private static SubgroupOfPoints CountSumOfDistancesFromPlane(double[,] matrix, double[,] vector, List <LasPoint> points, int pointsCount, ref double avgIntensity, ref double avgHeight, int classIndex)
{
avgIntensity /= pointsCount;
avgHeight /= pointsCount;
matrix[2, 2] = pointsCount;
Matrix <double> A = DenseMatrix.OfArray(matrix);
Matrix <double> b = DenseMatrix.OfArray(vector);
Matrix <double> x;
A = A.Inverse();
x = b.Multiply(A);
double[,] myarray;
myarray = x.ToArray();
Vector3 abc = new Vector3(Convert.ToSingle(myarray[0, 0]), Convert.ToSingle(myarray[0, 1]), Convert.ToSingle(myarray[0, 2]));
double d = 0;
for (int k = 0; k < pointsCount; k++)
{
d += Math.Abs((abc.X * points[k].X) + (abc.Y * points[k].Y) + (abc.Z * points[k].Z)) / Math.Sqrt((abc.X * abc.X) + (abc.Y * abc.Y) + (abc.Z * abc.Z));
}
d = d / pointsCount;
SubgroupOfPoints group = new SubgroupOfPoints(classIndex, Convert.ToSingle(d), Convert.ToSingle(avgIntensity), abc, Convert.ToSingle(avgHeight));
return(group);
}
public static Tuple <double[][], int[]> MakeInputOutputs(List <SubgroupOfPoints> values)
{
Console.WriteLine("Preparing training dataset... ");
Stopwatch sw = Stopwatch.StartNew();
int count = values.Count;
int[] outputs = new int[count];
double[][] inputs = new double[count][];
for (int i = 0; i < count; i++)
{
SubgroupOfPoints value = values[i];
// ClassificationType.Ground
if (value.classIndex == 4)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 0;
}
// ClassificationType.HighVegetation
else if (value.classIndex == 2)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 1;
}
// ClassificationType.Building
else if (value.classIndex == 0)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 2;
}
// ClassificationType.MediumVegetation
else if (value.classIndex == 1)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 3;
}
// ClassificationType.LowVegetation
else if (value.classIndex == 3)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 4;
}
// ClassificationType.Water
else if (value.classIndex == 5)
{
inputs[i] = new double[] { value.avgDistance, value.avgHeight, value.avgIntensity, value.slopeVector.X, value.slopeVector.Y, value.slopeVector.Z };
outputs[i] = 5;
}
}
sw.Stop();
Console.WriteLine("Preparing training dataset completed [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
return(new Tuple <double[][], int[]>(inputs, outputs));
}
public static SubgroupOfPoints[,] GroupPoints(LasFile file, int divisionCountX, int divisionCountY)
{
SubgroupOfPoints[,] results = new SubgroupOfPoints[divisionCountX, divisionCountY];
bool enoughPoints;
float percentOfClassTypeThreshold;
int PointsCount;
double minX, minY, ratioX, ratioY;
double[,] matrix, vector;
List <LasPoint>[,] pieces;
PrepareGroupPointsData(file, divisionCountX, divisionCountY, out enoughPoints, out percentOfClassTypeThreshold, out PointsCount, out minX, out minY, out ratioX, out ratioY, out matrix, out vector, out pieces);
for (int i = 0; i < PointsCount; i++)
{
var xReal = (float)file.LasPointDataRecords[i].X;
var yReal = (float)file.LasPointDataRecords[i].Y;
int arrayX = (int)((xReal - minX) / ratioX);
int arrayY = (int)((yReal - minY) / ratioY);
if ((arrayX < divisionCountX) && (arrayY < divisionCountY))
{
pieces[arrayX, arrayY].Add(file.LasPointDataRecords[i]);
}
}
for (int i = 0; i < divisionCountX; i++)
{
for (int j = 0; j < divisionCountY; j++)
{
List <LasPoint> points;
int pointsCount;
double avgIntensity, avgHeight;
int[] groupClass;
CountGroupPointsParams(out enoughPoints, matrix, vector, pieces, i, j, out points, out pointsCount, out avgIntensity, out avgHeight, out groupClass);
int classIndex = 6;
if (pointsCount < 12)
{
results[i, j] = new SubgroupOfPoints(7);
enoughPoints = false;
}
else
{
if (groupClass[0] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 0;
}
else if (groupClass[1] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 1;
}
else if (groupClass[2] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 2;
}
else if (groupClass[3] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 3;
}
else if (groupClass[4] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 4;
}
else if (groupClass[5] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 5;
}
}
if (enoughPoints)
{
SubgroupOfPoints group = CountSumOfDistancesFromPlane(matrix, vector, points, pointsCount, ref avgIntensity, ref avgHeight, classIndex);
results[i, j] = group;
}
}
}
return(results);
}
public static List <SubgroupOfPoints> GroupPointsList(LasFile file, int divisionCountX, int divisionCountY)
{
Console.WriteLine("Preparing input dataset... ");
Stopwatch sw = Stopwatch.StartNew();
List <SubgroupOfPoints> results = new List <SubgroupOfPoints>();
bool enoughPoints;
float percentOfClassTypeThreshold;
int PointsCount;
double minX, minY, ratioX, ratioY;
double[,] matrix, vector;
List <LasPoint>[,] pieces;
PrepareGroupPointsData(file, divisionCountX, divisionCountY, out enoughPoints, out percentOfClassTypeThreshold, out PointsCount, out minX, out minY, out ratioX, out ratioY, out matrix, out vector, out pieces);
for (int i = 0; i < PointsCount; i++)
{
var xReal = (float)file.LasPointDataRecords[i].X;
var yReal = (float)file.LasPointDataRecords[i].Y;
int arrayX = (int)((xReal - minX) / ratioX);
int arrayY = (int)((yReal - minY) / ratioY);
if ((arrayX < divisionCountX) && (arrayY < divisionCountY))
{
pieces[arrayX, arrayY].Add(file.LasPointDataRecords[i]);
}
}
for (int i = 0; i < divisionCountX; i++)
{
for (int j = 0; j < divisionCountY; j++)
{
List <LasPoint> points;
int pointsCount;
double avgIntensity, avgHeight;
int[] groupClass;
CountGroupPointsParams(out enoughPoints, matrix, vector, pieces, i, j, out points, out pointsCount, out avgIntensity, out avgHeight, out groupClass);
int classIndex = 6;
if (pointsCount >= 12)
{
if (groupClass[0] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 0;
}
else if (groupClass[1] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 1;
}
else if (groupClass[2] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 2;
}
else if (groupClass[3] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 3;
}
else if (groupClass[4] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 4;
}
else if (groupClass[5] >= (percentOfClassTypeThreshold * pointsCount))
{
classIndex = 5;
}
}
else
{
enoughPoints = false;
}
if (enoughPoints)
{
SubgroupOfPoints group = CountSumOfDistancesFromPlane(matrix, vector, points, pointsCount, ref avgIntensity, ref avgHeight, classIndex);
results.Add(group);
}
}
}
int[] classCount = new int[8];
float[] percentOfClassInputs = { 0.25f, 0.25f, 0.25f, 0.25f, 0.25f, 0.25f, 0.25f };
foreach (SubgroupOfPoints group in results)
{
if (group.classIndex == 0)
{
classCount[0]++;
}
else if (group.classIndex == 1)
{
classCount[1]++;
}
else if (group.classIndex == 2)
{
classCount[2]++;
}
else if (group.classIndex == 3)
{
classCount[3]++;
}
else if (group.classIndex == 4)
{
classCount[4]++;
}
else if (group.classIndex == 5)
{
classCount[5]++;
}
else if (group.classIndex == 6)
{
classCount[6]++;
}
}
results.Sort((a, b) => a.classIndex.CompareTo(b.classIndex));
List <SubgroupOfPoints> properResults = new List <SubgroupOfPoints>();
properResults.AddRange(results.GetRange(0, (int)(classCount[0] * percentOfClassInputs[0])));
properResults.AddRange(results.GetRange(classCount[0], (int)(classCount[1] * percentOfClassInputs[1])));
properResults.AddRange(results.GetRange(classCount[0] + classCount[1], (int)(classCount[2] * percentOfClassInputs[2])));
properResults.AddRange(results.GetRange(classCount[0] + classCount[1] + classCount[2], (int)(classCount[3] * percentOfClassInputs[3])));
properResults.AddRange(results.GetRange(classCount[0] + classCount[1] + classCount[2] + classCount[3], (int)(classCount[4] * percentOfClassInputs[4])));
properResults.AddRange(results.GetRange(classCount[0] + classCount[1] + classCount[2] + classCount[3] + classCount[4], (int)(classCount[5] * percentOfClassInputs[5])));
sw.Stop();
Console.WriteLine("Preparing input dataset completed [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
return(properResults);
}
