136 lines
5.4 KiB
Python
136 lines
5.4 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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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 (paired_paths_from_folder,
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paired_paths_from_lmdb,
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paired_paths_from_meta_info_file)
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from basicsr.data.transforms import augment, paired_random_crop
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from basicsr.utils import FileClient, imfrombytes, img2tensor, padding
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class PairedImageDataset(data.Dataset):
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"""Paired image dataset for image restoration.
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Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
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GT image pairs.
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There are three modes:
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1. 'lmdb': Use lmdb files.
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If opt['io_backend'] == lmdb.
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2. 'meta_info_file': Use meta information file to generate paths.
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If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
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3. 'folder': Scan folders to generate paths.
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The rest.
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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_gt (str): Data root path for gt.
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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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filename_tmpl (str): Template for each filename. Note that the
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template excludes the file extension. Default: '{}'.
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gt_size (int): Cropped patched size for gt patches.
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use_flip (bool): Use horizontal flips.
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use_rot (bool): Use rotation (use vertical flip and transposing h
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and w for implementation).
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scale (bool): Scale, which will be added automatically.
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phase (str): 'train' or 'val'.
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"""
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def __init__(self, opt):
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super(PairedImageDataset, self).__init__()
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self.opt = opt
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# file client (io backend)
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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.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
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if 'filename_tmpl' in opt:
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self.filename_tmpl = opt['filename_tmpl']
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else:
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self.filename_tmpl = '{}'
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if self.io_backend_opt['type'] == 'lmdb':
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self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
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self.io_backend_opt['client_keys'] = ['lq', 'gt']
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self.paths = paired_paths_from_lmdb(
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[self.lq_folder, self.gt_folder], ['lq', 'gt'])
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elif 'meta_info_file' in self.opt and self.opt[
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'meta_info_file'] is not None:
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self.paths = paired_paths_from_meta_info_file(
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[self.lq_folder, self.gt_folder], ['lq', 'gt'],
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self.opt['meta_info_file'], self.filename_tmpl)
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else:
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self.paths = paired_paths_from_folder(
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[self.lq_folder, self.gt_folder], ['lq', 'gt'],
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self.filename_tmpl)
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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(
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self.io_backend_opt.pop('type'), **self.io_backend_opt)
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scale = self.opt['scale']
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# Load gt and lq images. Dimension order: HWC; channel order: BGR;
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# image range: [0, 1], float32.
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gt_path = self.paths[index]['gt_path']
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# print('gt path,', gt_path)
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img_bytes = self.file_client.get(gt_path, 'gt')
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try:
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img_gt = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("gt path {} not working".format(gt_path))
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lq_path = self.paths[index]['lq_path']
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# print(', lq path', lq_path)
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img_bytes = self.file_client.get(lq_path, 'lq')
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try:
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img_lq = imfrombytes(img_bytes, float32=True)
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except:
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raise Exception("lq path {} not working".format(lq_path))
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# augmentation for training
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if self.opt['phase'] == 'train':
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gt_size = self.opt['gt_size']
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# padding
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img_gt, img_lq = padding(img_gt, img_lq, gt_size)
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# random crop
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img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale,
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gt_path)
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# flip, rotation
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img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'],
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self.opt['use_rot'])
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# TODO: color space transform
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# BGR to RGB, HWC to CHW, numpy to tensor
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img_gt, img_lq = img2tensor([img_gt, img_lq],
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bgr2rgb=True,
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float32=True)
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# normalize
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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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normalize(img_gt, self.mean, self.std, inplace=True)
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return {
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'lq': img_lq,
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'gt': img_gt,
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'lq_path': lq_path,
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'gt_path': gt_path
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}
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def __len__(self):
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return len(self.paths)
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