OpenCV/opencv/modules/video/test/test_kalman.cpp

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#include "test_precomp.hpp"
#include "opencv2/video/tracking.hpp"

namespace opencv_test { namespace {

class CV_KalmanTest : public cvtest::BaseTest
{
public:
    CV_KalmanTest();
protected:
    void run(int);
};


CV_KalmanTest::CV_KalmanTest()
{
}

void CV_KalmanTest::run( int )
{
    int code = cvtest::TS::OK;
    const int Dim = 7;
    const int Steps = 100;
    const double max_init = 1;
    const double max_noise = 0.1;

    const double EPSILON = 1.000;
    RNG& rng = ts->get_rng();
    int i, j;

    cv::Mat Sample(Dim,1,CV_32F);
    cv::Mat Temp(Dim,1,CV_32F);

    cv::KalmanFilter Kalm(Dim, Dim);
    Kalm.transitionMatrix = cv::Mat::eye(Dim, Dim, CV_32F);
    Kalm.measurementMatrix = cv::Mat::eye(Dim, Dim, CV_32F);
    Kalm.processNoiseCov = cv::Mat::eye(Dim, Dim, CV_32F);
    Kalm.errorCovPre = cv::Mat::eye(Dim, Dim, CV_32F);
    Kalm.errorCovPost = cv::Mat::eye(Dim, Dim, CV_32F);
    Kalm.measurementNoiseCov = cv::Mat::zeros(Dim, Dim, CV_32F);
    Kalm.statePre = cv::Mat::zeros(Dim, 1, CV_32F);
    Kalm.statePost = cv::Mat::zeros(Dim, 1, CV_32F);
    cvtest::randUni(rng, Sample, Scalar::all(-max_init), Scalar::all(max_init));
    Kalm.correct(Sample);
    for(i = 0; i<Steps; i++)
    {
        Kalm.predict();
        const Mat& Dyn = Kalm.transitionMatrix;
        for(j = 0; j<Dim; j++)
        {
            float t = 0;
            for(int k=0; k<Dim; k++)
            {
                t += Dyn.at<float>(j,k)*Sample.at<float>(k);
            }
            Temp.at<float>(j) = (float)(t+(cvtest::randReal(rng)*2-1)*max_noise);
        }
        Temp.copyTo(Sample);
        Kalm.correct(Temp);
    }

    Mat _state_post = Kalm.statePost;
    code = cvtest::cmpEps2( ts, Sample, _state_post, EPSILON, false, "The final estimated state" );

    if( code < 0 )
        ts->set_failed_test_info( code );
}

TEST(Video_Kalman, accuracy) { CV_KalmanTest test; test.safe_run(); }

}} // namespace
/* End of file. */