[OpenCV] Samples 14: kalman filter

Ref: http://blog.csdn.net/gdfsg/article/details/50904811

 

#include "opencv2/video/tracking.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;

//计算相对窗口的坐标值，因为坐标原点在左上角，所以sin前有个负号
static inline Point calcPoint(Point2f center, double R, double angle)
{
    return center + Point2f((float)cos(angle), (float)-sin(angle))*(float)R;
}

static void help()
{
    printf( "\nExamle of c calls to OpenCV's Kalman filter.\n"
"   Tracking of rotating point.\n"
"   Rotation speed is constant.\n"
"   Both state and measurements vectors are 1D (a point angle),\n"
"   Measurement is the real point angle + gaussian noise.\n"
"   The real and the estimated points are connected with yellow line segment,\n"
"   the real and the measured points are connected with red line segment.\n"
"   (if Kalman filter works correctly,\n"
"    the yellow segment should be shorter than the red one).\n"
            "\n"
"   Pressing any key (except ESC) will reset the tracking with a different speed.\n"
"   Pressing ESC will stop the program.\n"
            );
}

int main(int, char**)
{
    help();
    Mat img(500, 500, CV_8UC3);
    KalmanFilter KF(2, 1, 0);                                    //创建卡尔曼滤波器对象KF
    Mat state(2, 1, CV_32F);                                     //state(角度，△角度)
    Mat processNoise(2, 1, CV_32F);
    Mat measurement = Mat::zeros(1, 1, CV_32F);                 //定义测量值
    char code = (char)-1;

    for(;;)
    {
        //1.初始化
        randn( state, Scalar::all(0), Scalar::all(0.1) );        KF.transitionMatrix = *(Mat_<float>(2, 2) << 1, 1, 0, 1);  //转移矩阵A[1,1;0,1]    
        

        //将下面几个矩阵设置为对角阵
        setIdentity(KF.measurementMatrix);                             //测量矩阵H
        setIdentity(KF.processNoiseCov,     Scalar::all(1e-5));        //系统噪声方差矩阵Q
        setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));        //测量噪声方差矩阵R
        setIdentity(KF.errorCovPost,        Scalar::all(1));           //后验错误估计协方差矩阵P

        randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));          //x(0)初始化
        
        for(;;)
        {
            Point2f center(img.cols*0.5f, img.rows*0.5f);          //center图像中心点
            float R = img.cols/3.f;                                //半径
            double stateAngle = state.at<float>(0);                //跟踪点角度
            Point     statePt = calcPoint(center, R, stateAngle);  //跟踪点坐标statePt

            //2. 预测
            Mat prediction = KF.predict();                         //计算预测值，返回x'
            double predictAngle = prediction.at<float>(0);         //预测点的角度
            Point predictPt = calcPoint(center, R, predictAngle);  //预测点坐标predictPt


            //3.更新
//measurement是测量值
            randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));     //给measurement赋值N(0,R)的随机值

            // generate measurement
            measurement += KF.measurementMatrix*state;  //z = z + H*x;
            
            double measAngle = measurement.at<float>(0);
            Point     measPt = calcPoint(center, R, measAngle);

            // plot points
            //定义了画十字的方法，值得学习下
            #define drawCross( center, color, d )                                    \
                line( img, Point( center.x - d, center.y - d ),                      \
                           Point( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
                line( img, Point( center.x + d, center.y - d ),                      \
                           Point( center.x - d, center.y + d ), color, 1, CV_AA, 0 )

            img = Scalar::all(0);
            drawCross( statePt,   Scalar(255,255,255), 3 );
            drawCross( measPt,    Scalar(0,0,255),     3 );
            drawCross( predictPt, Scalar(0,255,0),     3 );
            line( img, statePt, measPt,    Scalar(0,0,255),   3, CV_AA, 0 );
            line( img, statePt, predictPt, Scalar(0,255,255), 3, CV_AA, 0 );


            //调用kalman这个类的correct方法得到加入观察值校正后的状态变量值矩阵
            if(theRNG().uniform(0,4) != 0)
                KF.correct(measurement);

            //不加噪声的话就是匀速圆周运动，加了点噪声类似匀速圆周运动，因为噪声的原因，运动方向可能会改变
            randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));   //vk
            state = KF.transitionMatrix*state + processNoise;   

            imshow( "Kalman", img );
            code = (char)waitKey(100);

            if( code > 0 )
                break;
        }
        if( code == 27 || code == 'q' || code == 'Q' )
            break;
    }

    return 0;
}

Result: