dataset/datasets.py

import torch
import os
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

from utils.utils import generate_mask


class TrainDataset(torch.utils.data.Dataset):
    def __init__(self, data_path, transform=None, mults_amount=1):
        self.data = os.listdir(os.path.join(data_path, "color"))
        self.data_path = data_path
        self.transform = transform
        self.mults_amount = mults_amount

        self.ToTensor = transforms.ToTensor()

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

    def __getitem__(self, idx):
        image_name = self.data[idx]
        
        try:
            color_img = plt.imread(os.path.join(self.data_path, 'color', image_name))
        except SyntaxError:
            print(f"Archivo {image_name} no es un PNG válido. Saltando...")
            return None  # O alguna otra acción que prefieras

        if self.mults_amount > 1:
            mult_number = np.random.choice(range(self.mults_amount))

            bw_name = (
                image_name[: image_name.rfind(".")] + "_" + str(mult_number) + ".png"
            )
            dfm_name = (
                image_name[: image_name.rfind(".")]
                + "_"
                + str(mult_number)
                + "_dfm.png"
            )
        else:
            bw_name = self.data[idx]
            dfm_name = os.path.splitext(self.data[idx])[0] + "0_dfm.png"

        bw_img = np.expand_dims(
            plt.imread(os.path.join(self.data_path, "bw", bw_name)), 2
        )
        dfm_img = np.expand_dims(
            plt.imread(os.path.join(self.data_path, "bw", dfm_name)), 2
        )

        bw_img = np.concatenate([bw_img, dfm_img], axis=2)

        if self.transform:
            result = self.transform(image=color_img, mask=bw_img)
            color_img = result["image"]
            bw_img = result["mask"]

        dfm_img = bw_img[:, :, 1]
        bw_img = bw_img[:, :, 0]

        color_img = self.ToTensor(color_img)
        bw_img = self.ToTensor(bw_img)

        dfm_img = self.ToTensor(dfm_img)

        color_img = (color_img - 0.5) / 0.5

        mask = generate_mask(bw_img.shape[1], bw_img.shape[2])
        hint = torch.cat((color_img * mask, mask), 0)

        return bw_img, color_img, hint, dfm_img


class FineTuningDataset(torch.utils.data.Dataset):
    def __init__(self, data_path, transform=None, mult_amount=1):
        self.data = [
            x
            for x in os.listdir(os.path.join(data_path, "real_manga"))
            if x.find("_dfm") == -1
        ]
        self.color_data = [x for x in os.listdir(os.path.join(data_path, "color"))]
        self.data_path = data_path
        self.transform = transform
        self.mults_amount = mult_amount

        np.random.shuffle(self.color_data)

        self.ToTensor = transforms.ToTensor()

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

    def __getitem__(self, idx):
        color_img = plt.imread(
            os.path.join(self.data_path, "color", self.color_data[idx])
        )

        image_name = self.data[idx]
        if self.mults_amount > 1:
            mult_number = np.random.choice(range(self.mults_amount))

            bw_name = (
                image_name[: image_name.rfind(".")]
                + "_"
                + str(self.mults_amount)
                + ".png"
            )
            dfm_name = (
                image_name[: image_name.rfind(".")]
                + "_"
                + str(self.mults_amount)
                + "_dfm.png"
            )
        else:
            bw_name = self.data[idx]
            dfm_name = os.path.splitext(self.data[idx])[0] + "_dfm.png"

        bw_img = np.expand_dims(
            plt.imread(os.path.join(self.data_path, "real_manga", image_name)), 2
        )
        dfm_img = np.expand_dims(
            plt.imread(os.path.join(self.data_path, "real_manga", dfm_name)), 2
        )

        if self.transform:
            result = self.transform(image=color_img)
            color_img = result["image"]

            result = self.transform(image=bw_img, mask=dfm_img)
            bw_img = result["image"]
            dfm_img = result["mask"]

        color_img = self.ToTensor(color_img)
        bw_img = self.ToTensor(bw_img)
        dfm_img = self.ToTensor(dfm_img)

        color_img = (color_img - 0.5) / 0.5

        return bw_img, dfm_img, color_img