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from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
@DATASET_REGISTRY.register()
class PairedImageDataset(data.Dataset):
"""Paired image dataset for image restoration.
Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
GT image pairs.
There are three modes:
1. 'lmdb': Use lmdb files.
If opt['io_backend'] == lmdb.
2. 'meta_info_file': Use meta information file to generate paths.
If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None.
3. 'folder': Scan folders to generate paths.
The rest.
Args:
opt (dict): Config for train datasets. It contains the following keys:
dataroot_gt (str): Data root path for gt.
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.
filename_tmpl (str): Template for each filename. Note that the
template excludes the file extension. Default: '{}'.
gt_size (int): Cropped patched size for gt patches.
use_flip (bool): Use horizontal flips.
use_rot (bool): Use rotation (use vertical flip and transposing h
and w for implementation).
scale (bool): Scale, which will be added automatically.
phase (str): 'train' or 'val'.
"""
def __init__(self, opt):
super(PairedImageDataset, self).__init__()
self.opt = opt
# file client (io backend)
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.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
if 'filename_tmpl' in opt:
self.filename_tmpl = opt['filename_tmpl']
else:
self.filename_tmpl = '{}'
if self.io_backend_opt['type'] == 'lmdb':
self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
self.io_backend_opt['client_keys'] = ['lq', 'gt']
self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None:
self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'],
self.opt['meta_info_file'], self.filename_tmpl)
else:
self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
def __getitem__(self, index):
if self.file_client is None:
self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
scale = self.opt['scale']
# Load gt and lq images. Dimension order: HWC; channel order: BGR;
# image range: [0, 1], float32.
gt_path = self.paths[index]['gt_path']
img_bytes = self.file_client.get(gt_path, 'gt')
img_gt = imfrombytes(img_bytes, float32=True)
lq_path = self.paths[index]['lq_path']
img_bytes = self.file_client.get(lq_path, 'lq')
img_lq = imfrombytes(img_bytes, float32=True)
# augmentation for training
if self.opt['phase'] == 'train':
gt_size = self.opt['gt_size']
# random crop
img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
# flip, rotation
img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_flip'], self.opt['use_rot'])
# TODO: color space transform
# BGR to RGB, HWC to CHW, numpy to tensor
img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
# normalize
if self.mean is not None or self.std is not None:
normalize(img_lq, self.mean, self.std, inplace=True)
normalize(img_gt, self.mean, self.std, inplace=True)
return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
def __len__(self):
return len(self.paths)
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