matlab unet图像分割代码

以下是一份使用MATLAB实现UNet图像分割的代码示例：

%% 加载图像，将其转换为灰度图像
img = imread('example.png');
img = rgb2gray(img);

%% 定义训练数据
imds = imageDatastore('training_images', 'FileExtensions', '.png', 'LabelSource', 'foldernames');

%% 定义网络架构
inputSize = [size(img,1) size(img,2) 1];
numClasses = 2;
layers = [
    imageInputLayer(inputSize)

    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,128,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    transposedConv2dLayer(2,64,'Stride',2,'Cropping','same')
    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer

    transposedConv2dLayer(2,32,'Stride',2,'Cropping','same')
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    transposedConv2dLayer(2,16,'Stride',2,'Cropping','same')
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer

    convolution2dLayer(1,numClasses)
    softmaxLayer
    pixelClassificationLayer];

%% 定义训练选项
options = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',20, ...
    'MiniBatchSize',16, ...
    'Shuffle','every-epoch', ...
    'Verbose',false, ...
    'Plots','training-progress');

%% 训练网络
net = trainNetwork(imds,layers,options);

%% 对测试图像进行分割
testImg = imread('test.png');
testImg = rgb2gray(testImg);
segmentedImg = semanticseg(testImg, net);

这段代码将图像转换为灰度图像，定义了训练数据，然后定义了一个具有UNet架构的神经网络，使用训练选项进行训练，并对测试图像进行分割。请确保图像和文件夹路径正确，并且在运行代码之前安装了MATLAB的深度学习工具箱。