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