python基于gan的图像加雾
时间: 2023-09-02 17:02:41 浏览: 93
GAN(生成对抗网络)是一种机器学习模型,可以通过生成器和判别器相互对抗的方式来生成逼真的样本。在Python中,可以使用TensorFlow、Keras或PyTorch等库来构建GAN模型。
要基于GAN给图像加雾,首先需要准备两个网络模型:生成器和判别器。生成器负责生成具有逼真雾化效果的图像,判别器负责判断生成的图像是否真实。两个网络通过对抗训练的方式来相互提升。
在训练过程中,首先加载训练集中的清晰图像,并对其进行预处理。然后生成器接收一个随机噪声向量作为输入,并将其转换为一张雾化图像。判别器则接收两张图像作为输入:清晰图像和生成器生成的雾化图像,并预测哪张图像是真实的。
生成器生成的雾化图像与真实清晰图像一起输入判别器,判别器将生成的雾化图像与真实清晰图像进行比较,并根据差异性训练自身。同时,生成器也会根据判别器的反馈进行更新,以生成更逼真的雾化图像。这个过程交替进行,直到生成器和判别器的性能都达到理想水平。
一旦训练完成,我们可以使用生成器来给任意一张清晰图像添加雾化效果。只需将清晰图像输入生成器中,它将会输出一张具有逼真雾化效果的图像。
需要注意的是,GAN训练过程可能会比较耗时,需要足够的计算资源和时间。同时,模型的性能也受到数据集的质量和规模的影响。因此,在使用基于GAN的图像加雾技术时,需要选择适合的训练集和网络参数,以获得更好的结果。
相关问题
用python写一段基于GAN的图像去雾代码
好的,以下是一段基于GAN的图像去雾代码,使用的是Python语言和TensorFlow框架。需要注意的是,这段代码仅供参考,实际效果可能需要根据具体情况调整。
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Reshape, BatchNormalization, LeakyReLU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import cifar10
import numpy as np
import matplotlib.pyplot as plt
# 加载数据
(x_train, _), (x_test, _) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# 添加噪声,模拟雾化效果
noise_factor = 0.3
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
# 定义生成器
def generator():
input_layer = Input(shape=(32, 32, 3))
x = Conv2D(32, (3, 3), padding='same')(input_layer)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = Conv2D(128, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = Conv2D(256, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = Conv2D(3, (3, 3), padding='same', activation='tanh')(x)
output_layer = x
model = Model(input_layer, output_layer)
return model
# 定义判别器
def discriminator():
input_layer = Input(shape=(32, 32, 3))
x = Conv2D(32, (3, 3), padding='same')(input_layer)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = MaxPooling2D()(x)
x = Conv2D(64, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = MaxPooling2D()(x)
x = Conv2D(128, (3, 3), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU()(x)
x = MaxPooling2D()(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid')(x)
output_layer = x
model = Model(input_layer, output_layer)
return model
# 定义GAN模型
def gan(generator, discriminator):
discriminator.trainable = False
input_layer = Input(shape=(32, 32, 3))
x = generator(input_layer)
output_layer = discriminator(x)
model = Model(input_layer, output_layer)
return model
# 编译模型
gen = generator()
dis = discriminator()
gan = gan(gen, dis)
gen_opt = Adam(lr=0.0002, beta_1=0.5)
dis_opt = Adam(lr=0.0002, beta_1=0.5)
gen.compile(loss='binary_crossentropy', optimizer=gen_opt)
dis.compile(loss='binary_crossentropy', optimizer=dis_opt)
gan.compile(loss='binary_crossentropy', optimizer=gen_opt)
# 训练模型
epochs = 100
batch_size = 128
for epoch in range(epochs):
for i in range(x_train_noisy.shape[0] // batch_size):
real_images = x_train_noisy[i * batch_size:(i + 1) * batch_size]
real_labels = np.ones((batch_size, 1))
fake_images = gen.predict(real_images)
fake_labels = np.zeros((batch_size, 1))
dis_loss_real = dis.train_on_batch(real_images, real_labels)
dis_loss_fake = dis.train_on_batch(fake_images, fake_labels)
dis_loss = 0.5 * np.add(dis_loss_real, dis_loss_fake)
gen_loss = gan.train_on_batch(real_images, real_labels)
print('Epoch %d: dis_loss=%.4f, gen_loss=%.4f' % (epoch + 1, dis_loss, gen_loss))
# 预测并可视化结果
decoded_imgs = gen.predict(x_test_noisy)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# 原始图像
ax = plt.subplot(3, n, i + 1)
plt.imshow(x_test_noisy[i])
plt.title('Noisy')
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# 去雾后的图像
ax = plt.subplot(3, n, i + 1 + n)
plt.imshow(decoded_imgs[i])
plt.title('De-noised')
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# 原始图像
ax = plt.subplot(3, n, i + 1 + n * 2)
plt.imshow(x_test[i])
plt.title('Original')
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
```
以上代码使用了一个简单的生成器和判别器,训练了100轮,然后对测试集数据进行了去雾处理。最后,将原始图像、去雾后的图像和有噪声的图像进行对比展示,以便评估去雾效果。
基于GAN的图像压缩代码
以下是基于GAN的图像压缩代码的示例:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Conv2D, Conv2DTranspose
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import mnist
import numpy as np
# 定义图像大小和压缩因子
img_rows = 28
img_cols = 28
channels = 1
img_shape = (img_rows, img_cols, channels)
latent_dim = 100
# 构建生成器
def build_generator():
noise = Input(shape=(latent_dim,))
x = Dense(128 * 7 * 7, activation="relu")(noise)
x = Reshape((7, 7, 128))(x)
x = Conv2DTranspose(128, kernel_size=3, strides=2, padding="same")(x)
x = Conv2DTranspose(64, kernel_size=3, strides=1, padding="same")(x)
x = Conv2DTranspose(channels, kernel_size=3, strides=2, padding="same", activation="tanh")(x)
generator = Model(noise, x)
return generator
# 构建判别器
def build_discriminator():
img = Input(shape=img_shape)
x = Conv2D(32, kernel_size=3, strides=2, padding="same")(img)
x = Conv2D(64, kernel_size=3, strides=2, padding="same")(x)
x = Conv2D(128, kernel_size=3, strides=2, padding="same")(x)
x = Flatten()(x)
x = Dense(1, activation="sigmoid")(x)
discriminator = Model(img, x)
discriminator.compile(loss="binary_crossentropy", optimizer=Adam(), metrics=["accuracy"])
return discriminator
# 构建GAN模型
def build_gan(generator, discriminator):
discriminator.trainable = False
noise = Input(shape=(latent_dim,))
img = generator(noise)
valid = discriminator(img)
gan = Model(noise, valid)
gan.compile(loss="binary_crossentropy", optimizer=Adam())
return gan
# 训练GAN模型
def train(epochs, batch_size=128, save_interval=50):
# 加载数据集
(X_train, _), (_, _) = mnist.load_data()
# 归一化数据
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)
# 初始化生成器和判别器
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)
# 开始训练
for epoch in range(epochs):
# 训练判别器
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, latent_dim))
gen_imgs = generator.predict(noise)
d_loss_real = discriminator.train_on_batch(imgs, np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# 训练生成器
noise = np.random.normal(0, 1, (batch_size, latent_dim))
g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
# 每个epoch保存一次生成的图像
if epoch % save_interval == 0:
print("Epoch %d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))
save_images(epoch, generator)
# 生成图像并保存
def save_images(epoch, generator):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, latent_dim))
gen_imgs = generator.predict(noise)
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c, figsize=(10,10))
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/mnist_%d.png" % epoch)
plt.close()
# 训练模型
train(epochs=20000, batch_size=32, save_interval=100)
```
上述代码使用了MNIST数据集进行训练,并生成了一系列压缩后的图像。你可以根据自己的需求修改代码以适应不同的数据集和压缩比例。