1 函数介绍

OpenCV自带的CascadeClassifier这个类下的detectMultiScale函数,其检测效果并不是很好
void CascadeClassifier::detectMultiScale(InputArray image, vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
总共有7个参数,分别是

第一个参数image:  要检测的图片,一般为灰度图;
第二个参数objects:  Rect型的容器,存放所有检测出的人脸,每个人脸是一个矩形;
第三个参数scaleFactor:  缩放因子,对图片进行缩放,默认为1.1;
第四个参数minNeighbors: 最小邻居数,默认为3;
第五个参数flags:  兼容老版本的一个参数,在3.0版本中没用处。默认为0;
第六个参数minSize: 最小尺寸,检测出的人脸最小尺寸;
第七个参数maxSize: 最大尺寸,检测出的人脸最大尺寸;

1.1 静态图片上的人脸检测

1.1.1 示例代码


\#include "opencv2/core/core.hpp" #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> #include <stdio.h> using namespace std; using namespace cv; string face_cascade_name = "D:\Program Files\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml"; CascadeClassifier face_cascade; void detectAndDisplay( Mat frame ); int main( int argc, char** argv ){ Mat image; image =imread("E:/snsd.jpg",1); //当前工程的image目录下的mm.jpg文件,注意目录符号 if( !face_cascade.load( face_cascade_name ) ){ cout<<"xml文件加载失败"<<endl; return -1; } detectAndDisplay(image); //调用人脸检测函数 waitKey(0); } void detectAndDisplay( Mat face ){ std::vector<Rect> faces; Mat face_gray; cvtColor( face, face_gray, CV_BGR2GRAY ); equalizeHist( face_gray, face_gray ); face_cascade.detectMultiScale( face_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(1, 1) ); for( int i = 0; i < faces.size(); i++ ){ Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 ); ellipse( face, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 0), 2,7, 0 ); } imshow("静态图片人脸识别", face ); }

或者


\#include "opencv2\opencv.hpp" #include <iostream> using namespace std; using namespace cv; int main() { string xmlPath="D:\Program Files\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml"; CascadeClassifier ccf; //创建分类器对象 Mat img=imread("E:/snsd.jpg"); if(!ccf.load(xmlPath)) //加载训练文件 { cout<<"不能加载指定的xml文件"<<endl; return 0; } vector<Rect> faces; //创建一个容器保存检测出来的脸 Mat gray; cvtColor(img,gray,CV_BGR2GRAY); //转换成灰度图,因为harr特征从灰度图中提取 equalizeHist(gray,gray); //直方图均衡行 ccf.detectMultiScale(gray,faces,1.1,3,0,Size(10,10),Size(100,100)); //检测人脸 for(vector<Rect>::const_iterator iter=faces.begin();iter!=faces.end();iter++) { rectangle(img,*iter,Scalar(0,0,255),2,8); //画出脸部矩形 } imshow("faces",img); waitKey(0); return 1; }

1.1.2 结果示例

OpenCV - 静态图片人脸检测和摄像头人脸检测-StubbornHuang Blog

从检测的结果可以看出,有些人脸没有检测出来,或者是检测出来有位置错误。

1.2 摄像头人脸检测

1.2.1 示例代码

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
//命名空间
using namespace std;
using namespace cv;
//函数声明
void detectAndDisplay( Mat frame );
//全局变量
//-- Note, either copy these two files from opencv/data/haarscascades to your current folder, or change these locations
string face_cascade_name = "D:\\Program Files\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
string eyes_cascade_name = "D:\\Program Files\\opencv\\sources\\data\\haarcascades\\haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
int main( void )
{
CvCapture* capture;
Mat frame;
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
//-- 2. Read the video stream
capture = cvCaptureFromCAM( 0);
if( capture )
{
for(;;)
{
frame = cvQueryFrame( capture );
//-- 3. Apply the classifier to the frame
if( !frame.empty() )
{ detectAndDisplay( frame ); }
else
{ printf(" --(!) No captured frame -- Break!"); break; }
int c = waitKey(10);
if( (char)c == 'c' ) { break; }
}
}
return 0;
}
void detectAndDisplay( Mat frame )
{
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, CV_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
for( size_t i = 0; i < faces.size(); i++ )
{
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 );
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
for( size_t j = 0; j < eyes.size(); j++ )
{
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 );
}
}
//-- Show what you got
imshow( window_name, frame );
}

1.2.2 结果示例

OpenCV - 静态图片人脸检测和摄像头人脸检测-StubbornHuang Blog