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import os
from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tensor
from basicsr.utils.registry import DATASET_REGISTRY
from torch.utils import data as data
from torchvision.transforms.functional import normalize
@DATASET_REGISTRY.register()
class RealESRGANPairedDataset(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': Use meta information file to generate paths.
If opt['io_backend'] != lmdb and opt['meta_info'] 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 (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_hflip (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(RealESRGANPairedDataset, self).__init__()
self.opt = opt
self.file_client = None
self.io_backend_opt = opt["io_backend"]
# mean and std for normalizing the input images
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"]
self.filename_tmpl = opt["filename_tmpl"] if "filename_tmpl" in opt else "{}"
# file client (lmdb io backend)
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" in self.opt and self.opt["meta_info"] is not None:
# disk backend with meta_info
# Each line in the meta_info describes the relative path to an image
with open(self.opt["meta_info"]) as fin:
paths = [line.strip() for line in fin]
self.paths = []
for path in paths:
gt_path, lq_path = path.split(", ")
gt_path = os.path.join(self.gt_folder, gt_path)
lq_path = os.path.join(self.lq_folder, lq_path)
self.paths.append(dict([("gt_path", gt_path), ("lq_path", lq_path)]))
else:
# disk backend
# it will scan the whole folder to get meta info
# it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
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_hflip"], self.opt["use_rot"]
)
# 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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