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from PIL import Image
import torch
import os
import numpy as np
import torchvision.transforms as transforms
from utils.utils import generate_mask
class TrainDataset(torch.utils.data.Dataset):
def __init__(self, data_path, transform=None):
self.data = os.listdir(os.path.join(data_path, 'color'))
self.data_path = data_path
self.transform = transform
self.ToTensor = transforms.ToTensor()
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
image_name = self.data[idx]
color_img = Image.open(os.path.join(self.data_path, 'color', image_name)).convert('RGB')
bw_name = self.data[idx]
dfm_name = 'dfm_' + self.data[idx]
bw_img = Image.open(os.path.join(self.data_path, 'bw', bw_name)).convert('L')
dfm_img = Image.open(os.path.join(self.data_path, 'bw', dfm_name)).convert('L')
color_img = np.array(color_img)
bw_img = np.array(bw_img)
dfm_img = np.array(dfm_img)
bw_img = np.expand_dims(bw_img, 2)
dfm_img = np.expand_dims(dfm_img, 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']
color_img = self.ToTensor(color_img)
bw_img = self.ToTensor(bw_img)
color_img = (color_img - 0.5) / 0.5 # Normalización de color_img
mask = generate_mask(bw_img.shape[1], bw_img.shape[2])
hint = torch.cat((color_img * mask, mask), 0)
return bw_img, bw_img, color_img, hint
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):
image_name = self.data[idx]
color_img = Image.open(os.path.join(self.data_path, 'color', image_name)).convert('RGB')
bw_name = self.data[idx]
dfm_name = 'dfm_' + self.data[idx]
bw_img = Image.open(os.path.join(self.data_path, 'bw', bw_name)).convert('L')
dfm_img = Image.open(os.path.join(self.data_path, 'bw', dfm_name)).convert('L')
color_img = np.array(color_img)
bw_img = np.array(bw_img)
dfm_img = np.array(dfm_img)
bw_img = np.expand_dims(bw_img, 2)
dfm_img = np.expand_dims(dfm_img, 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']
color_img = self.ToTensor(color_img)
bw_img = self.ToTensor(bw_img)
color_img = (color_img - 0.5) / 0.5 # Normalización de color_img
return bw_img, color_img # Devuelve bw_img una vez y color_img
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