def _create(n_fold, parser, file_dir):
# ID抽出用正規表現
re_compile = re.compile("\d+")
cla_items = parse_cla(PATH_RES_SHAPE_SHREC_CLASS_TEST_CLA).items()
def find_class(binvox_id):
for i, pair in enumerate(cla_items):
name, ids = pair
if binvox_id in ids:
return i
raise IndexError
x = []
y = []
for f in tqdm.tqdm(os.listdir(file_dir)):
# binvoxファイルフルパス
fullpath = os.path.join(file_dir, f)
# binvox配列
shrec_voxel = parser(fullpath)
# shrecデータに割り振られたID
shrec_voxel_id = int(re_compile.findall(f)[0])
x.append([shrec_voxel, ])
y.append(find_class(shrec_voxel_id))
x = np.vstack(x).astype(np.uint8)
x = x.reshape((x.shape[0], 1, x.shape[1], x.shape[2], x.shape[3]))
y = np.asarray(y, dtype=np.uint8)
x_train, x_test, y_train, y_test = train_test_split(x, y,
train_size=1. - 1. / n_fold)
return SHRECVoxel(x_train, x_test, y_train, y_test)
def test_shrec_voxel():
"""
unittestを使わないSHRECVoxelの簡易テスト
:return:
"""
# TODO:SHRECデータセットをダウンロード・binvox変換の機構ができたら適宜unittest対応
classes = parse_cla(PATH_RES_SHAPE_SHREC_CLASS_TEST_CLA).keys()
shrec = SHRECVoxel.create_shrec_voxel(n_fold=6)
for x, y in zip(shrec.x_train, shrec.y_train):
print classes[y]
plot_voxel(x[0])
def _create(n_fold, parser, file_dir):
# ID抽出用正規表現
re_compile = re.compile("\d+")
cla_items = parse_cla(PATH_RES_SHAPE_SHREC_CLASS_TEST_CLA).items()
def find_class(binvox_id):
for i, pair in enumerate(cla_items):
name, ids = pair
if binvox_id in ids:
return i
raise IndexError
x = []
y = []
for f in tqdm.tqdm(os.listdir(file_dir)):
# binvoxファイルフルパス
fullpath = os.path.join(file_dir, f)
# binvox配列
shrec_voxel = parser(fullpath)
# shrecデータに割り振られたID
shrec_voxel_id = int(re_compile.findall(f)[0])
x.append([
shrec_voxel,
])
y.append(find_class(shrec_voxel_id))
x = np.vstack(x).astype(np.uint8)
x = x.reshape((x.shape[0], 1, x.shape[1], x.shape[2], x.shape[3]))
y = np.asarray(y, dtype=np.uint8)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
train_size=1. -
1. / n_fold)
return SHRECVoxel(x_train, x_test, y_train, y_test)
def create(is_co_class=False, is_cached=False, from_cached=False,
align_data=False):
if from_cached:
print 'load voxels from .npy ...'
if is_co_class:
load_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_CO_CLASS_ORIGIN
else:
load_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_DEFAULT_ORIGIN
try:
return PSBVoxel.load(load_path)
except IOError:
warnings.warn('psb_voxel data was not loaded.')
print 'load voxels from .off ...'
cla_train = parse_cla(
os.path.join(PATH_RES_SHAPE_PSB_CLASS, PSB_CLS_TRAIN))
cla_test = parse_cla(
os.path.join(PATH_RES_SHAPE_PSB_CLASS, PSB_CLS_TEST))
if is_co_class:
cla_list = list(
set(cla_train.keys()).intersection(set(cla_test.keys())))
else:
cla_list = list(set(cla_train.keys() + cla_test.keys()))
re_compile = re.compile("\d+")
def check_binvox_y(binvox_id, is_train):
cla = cla_train if is_train else cla_test
for c_label, c_ids in cla.items():
if binvox_id in c_ids:
try:
return cla_list.index(c_label)
except ValueError:
break
return None
x_train = []
x_test = []
y_train = []
y_test = []
for f in tqdm.tqdm(os.listdir(PATH_RES_SHAPE_PSB_BINVOX)):
binvox_path = os.path.join(PATH_RES_SHAPE_PSB_BINVOX, f)
if os.path.isdir(binvox_path):
continue
binvox = parse_binvox(binvox_path)
binvox_id = int(re_compile.findall(f)[0])
train_binvox_y = check_binvox_y(binvox_id, True)
if train_binvox_y is not None:
x_train.append(binvox)
y_train.append(train_binvox_y)
else:
test_binvox_y = check_binvox_y(binvox_id, False)
if test_binvox_y is not None:
x_test.append(binvox)
y_test.append(test_binvox_y)
else:
continue
x_train = np.asarray(x_train, dtype=np.uint8)
x_test = np.asarray(x_test, dtype=np.uint8)
y_train = np.asarray(y_train, dtype=np.uint8)
y_test = np.asarray(y_test, dtype=np.uint8)
x_train = x_train.reshape(
[x_train.shape[0], 1] + list(x_train.shape[1:]))
x_test = x_test.reshape([x_test.shape[0], 1] + list(x_test.shape[1:]))
if align_data:
aligned_size = min([len(x_train), len(x_test)])
x_train = x_train[:aligned_size]
x_test = x_test[:aligned_size]
y_train = y_train[:aligned_size]
y_test = y_test[:aligned_size]
data = PSBVoxel(x_train, x_test, y_train, y_test)
if is_cached:
if is_co_class:
save_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_CO_CLASS_ORIGIN
else:
save_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_DEFAULT_ORIGIN
data.save(save_path)
return data
def create(is_co_class=False,
is_cached=False,
from_cached=False,
align_data=False):
if from_cached:
print 'load voxels from .npy ...'
if is_co_class:
load_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_CO_CLASS_ORIGIN
else:
load_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_DEFAULT_ORIGIN
try:
return PSBVoxel.load(load_path)
except IOError:
warnings.warn('psb_voxel data was not loaded.')
print 'load voxels from .off ...'
cla_train = parse_cla(
os.path.join(PATH_RES_SHAPE_PSB_CLASS, PSB_CLS_TRAIN))
cla_test = parse_cla(
os.path.join(PATH_RES_SHAPE_PSB_CLASS, PSB_CLS_TEST))
if is_co_class:
cla_list = list(
set(cla_train.keys()).intersection(set(cla_test.keys())))
else:
cla_list = list(set(cla_train.keys() + cla_test.keys()))
re_compile = re.compile("\d+")
def check_binvox_y(binvox_id, is_train):
cla = cla_train if is_train else cla_test
for c_label, c_ids in cla.items():
if binvox_id in c_ids:
try:
return cla_list.index(c_label)
except ValueError:
break
return None
x_train = []
x_test = []
y_train = []
y_test = []
for f in tqdm.tqdm(os.listdir(PATH_RES_SHAPE_PSB_BINVOX)):
binvox_path = os.path.join(PATH_RES_SHAPE_PSB_BINVOX, f)
if os.path.isdir(binvox_path):
continue
binvox = parse_binvox(binvox_path)
binvox_id = int(re_compile.findall(f)[0])
train_binvox_y = check_binvox_y(binvox_id, True)
if train_binvox_y is not None:
x_train.append(binvox)
y_train.append(train_binvox_y)
else:
test_binvox_y = check_binvox_y(binvox_id, False)
if test_binvox_y is not None:
x_test.append(binvox)
y_test.append(test_binvox_y)
else:
continue
x_train = np.asarray(x_train, dtype=np.uint8)
x_test = np.asarray(x_test, dtype=np.uint8)
y_train = np.asarray(y_train, dtype=np.uint8)
y_test = np.asarray(y_test, dtype=np.uint8)
x_train = x_train.reshape([x_train.shape[0], 1] +
list(x_train.shape[1:]))
x_test = x_test.reshape([x_test.shape[0], 1] + list(x_test.shape[1:]))
if align_data:
aligned_size = min([len(x_train), len(x_test)])
x_train = x_train[:aligned_size]
x_test = x_test[:aligned_size]
y_train = y_train[:aligned_size]
y_test = y_test[:aligned_size]
data = PSBVoxel(x_train, x_test, y_train, y_test)
if is_cached:
if is_co_class:
save_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_CO_CLASS_ORIGIN
else:
save_path = PATH_RES_SHAPE_PSB_BINVOX_CACHE_DEFAULT_ORIGIN
data.save(save_path)
return data
