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 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 ClassificationType[,] Classify(LasFile file)
{
var sw = Stopwatch.StartNew();
ClassificationType[,] output = new ClassificationType[_divisionCountX, _divisionCountY];
SubgroupOfPoints[,] values = Utills.GroupPoints(file, _divisionCountX, _divisionCountY);
Statistics stats = new Statistics();
stats.Count = _divisionCountX * _divisionCountY;
for (int i = 0; i < _divisionCountX; i++)
{
for (int j = 0; j < _divisionCountY; j++)
{
double avgHeight = values[i, j].avgHeight;
double avgIntensity = values[i, j].avgIntensity;
double avgDistance = values[i, j].avgDistance;
OpenTK.Vector3 slopeVector = values[i, j].slopeVector;
IMLData classed = Network.Compute(new BasicMLData(
new double[] { avgDistance, avgHeight, avgIntensity, slopeVector[0], slopeVector[1], slopeVector[2] }));
output[i, j] = Utills.QuickClassess[classed.IndexOfMax()];
ClassificationType ct;
if (!Utills.QuickClassess.TryGetValue(values[i, j].classIndex, out ct))
{
continue;
}
if (output[i, j] != ct)
{
stats.ClassErrors[(int)ct]++;
}
stats.PredictionMatrix[(int)output[i, j], (int)ct]++;
stats.PredictionMatrix[(int)ct, (int)output[i, j]]++;
stats.ClassCount[(int)output[i, j]]++;
stats.ClassRealCount[(int)ct]++;
}
Console.WriteLine(i);
}
Console.Write(stats.ToString());
sw.Stop();
Console.WriteLine("Czas trwania [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
stats.SaveMatrixAsCSV();
return(output);
}
