samples/cpp/kalman.cpp

使用标准卡尔曼滤波器的示例

#include "opencv2/video/tracking.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/cvdef.h"
#include <stdio.h>
 
using namespace cv;
 
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( "\nOpenCV卡尔曼滤波器C语言调用的示例。\n"
" 跟踪旋转点。\n"
" 点在一个圆中移动，并由一个 1D 状态描述。\n"
" state_k+1 = state_k + speed + 过程噪声 N(0, 1e-5)\n"
" 速度是恒定的。\n"
" 状态和测量向量都是 1D 的（一个点角度），\n"
" 测量是真实状态 + 高斯噪声 N(0, 1e-1)。\n"
" 真实点和测量点用红色线段连接，\n"
" 真实点和估计点用黄色线段连接，\n"
" 真实点和校正后的估计点用绿色线段连接。\n"
"（如果卡尔曼滤波器工作正常，\n"
" 黄色线段应该比红色线段短，\n"
" 绿色线段应该比黄色线段短。）"
            "\n"
" 按下任意键（除了 ESC）将重置跟踪。\n"
" 按下 ESC 将停止程序。\n"
            );
}
 
int main(int, char**)
{
help();
    Mat img(500, 500, CV_8UC3);
    KalmanFilter KF(2, 1, 0);
    Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
    Mat processNoise(2, 1, CV_32F);
    Mat measurement = Mat::zeros(1, 1, CV_32F);
    char code = (char)-1;
 
    for(;;)
    {
img = Scalar::all(0);
state.at<float>(0) = 0.0f;
state.at<float>(1) = 2.f * (float)CV_PI / 6;
KF.transitionMatrix = (Mat_<float>(2, 2) << 1, 1, 0, 1);
 
        setIdentity(KF.measurementMatrix);
        setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
        setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
        setIdentity(KF.errorCovPost, Scalar::all(1));
 
        randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));
 
        for(;;)
        {
            Point2f center(img.cols*0.5f, img.rows*0.5f);
            float R = img.cols/3.f;
            double stateAngle = state.at<float>(0);
            Point statePt = calcPoint(center, R, stateAngle);
 
            Mat prediction = KF.predict();
            double predictAngle = prediction.at<float>(0);
            Point predictPt = calcPoint(center, R, predictAngle);
 
            // 生成测量
            randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));
measurement += KF.measurementMatrix*state;
 
            double measAngle = measurement.at<float>(0);
            Point measPt = calcPoint(center, R, measAngle);
 
            // 基于测量校正状态估计
            // 更新 statePost & errorCovPost
KF.correct(measurement);
            double improvedAngle = KF.statePost.at<float>(0);
            Point improvedPt = calcPoint(center, R, improvedAngle);
 
            // 绘制点
img = img * 0.2;
            drawMarker(img, measPt, Scalar(0, 0, 255), cv::MARKER_SQUARE, 5, 2);
            drawMarker(img, predictPt, Scalar(0, 255, 255), cv::MARKER_SQUARE, 5, 2);
            drawMarker(img, improvedPt, Scalar(0, 255, 0), cv::MARKER_SQUARE, 5, 2);
            drawMarker(img, statePt, Scalar(255, 255, 255), cv::MARKER_STAR, 10, 1);
            // 预测一步
            Mat test = Mat(KF.transitionMatrix*KF.statePost);
            drawMarker(img, calcPoint(center, R, Mat(KF.transitionMatrix*KF.statePost).at<float>(0)),
                       Scalar(255, 255, 0), cv::MARKER_SQUARE, 12, 1);
 
            line( img, statePt, measPt, Scalar(0,0,255), 1, LINE_AA, 0 );
            line( img, statePt, predictPt, Scalar(0,255,255), 1, LINE_AA, 0 );
            line( img, statePt, improvedPt, Scalar(0,255,0), 1, LINE_AA, 0 );
 
 
            randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
state = KF.transitionMatrix*state + processNoise;
 
            imshow( "Kalman", img );
code = (char)waitKey(1000);
 
            if( code > 0 )
                break;
        }
        if( code == 27 || code == 'q' || code == 'Q' )
            break;
    }
 
    return 0;
}