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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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using namespace cv;
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class CV_KalmanTest : public cvtest::BaseTest
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{
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public:
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CV_KalmanTest();
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protected:
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void run(int);
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};
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CV_KalmanTest::CV_KalmanTest()
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{
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}
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void CV_KalmanTest::run( int )
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{
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int code = cvtest::TS::OK;
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const int Dim = 7;
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const int Steps = 100;
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const double max_init = 1;
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const double max_noise = 0.1;
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const double EPSILON = 1.000;
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RNG& rng = ts->get_rng();
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CvKalman* Kalm;
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int i, j;
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CvMat* Sample = cvCreateMat(Dim,1,CV_32F);
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CvMat* Temp = cvCreateMat(Dim,1,CV_32F);
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Kalm = cvCreateKalman(Dim, Dim);
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CvMat Dyn = cvMat(Dim,Dim,CV_32F,Kalm->DynamMatr);
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CvMat Mes = cvMat(Dim,Dim,CV_32F,Kalm->MeasurementMatr);
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CvMat PNC = cvMat(Dim,Dim,CV_32F,Kalm->PNCovariance);
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CvMat MNC = cvMat(Dim,Dim,CV_32F,Kalm->MNCovariance);
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CvMat PriErr = cvMat(Dim,Dim,CV_32F,Kalm->PriorErrorCovariance);
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CvMat PostErr = cvMat(Dim,Dim,CV_32F,Kalm->PosterErrorCovariance);
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CvMat PriState = cvMat(Dim,1,CV_32F,Kalm->PriorState);
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CvMat PostState = cvMat(Dim,1,CV_32F,Kalm->PosterState);
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cvSetIdentity(&PNC);
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cvSetIdentity(&PriErr);
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cvSetIdentity(&PostErr);
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cvSetZero(&MNC);
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cvSetZero(&PriState);
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cvSetZero(&PostState);
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cvSetIdentity(&Mes);
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cvSetIdentity(&Dyn);
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Mat _Sample = cvarrToMat(Sample);
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cvtest::randUni(rng, _Sample, cvScalarAll(-max_init), cvScalarAll(max_init));
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cvKalmanCorrect(Kalm, Sample);
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for(i = 0; i<Steps; i++)
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{
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cvKalmanPredict(Kalm);
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for(j = 0; j<Dim; j++)
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{
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float t = 0;
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for(int k=0; k<Dim; k++)
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{
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t += Dyn.data.fl[j*Dim+k]*Sample->data.fl[k];
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}
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Temp->data.fl[j]= (float)(t+(cvtest::randReal(rng)*2-1)*max_noise);
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}
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cvCopy( Temp, Sample );
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cvKalmanCorrect(Kalm,Temp);
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}
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Mat _state_post = cvarrToMat(Kalm->state_post);
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code = cvtest::cmpEps2( ts, _Sample, _state_post, EPSILON, false, "The final estimated state" );
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cvReleaseMat(&Sample);
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cvReleaseMat(&Temp);
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cvReleaseKalman(&Kalm);
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if( code < 0 )
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ts->set_failed_test_info( code );
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}
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TEST(Video_Kalman, accuracy) { CV_KalmanTest test; test.safe_run(); }
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/* End of file. */
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