DL-Art-School/codes/data_scripts/create_lmdb.py

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2019-08-23 13:42:47 +00:00
"""Create lmdb files for [General images (291 images/DIV2K) | Vimeo90K | REDS] training datasets"""
import sys
import os.path as osp
import glob
import pickle
from multiprocessing import Pool
import numpy as np
import lmdb
import cv2
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
import data.util as data_util # noqa: E402
import utils.util as util # noqa: E402
def main():
dataset = 'DIV2K_demo' # vimeo90K | REDS | general (e.g., DIV2K, 291) | DIV2K_demo |test
mode = 'GT' # used for vimeo90k and REDS datasets
# vimeo90k: GT | LR | flow
# REDS: train_sharp, train_sharp_bicubic, train_blur_bicubic, train_blur, train_blur_comp
# train_sharp_flowx4
if dataset == 'vimeo90k':
vimeo90k(mode)
elif dataset == 'REDS':
REDS(mode)
elif dataset == 'general':
opt = {}
opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub'
opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub.lmdb'
opt['name'] = 'DIV2K800_sub_GT'
general_image_folder(opt)
elif dataset == 'DIV2K_demo':
opt = {}
## GT
opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub'
opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub.lmdb'
opt['name'] = 'DIV2K800_sub_GT'
general_image_folder(opt)
## LR
opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4'
opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4.lmdb'
opt['name'] = 'DIV2K800_sub_bicLRx4'
general_image_folder(opt)
elif dataset == 'test':
test_lmdb('../../datasets/REDS/train_sharp_wval.lmdb', 'REDS')
def read_image_worker(path, key):
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
return (key, img)
def general_image_folder(opt):
"""Create lmdb for general image folders
Users should define the keys, such as: '0321_s035' for DIV2K sub-images
If all the images have the same resolution, it will only store one copy of resolution info.
Otherwise, it will store every resolution info.
"""
#### configurations
read_all_imgs = False # whether real all images to memory with multiprocessing
# Set False for use limited memory
BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
n_thread = 40
########################################################
img_folder = opt['img_folder']
lmdb_save_path = opt['lmdb_save_path']
meta_info = {'name': opt['name']}
if not lmdb_save_path.endswith('.lmdb'):
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
if osp.exists(lmdb_save_path):
print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
sys.exit(1)
#### read all the image paths to a list
print('Reading image path list ...')
all_img_list = sorted(glob.glob(osp.join(img_folder, '*')))
keys = []
for img_path in all_img_list:
keys.append(osp.splitext(osp.basename(img_path))[0])
if read_all_imgs:
#### read all images to memory (multiprocessing)
dataset = {} # store all image data. list cannot keep the order, use dict
print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
pbar = util.ProgressBar(len(all_img_list))
def mycallback(arg):
'''get the image data and update pbar'''
key = arg[0]
dataset[key] = arg[1]
pbar.update('Reading {}'.format(key))
pool = Pool(n_thread)
for path, key in zip(all_img_list, keys):
pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
pool.close()
pool.join()
print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
#### create lmdb environment
data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
print('data size per image is: ', data_size_per_img)
data_size = data_size_per_img * len(all_img_list)
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
#### write data to lmdb
pbar = util.ProgressBar(len(all_img_list))
txn = env.begin(write=True)
resolutions = []
for idx, (path, key) in enumerate(zip(all_img_list, keys)):
pbar.update('Write {}'.format(key))
key_byte = key.encode('ascii')
data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
if data.ndim == 2:
H, W = data.shape
C = 1
else:
H, W, C = data.shape
txn.put(key_byte, data)
resolutions.append('{:d}_{:d}_{:d}'.format(C, H, W))
if not read_all_imgs and idx % BATCH == 0:
txn.commit()
txn = env.begin(write=True)
txn.commit()
env.close()
print('Finish writing lmdb.')
#### create meta information
# check whether all the images are the same size
assert len(keys) == len(resolutions)
if len(set(resolutions)) <= 1:
meta_info['resolution'] = [resolutions[0]]
meta_info['keys'] = keys
print('All images have the same resolution. Simplify the meta info.')
else:
meta_info['resolution'] = resolutions
meta_info['keys'] = keys
print('Not all images have the same resolution. Save meta info for each image.')
pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
print('Finish creating lmdb meta info.')
def vimeo90k(mode):
"""Create lmdb for the Vimeo90K dataset, each image with a fixed size
GT: [3, 256, 448]
Now only need the 4th frame, e.g., 00001_0001_4
LR: [3, 64, 112]
1st - 7th frames, e.g., 00001_0001_1, ..., 00001_0001_7
key:
Use the folder and subfolder names, w/o the frame index, e.g., 00001_0001
flow: downsampled flow: [3, 360, 320], keys: 00001_0001_4_[p3, p2, p1, n1, n2, n3]
Each flow is calculated with GT images by PWCNet and then downsampled by 1/4
Flow map is quantized by mmcv and saved in png format
"""
#### configurations
read_all_imgs = False # whether real all images to memory with multiprocessing
# Set False for use limited memory
BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
if mode == 'GT':
img_folder = '../../datasets/vimeo90k/vimeo_septuplet/sequences'
lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_GT.lmdb'
txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
H_dst, W_dst = 256, 448
elif mode == 'LR':
img_folder = '../../datasets/vimeo90k/vimeo_septuplet_matlabLRx4/sequences'
lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_LR7frames.lmdb'
txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
H_dst, W_dst = 64, 112
elif mode == 'flow':
img_folder = '../../datasets/vimeo90k/vimeo_septuplet/sequences_flowx4'
lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_flowx4.lmdb'
txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
H_dst, W_dst = 128, 112
else:
raise ValueError('Wrong dataset mode: {}'.format(mode))
n_thread = 40
########################################################
if not lmdb_save_path.endswith('.lmdb'):
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
if osp.exists(lmdb_save_path):
print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
sys.exit(1)
#### read all the image paths to a list
print('Reading image path list ...')
with open(txt_file) as f:
train_l = f.readlines()
train_l = [v.strip() for v in train_l]
all_img_list = []
keys = []
for line in train_l:
folder = line.split('/')[0]
sub_folder = line.split('/')[1]
all_img_list.extend(glob.glob(osp.join(img_folder, folder, sub_folder, '*')))
if mode == 'flow':
for j in range(1, 4):
keys.append('{}_{}_4_n{}'.format(folder, sub_folder, j))
keys.append('{}_{}_4_p{}'.format(folder, sub_folder, j))
else:
for j in range(7):
keys.append('{}_{}_{}'.format(folder, sub_folder, j + 1))
all_img_list = sorted(all_img_list)
keys = sorted(keys)
if mode == 'GT': # only read the 4th frame for the GT mode
print('Only keep the 4th frame.')
all_img_list = [v for v in all_img_list if v.endswith('im4.png')]
keys = [v for v in keys if v.endswith('_4')]
if read_all_imgs:
#### read all images to memory (multiprocessing)
dataset = {} # store all image data. list cannot keep the order, use dict
print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
pbar = util.ProgressBar(len(all_img_list))
def mycallback(arg):
"""get the image data and update pbar"""
key = arg[0]
dataset[key] = arg[1]
pbar.update('Reading {}'.format(key))
pool = Pool(n_thread)
for path, key in zip(all_img_list, keys):
pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
pool.close()
pool.join()
print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
#### write data to lmdb
data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
print('data size per image is: ', data_size_per_img)
data_size = data_size_per_img * len(all_img_list)
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
txn = env.begin(write=True)
pbar = util.ProgressBar(len(all_img_list))
for idx, (path, key) in enumerate(zip(all_img_list, keys)):
pbar.update('Write {}'.format(key))
key_byte = key.encode('ascii')
data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
if 'flow' in mode:
H, W = data.shape
assert H == H_dst and W == W_dst, 'different shape.'
else:
H, W, C = data.shape
assert H == H_dst and W == W_dst and C == 3, 'different shape.'
txn.put(key_byte, data)
if not read_all_imgs and idx % BATCH == 0:
txn.commit()
txn = env.begin(write=True)
txn.commit()
env.close()
print('Finish writing lmdb.')
#### create meta information
meta_info = {}
if mode == 'GT':
meta_info['name'] = 'Vimeo90K_train_GT'
elif mode == 'LR':
meta_info['name'] = 'Vimeo90K_train_LR'
elif mode == 'flow':
meta_info['name'] = 'Vimeo90K_train_flowx4'
channel = 1 if 'flow' in mode else 3
meta_info['resolution'] = '{}_{}_{}'.format(channel, H_dst, W_dst)
key_set = set()
for key in keys:
if mode == 'flow':
a, b, _, _ = key.split('_')
else:
a, b, _ = key.split('_')
key_set.add('{}_{}'.format(a, b))
meta_info['keys'] = list(key_set)
pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
print('Finish creating lmdb meta info.')
def REDS(mode):
"""Create lmdb for the REDS dataset, each image with a fixed size
GT: [3, 720, 1280], key: 000_00000000
LR: [3, 180, 320], key: 000_00000000
key: 000_00000000
flow: downsampled flow: [3, 360, 320], keys: 000_00000005_[p2, p1, n1, n2]
Each flow is calculated with the GT images by PWCNet and then downsampled by 1/4
Flow map is quantized by mmcv and saved in png format
"""
#### configurations
read_all_imgs = False # whether real all images to memory with multiprocessing
# Set False for use limited memory
BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
if mode == 'train_sharp':
img_folder = '../../datasets/REDS/train_sharp'
lmdb_save_path = '../../datasets/REDS/train_sharp_wval.lmdb'
H_dst, W_dst = 720, 1280
elif mode == 'train_sharp_bicubic':
img_folder = '../../datasets/REDS/train_sharp_bicubic'
lmdb_save_path = '../../datasets/REDS/train_sharp_bicubic_wval.lmdb'
H_dst, W_dst = 180, 320
elif mode == 'train_blur_bicubic':
img_folder = '../../datasets/REDS/train_blur_bicubic'
lmdb_save_path = '../../datasets/REDS/train_blur_bicubic_wval.lmdb'
H_dst, W_dst = 180, 320
elif mode == 'train_blur':
img_folder = '../../datasets/REDS/train_blur'
lmdb_save_path = '../../datasets/REDS/train_blur_wval.lmdb'
H_dst, W_dst = 720, 1280
elif mode == 'train_blur_comp':
img_folder = '../../datasets/REDS/train_blur_comp'
lmdb_save_path = '../../datasets/REDS/train_blur_comp_wval.lmdb'
H_dst, W_dst = 720, 1280
elif mode == 'train_sharp_flowx4':
img_folder = '../../datasets/REDS/train_sharp_flowx4'
lmdb_save_path = '../../datasets/REDS/train_sharp_flowx4.lmdb'
H_dst, W_dst = 360, 320
n_thread = 40
########################################################
if not lmdb_save_path.endswith('.lmdb'):
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
if osp.exists(lmdb_save_path):
print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
sys.exit(1)
#### read all the image paths to a list
print('Reading image path list ...')
all_img_list = data_util._get_paths_from_images(img_folder)
keys = []
for img_path in all_img_list:
split_rlt = img_path.split('/')
folder = split_rlt[-2]
img_name = split_rlt[-1].split('.png')[0]
keys.append(folder + '_' + img_name)
if read_all_imgs:
#### read all images to memory (multiprocessing)
dataset = {} # store all image data. list cannot keep the order, use dict
print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
pbar = util.ProgressBar(len(all_img_list))
def mycallback(arg):
'''get the image data and update pbar'''
key = arg[0]
dataset[key] = arg[1]
pbar.update('Reading {}'.format(key))
pool = Pool(n_thread)
for path, key in zip(all_img_list, keys):
pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
pool.close()
pool.join()
print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
#### create lmdb environment
data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
print('data size per image is: ', data_size_per_img)
data_size = data_size_per_img * len(all_img_list)
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
#### write data to lmdb
pbar = util.ProgressBar(len(all_img_list))
txn = env.begin(write=True)
for idx, (path, key) in enumerate(zip(all_img_list, keys)):
pbar.update('Write {}'.format(key))
key_byte = key.encode('ascii')
data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
if 'flow' in mode:
H, W = data.shape
assert H == H_dst and W == W_dst, 'different shape.'
else:
H, W, C = data.shape
assert H == H_dst and W == W_dst and C == 3, 'different shape.'
txn.put(key_byte, data)
if not read_all_imgs and idx % BATCH == 0:
txn.commit()
txn = env.begin(write=True)
txn.commit()
env.close()
print('Finish writing lmdb.')
#### create meta information
meta_info = {}
meta_info['name'] = 'REDS_{}_wval'.format(mode)
channel = 1 if 'flow' in mode else 3
meta_info['resolution'] = '{}_{}_{}'.format(channel, H_dst, W_dst)
meta_info['keys'] = keys
pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
print('Finish creating lmdb meta info.')
def test_lmdb(dataroot, dataset='REDS'):
env = lmdb.open(dataroot, readonly=True, lock=False, readahead=False, meminit=False)
meta_info = pickle.load(open(osp.join(dataroot, 'meta_info.pkl'), "rb"))
print('Name: ', meta_info['name'])
print('Resolution: ', meta_info['resolution'])
print('# keys: ', len(meta_info['keys']))
# read one image
if dataset == 'vimeo90k':
key = '00001_0001_4'
else:
key = '000_00000000'
print('Reading {} for test.'.format(key))
with env.begin(write=False) as txn:
buf = txn.get(key.encode('ascii'))
img_flat = np.frombuffer(buf, dtype=np.uint8)
C, H, W = [int(s) for s in meta_info['resolution'].split('_')]
img = img_flat.reshape(H, W, C)
cv2.imwrite('test.png', img)
if __name__ == "__main__":
main()