用Python做有趣的AI项目5：AI 画画机器人（图像风格迁移）

这个项目将使用 PyTorch 实现图像风格迁移（Neural Style Transfer），让一张图片看起来具有另一张图片的"艺术风格"。

🔧 开发环境建议

Python 3.8+

PyTorch（pip install torch torchvision）

PIL（pip install pillow）

CUDA（可选，但建议有 GPU）

🗂️ 项目结构示例

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style_transfer/
├── style.jpg         # 风格图像（如：星夜）
├── content.jpg       # 内容图像（如：你的自拍）
├── style_transfer.py # 主程序

✅ Step-by-step：图像风格迁移项目详解（含代码）

✅ Step 1：导入库

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

✅ Step 2：设备设置（使用 GPU 优先）

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

✅ Step 3：图像加载与预处理函数

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def load_image(img_path, max_size=400):
    image = Image.open(img_path).convert('RGB')
    
    # 限制最大尺寸
    if max(image.size) > max_size:
        size = max_size
    else:
        size = max(image.size)

    in_transform = transforms.Compose([
        transforms.Resize(size),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])

    image = in_transform(image).unsqueeze(0)
    return image.to(device)

✅ Step 4：定义函数来展示图像

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def im_convert(tensor):
    image = tensor.to("cpu").clone().detach()
    image = image.squeeze(0)
    image = transforms.ToPILImage()(image)
    return image

✅ Step 5：加载内容图像和风格图像

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content = load_image("content.jpg").to(device)
style = load_image("style.jpg").to(device)

✅ Step 6：加载预训练的 VGG 模型（VGG19）

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vgg = models.vgg19(pretrained=True).features.to(device).eval()

我们只取模型中的某些层用于提取风格与内容特征。

✅ Step 7：定义提取内容和风格特征的函数

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def get_features(image, model, layers=None):
    if layers is None:
        layers = {
            '0': 'conv1_1',
            '5': 'conv2_1',
            '10': 'conv3_1',
            '19': 'conv4_1',
            '21': 'conv4_2',  # content representation
            '28': 'conv5_1'
        }
        
    features = {}
    x = image
    for name, layer in model._modules.items():
        x = layer(x)
        if name in layers:
            features[layers[name]] = x
            
    return features

✅ Step 8：定义 Gram 矩阵函数（风格提取核心）

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def gram_matrix(tensor):
    b, d, h, w = tensor.size()
    tensor = tensor.view(d, h * w)
    gram = torch.mm(tensor, tensor.t())
    return gram

✅ Step 9：提取内容和风格特征

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content_features = get_features(content, vgg)
style_features = get_features(style, vgg)


# 为每个风格层计算 Gram 矩阵
style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}

✅ Step 10：初始化目标图像（从内容图像复制）

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target = content.clone().requires_grad_(True).to(device)

✅ Step 11：设置超参数

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style_weights = {
    'conv1_1': 1.0,
    'conv2_1': 0.75,
    'conv3_1': 0.2,
    'conv4_1': 0.2,
    'conv5_1': 0.2
}
content_weight = 1e4  # α
style_weight = 1e2    # β

✅ Step 12：设置优化器

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optimizer = optim.Adam([target], lr=0.003)

✅ Step 13：训练模型

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steps = 300

for i in range(1, steps+1):
    target_features = get_features(target, vgg)

    content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2']) ** 2)

    style_loss = 0
    for layer in style_weights:
        target_feature = target_features[layer]
        target_gram = gram_matrix(target_feature)
        style_gram = style_grams[layer]
        layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram) ** 2)
        style_loss += layer_style_loss / (target_feature.shape[1] ** 2)

    total_loss = content_weight * content_loss + style_weight * style_loss

    optimizer.zero_grad()
    total_loss.backward()
    optimizer.step()

    if i % 50 == 0:
        print(f"Step {i}, Total loss: {total_loss.item():.4f}")

✅ Step 14：保存并显示结果图像

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final_img = im_convert(target)
final_img.save("result.jpg")
final_img.show()

🖼️ 示例效果

将自拍和《星夜.jpg》结合，输出一张油画风格的人像。

附上完整代码：

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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import copy


# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


# 图像加载和预处理
def load_image(img_path, max_size=400):
    image = Image.open(img_path).convert('RGB')

    if max(image.size) > max_size:
        size = max_size
    else:
        size = max(image.size)

    in_transform = transforms.Compose([
        transforms.Resize(size),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]
        )
    ])

    image = in_transform(image).unsqueeze(0)
    return image.to(device)


# 还原为可视图像
def im_convert(tensor):
    image = tensor.to("cpu").clone().detach()
    image = image.squeeze(0)
    image = transforms.ToPILImage()(image)
    return image


# 提取特征
def get_features(image, model, layers=None):
    if layers is None:
        layers = {
            '0': 'conv1_1',
            '5': 'conv2_1',
            '10': 'conv3_1',
            '19': 'conv4_1',
            '21': 'conv4_2',  # 内容层
            '28': 'conv5_1'
        }

    features = {}
    x = image
    for name, layer in model._modules.items():
        x = layer(x)
        if name in layers:
            features[layers[name]] = x
    return features


# Gram矩阵
def gram_matrix(tensor):
    b, d, h, w = tensor.size()
    tensor = tensor.view(d, h * w)
    gram = torch.mm(tensor, tensor.t())
    return gram


# 主程序入口
def main():
    # 加载图像
    content = load_image("content.jpg")
    style = load_image("style.jpg")

    # 加载预训练模型
    vgg = models.vgg19(pretrained=True).features.to(device).eval()

    content_features = get_features(content, vgg)
    style_features = get_features(style, vgg)

    style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}

    target = content.clone().requires_grad_(True).to(device)

    # 权重设置
    style_weights = {
        'conv1_1': 1.0,
        'conv2_1': 0.75,
        'conv3_1': 0.2,
        'conv4_1': 0.2,
        'conv5_1': 0.2
    }

    content_weight = 1e4
    style_weight = 1e2

    optimizer = optim.Adam([target], lr=0.003)
    steps = 300

    print("开始风格迁移...")
    for i in range(1, steps + 1):
        target_features = get_features(target, vgg)

        content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2']) ** 2)

        style_loss = 0
        for layer in style_weights:
            target_feature = target_features[layer]
            target_gram = gram_matrix(target_feature)
            style_gram = style_grams[layer]
            layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram) ** 2)
            style_loss += layer_style_loss / (target_feature.shape[1] ** 2)

        total_loss = content_weight * content_loss + style_weight * style_loss

        optimizer.zero_grad()
        total_loss.backward()
        optimizer.step()

        if i % 50 == 0:
            print(f"Step {i}/{steps}, Total loss: {total_loss.item():.4f}")

    # 保存结果
    result = im_convert(target)
    result.save("result.jpg")
    print("风格迁移完成！结果保存在 result.jpg")


# 运行主函数
if __name__ == "__main__":
    main()

✅ 使用说明

🖼️ 准备：

把你的内容图命名为 content.jpg

把你的风格图命名为 style.jpg

放在与 style_transfer.py 同一个目录下

▶️ 运行：

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python style_transfer.py

#🖼️ 输出：

运行成功后，生成的图像将保存在：

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result.jpg