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()
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)
# np.save('{}/{}_label.npy'.format(out_path, part), sample_label)
fp = open_memmap(
'{}/{}_data.npy'.format(out_path, part),
dtype='float32',
mode='w+',
shape=(len(sample_label), 3, max_frame, num_joint, max_body))
for i, s in enumerate(sample_name):
print_toolbar(i * 1.0 / len(sample_label),
'({:>5}/{:<5}) Processing {:>5}-{:<5} data: '.format(
i + 1, len(sample_name), benchmark, part))
data = read_xyz(
os.path.join(data_path, s), max_body=max_body, num_joint=num_joint)
fp[i, :, 0:data.shape[1], :, :] = data
end_toolbar()
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()
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)
# np.save('{}/{}_label.npy'.format(out_path, part), sample_label)
fp = open_memmap(
'{}/{}_data.npy'.format(out_path, part),
dtype='float32',
mode='w+',
shape=(len(sample_label), 3, max_frame, num_joint, max_body))
for i, s in enumerate(sample_name):
print_toolbar(i * 1.0 / len(sample_label),
'({:>5}/{:<5}) Processing {:>5}-{:<5} data: '.format(
i + 1, len(sample_name), benchmark, part))
data = read_xyz(
os.path.join(data_path, s), max_body=max_body, num_joint=num_joint)
fp[i, :, 0:data.shape[1], :, :] = data
end_toolbar()
def gendata(data_path,
out_path,
ignored_sample_path=None,
benchmark='cross_view_data',
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 = []
data_lens = []
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])
camera_id = int(
filename[filename.find('S') + 1:filename.find('S') + 4])
if benchmark == 'cross_view_data':
istraining = (camera_id in training_cameras)
elif benchmark == 'cross_subject_data':
istraining = (subject_id in training_subjects)
else:
raise ValueError()
if part == 'train':
issample = istraining
elif part == 'test':
issample = not (istraining)
else:
raise ValueError()
if issample:
# print("in here")
# if action_class == 120 :
# print("120")
# if action_class == 1:
# print("0")
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)
pickle.dump( list(sample_label ), f)
# np.save('{}/{}_label.npy'.format(out_path, part), sample_label)
fp = open_memmap(
'{}/trans_{}_data.npy'.format(out_path, part),
dtype='float32',
mode='w+',
shape=(len(sample_label), max_frame, 3 * num_joint * max_body))
for i, s in enumerate(sample_name):
print_toolbar(i * 1.0 / len(sample_label),
'({:>5}/{:<5}) Processing {:>5}-{:<5} data: '.format(
i + 1, len(sample_name), benchmark, part))
data = read_xyz(
os.path.join(data_path, s), max_body=max_body, num_joint=num_joint) # C, T, V, M
C, T, V, M = data.shape
data = data.transpose([1, -1, -2, 0]).reshape(T, -1) # T, M * V * C
data = ntu_tranform_skeleton(data)
# attention !!
if data.shape[0] > max_frame:
fp[i] = data[0:max_frame]
else:
# print(data.shape)
fp[i, 0:data.shape[0]] = data
data_lens.append(data.shape[0])
with open('{}/{}_sample_len.pkl'.format(out_path, part), 'wb') as f:
pickle.dump( data_lens, f)
end_toolbar()