|
public RadiusSearch ( Matrix |
||
| queries | Matrix |
The query points, one per row |
| indices | Matrix |
Indices of the nearest neighbors found |
| squareDistances | Matrix |
The square of the Eculidean distance between the neighbours |
| radius | float | The search radius |
| checks | int | The number of times the tree(s) in the index should be recursively traversed. A /// higher value for this parameter would give better search precision, but also take more /// time. If automatic configuration was used when the index was created, the number of /// checks required to achieve the specified precision was also computed, in which case /// this parameter is ignored |
| return | int | |
public int RadiusSearch(Matrix<float> queries, Matrix<int> indices, Matrix<float> squareDistances, float radius, int checks)
{
return CvInvoke.CvFlannIndexRadiusSearch(_ptr, queries, indices, squareDistances, radius, checks);
}
/// <summary>
/// Perform a search within the given radius
/// </summary>
/// <param name="position">The center of the search area</param>
/// <param name="radius">The radius of the search</param>
/// <param name="maxResults">The maximum number of results to return</param>
/// <returns>The neighbors found</returns>
public Neighbor[] RadiusSearch(MCvPoint3D32f position, double radius, int maxResults)
{
_query.Data[0, 0] = position.X;
_query.Data[0, 1] = position.Y;
_query.Data[0, 2] = position.Z;
using (Mat indicies = new Mat(new Size(maxResults, 1), DepthType.Cv32S, 1))
using (Mat sqrDistances = new Mat(new Size(maxResults, 1), DepthType.Cv32F, 1))
{
indicies.SetTo(new MCvScalar(-1));
sqrDistances.SetTo(new MCvScalar(-1));
_flannIndex.RadiusSearch(_query, indicies, sqrDistances, radius, maxResults);
int[] indiciesVal = new int[indicies.Rows * indicies.Cols];
indicies.CopyTo(indiciesVal);
float[] sqrDistancesVal = new float[sqrDistances.Rows * sqrDistances.Cols];
sqrDistances.CopyTo(sqrDistancesVal);
List <Neighbor> neighbors = new List <Neighbor>();
for (int j = 0; j < maxResults; j++)
{
if (indiciesVal[j] <= 0)
{
break;
}
Neighbor n = new Neighbor();
n.Index = indiciesVal[j];
n.SquareDist = sqrDistancesVal[j];
neighbors.Add(n);
}
return(neighbors.ToArray());
}
}
