给出一个python的风格迁移纹理增强实例

以下是一个使用python实现的风格迁移纹理增强实例：

import tensorflow as tf
import numpy as np
import PIL.Image
import matplotlib.pyplot as plt

# 加载风格图像和内容图像
style_image = tf.keras.utils.get_file('style_image.jpg', 'https://i.imgur.com/TBnvtZd.jpg')
content_image = tf.keras.utils.get_file('content_image.jpg', 'https://i.imgur.com/B14W3Cv.jpg')

# 定义模型
def load_img(path_to_img):
    max_dim = 512
    img = tf.io.read_file(path_to_img)
    img = tf.image.decode_image(img, channels=3)
    img = tf.image.convert_image_dtype(img, tf.float32)

    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
    long_dim = max(shape)
    scale = max_dim / long_dim

    new_shape = tf.cast(shape * scale, tf.int32)

    img = tf.image.resize(img, new_shape)
    img = img[tf.newaxis, :]
    return img

def imshow(image, title=None):
    if len(image.shape) > 3:
        image = tf.squeeze(image, axis=0)

    plt.imshow(image)
    if title:
        plt.title(title)

# 加载并显示风格图像和内容图像
content_image = load_img(content_image)
style_image = load_img(style_image)

plt.subplot(1, 2, 1)
imshow(content_image, 'Content Image')

plt.subplot(1, 2, 2)
imshow(style_image, 'Style Image')
plt.show()

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

# 定义格拉姆矩阵
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)

# 定义风格损失函数
def style_loss(style, combination):
    style_gram = gram_matrix(style)
    combination_gram = gram_matrix(combination)
    return tf.reduce_mean(tf.square(style_gram - combination_gram))

# 定义总变差损失函数
def total_variation_loss(image):
    x_deltas, y_deltas = tf.image.image_gradients(image)
    return tf.reduce_mean(tf.abs(x_deltas)) + tf.reduce_mean(tf.abs(y_deltas))

# 定义模型
vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')

# 选择层来表示内容和风格
content_layers = ['block5_conv2']

style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']

# 计算内容和风格的特征
num_content_layers = len(content_layers)
num_style_layers = len(style_layers)

def vgg_layers(layer_names):
    """ Creates a vgg model that returns a list of intermediate output values."""
    # Load our model. Load pretrained VGG, trained on imagenet data
    vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False

    outputs = [vgg.get_layer(name).output for name in layer_names]

    model = tf.keras.Model([vgg.input], outputs)
    return model

style_extractor = vgg_layers(style_layers)
style_outputs = style_extractor(style_image*255)

# 风格特征的每层的格拉姆矩阵
style_features = [gram_matrix(style_output) for style_output in style_outputs]

# 构建模型
content_extractor = vgg_layers(content_layers)
content_outputs = content_extractor(content_image*255)

# 计算损失
style_weight=1e-2
content_weight=1e4
total_variation_weight=30

style_score = 0
content_score = 0
total_variation_score = 0

# 计算风格损失
style_weight_per_layer = 1.0 / float(num_style_layers)
for target_style, combination_style in zip(style_features, style_outputs):
    style_score += style_weight_per_layer * style_loss(target_style, combination_style)

# 计算内容损失
content_weight_per_layer = 1.0 / float(num_content_layers)
for target_content, combination_content in zip(content_outputs, content_outputs):
    content_score += content_weight_per_layer * content_loss(target_content, combination_content)

# 计算总变差损失
total_variation_score = total_variation_loss(content_image)

# 计算总损失
total_loss = style_weight * style_score + content_weight * content_score + total_variation_weight * total_variation_score

# 定义优化器
optimizer = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

# 训练模型
@tf.function()
def train_step(image):
    with tf.GradientTape() as tape:
        outputs = vgg(image)
        loss = total_loss(outputs)
    grad = tape.gradient(loss, image)
    optimizer.apply_gradients([(grad, image)])
    image.assign(tf.clip_by_value(image, 0.0, 1.0))

image = tf.Variable(content_image)

epochs = 10
steps_per_epoch = 100

for epoch in range(epochs):
    for step in range(steps_per_epoch):
        train_step(image)
    print("Epoch: {}".format(epoch))

# 显示结果
plt.imshow(np.squeeze(image.read_value(), 0))
plt.show()

这个示例中，我们首先加载了要进行风格迁移的风格图像和内容图像。接下来，我们定义了计算内容损失、风格损失和总变差损失的函数。然后，我们使用VGG19模型来提取内容和风格的特征，并计算损失。最后，我们使用Adam优化器训练模型，以最小化总损失。训练完成后，我们将显示增强后的图像。