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I have the following MATLAB code, but there is an issue. It's not running. Can someone help me with that?
clc;
clear all;
close all;
DatasetPath='C:\Users\4u\OneDrive\Desktop\Jaffedbase'
imds=imageDatastore(DatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames')
tic;
train_data = dir('C:\Users\4u\OneDrive\Desktop\Jaffedbase*.tiff');
trainl=length(train_data);
trainf=[];
v1=[];
%Traing images loop
for trainloop = 1:trainl
file_train=strcat('C:\Users\4u\OneDrive\Desktop\Jaffedbase',train_data(trainloop).name);
im_train= (imread(file_train));
facecet = vision.CascadeObjectDetector(); % face detection in a given image
bbox=step(facedet,im_train);
face=imcrop(im_train,bbox);
Mouthdet= vision.CascadeObjectDetector('Mouth'); % mouth detection in a given face image
BB==step(Mouthdet,face);
for i= 1:size(BB,1)
a=BB(i,:);
z=a(2);
if z > 97
b=face(a(2):a(2)+a(4),a(1):a(1)+a(3));
figure(1);
subplot (5,4,trainloop);
imshow(file_train);
figure(2);
subplot (5,4,trainloop);
imshow(b);
[featureVector1,hogVisualization1] = extractHOGFeatures(b); %Extracting featues using HOG
figure(3);
subplot (5,4,trainloop);
plot(hogVisualization1);
Train_coeff1=featureVector1';
v1=[v1,Train_coeff1(1:120,1:1)];
trainfv1';
end
end
end
Happy = [trainf(1,:);trainf(3,:);trainf(5,:);trainf(6,:);trainf(15,:);trainf(16,:)];
Surprise = [trainf(2,:);trainf(4,:);trainf(7,:);trainf(8,:);trainf(17,:);trainf(18,:)];
Sad = [trainf(9,:);trainf(10,:);trainf(11,:);trainf(12,:);trainf(19,:)];
Anger = [trainf(13,:);trainf(14,:);trainf(20,:)];
aT1=mean(Happy);
aT2=mean(Surprise);
aT3=mean(Sad);
aT4=mean(Anger);
train_group=[1;2;1;2;1;1;2;2;3;3;3;3;4;4;1;1;2;2;3;4];
Model=fitcknn(trainf,train_group','Distance','euclidean'); % Training the KNN Classifier
test_data = dir('C:\Users\4u\OneDrive\Desktop\Jaffedbase\test1\*.tiff');
testl=length(test_data);
testf=[];
test_group=[1;2;3;2;3;1;4;4;1;2;3;2;4;4;4;1;3;3;2;1];
k=1;
for testloop = 1:testl
file_test = strcat('C:\Users\4u\OneDrive\Desktop\Jaffedbase\test1\',test_data(testloop).name);
im_test= imread(file_test);
facedet1= vision.CascadeObjectDetector(); % face detection in a given image
bbox=step(facedet1,im_test);
face1=imcrop(im_test,bbox1);
Mouthdet1= vision.CascadeObjectDetector('Mouth'); % mouth detection in a given face image
BB1=step(Mouthdet1,face1);
y1=size(BB1,1);
for j = 1:y1
a1=BB1(j,:);
z1=a1(2);
if z1 > 100
b1=face1(a1(2):a1(2)+a1(4),a1(1):a1(1)+a1(3));
figure(4);
subplot(5,4,testloop);
imshow(file_test);
figure(5);
subplot (5,4,testloop);
imshow(b1);
[ffeatureVector2,hogVisualization2] = extractHOGFeatures(b1); %Extracting featues using HOG
figure(6);
subplot(5,4,testloop);
plot(hogVisualization2);
Train_coeff2=featureVector2';
tc=Train_coeff2(1:120,1:1);
tc=tc';
[llabel,score,cost] = predict(Model,tc);
TestPredict(k)=round(label);
if test_group(testloop) == 1
ref_tc=aT1;
elseif test_group(testloop) == 2
ref_tc=aT2;
elseif test_group(testloop) == 3
ref_tc=aT3;
elseif test_group(testloop) == 4
ref_tc=aT4;
end
PSNR_cal(k)=psnr(tc,ref_tc);
mse_cal(k)=immse(tc,ref_tc);
entropy_cal(k)=entropy(b1);
k=k+1;
end
end
end
% Accuracy, mean time of executions, PSNR, MSE and Entropy Calculations
cMatf = confusionmat(test_group,TestPredict) %compare test data with new features
acc = 100*(sum(diag(cMatf))/length(TestPredict));
fprintf('Accuracy: ');
fprintf('%d\n', acc);
execution_time=toc
X=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20];
figure(7);
plot(X,PSNR_cal,'--bs');
xlabel ('Image Numbers');
ylabel ('Signal to Noise Ratio');
title('PSNR (Peak Signal to Noise Ratio)');
figure(8);
plot(X,mse_cal,'--ro');
xlabel ('Image Numbers');
ylabel ('Mean square error');
title('MSE (Mean Square Error)');
figure(9);
plot(X,entropy_cal,'--ms');
xlabel ('Image Numbers');
ylabel ('Entropy');
title('Entropy');
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