| from os import path as osp
|
| from torch.utils import data as data
|
| from torchvision.transforms.functional import normalize
|
|
|
| from basicsr.data.data_util import paths_from_lmdb
|
| from basicsr.utils import FileClient, imfrombytes, img2tensor, scandir
|
|
|
|
|
| class SingleImageDataset(data.Dataset):
|
| """Read only lq images in the test phase.
|
|
|
| Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc).
|
|
|
| There are two modes:
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| 1. 'meta_info_file': Use meta information file to generate paths.
|
| 2. 'folder': Scan folders to generate paths.
|
|
|
| Args:
|
| opt (dict): Config for train datasets. It contains the following keys:
|
| dataroot_lq (str): Data root path for lq.
|
| meta_info_file (str): Path for meta information file.
|
| io_backend (dict): IO backend type and other kwarg.
|
| """
|
|
|
| def __init__(self, opt):
|
| super(SingleImageDataset, self).__init__()
|
| self.opt = opt
|
|
|
| self.file_client = None
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| self.io_backend_opt = opt['io_backend']
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| self.mean = opt['mean'] if 'mean' in opt else None
|
| self.std = opt['std'] if 'std' in opt else None
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| self.lq_folder = opt['dataroot_lq']
|
|
|
| if self.io_backend_opt['type'] == 'lmdb':
|
| self.io_backend_opt['db_paths'] = [self.lq_folder]
|
| self.io_backend_opt['client_keys'] = ['lq']
|
| self.paths = paths_from_lmdb(self.lq_folder)
|
| elif 'meta_info_file' in self.opt:
|
| with open(self.opt['meta_info_file'], 'r') as fin:
|
| self.paths = [
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| osp.join(self.lq_folder,
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| line.split(' ')[0]) for line in fin
|
| ]
|
| else:
|
| self.paths = sorted(list(scandir(self.lq_folder, full_path=True)))
|
|
|
| def __getitem__(self, index):
|
| if self.file_client is None:
|
| self.file_client = FileClient(
|
| self.io_backend_opt.pop('type'), **self.io_backend_opt)
|
|
|
|
|
| lq_path = self.paths[index]
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| img_bytes = self.file_client.get(lq_path, 'lq')
|
| img_lq = imfrombytes(img_bytes, float32=True)
|
|
|
|
|
|
|
| img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True)
|
|
|
| if self.mean is not None or self.std is not None:
|
| normalize(img_lq, self.mean, self.std, inplace=True)
|
| return {'lq': img_lq, 'lq_path': lq_path}
|
|
|
| def __len__(self):
|
| return len(self.paths)
|
|
|
