matlab2.m

doTrainingAndEval = true;

load('myResizedData.mat')
trainingData = myResizedData.TrainData;
testData = myResizedData.TestData;

trainingData = trainingData(1:200,:);
testData = testData(20:50,:);

% 
% 
% 
% % Load a pretrained ResNet-50.
% net = resnet50;
% lgraph = layerGraph(net);
% 
% % Remove the the last 3 layers. 
% layersToRemove = {
%     'fc1000'
%     'fc1000_softmax'
%     'ClassificationLayer_fc1000'
%     };
% lgraph = removeLayers(lgraph, layersToRemove);
% 
% % Specify the number of classes the network should classify.
% numClasses = 1;
% numClassesPlusBackground = numClasses + 1;
% 
% % Define new classification layers.
% newLayers = [
%     fullyConnectedLayer(numClassesPlusBackground, 'Name', 'rcnnFC')
%     softmaxLayer('Name', 'rcnnSoftmax')
%     classificationLayer('Name', 'rcnnClassification')
%     ];
% 
% % Add new object classification layers.
% lgraph = addLayers(lgraph, newLayers);
% 
% % Connect the new layers to the network. 
% lgraph = connectLayers(lgraph, 'avg_pool', 'rcnnFC');
% 
% % Define the number of outputs of the fully connected layer.
% numOutputs = 4 * numClasses;
% 
% % Create the box regression layers.
% boxRegressionLayers = [
%     fullyConnectedLayer(numOutputs,'Name','rcnnBoxFC')
%     rcnnBoxRegressionLayer('Name','rcnnBoxDeltas')
%     ];
% 
% % Add the layers to the network.
% lgraph = addLayers(lgraph, boxRegressionLayers);
% 
% % Connect the regression layers to the layer named 'avg_pool'.
% lgraph = connectLayers(lgraph,'avg_pool','rcnnBoxFC');
% 
% % Select a feature extraction layer.
% featureExtractionLayer = 'activation_40_relu';
% 
% % Disconnect the layers attached to the selected feature extraction layer.
% lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch2a');
% lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch1');
% 
% % Add ROI max pooling layer.
% outputSize = [14 14];
% roiPool = roiMaxPooling2dLayer(outputSize,'Name','roiPool');
% lgraph = addLayers(lgraph, roiPool);
% 
% % Connect feature extraction layer to ROI max pooling layer.
% lgraph = connectLayers(lgraph, featureExtractionLayer,'roiPool/in');
% 
% % Connect the output of ROI max pool to the disconnected layers from above.
% lgraph = connectLayers(lgraph, 'roiPool','res5a_branch2a');
% lgraph = connectLayers(lgraph, 'roiPool','res5a_branch1');
% 
% % Define anchor boxes.
% anchorBoxes = [
%     16 16
%     32 16
%     ];
% 
% % Create the region proposal layer.
% proposalLayer = regionProposalLayer(anchorBoxes,'Name','regionProposal');
% 
% lgraph = addLayers(lgraph, proposalLayer);
% 
% % Number of anchor boxes.
% numAnchors = size(anchorBoxes,1);
% 
% % Number of feature maps in coming out of the feature extraction layer. 
% numFilters = 1024;
% 
% rpnLayers = [
%     convolution2dLayer(3, numFilters,'padding',[1 1],'Name','rpnConv3x3')
%     reluLayer('Name','rpnRelu')
%     ];
% 
% lgraph = addLayers(lgraph, rpnLayers);
% 
% % Connect to RPN to feature extraction layer.
% lgraph = connectLayers(lgraph, featureExtractionLayer, 'rpnConv3x3');
% 
% % Add RPN classification layers.
% rpnClsLayers = [
%     convolution2dLayer(1, numAnchors*2,'Name', 'rpnConv1x1ClsScores')
%     rpnSoftmaxLayer('Name', 'rpnSoftmax')
%     rpnClassificationLayer('Name','rpnClassification')
%     ];
% lgraph = addLayers(lgraph, rpnClsLayers);
% 
% % Connect the classification layers to the RPN network.
% lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1ClsScores');
% 
% % Add RPN regression layers.
% rpnRegLayers = [
%     convolution2dLayer(1, numAnchors*4, 'Name', 'rpnConv1x1BoxDeltas')
%     rcnnBoxRegressionLayer('Name', 'rpnBoxDeltas');
%     ];
% 
% lgraph = addLayers(lgraph, rpnRegLayers);
% 
% % Connect the regression layers to the RPN network.
% lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1BoxDeltas');
% 
% % Connect region proposal network.
% lgraph = connectLayers(lgraph, 'rpnConv1x1ClsScores', 'regionProposal/scores');
% lgraph = connectLayers(lgraph, 'rpnConv1x1BoxDeltas', 'regionProposal/boxDeltas');
% 
% % Connect region proposal layer to roi pooling.
% lgraph = connectLayers(lgraph, 'regionProposal', 'roiPool/roi');
% 
% % Show the network after adding the RPN layers.
% figure
% plot(lgraph)
% ylim([30 42])
% 
% 
% 
% optionsStage1 = trainingOptions('sgdm', ...
%     'MaxEpochs', 10, ...
%     'MiniBatchSize', 1, ...
%     'InitialLearnRate', 1e-3);
% 
% optionsStage2 = trainingOptions('sgdm', ...
%     'MaxEpochs', 10, ...
%     'MiniBatchSize', 1, ...
%     'InitialLearnRate', 1e-3);
% 
% optionsStage3 = trainingOptions('sgdm', ...
%     'MaxEpochs', 10, ...
%     'MiniBatchSize', 1, ...
%     'InitialLearnRate', 1e-3);
% 
% optionsStage4 = trainingOptions('sgdm', ...
%     'MaxEpochs', 10, ...
%     'MiniBatchSize', 1, ...
%     'InitialLearnRate', 1e-3);
% 
% options = [
%     optionsStage1
%     optionsStage2
%     optionsStage3
%     optionsStage4
%     ];
% 
% if doTrainingAndEval
%     rng(0);
%     detector = trainFasterRCNNObjectDetector(trainingData, lgraph, options, ...
%         'NegativeOverlapRange', [0 0.3], ...
%         'PositiveOverlapRange', [0.6 1], ...
%         'NumRegionsToSample', [256 128 256 128]);
% else
% %     detector = data.detector;
% end
% 
% save detector

% Test on one image
I = imread(testData.imageFilename{1});
% I = imread('IraniData/X.jpg');
% I = imresize(I,[300, NaN]);
[bboxes, scores] = detect(detector, I);
if bboxes
    I = insertObjectAnnotation(I, 'rectangle', bboxes, scores);
    figure
    imshow(I)
end
% 
% % Evaluate Detector Using Test Set
% if doTrainingAndEval
%     resultsStruct = struct([]);
%     for i = 1:height(testData)
%         I = imread(testData.imageFilename{i});
%         [bboxes, scores, labels] = detect(detector, I);
%         resultsStruct(i).Boxes = bboxes;
%         resultsStruct(i).Scores = scores;
%         resultsStruct(i).Labels = labels;
%         
% %         disp (i)
% %         disp(bboxes)
% %         if bboxes
% %             disp (i)
% %             disp(bboxes)
% %             I = insertObjectAnnotation(I, 'rectangle', bboxes, scores);
% %             figure
% %             imshow(I)
% %         end
%     end
%     results = struct2table(resultsStruct);
% else
%     results = data.results;
% end
% 
% expectedResults = testData(:, 2:end);
% [ap, recall, precision] = evaluateDetectionPrecision(results, expectedResults);
% 
% figure
% plot(recall, precision)
% xlabel('Recall')
% ylabel('Precision')
% grid on
% title(sprintf('Average Precision = %.2f', ap))