畳み込みニューラル ネットワークを使用した画像スタイル転送のコード例

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()