Math lab projesi

Merhaba bu proje ödevinde yardımcı olabilecek arkadaşlar varsa detaylı bilgi için iletişime geçerse sevinirim.

MATLAB programı üzerinde Derin Öğrenme kullanarak sizi tespit eden network tasarlayacaksınız yani yüzünüzü eğiteceksiniz. Test imgelerinde birden fazla kişi olmalıdır mümkünse en az 3 kişi, 100 adet test resmindeki başarı oranınızı da hesaplamanız gerekmektedir. Teslim edeceğiniz ödev dosyasında test resimleriniz ve oluşturacağınız sizi tespit eden network bulunmalıdır.

aşağıdaki dosyayı oluşturun

displayFaceGallery(faceGallery,galleryNames)

içine şunları yapıştırın ;

%% Display Face Gallery
%Copyright 2014-2015 The MathWorks, Inc.
function displayFaceGallery(faceGallery,galleryNames)
% displaying all the gallery images
figure
for i = 1:length(faceGallery)
I = cell(1, faceGallery(i).Count);
% concatenate all the images of a person side-by-side
for j = 1:faceGallery(i).Count
image = read(faceGallery(i), j);
% scale images to have same height, maintaining the aspect ratio
scaleFactor = 150/size(image, 1);
image = imresize(image, scaleFactor);
I{j} = image;
end
subplot(length(faceGallery), 1, i);
imshow(cell2mat(I));
title(galleryNames{i}, 'FontWeight', 'normal');
end
annotation('textbox', [0 0.9 1 0.1], 'String', 'Face Gallery', ...
'EdgeColor', 'none', 'FontWeight', 'bold', ...
'FontSize', 12, 'HorizontalAlignment', 'center')


--------------------------
şimdi şu dosyayı oluşturun

SimpleFaceRecognition.m



%% Simple Face Recognition Example
% Copyright 2014-2015 The MathWorks, Inc.
%% Load Image Information from ATT Face Database Directory
faceDatabase = imageSet('FaceDatabaseATT','recursive');
%% Display Montage of First Face
figure;
montage(faceDatabase(1).ImageLocation);
title('Images of Single Face');
%% Display Query Image and Database Side-Side
personToQuery = 1;
galleryImage = read(faceDatabase(personToQuery),1);
figure;
for i=1:size(faceDatabase,2)
imageList(i) = faceDatabase(i).ImageLocation(5);
end
subplot(1,2,1);imshow(galleryImage);
subplot(1,2,2);montage(imageList);
diff = zeros(1,9);
%% Split Database into Training & Test Sets
[training,test] = partition(faceDatabase,[0.8 0.2]);
%% Extract and display Histogram of Oriented Gradient Features for single face
person = 5;
[hogFeature, visualization]= ...
extractHOGFeatures(read(training(person),1));
figure;
subplot(2,1,1);imshow(read(training(person),1));title('Input Face');
subplot(2,1,2);plot(visualization);title('HoG Feature');
%% Extract HOG Features for training set
trainingFeatures = zeros(size(training,2)*training(1).Count,4680);
featureCount = 1;
for i=1:size(training,2)
for j = 1:training(i).Count
trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j));
trainingLabel{featureCount} = training(i).Description;
featureCount = featureCount + 1;
end
personIndex{i} = training(i).Description;
end
%% Create 40 class classifier using fitcecoc
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);
%% Test Images from Test Set
person = 1;
queryImage = read(test(person),1);
queryFeatures = extractHOGFeatures(queryImage);
personLabel = predict(faceClassifier,queryFeatures);
% Map back to training set to find identity
booleanIndex = strcmp(personLabel, personIndex);
integerIndex = find(booleanIndex);
subplot(1,2,1);imshow(queryImage);title('Query Face');
subplot(1,2,2);imshow(read(training(integerIndex),1));title('Matched Class');
%% Test First 5 People from Test Set
figure;
figureNum = 1;
for person=1:5
for j = 1:test(person).Count
queryImage = read(test(person),j);
queryFeatures = extractHOGFeatures(queryImage);
personLabel = predict(faceClassifier,queryFeatures);
% Map back to training set to find identity
booleanIndex = strcmp(personLabel, personIndex);
integerIndex = find(booleanIndex);
subplot(2,2,figureNum);imshow(imresize(queryImage,3));title('Query Face');
subplot(2,2,figureNum+1);imshow(imresize(read(training(integerIndex),1),3));title('Matched Class');
figureNum = figureNum+2;

end
figure;
figureNum = 1;
end



------------------------

ve son dosya aşağıdaki ad ile oluşturun


SimpleFaceRecognitionMathWorks.m


%% Face Recognition with MathWorks Faces
% Copyright 2014-2015 The MathWorks, Inc.
clear all;
close all;
clc;
%% Read Mathworks Face Gallery
faceGallery = imageSet('MathWorksGallery', 'recursive');
galleryNames = {faceGallery.Description};
displayFaceGallery(faceGallery,galleryNames);
%% Create HoG training features from face gallery
trainingFeatures = zeros(19,10404);
featureCount = 1;
for i=1:size(faceGallery,2)
for j = 1:faceGallery(i).Count
sizeNormalizedImage = imresize(rgb2gray(read(faceGallery(i),j)),[150 150]);
trainingFeatures(featureCount,:) = extractHOGFeatures(sizeNormalizedImage);
trainingLabel{featureCount} = faceGallery(i).Description;
featureCount = featureCount + 1;
end
personIndex{i} = faceGallery(i).Description;
end
%% Create Classifier
faceClassifier = fitcecoc(trainingFeatures,trainingLabel);
%% Read test data
testSet = imageSet('MathWorksTestImages');
figure;
figureNum = 1;
for i= 1: testSet.Count
queryImage = read(testSet,i);
queryFeatures = extractHOGFeatures(rgb2gray(queryImage));
personLabel = predict(faceClassifier,queryFeatures);
booleanIndex = strcmp(personLabel, personIndex);
integerIndex = find(booleanIndex);
subplot(5,2,figureNum);imshow(queryImage);title('Query Face');
subplot(5,2,figureNum+1);imshow(read(faceGallery(integerIndex),1));title('Matched Class');
figureNum = figureNum+2;

end

Eyvallah hocam sağolun hiç bir bilgim yoktu nasıl yapılacağıyla ilgili teşekkür ediyorum🙏