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import os
from PIL import Image
import torch
from torch.utils.data import Dataset
from torchvision import transforms
import numpy as np
def test_transform(size, crop):
transform_list = []
if size != 0:
transform_list.append(transforms.Resize(size))
if crop:
transform_list.append(transforms.CenterCrop(size))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
def style_transform(h, w):
k = (h, w)
size = int(np.max(k))
print(type(size))
transform_list = []
transform_list.append(transforms.CenterCrop((h, w)))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
return transform
def content_transform():
transform_list = []
transform_list.append(transforms.Resize(256)) # Thay đổi kích thước trước
transform_list.append(transforms.ToTensor()) # Sau đó chuyển đổi thành tensor
transform = transforms.Compose(transform_list)
return transform # Trả về một đối tượng biến đổi
class Summer2YosemiteDataset(Dataset):
def __init__(self, content_dir, style_dir, transform=None):
self.content_dir = content_dir
self.style_dir = style_dir
self.transform = transform
self.content_images = sorted([os.path.join(content_dir, img) for img in os.listdir(content_dir)])
self.style_images = sorted([os.path.join(style_dir, img) for img in os.listdir(style_dir)])
def __len__(self):
return min(len(self.content_images), len(self.style_images))
def __getitem__(self, index):
content_path = self.content_images[index]
style_path = self.style_images[index]
# Load và áp dụng các biến đổi ảnh
content_image = Image.open(content_path).convert("RGB")
style_image = Image.open(style_path).convert("RGB")
if self.transform:
content_image = self.transform(content_image)
style_image = self.transform(style_image)
return {'label': content_image, 'image': style_image,'cpath': content_path}
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