public LasPoint.ClassificationType[] Classify(LasFile file)
{
Stopwatch sw = Stopwatch.StartNew();
LasPointDataRecords points = file.LasPointDataRecords;
LasPoint.ClassificationType[] output = new LasPoint.ClassificationType[points.Count];
Statistics stats = new Statistics();
stats.Count = points.Count;
for (int i = 0; i < points.Count; i++)
{
LasPoint3Short point = (LasPoint3Short)points[i];
double green = point.Green - (point.Red + point.Blue) / 2;
IMLData classed = Network.Compute(
new BasicMLData(new double[] { file.LasHeader.ScaleZ(point.Z), point.Intensity, green }));
output[i] = Utills.QuickClassess[classed.IndexOfMax()];
if (output[i] != points[i].Classification)
{
stats.ClassErrors[(int)points[i].Classification]++;
}
stats.PredictionMatrix[(int)points[i].Classification, (int)output[i]]++;
stats.ClassCount[(int)output[i]]++;
stats.ClassRealCount[(int)points[i].Classification]++;
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 double[] ClassToVector(LasPoint.ClassificationType classType)
{
double[] ideal = new double[] { 0, 0, 0, 0, 0, 0 };
if (classType == LasPoint.ClassificationType.Building)
{
ideal[0] = 1;
}
else if (classType == LasPoint.ClassificationType.MediumVegetation)
{
ideal[1] = 1;
}
else if (classType == LasPoint.ClassificationType.HighVegetation)
{
ideal[2] = 1;
}
else if (classType == LasPoint.ClassificationType.LowVegetation)
{
ideal[3] = 1;
}
else if (classType == LasPoint.ClassificationType.Ground)
{
ideal[4] = 1;
}
else if (classType == LasPoint.ClassificationType.Water)
{
ideal[5] = 1;
}
return(ideal);
}
public List <LasPoint> GetPointsByClassification(LasPoint.ClassificationType classType, double frac)
{
var random = new Random();
List <int> points;
if (!_classificationMap.TryGetValue(classType, out points))
{
points = new List <int>();
}
int count = (int)Math.Floor(points.Count * frac);
return(Enumerable.Range(0, count).Select(i => points[random.Next(points.Count - 1)]).Select(el => this[el]).ToList());
}
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 static bool IsSignificant(LasPoint.ClassificationType clType)
{
switch (clType)
{
case LasPoint.ClassificationType.Ground:
case LasPoint.ClassificationType.LowVegetation:
case LasPoint.ClassificationType.MediumVegetation:
case LasPoint.ClassificationType.HighVegetation:
case LasPoint.ClassificationType.Building:
case LasPoint.ClassificationType.Water:
return(true);
}
return(false);
}
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 List <LasPoint> GetPointsByClassification(LasPoint.ClassificationType classType)
{
return(_classificationMap[classType].Select(el => this[el]).ToList());
}
