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103 lines
3.3 KiB
Mathematica
103 lines
3.3 KiB
Mathematica
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#! /usr/bin/env octave
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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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cv;
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highgui;
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global img;
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function ret=calc_point(angle)
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global img;
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ret=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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endfunction
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function draw_cross( center, color, d )
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global img;
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global CV_AA;
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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 ), \
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color, 1, CV_AA, 0 );
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endfunction
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A = [ 1, 1; 0, 1 ];
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img = cvCreateImage( cvSize(500,500), 8, 3 );
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kalman = cvCreateKalman( 2, 1, 0 );
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state = cvCreateMat( 2, 1, CV_32FC1 ); # (phi, delta_phi)
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process_noise = cvCreateMat( 2, 1, CV_32FC1 );
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measurement = cvCreateMat( 1, 1, CV_32FC1 );
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rng = cvRNG(-1);
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code = -1;
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cvZero( measurement );
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cvNamedWindow( "Kalman", 1 );
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while (true),
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cvRandArr( rng, state, CV_RAND_NORMAL, cvRealScalar(0), cvRealScalar(0.1) );
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kalman.transition_matrix = mat2cv(A, CV_32FC1);
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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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while (true),
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state_angle = state(0);
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state_pt = calc_point(state_angle);
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prediction = cvKalmanPredict( kalman );
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predict_angle = prediction(0);
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predict_pt = calc_point(predict_angle);
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cvRandArr( rng, measurement, CV_RAND_NORMAL, cvRealScalar(0), \
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cvRealScalar(sqrt(kalman.measurement_noise_cov(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(0);
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measurement_pt = calc_point(measurement_angle);
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## plot points
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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(0)(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 = cvWaitKey( 100 );
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if( code > 0 )
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break;
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endif
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endwhile
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if( code == '\x1b' || code == 'q' || code == 'Q' )
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break;
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endif
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endwhile
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cvDestroyWindow("Kalman");
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