利用卷积神经网络实现图像风格迁移代码

以下是利用卷积神经网络实现图像风格迁移的代码：

import tensorflow as tf
import numpy as np
import PIL.Image
import time
import functools

# 定义常量
IMAGE_WIDTH = 512
IMAGE_HEIGHT = 512
CONTENT_WEIGHT = 1e3
STYLE_WEIGHT = 1e-2
TOTAL_VARIATION_WEIGHT = 30

# 加载图片
def load_image(image_path):
    max_dim = 512
    img = tf.io.read_file(image_path)
    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

# 显示图片
def show_image(image):
    if len(image.shape) > 3:
        image = tf.squeeze(image, axis=0)

    img = PIL.Image.fromarray(np.array(image * 255, dtype=np.uint8))
    img.show()

# 加载模型
def load_model():
    # 加载预训练的VGG19模型
    vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    vgg.trainable = False

    # 获取需要的层
    content_layers = ['block5_conv2']
    style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']

    # 构建模型
    content_outputs = [vgg.get_layer(name).output for name in content_layers]
    style_outputs = [vgg.get_layer(name).output for name in style_layers]
    outputs = content_outputs + style_outputs
    model = tf.keras.models.Model(vgg.input, outputs)

    return model, style_layers, content_layers

# 计算内容损失
def calculate_content_loss(content_output, target_content):
    return tf.reduce_mean(tf.square(content_output - target_content))

# 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)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

# 计算风格损失
def calculate_style_loss(style_outputs, target_style):
    style_loss = 0
    for style_output in style_outputs:
        style_output_gram = gram_matrix(style_output)
        target_style_gram = gram_matrix(target_style)
        style_loss += tf.reduce_mean(tf.square(style_output_gram - target_style_gram))
    style_loss /= len(style_outputs)
    return style_loss

# 计算总变差损失
def calculate_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))

# 计算总损失
def calculate_total_loss(model, loss_weights, init_image, gram_style_features, content_features):
    style_weight, content_weight, tv_weight = loss_weights

    model_outputs = model(init_image)

    content_output_features = model_outputs[len(gram_style_features):]
    style_output_features = model_outputs[:len(gram_style_features)]

    content_loss = 0
    for target_content, content_output in zip(content_features, content_output_features):
        content_loss += calculate_content_loss(content_output, target_content)

    content_loss *= content_weight / len(content_features)

    style_loss = 0
    for target_style, style_output in zip(gram_style_features, style_output_features):
        style_loss += calculate_style_loss(style_output, target_style)

    style_loss *= style_weight / len(gram_style_features)

    tv_loss = calculate_total_variation_loss(init_image)
    tv_loss *= tv_weight

    total_loss = content_loss + style_loss + tv_loss

    return total_loss

# 计算梯度
@tf.function
def calculate_gradients(model, loss_weights, init_image, gram_style_features, content_features):
    with tf.GradientTape() as tape:
        loss = calculate_total_loss(model, loss_weights, init_image, gram_style_features, content_features)
    gradients = tape.gradient(loss, init_image)
    return gradients, loss

# 执行风格迁移
def style_transfer(content_image_path, style_image_path, epochs=10, steps_per_epoch=100, learning_rate=0.01):
    # 加载模型
    model, style_layers, content_layers = load_model()

    # 加载图片并提取特征
    content_image = load_image(content_image_path)
    style_image = load_image(style_image_path)

    content_features = [model(content_image)[idx] for idx in range(len(content_layers))]
    gram_style_features = [gram_matrix(model(style_image)[idx]) for idx in range(len(style_layers))]

    # 初始化生成的图像
    init_image = tf.Variable(content_image)

    # 定义损失权重
    loss_weights = (STYLE_WEIGHT, CONTENT_WEIGHT, TOTAL_VARIATION_WEIGHT)

    # 优化器
    optimizer = tf.optimizers.Adam(learning_rate=learning_rate)

    # 记录时间
    start_time = time.time()

    # 迭代训练
    for epoch in range(epochs):
        for step in range(steps_per_epoch):
            gradients, loss = calculate_gradients(model, loss_weights, init_image, gram_style_features, content_features)
            optimizer.apply_gradients([(gradients, init_image)])
            init_image.assign(tf.clip_by_value(init_image, 0, 1))

            print(".", end='')

        print("Train step: {}".format(epoch))

    end_time = time.time()
    print("Total time: {:.1f}".format(end_time - start_time))

    # 显示生成的图像
    show_image(init_image.numpy())

使用方法：

style_transfer("content.jpg", "style.jpg")

其中，`content.jpg`是原始图片的路径，`style.jpg`是想要迁移的风格图片的路径。默认使用10个epochs进行风格迁移，每个epoch训练100步，学习率为0.01。可以根据需要进行调整。