from torch.utils.data import Dataset
import PIL.Image as Image
import os


# def make_dataset(root):
#     # root = "./data/train"
#     imgs = []
#     ori_path = os.path.join(root, "Data")
#     ground_path = os.path.join(root, "Ground")
#     names = os.listdir(ori_path)
#     n = len(names)
#     for i in range(n):
#         img = os.path.join(ori_path, names[i])
#         mask = os.path.join(ground_path, names[i])
#         imgs.append((img, mask))
#     return imgs


class LiverDataset(Dataset):
    def __init__(self, root, transform=None, target_transform=None):
        imgs = "/home/shared/wy/flask_web/Unet_liver_seg-master/data/val/Data/P2_T1_00018.png"
        
        self.imgs = imgs
        self.transform = transform
        self.target_transform = target_transform

    def __getitem__(self, index):
        # x_path, y_path = self.imgs[index]
        x_path = self.imgs
        img_x = Image.open(x_path).convert('L')
        # img_y = Image.open(y_path).convert('L')
        if self.transform is not None:
            img_x = self.transform(img_x)
        # if self.target_transform is not None:
        #     img_y = self.target_transform(img_y)
        return img_x

    def __len__(self):
        return len(self.imgs)