mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
124 lines
4.3 KiB
124 lines
4.3 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// 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. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// Redistribution and use in source and binary forms, with or without modification, |
|
// are permitted provided that the following conditions are met: |
|
// |
|
// * Redistribution's of source code must retain the above copyright notice, |
|
// this list of conditions and the following disclaimer. |
|
// |
|
// * Redistribution's in binary form must reproduce the above copyright notice, |
|
// this list of conditions and the following disclaimer in the documentation |
|
// and/or other materials provided with the distribution. |
|
// |
|
// * The name of Intel Corporation may not be used to endorse or promote products |
|
// derived from this software without specific prior written permission. |
|
// |
|
// This software is provided by the copyright holders and contributors "as is" and |
|
// any express or implied warranties, including, but not limited to, the implied |
|
// warranties of merchantability and fitness for a particular purpose are disclaimed. |
|
// In no event shall the Intel Corporation or contributors be liable for any direct, |
|
// indirect, incidental, special, exemplary, or consequential damages |
|
// (including, but not limited to, procurement of substitute goods or services; |
|
// loss of use, data, or profits; or business interruption) however caused |
|
// and on any theory of liability, whether in contract, strict liability, |
|
// or tort (including negligence or otherwise) arising in any way out of |
|
// the use of this software, even if advised of the possibility of such damage. |
|
// |
|
//M*/ |
|
|
|
#include "test_precomp.hpp" |
|
|
|
using namespace cv; |
|
|
|
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(); |
|
CvKalman* Kalm; |
|
int i, j; |
|
|
|
CvMat* Sample = cvCreateMat(Dim,1,CV_32F); |
|
CvMat* Temp = cvCreateMat(Dim,1,CV_32F); |
|
|
|
Kalm = cvCreateKalman(Dim, Dim); |
|
CvMat Dyn = cvMat(Dim,Dim,CV_32F,Kalm->DynamMatr); |
|
CvMat Mes = cvMat(Dim,Dim,CV_32F,Kalm->MeasurementMatr); |
|
CvMat PNC = cvMat(Dim,Dim,CV_32F,Kalm->PNCovariance); |
|
CvMat MNC = cvMat(Dim,Dim,CV_32F,Kalm->MNCovariance); |
|
CvMat PriErr = cvMat(Dim,Dim,CV_32F,Kalm->PriorErrorCovariance); |
|
CvMat PostErr = cvMat(Dim,Dim,CV_32F,Kalm->PosterErrorCovariance); |
|
CvMat PriState = cvMat(Dim,1,CV_32F,Kalm->PriorState); |
|
CvMat PostState = cvMat(Dim,1,CV_32F,Kalm->PosterState); |
|
cvSetIdentity(&PNC); |
|
cvSetIdentity(&PriErr); |
|
cvSetIdentity(&PostErr); |
|
cvSetZero(&MNC); |
|
cvSetZero(&PriState); |
|
cvSetZero(&PostState); |
|
cvSetIdentity(&Mes); |
|
cvSetIdentity(&Dyn); |
|
Mat _Sample = cvarrToMat(Sample); |
|
cvtest::randUni(rng, _Sample, cvScalarAll(-max_init), cvScalarAll(max_init)); |
|
cvKalmanCorrect(Kalm, Sample); |
|
for(i = 0; i<Steps; i++) |
|
{ |
|
cvKalmanPredict(Kalm); |
|
for(j = 0; j<Dim; j++) |
|
{ |
|
float t = 0; |
|
for(int k=0; k<Dim; k++) |
|
{ |
|
t += Dyn.data.fl[j*Dim+k]*Sample->data.fl[k]; |
|
} |
|
Temp->data.fl[j]= (float)(t+(cvtest::randReal(rng)*2-1)*max_noise); |
|
} |
|
cvCopy( Temp, Sample ); |
|
cvKalmanCorrect(Kalm,Temp); |
|
} |
|
|
|
Mat _state_post = cvarrToMat(Kalm->state_post); |
|
code = cvtest::cmpEps2( ts, _Sample, _state_post, EPSILON, false, "The final estimated state" ); |
|
|
|
cvReleaseMat(&Sample); |
|
cvReleaseMat(&Temp); |
|
cvReleaseKalman(&Kalm); |
|
|
|
if( code < 0 ) |
|
ts->set_failed_test_info( code ); |
|
} |
|
|
|
TEST(Video_Kalman, accuracy) { CV_KalmanTest test; test.safe_run(); } |
|
|
|
/* End of file. */
|
|
|