clear;

clc;

train_path='E:\TrainingSet\angry\positive\';

images = dir('E:\TrainingSet\angry\positive\*.bmp'); 

phi=zeros(30,64*64);



% 加载样本图像到 30*(64*64)的矩阵中，每一行代表一幅图像

for i=1:30

    path=strcat(train_path,images(i).name);

    Image=imread(path);

    Image=imresize(Image,[64,64]);

    phi(i,:)=double(reshape(Image,1,[]));

end;



% 计算平均脸，并保存用以查看

mean_phi=mean(phi,1);

mean_face=reshape(mean_phi,64,64);

Image_mean=mat2gray(mean_face);

imwrite(Image_mean,'meanface2.bmp','bmp');



% 使用matlab自带的pca进行降维

[coeff, score, latent, TSQUARED] = princomp(phi,'econ');



%display Eigenface

for i=1:29

    Eigenface=reshape(coeff(:,i),[64,64]);

    figure(i);

    imshow(mat2gray(Eigenface));

end



% 进行测试

%使用测试样本进行测试

clc;

test_path='E:\BIT\code\FER\meanface.bmp';

error=zeros([1,4]);



Image=imread(test_path);

Image=double(imresize(Image,[64,64]));

phi_test=zeros(1,64*64);

phi_test(1,:)=double(reshape(Image,1,[])); % 读入的测试图像保存为一行，行向量

X_test=phi_test-mean_phi;

Y_test=X_test*coeff;

X_test_re=Y_test*coeff';

Face_re=X_test_re+mean_phi;

%calculate error rate

e=Face_re-phi_test;



%%display figure

Face_re_2=reshape(Face_re(1,:),[64,64]);

figure(i);



imshow(mat2gray(Image));

title('Original');

figure(10+i);

imshow(mat2gray(Face_re_2));

title('Reconstruct');

error(1,i)=norm(e);



%dispaly error rate

error_rate=error(1,i);

display(error_rate);

X = [2.5 2.4;

     0.5 0.7;

     2.2 2.9;

     1.9 2.2;

     3.1 3.0;

     2.3 2.7;

     2 1.6;

     1 1.1;

     1.5 1.6;

     1.1 0.9];

 

 % 调用princomp进行PCA计算

[pc,score,latent,tsquare] = princomp(X);



% 数据降维:X-〉X1，由2维降为1维

X_mean = mean(X);

X_adjust = [X(:,1)-X_mean(1) X(:,2)-X_mean(2)];



tranMatrix = pc(:,1);

X1 = X_adjust * tranMatrix * tranMatrix';

X1 = [X1(:,1)+X_mean(1)];



% 数据还原：

X_re = X_adjust * pc * pc';

X_re = [X_re(:,1)+X_mean(1) X_re(:,2)+X_mean(2)];