MATLAB下人脸图像的PCA重构matlab代码

MATLAB下人脸图像的PCA重构matlab代码
主函数
clc;
clear all;
newimage=double(zeros(112*6,92*5));
[M,sort_V,avg_face] = averageface( );%返回去均值的矩阵和协方差，以及排列好的特征向量
k=1;
new_sort_V =sort_V(1:10304,1:30);%取特征向量的前30个，并进行显示for i=1:112:5*112+1
for j=1:92:4*92+1
newimage(i:i+111,j:j+91)=reshape(new_sort_V(:,k),112,92);
k=k+1;
end
end
minum=min(min(newimage));%对特征向量进行标定
maxum=max(max(newimage));
d=maxum-minum;
newimage(:,:)=newimage(:,:)-minum;
newimage(:,:)=newimage(:,:)/d;
newimage(:,:)=newimage(:,:)*255;
newimage=uint8(newimage);
subplot(1,3,1);
title('特征向量图');
imshow(newimage);%显示前30个特征向量
avg_face=uint8(avg_face);
subplot(1,3,2)
title('平均脸');
imshow(avg_face);
subplot(1,3,3)
[ tyxs,re_newimage] = touyingxishu( sort_V ,avg_face);
imshow(re_newimage);
子函数
function []=img2txt()
maindir = 'D:\Image';
subdir = dir( maindir ); % 先确定子文件夹
m=1;
k=1;
for i = 1 : length( subdir )
if( isequal( subdir( i ).name, '.') || isequal( subdir( i ).name, '..') || ~subdir( i ).isdir ) % 如果不是目录跳过
continue;
end
subdirpath = fullfile( maindir, subdir( i ).name, '*.pgm' );
images = dir( subdirpath ); % 在这个子文件夹下找后缀为pgm的文件
% 遍历每张图片
for j = 1 : length( images )
imagepath = fullfile( maindir, subdir( i ).name,
images( j ).name );
imgdata = imread( imagepath ); % 这里进行你的读取操作
[row, col]=size(imgdata); %将数据写入txt中
fid = fopen(['C:\Users\Administrator\Desktop\编程实验材料\att_faces','\s',int2str(m),'\',int2str(k),'.txt'],'w'); %新建
txt文件存储图像的坐标和灰度值
for I=1:row
for J=1:col
fprintf(fid,'%03d\t',imgdata(I,J));
end
fprintf(fid,'\n') ;
end
fclose(fid);
k=k+1;
end
m=m+1;
k=1;
end
function [M,sort_V,avg_face] = averageface()
big_array=double(zeros(10304,400));
new_big_array=double(zeros(10304,400));
avg_face=double(zeros(112,92));
i=1;
for m=1:40;
for k=1:10
FileName = ['C:\Users\Administrator\Desktop\编程实验材料
\att_faces','\s',int2str(m),'\',int2str(k),'.txt'];
p = load (FileName);
big_array(:,i)=reshape(p,10304,1); %将400张脸转换成10304*400的大矩阵
new_p=reshape(p,112,92);
avg_face(:,:)=avg_face(:,:)+new_p(:,:)/400; %求平均脸
i=i+1;
end
end
M=mean(big_array); %对矩阵的每一列求均值
for i=1:400
new_big_array(:,i)=big_array(:,i)-M(i);%去均值后的矩阵
end
%用SVD求特征向量
C0=new_big_array'*new_big_array;%求低维度的协方差
[V0,~]=eig(C0);
k=0;
V=new_big_array*V0;%特征向量
for i=1:400 %将特征向量进行归一化10304*400
for j=1:10304
k=k+V(j,i)^2;
end
k=k^(0.5);
V(1:10304,i)= V(1:10304,i)/k;
k=0;
end
sort_V=double(zeros(10304,400));
k=400;
for i=1:400 %将特征向量的列按特征值从大到小排列好
sort_V(1:10304,i)=V(1:10304,k);
k=k-1;
end
function [ tyxs,re_newimage] = touyingxishu( sort_V ,avg_face)
FileName = ['C:\Users\Administrator\Desktop\编程实验材料
\att_faces','\s',int2str(1),'\',int2str(1),'.txt'];
p = load (FileName);
avg_face=double(avg_face);
big_array=reshape(p,1,10304);
a_big_array=double(reshape(big_array,112,92));
big_array=a_big_array(:,:)-avg_face(:,:);
big_array=reshape(big_array,1,10304);
tyxs=big_array*sort_V;
re_newimage=double(zeros(10304,1));
for i=1:400
re_newimage(:,:)= re_newimage(:,:)+tyxs(i)*sort_V(1:10304,i);
end
re_newimage= reshape(re_newimage,112,92);
re_newimage=re_newimage(:,:)+avg_face(:,:);
minum=min(min(re_newimage));%对特征向量进行标定
maxum=max(max(re_newimage));
d=maxum-minum;
re_newimage(:,:)=re_newimage(:,:)-minum;
re_newimage(:,:)=re_newimage(:,:)/d;
re_newimage(:,:)=re_newimage(:,:)*255;
re_newimage=uint8(re_newimage);
end。