Opencv学习（人脸识别）

今天跑了下Opencv的人脸识别，简化了一下代码，不用在Dos下就可以操作了，当然，用的model说Opencv里面自带的。

haarcascade_frontalface_alt.xml

haarcascade_frontalface_alt2.xml

学习了下分类器使用，发现很简单

load进去后使用detectMultiScale就可以检测了。

但是好像和Opencv中的Hog.detectMultiScale不太一样，hog类和分类器的类没有实质的关系。

修改后的代码如下：

#include <fstream>
#include <string>
#include <cv.h>
#include <highgui.h>
#include <ml.h>
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include "cvaux.h"
#include <iostream>
#include <stdio.h>
#include <string.h>
#include <ctype.h>

using namespace cv;
using namespace std;

void detectAndDraw( Mat& img,
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale);
String cascadeName =
"D:\\My Documents\\Visual Studio 2008\\Projects\\webCam\\haarcascade_frontalface_alt.xml";
String nestedCascadeName =
"D:\\My Documents\\Visual Studio 2008\\Projects\\webCam\\haarcascade_eye_tree_eyeglasses.xml";

int main()
{

IplImage* aplace = 0;
IplImage* colorlaplace = 0;

IplImage* planes[3] = {0,0,0};
CvCapture* capture = 0;
Mat frame, frameCopy, image;
double scale = 1;
CascadeClassifier cascade, nestedCascade;
capture = cvCaptureFromCAM(0);

if(!cascade.load(cascadeName))
{
cout<<"Load 1 is false!"<<endl;
}
//if(!nestedCascade.load(nestedCascadeName))
//{
// cout<<"Load 2 is false!"<<endl;
//}

cvNamedWindow("Example",0);
for(;;)
{
IplImage* frame0 = 0;
int i;

frame0 = cvQueryFrame(capture);
frame = frame0;
if(!frame0)
{
break;
}
if( frame0->origin == IPL_ORIGIN_TL )
frame.copyTo( frameCopy );
else
flip( frame, frameCopy, 0 );

detectAndDraw( frameCopy, cascade, nestedCascade, scale );
//cvShowImage("Example",frame);
cvWaitKey(1);
}
cvReleaseCapture(&capture);
cvDestroyWindow("Example");
return 0;
}
void detectAndDraw( Mat& img,
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale)
{
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_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
t = (double)cvGetTickCount() - t;
printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
// Scalar color = colors[i%8];
Scalar color = colors[3];
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 );
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
{
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
}
cv::imshow( "Example", img );
}

上传一张截图：



运行环境：vs2008，Opencv版本为2.0

最近在研究人体识别，目标跟踪，有兴趣的同学一起讨论！