def __init__(self, opt):
super(LRHRSeg_BG_Dataset, self).__init__(opt, keys_ds=['LR', 'HR'])
self.znorm = opt.get(
'znorm',
False) # Alternative: images are z-normalized to the [-1,1] range
# self.opt = opt
# self.paths_LR = None
# self.paths_HR = None
self.paths_HR_bg = None # HR images for background scenes
self.LR_env = None
self.HR_env = None
self.HR_env_bg = None # environment for lmdb
# get images paths (and optional environments for lmdb) from dataroots
self.paths_LR, self.paths_HR = get_dataroots_paths(
opt, strict=False, keys_ds=self.keys_ds)
# read backgrounds image list from lmdb or image files
self.paths_HR_bg = util.get_image_paths(opt['data_type'],
opt['dataroot_HR_bg'])
if self.opt.get('data_type') == 'lmdb':
self.LR_env = util._init_lmdb(
opt.get('dataroot_' + self.keys_ds[0]))
self.HR_env = util._init_lmdb(
opt.get('dataroot_' + self.keys_ds[1]))
self.HR_env_bg = util._init_lmdb(opt.get('dataroot_HR_bg'))
assert len(self.paths_HR) == len(self.paths_HR_bg)
self.random_scale_list = [1, 0.9, 0.8, 0.7, 0.6, 0.5]
self.ratio = 10 # 10 OST data samples and 1 DIV2K general data samples(background)
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option dictionary): stores all the experiment flags
"""
super(AlignedDataset, self).__init__(opt, keys_ds=['LR', 'HR'])
self.vars = self.opt.get('outputs', 'LRHR') #'AB'
self.ds_kernels = get_ds_kernels(self.opt)
self.noise_patches = get_noise_patches(self.opt)
set_transforms(self.opt.get('img_loader', 'cv2'))
# get images paths (and optional environments for lmdb) from dataroots
dir_AB = self.opt.get('dataroot', None) or self.opt.get(
'dataroot_AB', None)
if dir_AB:
self.AB_env = None # environment for lmdb
self.AB_paths = get_single_dataroot_path(self.opt, dir_AB)
if self.opt.get('data_type') == 'lmdb':
self.AB_env = _init_lmdb(dir_AB)
else:
self.A_paths, self.B_paths = get_dataroots_paths(
self.opt, strict=False, keys_ds=self.keys_ds)
self.AB_paths = None
self.A_env, self.B_env = None, None # environment for lmdb
if self.opt.get('data_type') == 'lmdb':
self.A_env = _init_lmdb(
self.opt.get(f'dataroot_{self.keys_ds[0]}'))
self.B_env = _init_lmdb(
self.opt.get(f'dataroot_{self.keys_ds[1]}'))
# get reusable totensor params
self.totensor_params = get_totensor_params(self.opt)
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option dictionary): stores all the experiment flags
"""
super(UnalignedDataset, self).__init__(opt, keys_ds=['A', 'B'])
self.vars = self.opt.get('outputs', 'AB')
self.A_env, self.B_env = None, None # environment for lmdb
self.noise_patches = get_noise_patches(self.opt)
self.idx_case = 'serial' if self.opt.get('serial_batches',
None) else 'random'
set_transforms(self.opt.get('img_loader', 'cv2'))
# get images paths (and optional environments for lmdb) from dataroots
dir_A = self.opt.get(f'dataroot_{self.keys_ds[0]}'
) # create a path '/path/to/data/trainA'
self.A_paths = get_single_dataroot_path(
self.opt, dir_A) # load images from '/path/to/data/trainA'
dir_B = self.opt.get(f'dataroot_{self.keys_ds[1]}'
) # create a path '/path/to/data/trainB'
self.B_paths = get_single_dataroot_path(
self.opt, dir_B) # load images from '/path/to/data/trainB'
if self.opt.get('data_type') == 'lmdb':
self.A_env = _init_lmdb(dir_A)
self.B_env = _init_lmdb(dir_B)
assert self.A_paths, f'Error: image path {dir_A} empty.'
assert self.B_paths, f'Error: image path {dir_B} empty.'
self.A_size = len(self.A_paths) # get the size of dataset A
self.B_size = len(self.B_paths) # get the size of dataset B
# TODO: fix with change color space, etc
# BtoA = self.opt.get('direction') == 'BtoA'
# # get the number of channels of input image
# input_nc = self.opt.get('output_nc') if BtoA else self.opt.get('input_nc')
# # get the number of channels of output image
# output_nc = self.opt.get('input_nc') if BtoA else self.opt.get('output_nc')
# get reusable totensor params
self.totensor_params = get_totensor_params(self.opt)
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option dictionary): stores all the experiment flags
"""
super(SingleDataset, self).__init__(opt, keys_ds=['LR', 'HR'])
self.vars = self.opt.get('outputs', 'LR') #'A'
self.A_env = None # environment for lmdb
set_transforms(self.opt.get('img_loader', 'cv2'))
# get images paths (and optional environments for lmdb) from dataroots
dir_A = self.opt.get(f'dataroot_{self.keys_ds[0]}')
self.A_paths = get_single_dataroot_path(self.opt, dir_A)
if self.opt.get('data_type') == 'lmdb':
self.A_env = _init_lmdb(dir_A)
# get reusable totensor transform
totensor_params = get_totensor_params(self.opt)
self.tensor_transform = get_totensor(self.opt,
params=totensor_params,
toTensor=True,
grayscale=False)
assert self.A_paths, f'Error: image path {dir_A} empty.'