def generate_data(data_path,
out_path,
ignore_sample_path=None,
benchmark='cv',
dataset='test'):
if ignore_sample_path != None:
with open(ignore_sample_path, 'r') as f:
ignore_samples = [line.strip() + '.skeleton' for line in f.readlines()]
else:
ignore_samples = []
sample_name = []
sample_label = []
for filename in os.listdir(data_path):
if filename in ignore_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 == 'cv':
training = (camera_id in training_cameras)
elif benchmark == 'cs':
training = (subject_id in training_subjects)
else:
raise ValueError()
if dataset == 'train':
training = training
elif dataset == 'test':
training = not training
else:
raise ValueError()
if training:
sample_name.append(filename)
sample_label.append(action_class - 1)
if dataset == 'train':
sample_name, val_name, sample_label, val_label = train_test_split(sample_name, sample_label, test_size=0.05,
random_state=10000)
with open('{}/val_label.pkl'.format(out_path), 'wb') as f:
pickle.dump((val_name, list(val_label)), f)
f_data = open_memmap('{}/val_data.npy'.format(out_path),
dtype='float32',
mode='w+',
shape=(len(val_label), 3, max_frame, num_joint, max_body))
for idx, s in enumerate(val_name):
print_output(idx * 1.0 / len(val_label), '({:>5}/{:<5}) Processing {:>5}-{:<5} data: '
.format(idx + 1, len(val_name), benchmark, 'val'))
data = read_xyz(os.path.join(data_path, s), max_body=max_body, num_joint=num_joint)
f_data[idx, :, 0:data.shape[1], :, :] = data
with open('{}/{}_label.pkl'.format(out_path, dataset), 'wb') as f:
pickle.dump((sample_name, list(sample_label)), f)
f_data = open_memmap('{}/{}_data.npy'.format(out_path, dataset),
dtype='float32',
mode='w+',
shape=(len(sample_label), 3, max_frame, num_joint, max_body))
for idx, s in enumerate(sample_name):
print_output(idx * 1.0 / len(sample_label), '({:>5}/{:<5}) Processing {:>5}-{:<5} data: '
.format(idx + 1, len(sample_name), benchmark, dataset))
data = read_xyz(os.path.join(data_path, s), max_body=max_body, num_joint=num_joint)
f_data[idx, :, 0:data.shape[1], :, :] = data
sys.stdout.write('\n')
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), 'w') 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 make_data_ntu(origin_path, output_path, benchmark, part):
# ignore sample
if ignore_sample_path != None:
with open(ignore_sample_path, 'r') as f:
ignored_sample = [
line.strip() + '.skeleton' for line in f.readlines()
]
else:
ignored_sample = []
sample_name = []
sample_label = []
# start
for filename in os.listdir(origin_path):
# pass: ignore sample
if filename in ignored_sample:
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])
# xsub or xview
if benchmark == 'xview':
istraining = (camera_id in training_cameras)
elif benchmark == 'xsub':
istraining = (subject_id in training_subjects)
else:
raise ValueError()
# train or test
if part == 'train':
issample = istraining
elif part == 'test':
issample = not (istraining)
else:
raise ValueError()
if issample:
# storage: data name
sample_name.append(filename)
# storage: label
sample_label.append(action_class - 1)
# store: label
with open('{}/{}_label.pkl'.format(output_path, part), 'wb') as f:
pickle.dump((sample_name, list(sample_label)), f)
# store: data
fp = open_memmap('{}/{}_joint.npy'.format(output_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}) Writing {:>5}-{:<5} data: '.format(
i + 1, len(sample_name), benchmark, part))
data = read_xyz(os.path.join(origin_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()
####OVERIDING FOR ZERO SHOT
if part == 'train' and action_class in unseen_classes:
issample = False
if part == 'val' and action_class in unseen_classes:
issample = True
###################TODO: SO in our train seen classes split we only include this 2 subjects and cameras and only seen classes, but
########for test data we include the remaining classes of all cameras and subjects irrespective
#### SO DURING TRAINING IF YOU FIND THAT UNSEEN CLASS JUST NEGLECT IT DON"T EVEN LOOK AT CAMERAS AND SUBJECTS, AND DURING TEST DATA, IF YOU
#### FIND THE UNSEEN CLASSES INCLUDE THEM NO MATTER WHAT THERE CAMERA AND SUBJECT ID IS ......
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 = []
pdb.set_trace()
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()
###CHECKS IF THE FILE IS PART OF TRANING DATA OR TEST DATA
###################TODO: SO in our train seen classes split we only include this 2 subjects and cameras and only seen classes, but
########for test data we include the remaining classes of all cameras and subjects irrespective
#### SO DURING TRAINING IF YOU FIND THAT UNSEEN CLASS JUST NEGLECT IT DON"T EVEN LOOK AT CAMERAS AND SUBJECTS, AND DURING TEST DATA, IF YOU
#### FIND THE UNSEEN CLASSES INCLUDE THEM NO MATTER WHAT THERE CAMERA AND SUBJECT ID IS ......
######TODO IMP.....WE NEED TO ENSURE THIS HAPPENS AND WE STORE IN THE SAME ORDER FOR FEATURES AS WELL
#### THIS IS NOT AN ISSUE BECAUSE FOR FINDING FEATURES IT READS THE FILE NAME STORED IN THE LABELS ACCORDINGLY
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)
####SUBTRACTING -1 from the action class
####TILL NOW U READ ALL THE ACTION LABELS APPEND THE FILE NAME AND THE ACTION IN A LIST AND SAVE IT AS A PICKE FILE
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)
pdb.set_trace()
fp = open_memmap('{}/{}_data.npy'.format(out_path, part),
dtype='float32',
mode='w+',
shape=(len(sample_label), 3, max_frame, num_joint,
max_body))
###fp is just a numpy array
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)
####data.shape = (3, 178, 25, 2)
###so if there frames less then 300, then the remaining ones are assigned 0
fp[i, :, 0:data.shape[1], :, :] = data
end_toolbar()
