| | from os import path as osp |
| | from torch.utils import data as data |
| | from torchvision.transforms.functional import normalize |
| |
|
| | from r_basicsr.data.data_util import paths_from_lmdb |
| | from r_basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir |
| | from r_basicsr.utils.registry import DATASET_REGISTRY |
| |
|
| |
|
| | @DATASET_REGISTRY.register() |
| | 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: |
| | 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 |
| | self.io_backend_opt = opt['io_backend'] |
| | self.mean = opt['mean'] if 'mean' in opt else None |
| | self.std = opt['std'] if 'std' in opt else None |
| | 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 = [osp.join(self.lq_folder, line.rstrip().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] |
| | img_bytes = self.file_client.get(lq_path, 'lq') |
| | img_lq = imfrombytes(img_bytes, float32=True) |
| |
|
| | |
| | if 'color' in self.opt and self.opt['color'] == 'y': |
| | img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] |
| |
|
| | |
| | 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) |
| |
|
