帮我用Tensorflow框架写一个基于深度神经网络的图像去雨的程序
时间: 2023-11-27 16:04:30 浏览: 34
好的,下面是一个基于Tensorflow框架的图像去雨程序:
```python
import tensorflow as tf
import numpy as np
import cv2
# 定义输入图像的大小
IMAGE_SIZE = 256
# 定义卷积层的参数
CONV_PARAMS = {
'kernel_size': 3,
'stride': 1,
'padding': 'SAME',
'activation': tf.nn.relu
}
# 定义反卷积层的参数
DECONV_PARAMS = {
'kernel_size': 3,
'stride': 2,
'padding': 'SAME',
'activation': tf.nn.relu
}
# 定义残差块
def residual_block(x, filters, name):
with tf.variable_scope(name):
# 定义卷积层1
conv1 = tf.layers.conv2d(x, filters, name='conv1', **CONV_PARAMS)
# 定义卷积层2
conv2 = tf.layers.conv2d(conv1, filters, name='conv2', **CONV_PARAMS)
# 定义残差块
residual = x + conv2
return residual
# 定义图像去雨的网络模型
def image_deraining_network(x):
with tf.variable_scope('deraining_network'):
# 定义卷积层1
conv1 = tf.layers.conv2d(x, 64, name='conv1', **CONV_PARAMS)
# 定义残差块1
res1 = residual_block(conv1, 64, 'res1')
# 定义残差块2
res2 = residual_block(res1, 64, 'res2')
# 定义反卷积层1
deconv1 = tf.layers.conv2d_transpose(res2, 32, name='deconv1', **DECONV_PARAMS)
# 定义反卷积层2
deconv2 = tf.layers.conv2d_transpose(deconv1, 3, name='deconv2', **DECONV_PARAMS)
return deconv2
# 定义输入占位符
x = tf.placeholder(tf.float32, [None, IMAGE_SIZE, IMAGE_SIZE, 3])
# 加载图像数据
image = cv2.imread('input.jpg')
image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE))
image = image.astype(np.float32) / 255.0
# 构建图像去雨的网络模型
derained_image = image_deraining_network(x)
# 定义损失函数
loss = tf.reduce_mean(tf.square(x - derained_image))
# 定义优化器
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# 定义会话
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# 训练模型
for i in range(1000):
_, l = sess.run([optimizer, loss], feed_dict={x: [image]})
print('step %d: loss = %f' % (i, l))
# 测试模型
result = sess.run(derained_image, feed_dict={x: [image]})
result = np.clip(result[0], 0, 1) * 255.0
result = result.astype(np.uint8)
cv2.imwrite('output.jpg', result)
```
这个程序中,我们首先定义了输入图像的大小和卷积层、反卷积层的参数。然后,我们定义了一个残差块和一个图像去雨的网络模型,其中图像去雨的网络模型由两个卷积层、两个残差块和两个反卷积层组成。接着,我们定义了输入占位符、损失函数和优化器,并使用加载的图像数据来训练模型。最后,我们使用训练好的模型对输入图像进行去雨处理,并将结果保存到输出图像中。