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@happynear
happynear committed Jun 13, 2015
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205 changes: 205 additions & 0 deletions FaceExtract.cpp
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#include <io.h>
#include <direct.h>
#include <string>
#include <iomanip>
#include <Windows.h>
#include <AtlBase.h>
#include <opencv2/opencv.hpp>

#include "stdafx.h"
//https://github.com/ShiqiYu/libfacedetection
#include "facedetect-dll.h"
//#pragma comment(lib,"libfacedetect.lib")


using namespace cv;
using namespace std;

void getFiles( string path, vector<pair<string,int> >& files ) {
//文件句柄
long hFile = 0;
//文件信息
struct _finddata_t fileinfo;

string p;

static int auto_label = 1;
int label=0;
p.clear();
FILE *fp = fopen(p.assign(path).append("/label.txt").c_str(),"r");
if(fp==NULL)
{
label = auto_label++;
cout<<path<<" label:"<<label<<endl;
}
else
{
fscanf(fp,"%d",&label);
if(auto_label<label) auto_label = label + 1;
fclose(fp);
}


if ((hFile = _findfirst(p.assign(path).append("/*").c_str(),&fileinfo)) != -1) {

do {
//如果是目录,迭代之
//如果不是,加入列表
if ((fileinfo.attrib & _A_SUBDIR)) {
if (strcmp(fileinfo.name,".") != 0 && strcmp(fileinfo.name,"..") != 0)
getFiles( p.assign(path).append("/").append(fileinfo.name), files );
} else {
if(strstr(fileinfo.name,"png")!=NULL||strstr(fileinfo.name,"bmp")!=NULL||strstr(fileinfo.name,"jpg")!=NULL||strstr(fileinfo.name,"tif")!=NULL)
files.push_back( make_pair(p.assign(path).append("\\").append(fileinfo.name),label) );
}
} while (_findnext( hFile, &fileinfo ) == 0);

_findclose(hFile);
}
}
//static void getFiles(LPCTSTR path, vector<pair<LPCTSTR,int> > &filesPathVector)
//{
// struct _tfinddata64_t c_file;
// intptr_t hFile;
// TCHAR root[MAX_PATH];
// string p;
// hFile=_tfindfirst64(p.assign(path).append("/*.*").c_str(),&c_file);
// if( hFile == -1)
// return;
// static int auto_label = 1;
// int label=0;
// p.clear();
// FILE *fp = fopen(p.assign(path).append("/label.txt").c_str(),"r");
// if(fp==NULL)
// {
// label = auto_label++;
// cout<<path<<" label:"<<label<<endl;
// }
// else
// {
// fscanf(fp,"%d",&label);
// if(auto_label<label) auto_label = label + 1;
// }
//
// //search all files recursively.
// do
// {
// if(_tcslen(c_file.name)==1&&c_file.name[0]==_T('.')
// ||_tcslen(c_file.name)==2&&c_file.name[0]==_T('.')&&c_file.name[1]==_T('.'))
// continue;
// TCHAR *fullPath =new TCHAR[MAX_PATH];
// _tcscpy(fullPath,path);
// _tcscat(fullPath,_T("\\"));
// _tcscat(fullPath,c_file.name);
// if(c_file.attrib&_A_SUBDIR)
// {
// getFiles(fullPath,filesPathVector);
// }
// else
// {
// if(strstr(c_file.name,"png")!=NULL||strstr(c_file.name,"bmp")!=NULL||strstr(c_file.name,"JPG")!=NULL
// ||strstr(c_file.name,"jpg")!=NULL||strstr(c_file.name,"tif")!=NULL)
// //if(strstr(c_file.name,"yml")!=NULL)
// filesPathVector.push_back(make_pair(fullPath,label));
// }
// }
// while( _tfindnext64( hFile, &c_file ) == 0);
// //close search handle
// _findclose(hFile);
//}

int _tmain(int argc, char* argv[])
{
char * filename = new char[100];
char folder[]="G:\\CASIA-WebFace";
vector<pair<string, int> > files;
string p;
FILE *fp = fopen(p.assign(folder).append("/list.txt").c_str(),"r");
if(fp==NULL)
{
fp = fopen(p.assign(folder).append("/list.txt").c_str(),"w");
getFiles(folder,files);
int fileSize=files.size();
for(int f=0;f<fileSize;f++){
fprintf(fp,"%s %d\n",files[f].first.c_str(),files[f].second);
}

}
else
{
char imgpath[256];
int label;
while(!feof(fp))
{
fscanf(fp,"%s %d",imgpath,&label);
files.push_back( make_pair(imgpath,label) );
}
}
fclose(fp);
int fileSize=files.size();
int * pResults = NULL;


for(int f=0;f<fileSize;f++){
Mat gray = imread(files[f].first,0);
imshow("original",gray);

/////////////////////////////////////////////
//// frontal face detection
//// it's fast, but cannot detect side view faces
////////////////////////////////////////////
////!!! The input image must be a gray one (single-channel)
////!!! DO NOT RELEASE pResults !!!
//pResults = facedetect_frontal((unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, gray.step,
//1.2f, 2, 24);
//printf("%d frontal faces detected.\n", (pResults ? *pResults : 0));
////print the detection results
//for(int i = 0; i < (pResults ? *pResults : 0); i++)
//{
// short * p = ((short*)(pResults+1))+6*i;
// int x = p[0];
// int y = p[1];
// int w = p[2];
// int h = p[3];
// int neighbors = p[4];
// imshow("face",gray(Rect(x,y,w,h)));
// printf("face_rect=[%d, %d, %d, %d], neighbors=%d\n", x,y,w,h,neighbors);
//}

///////////////////////////////////////////
// multiview face detection
// it can detection side view faces, but slower than the frontal face detection.
//////////////////////////////////////////
//!!! The input image must be a gray one (single-channel)
//!!! DO NOT RELEASE pResults !!!
pResults = facedetect_multiview((unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, gray.step,
1.2f, 4, 24);
printf("%d faces detected.\n", (pResults ? *pResults : 0));

//print the detection results
for(int i = 0; i < (pResults ? *pResults : 0); i++)
{
short * p = ((short*)(pResults+1))+6*i;
int x = p[0];
int y = p[1];
int w = p[2];
int h = p[3];
int neighbors = p[4];
int angle = p[5];
imshow("face",gray(Rect(x,y,w,h)));
printf("face_rect=[%d, %d, %d, %d], neighbors=%d, angle=%d\n", x,y,w,h,neighbors, angle);
//waitKey(1);
}
if(pResults==NULL)
{
waitKey(0);
}
else
{
waitKey(0);
}
}

return 0;
}

137 changes: 137 additions & 0 deletions JointBayesian.m
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function [mappedX, mapping] = JointBayesian(X, labels)
%LDA Perform the LDA algorithm
%
% [mappedX, mapping] = lda(X, labels, no_dims)
%
% The function runs LDA on a set of datapoints X. The variable
% no_dims sets the number of dimensions of the feature points in the
% embedded feature space (no_dims >= 1, default = 2). The maximum number
% for no_dims is the number of classes in your data minus 1.
% The function returns the coordinates of the low-dimensional data in
% mappedX. Furthermore, it returns information on the mapping in mapping.
%
%

% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of the original author.
%
% (C) Laurens van der Maaten, Delft University of Technology
% Make sure data is zero mean
% mapping.mean = mean(X, 1);
% [COEFF,SCORE] = princomp(X,'econ');
% X = SCORE(:,1:400);
% X = bsxfun(@minus,X,mapping.mean);
m = length(labels);
n = size(X,2);

% Make sure labels are nice
[classes, bar, labels] = unique(labels);
nc = length(classes);

% Intialize Sw
Sw = zeros(size(X, 2), size(X, 2));

cur = {};
withinCount = 0;
numberBuff = zeros(1000,1);
% numberInvert = zeros(1000,1);
maxNumberInOneClass = [];
for i=1:nc
% Get all instances with class i
cur{i} = X(labels == i,:);
if size(cur{i},1)>1
withinCount = withinCount + size(cur{i},1);
end;
if numberBuff(size(cur{i},1)) ==0
numberBuff(size(cur{i},1)) = 1;
maxNumberInOneClass = [maxNumberInOneClass;size(cur{i},1)];
end;
end;
disp([nc withinCount]);
fprintf('prepare done, maxNumberInOneClass=%d.\n',length(maxNumberInOneClass));
tic;
u = zeros(n,nc);
ep = zeros(n,withinCount);
nowp = 1;
% Sum over classes
for i=1:nc
% Update within-class scatter
u(:,i) = mean(cur{i},1)';

if size(cur{i},1)>1
ep(:,nowp:nowp+ size(cur{i}, 1)-1) = bsxfun(@minus,cur{i}',u(:,i));
nowp = nowp + size(cur{i}, 1);
% C = cov(cur{i});
% p = size(cur{i}, 1) / withinCount;
% Sw = Sw + (p * C);
end;
end;
Su = cov(u');
Sw = cov(ep');

% Su = u*u'/nc;
% Sw = ep*ep'/withinCount;
% Su = cov(rand(n,n));
% Sw = cov(rand(5*n,n));
fprintf('LDA matrix done.\n');
toc;
% F = inv(Sw);
% mapping.Su = Su;
% mapping.Sw = Sw;
% mapping.G = -1 .* (2 * Su + Sw) \ Su / Sw;
% mapping.A = inv(Su + Sw) - (F + mapping.G);
% mappedX = X;
% end

oldSw = Sw;
% Gs = cell(maxNumberInOneClass,1);
SuFG = cell(1000,1);
SwG = cell(1000,1);

for l=1:500
% tic;
F = inv(Sw);
ep =zeros(n,m);
nowp = 1;
for g = 1:1000
if numberBuff(g)==1
G = -1 .* (g .* Su + Sw) \ Su / Sw;
SuFG{g} = Su * (F + g.*G);
SwG{g} = Sw*G;
end;
end;
for i=1:nc
nnc = size(cur{i}, 1);
% G = Gs{nnc};
u(:,i) = sum(SuFG{nnc} * cur{i}',2);
ep(:,nowp:nowp+ size(cur{i}, 1)-1) = bsxfun(@plus,cur{i}',sum(SwG{nnc}*cur{i}',2));
nowp = nowp+ nnc;
end;
Su = cov(u');
Sw = cov(ep');
% Su = u*u'/nc;
% Sw = ep*ep'/withinCount;
fprintf('%d %f\n',l,norm(Sw - oldSw)/norm(Sw));
% toc;
if norm(Sw - oldSw)/norm(Sw)<1e-6
break;
end;
oldSw = Sw;
end;
F = inv(Sw);
mapping.G = -1 .* (2 * Su + Sw) \ Su / Sw;
mapping.A = inv(Su + Sw) - (F + mapping.G);
mapping.Sw = Sw;
mapping.Su = Su;
% mapping.U = chol(-G,'upper');
% mapping.COEFF = COEFF;
% mapping.y = mapping.U * X';
mapping.c = zeros(m,1);
for i = 1:m
mapping.c(i) = X(i,:) * mapping.A * X(i,:)';
end;
mappedX = X;

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