import glob

import os

def regroup_reds_dataset(train_path, val_path):

"""Regroup original REDS datasets.

We merge train and validation data into one folder, and separate the

validation clips in reds_dataset.py.

There are 240 training clips (starting from 0 to 239),

so we name the validation clip index starting from 240 to 269 (total 30

validation clips).

Args:

train_path (str): Path to the train folder.

val_path (str): Path to the validation folder.

"""

# move the validation data to the train folder

val_folders = glob.glob(os.path.join(val_path, '*'))

for folder in val_folders:

new_folder_idx = int(folder.split('/')[-1]) + 240

os.system(f'cp -r {folder} {os.path.join(train_path, str(new_folder_idx))}')

if __name__ == '__main__':

# train_sharp

train_path = 'trainsets/REDS/train_sharp'

val_path = 'trainsets/REDS/val_sharp'

regroup_reds_dataset(train_path, val_path)

# train_sharp_bicubic

train_path = 'trainsets/REDS/train_sharp_bicubic/X4'

val_path = 'trainsets/REDS/val_sharp_bicubic/X4'

regroup_reds_dataset(train_path, val_path)

# train_blur (for video deblurring)

train_path = 'trainsets/REDS/train_blur'

val_path = 'trainsets/REDS/val_blur'

regroup_reds_dataset(train_path, val_path)