用unet算法实现图像分割
时间: 2023-10-15 18:05:19 浏览: 43
1. 导入必要的库
```
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
```
2. 定义UNet模型
```
def unet(input_shape=(256, 256, 3), num_classes=2):
inputs = Input(input_shape)
# 编码器部分
conv1 = Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(inputs)
conv1 = Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(pool1)
conv2 = Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(pool2)
conv3 = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(filters=512, kernel_size=3, padding='same', activation='relu')(pool3)
conv4 = Conv2D(filters=512, kernel_size=3, padding='same', activation='relu')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(filters=1024, kernel_size=3, padding='same', activation='relu')(pool4)
conv5 = Conv2D(filters=1024, kernel_size=3, padding='same', activation='relu')(conv5)
drop5 = Dropout(0.5)(conv5)
# 解码器部分
up6 = Conv2D(filters=512, kernel_size=2, padding='same', activation='relu')(UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(filters=512, kernel_size=3, padding='same', activation='relu')(merge6)
conv6 = Conv2D(filters=512, kernel_size=3, padding='same', activation='relu')(conv6)
up7 = Conv2D(filters=256, kernel_size=2, padding='same', activation='relu')(UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(merge7)
conv7 = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(conv7)
up8 = Conv2D(filters=128, kernel_size=2, padding='same', activation='relu')(UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(merge8)
conv8 = Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(conv8)
up9 = Conv2D(filters=64, kernel_size=2, padding='same', activation='relu')(UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(merge9)
conv9 = Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(conv9)
outputs = Conv2D(filters=num_classes, kernel_size=1, activation='softmax')(conv9)
model = Model(inputs, outputs)
return model
```
3. 编译模型
```
model = unet()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
```
4. 训练模型
```
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))
```
其中,x_train、y_train、x_val、y_val分别是训练集和验证集的输入图像和标签图像。