dataset/datasets.py

import os
import torch
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()

        # Directorio para guardar las imágenes en blanco y negro
        self.bw_directory = os.path.join(data_path, 'bw')
        if not os.path.exists(self.bw_directory):
            os.makedirs(self.bw_directory)

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

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

        if self.mults_amount > 1:
            mult_number = np.random.choice(range(self.mults_amount))
        else:
            mult_number = 0

        bw_name = f"{os.path.splitext(image_name)[0]}_{mult_number}.png"
        dfm_name = f"{os.path.splitext(image_name)[0]}_{mult_number}_dfm.png"

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

        # Normalización y generación de máscara
        bw_img = self.ToTensor(bw_img)
        color_img = self.ToTensor(color_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

# Resto del código...

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
        self.ToTensor = transforms.ToTensor()

        # Directorio para guardar las imágenes en blanco y negro
        self.bw_directory = os.path.join(data_path, 'bw')
        if not os.path.exists(self.bw_directory):
            os.makedirs(self.bw_directory)

    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 = image_name
            dfm_name = os.path.splitext(image_name)[0] + '_dfm.png'

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

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

        color_img = (color_img - 0.5) / 0.5

        return bw_img, dfm_img, color_img