/// <summary>
/// Find the k-nearest match
/// </summary>
/// <param name="queryDescriptor">An n x m matrix of descriptors to be query for nearest neighbours. n is the number of descriptor and m is the size of the descriptor</param>
/// <param name="k">Number of nearest neighbors to search for</param>
/// <param name="mask">Can be null if not needed. An n x 1 matrix. If 0, the query descriptor in the corresponding row will be ignored.</param>
/// <param name="matches">Matches. Each matches[i] is k or less matches for the same query descriptor.</param>
public void KnnMatch(IInputArray queryDescriptor, VectorOfVectorOfDMatch matches, int k, IInputArray mask)
{
using (InputArray iaQueryDesccriptor = queryDescriptor.GetInputArray())
using (InputArray iaMask = mask == null ? InputArray.GetEmpty() : mask.GetInputArray())
DescriptorMatcherInvoke.CvDescriptorMatcherKnnMatch(_descriptorMatcherPtr, iaQueryDesccriptor, matches, k, iaMask);
}
/// <summary>
/// Find the k-nearest match for DistanceType of Hamming or HammingLUT
/// </summary>
/// <param name="queryDescriptor">An n x m matrix of descriptors to be query for nearest neighbours. n is the number of descriptor and m is the size of the descriptor</param>
/// <param name="trainIdx">The resulting n x <paramref name="k"/> matrix of descriptor index from the training descriptors</param>
/// <param name="distance">The resulting n x <paramref name="k"/> matrix of distance value from the training descriptors</param>
/// <param name="k">Number of nearest neighbors to search for</param>
/// <param name="mask">Can be null if not needed. An n x 1 matrix. If 0, the query descriptor in the corresponding row will be ignored.</param>
public void KnnMatch(Matrix <Byte> queryDescriptor, Matrix <int> trainIdx, Matrix <float> distance, int k, Matrix <Byte> mask)
{
Debug.Assert(_distanceType == DistanceType.Hamming || _distanceType == DistanceType.HammingLUT);
DescriptorMatcherInvoke.CvDescriptorMatcherKnnMatch(Ptr, queryDescriptor, trainIdx, distance, k, mask);
}