KalmanMouse: Restructure a bit

KalmanMouse.cpp

@@ -1,82 +1,116 @@
 // http://opencvexamples.blogspot.com/2014/01/kalman-filter-implementation-tracking.html
-// (slightly cleaned up and patched to use opencv's gui functions)
+// (slightly cleaned up, restructured and patched to use opencv's gui functions)
 
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/video/tracking.hpp"
 
-#define drawCross( center, color, d )                                 \
-line( img, Point( center.x - d, center.y - d ), Point( center.x + d, center.y + d ), color, 2, CV_AA, 0); \
-line( img, Point( center.x + d, center.y - d ), Point( center.x - d, center.y + d ), color, 2, CV_AA, 0 )
-
 using namespace cv;
 using namespace std;
 
 Point mousePos;
 
-void mouseCallback(int event, int x, int y, int flags, void* userdata) {
+// save mouse position in the global mousePos.
-     if ( event == EVENT_MOUSEMOVE ) {
+void saveMousePosCallback(int event, int x, int y, int flags, void* userdata) {
+     if (event == EVENT_MOUSEMOVE) {
          mousePos.x = x;
          mousePos.y = y;
      }
 }
 
-int main() {
+#define ADDNOISE 1
 
-    KalmanFilter KF(4, 2, 0);
+// measures the mouse position by reading from mousePos and adding some
-
+// artificial noise.
-    // intialization of KF...
+Mat_<float> measure() {
-    KF.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0,   0,1,0,1,  0,0,1,0,  0,0,0,1);
     Mat_<float> measurement(2,1);
+    Mat_<float> measurementNoise(2,1);
 
-    KF.statePre.at<float>(0) = mousePos.x;
+    measurement(0) = mousePos.x;
-    KF.statePre.at<float>(1) = mousePos.y;
+    measurement(1) = mousePos.y;
-    KF.statePre.at<float>(2) = 0;
-    KF.statePre.at<float>(3) = 0;
-    setIdentity(KF.measurementMatrix);
-    setIdentity(KF.processNoiseCov, Scalar::all(1e-4));
-    setIdentity(KF.measurementNoiseCov, Scalar::all(10));
-    setIdentity(KF.errorCovPost, Scalar::all(.1));
-    // Image to show mouse tracking
-    Mat img(600, 800, CV_8UC3);
-    vector<Point> mousev,kalmanv;
-    mousev.clear();
-    kalmanv.clear();
 
-    namedWindow("mouse kalman", 1);
+#if ADDNOISE == 1
-    setMouseCallback("mouse kalman", mouseCallback, NULL);
+    Mat mean  = Mat::zeros(1,1,CV_64FC1);
+    Mat sigma = Mat::ones(1,1,CV_64FC1) * 5;
+    randn(measurementNoise, mean, sigma);
+    measurement += measurementNoise;
+#endif
 
-    while(1) {
+    return measurement;
-        // First predict, to update the internal statePre variable
+}
-        Mat prediction = KF.predict();
 
 
-        // The update phase
+// draw a cross
-        measurement(0) = mousePos.x;
+void drawCross(Mat img, Point center, Scalar color, int d) {
-        measurement(1) = mousePos.y;
+    line(img, Point(center.x - d, center.y - d),
-        Mat estimated = KF.correct(measurement);
+              Point(center.x + d, center.y + d), color, 2, CV_AA, 0);
+    line(img, Point(center.x + d, center.y - d),
+              Point(center.x - d, center.y + d), color, 2, CV_AA, 0);
+}
 
+Mat img(600, 800, CV_8UC3);
+vector<Point> mousev, kalmanv;
 
-        // Plot
+void plot() {
         img = Scalar::all(0);
 
-        Point statePt(estimated.at<float>(0),estimated.at<float>(1));
-        Point measPt(measurement(0),measurement(1));
-        drawCross(statePt, Scalar(255,255,255), 5);
-        drawCross(measPt, Scalar(0,0,255), 5);
 
-        mousev.push_back(measPt);
+        Point statePt = kalmanv.back();
-        kalmanv.push_back(statePt);
+        Point measPt = mousev.back();
+        drawCross(img, statePt, Scalar(255,255,255), 5);
+        drawCross(img, measPt, Scalar(0,0,255), 5);
+
 
         for (int i = 0; i < mousev.size()-1; i++)
             line(img, mousev[i], mousev[i+1], Scalar(255,255,0), 1);
 
         for (int i = 0; i < kalmanv.size()-1; i++)
             line(img, kalmanv[i], kalmanv[i+1], Scalar(0,155,255), 1);
-
+}
-        imshow("mouse kalman", img);
+
-
+
-        waitKey(10);
+
-    }
+int main() {
-
+    namedWindow("mouse kalman", 1);
-    return 0;
+    setMouseCallback("mouse kalman", saveMousePosCallback, NULL);
+
+
+
+    // 4 state dimensions: x, y, dx, dy
+    // 2 measurement dimensions: x, y
+    KalmanFilter KF(4, 2, 0);
+
+    // transition matrix models: x' = x + dx, y' = y + dy, dx' = dx, dy' = dy
+    KF.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0,   0,1,0,1,  0,0,1,0,  0,0,0,1);
+
+    setIdentity(KF.measurementMatrix);
+    setIdentity(KF.processNoiseCov, Scalar::all(1e-3));
+    setIdentity(KF.measurementNoiseCov, Scalar::all(10));
+    setIdentity(KF.errorCovPost, Scalar::all(.1));
+
+
+
+    while (waitKey(10) < 0) {
+        // First predict, to update the internal statePre variable
+        Mat prediction = KF.predict();
+
+
+        // Measure
+        Mat_<float> measurement = measure();
+
+
+        // Update
+        Mat_<float> estimated = KF.correct(measurement);
+
+
+        // Save history
+        Point statePt(estimated(0),estimated(1));
+        Point measPt(measurement(0),measurement(1));
+        mousev.push_back(measPt);
+        kalmanv.push_back(statePt);
+
+
+        // Plot
+        plot();
+        imshow("mouse kalman", img);
+    }
 }