converted Kalman sample to C++

samples/c/kalman.c

-#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

samples/cpp/kalman.cpp

+#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;
+}