卷积神经网络之图像风格迁移视觉效果

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鱼弦CTO 2024-07-25 16:08:58 博主文章分类：宗师 ©著作权

文章标签 Image tensorflow 预处理 文章分类 llama AIGC AIGC二三事

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卷积神经网络之图像风格迁移视觉效果_预处理

1. 介绍

DeepArt 是一种将普通照片转化为具有特定艺术风格图片的应用。它利用深度学习技术，尤其是卷积神经网络（CNN），来实现图像风格迁移（style transfer）。DeepArt 可以将任意输入图像转换成类似于著名艺术家的绘画风格，从而生成独特的、充满艺术感的作品。

2. 应用使用场景

数码艺术创作：数字艺术家可以快速将照片转换为不同艺术风格的作品。
社交媒体内容制作：用户可以创建个性化和吸引人的图像，以提高社交媒体关注度。
广告和品牌宣传：企业可以生成符合品牌调性的艺术风格图像，用于营销和推广活动。
文化教育：教育机构可以以艺术风格转换的方式激发学生的兴趣，进行艺术欣赏教育。
纪念品和礼品制作：用户可以将自己的照片转换为艺术风格图像，作为特别的礼物或者纪念品。

3. 原理解释

核心技术

DeepArt 主要基于卷积神经网络（CNN）实现图像风格迁移。其核心思想是通过优化一个目标图像，使其同时拥有内容图像的结构和风格图像的视觉效果。

算法原理流程图

+-------------------------+
| Content Image           |
+-------------------------+
            |
            v
+-------------------------+
| Convolutional Neural    |
| Network (CNN)           |
+-------------------------+
            |
            v
+-------------------------+
| Style Transfer Process  |
+-------------------------+
            |
            v
+-------------------------+
| Generated Art Image     |
+-------------------------+

算法原理解释

内容图像与风格图像输入：选择一张内容图像（普通照片）和一张风格图像（艺术风格的作品）。
卷积神经网络：通过预训练的卷积神经网络提取内容图像和风格图像的特征表示。
损失函数：定义内容损失和风格损失，并通过优化过程使生成图像最小化这两个损失。

内容损失：确保生成图像与内容图像在高层次特征上的相似性。
风格损失：确保生成图像与风格图像在低层次特征上的相似性。

优化生成图像：通过梯度下降等优化算法，逐步调整初始随机产生的图像，使其成为最终的艺术风格图像。

4. 应用场景代码示例实现

以下是一个使用 TensorFlow 和 Keras 实现图像风格迁移的简单示例。

安装必要包

pip install tensorflow pillow numpy

代码示例

import tensorflow as tf
import numpy as np
from PIL import Image
from tensorflow.keras.applications import VGG19
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import img_to_array, array_to_img

# 加载并预处理图像
def load_and_process_image(image_path, target_shape):
    image = Image.open(image_path)
    image = image.resize(target_shape)
    image = img_to_array(image)
    image = np.expand_dims(image, axis=0)
    return tf.keras.applications.vgg19.preprocess_input(image)

# 反向预处理图像
def deprocess_image(image):
    image = image.reshape((image.shape[1], image.shape[2], image.shape[3]))
    image[:, :, 0] += 103.939
    image[:, :, 1] += 116.779
    image[:, :, 2] += 123.68
    image = image[:, :, ::-1]
    return np.clip(image, 0, 255).astype('uint8')

# 内容损失
def content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))

# 风格损失
def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    return tf.reduce_mean(tf.square(S - C))

# 总变差损失
def total_variation_loss(x):
    a = tf.square(x[:, : -1, : -1, :] - x[:, 1:, : -1, :])
    b = tf.square(x[:, : -1, : -1, :] - x[:, : -1, 1:, :])
    return tf.reduce_sum(tf.pow(a + b, 1.25))

# 设置模型
content_layer = 'block5_conv2'
style_layers = [
    'block1_conv1',
    'block2_conv1',
    'block3_conv1',
    'block4_conv1',
    'block5_conv1'
]

num_style_layers = len(style_layers)

vgg = VGG19(include_top=False, weights='imagenet')
vgg.trainable = False

outputs = [vgg.get_layer(name).output for name in style_layers]
outputs.append(vgg.get_layer(content_layer).output)
model = Model([vgg.input], outputs)

# 提取特征
def get_feature_representations(model, content_path, style_path, target_shape):
    content_image = load_and_process_image(content_path, target_shape)
    style_image = load_and_process_image(style_path, target_shape)
    
    style_outputs = model(style_image)
    content_outputs = model(content_image)
    
    style_features = [style_layer[0] for style_layer in style_outputs[:num_style_layers]]
    content_feature = content_outputs[num_style_layers][0]
    return style_features, content_feature

# 计算损失
def compute_loss(model, loss_weights, init_image, gram_style_features, content_feature):
    input_tensor = tf.concat([init_image], axis=0)
    features = model(input_tensor)
    
    style_output_features = features[:num_style_layers]
    content_output_features = features[num_style_layers]
    
    style_score = 0
    content_score = 0
    
    weight_per_style_layer = 1.0 / float(num_style_layers)
    for target_style, comb_style in zip(gram_style_features, style_output_features):
        style_score += weight_per_style_layer * style_loss(target_style, comb_style[0])
    
    content_score = content_loss(content_feature, content_output_features[0])
    
    style_score *= loss_weights[0]
    content_score *= loss_weights[1]
    
    loss = style_score + content_score
    return loss

# 梯度计算
@tf.function
def compute_grads(cfg):
    with tf.GradientTape() as tape:
        all_loss = compute_loss(**cfg)
    total_loss = all_loss
    return tape.gradient(total_loss, cfg['init_image']), all_loss

# 风格迁移主函数
def run_style_transfer(content_path, style_path, num_iterations=1000, content_weight=1e3, style_weight=1e-2):
    target_shape = (512, 512)
    
    model = get_model()
    for layer in model.layers:
        layer.trainable = False
        
    style_features, content_feature = get_feature_representations(model, content_path, style_path, target_shape)
    gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]
    
    init_image = load_and_process_image(content_path, target_shape)
    init_image = tf.Variable(init_image, dtype=tf.float32)
    
    opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
    
    loss_weights = (style_weight, content_weight)
    cfg = {
        'model': model,
        'loss_weights': loss_weights,
        'init_image': init_image,
        'gram_style_features': gram_style_features,
        'content_feature': content_feature
    }
    
    best_loss, best_img = float('inf'), None
    
    for i in range(num_iterations):
        grads, all_loss = compute_grads(cfg)
        loss = all_loss
        opt.apply_gradients([(grads, init_image)])
        clipped = tf.clip_by_value(init_image, -103.939, 255.0 - 103.939)
        init_image.assign(clipped)
        
        if loss < best_loss:
            best_loss = loss
            best_img = deprocess_image(init_image.numpy())
            
        if i % 100 == 0:
            print(f"Iteration: {i}, Loss: {loss}")
    
    return best_img

# 执行风格迁移
best_img = run_style_transfer('path_to_content_image.jpg', 'path_to_style_image.jpg')
Image.fromarray(best_img).save('stylized_image.jpg')

5. 部署测试场景

使用 Flask 部署一个简单的 Web 服务，让用户能够上传照片和选择风格图片，进行风格迁移。

安装 Flask

bashbash pip install Flask

#### 代码示例

```python
from flask import Flask, request, send_file
import tensorflow as tf
from PIL import Image
import numpy as np
from io import BytesIO

app = Flask(__name__)

# 加载并预处理图像
def load_and_process_image(image, target_shape):
    image = image.resize(target_shape)
    image = np.array(image)
    image = np.expand_dims(image, axis=0)
    return tf.keras.applications.vgg19.preprocess_input(image)

# 反向预处理图像
def deprocess_image(image):
    image = image.reshape((image.shape[1], image.shape[2], image.shape[3]))
    image[:, :, 0] += 103.939
    image[:, :, 1] += 116.779
    image[:, :, 2] += 123.68
    image = image[:, :, ::-1]
    return np.clip(image, 0, 255).astype('uint8')

# 内容损失
def content_loss(base_content, target):
    return tf.reduce_mean(tf.square(base_content - target))

# 风格损失
def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    return tf.reduce_mean(tf.square(S - C))

# 设置模型
content_layer = 'block5_conv2'
style_layers = [
    'block1_conv1',
    'block2_conv1',
    'block3_conv1',
    'block4_conv1',
    'block5_conv1'
]

num_style_layers = len(style_layers)

vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
vgg.trainable = False

outputs = [vgg.get_layer(name).output for name in style_layers]
outputs.append(vgg.get_layer(content_layer).output)
model = tf.keras.models.Model([vgg.input], outputs)

# 提取特征
def get_feature_representations(model, content_image, style_image, target_shape):
    content_image = load_and_process_image(content_image, target_shape)
    style_image = load_and_process_image(style_image, target_shape)
    
    style_outputs = model(style_image)
    content_outputs = model(content_image)
    
    style_features = [style_layer[0] for style_layer in style_outputs[:num_style_layers]]
    content_feature = content_outputs[num_style_layers][0]
    return style_features, content_feature

# 计算损失
def compute_loss(model, loss_weights, init_image, gram_style_features, content_feature):
    input_tensor = tf.concat([init_image], axis=0)
    features = model(input_tensor)
    
    style_output_features = features[:num_style_layers]
    content_output_features = features[num_style_layers]
    
    style_score = 0
    content_score = 0
    
    weight_per_style_layer = 1.0 / float(num_style_layers)
    for target_style, comb_style in zip(gram_style_features, style_output_features):
        style_score += weight_per_style_layer * style_loss(target_style, comb_style[0])
    
    content_score = content_loss(content_feature, content_output_features[0])
    
    style_score *= loss_weights[0]
    content_score *= loss_weights[1]
    
    loss = style_score + content_score
    return loss

# 梯度计算
@tf.function
def compute_grads(cfg):
    with tf.GradientTape() as tape:
        all_loss = compute_loss(**cfg)
    total_loss = all_loss
    return tape.gradient(total_loss, cfg['init_image']), all_loss

# 风格迁移主函数
def run_style_transfer(content_image, style_image, num_iterations=1000, content_weight=1e3, style_weight=1e-2):
    target_shape = (512, 512)
    
    model = get_model()
    for layer in model.layers:
        layer.trainable = False
        
    style_features, content_feature = get_feature_representations(model, content_image, style_image, target_shape)
    gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]
    
    init_image = load_and_process_image(content_image, target_shape)
    init_image = tf.Variable(init_image, dtype=tf.float32)
    
    opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)
    
    loss_weights = (style_weight, content_weight)
    cfg = {
        'model': model,
        'loss_weights': loss_weights,
        'init_image': init_image,
        'gram_style_features': gram_style_features,
        'content_feature': content_feature
    }
    
    best_loss, best_img = float('inf'), None
    
    for i in range(num_iterations):
        grads, all_loss = compute_grads(cfg)
        loss = all_loss
        opt.apply_gradients([(grads, init_image)])
        clipped = tf.clip_by_value(init_image, -103.939, 255.0 - 103.939)
        init_image.assign(clipped)
        
        if loss < best_loss:
            best_loss = loss
            best_img = deprocess_image(init_image.numpy())
            
        if i % 100 == 0:
            print(f"Iteration: {i}, Loss: {loss}")
    
    return best_img

@app.route('/upload', methods=['POST'])
def upload():
    content_file = request.files['content']
    style_file = request.files['style']
    
    content_image = Image.open(content_file)
    style_image = Image.open(style_file)
    
    stylized_image = run_style_transfer(content_image, style_image)
    
    img_byte_arr = BytesIO()
    Image.fromarray(stylized_image).save(img_byte_arr, format='JPEG')
    img_byte_arr.seek(0)
    
    return send_file(img_byte_arr, mimetype='image/jpeg')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

启动 Flask 应用后，可以通过 POST 请求上传照片和风格图片，完成风格迁移操作。请求示例如下：

curl -F "content=@path_to_content_image.jpg" -F "style=@path_to_style_image.jpg" http://localhost:5000/upload -o stylized_image.jpg

6. 材料链接

7. 总结

本文详细介绍了 DeepArt 的基本概念、应用场景及其算法原理，并通过代码示例展示了如何利用深度学习技术实现图像风格迁移。通过简单的 API 调用和模型优化，用户可以轻松地将普通照片转换为具有艺术风格的图像，适用于多种创意应用场景。

8. 未来展望

随着人工智能技术的不断发展，DeepArt 及类似应用可能会出现以下趋势：

更多风格支持：添加更多类型的艺术风格，使用户有更广泛的选择。
实时处理：提高模型推理速度，实现实时图像风格迁移。
高分辨率支持：支持更高分辨率的图像生成，以满足专业创作需求。
多模态融合：结合文本、音频等其他模态，实现跨模态的艺术创作。

通过不断创新与优化，AI 工具将在创意产业中发挥越来越重要的作用，为创作者提供强有力的支持和更多的可能性。

上一篇：【全网独家】AIGC 最佳实践：图像+音频+文本+建模+视频跨技术融合（代码+测试部署）

下一篇：深度学习(RNN+VAE)：高质量的音乐作品让音符飞舞起来

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