matlab unet图像分割代码
时间: 2023-08-27 07:05:05 浏览: 53
以下是MATLAB中用于图像分割的UNet代码示例:
```matlab
% 加载数据
data = imageDatastore('path/to/images', ...
'IncludeSubfolders',true,'LabelSource','foldernames');
% 定义UNet架构
inputSize = [256 256 3];
numClasses = 2;
layers = [
imageInputLayer(inputSize)
convolution2dLayer(3,64,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,128,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,256,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,512,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,1024,'Padding','same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2,1024,'Stride',2)
concatenationLayer(2)
convolution2dLayer(3,512,'Padding','same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2,512,'Stride',2)
concatenationLayer(2)
convolution2dLayer(3,256,'Padding','same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2,256,'Stride',2)
concatenationLayer(2)
convolution2dLayer(3,128,'Padding','same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2,128,'Stride',2)
concatenationLayer(2)
convolution2dLayer(3,64,'Padding','same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3,numClasses,'Padding','same')
softmaxLayer
pixelClassificationLayer
];
% 训练网络
options = trainingOptions('sgdm', ...
'MaxEpochs',5, ...
'InitialLearnRate',1e-3, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(data,layers,options);
% 对测试图像进行分割
testImage = imread('path/to/test/image');
segmentedImage = semanticseg(testImage,net);
```
在这个示例中,我们首先使用 `imageDatastore` 函数来加载训练数据。然后,我们定义了UNet网络架构,这是一种广泛用于图像分割的卷积神经网络。我们使用 `trainingOptions` 函数来定义训练选项,包括优化器类型、最大迭代次数和学习率。最后,我们使用 `trainNetwork` 函数来训练网络,并使用 `semanticseg` 函数对测试图像进行分割。
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