卡尔曼滤波+opencv 实现人脸跟踪 小demo

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/tracking.hpp"
#include 
#include 

using namespace std;
using namespace cv;

/** 函数声明 */
void detectAndDisplay(Mat& frame);

/** 全局变量 */
string face_cascade_name = "haarcascade_frontalface_alt.xml";
//string eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
//CascadeClassifier eyes_cascade;
string window_name = "Face detection with Kalman";
RNG rng(12345);
struct face{
    Point leftTop=0;
    int width=0;
    int height=0;
};
face preFace;
/** @主函数 */
int main()
{
    //kalman参数设置
    
    int stateNum = 4;
    int measureNum = 2;
    KalmanFilter KF(stateNum, measureNum, 0);
    //Mat processNoise(stateNum, 1, CV_32F);
    Mat measurement = Mat::zeros(measureNum, 1, CV_32F);
    KF.transitionMatrix = *(Mat_(stateNum, stateNum) << 1, 0, 1, 0,//A 状态转移矩阵
        0, 1, 0, 1,
        0, 0, 1, 0,
        0, 0, 0, 1);
    //这里没有设置控制矩阵B，默认为零
    setIdentity(KF.measurementMatrix);//H=[1,0,0,0;0,1,0,0] 测量矩阵
    setIdentity(KF.processNoiseCov, Scalar::all(1e-5));//Q高斯白噪声，单位阵
    setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));//R高斯白噪声，单位阵
    setIdentity(KF.errorCovPost, Scalar::all(1));//P后验误差估计协方差矩阵，初始化为单位阵
    randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));//初始化状态为随机值

    //读入视频
    
    if (!face_cascade.load(face_cascade_name)){ cout << "--(!)Error loading\n" << endl; };
    Mat frame, frame2;
    VideoCapture cap;
    cap.open("me1.mp4");
    //cap.open("me2.mp4");
    //cap.open("me3.mp4");
    while (true){
        for (int i = 0; i < 1; i++){
            cap >> frame;
        }
        if (!frame.empty())
        {
            resize(frame, frame2, Size(), 0.5, 0.5, INTER_LINEAR);
            Mat prediction = KF.predict();
            Point predict_pt = Point((int)prediction.at(0), (int)prediction.at(1));
            detectAndDisplay(frame2);
            measurement.at(0) = (float)preFace.leftTop.x;
            measurement.at(1) = (float)preFace.leftTop.y;
            KF.correct(measurement);
            //画卡尔曼的效果
            Point center(predict_pt.x + preFace.width*0.5, predict_pt.y + preFace.height*0.5);
            ellipse(frame2, center, Size(preFace.width*0.3, preFace.height*0.3), 0, 0, 360, Scalar(0, 0, 255), 4, 8, 0);
            circle(frame2, center, 3, Scalar(0, 0, 255), -1);
            imshow(window_name, frame2);
            waitKey(1);
        }
        else
        {
            printf(" --(!) No frame -- Break!");
            break; 
        }
    }
    return 0;
}

/** @函数 detectAndDisplay */
void detectAndDisplay(Mat& frame)
{
    std::vector faces;
    Mat frame_gray;
    int Max_area=0;
    int faceID=0;

    cvtColor(frame, frame_gray, CV_BGR2GRAY);
    equalizeHist(frame_gray, frame_gray);

    //-- 多尺寸检测人脸
    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));
    //找出最大的脸，可以去除不是脸的误检，这些误检一般比较小
    for (int i = 0; i < faces.size(); i++)
    {
        if ((int)(faces[i].width*faces[i].height) > Max_area){
            Max_area =(int) faces[i].width*faces[i].height;
            faceID=i;
        }    
    }

    if (faces.size() > 0)//必须是检测到脸才绘制当前人脸圆圈，并且只能绘制最大的脸
    {
        preFace.leftTop.x = faces[faceID].x;
        preFace.leftTop.y = faces[faceID].y;
        preFace.height = faces[faceID].height;
        preFace.width = faces[faceID].width;
        Point center(faces[faceID].x + faces[faceID].width*0.5, faces[faceID].y + faces[faceID].height*0.5);
        ellipse(frame, center, Size(faces[faceID].width*0.5, faces[faceID].height*0.5), 0, 0, 360, Scalar(0, 255, 0), 1, 8, 0);
        circle(frame, center, 3, Scalar(0, 255,0), -1);
    }
    else{//没检测到人脸绘制之前的人脸
        Point center(preFace.leftTop.x + preFace.width*0.5, preFace.leftTop.y + preFace.height*0.5);
        ellipse(frame, center, Size(preFace.width*0.5, preFace.height*0.5), 0, 0, 360, Scalar(0, 255, 0), 1, 8, 0);
        circle(frame, center, 3, Scalar(0, 255, 0), -1);
    }
    
    
}