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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
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