from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize

from modules.basic.data.data_util import paths_from_lmdb
from modules.basic.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir
from modules.basic.utils.registry import DATASET_REGISTRY


@DATASET_REGISTRY.register()
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.rstrip().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)

        # color space transform
        if "color" in self.opt and self.opt["color"] == "y":
            img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None]

        # 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)
