outputFolder = pwd;
dataURL = ('https://ssd.mathworks.com/supportfiles/radar/data/MSTAR_ClutterDataset.tar.gz');
helperDownloadMSTARClutterData(outputFolder,dataURL);

pretrainedNetURL = ('https://ssd.mathworks.com/supportfiles/radar/data/TrainedSARDetectorNet.tar.gz');
doTrain = false;
if ~doTrain
    helperDownloadPretrainedSARDetectorNet (outputFolder, pretrainedNetURL);
end

load('groundTruthMSTARClutterDataset.mat', "trainingData", "testData");

% Display the first few rows of the data set
trainingData(1:4,:)

img = imread(trainingData.imageFilename(1));
bbox = reshape(cell2mat(trainingData{1,2:end}),[4,5])';
labels = {'SLICY', 'BTR_60', 'BRDM_2',  'ZSU_23_4', 'T62'};
annotatedImage = insertObjectAnnotation(img,'rectangle',bbox,labels,...
    'TextBoxOpacity',0.9,'FontSize',50);
figure
imshow(annotatedImage);
title('Sample Training image with bounding boxes and labels')

objectClasses = {'SLICY', 'BTR_60', 'BRDM_2', 'ZSU_23_4', 'T62'};

numClassesPlusBackground = numel(objectClasses) + 1;

% Define input size 
inputSize = [128,128,1];

% Define network
layers = createNetwork(inputSize,numClassesPlusBackground);

% Set training options
options = trainingOptions('sgdm', ...
    'MiniBatchSize', 128, ...
    'InitialLearnRate', 1e-3, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.1, ...
    'LearnRateDropPeriod', 100, ...
    'MaxEpochs', 10, ...
    'Verbose', true, ...
    'CheckpointPath',tempdir,...
    'ExecutionEnvironment','auto');

if doTrain
    % Train an R-CNN object detector. This will take several minutes
    detector = trainRCNNObjectDetector(trainingData, layers, options,'PositiveOverlapRange',[0.5 1], 'NegativeOverlapRange', [0.1 0.5]);   
else
    % Load a previously trained detector
    preTrainedMATFile = fullfile(outputFolder,'TrainedSARDetectorNet.mat');
    load(preTrainedMATFile);
end

% Read test image
imgIdx = randi(height(testData));
testImage = imread(testData.imageFilename(imgIdx));

% Detect SAR targets in the test image
[bboxes,score,label] = detect(detector,testImage,'MiniBatchSize',16);

scoreThreshold = 0.8;

% Display the detection results
outputImage = testImage;
for idx = 1:length (счет)
    bbox = поля (idx, :);
    thisScore = счет (idx);
    
    if thisScore> scoreThreshold
        аннотация = sprintf ('%s: (Confidence = %0.2f)', пометьте (idx),...
            вокруг (thisScore, 2));
        outputImage = insertObjectAnnotation (outputImage, 'rectangle', bbox,...
            аннотация,'TextBoxOpacity',0.9,'FontSize',45,'LineWidth',2);
    end
end
f = число;
f.Position (3:4) = [860,740];
imshow (outputImage)
заголовок'Predicted boxes and labels on test image')

% Create a table to hold the bounding boxes, scores and labels output by the detector
numImages = height(testData);
results = table('Size',[numImages 3],...
    'VariableTypes',{'cell','cell','cell'},...
    'VariableNames',{'Boxes','Scores','Labels'});

% Run detector on each image in the test set and collect results
for i = 1:numImages
    imgFilename = testData.imageFilename{i};
    
    % Read the image
    I = imread(imgFilename);
    
    % Run the detector
    [bboxes, scores, labels] = detect(detector, I,'MiniBatchSize',16);
    
    % Collect the results
    results.Boxes{i} = bboxes;
    results.Scores{i} = scores;
    results.Labels{i} = labels;
end

% Extract expected bounding box locations from test data
expectedResults = testData(:, 2:end);

threshold = 0.5;
% Evaluate the object detector using average precision metric
[ap, recall, precision] = evaluateDetectionPrecision(results, expectedResults,threshold);

% Plot precision recall curve
f = figure; ax = gca; f.Position(3:4) = [860,740];
xlabel('Recall')
ylabel('Precision')
grid on; hold on; legend('Location', 'southeast');
title('Precision Vs Recall curve for threshold value 0.5 for different classes');    

for i = 1:length(ap)
% Plot precision/recall curve
    plot(ax,recall{i},precision{i},'DisplayName',['Average Precision for class ' trainingData.Properties.VariableNames{i+1} ' is ' num2str(round(ap(i),3))])
end

function layers = createNetwork(inputSize,numClassesPlusBackground)
    layers = [
        imageInputLayer(inputSize)                      % Input Layer
        convolution2dLayer(3,32,'Padding','same')       % Convolution Layer
        reluLayer                                       % Relu Layer
        convolution2dLayer(3,32,'Padding','same')
        batchNormalizationLayer                         % Batch normalization Layer
        reluLayer
        maxPooling2dLayer(2,'Stride',2)                 % Max Pooling Layer
        
        convolution2dLayer(3,64,'Padding','same')
        reluLayer
        convolution2dLayer(3,64,'Padding','same')
        batchNormalizationLayer
        reluLayer
        maxPooling2dLayer(2,'Stride',2)
        
        convolution2dLayer(3,128,'Padding','same')
        reluLayer
        convolution2dLayer(3,128,'Padding','same')
        batchNormalizationLayer
        reluLayer
        maxPooling2dLayer(2,'Stride',2)

        convolution2dLayer(3,256,'Padding','same')
        reluLayer
        convolution2dLayer(3,256,'Padding','same')
        batchNormalizationLayer
        reluLayer
        maxPooling2dLayer(2,'Stride',2)
    
        convolution2dLayer(6,512)
        reluLayer
        
        dropoutLayer(0.5)                               % Dropout Layer
        fullyConnectedLayer(512)                        % Fully connected Layer.
        reluLayer
        fullyConnectedLayer(numClassesPlusBackground)
        softmaxLayer                                    % Softmax Layer
        classificationLayer                             % Classification Layer
        ];

end

function helperDownloadMSTARClutterData(outputFolder,DataURL)
% Download the data set from the given URL to the output folder.

    radarDataTarFile = fullfile(outputFolder,'MSTAR_ClutterDataset.tar.gz');
    
    if ~exist(radarDataTarFile,'file')
        
        disp('Downloading MSTAR Clutter data (1.6 GB)...');
        websave(radarDataTarFile,DataURL);
        untar(radarDataTarFile,outputFolder);
    end
end

function helperDownloadPretrainedSARDetectorNet(outputFolder,pretrainedNetURL)
% Download the pretrained network.

    preTrainedMATFile = fullfile(outputFolder,'TrainedSARDetectorNet.mat');
    preTrainedZipFile = fullfile(outputFolder,'TrainedSARDetectorNet.tar.gz');
    
    if ~exist(preTrainedMATFile,'file')
        if ~exist(preTrainedZipFile,'file')
            disp('Downloading pretrained detector (29.4 MB)...');
            websave(preTrainedZipFile,pretrainedNetURL);
        end
        untar(preTrainedZipFile,outputFolder);   
    end       
end