UNET MATLAB 代码

以下是一个基本的UNET MATLAB代码示例：

% 加载图像和标签
img = imread('image.jpg');
label = imread('label.jpg');

% 将图像和标签调整为模型输入大小
img = imresize(img, [256, 256]);
label = imresize(label, [256, 256]);

% 将标签转换为二进制掩码
mask = label == 255;

% 创建UNET模型
model = unet;

% 训练模型
model = trainNetwork(img, mask, model);

% 对新图像进行预测
newImg = imread('new_image.jpg');
newImg = imresize(newImg, [256, 256]);
predictedMask = predict(model, newImg);

% 将预测的掩码转换为图像
predictedImg = uint8(predictedMask) * 255;

% 显示结果
subplot(1, 2, 1), imshow(newImg), title('Original Image');
subplot(1, 2, 2), imshow(predictedImg), title('Predicted Mask');

请注意，此示例仅包含基本的UNET代码，您需要根据自己的数据集和应用程序进行修改和调整。

相关问题

UNET MATLAB代码

以下是一个基本的UNET MATLAB代码实现：

首先，需要加载数据集。假设你的训练图像和标签存储在“train_images”和“train_labels”文件夹中，可以使用以下代码读取它们：

train_image_folder = fullfile(pwd,'train_images');
train_label_folder = fullfile(pwd,'train_labels');
imds = imageDatastore(train_image_folder);
pxds = pixelLabelDatastore(train_label_folder, classes);

其中“classes”是你的标签类别。

接下来，需要定义UNET模型。可以使用以下代码：

numClasses = length(classes);
lgraph = unetLayers([256 256 numClasses])

然后，可以使用以下代码训练UNET模型：

options = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',30, ...
    'MiniBatchSize',16, ...
    'Shuffle','every-epoch', ...
    'VerboseFrequency',10, ...
    'Plots','training-progress', ...
    'ExecutionEnvironment','gpu');
trainedNet = trainNetwork(imds,pxds,lgraph,options);

其中，“options”包含了训练UNET模型需要的参数，包括最大训练轮数、每批训练的图像数量和学习率等。

最后，可以使用以下代码测试UNET模型：

test_image = imread('test_image.png');
test_label = semanticseg(test_image, trainedNet);

其中，“test_image.png”是你的测试图像文件名，“test_label”是模型预测的标签图像。

这只是一个基本的UNET MATLAB代码实现，你可以根据自己的需求进行调整和优化。

UNET MATLAB 池化代码

以下是一个简单的UNet MATLAB池化代码示例：

%% 定义 UNet 模型
input = imageInputLayer([256 256 3],'Name','InputLayer');
conv1 = convolution2dLayer(3,64,'Padding','same','Name','Conv1_1');
relu1 = reluLayer('Name','ReLU1_1');
conv2 = convolution2dLayer(3,64,'Padding','same','Name','Conv1_2');
relu2 = reluLayer('Name','ReLU1_2');
pool1 = maxPooling2dLayer(2,'Stride',2,'Name','Pool1');

% 编码器
conv3 = convolution2dLayer(3,128,'Padding','same','Name','Conv2_1');
relu3 = reluLayer('Name','ReLU2_1');
conv4 = convolution2dLayer(3,128,'Padding','same','Name','Conv2_2');
relu4 = reluLayer('Name','ReLU2_2');
pool2 = maxPooling2dLayer(2,'Stride',2,'Name','Pool2');

conv5 = convolution2dLayer(3,256,'Padding','same','Name','Conv3_1');
relu5 = reluLayer('Name','ReLU3_1');
conv6 = convolution2dLayer(3,256,'Padding','same','Name','Conv3_2');
relu6 = reluLayer('Name','ReLU3_2');
pool3 = maxPooling2dLayer(2,'Stride',2,'Name','Pool3');

conv7 = convolution2dLayer(3,512,'Padding','same','Name','Conv4_1');
relu7 = reluLayer('Name','ReLU4_1');
conv8 = convolution2dLayer(3,512,'Padding','same','Name','Conv4_2');
relu8 = reluLayer('Name','ReLU4_2');

% 解码器
trans1 = transposedConv2dLayer(2,512,'Stride',2,'Name','TransConv1');
concat1 = concatenationLayer(3,{'Conv4_2','TransConv1'},'Name','Concat1');
conv9 = convolution2dLayer(3,256,'Padding','same','Name','Conv5_1');
relu9 = reluLayer('Name','ReLU5_1');
conv10 = convolution2dLayer(3,256,'Padding','same','Name','Conv5_2');
relu10 = reluLayer('Name','ReLU5_2');

trans2 = transposedConv2dLayer(2,256,'Stride',2,'Name','TransConv2');
concat2 = concatenationLayer(3,{'Conv3_2','TransConv2'},'Name','Concat2');
conv11 = convolution2dLayer(3,128,'Padding','same','Name','Conv6_1');
relu11 = reluLayer('Name','ReLU6_1');
conv12 = convolution2dLayer(3,128,'Padding','same','Name','Conv6_2');
relu12 = reluLayer('Name','ReLU6_2');

trans3 = transposedConv2dLayer(2,128,'Stride',2,'Name','TransConv3');
concat3 = concatenationLayer(3,{'Conv2_2','TransConv3'},'Name','Concat3');
conv13 = convolution2dLayer(3,64,'Padding','same','Name','Conv7_1');
relu13 = reluLayer('Name','ReLU7_1');
conv14 = convolution2dLayer(3,64,'Padding','same','Name','Conv7_2');
relu14 = reluLayer('Name','ReLU7_2');

output = convolution2dLayer(1,1,'Padding','same','Name','OutputLayer');
softmax = softmaxLayer('Name','Softmax');
class = classificationLayer('Name','ClassLayer');

% 建立网络
layers = [input
          conv1
          relu1
          conv2
          relu2
          pool1
          conv3
          relu3
          conv4
          relu4
          pool2
          conv5
          relu5
          conv6
          relu6
          pool3
          conv7
          relu7
          conv8
          relu8
          trans1
          concat1
          conv9
          relu9
          conv10
          relu10
          trans2
          concat2
          conv11
          relu11
          conv12
          relu12
          trans3
          concat3
          conv13
          relu13
          conv14
          relu14
          output
          softmax
          class];

% 定义训练参数
options = trainingOptions('adam', ...
    'MaxEpochs', 25, ...
    'InitialLearnRate', 1e-3, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% 训练模型
net = trainNetwork(XTrain,YTrain,layers,options);

在这个示例中，我们定义了一个包含了池化操作的UNet模型，并使用了`maxPooling2dLayer`来实现池化操作。我们还定义了训练参数和训练模型的代码。需要注意的是，这个示例是一个简单的UNet模型，实际应用中可能需要更复杂的结构和更多的层来实现更好的性能。