OpenCV教程(41) 人脸特征检测

在OpenCV中，自带着Harr分类器人脸特征训练的文件，利用这些文件，我们可以很方面的进行人脸，眼睛，鼻子，表情等的检测。

人脸特征文件目录： ../opencv2.46/opencv/data/haarcascades

人脸检测Harr分类器的介绍：http://www.cnblogs.com/mikewolf2002/p/3437883.html

分类器的训练步骤：http://note.sonots.com/SciSoftware/haartraining.html

本文中，我们通过代码了解一下在OpenCV中如何通过harr分类器进行人脸特征检测。

#include <opencv2/core/core.hpp>
#include <opencv2/highgui//highgui.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <string>
#include <vector>
using namespace std;

int main()
    {

    cv::CascadeClassifier mFaceDetector;
    cv::CascadeClassifier mEyeDetector;
    cv::CascadeClassifier mMouthDetector;
    cv::CascadeClassifier mNoseDetector;
    //载入四个人脸特征分类器文件，可以从opencv的安装目录中找到
    if( mFaceDetector.empty() )
        mFaceDetector.load( "haarcascade_frontalface_default.xml" );
    if( mEyeDetector.empty() )
        mEyeDetector.load( "haarcascade_mcs_eyepair_big.xml" );
    if( mNoseDetector.empty() )
        mNoseDetector.load("haarcascade_mcs_nose.xml" );
    if( mMouthDetector.empty() )
        mMouthDetector.load( "haarcascade_mcs_mouth.xml" );

    //打开视频文件
    //cv::VideoCapture capture("bike.avi");
    //0 open default camera
    cv::VideoCapture capture(0);
    //检查视频是否打开
    if(!capture.isOpened())
        return 1;

    // 得到帧率
    double rate= capture.get(CV_CAP_PROP_FPS);
    bool stop(false);
    cv::Mat frame; // 现在的视频帧
    cv::Mat mElabImage;//备份frame图像

    cv::namedWindow("Extracted Frame");

    // 两帧之间的间隔时间
    int delay= 1000/rate;
    // 循环播放所有的帧
    while (!stop) {
        // 读下一帧
        if (!capture.read(frame))
            break;
        frame.copyTo( mElabImage );
        //检测脸
        //缩放因子
        float scaleFactor = 3.0f; 
        vector< cv::Rect > faceVec;
        mFaceDetector.detectMultiScale( frame, faceVec, scaleFactor );
        int i, j;
        for(  i=0; i<faceVec.size(); i++ )
            {
            cv::rectangle( mElabImage, faceVec[i], CV_RGB(255,0,0), 2 );
            cv::Mat face = frame( faceVec[i] );
            //检测眼睛
            vector< cv::Rect > eyeVec;
            mEyeDetector.detectMultiScale( face, eyeVec );

            for( j=0; j<eyeVec.size(); j++ )
                {
                cv::Rect rect = eyeVec[j];
                rect.x += faceVec[i].x;
                rect.y += faceVec[i].y;

                cv::rectangle( mElabImage, rect, CV_RGB(0,255,0), 2 );
                }
            //检测鼻子
            vector< cv::Rect > noseVec;

            mNoseDetector.detectMultiScale( face, noseVec, 3 );

            for( j=0; j<noseVec.size(); j++ )
                {
                cv::Rect rect = noseVec[j];
                rect.x += faceVec[i].x;
                rect.y += faceVec[i].y;

                cv::rectangle( mElabImage, rect, CV_RGB(0,0,255), 2 );
                }

            //检测嘴巴
            vector< cv::Rect > mouthVec;
            cv::Rect halfRect = faceVec[i];
            halfRect.height /= 2;
            halfRect.y += halfRect.height;

            cv::Mat halfFace = frame( halfRect );

            mMouthDetector.detectMultiScale( halfFace, mouthVec, 3 );

            for(  j=0; j<mouthVec.size(); j++ )
                {
                cv::Rect rect = mouthVec[j];
                rect.x += halfRect.x;
                rect.y += halfRect.y;

                cv::rectangle( mElabImage, rect, CV_RGB(255,255,255), 2 );
                }
            }


        //在窗口中显示图像
        cv::imshow("Extracted Frame",mElabImage);
        // 按任意键停止视频播放
        //if (cv::waitKey(delay)>=0)
        //    stop= true;
        cv::waitKey(20);
        }
    // 关闭视频文件
    capture.release();
    return 0;
    }