pythonwebcoder
diff --git a/‎samples/c/kalman.c‎
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diff --git a/‎samples/cpp/kalman.cpp‎
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+#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;
+}