使用VGG16模型进行图像风格转换的python代码示例

以下是使用VGG16模型进行图像风格转换的Python代码示例：

首先，我们需要导入必要的库和模块：

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input

接下来，我们加载预训练的VGG16模型并设置其权重为不可训练：

def load_vgg_model():
    vgg = VGG16(include_top=False, weights='imagenet')
    vgg.trainable = False
    return vgg

定义图像处理函数，用于调整图像大小以适应VGG16模型的要求，并添加适当的维度：

def preprocess_image(image_path):
    image = load_img(image_path, target_size=(224, 224))
    image = img_to_array(image)
    image = np.expand_dims(image, axis=0)
    image = tf.keras.applications.vgg16.preprocess_input(image)
    return image

定义解码函数，用于将VGG16的输出转换为可用的图像：

def deprocess_image(image):
    image = image.reshape((224, 224, 3))
    image[:, :, 0] += 103.939
    image[:, :, 1] += 116.779
    image[:, :, 2] += 123.68
    image = image[:, :, ::-1]
    image = np.clip(image, 0, 255).astype('uint8')
    return image

定义计算风格损失的函数：

def compute_style_loss(style_features, generated_features):
    style_loss = 0.0
    for style_feature, gen_feature in zip(style_features, generated_features):
        style_loss += tf.reduce_mean(tf.square(style_feature - gen_feature))
    return style_loss

def compute_content_loss(content_features, generated_features):
    content_loss = tf.reduce_mean(tf.square(content_features - generated_features))
    return content_loss

def compute_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))

def compute_loss(vgg_model, content_image, style_image, generated_image):
    content_outputs = vgg_model(content_image)['block4_conv2']
    style_outputs = vgg_model(style_image)['block1_conv2', 'block2_conv2', 'block3_conv3', 'block4_conv3']
    generated_outputs = vgg_model(generated_image)['block4_conv2']
    
    style_loss = compute_style_loss(style_outputs, generated_outputs)
    content_loss = compute_content_loss(content_outputs, generated_outputs)
    tv_loss = compute_total_variation_loss(generated_image)
    
    total_loss = style_loss + content_loss + tv_loss
    return total_loss

定义训练函数，利用梯度下降优化器最小化损失函数，生成具有风格的图像：

@tf.function()
def train_step(vgg_model, optimizer, content_image, style_image, generated_image):
    with tf.GradientTape() as tape:
        loss = compute_loss(vgg_model, content_image, style_image, generated_image)
    gradients = tape.gradient(loss, generated_image)
    optimizer.apply_gradients([(gradients, generated_image)])
    generated_image.assign(tf.clip_by_value(generated_image, clip_value_min=0.0, clip_value_max=255.0))

最后，我们定义主函数，并加载内容图像和风格图像，然后使用VGG16模型训练生成新的图像：

def main(content_image_path, style_image_path, num_iterations):
    content_image = preprocess_image(content_image_path)
    style_image = preprocess_image(style_image_path)
    generated_image = tf.Variable(content_image, dtype=tf.float32)

    vgg_model = load_vgg_model()

    optimizer = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

    for i in range(num_iterations):
        train_step(vgg_model, optimizer, content_image, style_image, generated_image)

    generated_image = deprocess_image(generated_image.numpy())
    return generated_image

使用示例：

content_image_path = 'content.jpg'
style_image_path = 'style.jpg'
num_iterations = 1000

generated_image = main(content_image_path, style_image_path, num_iterations)

# 保存生成的图像
save_img('generated.jpg', generated_image)

这是一个简单的使用VGG16模型进行图像风格转换的示例。通过调整迭代次数和优化器参数等超参数，可以获得不同风格的生成图像。