def create_lmdb_for_reds():
"""Create lmdb files for REDS dataset.
Usage:
Before run this script, please run `merge_reds_train_val.py`.
We take two folders for example:
train_sharp
train_sharp_bicubic
Remember to modify opt configurations according to your settings.
"""
# train_sharp
folder_path = 'datasets/REDS/train_sharp'
lmdb_path = 'datasets/REDS/train_sharp_with_val.lmdb'
img_path_list, keys = prepare_keys_reds(folder_path)
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
multiprocessing_read=True)
# train_sharp_bicubic
folder_path = 'datasets/REDS/train_sharp_bicubic'
lmdb_path = 'datasets/REDS/train_sharp_bicubic_with_val.lmdb'
img_path_list, keys = prepare_keys_reds(folder_path)
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
multiprocessing_read=True)
def create_lmdb_for_vimeo90k():
"""Create lmdb files for Vimeo90K dataset.
Usage:
Remember to modify opt configurations according to your settings.
"""
# GT
folder_path = 'datasets/vimeo90k/vimeo_septuplet/sequences'
lmdb_path = 'datasets/vimeo90k/vimeo90k_train_GT_only4th.lmdb'
train_list_path = 'datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
img_path_list, keys = prepare_keys_vimeo90k(folder_path, train_list_path,
'gt')
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
multiprocessing_read=True)
# LQ
folder_path = 'datasets/vimeo90k/vimeo_septuplet_matlabLRx4/sequences'
lmdb_path = 'datasets/vimeo90k/vimeo90k_train_LR7frames.lmdb'
train_list_path = 'datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
img_path_list, keys = prepare_keys_vimeo90k(folder_path, train_list_path,
'lq')
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
multiprocessing_read=True)
def create_lmdb_for_rain13k():
folder_path = './datasets/Rain13k/train/input'
lmdb_path = './datasets/Rain13k/train/input.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'jpg')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/Rain13k/train/target'
lmdb_path = './datasets/Rain13k/train/target.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'jpg')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
def create_lmdb_for_div2k():
"""Create lmdb files for DIV2K dataset.
Usage:
Before run this script, please run `extract_subimages.py`.
Typically, there are four folders to be processed for DIV2K dataset.
DIV2K_train_HR_sub
DIV2K_train_LR_bicubic/X2_sub
DIV2K_train_LR_bicubic/X3_sub
DIV2K_train_LR_bicubic/X4_sub
Remember to modify opt configurations according to your settings.
"""
# HR images
folder_path = 'datasets/DIV2K/DIV2K_train_HR_sub'
lmdb_path = 'datasets/DIV2K/DIV2K_train_HR_sub.lmdb'
img_path_list, keys = prepare_keys_div2k(folder_path)
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
# LRx2 images
folder_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X2_sub'
lmdb_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic_X2_sub.lmdb'
img_path_list, keys = prepare_keys_div2k(folder_path)
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
# LRx3 images
folder_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X3_sub'
lmdb_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic_X3_sub.lmdb'
img_path_list, keys = prepare_keys_div2k(folder_path)
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
# LRx4 images
folder_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic/X4_sub'
lmdb_path = 'datasets/DIV2K/DIV2K_train_LR_bicubic_X4_sub.lmdb'
img_path_list, keys = prepare_keys_div2k(folder_path)
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
def create_lmdb_for_SIDD():
folder_path = './datasets/SIDD/train/input_crops'
lmdb_path = './datasets/SIDD/train/input_crops.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'PNG')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/SIDD/train/gt_crops'
lmdb_path = './datasets/SIDD/train/gt_crops.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'PNG')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
#for val
folder_path = './datasets/SIDD/val/input_crops'
lmdb_path = './datasets/SIDD/val/input_crops.lmdb'
mat_path = './datasets/SIDD/ValidationNoisyBlocksSrgb.mat'
if not osp.exists(folder_path):
os.makedirs(folder_path)
assert osp.exists(mat_path)
data = scio.loadmat(mat_path)['ValidationNoisyBlocksSrgb']
N, B, H, W, C = data.shape
data = data.reshape(N * B, H, W, C)
for i in tqdm(range(N * B)):
cv2.imwrite(
osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)),
cv2.cvtColor(data[i, ...], cv2.COLOR_RGB2BGR))
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/SIDD/val/gt_crops'
lmdb_path = './datasets/SIDD/val/gt_crops.lmdb'
mat_path = './datasets/SIDD/ValidationGtBlocksSrgb.mat'
if not osp.exists(folder_path):
os.makedirs(folder_path)
assert osp.exists(mat_path)
data = scio.loadmat(mat_path)['ValidationGtBlocksSrgb']
N, B, H, W, C = data.shape
data = data.reshape(N * B, H, W, C)
for i in tqdm(range(N * B)):
cv2.imwrite(
osp.join(folder_path, 'ValidationBlocksSrgb_{}.png'.format(i)),
cv2.cvtColor(data[i, ...], cv2.COLOR_RGB2BGR))
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
def create_lmdb_for_reds():
folder_path = './datasets/REDS/val/sharp_300'
lmdb_path = './datasets/REDS/val/sharp_300.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
#
folder_path = './datasets/REDS/val/blur_300'
lmdb_path = './datasets/REDS/val/blur_300.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'jpg')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/REDS/train/train_sharp'
lmdb_path = './datasets/REDS/train/train_sharp.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/REDS/train/train_blur_jpeg'
lmdb_path = './datasets/REDS/train/train_blur_jpeg.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'jpg')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
def create_lmdb_for_gopro():
folder_path = './datasets/GoPro/train/blur_crops'
lmdb_path = './datasets/GoPro/train/blur_crops.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/GoPro/train/sharp_crops'
lmdb_path = './datasets/GoPro/train/sharp_crops.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/GoPro/test/target'
lmdb_path = './datasets/GoPro/test/target.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
folder_path = './datasets/GoPro/test/input'
lmdb_path = './datasets/GoPro/test/input.lmdb'
img_path_list, keys = prepare_keys(folder_path, 'png')
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)