/// <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>
/// Algorithm based on ignoring points with less neighbours int thresholdNeighboursCount = 10; float thresholdDistance = 15e-5f;
/// at given distance.
/// </summary>
/// <param name="source"></param>
/// <param name="threshold"></param>
/// <returns></returns>
public static PointCloud ByLessNeighbours(PointCloud source, float thresholdDistance, int thresholdNeighboursCount)
{
PointCloud pcResult = new PointCloud();
KDTreeKennell kdTree = new KDTreeKennell();
kdTree.Build(source);
VertexKDTree[] resultArray = new VertexKDTree[source.Count];
try
{
List <Vector3> listV = new List <Vector3>();
List <Vector3> listC = new List <Vector3>();
System.Threading.Tasks.Parallel.For(0, source.Count, i =>
{
VertexKDTree vSource = new VertexKDTree(source.Vectors[i], source.Colors[i], i);
int neighboursCount = 0;
kdTree.FindClosestPoints_Radius(vSource, thresholdDistance, ref neighboursCount);
if (neighboursCount >= thresholdNeighboursCount)
{
resultArray[i] = vSource;
}
});
for (int i = 0; i < source.Count; i++)
{
if (resultArray[i] != null)
{
listV.Add(resultArray[i].Vector);
listC.Add(resultArray[i].Color);
}
}
pcResult.Vectors = listV.ToArray();
pcResult.Colors = listC.ToArray();
pcResult.SetDefaultIndices();
}
catch (Exception err)
{
System.Windows.Forms.MessageBox.Show("Error in KDTreeKennnellRemoveDuplicates: " + err.Message);
}
return(pcResult);
}
/// <summary>
/// mergedPoints
/// </summary>
/// <param name="pointsTransformed"></param>
/// <param name="pointsTarget"></param>
/// <param name="kdTree"></param>
/// <param name="meanDistance"></param>
/// <returns></returns>
public static PointCloud CalculateMergedPoints(PointCloud pointsTransformed, PointCloud pointsTarget, IKDTree kdTree, float threshold, out int pointsAdded, bool changeColorsOfMergedPoints)
{
if (pointsTransformed.Colors == null || pointsTransformed.Colors.Length != pointsTransformed.Vectors.Length)
{
pointsTransformed.Colors = new Vector3[pointsTransformed.Vectors.Length];
}
if (pointsTarget.Colors == null || pointsTarget.Colors.Length != pointsTarget.Vectors.Length)
{
pointsTarget.Colors = new Vector3[pointsTarget.Vectors.Length];
}
//search in tree
// PointCloud resultKDTree = kdTree.FindClosestPointCloud_Parallel(result);
KDTreeKennell kdTreeKennell = kdTree as KDTreeKennell;
PointCloud pcToAdd = kdTreeKennell.RemoveDuplicates(pointsTransformed, threshold);
pointsAdded = pcToAdd.Vectors.Length;
//System.Diagnostics.Debug.WriteLine("target points added : " + pcToAdd.Vectors.Length.ToString() + " - outof " + result.Vectors.Length.ToString());
if (changeColorsOfMergedPoints)
{
pcToAdd.SetColor(new Vector3(0, 1, 1));
}
List <Vector3> listV = pointsTarget.ListVectors;
List <Vector3> listC = pointsTarget.ListColors;
listV.AddRange(pcToAdd.ListVectors);
listC.AddRange(pcToAdd.ListColors);
PointCloud pMerged = new PointCloud(listV, listC, null, null, null, null);
return(pMerged);
}
public KDTreeNodeKennell(KDTreeKennell parent)
{
Parent = parent;
}
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);
}
}
