能够通过python 实现,输入一张图片,通过卷积神经网络实现对图片进行压缩,然后再输出压缩之后的图片。
时间: 2023-07-16 13:12:24 浏览: 32
是的,可以通过Python和卷积神经网络实现对图片进行压缩。其中,卷积神经网络通常用于图像处理任务,如图像分类和图像压缩等。
以下是一个基本的图像压缩示例,使用Python和Keras库。这个例子将输入一张28x28像素的灰度图像,并将其压缩到14x14像素。
```python
import numpy as np
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
# 加载MNIST数据集
from keras.datasets import mnist
(x_train, _), (x_test, _) = mnist.load_data()
# 归一化并重塑输入图像
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
# 定义编码器
input_img = Input(shape=(28, 28, 1))
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# 定义解码器
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
# 定义模型
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
# 训练模型
autoencoder.fit(x_train, x_train,
epochs=10,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test))
# 压缩测试图像并输出重构图像
encoded_imgs = autoencoder.predict(x_test)
decoded_imgs = autoencoder.predict(encoded_imgs)
# 显示重构图像
import matplotlib.pyplot as plt
n = 10 # 显示的数字图像数量
plt.figure(figsize=(20, 4))
for i in range(n):
# 显示原始图像
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# 显示重构图像
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
```
运行代码后,将会输出原始图像和重构图像。注意,重构图像与原始图像相比,已经被压缩并有一定程度的失真。