def quantization(high_bitdepth=16, low_bitdepth=4):
# parent directory of all GT video clips
gt_data_dir = "/home/medialab/workspace/hdd/zhen/EDVR/datasets/SDR4k/train/SDR_10BIT_patch"
gt_video_list = glob.glob(os.path.join(gt_data_dir, "*"))
pbar = ProgressBar(len(gt_video_list))
#print(gt_video_list)
parent_dir = os.path.dirname(gt_data_dir)
zp_data_dir = os.path.join(parent_dir, "SDR_4BIT_patch")
if not os.path.exists(zp_data_dir):
os.mkdir(zp_data_dir)
for video_path in gt_video_list:
pbar.update()
filepath, filename = os.path.split(video_path)
zp_filepath = os.path.join(zp_data_dir, filename)
if not os.path.exists(zp_filepath):
os.mkdir(zp_filepath)
gt_imglist = glob.glob(os.path.join(video_path, "*.png"))
print("Start quantizing video clip %s ..." % filename)
for gt_img in gt_imglist:
#print(gt_img)
imgpath, imgname = os.path.split(gt_img)
image = cv2.imread(gt_img, 3)
image = image // np.power(
2, high_bitdepth - low_bitdepth) * np.power(
2, high_bitdepth - low_bitdepth)
cv2.imwrite(os.path.join(zp_filepath, imgname), image)
def create_LMDB(img_folder, lmdb_save_path, LRGT):
# configurations
img_folder = img_folder + '/*'
lmdb_save_path = lmdb_save_path + '/DIV2K800_sub.lmdb'
meta_info = {'name': 'DIV2K800_sub_' + LRGT}
mode = 2 # 1 for reading all the images to memory and then writing to lmdb (more memory);
# 2 for reading several images and then writing to lmdb, loop over (less memory)
batch = 1 # Used in mode 2. After batch images, lmdb commits.
###########################################
if not lmdb_save_path.endswith('.lmdb'):
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
#### whether the lmdb file exist
if osp.exists(lmdb_save_path):
print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
sys.exit(1)
img_list = sorted(glob.glob(img_folder))
if mode == 1:
print('Read images...')
dataset = [cv2.imread(v, cv2.IMREAD_UNCHANGED) for v in img_list]
data_size = sum([img.nbytes for img in dataset])
elif mode == 2:
print('Calculating the total size of images...')
data_size = sum(os.stat(v).st_size for v in img_list)
else:
raise ValueError('mode should be 1 or 2')
key_l = []
resolution_l = []
pbar = ProgressBar(len(img_list))
env = lmdb.open(lmdb_save_path, map_size=data_size * 30)
txn = env.begin(write=True) # txn is a Transaction object
for i, v in enumerate(img_list):
pbar.update('Write {}'.format(v))
base_name = osp.splitext(osp.basename(v))[0]
key = base_name.encode('ascii')
data = dataset[i] if mode == 1 else cv2.imread(v, cv2.IMREAD_UNCHANGED)
if data.ndim == 2:
H, W = data.shape
C = 1
else:
H, W, C = data.shape
txn.put(key, data)
key_l.append(base_name)
resolution_l.append('{:d}_{:d}_{:d}'.format(C, H, W))
# commit in mode 2
if mode == 2 and i % batch == 1:
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
same_resolution = (len(set(resolution_l)) <= 1)
if same_resolution:
meta_info['resolution'] = [resolution_l[0]]
meta_info['keys'] = key_l
print('All images have the same resolution. Simplify the meta info...')
else:
meta_info['resolution'] = resolution_l
meta_info['keys'] = key_l
print(
'Not all images have the same resolution. Save meta info for each image...'
)
#### pickle dump
pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'),
"wb"))
print('Finish creating lmdb meta info.')
def main(split):
kept_indices = None
for quality in ("LR", "HR"):
img_folder = '../../datasets/{}_{}_bicLRx4/{}/x4/*'.format( # glob matching pattern
dataset_name, split, quality)
lmdb_save_path = '../../datasets/{}_{}_bicLRx4/{}/x4{}.lmdb'.format(
dataset_name, split, quality,
removed_fraction if removed_fraction else "")
meta_info = {'name': 'DIV2K800_sub_GT'}
mode = 2 # 1 for reading all the images to memory and then writing to lmdb (more memory);
# 2 for reading several images and then writing to lmdb, loop over (less memory)
batch = 1000 # Used in mode 2. After batch images, lmdb commits.
###########################################
if not lmdb_save_path.endswith('.lmdb'):
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
#### whether the lmdb file exist
if osp.exists(lmdb_save_path):
print(
'Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
sys.exit(1)
img_list = sorted(glob.glob(img_folder))
if removed_fraction and split == 'valid':
if kept_indices is None:
kept_indices = binomial(1, (1 - removed_fraction),
len(img_list))
img_list = [
item for k, item in enumerate(img_list) if kept_indices[k]
]
if mode == 1:
print('Read images...')
dataset = [cv2.imread(v, cv2.IMREAD_UNCHANGED) for v in img_list]
data_size = sum([img.nbytes for img in dataset])
elif mode == 2:
print('Calculating the total size of images...')
data_size = sum(os.stat(v).st_size for v in img_list)
else:
raise ValueError('mode should be 1 or 2')
key_l = []
resolution_l = []
pbar = ProgressBar(len(img_list))
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
txn = env.begin(write=True) # txn is a Transaction object
for i, v in enumerate(img_list):
pbar.update('Write {}'.format(v))
base_name = osp.splitext(osp.basename(v))[0]
key = base_name.encode('ascii')
data = dataset[i] if mode == 1 else cv2.imread(
v, cv2.IMREAD_UNCHANGED)
if data.ndim == 2:
H, W = data.shape
C = 1
else:
H, W, C = data.shape
txn.put(key, data)
key_l.append(base_name)
resolution_l.append('{:d}_{:d}_{:d}'.format(C, H, W))
# commit in mode 2
if mode == 2 and i % batch == 1:
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
same_resolution = (len(set(resolution_l)) <= 1)
if same_resolution:
meta_info['resolution'] = [resolution_l[0]]
meta_info['keys'] = key_l
print(
'All images have the same resolution. Simplify the meta info...'
)
else:
meta_info['resolution'] = resolution_l
meta_info['keys'] = key_l
print(
'Not all images have the same resolution. Save meta info for each image...'
)
#### pickle dump
pickle.dump(meta_info,
open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
print('Finish creating lmdb meta info.')
elif mode == 2:
print('Calculating the total size of images...')
data_size = sum(os.stat(v).st_size
for v in img_list) * (memory_len * 2 + 1)
else:
raise ValueError('mode should be 1 or 2')
env = lmdb.open(lmdb_save_path, map_size=data_size * 20)
txn = env.begin(write=True) # txn is a Transaction object
key_l = []
resolution_l = []
pbar = ProgressBar(frame_total)
for frame_i in range(memory_len, frame_total - memory_len, 1):
pbar.update('Write frame {}'.format(frame_i))
for block_i in range(num_sub):
base_name = 'frame_' + str(frame_i) + '_' + str(block_i)
key = base_name.encode('ascii')
data = []
if info_mode == 'LQ':
for cur_frame_in_batch in range(2 * memory_len + 1):
cur_data = dataset[
(frame_i + cur_frame_in_batch - memory_len) * num_sub +
block_i]
data.append(cur_data)
elif info_mode == 'GT':
cur_data = dataset[frame_i * num_sub + block_i]
data.append(cur_data)
print("Read images...")
dataset = [cv2.imread(v, cv2.IMREAD_UNCHANGED) for v in img_list]
data_size = sum([img.nbytes for img in dataset])
elif mode == 2:
print("Calculating the total size of images...")
data_size = sum(os.stat(v).st_size for v in img_list)
else:
raise ValueError("mode should be 1 or 2")
key_l = []
resolution_l = []
pbar = ProgressBar(len(img_list))
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
txn = env.begin(write=True) # txn is a Transaction object
for i, v in enumerate(img_list):
pbar.update("Write {}".format(v))
base_name = osp.splitext(osp.basename(v))[0]
key = base_name.encode("ascii")
data = dataset[i] if mode == 1 else cv2.imread(v, cv2.IMREAD_UNCHANGED)
if data.ndim == 2:
H, W = data.shape
C = 1
else:
H, W, C = data.shape
txn.put(key, data)
key_l.append(base_name)
resolution_l.append("{:d}_{:d}_{:d}".format(C, H, W))
# commit in mode 2
if mode == 2 and i % batch == 1:
txn.commit()
txn = env.begin(write=True)
import sys
import glob
import os.path as osp
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
from utils.util import ProgressBar
dataset_path = '../../datasets/SDR4k/train/SDR_10BIT_sub/'
video_path_list = glob.glob(os.path.join(dataset_path, '*'))
pbar = ProgressBar(len(video_path_list))
for video_path in video_path_list:
print('Processing video', video_path)
frame_path_list = glob.glob(os.path.join(video_path, '*'))
# print(frame_path_list)
patch_name_list = sorted(os.listdir(frame_path_list[0]))
# print(patch_name_list)
video_path, video_name = os.path.split(video_path)
# print(video_path, video_name)
for patch_name in patch_name_list:
save_name = '{}_{}'.format(video_name, os.path.splitext(patch_name)[0])
save_dir = os.path.join(video_path, save_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print("Making dir {}".format(save_dir))
for frame_path in frame_path_list:
frame_name = '{}.png'.format(osp.basename(frame_path))
os.system('mv {} {}'.format(os.path.join(frame_path, patch_name),
osp.join(save_dir, frame_name)))
# print('mv {} {}'.format(os.path.join(frame_path, patch_name), osp.join(save_dir, frame_name)))
pbar.update()
def extract_single(opt):
input_folder = opt['input_folder']
save_folder = opt['save_folder']
if not osp.exists(save_folder):
os.makedirs(save_folder)
# print('mkdir [{:s}] ...'.format(save_folder))
else:
print('Folder [{:s}] already exists. Continue...'.format(save_folder))
# sys.exit(1)
# img_list = data_util._get_paths_from_images(input_folder)
video_list, _ = data_util.get_video_paths(input_folder)
# def update(arg):
# pbar.update(arg)
pbar = ProgressBar(len(video_list))
# pool = Pool(opt['n_thread'])
# for path in img_list:
# pool.apply_async(worker, args=(path, opt), callback=update)
# pool.close()
# pool.join()
# print('All subprocesses done.')
crop_sz = opt['crop_sz']
step = opt['step']
thres_sz = opt['thres_sz']
for video_path in video_list:
pbar.update()
video_name = osp.basename(video_path)
save_video_folder = osp.join(opt['save_folder'], video_name)
if not osp.exists(save_video_folder):
os.makedirs(save_video_folder)
# print('mkdir [{:s}] ...'.format(save_folder))
else:
print('Folder [{:s}] already exists. Continue...'.format(
save_video_folder))
continue
print('Processing video {:s}...'.format(video_name))
left, right, bottom, top = rm_border(osp.join(
video_path, "001.png")) # cut for all images in one video
for img_name in sorted(os.listdir((video_path))):
save_folder = osp.join(save_video_folder,
osp.splitext(img_name)[0])
# print("save_folder", save_folder)
if not osp.exists(save_folder):
os.makedirs(save_folder)
# print('mkdir [{:s}] ...'.format(save_folder))
else:
print('Folder [{:s}] already exists. Continue...'.format(
save_folder))
continue
print('Processing frame {:s} ...'.format(img_name))
img_path = osp.join(video_path, img_name)
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
img = img[left:right, bottom:top, :]
n_channels = len(img.shape)
if n_channels == 2:
h, w = img.shape
elif n_channels == 3:
h, w, c = img.shape
else:
raise ValueError('Wrong image shape - {}'.format(n_channels))
h_space = np.arange(0, h - crop_sz + 1, step)
if h - (h_space[-1] + crop_sz) > thres_sz:
h_space = np.append(h_space, h - crop_sz)
w_space = np.arange(0, w - crop_sz + 1, step)
if w - (w_space[-1] + crop_sz) > thres_sz:
w_space = np.append(w_space, w - crop_sz)
index = 0
for x in h_space:
for y in w_space:
index += 1
if n_channels == 2:
crop_img = img[x:x + crop_sz, y:y + crop_sz]
else:
crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
crop_img = np.ascontiguousarray(crop_img)
cv2.imwrite(
osp.join(save_folder, 'p{:03d}.png'.format(index)),
crop_img, [
cv2.IMWRITE_PNG_COMPRESSION,
opt['compression_level']
])
print('Read images...')
dataset = [cv2.imread(v, cv2.IMREAD_UNCHANGED) for v in img_list]
data_size = sum([img.nbytes for img in dataset])
elif mode == 2:
print('Calculating the total size of images...')
data_size = sum(os.stat(v).st_size for v in img_list)
else:
raise ValueError('mode should be 1 or 2')
key_l = []
resolution_l = []
pbar = ProgressBar(len(img_list))
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
txn = env.begin(write=True) # txn is a Transaction object
for i, v in enumerate(img_list):
pbar.update('Write {}'.format(v))
base_name = osp.splitext(osp.basename(v))[0]
key = base_name.encode('ascii')
data = dataset[i] if mode == 1 else cv2.imread(v, cv2.IMREAD_UNCHANGED)
if data.ndim == 2:
H, W = data.shape
C = 1
else:
H, W, C = data.shape
txn.put(key, data)
key_l.append(base_name)
resolution_l.append('{:d}_{:d}_{:d}'.format(C, H, W))
# commit in mode 2
if mode == 2 and i % batch == 1:
txn.commit()
txn = env.begin(write=True)