vgg19图像风格迁移代码

VGG19图像风格迁移是一种通过深度学习实现图像风格转移的方法，其基于深度神经网络VGG19。其代码实现可以分为以下几步：

1. 导入必要的库：包括numpy、tensorflow等。
2. 加载预训练的VGG19模型：使用tensorflow的tf.keras.applications中的VGG19模型进行预训练。
3. 提取内容图片和风格图片的特征：使用预训练的VGG19模型提取内容图片和风格图片的特征。
4. 定义损失函数：将内容损失和风格损失加权求和作为总损失函数。
5. 使用优化器进行训练：通过梯度下降优化器来更新生成图片。

以下是简单的代码实现：

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG19
from tensorflow.keras.preprocessing.image import load_img, img_to_array

# 加载VGG19模型，去掉最后一层输出
vgg = VGG19(include_top=False, weights='imagenet')

# 定义内容图片和风格图片的路径
content_path = 'content.jpg'
style_path = 'style.jpg'

# 加载图片并进行预处理
def load_and_process_img(path):
    img = load_img(path)
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = tf.keras.applications.vgg19.preprocess_input(img)
    return img

# 提取内容图片和风格图片的特征
def get_feature_representations(model, content_path, style_path):
    content_image = load_and_process_img(content_path)
    style_image = load_and_process_img(style_path)
    content_feature_maps = model.predict(content_image)
    style_feature_maps = model.predict(style_image)
    return content_feature_maps, style_feature_maps

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

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

# 定义风格损失
def get_style_loss(base_style, gram_target):
    return tf.reduce_mean(tf.square(gram_matrix(base_style) - gram_target))

# 定义总损失函数
def compute_loss(model, loss_weights, init_image, gram_style_features, content_features):
    style_weight, content_weight = loss_weights
    
    model_outputs = model(init_image)
    
    style_output_features = model_outputs[:len(gram_style_features)]
    content_output_features = model_outputs[len(gram_style_features):]
    
    style_score = 0
    content_score = 0

    weight_per_style_layer = 1.0 / float(len(style_output_features))

    for target_style, comb_style in zip(gram_style_features, style_output_features):
        style_score += weight_per_style_layer * get_style_loss(comb_style, target_style)

    weight_per_content_layer = 1.0 / float(len(content_output_features))
    
    for target_content, comb_content in zip(content_features, content_output_features):
        content_score += weight_per_content_layer* get_content_loss(comb_content, target_content)
  
    style_score *= style_weight
    content_score *= content_weight

    loss = style_score + content_score 
    return loss

# 定义优化器
def run_style_transfer(content_path, style_path,
                       num_iterations=1000,
                       content_weight=1e3,
                       style_weight=1e-2): 
    
    # 提取内容图片和风格图片的特征
    content_features, style_features = get_feature_representations(vgg, content_path, style_path)
    
    gram_style_features = [gram_matrix(feature) for feature in style_features]

    init_image = load_and_process_img(content_path)
    init_image = tf.Variable(init_image, dtype=tf.float32)

    opt = tf.optimizers.Adam(learning_rate=5, beta_1=0.99, epsilon=1e-1)

    # 定义损失权重
    loss_weights = (style_weight, content_weight)

    # 进行训练
    for i in range(num_iterations):
        with tf.GradientTape() as tape: 
            loss = compute_loss(vgg, loss_weights, init_image, gram_style_features, content_features)

        grad = tape.gradient(loss, init_image)

        opt.apply_gradients([(grad, init_image)])
        clipped = tf.clip_by_value(init_image, clip_value_min=0.0, clip_value_max=255.0)
        init_image.assign(clipped)

    return init_image.numpy()