Question

我的代码以下列方式工作：

列出项目。首先，它从训练集中获得几个图像加载这些图像后，我们找到标准化的面，平均面并执行多次计算。接下来，我们要求我们想要识别的图像的名称然后我们将输入图像投影到本征空间中，并根据与特征脸的差异我们做出决定。根据每个输入图像的特征权重向量，我们使用kmeans命令创建聚类。我将总代码分为5个部分，并显示了5个部分的运行时间。

PART1

tic
clear all
close all
clc
% number of images on your training set.
M=50;

%Chosen std and mean. 
%It can be any number that it is close to the std and mean of most of the images.
um=60;
ustd=32;

%read and show images(bmp);
S=[];   %img matrix

for i=1:M
    str=strcat(int2str(i),'.jpg');   %concatenates two strings that form the name of the image
    eval('img=imread(str);');

       [irow icol d]=size(img); % get the number of rows (N1) and columns (N2)
       temp=reshape(permute(img,[2,1,3]),[irow*icol,d]);     %creates a (N1*N2)x1 matrix
    S=[S temp];         %X is a N1*N2xM matrix after finishing the sequence
                        %this is our S
end


%Here we change the mean and std of all images. We normalize all images.
%This is done to reduce the error due to lighting conditions.
for i=1:size(S,2)
    temp=double(S(:,i));
    m=mean(temp);
    st=std(temp);
    S(:,i)=(temp-m)*ustd/st+um;
end
toc

经过的时间是0.425070秒。

PART2

tic
%show normalized images

for i=1:M
    str=strcat(int2str(i),'.jpg');
    img=reshape(S(:,i),icol,irow);
    img=img';

end


%mean image;
m=mean(S,2);   %obtains the mean of each row instead of each column
tmimg=uint8(m);   %converts to unsigned 8-bit integer. Values range from 0 to 255
img=reshape(tmimg,icol,irow);    %takes the N1*N2x1 vector and creates a N2xN1 matrix
img=img';       %creates a N1xN2 matrix by transposing the image.

% Change image for manipulation
dbx=[];   % A matrix
for i=1:M
    temp=double(S(:,i));
    dbx=[dbx temp];
end

%Covariance matrix C=A'A, L=AA'
A=dbx';
L=A*A';
% vv are the eigenvector for L
% dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx';
[vv dd]=eig(L);
% Sort and eliminate those whose eigenvalue is zero
v=[];
d=[];
for i=1:size(vv,2)
    if(dd(i,i)>1e-4)
        v=[v vv(:,i)];
        d=[d dd(i,i)];
    end
 end

 %sort,  will return an ascending sequence
 [B index]=sort(d);
 ind=zeros(size(index));
 dtemp=zeros(size(index));
 vtemp=zeros(size(v));
 len=length(index);
 for i=1:len
    dtemp(i)=B(len+1-i);
    ind(i)=len+1-index(i);
    vtemp(:,ind(i))=v(:,i);
 end
 d=dtemp;
 v=vtemp;


%Normalization of eigenvectors
 for i=1:size(v,2)       %access each column
   kk=v(:,i);
   temp=sqrt(sum(kk.^2));
   v(:,i)=v(:,i)./temp;
end

%Eigenvectors of C matrix
u=[];
for i=1:size(v,2)
    temp=sqrt(d(i));
    u=[u (dbx*v(:,i))./temp];
end

%Normalization of eigenvectors
for i=1:size(u,2)
   kk=u(:,i);
   temp=sqrt(sum(kk.^2));
    u(:,i)=u(:,i)./temp;
end

toc

经过的时间是0.350561秒。

PART3

tic
% show eigenfaces;

for i=1:size(u,2)
    img=reshape(u(:,i),icol,irow);
    img=img';
    img=histeq(img,255);

end


% Find the weight of each face in the training set.
omega = [];
for h=1:size(dbx,2)
    WW=[];    
    for i=1:size(u,2)
        t = u(:,i)';    
        WeightOfImage = dot(t,dbx(:,h)');
        WW = [WW; WeightOfImage];
    end
    omega = [omega WW];
end

toc

经过的时间是0.991261秒。

PART4：

tic
% Acquire new image
% Note: the input image must have a bmp or jpg extension. 
%       It should have the same size as the ones in your training set. 
%       It should be placed on your desktop
ed_min=[];


srcFiles = dir('G:\newdatabase\*.jpg');  % the folder in which ur images exists
for b = 1 : length(srcFiles)
    filename = strcat('G:\newdatabase\',srcFiles(b).name);
    Imgdata = imread(filename);

        InputImage=Imgdata;

InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]);
temp=InImage;
me=mean(temp);
st=std(temp);
temp=(temp-me)*ustd/st+um;
NormImage = temp;
Difference = temp-m;

p = [];
aa=size(u,2);
for i = 1:aa
    pare = dot(NormImage,u(:,i));
    p = [p; pare];
end


InImWeight = [];
for i=1:size(u,2)
    t = u(:,i)';
    WeightOfInputImage = dot(t,Difference');
    InImWeight = [InImWeight; WeightOfInputImage];
end
noe=numel(InImWeight);


% Find Euclidean distance
e=[];
for i=1:size(omega,2)
    q = omega(:,i);
    DiffWeight = InImWeight-q;
    mag = norm(DiffWeight);
    e = [e mag];

end
format long

z(b,:)=InImWeight;

end
toc

经过的时间是36.058315秒。

PART5

tic
%disp(ed_min);
%z

IDX = kmeans(z,5);
clustercount=accumarray(IDX, ones(size(IDX)));

disp(clustercount);
toc

经过的时间是3.219573秒。

个人资料摘要快照

enter image description here

问题：PART4代码需要更多时间（36秒）。解决方案是什么？我只使用了50个训练图像，但我需要处理1200个训练图像。提高性能的方法是什么？

如何减少用于人脸识别的matlab代码（基于PCA）的运行时间？

0 个答案: