/// <summary>
/// Estimates LK optical flow.
/// </summary>
/// <param name="prevImg">Previous image.</param>
/// <param name="currImg">Current image.</param>
/// <param name="prevFeatures">Previous features.</param>
/// <param name="currFeatures">Current features.</param>
/// <param name="status">Feature status.</param>
/// <param name="error">Normalized tracking error [0..1].</param>
/// <param name="windowSize">Aperture size.</param>
/// <param name="iterations">Maximal number of iterations. If <paramref name="minFeatureShift"/> is reached then number of iterations will be lower.</param>
/// <param name="minFeatureShift">Minimal feature shift in horizontal or vertical direction.</param>
/// <param name="minEigenValue">Minimal eigen value.
/// Eigen values could be interpreted as lengths of ellipse's axes.
/// If zero ellipse turn into line therefore there is no corner.</param>
/// <param name="maxError">Maximal allowable error for <paramref name="error"/>.</param>
/// <param name="initialEstimate">Initial estimate shifts input features by specified amount. Default is zero.
/// Used for pyramidal implementation.</param>
public static void EstimateFlow(Image <TColor, float> prevImg, Image <TColor, float> currImg,
PointF[] prevFeatures, out PointF[] currFeatures,
out KLTFeatureStatus[] status, out float[] error,
int windowSize = 15, int iterations = 30, float minFeatureShift = 0.1f, float minEigenValue = 0.01f, float maxError = 0.1f, PointF[] initialEstimate = null)
{
storage = new PyrLKStorage <TColor>(0);
storage.Process(prevImg, currImg);
EstimateFlow(storage,
prevFeatures, out currFeatures, out status, out error,
windowSize, iterations, minFeatureShift, minEigenValue, maxError, initialEstimate, 0);
storage.Dispose();
}
/// <summary>
/// Estimates LK optical flow.
/// </summary>
/// <param name="storage">Used storage. Number of pyramid levels is specified within storage. Use storage to gain performance in video* by 2x! </param>
/// <param name="prevFeatures">Previous features.</param>
/// <param name="currFeatures">Current features.</param>
/// <param name="status">Feature status.</param>
/// <param name="error">Normalized tracking error [0..1].</param>
/// <param name="windowSize">Aperture size.</param>
/// <param name="iterations">Maximal number of iterations. If <paramref name="minFeatureShift"/> is reached then number of iterations will be lower.</param>
/// <param name="minFeatureShift">Minimal feature shift in horizontal or vertical direction.</param>
/// <param name="minEigenValue">Minimal eigen value.
/// Eigen values could be interpreted as lengths of ellipse's axes.
/// If zero ellipse turn into line therefore there is no corner.</param>
/// <param name="maxError">Maximal allowable error for <paramref name="error"/>.</param>
public static void EstimateFlow(PyrLKStorage <TColor> storage,
PointF[] prevFeatures, out PointF[] currFeatures,
out KLTFeatureStatus[] status, out float[] error,
int windowSize = 15, int iterations = 30, float minFeatureShift = 0.1f, float minEigenValue = 0.001f, float maxError = 0.1f)
{
var initialEstimate = new PointF[prevFeatures.Length];
currFeatures = new PointF[prevFeatures.Length];
error = new float[prevFeatures.Length];
status = new KLTFeatureStatus[prevFeatures.Length];
var scaledPrevFeatures = prevFeatures.Apply(x => x.DownScale(storage.PyrLevels));
var usedIndicies = Enumerable.Range(0, prevFeatures.Length).ToArray();
for (int pyrLevel = storage.PyrLevels; pyrLevel >= 0; pyrLevel--)
{
PointF[] levelCurrFeatures;
float[] levelError;
KLTFeatureStatus[] levelStatus;
LKOpticalFlow <TColor> .EstimateFlow(storage,
scaledPrevFeatures.GetAt(usedIndicies), out levelCurrFeatures, out levelStatus, out levelError,
windowSize, iterations, minFeatureShift, minEigenValue, maxError, initialEstimate, pyrLevel);
//update data
currFeatures.SetAt(usedIndicies, levelCurrFeatures);
error.SetAt(usedIndicies, levelError);
status.SetAt(usedIndicies, levelStatus);
if (pyrLevel != 0)
{
scaledPrevFeatures.ApplyInPlace(usedIndicies, x => x.UpScale());
currFeatures.ApplyInPlace(usedIndicies, x => x.UpScale());
initialEstimate = Sub(currFeatures, scaledPrevFeatures);
usedIndicies = getValidFeatureIndicies(status);
}
}
}
/// <summary>
///
/// </summary>
/// <param name="storage">Memory storage used for pyramid and derivative calculation.
/// By using storage instead images if using sequential image input (e.g. frame by frame video) </param>
/// <param name="prevFeatures">Previous features.</param>
/// <param name="currFeatures">Current features.</param>
/// <param name="status">Feature status.</param>
/// <param name="error">Normalized tracking error [0..1].</param>
/// <param name="windowSize">Aperture size.</param>
/// <param name="iterations">Maximal number of iterations. If <paramref name="minFeatureShift"/> is reached then number of iterations will be lower.</param>
/// <param name="minFeatureShift">Minimal feature shift in horizontal or vertical direction.</param>
/// <param name="minEigenValue">Minimal eigen value.
/// Eigen values could be interpreted as lengths of ellipse's axes.
/// If zero ellipse turn into line therefore there is no corner.</param>
/// <param name="maxError">Maximal allowable error for <paramref name="error"/>.</param>
/// <param name="initialEstimate">Initial estimate shifts input features by specified amount. Default is zero.</param>
/// <param name="storagePyrLevel">Used pyramid level for the input storage. Default is zero.</param>
public static void EstimateFlow(PyrLKStorage <TColor> storage,
PointF[] prevFeatures, out PointF[] currFeatures,
out KLTFeatureStatus[] status, out float[] error,
int windowSize = 15, int iterations = 30, float minFeatureShift = 0.1f, float minEigenValue = 0.01f, float maxError = 0.1f,
PointF[] initialEstimate = null, int storagePyrLevel = 0)
{
LKOpticalFlow <TColor> .storage = storage;
LKOpticalFlow <TColor> .storagePyrLevel = storagePyrLevel;
initialEstimate = initialEstimate ?? new PointF[prevFeatures.Length];
PointF[] _currFeatures = new PointF[prevFeatures.Length];
KLTFeatureStatus[] _status = new KLTFeatureStatus[prevFeatures.Length];
float[] _error = new float[prevFeatures.Length];
Parallel.For(0, prevFeatures.Length, (int featureIdx) =>
//for (int featureIdx = 0; featureIdx < prevFeatures.Length; featureIdx++)
{
PointF currFeature;
float featureError;
KLTFeatureStatus featureStatus;
EstimateFeatureFlow(storage.PrevImgPyr[storagePyrLevel], storage.CurrImgPyr[storagePyrLevel],
prevFeatures[featureIdx], out currFeature, out featureStatus, out featureError, windowSize, iterations, minFeatureShift, minEigenValue, maxError, initialEstimate[featureIdx]);
lock (_currFeatures)
lock (_error)
lock (_status)
{
_currFeatures[featureIdx] = currFeature;
_error[featureIdx] = featureError;
_status[featureIdx] = featureStatus;
}
});
currFeatures = _currFeatures;
error = _error;
status = _status;
}