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114 lines
4.1 KiB
C
114 lines
4.1 KiB
C
/*
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Tracking of rotating point.
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Rotation speed is constant.
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Both state and measurements vectors are 1D (a point angle),
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Measurement is the real point angle + gaussian noise.
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The real and the estimated points are connected with yellow line segment,
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the real and the measured points are connected with red line segment.
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(if Kalman filter works correctly,
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the yellow segment should be shorter than the red one).
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Pressing any key (except ESC) will reset the tracking with a different speed.
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Pressing ESC will stop the program.
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*/
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#ifdef _CH_
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#pragma package <opencv>
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#endif
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#define CV_NO_BACKWARD_COMPATIBILITY
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#ifndef _EiC
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#include "cv.h"
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#include "highgui.h"
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#include <math.h>
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#endif
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int main(int argc, char** argv)
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{
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const float A[] = { 1, 1, 0, 1 };
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IplImage* img = cvCreateImage( cvSize(500,500), 8, 3 );
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CvKalman* kalman = cvCreateKalman( 2, 1, 0 );
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CvMat* state = cvCreateMat( 2, 1, CV_32FC1 ); /* (phi, delta_phi) */
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CvMat* process_noise = cvCreateMat( 2, 1, CV_32FC1 );
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CvMat* measurement = cvCreateMat( 1, 1, CV_32FC1 );
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CvRNG rng = cvRNG(-1);
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char code = -1;
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cvZero( measurement );
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cvNamedWindow( "Kalman", 1 );
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for(;;)
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{
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cvRandArr( &rng, state, CV_RAND_NORMAL, cvRealScalar(0), cvRealScalar(0.1) );
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memcpy( kalman->transition_matrix->data.fl, A, sizeof(A));
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cvSetIdentity( kalman->measurement_matrix, cvRealScalar(1) );
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cvSetIdentity( kalman->process_noise_cov, cvRealScalar(1e-5) );
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cvSetIdentity( kalman->measurement_noise_cov, cvRealScalar(1e-1) );
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cvSetIdentity( kalman->error_cov_post, cvRealScalar(1));
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cvRandArr( &rng, kalman->state_post, CV_RAND_NORMAL, cvRealScalar(0), cvRealScalar(0.1) );
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for(;;)
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{
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#define calc_point(angle) \
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cvPoint( cvRound(img->width/2 + img->width/3*cos(angle)), \
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cvRound(img->height/2 - img->width/3*sin(angle)))
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float state_angle = state->data.fl[0];
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CvPoint state_pt = calc_point(state_angle);
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const CvMat* prediction = cvKalmanPredict( kalman, 0 );
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float predict_angle = prediction->data.fl[0];
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CvPoint predict_pt = calc_point(predict_angle);
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float measurement_angle;
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CvPoint measurement_pt;
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cvRandArr( &rng, measurement, CV_RAND_NORMAL, cvRealScalar(0),
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cvRealScalar(sqrt(kalman->measurement_noise_cov->data.fl[0])) );
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/* generate measurement */
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cvMatMulAdd( kalman->measurement_matrix, state, measurement, measurement );
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measurement_angle = measurement->data.fl[0];
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measurement_pt = calc_point(measurement_angle);
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/* plot points */
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#define draw_cross( center, color, d ) \
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cvLine( img, cvPoint( center.x - d, center.y - d ), \
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cvPoint( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
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cvLine( img, cvPoint( center.x + d, center.y - d ), \
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cvPoint( center.x - d, center.y + d ), color, 1, CV_AA, 0 )
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cvZero( img );
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draw_cross( state_pt, CV_RGB(255,255,255), 3 );
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draw_cross( measurement_pt, CV_RGB(255,0,0), 3 );
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draw_cross( predict_pt, CV_RGB(0,255,0), 3 );
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cvLine( img, state_pt, measurement_pt, CV_RGB(255,0,0), 3, CV_AA, 0 );
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cvLine( img, state_pt, predict_pt, CV_RGB(255,255,0), 3, CV_AA, 0 );
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cvKalmanCorrect( kalman, measurement );
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cvRandArr( &rng, process_noise, CV_RAND_NORMAL, cvRealScalar(0),
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cvRealScalar(sqrt(kalman->process_noise_cov->data.fl[0])));
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cvMatMulAdd( kalman->transition_matrix, state, process_noise, state );
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cvShowImage( "Kalman", img );
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code = (char) cvWaitKey( 100 );
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if( code > 0 )
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break;
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}
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if( code == 27 || code == 'q' || code == 'Q' )
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break;
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}
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cvDestroyWindow("Kalman");
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return 0;
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}
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#ifdef _EiC
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main(1, "kalman.c");
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#endif
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