def __init__(self, config, train=True):
self.config = config
self.train = train
self.formatdata = FormatData(config)
if train:
subjects = os.listdir('{0}/{1}/{2}'.format(config.data_root,
'train',
config.filename))
else:
subjects = os.listdir('{0}/{1}/{2}'.format(config.data_root,
'test',
config.filename))
set = []
complete_train = []
for sub in subjects:
if train:
folderdir = '{0}/{1}/{2}/{3}'.format(config.data_root, 'train',
config.filename, sub)
else:
folderdir = '{0}/{1}/{2}/{3}'.format(config.data_root, 'test',
config.filename, sub)
for file in os.listdir(folderdir):
filedir = '{0}/{1}'.format(folderdir, file)
rawdata = np.load(filedir)['poses'][:, :66]
rawdata = self.frame_filter(rawdata)
# 去除帧太少的序列
if rawdata.shape[0] > 150:
set.append(rawdata)
if len(complete_train) == 0:
complete_train = copy.deepcopy(
set[-1]) #每个subjects取最后一个动作序列计算均值方差
else:
complete_train = np.append(complete_train, set[-1], axis=0)
if train:
print('video num for training：', len(set))
else:
print('video num for test：', len(set))
if not train and config.data_mean is None:
print('Load train dataset first!')
if train:
data_mean, data_std, dim_to_ignore, dim_to_use = utils.normalization_stats(
complete_train)
config.data_mean = data_mean
config.data_std = data_std
config.dim_to_ignore = dim_to_ignore
config.dim_to_use = dim_to_use
set = utils.normalize_data(set, config.data_mean, config.data_std,
config.dim_to_use)
# [S_num, frame_for_S, 60]
self.data = set
def __init__(self, config, train=True):
self.config = config
self.train = train
self.formatdata = FormatData(config)
if config.datatype == 'smpl':
train_path = config.data_root
else:
print('CMUDataset only support the smpl datatype')
sys.exit(1)
if config.filename != 'all':
if train:
subjects = config.subjects_train
else:
subjects = config.subjects_test
else:
print('Only support walking and dance action')
sys.exit(1)
set = []
complete_train = []
for sub in subjects:
folderdir = '{0}/{1}'.format(train_path, sub)
for file in os.listdir(folderdir):
filedir = '{0}/{1}'.format(folderdir, file)
rawdata = np.load(filedir)['poses'][:, :66]
rawdata = self.frame_filter(rawdata)
if rawdata.shape[0] > 70:
set.append(rawdata)
if len(complete_train) == 0:
complete_train = copy.deepcopy(
set[-1]) #每个subjects取最后一个动作序列计算均值方差
else:
complete_train = np.append(complete_train, set[-1], axis=0)
print('视频个数：', len(set))
if not train and config.data_mean is None:
print('Load train dataset first!')
if train and config.datatype == 'smpl':
data_mean, data_std, dim_to_ignore, dim_to_use = utils.normalization_stats(
complete_train)
config.data_mean = data_mean
config.data_std = data_std
config.dim_to_ignore = dim_to_ignore
config.dim_to_use = dim_to_use
set = utils.normalize_data(set, config.data_mean, config.data_std,
config.dim_to_use)
# [S_num, frame_for_S, 66]
self.data = set
def __init__(self, opt, training=True):
self.dataset_path = os.path.join(opt.dataset_root, opt.dataset)
train_data = []
self.seq_num = 0
self.set_start_nums = []
if opt.dataset == 'SBU':
# x and y are normalized as [0, 1] while z is normalized as[0, 7.8125]
if training:
sets = range(1, 19) # train
print(
"************start loading SBU training set!!!*************"
)
else:
sets = range(19, 22) # test
print(
"************start loading SBU testing set!!!*************"
)
self.train_data = {}
complete_train = None
for i in tqdm(sets): # all (1,22)
self.set_start_nums.append(self.seq_num)
for cat in range(1, 9):
tmp_path = os.path.join(self.dataset_path, '%02d' % i,
'%02d' % cat)
if not os.path.exists(tmp_path):
continue
for txt_file in os.listdir(tmp_path):
self.seq_num += 1
txt_path = os.path.join(tmp_path, txt_file)
one_seq_data = utils.read_SBU_txt(
txt_path) # np.array float32
if complete_train is None:
complete_train = one_seq_data
else:
complete_train = np.concatenate(
(complete_train, one_seq_data), axis=0)
train_data.append(one_seq_data)
print(complete_train.shape)
# calculate the mean and std
data_mean, data_std = utils.normalization_stats(complete_train)
opt.train_mean = data_mean
opt.train_std = data_std
self.train_data = utils.normalize_SBU_data(train_data, data_mean,
data_std)
def __init__(self, config, train=True):
self.config = config
self.train = train
self.lie_tsfm = LieTsfm(config)
self.formatdata = FormatData(config)
if config.datatype == 'lie':
if train:
train_path = './data/h3.6m/Train/train_lie'
else:
train_path = './data/h3.6m/Test/test_lie'
elif config.datatype == '':
train_path = './data/h3.6m/Train/train_xyz'
if train:
subjects = ['S1', 'S6', 'S7', 'S8', 'S9', 'S11']
else:
subjects = ['S5']
if config.filename == 'all':
actions = [
'directions', 'discussion', 'eating', 'greeting', 'phoning',
'posing', 'purchases', 'sitting', 'sittingdown', 'smoking',
'takingphoto', 'waiting', 'walking', 'walkingdog',
'walkingtogether'
]
else:
actions = [config.filename]
set = []
complete_train = []
for id in subjects:
for action in actions:
for i in range(2):
if config.datatype == 'lie':
filename = '{0}/{1}_{2}_{3}_lie.mat'.format(
train_path, id, action, i + 1)
rawdata = sio.loadmat(filename)['lie_parameters']
set.append(rawdata)
elif config.datatype == 'xyz':
filename = '{0}/{1}_{2}_{3}_xyz.mat'.format(
train_path, id, action, i + 1)
rawdata = sio.loadmat(filename)['joint_xyz']
set.append(rawdata.reshape(rawdata.shape[0], -1))
if len(complete_train) == 0:
complete_train = copy.deepcopy(set[-1])
else:
complete_train = np.append(complete_train, set[-1], axis=0)
if not train and config.data_mean is None:
print('Load train dataset first!')
if train and config.datatype == 'lie':
data_mean, data_std, dim_to_ignore, dim_to_use = utils.normalization_stats(
complete_train)
config.data_mean = data_mean
config.data_std = data_std
config.dim_to_ignore = dim_to_ignore
config.dim_to_use = dim_to_use
set = utils.normalize_data(set, config.data_mean, config.data_std,
config.dim_to_use)
# [S_num, frame_for_S, 54]
self.data = set