def __init__(self, opt):
super(LRHRDataset, self).__init__()
# self.args = args
self.opt = opt
self.train = (opt['phase'] == 'train')
self.split = 'train' if self.train else 'test'
self.repeat = 1
# TODO
# self.benchmark = benchmark
self.scale = self.opt['scale']
self.idx_scale = 0
# read image list from lmdb or image files
self.HR_env, self.paths_HR = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_HR'])
self.LR_env, self.paths_LR = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_LR'])
assert self.paths_HR, 'Error: HR paths are empty.'
if self.paths_LR and self.paths_HR:
assert len(self.paths_LR) == len(self.paths_HR), \
'HR and LR datasets have different number of images - {}, {}.'.format(\
len(self.paths_LR), len(self.paths_HR))
# TODO: random_scale
# self.random_scale_list = [1, 0.9, 0.8, 0.7, 0.6, 0.5]
self.random_scale_list = None
def __init__(self, opt):
super(LRHRlmdbDataset, self).__init__()
self.opt = opt
self.paths_LR = None
self.paths_HR = None
self.LR_env = None # environment for lmdb
self.HR_env = None
# read image list from subset list txt
if opt['subset_file'] is not None and opt['phase'] == 'train':
with open(opt['subset_file']) as f:
self.paths_HR = sorted([os.path.join(opt['dataroot_HR'], line.rstrip('\n')) \
for line in f])
if opt['dataroot_LR'] is not None:
raise NotImplementedError('Now subset only supports generating LR on-the-fly.')
# read image list from lmdb or image files
self.HR_env, self.paths_HR = common.get_image_paths(opt['data_type'], opt['dataroot_HR'])
self.LR_env, self.paths_LR = common.get_image_paths(opt['data_type'], opt['dataroot_LR'])
assert self.paths_HR, 'Error: HR paths are empty.'
if self.paths_LR and self.paths_HR:
assert len(self.paths_LR) == len(self.paths_HR), \
'HR and LR datasets have different number of images - {}, {}.'.format( \
len(self.paths_LR), len(self.paths_HR))
self.random_scale_list = None
def __init__(self, opt):
super(MLRMHRDataset, self).__init__()
self.opt = opt
self.train = (opt['phase'] == 'train')
self.split = 'train' if self.train else 'test'
self.scale = self.opt['scale']
self.paths_HR, self.paths_LR = [], []
# change the length of train dataset (influence the number of iterations in each epoch)
self.repeat = 2
# read image list from image/binary files
for scale in self.scale:
paths_LR = common.get_image_paths(
self.opt['data_type'],
os.path.join(self.opt['dataroot_LR'], 'x' + str(scale)))
paths_HR = common.get_image_paths(
self.opt['data_type'],
os.path.join(self.opt['dataroot_HR'], 'x' + str(scale)))
assert paths_LR, '[Error] x%s LR paths are empty.' % scale
assert paths_HR, '[Error] x%s LR paths are empty.' % scale
assert len(paths_HR) == len(paths_LR), \
'[Error] x%s HR: [%d] and LR: [%d] have different number of images.' % (
scale, len(paths_HR), len(paths_LR))
self.paths_LR.append(paths_LR)
self.paths_HR.append(paths_HR)
def __init__(self, opt):
super(LRHRDataset, self).__init__()
self.opt = opt
self.train = (opt['phase'] == 'train')
self.split = 'train' if self.train else 'test'
self.scale = self.opt['scale']
self.paths_HR, self.paths_LR1, self.paths_LR2 = None, None, None
# change the length of train dataset (influence the number of iterations in each epoch)
self.repeat = 2
# read image list from image/binary files
self.paths_HR = common.get_image_paths(self.opt['data_type'],
self.opt['dataroot_HR'])
self.paths_LR1 = common.get_image_paths(self.opt['data_type'],
self.opt['dataroot_LR1'])
self.paths_LR2 = common.get_image_paths(self.opt['data_type'],
self.opt['dataroot_LR2'])
self.paths_LR3 = common.get_image_paths(self.opt['data_type'],
self.opt['dataroot_LR3'])
assert self.paths_HR, '[Error] HR paths are empty.'
if self.paths_LR1 and self.paths_LR2 and self.paths_LR3 and self.paths_HR:
assert len(self.paths_LR1) == len(self.paths_HR) and len(self.paths_LR2) == len(self.paths_HR) and len(self.paths_LR3) == len(self.paths_HR), \
'[Error] HR: [%d], LR1: [%d], LR2: [%d], and LR3: [%d] have different number of images.'%(
len(self.paths_HR), len(self.paths_LR1), len(self.paths_LR2), len(self.paths_LR3))
def __init__(self, opt):
super(HRDataset, self).__init__()
self.opt = opt
self.train = (opt['phase'] == 'train')
self.split = 'train' if self.train else 'test'
self.scale = self.opt['scale']
self.paths_HR, self.paths_LR = None, None
if self.train:
self.lr_size = self.opt['LR_size']
self.hr_size = self.lr_size * self.scale
# change the length of train dataset (influence the number of iterations in each epoch)
self.repeat = 1
# read image list from image/binary files
self.paths_HR = common.get_image_paths(self.opt['data_type'],
self.opt['dataroot_HR'])
assert self.paths_HR, '[Error] HR paths are empty.'
self.filters_path = opt['blur_kernel_path']
self.filter_bank = common.get_filters(self.filters_path)
assert (len(self.filter_bank) > 0)
self.n_filters = len(self.filter_bank)
if opt['noise_patch_path']:
self.paths_noise_patches = common.get_image_paths(
self.opt['data_type'], self.opt['noise_patch_path'])
self.n_noise_patches = len(self.paths_noise_patches)
assert (self.n_noise_patches > 0)
print("Number of noise patches = {}".format(self.n_noise_patches))
def __init__(self, opt):
super(LRPANDataset, self).__init__()
self.opt = opt
self.scale = self.opt['scale']
self.paths_LR = None
# read image list from image/binary files
self.paths_LR = common.get_image_paths(opt['data_type'], opt['dataroot_LR'])
self.paths_PAN = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_PAN'])
assert self.paths_LR and len(self.paths_PAN)==len(self.paths_LR), '[Error] LR paths are empty.'
def __init__(self, opt):
super(IRVISCoGAN_dataset, self).__init__()
# self.args = args
self.opt = opt
self.train = (opt['phase'] == 'train')
self.split = 'train' if self.train else 'test'
self.repeat = 1
# read image list from lmdb or image files
self.VIS_env, self.paths_VIS = common.get_image_paths(
self.opt['data_type'], self.opt['dataroot_VI'])
self.IR_env, self.paths_IR = common.get_image_paths(
self.opt['data_type'], self.opt['dataroot_IR'])
self.FUS_env, self.paths_PF = common.get_image_paths(
self.opt['data_type'], self.opt['dataroot_PF'])
def __init__(self, opt):
super(LRDataset, self).__init__()
self.opt = opt
self.paths_LR = None
self.LR_env = None # environment for lmdb
# read image list from lmdb or image files
self.LR_env, self.paths_LR = util.get_image_paths(
opt['data_type'], opt['dataroot_LR'])
assert self.paths_LR, 'Error: LR paths are empty.'
def __init__(self, opt):
super(LRHRSegDataset, self).__init__()
self.opt = opt
self.paths_LR = None
self.paths_HR = None
self.LR_env = None # environment for lmdb
self.HR_env = None
# read image list from lmdb or image files
self.HR_env, self.paths_HR = util.get_image_paths(opt['data_type'], opt['dataroot_HR'])
self.LR_env, self.paths_LR = util.get_image_paths(opt['data_type'], opt['dataroot_LR'])
assert self.paths_HR, 'Error: HR paths are empty.'
if self.paths_LR and self.paths_HR:
assert len(self.paths_LR) == len(self.paths_HR), \
'HR and LR datasets have different number of images - {}, {}.'.format(\
len(self.paths_LR), len(self.paths_HR))
# randomly scale list
self.random_scale_list = [1, 0.9, 0.8, 0.7, 0.6, 0.5]
def __init__(self, opt):
super(LRHRDataset, self).__init__()
self.opt = opt
self.msx2 = True if 'MSX2' in opt['dataroot_LRPAN'] else False
self.train = ('train' in opt['phase'])
self.split = 'train' if self.train else 'test'
self.scale = self.opt['scale']
self.paths_HR, self.paths_LR = None, None
# change the length of train dataset (influence the number of iterations in each epoch)
self.repeat = 2
# read image list from image/binary files
self.paths_HR = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_HR'], opt['subset'])
self.paths_LR = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_LR'], opt['subset'])
self.paths_PAN = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_PAN'], opt['subset'])
self.paths_LRPAN = common.get_image_paths(self.opt['data_type'], self.opt['dataroot_LRPAN'], opt['subset'])
assert self.paths_HR, '[Error] HR paths are empty.'
if self.paths_LR and self.paths_HR:
assert len(self.paths_LR) == len(self.paths_HR), \
'[Error] HR: [%d] and LR: [%d] have different number of images.'%(
len(self.paths_LR), len(self.paths_HR))
