haarcascade_eye.xml
haarcascade_frontalface_alt2.xml
放在程序目录下：
#include <opencv\cv.h>
#include <opencv\highgui.h>
#include <opencv\cxcore.h>
#include <stdio.h>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/ml/ml.hpp"
#include <iostream>
using namespace std;
using namespace cv;
void detectAndDraw( Mat& img,
CascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale);
String cascadeName = "haarcascade_frontalface_alt2.xml";//人脸的训练数据
//String nestedCascadeName = "./haarcascade_eye_tree_eyeglasses.xml";//人眼的训练数据
String nestedCascadeName = "haarcascade_eye.xml";//人眼的训练数据
int main( int argc, const char** argv )
{
Mat image;
CascadeClassifier cascade, nestedCascade;//创建级联分类器对象
double scale = 1.3;
image = imread( "lena.jpg", 1 );//读入lena图片
//image = imread("people_with_hands.png",1);
namedWindow( "result", 1 );//opencv2.0以后用namedWindow函数会自动销毁窗口
if( !cascade.load( cascadeName ) )//从指定的文件目录中加载级联分类器
{
cerr << "ERROR: Could not load classifier cascade" << endl;
return 0;
}
if( !nestedCascade.load( nestedCascadeName ) )
{
cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
return 0;
}
if( !image.empty() )//读取图片数据不能为空
{
detectAndDraw( image, cascade, nestedCascade, scale );
waitKey(0);
}
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)} ;//用不同的颜色表示不同的人脸
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );//将图片缩小，加快检测速度
cvtColor( img, gray, CV_BGR2GRAY );//因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );//将尺寸缩小到1/scale,用线性插值
equalizeHist( smallImg, smallImg );//直方图均衡
t = (double)cvGetTickCount();//用来计算算法执行时间
//检测人脸
//detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，faces表示检测到的人脸目标序列，1.1表示
//每次图像尺寸减小的比例为1.1，2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大
//小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(30, 30)为目标的
//最小最大尺寸
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;//相减为算法执行的时间
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];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);//还原成原来的大小
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 );
//检测人眼，在每幅人脸图上画出人眼
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
//和上面的函数功能一样
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 );//将眼睛也画出来，和对应人脸的图形是一样的
}
}
cv::imshow( "result", img );
}
效果：
