Neural Color Transfer代码

以下是基于PyTorch的神经色彩转移的示例代码：

import numpy as np
import torch
import torchvision.transforms.functional as F
from PIL import Image

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 加载预训练的VGG网络
vgg = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19', pretrained=True).features.to(device).eval()

# 定义图像处理函数
def load_image(filename, size=None, scale=None):
    img = Image.open(filename).convert('RGB')
    if size is not None:
        img = img.resize((size, size), Image.BICUBIC)
    elif scale is not None:
        img = img.resize((int(img.width / scale), int(img.height / scale)), Image.BICUBIC)
    return F.to_tensor(img).unsqueeze(0).to(device)

def save_image(tensor, filename):
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = F.to_pil_image(image)
    image.save(filename)

# 定义Gram矩阵函数
def gram_matrix(tensor):
    batch, depth, height, width = tensor.size()
    tensor = tensor.view(batch * depth, height * width)
    gram = torch.mm(tensor, tensor.t())
    return gram.div(batch * depth * height * width)

# 定义神经色彩转移函数
def neural_transfer(content, style, alpha=1.0, beta=1e6, iterations=1000):
    # 加载内容和风格图像
    content_tensor = load_image(content)
    style_tensor = load_image(style, size=content_tensor.size(-1))
    
    # 定义生成的图像为内容图像
    target = content_tensor.clone().requires_grad_(True)
    
    # 定义优化器
    optimizer = torch.optim.Adam([target], lr=0.01)
    
    # 迭代
    for i in range(iterations):
        target_features = vgg(target)
        content_features = vgg(content_tensor)
        style_features = vgg(style_tensor)
        
        # 计算内容损失
        content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2']) ** 2)
        
        # 计算风格损失
        style_loss = 0
        for layer in ['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1']:
            target_feature = target_features[layer]
            target_gram = gram_matrix(target_feature)
            style_feature = style_features[layer]
            style_gram = gram_matrix(style_feature)
            layer_style_loss = torch.mean((target_gram - style_gram) ** 2)
            style_loss += layer_style_loss
        
        # 计算总损失
        total_loss = alpha * content_loss + beta * style_loss
        
        # 更新生成的图像
        optimizer.zero_grad()
        total_loss.backward()
        optimizer.step()
        
        # 输出损失
        if i % 100 == 0:
            print('Iteration [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}, Total Loss: {:.4f}'.format(
                i+1, iterations, content_loss.item(), style_loss.item(), total_loss.item()))
    
    # 返回生成的图像
    return target.detach()

# 运行神经色彩转移函数
content = 'content.jpg'
style = 'style.jpg'
output = 'output.jpg'
result = neural_transfer(content, style, iterations=1000)
save_image(result, output)

在这个示例代码中，我们首先加载了预训练的VGG网络，并定义了图像处理函数和Gram矩阵函数。然后，我们定义了神经色彩转移函数，其中我们通过迭代更新生成的图像，并计算内容损失和风格损失。最后，我们运行神经色彩转移函数，并将生成的图像保存到文件中。

请注意，这个示例代码仅用于演示神经色彩转移的基本原理，实际的应用中可能需要进行更多的参数调整和优化。