74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
# ------------------------------------------------------------------------
|
|
# Copyright (c) 2022 megvii-model. All Rights Reserved.
|
|
# ------------------------------------------------------------------------
|
|
# Modified from BasicSR (https://github.com/xinntao/BasicSR)
|
|
# Copyright 2018-2020 BasicSR Authors
|
|
# ------------------------------------------------------------------------
|
|
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:
|
|
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
|
|
# 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.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.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)
|
|
|
|
# load lq image
|
|
lq_path = self.paths[index]
|
|
img_bytes = self.file_client.get(lq_path, 'lq')
|
|
img_lq = imfrombytes(img_bytes, float32=True)
|
|
|
|
# TODO: color space transform
|
|
# BGR to RGB, HWC to CHW, numpy to tensor
|
|
img_lq = img2tensor(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)
|
|
return {'lq': img_lq, 'lq_path': lq_path}
|
|
|
|
def __len__(self):
|
|
return len(self.paths)
|