private PointF[] FilterPoint(PointF pt)
{
_syntheticData.State[0, 0] = pt.X;
_syntheticData.State[1, 0] = pt.Y;
var prediction = _kalman.Predict();
var predictPoint = new PointF(prediction[0, 0], prediction[1, 0]);
var estimated = _kalman.Correct(_syntheticData.GetMeasurement());
var estimatedPoint = new PointF(estimated[0, 0], estimated[1, 0]);
var results = new PointF[2];
results[0] = predictPoint;
results[1] = estimatedPoint;
_syntheticData.GoToNextState();
return(results);
}
//[Test]
public void TestKalman()
{
Image<Bgr, Byte> img = new Image<Bgr, byte>(400, 400);
SyntheticData syntheticData = new SyntheticData();
//Matrix<float> state = new Matrix<float>(new float[] { 0.0f, 0.0f}); //initial guess
#region initialize Kalman filter
KalmanFilter tracker = new KalmanFilter(2, 1, 0);
syntheticData.TransitionMatrix.Mat.CopyTo(tracker.TransitionMatrix);
syntheticData.MeasurementMatrix.Mat.CopyTo(tracker.MeasurementMatrix);
syntheticData.ProcessNoise.Mat.CopyTo(tracker.ProcessNoiseCov);
syntheticData.MeasurementNoise.Mat.CopyTo(tracker.MeasurementNoiseCov);
syntheticData.ErrorCovariancePost.Mat.CopyTo(tracker.ErrorCovPost);
tracker.StatePost.SetTo(new float[] { 0.0f, 0.0f });
#endregion
System.Converter<double, PointF> angleToPoint =
delegate(double radianAngle)
{
return new PointF(
(float)(img.Width / 2 + img.Width / 3 * Math.Cos(radianAngle)),
(float)(img.Height / 2 - img.Width / 3 * Math.Sin(radianAngle)));
};
Action<PointF, Bgr> drawCross =
delegate(PointF point, Bgr color)
{
img.Draw(new Cross2DF(point, 15, 15), color, 1);
};
ImageViewer viewer = new ImageViewer();
System.Windows.Forms.Timer timer = new System.Windows.Forms.Timer();
timer.Interval = 200;
timer.Tick += new EventHandler(delegate(object sender, EventArgs e)
{
Matrix<float> measurement = syntheticData.GetMeasurement();
// adjust Kalman filter state
tracker.Correct(measurement.Mat);
tracker.Predict();
#region draw the state, prediction and the measurement
float[] correctedState = new float[2];
float[] predictedState = new float[2];
tracker.StatePost.CopyTo(correctedState);
tracker.StatePre.CopyTo(predictedState);
PointF statePoint = angleToPoint(correctedState[0]);
PointF predictPoint = angleToPoint(predictedState[0]);
PointF measurementPoint = angleToPoint(measurement[0, 0]);
img.SetZero(); //clear the image
drawCross(statePoint, new Bgr(Color.White)); //draw current state in White
drawCross(measurementPoint, new Bgr(Color.Red)); //draw the measurement in Red
drawCross(predictPoint, new Bgr(Color.Green)); //draw the prediction (the next state) in green
img.Draw(new LineSegment2DF(statePoint, predictPoint), new Bgr(Color.Magenta), 1); //Draw a line between the current position and prediction of next position
//Trace.WriteLine(String.Format("Velocity: {0}", tracker.CorrectedState[1, 0]));
#endregion
syntheticData.GoToNextState();
viewer.Image = img;
});
timer.Start();
viewer.Disposed += delegate(Object sender, EventArgs e) { timer.Stop(); };
viewer.Text = "Actual State: White; Measurement: Red; Prediction: Green";
viewer.ShowDialog();
}
public void TestKalman()
{
Image <Bgr, Byte> img = new Image <Bgr, byte>(400, 400);
SyntheticData syntheticData = new SyntheticData();
// state is (phi, delta_phi) - angle and angle increment
Matrix <float> state = new Matrix <float>(new float[] { 0.0f, 0.0f }); //initial guess
#region initialize Kalman filter
Kalman tracker = new Kalman(2, 1, 0);
tracker.TransitionMatrix = syntheticData.TransitionMatrix;
tracker.MeasurementMatrix = syntheticData.MeasurementMatrix;
tracker.ProcessNoiseCovariance = syntheticData.ProcessNoise;
tracker.MeasurementNoiseCovariance = syntheticData.MeasurementNoise;
tracker.ErrorCovariancePost = syntheticData.ErrorCovariancePost;
tracker.CorrectedState = state;
#endregion
System.Converter <double, PointF> angleToPoint =
delegate(double radianAngle)
{
return(new PointF(
(float)(img.Width / 2 + img.Width / 3 * Math.Cos(radianAngle)),
(float)(img.Height / 2 - img.Width / 3 * Math.Sin(radianAngle))));
};
Action <PointF, Bgr> drawCross =
delegate(PointF point, Bgr color)
{
img.Draw(new Cross2DF(point, 15, 15), color, 1);
};
ImageViewer viewer = new ImageViewer();
System.Windows.Forms.Timer timer = new System.Windows.Forms.Timer();
timer.Interval = 200;
timer.Tick += new EventHandler(delegate(object sender, EventArgs e)
{
Matrix <float> measurement = syntheticData.GetMeasurement();
// adjust Kalman filter state
tracker.Correct(measurement);
tracker.Predict();
#region draw the state, prediction and the measurement
PointF statePoint = angleToPoint(tracker.CorrectedState[0, 0]);
PointF predictPoint = angleToPoint(tracker.PredictedState[0, 0]);
PointF measurementPoint = angleToPoint(measurement[0, 0]);
img.SetZero(); //clear the image
drawCross(statePoint, new Bgr(Color.White)); //draw current state in White
drawCross(measurementPoint, new Bgr(Color.Red)); //draw the measurement in Red
drawCross(predictPoint, new Bgr(Color.Green)); //draw the prediction (the next state) in green
img.Draw(new LineSegment2DF(statePoint, predictPoint), new Bgr(Color.Magenta), 1); //Draw a line between the current position and prediction of next position
//Trace.WriteLine(String.Format("Velocity: {0}", tracker.CorrectedState[1, 0]));
#endregion
syntheticData.GoToNextState();
viewer.Image = img;
});
timer.Start();
viewer.Disposed += delegate(Object sender, EventArgs e) { timer.Stop(); };
viewer.Text = "Actual State: White; Measurement: Red; Prediction: Green";
viewer.ShowDialog();
}
