public LasPoint.ClassificationType[] Classify(LasFile file, int count = 0)
{
var sw = Stopwatch.StartNew();
LasPointDataRecords points = file.LasPointDataRecords;
if (count == 0 || count > points.Count)
{
count = points.Count;
}
LasPoint.ClassificationType[] output = new LasPoint.ClassificationType[count];
Statistics stats = new Statistics();
stats.Count = count;
OpenTK.Vector3[] abc = new OpenTK.Vector3[count];
Parallel.For(0, count, (i) =>
{
abc[i] = LinearRegression.ComputeRegressionPoint(file, points[i], regressionCount, regressionRange);
if (i % 1000 == 0)
{
Console.WriteLine(i);
}
});
for (int i = 0; i < count; i++)
{
//double[] regression = LinearRegression.ComputeRegressionNumerics(file, points[i], regressionCount, regressionRange);
LasPoint3Short point = (LasPoint3Short)points[i];
//OpenTK.Vector3 abc = LinearRegression.ComputeRegressionPoint(file, points[i], regressionCount, regressionRange);
double distanceFromPlane = Utills.DistanceFromPlane(point, abc[i]);
double green = point.Green - (point.Red + point.Blue) / 2;
IMLData classed = Network.Compute(new BasicMLData(new double[] { green, file.LasHeader.ScaleZ(point.Z), point.Intensity,
abc[i].X, abc[i].Y, abc[i].Z, distanceFromPlane }));
output[i] = Utills.QuickClassess[classed.IndexOfMax()];
if (output[i] != points[i].Classification)
{
stats.ClassErrors[(int)points[i].Classification]++;
}
stats.ClassCount[(int)output[i]]++;
stats.ClassRealCount[(int)points[i].Classification]++;
stats.PredictionMatrix[(int)points[i].Classification, (int)output[i]]++;
if (i % 1000 == 0)
{
Console.WriteLine(i);
}
}
Console.Write(stats.ToString());
sw.Stop();
Console.WriteLine("Czas trwania [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
stats.SaveMatrixAsCSV();
return(output);
}
public LasPoint.ClassificationType[] Classify(LasFile file)
{
var sw = Stopwatch.StartNew();
LasPointDataRecords points = file.LasPointDataRecords;
int pointsCount = points.Count();
LasPoint.ClassificationType[] output = new LasPoint.ClassificationType[pointsCount];
Statistics stats = new Statistics();
stats.Count = pointsCount;
OpenTK.Vector3[] slopeVector = new OpenTK.Vector3[pointsCount];
Parallel.For(0, pointsCount, (i) =>
{
slopeVector[i] = LinearRegression.ComputeRegressionPoint(file, points[i], regressionCount, regressionRange);
if (i % 1000 == 0)
{
Console.WriteLine("ComputeRegression " + i);
}
});
for (int i = 0; i < pointsCount; i++)
{
LasPoint3Short point = (LasPoint3Short)points[i];
double distanceFromPlane = Utills.DistanceFromPlane(point, slopeVector[i]);
double green = point.Green - (point.Red + point.Blue) / 2;
output[i] = Utills.ClassificationClasses[knn.Compute(new double[] { green,
file.LasHeader.ScaleZ(point.Z), point.Intensity, slopeVector[i].X,
slopeVector[i].Y, slopeVector[i].Z, distanceFromPlane })];
if (output[i] != points[i].Classification)
{
stats.ClassErrors[(int)points[i].Classification]++;
}
stats.ClassCount[(int)output[i]]++;
stats.ClassRealCount[(int)points[i].Classification]++;
stats.PredictionMatrix[(int)points[i].Classification, (int)output[i]]++;
if (i % 1000 == 0)
{
Console.WriteLine(i);
}
}
Console.Write(stats.ToString());
sw.Stop();
Console.WriteLine("Czas trwania [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
stats.SaveMatrixAsCSV();
return(output);
}
public static List <Vector3> GenerateVectorArray(List <LasPoint> classPointsRanged, LasFile file)
{
List <Vector3> vectors = new List <Vector3>();
Console.WriteLine("Generationg Vector Arrays in progress");
int pointNo = 0;
foreach (LasPoint point in classPointsRanged)
{
pointNo += 1;
vectors.Add(LinearRegression.ComputeRegressionPoint(file, point, 100, 2));
Console.WriteLine("Computing Vectors " + pointNo + "/" + classPointsRanged.Count);
}
return(vectors);
}
public static Tuple <double[], int> GetInputOutputFromClassificationType(LasPoint point, LasFile file)
{
var abc = LinearRegression.ComputeRegressionPoint(file, point, regressionCount, regressionRange);
double distanceFromPlane = Utills.DistanceFromPlane(point, abc);
LasPoint3Short pointShort = (LasPoint3Short)point;
double green = pointShort.Green - (pointShort.Red + pointShort.Blue) / 2;
var input = new double[] { green, file.LasHeader.ScaleZ(point.Z), point.Intensity, abc.X, abc.Y, abc.Z, distanceFromPlane };
int output;
switch (point.Classification)
{
case LasPoint.ClassificationType.Ground:
output = 0;
break;
case LasPoint.ClassificationType.HighVegetation:
output = 1;
break;
case LasPoint.ClassificationType.Building:
output = 2;
break;
case LasPoint.ClassificationType.MediumVegetation:
output = 3;
break;
case LasPoint.ClassificationType.LowVegetation:
output = 4;
break;
case LasPoint.ClassificationType.Water:
output = 5;
break;
default:
input = new double[] { -1, -1, -1, -1, -1, -1, -1 };
output = 6;
break;
}
return(Tuple.Create(input, output));
}
public EncogNeuralNetworkSlow(LasFile file)
{
var sw = Stopwatch.StartNew();
int count = 300000;
LasPointDataRecords points = file.LasPointDataRecords;
double[][] input = new double[count][];
double[][] ideal = new double[count][];
int waterCount = 0;
int groundCount = 0;
int lowCount = 0;
int mediumCount = 0;
int highCount = 0;
int buildingCount = 0;
for (int i = 0; i < count; i++)
{
int rndNumber;
while (true)
{
rndNumber = _rnd.Next(0, points.Count - 1);
if (points[rndNumber].Classification == LasPoint.ClassificationType.Water)
{
waterCount++;
if (waterCount - 25 < count / 6)
{
break;
}
}
else if (points[rndNumber].Classification == LasPoint.ClassificationType.Ground)
{
groundCount++;
if (groundCount - 25 < count / 6)
{
break;
}
}
else if (points[rndNumber].Classification == LasPoint.ClassificationType.Building)
{
buildingCount++;
if (buildingCount - 25 < count / 6)
{
break;
}
}
else if (points[rndNumber].Classification == LasPoint.ClassificationType.LowVegetation)
{
lowCount++;
if (lowCount - 25 < count / 6)
{
break;
}
}
else if (points[rndNumber].Classification == LasPoint.ClassificationType.MediumVegetation)
{
mediumCount++;
if (mediumCount - 25 < count / 6)
{
break;
}
}
else if (points[rndNumber].Classification == LasPoint.ClassificationType.HighVegetation)
{
highCount++;
if (highCount - 25 < count / 6)
{
break;
}
}
if (highCount > 5 * count)
{
highCount = 0;
}
if (buildingCount > 5 * count)
{
buildingCount = 0;
}
if (lowCount > 5 * count)
{
lowCount = 0;
}
if (mediumCount > 5 * count)
{
mediumCount = 0;
}
if (waterCount > 5 * count)
{
waterCount = 0;
}
if (groundCount > 5 * count)
{
groundCount = 0;
}
}
if (i % 1000 == 0)
{
Console.WriteLine("Selected point: " + i + "/" + count);
}
//double[] regression = LinearRegression.ComputeRegressionNumerics(file, points[rndNumber], regressionCount, regressionRange);
OpenTK.Vector3 abc = LinearRegression.ComputeRegressionPoint(file, points[rndNumber], regressionCount, regressionRange);
LasPoint3Short point = (LasPoint3Short)points[rndNumber];
double distanceFromPlane = Utills.DistanceFromPlane(point, abc);
double green = point.Green - (point.Red + point.Blue) / 2;
input[i] = new double[] { green, file.LasHeader.ScaleZ(point.Z), point.Intensity, abc.X, abc.Y, abc.Z, distanceFromPlane };
ideal[i] = Utills.ClassToVector(point.Classification);
}
inputNumber = input[0].Length;
init();
IMLDataSet trainingSet = new BasicMLDataSet(input, ideal);
IMLTrain train = new ResilientPropagation(Network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Train error: " + train.Error + ", iteration: " + epoch);
epoch++;
} while (epoch < 1000);
LearningError = train.Error;
train.FinishTraining();
sw.Stop();
Console.WriteLine("Czas trwania [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
}