
action_class = int(filename[filename.find('A') + 1:filename.find('A') +
4])
if action_class > 60: #60=>11, 61=>12
continue
#action_class -= 49 #55->6, 56->7, 58 -> 9 action_map = {"hand_shake":"58", "pass_object":"56", "hug":"55"}
#subject_id = int(
# filename[filename.find('P') + 1:filename.find('P') + 4])
camera_id = int(filename[filename.find('C') + 1:filename.find('C') +
4])
#istraining = (camera_id != 1)
istraining = (camera_id != 4)
if part == 'train':
issample = istraining
elif part == 'val':
issample = not (istraining)
else:
raise ValueError()
if issample:
sample_name.append(filename)
sample_label.append(action_class - 1)
with open('{}/{}_label.pkl'.format(out_path, part), 'wb') as f:
pickle.dump((sample_name, list(sample_label)), f)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true),
dtype=np.float32)
for i, s in enumerate(tqdm(sample_name)):
data = read_xyz(os.path.join(data_path, s),
max_body=max_body_kinect,
num_joint=num_joint)
#def read_xyz(file, max_body=4, num_joint=25):
fp[i, :, 0:data.shape[1], :, :] = data
fp = pre_normalization(fp)
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)
def gendata(data_path,
out_path,
ignored_sample_path=None,
benchmark='xview',
part='eval'):
if ignored_sample_path != None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [
line.strip() + '.skeleton' for line in f.readlines()
]
else:
ignored_samples = []
sample_name = []
sample_label = []
sample_info = []
for filename in os.listdir(data_path):
if filename in ignored_samples:
continue
info = extract(filename)
if benchmark == 'xview':
istraining = (info[1] in training_cameras)
elif benchmark == 'xsub':
istraining = (info[2] in training_subjects)
else:
raise ValueError()
if part == 'train':
issample = istraining
elif part == 'test':
issample = not (istraining)
else:
raise ValueError()
if issample:
sample_name.append(filename)
sample_label.append(info[4] - 1)
sample_info.append(info)
with open('{}/{}_label.pkl'.format(out_path, part), 'wb') as f:
pickle.dump((sample_info, list(sample_label)), f)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true),
dtype=np.float32)
for i, s in enumerate(tqdm(sample_name)):
data = read_xyz(os.path.join(data_path, s),
max_body=max_body_kinect,
num_joint=num_joint)
fp[i, :, 0:data.shape[1], :, :] = data
fp = pre_normalization(fp)
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)
def gendata(data_path, out_path, ignored_sample_path=None, benchmark='xsub', set_name='val'):
if ignored_sample_path != None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [line.strip() + '.skeleton' for line in f.readlines()]
else:
ignored_samples = []
sample_name = []
sample_label = []
for filename in os.listdir(data_path):
if filename in ignored_samples:
continue
action_class = int(filename[filename.find('A') + 1:filename.find('A') + 4])
subject_id = int(filename[filename.find('P') + 1:filename.find('P') + 4])
camera_id = int(filename[filename.find('C') + 1:filename.find('C') + 4])
if benchmark == 'xview':
istraining = (camera_id in training_cameras)
elif benchmark == 'xsub':
istraining = (subject_id in training_subjects)
else:
raise ValueError()
if set_name == 'train':
issample = istraining
elif set_name == 'val':
issample = not (istraining)
else:
raise ValueError()
if issample:
sample_name.append(filename)
sample_label.append(action_class - 1)
print(len(sample_label))
with open('{}/{}_label.pkl'.format(out_path, set_name), 'wb') as f:
pickle.dump((sample_name, list(sample_label)), f)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true), dtype=np.float32)
for i, s in enumerate(tqdm(sample_name)):
print(s)
data = read_xyz(os.path.join(data_path, s), max_body=max_body_kinect, num_joint=num_joint)
fp[i, :, 0:data.shape[1], :, :] = data
fp = pre_normalization(fp)
np.save('{}/{}_data_joint_pad.npy'.format(out_path, set_name), fp)
def gendata(data_path,
out_path,
ignored_sample_path=None,
benchmark='xview',
part='eval'):
if ignored_sample_path != None:
with open(ignored_sample_path, 'r') as f:
ignored_samples = [
line.strip() + '.skeleton' for line in f.readlines()
]
else:
ignored_samples = []
sample_name = []
sample_label = []
for filename in os.listdir(data_path):
if filename in ignored_samples:
continue
action_class = int(filename[filename.find('A') + 1:filename.find('A') +
4])
subject_id = int(filename[filename.find('P') + 1:filename.find('P') +
4])
camera_id = int(filename[filename.find('C') + 1:filename.find('C') +
4])
############################################################
action_class -= 49 #50->1 #50-60->1-11 #50-65 => 1-16
if action_class < 1:
continue
set_up = int(filename[filename.find('S') + 1:filename.find('S') + 4])
if set_up > 5:
break
############################################################
if benchmark == 'xview':
istraining = (camera_id in training_cameras)
elif benchmark == 'xsub':
istraining = (subject_id in training_subjects)
else:
raise ValueError()
if part == 'train':
issample = istraining
elif part == 'val':
issample = not (istraining)
else:
raise ValueError()
if issample:
sample_name.append(filename)
sample_label.append(action_class - 1)
with open('{}/{}_label.pkl'.format(out_path, part), 'wb') as f: #?????
pickle.dump((sample_name, list(sample_label)), f)
fp = np.zeros((len(sample_label), 3, max_frame, num_joint, max_body_true),
dtype=np.float32) #N,C,T,V,M
for i, s in enumerate(tqdm(sample_name)):
data = read_xyz(os.path.join(data_path, s),
max_body=max_body_kinect,
num_joint=num_joint)
fp[i, :, 0:data.shape[1], :, :] = data #50000,3,300,25,2
fp = pre_normalization(fp) #preprocess.py
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)
