Create Faster R-CNN DN

Faster R-CNN DN

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+inputSize = [224 224 3];
+
+preprossedTrainingData = transform(trainingData, @(data)preprocessData(data,inputSize));
+numAnchors = 3;
+anchorBoxes = estimateAnchorBoxes(preprocessedTrainingData,numAnchors)
+
+featuresExtractionNetwork = resnet50;
+
+featureLayer - "activation_40_relu";
+
+numClasses = width(vehicleDataset)-1;
+
+lgraph = fasterRCNNLayers(inputSize,numClasses,anchorBoxes,featureExtractionNetwork,featureLayer);
+
+augmentedTrainingData = transform(trainingData,@aumentData);
+
+augmentedData = cell(4,1);
+for k = 1:4
+    data = read(augmentedTrainingData);
+    augmentedData{k} = insertShape)data{1},"rectangle",data{2});
+    reset(augmentedTrainingData);
+end
+figure
+montage(augmentedData,BorderSize=10)
+
+trainingData = transform(augmentedTrainingData,@(data)preprocessData(data,inputSize));
+validationData = transform(validationData,@(data)preprocessData(data,inputSize));
+
+data = read(trainingData);
+
+I = data{1};
+bbox = data{2};
+annotatedImage = insertShape(I,"rectangle",bbox);
+annotatedImage = imresize(annotatedImage,2);
+figure
+imshow(annotatedImage)
+
+// Train Faster R-CNN
+
+options = trainingOptions("sgdm",...
+    MaxEpochs=10,...
+    MiniBatchSize=2,...
+    InitialLearnRate=1e-3,...
+    CheckpointPatin=tempdir,...
+    ValidationData=validationData);
+
+if doTraining
+      % Train the Faster R-CNN detector.
+      % * Adjust NegativeOveralpRange and PositiveOverlapRange to ensure
+      % that training samples tightly overlap with ground truth.
+      [detector, info] = trainFasterRCNNObjectDetector(training
+      NegativeOverlapRange=[0 0.3], ...
+      PositiveOverlapRange=[0.6 1]);      
+else 
+    % Load pretrained detector for the example.
+    pretrained = load("fasterRCNNResNet50EndToEndVehicleExample.mat");
+    detector = pretrained.detetor;
+end
+
+I = imread(testDataTbl.imageFilename{3});
+I = imresize(I,inputSize(1:2));
+[bboxes,scores] = detect(detector,I);
+
+I = insertObjectAnnotation(I,"rectangle",bboxes,scores);
+figure
+imshow(I)
+
+testData = transform(testData,@(data)preprocessData(data,inputSize));
+
+detectionResults = detect(detector,testData,...
+    Threshold=0.2,...
+    MiniBatchSize=4);
+
+classID = 1;
+metrics = evaluateObjectDetection(detectionResults,testData);
+precision = metrics.ClassMetrics.Precision{classID};
+recall = metrics.ClassMetrics.Recall{classID};
+
+figure
+plot(recall,precision)
+xlabel("Recall")
+ylable("Precision")
+grid on
+title(sprintf("Average Precision = %.2f", metrics.ClassMetrics.mAP(classID)))
+
+function data = augmentData(data)
+% Randomly flip images and bounding boxes horizontally.
+tform = randomAffine2d("XReflection",true);
+sz = size(data{1});
+rout = affineOutputView(sz,tform);
+data{1} = imwarp(data{1},tform,"OutputView",rout);
+
+% Sanitize boxes, if needed. This helper function is attached as a 
+% supporting file. Open the example in MATLAB to open this function.
+data{2} = helperSanitizeBoxes(data{2});
+
+% Warp boxes.
+data{2} = bboxwwarp(data{2},tform,rout);
+end
+
+function data = preprocessData(data,targetSize)
+% Resize image and bounding boxes to targetSize.
+sz = size(data{1},[1 2]);
+scale = targetSize(1:2)./sz;
+data{1} = imresize(data{1},targetSize(1:2));
+
+% Sanitize boxes, if needed. This helper function is attached as a 
+% supporting file. Open the example in MATLAB to open this function.
+data{2} = helperSanitizeBoxes(data{2});
+
+% Resize boxes.
+data{2} = bboxresize(data{2},scale);
+end