how to implement the Faster Rcnn using Densenet(201) to detect the intersection over union.

close all;

clear all;

clc;

%input image

[file,path]=uigetfile('*.jpg','select a input image');

str=strcat(path,file);

I=imread(str);

figure(1),imshow(I);

gray=rgb2gray(I);

figure(2),imshow(gray);

% noise removal

noise=fspecial('gaussian');

f=imfilter(gray,noise);

figure(3);

imshow(f)

%Gabor Filter

gabor=GaborFilterBank(4,4,32,32);

features=GaborFeatures(f,gabor,4,4);

save Features features

%gaborWavelet=mean(features)

%Pre-trained

File='D:\Project_1\dataset'

dataset=fullfile(File,'foldername','dataset')

imgdata=imageDatastore('dataset',...

'IncludeSubFolders',true,...

'LabelSource','foldernames','FileExtensions',{'.jpg','.png', '.tif'});

%count of total img

icount=countEachLabel(imgdata)

minsetCount=min(icount{:,2});

maxImages=60;

mincount=min(maxImages,minsetCount);

%split each label

imds=splitEachLabel(imgdata,mincount,'randomize');

countEachLabel(imds)

%DenseNet

net=densenet201();

figure(6),plot(net);

title('Densenet');

set(gca,'Ylim',[150 170]);

%First Layer

First=net.Layers(1);

%Last Layer

End=net.Layers(end);

%Number of Class names for ImageNet Classification Task

numel(End.ClassNames);

[trainingSet, testSet]=splitEachLabel(imds,0.3,'randomize');

%resize

imageSize=First.InputSize;

augmentedTrainingSet=augmentedImageDatastore(imageSize,trainingSet,'ColorPreprocessing','gray2rgb');

augmentedTestSet=augmentedImageDatastore(imageSize,testSet,'ColorPreprocessing','gray2rgb');

%Layers

layer=[(imageInputLayer([224 224 3]))

convolution2dLayer(5,16,'Padding','Same','Name','conv_1')

batchNormalizationLayer('Name','BN_1')

reluLayer('Name','relu_1')

maxPooling2dLayer(2,'stride',2)

convolution2dLayer(5,16,'Padding','Same','Name','conv_1')

batchNormalizationLayer('Name','BN_1')

reluLayer('Name','relu_1')

maxPooling2dLayer(2,'stride',2)

averagePooling2dLayer(2,'stride',2)

fullyConnectedLayer(4)

softmaxLayer

classificationLayer()];

featureLayer='fc1000';

trainingFeatures=activations(net,augmentedTrainingSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

%Get training labels from trainingset

trainingLabels=trainingSet.Labels;

classifier=fitcecoc(trainingFeatures,trainingLabels,'Learners','Linear','Coding','onevsall','ObservationsIn','columns');

%Test Features

testFeatures=activations(net,augmentedTestSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

predictedLabels =predict(classifier,testFeatures,'ObservationsIn','columns');

testLabels=testSet.Labels;

% %confusion matrix

% confMat=confusionmat(testLabels,predictedLabels);

% %convert confusionMatrix to precentage

% confMat=bsxfun(@rdivide,confMat,sum(confMat,2))

% mean(diag(confMat))

%

%automatic resize

aids=augmentedImageDatastore(imageSize,f,'ColorPreprocessing','gray2rgb');

% figure(12),imshow(aids)

imageFeatures= activations(net,aids,featureLayer,'OutputAs','columns');

save DensenetFeatures imageFeatures

% % sdf=imageFeatures'

% % save sdf sdf

% predictedLabels=predict(classifier,imageFeatures,'ObservationsIn','columns');

% g=reshape(imageFeatures,100,5,[])

% g=double(g)

% the=resize(g,[256,256])

% datastruct = load('DensenetFeatures.mat');

% fn = fieldnames(datastruct);

% firstvar = fn{1};

% data = datastruct.(firstvar);

% imwrite( data, 'DenseNet.jpg' );

% et=imread('DenseNet.jpg')

% et=double(et)

%

% % z=imresize(et,[256 256])

% z=reshape(et,256,256,[])

% save Dense z

% %

% r=im2double(z)

figure(7),imshow(f);

%concatenated features

load GaborFeatures.mat

load DensenetFeatures.mat

features = cat(3,imageFeatures,Gabor);

save ConcatenatedFeatures features

datastruct = load('ConcatenatedFeatures.mat');

fn1 = fieldnames(datastruct);

firstvar = fn1{1};

data = datastruct.(firstvar);

% imwrite( data, 'concat.jpg' );

% et1=imread('concat.jpg')

% et1=imread(data)

% figure(87),imshow(et1)

% et2=double(et1)

%

% z=imresize(et2,[256 256])

% Faster R-CNN

lgraph = layerGraph(net);

% Remove the last 3 layners.

layersToRemove = {

'fc1000'

'fc1000_softmax'

'ClassificationLayer_fc1000'

};

lgraph = removeLayers(lgraph, layersToRemove);

% Specify the number of classes the network should classify.

numClasses = 2;

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')

% 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');

% Add ROI max pooling layer.

% SZ=size(testImage)

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

16 32

];

% 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 = 1000;

img= f;

img1 = img < 65;

img2 = imclearborder(img1);

rp = regionprops(img2, 'BoundingBox', 'Area');

area = [rp.Area];

[~,ind] = max(area);

bb = rp(ind).BoundingBox;

figure(8),imshow(img);

%figure(9),imhist(img)

rectangle('Position', bb, 'EdgeColor', 'red');

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,'avg_pool','rpnConv3x3');

% Add RPN classification layers.

rpnClsLayers = convolution2dLayer(1, numAnchors*2,'Name', 'rpnConv1x1ClsScores')

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')

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');

%

% figure(6),

% imshow(testImage);

%

% Show the network after adding the RPN layers.

figure(9),

plot(lgraph)

ylim([30 42])

title('network after adding the RPN layers')

%GroundTruth

load('gTruth.mat');

%

trainingData = gTruth.DataSource;

%

dataset=fullfile(File,'foldername','dataset')

trainingData.imageFilename=imageDatastore('dataset',...

'IncludeSubFolders',true,...

'LabelSource','foldernames','FileExtensions',{'.jpg','.png', '.tif'});

trainingData.imageFilename = fullfile('dataset','trainingSet',...

trainingData.imageFilename);

trainingData.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ...

trainingData.imageFilename);

rng(0);

shuffledIdx = randperm(height(trainingData));

trainingData = trainingData(shuffledIdx,:);

%

imds = imageDatastore(trainingData.imageFilename);

%Create a box label datastore using the label columns from the table.

%

blds = boxLabelDatastore(trainingData(:,2:end));

%Combine the datastores.

ds = combine(imds, blds);

% % Set up the network layers.

%

% lgraph = layerGraph(data.detector.Network)

% Configure training options.

options = trainingOptions('sgdm', ...

'MiniBatchSize', 1, ...

'InitialLearnRate', 1e-3, ...

'MaxEpochs', 7, ...

'VerboseFrequency', 200, ...

'CheckpointPath', tempdir);

detector = trainFasterRCNNObjectDetector(gTruth,ppp, options, ...

'NegativeOverlapRange',[0 0.3], ...

'PositiveOverlapRange',[0.6 1], ...

'SmallestImageDimension',300);

[bbox, score, label] = detect(detector,img);

detectedImg = insertShape(img,'Rectangle',bbox);

figure(11)

imshow(detectedImg)