RealESRGANv030/realesrgan/data/realesrgan_paired_dataset.py

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)