/// <summary>
/// Create k-d feature trees using the SURF feature extracted from the model image.
/// </summary>
/// <param name="modelFeatures">The SURF feature extracted from the model image</param>
public SURFMatcher(SURFFeature[] modelFeatures)
{
Debug.Assert(modelFeatures.Length > 0, "Model Features should have size > 0");
_modelIndex = new Flann.Index(
Util.GetMatrixFromDescriptors(
Array.ConvertAll <SURFFeature, float[]>(
modelFeatures,
delegate(SURFFeature f) { return(f.Descriptor); })),
1);
_modelFeatures = modelFeatures;
}
/*
* private static int CompareSimilarFeature(SimilarFeature f1, SimilarFeature f2)
* {
* if (f1.Distance < f2.Distance)
* return -1;
* if (f1.Distance == f2.Distance)
* return 0;
* else
* return 1;
* }*/
/// <summary>
/// Match the SURF feature from the observed image to the features from the model image
/// </summary>
/// <param name="observedFeatures">The SURF feature from the observed image</param>
/// <param name="k">The number of neighbors to find</param>
/// <param name="emax">For k-d tree only: the maximum number of leaves to visit.</param>
/// <returns>The matched features</returns>
public MatchedSURFFeature[] MatchFeature(SURFFeature[] observedFeatures, int k, int emax)
{
if (observedFeatures.Length == 0)
{
return(new MatchedSURFFeature[0]);
}
float[][] descriptors = new float[observedFeatures.Length][];
for (int i = 0; i < observedFeatures.Length; i++)
{
descriptors[i] = observedFeatures[i].Descriptor;
}
using (Matrix <int> result1 = new Matrix <int>(descriptors.Length, k))
using (Matrix <float> dist1 = new Matrix <float>(descriptors.Length, k))
{
_modelIndex.KnnSearch(Util.GetMatrixFromDescriptors(descriptors), result1, dist1, k, emax);
int[,] indexes = result1.Data;
float[,] distances = dist1.Data;
MatchedSURFFeature[] res = new MatchedSURFFeature[observedFeatures.Length];
List <SimilarFeature> matchedFeatures = new List <SimilarFeature>();
for (int i = 0; i < res.Length; i++)
{
matchedFeatures.Clear();
for (int j = 0; j < k; j++)
{
int index = indexes[i, j];
if (index >= 0)
{
matchedFeatures.Add(new SimilarFeature(distances[i, j], _modelFeatures[index]));
}
}
res[i].ObservedFeature = observedFeatures[i];
res[i].SimilarFeatures = matchedFeatures.ToArray();
}
return(res);
}
}
/// <summary>
/// Create a spill tree from the specific feature descriptors
/// </summary>
/// <param name="descriptors">The array of feature descriptors</param>
/// <param name="naive">A good value is 50</param>
/// <param name="rho">A good value is .7</param>
/// <param name="tau">A good value is .1</param>
public FeatureTree(float[][] descriptors, int naive, double rho, double tau)
{
_descriptorMatrix = Util.GetMatrixFromDescriptors(descriptors);
_ptr = CvInvoke.cvCreateSpillTree(_descriptorMatrix.Ptr, naive, rho, tau);
}
/// <summary>
/// Create a k-d tree from the specific feature descriptors
/// </summary>
/// <param name="descriptors">The array of feature descriptors</param>
public FeatureTree(float[][] descriptors)
{
_descriptorMatrix = Util.GetMatrixFromDescriptors(descriptors);
_ptr = CvInvoke.cvCreateKDTree(_descriptorMatrix.Ptr);
}
/// <summary>
/// Finds (with high probability) the k nearest neighbors in tree for each of the given (row-)vectors in desc, using best-bin-first searching ([Beis97]). The complexity of the entire operation is at most O(m*emax*log2(n)), where n is the number of vectors in the tree
/// </summary>
/// <param name="descriptors">The m feature descriptors to be searched from the feature tree</param>
/// <param name="results">
/// The results of the best <paramref name="k"/> matched from the feature tree. A m x <paramref name="k"/> matrix. Contains -1 in some columns if fewer than k neighbors found.
/// For each row the k neareast neighbors are not sorted. To findout the closet neighbour, look at the output matrix <paramref name="dist"/>.
/// </param>
/// <param name="dist">
/// A m x <paramref name="k"/> matrix of the distances to k nearest neighbors
/// </param>
/// <param name="k">The number of neighbors to find</param>
/// <param name="emax">For k-d tree only: the maximum number of leaves to visit. Use 20 if not sure</param>
private void FindFeatures(float[][] descriptors, Matrix <Int32> results, Matrix <double> dist, int k, int emax)
{
using (Matrix <float> descriptorMatrix = Util.GetMatrixFromDescriptors(descriptors))
CvInvoke.cvFindFeatures(Ptr, descriptorMatrix.Ptr, results.Ptr, dist.Ptr, k, emax);
}
