如何使用Python函数实现图像风格迁移？

图像风格迁移是一种将一张图像的风格应用到另一张图像上的技术。下面是使用Python函数实现图像风格迁移的步骤：

1. 导入所需的模块和库[^2]：

import torch
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models

2. 加载预训练的VGG-19模型[^1]：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.vgg19(pretrained=True).features
model.to(device).eval()

3. 定义图像转换函数：

def image_transform(image_path):
    image = Image.open(image_path)
    image_transform = transforms.Compose([
        transforms.Resize(400),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    image = image_transform(image).unsqueeze(0)
    return image.to(device)

4. 加载内容图像和风格图像：

content_image = image_transform("content.jpg")
style_image = image_transform("style.jpg")

5. 定义内容损失函数和风格损失函数：

def content_loss(content_features, target_features):
    return torch.mean((content_features - target_features) ** 2)

def style_loss(style_features, target_features):
    _, c, h, w = style_features.size()
    style_features = style_features.view(c, h * w)
    target_features = target_features.view(c, h * w)
    gram_style = torch.mm(style_features, style_features.t())
    gram_target = torch.mm(target_features, target_features.t())
    return torch.mean((gram_style - gram_target) ** 2)

6. 定义总损失函数：

def total_loss(content_features, style_features, target_features):
    content_weight = 1
    style_weight = 1000
    content_loss_value = content_loss(content_features, target_features)
    style_loss_value = style_loss(style_features, target_features)
    total_loss = content_weight * content_loss_value + style_weight * style_loss_value
    return total_loss

7. 进行图像风格迁移：

input_image = content_image.clone().requires_grad_(True).to(device)
optimizer = torch.optim.Adam([input_image], lr=0.01)

num_epochs = 2000
for epoch in range(num_epochs):
    optimizer.zero_grad()
    input_features = model(input_image)
    content_features = model(content_image)
    style_features = model(style_image)
    loss = total_loss(content_features, style_features, input_features)
    loss.backward()
    optimizer.step()

    if (epoch+1) % 100 == 0:
        print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

output_image = input_image.detach().squeeze(0).cpu()
output_image = transforms.ToPILImage()(output_image)
output_image.save("output.jpg")

这样，你就可以使用Python函数实现图像风格迁移了。