def __getitem__(self, index):
HR_path, LR_path = None, None
scale = self.opt['scale']
HR_size = self.opt['HR_size']
# get HR image
HR_path = self.paths_HR[index]
img_HR = common.read_img(self.HR_env, HR_path)
# get LR image
if self.paths_LR:
LR_path = self.paths_LR[index]
img_LR = common.read_img(self.LR_env, LR_path)
else:
raise NotImplementedError('Low resolution images do not exist')
img_LR, img_HR = self._get_patch(img_LR, img_HR, scale, HR_size, self.opt['phase'])
# channel conversion
if self.opt['color']:
img_LR, img_HR = common.channel_convert(img_HR.shape[2], [img_LR, img_HR], self.opt['color'])
# HWC to CHW, BGR to RGB, numpy to tensor
tensor_LR, tensor_HR = common.np2Tensor([img_LR, img_HR])
if LR_path is None:
LR_path = HR_path
return {'LR': tensor_LR, 'HR': tensor_HR, 'LR_path': LR_path, 'HR_path': HR_path}
def __getitem__(self, index):
HR_path, LR_path = None, None
scale = self.opt['scale']
HR_size = self.opt['HR_size']
# get HR image
HR_path = self.paths_HR[index]
img_HR = common.read_img(self.HR_env, HR_path)
# get LR image
if self.paths_LR:
LR_path = self.paths_LR[index]
img_LR = common.read_img(self.LR_env, LR_path)
else:
raise NotImplementedError('Low resolution images do not exist')
# get coeff
coeff_path = self.path_coeff[index]
coeff = common.read_coeff(self.coeff_env, coeff_path)
coeff = [coeff[0][i] for i in range(coeff.size)]
# channel conversion
if self.opt['color']:
img_LR, img_HR = common.channel_convert(img_HR.shape[2], [img_LR, img_HR], self.opt['color'])
# HWC to CHW, BGR to RGB, numpy to tensor
tensor_LR = torch.from_numpy(np.ascontiguousarray(img_LR)).float()
tensor_HR = torch.from_numpy(np.ascontiguousarray(img_HR)).float()
if LR_path is None:
LR_path = HR_path
return {'LR': tensor_LR, 'HR': tensor_HR, 'LR_path': LR_path, 'HR_path': HR_path, 'coeff': coeff}
def __getitem__(self, idx):
lr, hr, lr_path, hr_path = self._load_file(idx)
lr, hr = self._get_patch(lr, hr)
# channel conversion
if self.opt['color']:
lr, hr = common.channel_convert(hr.shape[2], [lr, hr], self.opt['color'])
lr_tensor, hr_tensor = common.np2Tensor([lr, hr], self.opt['rgb_range'])
return {'LR': lr_tensor, 'HR': hr_tensor, 'LR_path': lr_path, 'HR_path': hr_path}
def __getitem__(self, index):
LR_path = None
# get LR image
LR_path = self.paths_LR[index]
img_LR = util.read_img(self.LR_env, LR_path)
H, W, C = img_LR.shape
# channel conversion
if self.opt['color']:
img_LR = util.channel_convert(C, self.opt['color'], [img_LR])[0]
# HWC to CHW, BGR to RGB, numpy to tensor
if img_LR.shape[2] == 3:
img_LR = img_LR[:, :, [2, 1, 0]]
img_LR = torch.from_numpy(
np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()
return {'LR': img_LR, 'LR_path': LR_path}