dataset.py

import numpy as np
import config
import os
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision.utils import save_image


class MapDataset(Dataset):
    def __init__(self, root_dir_comics, root_dir_face):
        self.root_dir_face = root_dir_face
        self.list_files_face = os.listdir(self.root_dir_face)
        self.root_dir_comics = root_dir_comics
        self.list_files_comics = os.listdir(self.root_dir_comics)

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

    def __getitem__(self, index):
        img_file_input = self.list_files_face[index]
        img_file_target = self.list_files_comics[index]
        img_path_input = os.path.join(self.root_dir_face, img_file_input)
        img_path_target = os.path.join(self.root_dir_comics, img_file_target)
        image_input = np.array(Image.open(img_path_input))
        image_target = np.array(Image.open(img_path_target))
        input_image = image_input
        target_image = image_target

        augmentations = config.both_transform(image=input_image, image0=target_image)
        input_image = augmentations["image"]
        target_image = augmentations["image0"]

        input_image = config.transform_only_input(image=input_image)["image"]
        target_image = config.transform_only_mask(image=target_image)["image"]

        return input_image, target_image


if __name__ == "__main__":
    dataset = MapDataset("data/train/")
    loader = DataLoader(dataset, batch_size=5)
    for x, y in loader:
        print(x.shape)
        save_image(x, "x.png")
        save_image(y, "y.png")
        import sys

        sys.exit()