///// <summary>
///// number of search result
///// </summary>
///// <param name="qv"></param>
///// <param name="r2"></param>
///// <returns></returns>
//public int r_count(Vector3 qv, float r2)
//{
// {
// // search for all within a ball of a certain radius
// ListKDTreeResultVectors result = new ListKDTreeResultVectors();
// SearchRecord sr = new SearchRecord(qv, this, result);
// sr.IndexCenter = -1;
// sr.correltime = 0;
// sr.IndexNeighbour = 0;
// sr.Ballsize = r2;
// root.search(sr);
// return (result.Count);
// }
//}
public ListKDTreeResultVectors r_nearest_around_point(int idxin, int correltime, float r2)
{
ListKDTreeResultVectors result = new ListKDTreeResultVectors();
Vector3 qv = TreeVectors[idxin].Vector.Clone(); // query vector
// copy the query vector.
SearchRecord sr = new SearchRecord(qv);
// construct the search record.
sr.Radius = r2;
sr.NumberOfNeighbours = 0;
root.search(sr);
//if (sort_results)
//{
// result.So
// sort(result.begin(), result.end());
//}
return(sr.SearchResult);
}
/// <summary>
/// Based upon the info of the nearest vertex (of each vertex), the triangles are created
/// </summary>
/// <param name="myModel"></param>
//private static List<Triangle> CreateTrianglesByNearestVertices(PointCloud pointCloud)
//{
// List<Triangle> listTriangles = new List<Triangle>();
// //create triangles
// //for (int i = pointCloud.Count - 1; i >= 0; i--)
// for (int i = 0; i < pointCloud.Count; i++)
// {
// Vertex v = pointCloud[i];
// if (v.KDTreeSearch.Count >= 2)
// {
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[0].Key, v.KDTreeSearch[1].Key, listTriangles, v);
// }
// if (v.KDTreeSearch.Count >= 3)
// {
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[0].Key, v.KDTreeSearch[2].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[1].Key, v.KDTreeSearch[2].Key, listTriangles, v);
// }
// if (v.KDTreeSearch.Count >= 4)
// {
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[0].Key, v.KDTreeSearch[3].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[1].Key, v.KDTreeSearch[3].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[2].Key, v.KDTreeSearch[3].Key, listTriangles, v);
// }
// if (v.KDTreeSearch.Count >= 5)
// {
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[0].Key, v.KDTreeSearch[4].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[1].Key, v.KDTreeSearch[4].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[2].Key, v.KDTreeSearch[4].Key, listTriangles, v);
// Triangle.AddTriangleToList(v.Index, v.KDTreeSearch[3].Key, v.KDTreeSearch[4].Key, listTriangles, v);
// }
// }
// //RemoveDuplicateTriangles(listTriangles);
// listTriangles.Sort(new TriangleComparer());
// return listTriangles;
//}
public void Triangulate_KDTree(int numberNeighbours)
{
KDTreeKennell kdTree = new KDTreeKennell();
kdTree.Build(this);
List <Triangle> listTriangles = new List <Triangle>();
for (int i = 0; i < this.Vectors.Length; i++)
{
VertexKDTree vSource = new VertexKDTree(this.Vectors[i], this.Colors[i], i);
uint indexI = Convert.ToUInt32(i);
ListKDTreeResultVectors listResult = kdTree.Find_N_Nearest(vSource.Vector, numberNeighbours);
for (int j = 1; j < listResult.Count; j++)
{
for (int k = j + 1; k < listResult.Count; k++)
{
Triangle t = new Triangle(indexI, listResult[j].IndexNeighbour, listResult[k].IndexNeighbour);
listTriangles.Add(t);
}
}
}
this.Triangles = listTriangles;
CreateIndicesFromTriangles();
}
/// <summary>
/// FInd the closest matching point using a full For-loop search: O(n)
/// </summary>
/// <param name="vertex">Vertex to match</param>
/// <param name="nearest_index">Index of matching vertex in the KDTree vertex array</param>
/// <returns>Nearest matching vertex</returns>
public VertexKDTree FindClosestPoint(VertexKDTree vertex, ref float nearestDistance, ref int nearest_index)
{
VertexKDTree v = new VertexKDTree();
ListKDTreeResultVectors listResult = Find_N_Nearest(vertex.Vector, 1);
if (listResult != null && listResult.Count > 0)
{
nearest_index = Convert.ToInt32(listResult[0].IndexNeighbour);
nearestDistance = listResult[0].Distance;
v = this.TreeVectors[Convert.ToInt32(listResult[0].IndexNeighbour)];
}
return(v);
}
public void FindClosestPoints_Radius(VertexKDTree vertex, float radius, ref int neighboursCount)
{
// search for all within a ball of a certain radius
//ListKDTreeResultVectors result = new ListKDTreeResultVectors();
SearchRecord sr = new SearchRecord(vertex.Vector);
// Vector3 vdiff = new Vector3();
sr.Radius = radius;
root.search(sr);
ListKDTreeResultVectors listResult = sr.SearchResult;
neighboursCount = listResult.Count;
}
public static void StandardDeviation(PointCloud source, int numberOfNeighbours, out float meanDistance, out float standardDeviation, out float[] distances)
{
meanDistance = 0;
standardDeviation = 0f;
distances = new float[source.Count];
KDTreeKennell kdTree = new KDTreeKennell();
kdTree.Build(source);
PointCloud pcResult = new PointCloud();
VertexKDTree[] resultArray = new VertexKDTree[source.Count];
VertexKDTree[] outliers = new VertexKDTree[source.Count];
try
{
List <Vector3> listV = new List <Vector3>();
List <Vector3> listC = new List <Vector3>();
//1. mean distance of one point to his next "numberOfNeighbours" neighbours - stored in the "distances" array
for (int i = 0; i < source.Count; i++)
{
VertexKDTree vSource = new VertexKDTree(source.Vectors[i], source.Colors[i], i);
ListKDTreeResultVectors listResult = kdTree.Find_N_Nearest(vSource.Vector, numberOfNeighbours);
float distSum = 0f;
for (int k = 1; k < listResult.Count; ++k) // k = 0 is the query point
{
distSum += listResult[k].Distance;
}
distances[i] = (distSum / (listResult.Count - 1));
}
//2. calculate the mean distance of ALL points
for (int i = 0; i < distances.Length; ++i)
{
meanDistance += distances[i];
}
meanDistance /= distances.Length;
//3. calculate the deviation of each data point from the mean, and square the result of each
for (int i = 0; i < distances.Length; i++)
{
float dev = distances[i] - meanDistance;
dev *= dev;
standardDeviation += dev;
}
standardDeviation /= distances.Length;
standardDeviation = Convert.ToSingle(Math.Sqrt(standardDeviation));
}
catch (Exception err)
{
System.Windows.Forms.MessageBox.Show("Error in KDTreeKennnellRemoveDuplicates: " + err.Message);
}
}