请帮我使用Python写一段根据已有图像生成新图像的代码

生成图像的方法有很多种，其中一种比较常用的方法是使用神经网络生成对抗网络（GAN）模型。以下是一个简单的使用GAN模型生成图像的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Dense, Reshape, Flatten, Input
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam

# 加载MNIST数据集
(X_train, _), (_, _) = mnist.load_data()
# 将像素值归一化到[-1,1]之间
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
# 将图像展平成一维向量
X_train = X_train.reshape(X_train.shape[0], -1)

# 定义生成器模型
def build_generator():
    generator = Sequential()
    generator.add(Dense(256, input_shape=(100,), activation=LeakyReLU(0.2)))
    generator.add(Dense(512, activation=LeakyReLU(0.2)))
    generator.add(Dense(1024, activation=LeakyReLU(0.2)))
    generator.add(Dense(28*28, activation='tanh'))
    generator.add(Reshape((28, 28, 1)))
    generator_input = Input(shape=(100,))
    generator_output = generator(generator_input)
    return Model(generator_input, generator_output)

# 定义判别器模型
def build_discriminator():
    discriminator = Sequential()
    discriminator.add(Flatten(input_shape=(28, 28, 1)))
    discriminator.add(Dense(512, activation=LeakyReLU(0.2)))
    discriminator.add(Dense(256, activation=LeakyReLU(0.2)))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator_input = Input(shape=(28, 28, 1))
    discriminator_output = discriminator(discriminator_input)
    return Model(discriminator_input, discriminator_output)

# 将判别器不可训练
def make_discriminator_untrainable(discriminator):
    discriminator.trainable = False
    for layer in discriminator.layers:
        layer.trainable = False

# 定义GAN模型
def build_gan(generator, discriminator):
    gan_input = Input(shape=(100,))
    gan_output = discriminator(generator(gan_input))
    gan = Model(gan_input, gan_output)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return gan

# 训练GAN模型
def train_gan(X_train, epochs=100, batch_size=128):
    generator = build_generator()
    discriminator = build_discriminator()
    gan = build_gan(generator, discriminator)
    for epoch in range(epochs):
        # 训练判别器
        make_discriminator_untrainable(discriminator)
        real_images = X_train[np.random.randint(0, X_train.shape[0], size=batch_size)]
        noise = np.random.normal(0, 1, size=(batch_size, 100))
        fake_images = generator.predict(noise)
        X = np.concatenate((real_images, fake_images))
        y = np.array([1] * batch_size + [0] * batch_size)
        discriminator_loss = discriminator.train_on_batch(X, y)
        # 训练生成器
        noise = np.random.normal(0, 1, size=(batch_size, 100))
        generator_loss = gan.train_on_batch(noise, np.array([1] * batch_size))
        # 输出损失值
        print("Epoch %d Discriminator Loss: %f Generator Loss: %f" % (epoch, discriminator_loss, generator_loss))
        # 保存生成的图像
        if epoch % 10 == 0:
            noise = np.random.normal(0, 1, size=(batch_size, 100))
            fake_images = generator.predict(noise)
            plt.figure(figsize=(10, 10))
            for i in range(25):
                plt.subplot(5, 5, i+1)
                plt.imshow(fake_images[i, :, :, 0], cmap='gray')
                plt.axis('off')
            plt.savefig('generated_images_epoch_%d.png' % epoch)
            plt.close()

train_gan(X_train)

这个代码使用了MNIST数据集，生成了手写数字的图像。你可以根据需要修改成适合你的应用场景的代码。