/// <summary>
/// Match the Image feature from the observed image to the features from the model image, using brute force matcher
/// </summary>
/// <param name="observedFeatures">The Image feature from the observed image</param>
/// <param name="k">The number of neighbors to find</param>
/// <returns>The matched features</returns>
public MatchedImageFeature[] MatchFeature(ImageFeature <TDescriptor>[] observedFeatures, int k)
{
VectorOfKeyPoint obsKpts;
Matrix <TDescriptor> observedDescriptors;
ImageFeature <TDescriptor> .ConvertToRaw(observedFeatures, out obsKpts, out observedDescriptors);
try
{
DistanceType dt = typeof(TDescriptor) == typeof(Byte) ? DistanceType.Hamming : DistanceType.L2;
using (Matrix <int> indices = new Matrix <int>(observedDescriptors.Rows, k))
using (Matrix <float> dists = new Matrix <float>(observedDescriptors.Rows, k))
using (BruteForceMatcher <TDescriptor> matcher = new BruteForceMatcher <TDescriptor>(dt))
{
matcher.Add(_modelDescriptors);
matcher.KnnMatch(observedDescriptors, indices, dists, k, null);
return(ConvertToMatchedImageFeature(_modelKeyPoints, _modelDescriptors, obsKpts, observedDescriptors, indices, dists, null));
}
}
finally
{
obsKpts.Dispose();
observedDescriptors.Dispose();
}
}
/// <summary>
/// Create a Image tracker, where Image is matched with flann
/// </summary>
/// <param name="modelFeatures">The Image feature from the model image</param>
public Features2DTracker(ImageFeature <TDescriptor>[] modelFeatures)
{
ImageFeature <TDescriptor> .ConvertToRaw(modelFeatures, out _modelKeyPoints, out _modelDescriptors);
}