在工作目录下建立/pic文件夹放入测试图片，建立/positive文件夹，放入自己的训练数据（我使用的是自己的相片中截获好的头像）建立list.txt，在里面写入pic/文件名以回车隔开，即可。
在工作目录下建立/pic文件夹放入测试图片，建立/positive文件夹，放入自己的训练数据（我使用的是自己的相片中截获好的头像）建立list.txt，在里面写入pic/文件名以回车隔开，即可。
#define CV_NO_BACKWARD_COMPATIBILITY
#include "cv.h"
#include "highgui.h"
#include <iostream>
#include <cstdio>
#include <string.h>
#ifdef _EiC
#define WIN32
#endif
using namespace std;
using namespace cv;
void detectAndDraw( Mat& img,
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale,int picid);
int compressPCA(const Mat& pcaset, int maxComponents,
const Mat& testset, Mat& compressed);
String cascadeName ="haarcascade_frontalface_alt.xml";
String nestedCascadeName ="haarcascade_eye_tree_eyeglasses.xml";
int WINDOWID=1;
int main( int argc, const char** argv )
{
Mat frame, frameCopy, image;
const String scaleOpt = "--scale=";
size_t scaleOptLen = scaleOpt.length();
const String cascadeOpt = "--cascade=";
size_t cascadeOptLen = cascadeOpt.length();
const String nestedCascadeOpt = "--nested-cascade";
size_t nestedCascadeOptLen = nestedCascadeOpt.length();
String inputName;
CascadeClassifier cascade, nestedCascade;
double scale = 1;
for( int i = 1; i < argc; i++ )
{
if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 )
cascadeName.assign( argv[i] + cascadeOptLen );
else if( nestedCascadeOpt.compare( 0, nestedCascadeOptLen, argv[i], nestedCascadeOptLen ) == 0 )
{
if( argv[i][nestedCascadeOpt.length()] == '=' )
nestedCascadeName.assign( argv[i] + nestedCascadeOpt.length() + 1 );
if( !nestedCascade.load( nestedCascadeName ) )
cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
}
else if( scaleOpt.compare( 0, scaleOptLen, argv[i], scaleOptLen ) == 0 )
{
if( !sscanf( argv[i] + scaleOpt.length(), "%lf", &scale ) || scale < 1 )
scale = 1;
}
else if( argv[i][0] == '-' )
{
cerr << "WARNING: Unknown option %s" << argv[i] << endl;
}
else
inputName.assign( argv[i] );
}
if( !cascade.load( cascadeName ) )
{
cerr << "ERROR: Could not load classifier cascade" << endl;
cerr << "Usage: facedetect [--cascade=\"<cascade_path>\"]\n"
" [--nested-cascade[=\"nested_cascade_path\"]]\n"
" [--scale[=<image scale>\n"
" [filename|camera_index]\n" ;
return -1;
}
image = imread( inputName, 1 );
if( !image.empty() )
{
detectAndDraw( image, cascade, nestedCascade, scale,0 );
waitKey(0);
}
else if( !inputName.empty() )
{
FILE* f = fopen( inputName.c_str(), "rt" );
if( f )
{
char buf[1000+1];
int picid=1;
printf("################################################################\n");
printf("\n");
while( fgets( buf, 1000, f ) )
{
int len = (int)strlen(buf), c;
while( len > 0 && isspace(buf[len-1]) )
len--;
buf[len] = '\0';
cout << "file " << buf << endl;
image = imread( buf, 1 );
if( !image.empty() )
{
detectAndDraw( image, cascade, nestedCascade, scale,picid );
picid++;
printf("\n");
printf("################################################################\n");
printf("\n");
printf("图片编号: %d \n",picid);
}
}
fclose(f);
waitKey(0);
}
}
return 0;
}
void detectAndDraw( Mat& img,
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale, int picid)
{
int i = 0;
double t = 0;
vector<Rect> faces;
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
cvtColor( img, gray, CV_BGR2GRAY );
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
t = (double)cvGetTickCount();
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
t = (double)cvGetTickCount() - t;
printf( "检测用时为： %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
int index=1;
char facenum[100];
char filenum[100];
char filename[255];
//pca算法处理图像开始
Mat p1,p2,p3,p4,compressed,UnknowFaceMat;
p1=cv::imread("positive/1.jpg",0);
p2=cv::imread("positive/2.jpg",0);
p3=cv::imread("positive/3.jpg",0);
p4=cv::imread("positive/4.jpg",0);
float dataArr[40000];
int start=0;
for(int i=0;i<p1.rows;i++)
{
for(int j=0;j<p1.cols;j++)
{
dataArr[start]=(float) p1.at<uchar>(i,j);
start++;
}
}
for(int i=0;i<p2.rows;i++)
{
for(int j=0;j<p2.cols;j++)
{
dataArr[start]=(float)p2.at<uchar>(i,j);
start++;
}
}
for(int i=0;i<p3.rows;i++)
{
for(int j=0;j<p3.cols;j++)
{
dataArr[start]=(float)p3.at<uchar>(i,j);
start++;
}
}
for(int i=0;i<p4.rows;i++)
{
for(int j=0;j<p4.cols;j++)
{
dataArr[start]=(float)p4.at<uchar>(i,j);
start++;
}
}
Mat positiveMat(4,10000,CV_32FC1,dataArr);
//pca算法结束
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI,tempMat;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
// circle( img, center, radius, color, 3, 8, 0 );
smallImgROI = smallImg(*r);
tempMat = smallImg(*r);
itoa(picid,filenum,10);
itoa(index,facenum,10);
strcpy(filename,"faces/");
strcat(filename,filenum);
strcat(filename,"_");
strcat(filename,facenum);
strcat(filename,".jpg");
cv::Mat graysmallface(Size(100,100),CV_8UC1);
cv::resize(tempMat,graysmallface,graysmallface.size(),0,0);
cv::imwrite(filename,graysmallface);
printf("检测到人脸，写入如下文件: %s\n", filename);
index++;
UnknowFaceMat=graysmallface;
float UnknowDataArr[10000];
start=0;
for(int i=0;i<UnknowFaceMat.rows;i++)
{
for(int j=0;j<UnknowFaceMat.cols;j++)
{
UnknowDataArr[start]=(float) UnknowFaceMat.at<uchar>(i,j);
start++;
}
}
Mat negativeMat(1,10000,CV_32FC1,UnknowDataArr);
int result=compressPCA(positiveMat,3,negativeMat,compressed);
if(result==-1)
{
continue;
}
//--use pca end---
if(result==1)
{
printf("检测到指定的人！\n点任意键继续\n");
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
circle( img, center, radius, color, 2, 8, 0 );
char windowname[255];
char wnum[10];
itoa(WINDOWID,wnum,10);
strcpy(windowname,"Match_");
strcat(windowname,wnum);
cv::Mat tempimg(Size(800,600),CV_8UC3);
cv::resize(img,tempimg,tempimg.size(),0,0);
cv::imshow(windowname,img);
WINDOWID++;
waitKey(0);
}
}
}
int compressPCA(const Mat& pcaset, int maxComponents,
const Mat& testset, Mat& compressed)
{
PCA pca(pcaset, // pass the data
Mat(), // we do not have a pre-computed mean vector,
// so let the PCA engine to compute it
CV_PCA_DATA_AS_ROW, // indicate that the vectors
// are stored as matrix rows
// (use CV_PCA_DATA_AS_COL if the vectors are
// the matrix columns)
maxComponents // specify, how many principal components to retain
);
// if there is no test data, just return the computed basis, ready-to-use
//if( !testset.data )
//return pca;
CV_Assert( testset.cols == pcaset.cols );
compressed.create(testset.rows, maxComponents, testset.type());
Mat reconstructed;
for( int i = 0; i < testset.rows; i++ )
{
Mat vec = testset.row(i), coeffs = compressed.row(i);
// compress the vector, the result will be stored
// in the i-th row of the output matrix
pca.project(vec, coeffs);
// and then reconstruct it
pca.backProject(coeffs, reconstructed);
// and measure the error
printf("%d. 偏移量为： %g\n", i+1, norm(vec, reconstructed, NORM_L2));
if(norm(vec, reconstructed, NORM_L2)>2000)
{
return -1;
}
}
return 1;
}源代码下载：
demo
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