converted Kalman sample to C++
This commit is contained in:
Vadim Pisarevsky
parent
103bbaf09c
commit
fe72a6eeb3
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#include "opencv2/video/tracking.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include <stdio.h>
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void help()
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{
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printf( "\nExamle of c calls to OpenCV's Kalman filter.\n"
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" Tracking of rotating point.\n"
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" Rotation speed is constant.\n"
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" Both state and measurements vectors are 1D (a point angle),\n"
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" Measurement is the real point angle + gaussian noise.\n"
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" The real and the estimated points are connected with yellow line segment,\n"
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" the real and the measured points are connected with red line segment.\n"
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" (if Kalman filter works correctly,\n"
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" the yellow segment should be shorter than the red one).\n"
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"\n"
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" Pressing any key (except ESC) will reset the tracking with a different speed.\n"
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" Pressing ESC will stop the program.\n"
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);
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}
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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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help();
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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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101
samples/cpp/kalman.cpp
Normal file
101
samples/cpp/kalman.cpp
Normal file
@ -0,0 +1,101 @@
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#include "opencv2/video/tracking.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include <stdio.h>
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using namespace cv;
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static inline Point calcPoint(Point2f center, double R, double angle)
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{
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return center + Point2f((float)cos(angle), (float)-sin(angle))*(float)R;
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}
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void help()
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{
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printf( "\nExamle of c calls to OpenCV's Kalman filter.\n"
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" Tracking of rotating point.\n"
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" Rotation speed is constant.\n"
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" Both state and measurements vectors are 1D (a point angle),\n"
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" Measurement is the real point angle + gaussian noise.\n"
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" The real and the estimated points are connected with yellow line segment,\n"
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" the real and the measured points are connected with red line segment.\n"
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" (if Kalman filter works correctly,\n"
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" the yellow segment should be shorter than the red one).\n"
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"\n"
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" Pressing any key (except ESC) will reset the tracking with a different speed.\n"
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" Pressing ESC will stop the program.\n"
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);
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}
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int main(int, char**)
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{
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help();
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Mat img(500, 500, CV_8UC3);
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KalmanFilter KF(2, 1, 0);
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Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
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Mat processNoise(2, 1, CV_32F);
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Mat measurement = Mat::zeros(1, 1, CV_32F);
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char code = (char)-1;
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for(;;)
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{
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randn( state, Scalar::all(0), Scalar::all(0.1) );
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KF.transitionMatrix = *(Mat_<float>(2, 2) << 1, 1, 0, 1);
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setIdentity(KF.measurementMatrix);
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setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
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setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
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setIdentity(KF.errorCovPost, Scalar::all(1));
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randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));
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for(;;)
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{
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Point2f center(img.cols*0.5f, img.rows*0.5f);
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float R = img.cols/3.f;
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double stateAngle = state.at<float>(0);
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Point statePt = calcPoint(center, R, stateAngle);
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Mat prediction = KF.predict();
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double predictAngle = prediction.at<float>(0);
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Point predictPt = calcPoint(center, R, predictAngle);
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randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));
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// generate measurement
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measurement += KF.measurementMatrix*state;
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double measAngle = measurement.at<float>(0);
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Point measPt = calcPoint(center, R, measAngle);
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// plot points
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#define drawCross( center, color, d ) \
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line( img, Point( center.x - d, center.y - d ), \
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Point( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
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line( img, Point( center.x + d, center.y - d ), \
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Point( center.x - d, center.y + d ), color, 1, CV_AA, 0 )
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img = Scalar::all(0);
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drawCross( statePt, Scalar(255,255,255), 3 );
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drawCross( measPt, Scalar(0,0,255), 3 );
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drawCross( predictPt, Scalar(0,255,0), 3 );
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line( img, statePt, measPt, Scalar(0,0,255), 3, CV_AA, 0 );
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line( img, statePt, predictPt, Scalar(0,255,255), 3, CV_AA, 0 );
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KF.correct(measurement);
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randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
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state = KF.transitionMatrix*state + processNoise;
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imshow( "Kalman", img );
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code = (char)waitKey(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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return 0;
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}
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