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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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// this list of conditions and the following disclaimer.
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// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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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. */
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