+++ /dev/null
-#include "opencv2/video/tracking.hpp"
-#include "opencv2/highgui/highgui.hpp"
-
-#include <stdio.h>
-void help()
-{
- printf( "\nExamle of c calls to OpenCV's Kalman filter.\n"
-" Tracking of rotating point.\n"
-" Rotation speed is constant.\n"
-" Both state and measurements vectors are 1D (a point angle),\n"
-" Measurement is the real point angle + gaussian noise.\n"
-" The real and the estimated points are connected with yellow line segment,\n"
-" the real and the measured points are connected with red line segment.\n"
-" (if Kalman filter works correctly,\n"
-" the yellow segment should be shorter than the red one).\n"
- "\n"
-" Pressing any key (except ESC) will reset the tracking with a different speed.\n"
-" Pressing ESC will stop the program.\n"
- );
-}
-
-
-
-int main(int argc, char** argv)
-{
- const float A[] = { 1, 1, 0, 1 };
- help();
- IplImage* img = cvCreateImage( cvSize(500,500), 8, 3 );
- CvKalman* kalman = cvCreateKalman( 2, 1, 0 );
- CvMat* state = cvCreateMat( 2, 1, CV_32FC1 ); /* (phi, delta_phi) */
- CvMat* process_noise = cvCreateMat( 2, 1, CV_32FC1 );
- CvMat* measurement = cvCreateMat( 1, 1, CV_32FC1 );
- CvRNG rng = cvRNG(-1);
- char code = -1;
-
- cvZero( measurement );
- cvNamedWindow( "Kalman", 1 );
-
- for(;;)
- {
- cvRandArr( &rng, state, CV_RAND_NORMAL, cvRealScalar(0), cvRealScalar(0.1) );
-
- memcpy( kalman->transition_matrix->data.fl, A, sizeof(A));
- cvSetIdentity( kalman->measurement_matrix, cvRealScalar(1) );
- cvSetIdentity( kalman->process_noise_cov, cvRealScalar(1e-5) );
- cvSetIdentity( kalman->measurement_noise_cov, cvRealScalar(1e-1) );
- cvSetIdentity( kalman->error_cov_post, cvRealScalar(1));
- cvRandArr( &rng, kalman->state_post, CV_RAND_NORMAL, cvRealScalar(0), cvRealScalar(0.1) );
-
- for(;;)
- {
- #define calc_point(angle) \
- cvPoint( cvRound(img->width/2 + img->width/3*cos(angle)), \
- cvRound(img->height/2 - img->width/3*sin(angle)))
-
- float state_angle = state->data.fl[0];
- CvPoint state_pt = calc_point(state_angle);
-
- const CvMat* prediction = cvKalmanPredict( kalman, 0 );
- float predict_angle = prediction->data.fl[0];
- CvPoint predict_pt = calc_point(predict_angle);
- float measurement_angle;
- CvPoint measurement_pt;
-
- cvRandArr( &rng, measurement, CV_RAND_NORMAL, cvRealScalar(0),
- cvRealScalar(sqrt(kalman->measurement_noise_cov->data.fl[0])) );
-
- /* generate measurement */
- cvMatMulAdd( kalman->measurement_matrix, state, measurement, measurement );
-
- measurement_angle = measurement->data.fl[0];
- measurement_pt = calc_point(measurement_angle);
-
- /* plot points */
- #define draw_cross( center, color, d ) \
- cvLine( img, cvPoint( center.x - d, center.y - d ), \
- cvPoint( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
- cvLine( img, cvPoint( center.x + d, center.y - d ), \
- cvPoint( center.x - d, center.y + d ), color, 1, CV_AA, 0 )
-
- cvZero( img );
- draw_cross( state_pt, CV_RGB(255,255,255), 3 );
- draw_cross( measurement_pt, CV_RGB(255,0,0), 3 );
- draw_cross( predict_pt, CV_RGB(0,255,0), 3 );
- cvLine( img, state_pt, measurement_pt, CV_RGB(255,0,0), 3, CV_AA, 0 );
- cvLine( img, state_pt, predict_pt, CV_RGB(255,255,0), 3, CV_AA, 0 );
-
- cvKalmanCorrect( kalman, measurement );
-
- cvRandArr( &rng, process_noise, CV_RAND_NORMAL, cvRealScalar(0),
- cvRealScalar(sqrt(kalman->process_noise_cov->data.fl[0])));
- cvMatMulAdd( kalman->transition_matrix, state, process_noise, state );
-
- cvShowImage( "Kalman", img );
- code = (char) cvWaitKey( 100 );
-
- if( code > 0 )
- break;
- }
- if( code == 27 || code == 'q' || code == 'Q' )
- break;
- }
-
- cvDestroyWindow("Kalman");
-
- return 0;
-}
-
-#ifdef _EiC
-main(1, "kalman.c");
-#endif
--- /dev/null
+#include "opencv2/video/tracking.hpp"
+#include "opencv2/highgui/highgui.hpp"
+
+#include <stdio.h>
+
+using namespace cv;
+
+static inline Point calcPoint(Point2f center, double R, double angle)
+{
+ return center + Point2f((float)cos(angle), (float)-sin(angle))*(float)R;
+}
+
+void help()
+{
+ printf( "\nExamle of c calls to OpenCV's Kalman filter.\n"
+" Tracking of rotating point.\n"
+" Rotation speed is constant.\n"
+" Both state and measurements vectors are 1D (a point angle),\n"
+" Measurement is the real point angle + gaussian noise.\n"
+" The real and the estimated points are connected with yellow line segment,\n"
+" the real and the measured points are connected with red line segment.\n"
+" (if Kalman filter works correctly,\n"
+" the yellow segment should be shorter than the red one).\n"
+ "\n"
+" Pressing any key (except ESC) will reset the tracking with a different speed.\n"
+" Pressing ESC will stop the program.\n"
+ );
+}
+
+int main(int, char**)
+{
+ help();
+ Mat img(500, 500, CV_8UC3);
+ KalmanFilter KF(2, 1, 0);
+ Mat state(2, 1, CV_32F); /* (phi, delta_phi) */
+ Mat processNoise(2, 1, CV_32F);
+ Mat measurement = Mat::zeros(1, 1, CV_32F);
+ char code = (char)-1;
+
+ for(;;)
+ {
+ randn( state, Scalar::all(0), Scalar::all(0.1) );
+ KF.transitionMatrix = *(Mat_<float>(2, 2) << 1, 1, 0, 1);
+
+ setIdentity(KF.measurementMatrix);
+ setIdentity(KF.processNoiseCov, Scalar::all(1e-5));
+ setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));
+ setIdentity(KF.errorCovPost, Scalar::all(1));
+
+ randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));
+
+ for(;;)
+ {
+ Point2f center(img.cols*0.5f, img.rows*0.5f);
+ float R = img.cols/3.f;
+ double stateAngle = state.at<float>(0);
+ Point statePt = calcPoint(center, R, stateAngle);
+
+ Mat prediction = KF.predict();
+ double predictAngle = prediction.at<float>(0);
+ Point predictPt = calcPoint(center, R, predictAngle);
+
+ randn( measurement, Scalar::all(0), Scalar::all(KF.measurementNoiseCov.at<float>(0)));
+
+ // generate measurement
+ measurement += KF.measurementMatrix*state;
+
+ double measAngle = measurement.at<float>(0);
+ Point measPt = calcPoint(center, R, measAngle);
+
+ // plot points
+ #define drawCross( center, color, d ) \
+ line( img, Point( center.x - d, center.y - d ), \
+ Point( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
+ line( img, Point( center.x + d, center.y - d ), \
+ Point( center.x - d, center.y + d ), color, 1, CV_AA, 0 )
+
+ img = Scalar::all(0);
+ drawCross( statePt, Scalar(255,255,255), 3 );
+ drawCross( measPt, Scalar(0,0,255), 3 );
+ drawCross( predictPt, Scalar(0,255,0), 3 );
+ line( img, statePt, measPt, Scalar(0,0,255), 3, CV_AA, 0 );
+ line( img, statePt, predictPt, Scalar(0,255,255), 3, CV_AA, 0 );
+
+ KF.correct(measurement);
+
+ randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));
+ state = KF.transitionMatrix*state + processNoise;
+
+ imshow( "Kalman", img );
+ code = (char)waitKey(100);
+
+ if( code > 0 )
+ break;
+ }
+ if( code == 27 || code == 'q' || code == 'Q' )
+ break;
+ }
+
+ return 0;
+}