def split_classify(img_path: str, n_splits: int, group_regex: str = None):
output = join(current_folder, 'fold.csv')
get_group = None
if group_regex:
pattern = re.compile(group_regex)
def get_group(x):
match = pattern.match(x)
if not match:
return str(uuid4())
return match.group(1)
images_labels = [(img, sub_folder) for sub_folder in os.listdir(img_path)
for img in os.listdir(join(img_path, sub_folder))]
file_group_kfold(n_splits,
output,
get_group=get_group,
image=[join(label, img) for img, label in images_labels],
label=[label for img, label in images_labels])
def split_segment(img_path: str,
mask_path: str,
n_splits: int,
group_regex: str = None):
output = join(current_folder, 'fold.csv')
get_group = None
if group_regex:
pattern = re.compile(group_regex)
def get_group(x):
match = pattern.match(x)
if not match:
return str(uuid4())
return match.group(1)
file_group_kfold(n_splits, output, get_group=get_group,
image=os.listdir(img_path),
mask=os.listdir(mask_path),
sort=True,
must_equal=['image', 'mask']
)
def split_pandas(path: str, n_splits: int):
output = join(current_folder, 'fold.csv')
df = pd.read_csv(path)
folds = file_group_kfold(n_splits, image=df[df.columns[0]])
df['fold'] = folds['fold']
df.to_csv(output)