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: #61->12, 65->16
# 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 != 3)
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 = []
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 != 55) and (action_class != 56) and (action_class !=
58): #6,7,9
continue
action_class -= 49
set_up = int(filename[filename.find('S') + 1:filename.find('S') + 4])
if set_up > 1:
continue
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)
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
print(filename)
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
############################################################
istraining = True
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
#testplt(fp[i,:,0,:,:])
print(fp.shape)
fp = pre_normalization(fp) #preprocess.py
for i in range(fp.shape[0]):
testplt(fp[i, :, 0, :, :])
np.save('{}/{}_data_joint.npy'.format(out_path, part), fp)