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_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 = 'data_dir/train_mod/HR'
lmdb_path = 'data_dir/train_mod/PLANET_HR_train.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 = 'data_dir/train_mod/LR'
lmdb_path = 'data_dir/train_mod/PLANET_LR_train.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_imagenet():
"""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 train images (256x)
# folder_path = '/disk_c/han/data/ImageNet_256x256/train/'
# lmdb_path = '/disk_c/han/data/ImageNet_lmdb/ImageNet_train_HR.lmdb'
# img_path_list, keys = prepare_keys_imagenet(folder_path)
# make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, n_thread=16)
# # HR val images (256x)
# folder_path = '/disk_c/han/data/ImageNet_256x256/val/'
# lmdb_path = '/disk_c/han/data/ImageNet_lmdb/ImageNet_val_HR.lmdb'
# img_path_list, keys = prepare_keys_imagenet(folder_path)
# make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, n_thread=16)
# LR train images (64x)
# folder_path = '/disk_c/han/data/ImageNet_64x64/train/'
# lmdb_path = '/disk_c/han/data/ImageNet_lmdb/ImageNet_train_LR.lmdb'
# img_path_list, keys = prepare_keys_imagenet(folder_path)
# make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, n_thread=16)
# # LR val images (64x)
# folder_path = '/disk_c/han/data/ImageNet_64x64/val/'
# lmdb_path = '/disk_c/han/data/ImageNet_lmdb/ImageNet_val_LR.lmdb'
# img_path_list, keys = prepare_keys_imagenet(folder_path)
# make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys, n_thread=16)
# LR val images (128x)
folder_path = '/disk_c/han/data/ImageNet_128x128/val/'
lmdb_path = '/disk_c/han/data/ImageNet_lmdb/ImageNet_val_128.lmdb'
img_path_list, keys = prepare_keys_imagenet(folder_path)
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
n_thread=16)
def create_lmdb_for_lsun_church_wt():
"""Create lmdb files for LSUN Church 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 train images
folder_path = '/disk_c/han/data/lsun_church_256x256/train/'
lmdb_path = '/disk_c/han/data/lsun_church_lmdb/lsun_church_train_HR.lmdb'
img_path_list, keys = prepare_keys_imagenet(folder_path)
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
n_thread=16)
# HR val images
folder_path = '/disk_c/han/data/lsun_church_256x256/val/'
lmdb_path = '/disk_c/han/data/lsun_church_lmdb/lsun_church_val_HR.lmdb'
img_path_list, keys = prepare_keys_imagenet(folder_path)
make_lmdb_from_imgs(folder_path,
lmdb_path,
img_path_list,
keys,
n_thread=16)
# WT train images
folder_path = '/disk_c/han/data/lsun_church_256x256/train/'
lmdb_path = '/disk_c/han/data/lsun_church_lmdb/lsun_church_train_WT.lmdb'
img_path_list, keys = prepare_keys_imagenet(folder_path)
make_wt_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
# WT val images
folder_path = '/disk_c/han/data/lsun_church_256x256/val/'
lmdb_path = '/disk_c/han/data/lsun_church_lmdb/lsun_church_val_WT.lmdb'
img_path_list, keys = prepare_keys_imagenet(folder_path)
make_wt_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
def create_lmdb_for_div2kp2():
"""Create lmdb files for DIV2K dataset (Project 2).
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 = '../../../data/DIV2K/Train/HR_sub'
lmdb_path = '../../../data/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)
# LRx4 images
folder_path = '../../../data/DIV2K/Train/LR_x4_sub'
lmdb_path = '../../../data/DIV2K/Train/LR_x4_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 = '../../../data/DIV2K/Train/LR_x2_sub'
lmdb_path = '../../../data/DIV2K/Train/LR_x2_sub.lmdb'
img_path_list, keys = prepare_keys_div2k(folder_path)
make_lmdb_from_imgs(folder_path, lmdb_path, img_path_list, keys)
