计算机视觉算法中图像生成（Image Generation）

pythonCopy codeimport numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Reshape
from keras.layers import LeakyReLU
from keras.optimizers import Adam
# 定义生成器网络
def build_generator():
    generator = Sequential()
    generator.add(Dense(128, input_dim=100))
    generator.add(LeakyReLU(alpha=0.01))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return generator
# 定义判别器网络
def build_discriminator():
    discriminator = Sequential()
    discriminator.add(Dense(128, input_dim=784))
    discriminator.add(LeakyReLU(alpha=0.01))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return discriminator
# 定义GAN网络
def build_gan(generator, discriminator):
    gan = Sequential()
    gan.add(generator)
    gan.add(discriminator)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return gan
# 定义训练函数
def train(epochs, batch_size, sample_interval):
    # 载入MNIST数据集
    (X_train, _), (_, _) = mnist.load_data()
    # 将图像像素归一化到[-1, 1]之间
    X_train = (X_train.astype(np.float32) - 127.5) / 127.5
    X_train = X_train.reshape((-1, 784))
    
    # 创建生成器和判别器
    generator = build_generator()
    discriminator = build_discriminator()
    gan = build_gan(generator, discriminator)
    
    for epoch in range(epochs):
        # 训练判别器
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        real_images = X_train[idx]
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_images = generator.predict(noise)
        X = np.concatenate((real_images, fake_images))
        y = np.concatenate((np.ones((batch_size, 1)), np.zeros((batch_size, 1))))
        discriminator_loss = discriminator.train_on_batch(X, y)
        
        # 训练生成器
        noise = np.random.normal(0, 1, (batch_size, 100))
        y = np.ones((batch_size, 1))
        generator_loss = gan.train_on_batch(noise, y)
        
        # 输出训练过程
        if (epoch + 1) % sample_interval == 0:
            print(f"Epoch {epoch+1}/{epochs}  判别器损失: {discriminator_loss}  生成器损失: {generator_loss}")
            # 生成示例图像
            generate_images(generator, epoch+1)
# 生成示例图像
def generate_images(generator, epoch):
    r, c = 5, 5
    noise = np.random.normal(0, 1, (r * c, 100))
    generated_images = generator.predict(noise)
    generated_images = 0.5 * generated_images + 0.5
    fig, axs = plt.subplots(r, c)
    cnt = 0
    for i in range(r):
        for j in range(c):
            axs[i, j].imshow(generated_images[cnt, :].reshape(28, 28), cmap='gray')
            axs[i, j].axis('off')
            cnt += 1
    fig.savefig(f"images/mnist_{epoch}.png")
    plt.close()
# 设置超参数并开始训练
epochs = 20000
batch_size = 128
sample_interval = 1000
train(epochs, batch_size, sample_interval)

pythonCopy codeimport cv2
# 读取图像
image = cv2.imread("image.jpg")
# 将图像转换为灰度图
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 检测边缘
edges = cv2.Canny(gray_image, 100, 200)
# 显示图像
cv2.imshow("Original Image", image)
cv2.imshow("Gray Image", gray_image)
cv2.imshow("Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()