Example code for image style transfer using convolutional neural networks

Image style transfer based on convolutional neural network is a technology that combines the content and style of an image to generate a new image. It utilizes a convolutional neural network (CNN) model to convert images into style feature vectors. This article will discuss this technology from the following three aspects:

1. Technical principles

Image style transfer based on convolutional neural network The implementation relies on two key concepts: content representation and style representation. Content representation refers to the abstract representation of objects and objects in an image, while style representation refers to the abstract representation of textures and colors in an image. In a convolutional neural network, we generate a new image by combining content representation and style representation to preserve the content of the original image and have the style of the new image.

To achieve this goal, we can use an algorithm called "Neural Style Transfer". The algorithm utilizes an already trained convolutional neural network to extract the content and style representation of the image. Specifically, we input an image into the network and extract the content representation of the image through the middle layer of the network, and use the last layer of the network to extract the style representation of the image. We can then generate a completely new image by minimizing the differences between the content and style representation of the original image and the target image. This way we can combine the content of one image with the style of another to create a unique work of art. This algorithm has achieved great success in the field of image processing and is widely used in various applications, such as image editing and artistic creation.

2. Example description

The following is an example of image style transfer based on convolutional neural network. Suppose we have a photo and a picture of a work of art. We hope to use the operation of a convolutional neural network to fuse the content and style of the two pictures to generate a picture that retains the content of the original photo and has the characteristics of the work of art. New pictures in style.

We can use pre-trained convolutional neural networks to extract the content representation and style representation of these two images. Then, a new image is generated by minimizing the distance between the original photo and the content representation of the target image and the style representation of the target image.

3. Code Implementation

The following is a code implementation example based on Python and Keras framework. The code uses the pre-trained VGG19 convolutional neural network to extract the content representation and style representation of the image, and uses gradient descent to minimize the distance between the original image and the target image to generate a new image.

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

# 加载图像
content_img = load_img("content.jpg", target_size=(224, 224))
style_img = load_img("style.jpg", target_size=(224, 224))

# 将图像转换成数组
content_array = img_to_array(content_img)
style_array = img_to_array(style_img)

# 将数组转换成张量
content_tensor = tf.keras.backend.variable(content_array)
style_tensor = tf.keras.backend.variable(style_array)
generated_tensor = tf.keras.backend.placeholder((1, 224, 224,3))

# 创建预训练的VGG19模型
model = VGG19(include_top=False, weights='imagenet')

# 定义内容损失函数
def content_loss(content, generated):
    return tf.reduce_sum(tf.square(content - generated))

# 定义风格损失函数
def gram_matrix(x):
    features = tf.keras.backend.batch_flatten(tf.keras.backend.permute_dimensions(x, (2, 0, 1)))
    gram = tf.matmul(features, tf.transpose(features))
    return gram

def style_loss(style, generated):
    S = gram_matrix(style)
    G = gram_matrix(generated)
    channels = 3
    size = 224 * 224
    return tf.reduce_sum(tf.square(S - G)) / (4.0 * (channels ** 2) * (size ** 2))

# 定义总损失函数
def total_loss(content, style, generated, alpha=0.5, beta=0.5):
    return alpha * content_loss(content, generated) + beta * style_loss(style, generated)

# 定义优化器和超参数
optimizer = tf.keras.optimizers.Adam(lr=2.0)
alpha = 0.5
beta = 0.5
epochs = 10

# 进行训练
for i in range(epochs):
    with tf.GradientTape() as tape:
        loss = total_loss(content_tensor, style_tensor, generated_tensor, alpha, beta)
    grads = tape.gradient(loss, generated_tensor)
    optimizer.apply_gradients([(grads, generated_tensor)])
    generated_tensor.assign(tf.clip_by_value(generated_tensor, 0.0, 255.0))

# 将张量转换成数组
generated_array = generated_tensor.numpy()
generated_array = generated_array.reshape((224, 224, 3))

# 将数组转换成图像
generated_img = np.clip(generated_array, 0.0, 255.0).astype('uint8')
generated_img = Image.fromarray(generated_img)

# 显示结果
generated_img.show()

In the above code, we use the pre-trained VGG19 model to extract the feature vector of the image, and define the content loss function and style loss function to measure the distance between the generated image and the target image. Then, we define a total loss function to calculate the trade-off between content loss and style loss, and use the Adam optimizer to minimize the total loss function. During training, we use gradient descent to update the generated images and limit them between 0 and 255 using the clip_by_value function. Finally, we convert the generated image back to array and image format and display the result.

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