private float[,] calcDist(EnumIndexableCollection <FeaturePoint, Vector3DF> fp)
{
int fpSize = fp.Count();
Vector3DF[] temp = new Vector3DF[fpSize];
float[,] fpRes = new float[fpSize, fpSize];
int k = 0;
foreach (Vector3DF p in fp)
{
temp[k] = p;
k++;
}
for (int i = 0; i < (fpSize - 1); i++)
{
for (int j = 0; j < (fpSize - 1); j++)
{
if (i == j)
{
}
// Do stuff to temp!
else
{
fpRes[i, j] = Math.Abs((float)Math.Sqrt((float)Math.Pow(temp[i].X - temp[j].X, 2) + (float)Math.Pow(temp[i].Y - temp[j].Y, 2) + (float)Math.Pow(temp[i].Z - temp[j].Z, 2)));
}
}
}
return(fpRes);
}
private int pointsCount(EnumIndexableCollection <FeaturePoint, Vector3DF> fp)
{
int size = fp.Count();
return(size);
}
/// <summary>
/// Prepares the 3d face points and calls the ERTreesClassifier.Predict method
/// </summary>
/// <param name="forest">ERTreesClassifier object</param>
/// <param name="face3DPoints">3d points of the face</param>
/// <returns>ID of the face</returns>
static public int Recognize(this ERTreesClassifier forest, EnumIndexableCollection<FeaturePoint, Vector3DF> face3DPoints)
{
var varCount = face3DPoints.Count; // x, y, z
Matrix<float> data;
data = new Matrix<float>(1, face3DPoints.Count()*3);
for (int j = 0; j < varCount; j++)
{
data[0, j * 3] = face3DPoints[j].X;
data[0, j * 3 + 1] = face3DPoints[j].Y;
data[0, j * 3 + 2] = face3DPoints[j].Z;
}
var r = forest.Predict(data);
return (int)r;
}