from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize
import torch.nn.functional as F
from basicsr.data.data_util import paths_from_lmdb
from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir
from basicsr.utils.registry import DATASET_REGISTRY
import cv2

@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)))
            from glob import glob
            self.paths = sorted(list(glob(self.lq_folder+"/*/*.png")))

    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
        # print(img_lq.shape)

        # img_lq = cv2.resize(img_lq, (1024, 1024), interpolation=cv2.INTER_CUBIC)
        img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True)
        # print(img_lq.shape)
        # img_lq = F.interpolate(img_lq, scale_factor=2, mode='bicubic')

        # 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)
