Rectangle on the detected class's position in an image using a trained cnn

camera = webcam;
camera.Resolution = '640x480';
net = googlenet;

h = figure;

while ishandle(h)
    im = snapshot(camera);
    image(im)
    I1 = imcrop(im, [ 1 1 160 479]);
    [label1,score1] = classify(trainNet,I1);
    I2 = imcrop(im, [ 161 1 160 479]);
     [label2,score2] = classify(trainNet,I2);
    I3 = imcrop(im, [ 321 1 160 479]);
     [label3,score3] = classify(trainNet,I3);
    I4 = imcrop(im, [ 480 1 160 479]);
     [label4,score4] = classify(trainNet,I4);
    
  
    if label1=='door'
        rectangle('Position',[ 1 1 160 480],'EdgeColor','b','LineWidth',3)
    end
        
    if  label2=='vrata'
        rectangle('door',[ 161 1 160 479],'EdgeColor','b','LineWidth',3)
    end
        
    if  label3=='vrata'
        rectangle('door',[ 321 1 160 479],'EdgeColor','b','LineWidth',3)
    end
        
    if  label4=='vrata'
        rectangle('door',[ 480 1 160 479],'EdgeColor','b','LineWidth',3)   
    end
    

    
end

I created a CNN with 2 classes 'door' and 'no door' and i'm using it to detect a door with this code that devides the screen from the camera into 4 parts and it uses rectangle on the the parts it detects a door, but i really want for the rectangle to appear on the exact position of the detected door, is there a way to do that?

[Image Analyst]

Your network does not return that information. It just returns the class name/label, and a score. You're just putting the rectangle over fixed locations because your network does not return the location of the door. You need to train a network to find the door and return its coordinates.

[vdk vdk]

How do i do that? Is there a specific layer , option or something to add while training the network?

[Image Analyst]

What did you do to train the network that you DO have right now? It should be pretty similar. Replace the last layer with your ground truth and feed in your labeled training images. What did you do to create your existing trainNet?

[vdk vdk]

This is the code i used to create the CNN, i'm using 7441 images for door and 7448 for no door.

clear
clc
imagesDir = 'D:\uchene\4ti kurs\diplomna chast\New folder\posleden test';
images = imageDatastore(imagesDir,'IncludeSubfolders',true,'FileExtensions','.jpg','LabelSource','foldernames');
[imdsTrain,imdsValidation] = ...
splitEachLabel(images,0.7,'randomized');
numClasses = numel(categories(imdsTrain.Labels));
trainingLabels = imdsTrain.Labels;
filterSize = [5 5];
numFilters = 42;
layers = [
    imageInputLayer([480 161 3]);
    convolution2dLayer(filterSize, numFilters, 'Padding', 2)
    reluLayer()
    maxPooling2dLayer(3, 'Stride', 2)
    convolution2dLayer(filterSize, numFilters, 'Padding', 2)
    reluLayer()
    convolution2dLayer(filterSize, numFilters, 'Padding', 2)
    reluLayer()
    maxPooling2dLayer(3, 'Stride',2)
    convolution2dLayer(filterSize, 2 * numFilters, 'Padding', 2)
    reluLayer()
    convolution2dLayer(filterSize, 2 * numFilters, 'Padding', 2)
    reluLayer()
    maxPooling2dLayer(3, 'Stride',2)
    fullyConnectedLayer(numClasses*32)
    reluLayer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
    softmaxLayer
    classificationLayer
    ];
miniBatchSize = 64;
numIterationsPerEpoch = floor(numel(imdsTrain.Labels)/miniBatchSize);
options = trainingOptions('sgdm',...
 'MiniBatchSize',miniBatchSize,...
 'MaxEpochs',50,...
 'InitialLearnRate',1e-5,...
 'L2Regularization', 0.004, ...
 'Verbose',false,...
 'Plots','training-progress',...
 'ValidationData',imdsValidation,...
 'ValidationFrequency',numIterationsPerEpoch,...
 'ExecutionEnvironment','gpu');
trainNet = trainNetwork(imdsTrain,layers,options);