public PointF[] filterPoints(PointF pt)
{
syntheticData.state[0, 0] = pt.X;
syntheticData.state[1, 0] = pt.Y;
Mat prediction = kal.Predict();
float[] data = GetMatData(prediction);
PointF predictPoint = new PointF(data[0], data[1]);
PointF measurePoint = new PointF(syntheticData.GetMeasurement()[0, 0],
syntheticData.GetMeasurement()[1, 0]);
Mat estimated = kal.Correct(syntheticData.GetMeasurement().Mat);
data = GetMatData(estimated);
PointF estimatedPoint = new PointF(data[0], data[1]);
syntheticData.GoToNextState();
PointF[] results = new PointF[2];
results[0] = predictPoint;
results[1] = estimatedPoint;
px = predictPoint.X;
py = predictPoint.Y;
cx = estimatedPoint.X;
cy = estimatedPoint.Y;
return(results);
}
private PointF[] filterPoints(PointF pt)
{
syntheticData.state[0, 0] = pt.X;
syntheticData.state[1, 0] = pt.Y;
Matrix <float> prediction = kal.Predict();
PointF predictPoint = new PointF(prediction[0, 0], prediction[1, 0]);
PointF measurePoint = new PointF(syntheticData.GetMeasurement()[0, 0],
syntheticData.GetMeasurement()[1, 0]);
Matrix <float> estimated = kal.Correct(syntheticData.GetMeasurement());
PointF estimatedPoint = new PointF(estimated[0, 0], estimated[1, 0]);
syntheticData.GoToNextState();
PointF[] results = new PointF[2];
results[0] = predictPoint;
results[1] = estimatedPoint;
px = predictPoint.X;
py = predictPoint.Y;
cx = estimatedPoint.X;
cy = estimatedPoint.Y;
return(results);
}
