private MWNumericArray transpose(MWNumericArray array)
{
double[] tmp = (double[])array.ToVector(MWArrayComponent.Real);
return new MWNumericArray(tmp.Length, 1, tmp);
}
public void GrayScaleImageTest(ImageData imageData)
{
byte[] a = Convert.FromBase64String(imageData.Base64);
//MemoryStream memoryStream = new MemoryStream(a);
//Image image = System.Drawing.Image.FromStream(memoryStream);
Analyse z = new Analyse();
MWNumericArray mWNumericArray = a;
MWArray I = z.analyseImage(mWNumericArray, imageData.CoordinateXY[0], imageData.CoordinateXY[1]);
MWNumericArray I_num = I.ToArray();
Byte[] I_bytes = (Byte[])I_num.ToVector(MWArrayComponent.Real);
int w = I.Dimensions[0];
int h = I.Dimensions[1];
Bitmap bmp = new Bitmap(w, h, PixelFormat.Format8bppIndexed);
ColorPalette cp = bmp.Palette;
for (Int32 i = 0; i < 256; ++i)
{
cp.Entries[i] = Color.FromArgb(255, i, i, i);
}
bmp.Palette = cp;
BitmapData data = bmp.LockBits((new Rectangle(0, 0, bmp.Width, bmp.Height)), ImageLockMode.WriteOnly, bmp.PixelFormat);
Marshal.Copy(I_bytes, 0, data.Scan0, I_bytes.Length);
bmp.UnlockBits(data);
bmp.Save("image.png", ImageFormat.Png);
}
public double[,] doFFT(double[] sinal, double freqAm)
{
double[] resY = new double[sinal.Length];
double[] resX = new double[sinal.Length];
double[,] res = new double[sinal.Length, 2];
MWNumericArray aux_res2 = new MWNumericArray(new double[sinal.Length]);
MWNumericArray aux_res2X = new MWNumericArray(new double[sinal.Length]);
MWNumericArray xdt = new MWNumericArray(new double[sinal.Length]);
Array aux_res3 = null;
Array aux_res3X = null;
fftMatlab2 obj = new fftMatlab2();
xdt = sinal;
MWArray aux_res = obj.IB_fft(xdt, freqAm);
MWArray aux_resX = obj.IB_fftx(xdt, freqAm);
aux_res2X = (MWNumericArray)aux_resX;
aux_res2 = (MWNumericArray)aux_res;
aux_res3X = aux_res2X.ToVector(MWArrayComponent.Real);
aux_res3 = aux_res2.ToVector(MWArrayComponent.Real);
resY = (double[])aux_res3;
resX = (double[])aux_res3X;
for (int i = 0; i < resX.Length; i++)
{
res[i, 0] = resY[i]; //linha 0 valores de Y
res[i, 1] = resX[i]; //linha 1 valores de X
}
return(res);
}
public FittingResult Predict(Spectrum specInput, bool needFilter = true, int numOfId = 5, int topK = 1)
{
var spec = specInput.Clone();
var y = new MWNumericArray(specInput.Data.Lenght, 1, specInput.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
y = Preprocesser.ProcessForPredict(this._filters, y);
spec.Data.Y = (double[])y.ToVector(MWArrayComponent.Real);
}
var handler = Tools.ModelHandler;
var rlst = handler.FitPredictor(5, this._lib.X, y, this._wind, (MWNumericArray)this._region.VarIndex, topK);
var tq = (MWNumericArray)rlst[0];
var sq = (MWNumericArray)rlst[1];
var ratio = (MWNumericArray)rlst[2];
var idx = (MWNumericArray)rlst[3];
var fit = (MWNumericArray)rlst[4];
var fitspec = spec.Clone();
fitspec.Components = this._lib.Components.Clone();
fitspec.Data.Y = (double[])fit.ToVector(MWArrayComponent.Real);
var r = new FittingResult()
{
SpecOriginal = spec,
SQ = sq.ToScalarDouble(),
TQ = tq.ToScalarDouble(),
FitSpec = fitspec,
MinSQ = this._minSQ,
MinTQ = this._TQ,
Wind = this._wind,
VarIndex = this._region.VarIndex
};
var flst = new List <FittingSpec>();
for (int i = 1; i <= ratio.NumberOfElements; i++)
{
if ((double)ratio[i] <= double.Epsilon)
{
break;
}
flst.Add(new FittingSpec()
{
Spec = this._lib[(int)idx[i] - 1],
Rate = (double)ratio[i]
});
}
r.Specs = flst.ToArray();
this.getResult(ref r);
r.Result = r.TQ >= this._TQ && r.SQ >= this._minSQ;
return(r);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
PointCloud Rhino_Cloud = new PointCloud();
/// read inputs
if (!DA.GetData("Point_Cloud", ref Rhino_Cloud))
{
return;
}
if (!DA.GetData("k", ref k))
{
return;
}
/// interal parameters
var Rhino_xyz = Rhino_Cloud.GetPoints();
List <double> xyz = new List <double>();
if (!Rhino_Cloud.ContainsNormals)
{
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
xyz.Add(Rhino_xyz[i].X);
xyz.Add(Rhino_xyz[i].Y);
xyz.Add(Rhino_xyz[i].Z);
}
}
///2.
var Matlab_xyz = new MWNumericArray(Rhino_Cloud.Count, 3, xyz.ToArray());
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
MWArray array = new MWNumericArray();
array = pointcloudutilities.G_compute_normals(Matlab_xyz, k);
/// 4.
MWNumericArray na = (MWNumericArray)array;
double[] dc = (double[])na.ToVector(0);
/// 5.%£%/:
Resolution = dc[0];
/// 6.
DA.SetData(0, Resolution);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
PointCloud Rhino_Cloud = new PointCloud();
double Samples = new double();
double Resolution = new double();
/// read inputs
if (!DA.GetData("Point_Cloud", ref Rhino_Cloud))
{
return;
}
if (!DA.GetData("Samples", ref Samples))
{
return;
}
/// interal parameters
var Rhino_xyz = Rhino_Cloud.GetPoints();
List <double> xyz = new List <double>();
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
xyz.Add(Rhino_xyz[i].X);
xyz.Add(Rhino_xyz[i].Y);
xyz.Add(Rhino_xyz[i].Z);
}
///2.
var Matlab_samples = new MWNumericArray(Samples);
var Matlab_xyz = new MWNumericArray(Rhino_Cloud.Count, 3, xyz.ToArray());
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
MWArray cluster = new MWNumericArray();
cluster = segment_mesh.G_Resolution_PCD(Matlab_xyz, Matlab_samples);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.%£%/:
Resolution = dc[0];
/// 6.
DA.SetData(0, Resolution);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
List <double> tform = new List <double>();
/// initialise parameters
///import
if (!DA.GetDataList("tform", tform))
{
return;
}
/// 0. error catching
/// 1. decompose tform into translation
/// 2. decompose tform in euler XYZ rotations
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
///0.
if (tform.Count != 16)
{
throw new Size_Exception(string.Format("Need[16, 1] parameters"));
}
/// 1.
Vector3d T = new Vector3d(tform[3], tform[7], tform[11]);
/// 2.
var param = new MWNumericArray(16, 1, tform.ToArray());
Scan2BIM_Matlab.General general = new General();
MWArray rotXYZ = new MWNumericArray();
rotXYZ = general.S2B_rotm2eul(param);
/// 4.
MWNumericArray na = (MWNumericArray)rotXYZ;
double[] dc = (double[])na.ToVector(0);
/// 5.
var Rhino_rotXYZ = new List <double>(dc);
Vector3d R = new Vector3d(Rhino_rotXYZ[0], Rhino_rotXYZ[1], Rhino_rotXYZ[2]);
/// 6.
DA.SetData("R", R);
DA.SetData("t", T);
}
public double[] passaAlta(double[] sinal, int polos, int fc, int fs)
{
double[] res = new double[sinal.Length];
MWNumericArray aux_res2 = new MWNumericArray(new double[sinal.Length]);
MWNumericArray xdt = new MWNumericArray(new double[sinal.Length]);
Array aux_res3 = null;
Fltros obj = new Fltros();
xdt = sinal;
MWArray aux_res = obj.HighPassFilt(xdt, polos, fc, fs);
aux_res2 = (MWNumericArray)aux_res;
aux_res3 = aux_res2.ToVector(MWArrayComponent.Real);
res = (double[])aux_res3;
return(res);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
PointCloud Rhino_Cloud1 = new PointCloud();
PointCloud Rhino_Cloud2 = new PointCloud();
/// initialise parameters
double Metric = 0.0, InlierRatio = 0.2, MaxIterations = 20, Downsampling1 = 1.0, Downsampling2 = 1.0;
///import
if (!DA.GetData("pc_moving", ref Rhino_Cloud1))
{
return;
}
if (!DA.GetData("pc_fixed", ref Rhino_Cloud2))
{
return;
}
if (!DA.GetData("Metric", ref Metric))
{
return;
}
if (!DA.GetData("InlierRatio", ref InlierRatio))
{
return;
}
if (!DA.GetData("MaxIterations", ref MaxIterations))
{
return;
}
if (!DA.GetData("Downsample pc_moving", ref Downsampling1))
{
return;
}
if (!DA.GetData("Downsample pc_fixed", ref Downsampling2))
{
return;
}
/// 0. error catching
/// 1. decompose points into doubles (rhinocommon => .NET)
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
if (Rhino_Cloud1.Count <= 1 || Rhino_Cloud2.Count <= 1)
{
throw new Size_Exception(string.Format("Use point clouds with more than 1 point"));
}
if (Downsampling1 == 0 || Downsampling2 == 0)
{
throw new Size_Exception(string.Format("Do not use 0 as downsampling"));
}
/// 1.
var Rhino_xyz1 = Rhino_Cloud1.GetPoints();
var Rhino_xyz2 = Rhino_Cloud2.GetPoints();
List <double> xyz1 = new List <double>();
List <double> xyz2 = new List <double>();
int interval1 = (int)(1 / Downsampling1);
int interval2 = (int)(1 / Downsampling2);
var Rhino_xyz11 = Rhino_xyz1
.Where((v, index) => index % interval1 == 0)
.ToList();
var Rhino_xyz22 = Rhino_xyz2
.Where((v, index) => index % interval2 == 0)
.ToList();
for (int i = 0; i < Rhino_xyz11.Count; i++)
{
xyz1.Add(Rhino_xyz11[i].X);
xyz1.Add(Rhino_xyz11[i].Y);
xyz1.Add(Rhino_xyz11[i].Z);
}
var Matlab_xyz1 = new MWNumericArray(Rhino_xyz11.Count, 3, xyz1.ToArray());
for (int j = 0; j < Rhino_xyz22.Count; j++)
{
xyz2.Add(Rhino_xyz22[j].X);
xyz2.Add(Rhino_xyz22[j].Y);
xyz2.Add(Rhino_xyz22[j].Z);
}
var Matlab_xyz2 = new MWNumericArray(Rhino_xyz22.Count, 3, xyz2.ToArray());
/// 3.
Scan2BIM_Matlab.General general = new General();
var mwca = (MWCellArray)general.S2B_ICP2(Matlab_xyz1, Matlab_xyz2, Metric, InlierRatio, MaxIterations);
/// 4.
MWNumericArray na0 = (MWNumericArray)mwca[1];
double[] dc0 = (double[])na0.ToVector(0);
MWNumericArray na1 = (MWNumericArray)mwca[2];
double[] dc1 = (double[])na1.ToVector(0);
/// 5.
var Rhino_param0 = new List <double>(dc0);
var Rhino_param1 = new List <double>(dc1);
/// 6.
DA.SetDataList(0, Rhino_param0);
DA.SetDataList(1, Rhino_param1);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
PointCloud Rhino_Cloud = new PointCloud();
Double k = 6;
/// read inputs
if (!DA.GetData(0, ref Rhino_Cloud))
{
return;
}
if (!DA.GetData("k", ref k))
{
return;
}
if (Rhino_Cloud.Count <= k)
{
throw new Size_Exception(string.Format("Use point clouds with more than k points"));
}
/// interal parameters
List <Vector3d> normals = new List <Vector3d>();
if (!Rhino_Cloud.ContainsNormals)
{
var X = Rhino_Cloud.Select(x => x.X).ToList();
var Y = Rhino_Cloud.Select(x => x.Y).ToList();
var Z = Rhino_Cloud.Select(x => x.Z).ToList();
///2.
var Matlab_X = new MWNumericArray(Rhino_Cloud.Count, 1, X.ToArray());
var Matlab_Y = new MWNumericArray(Rhino_Cloud.Count, 1, Y.ToArray());
var Matlab_Z = new MWNumericArray(Rhino_Cloud.Count, 1, Z.ToArray());
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
var mwca = (MWCellArray)segment_mesh.G_Normals(Matlab_X, Matlab_Y, Matlab_Z, k);
/// 4.
MWNumericArray na0 = (MWNumericArray)mwca[1];
double[] dc0 = (double[])na0.ToVector(0);
MWNumericArray na1 = (MWNumericArray)mwca[2];
double[] dc1 = (double[])na1.ToVector(0);
MWNumericArray na2 = (MWNumericArray)mwca[3];
double[] dc2 = (double[])na2.ToVector(0);
/// 5.
var Rhino_param0 = new List <double>(dc0);
var Rhino_param1 = new List <double>(dc1);
var Rhino_param2 = new List <double>(dc2);
Vector3d R = new Vector3d();
for (int i = 0; i < Rhino_param0.Count; i++)
{
R = new Vector3d(Rhino_param0[i], Rhino_param1[i], Rhino_param2[i]);
normals.Add(R);
}
}
else
{
normals = Rhino_Cloud.GetNormals().ToList();
}
PointCloud Rhino_Cloud_out = new PointCloud();
Rhino_Cloud_out.AddRange(Rhino_Cloud.GetPoints(), normals);
GH_Cloud Rhino_Cloud_out2 = new GH_Cloud(Rhino_Cloud_out);
/// 6.
DA.SetData(0, Rhino_Cloud_out2);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
List <Point3d> Rhino_C = new List <Point3d>();
List <Point3d> Rhino_P = new List <Point3d>();
/// initialise internal parameters
int k = 100, n = 4; double t = 0.015, w = 0.7, dw = 0.1;
if (!DA.GetDataList <Point3d>("C", Rhino_C))
{
return;
}
if (!DA.GetDataList <Point3d>("P", Rhino_P))
{
return;
}
if (!DA.GetData("k", ref k))
{
return;
}
if (!DA.GetData("t", ref t))
{
return;
}
if (!DA.GetData("dw", ref dw))
{
return;
}
if (!DA.GetData("w", ref w))
{
return;
}
if (!DA.GetData("n", ref n))
{
return;
}
/// 1. decompose P,C into lists of coordinates (rhinocommon => .NET)
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. compute proper geometry based on n=2,3,...n
/// 7. output data
///Point3d test = new Point3d();
List <double> Cx = new List <double>();
List <double> Cy = new List <double>();
List <double> Cz = new List <double>();
List <double> Px = new List <double>();
List <double> Py = new List <double>();
List <double> Pz = new List <double>();
/// List<double> n = new List<double>();
///List<double> c= new List<double>();
//MWNumericArray Matlab_Cx = new MWNumericArray();
//MWNumericArray Matlab_Cy = new MWNumericArray();
//MWNumericArray Matlab_Px = new MWNumericArray();
//MWNumericArray Matlab_Py = new MWNumericArray();
for (int i = 0; i < Rhino_C.Count; i++)
{
Cx.Add(Rhino_C[i].X);
Cy.Add(Rhino_C[i].Y);
Cz.Add(Rhino_C[i].Z);
}
for (int i = 0; i < Rhino_P.Count; i++)
{
Px.Add(Rhino_P[i].X);
Py.Add(Rhino_P[i].Y);
Pz.Add(Rhino_P[i].Z);
}
///2.
var Matlab_Cx = new MWNumericArray(Rhino_C.Count, 1, Cx.ToArray());
var Matlab_Cy = new MWNumericArray(Rhino_C.Count, 1, Cy.ToArray());
var Matlab_Px = new MWNumericArray(Rhino_P.Count, 1, Px.ToArray());
var Matlab_Py = new MWNumericArray(Rhino_P.Count, 1, Py.ToArray());
/// 3.
Reconstruction.fittwall fitt_wall = new Reconstruction.fittwall();
///MWArray result = new MWNumericArray(4);
var result = fitt_wall.G_fitwall(4, Matlab_Cx, Matlab_Cy, Matlab_Px, Matlab_Py, k, t, dw, w, n);
/// 4.
MWNumericArray out1 = (MWNumericArray)result[0];
double[] Mx = (double[])out1.ToVector(0);
MWNumericArray out2 = (MWNumericArray)result[1];
double[] My = (double[])out2.ToVector(0);
MWNumericArray out3 = (MWNumericArray)result[2];
double[] inlrNum = (double[])out3.ToVector(0);
MWNumericArray out4 = (MWNumericArray)result[3];
double[] error = (double[])out4.ToVector(0);
/// 5.
var Rhino_Mx = new List <double>(Mx);
var Rhino_My = new List <double>(My);
double Rhino_inlrNum = inlrNum[0];
double Rhino_error = error[0];
/// 6.
List <Point3d> Rhino_M = new List <Point3d>();
for (int i = 0; i < Rhino_Mx.Count; i++)
{
var temp = new Point3d(Rhino_Mx[i], Rhino_My[i], Cz.Min());
Rhino_M.Add(temp);
}
if (Rhino_M.Count == 2)
{
var geometry = new Line(Rhino_M[0], Rhino_M[1]);
DA.SetData(0, geometry);
}
if (Rhino_Mx.Count == 3)
{
var geometry = new Arc(Rhino_M[0], Rhino_M[1], Rhino_M[2]);
DA.SetData(0, geometry);
}
else
{
var geometry = new Polyline(Rhino_M);
DA.SetData(0, geometry);
}
/// 7.
DA.SetData(1, Rhino_inlrNum);
//DA.SetData(2, Rhino_error);
}
public IdentifyResult Predict(Spectrum speciii, bool needFilter = true, int numOfId = 5, int topK = 1)
{
if (this._lib == null || speciii == null)
{
throw new ArgumentNullException("");
}
var spec = speciii.Clone();
var y = new MWNumericArray(speciii.Data.Lenght, 1, speciii.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
y = Preprocesser.ProcessForPredict(this._filters, y);
spec.Data.Y = (double[])y.ToVector(MWArrayComponent.Real);
}
var handler = Tools.ModelHandler;
//Tools.ToolHandler.Save(this._lib.X, "x", "x");
//Tools.ToolHandler.Save(y, "y", "y");
//Tools.ToolHandler.Save(this._baseLib.X, "xa", "xa");
//this.LibBase.FullPath = "aaaa.lib";
//this.LibBase.Save();
this._maxNum = Math.Min(this._maxNum, this._lib.X.Dimensions[1]);
var rlst = handler.IdentifyPredictor(3, this._lib.X, y, this._wind, this._maxNum, this._p, this._w, this._t, topK);
var minSQ = (MWNumericArray)rlst[0];
var minTQ = (MWNumericArray)rlst[1];
var ad = (MWNumericArray)rlst[2];
var r = new IdentifyResult()
{
Spec = spec,
MinSQ = this.MinSQ,
MinTQ = this.TQ
};
//设置结果
var itemlst = new List <IdentifyItem>();
// r.Items = new IdentifyItem[ad.NumberOfElements];
for (int i = 0; i < this._maxNum; i++)
{
var adidx = (int)ad[i + 1] - 1;
if (adidx >= this._lib.Count || adidx < 0)
{
break;
}
itemlst.Add(new IdentifyItem()
{
Parent = r,
TQ = (double)minTQ[i + 1],
SQ = (double)minSQ[i + 1],
Spec = this._lib[adidx],
Wind = this._wind,
SpecOriginal = r.Spec
});
}
r.Items = itemlst.ToArray();
r = GetPredictValue(r, r.Items.Length, numOfId);
r.IsId = r.Items.Where(d => d.Result).Count() > 0;
return(r);
}
public void execute()
{
double[,] nm;
int m_b;// = noOfSuspects - 1;
double[,] Phi_w = new double[height * width, noOfImages];
int[,] t;
double[,] one;
double[,] mc;
double[,] res;
double[,] Phi_b;
int person = 0;
double[] eigenval;
double[,] eigenvtr;
double[] eigenval_sbt;
double[,] eigenvtr_sbt;
int[] index;
double[] aa;
double[] bb;
double[] tmp;
double[,] D_b;
double[,] mU_Sw_U;
double[] evl;
double[,] evr;
double[,] A;
double[] D_w;
//int m_b;
//int Sb_t;
//static int l=height*width;
//static int b=
for (int i = 0; i < noOfSuspects; i++)
{
breadth = imagePerClass[i];
t = new int[height * width, breadth];
one = new double[1, breadth];
for (int j = 0; j < breadth; j++)
{
one[0, j] = 1;
}
mc = new double[height * width, 1];
for (int j = 0; j < height * width; j++)
{
mc[j, 0] = mean_c[j, i];
for (int k = 0; k < imagePerClass[i]; k++)
{
t[j, k] = images[j, person + k];
}
}
nm = new double[mc.GetLength(0) * one.GetLength(0), mc.GetLength(1) * one.GetLength(1)];
k_t_p(ref mc, ref one, ref nm);
res = new double[nm.GetLength(0), nm.GetLength(1)];
for (int j = 0; j < nm.GetLength(0); j++)
{
for (int k = 0; k < nm.GetLength(1); k++)
{
res[j, k] = t[j, k] - nm[j, k];
}
}
for (int j = 0; j < height * width; j++)
{
for (int k = person; k < person + imagePerClass[i]; k++)
{
Phi_w[j, k] = res[j, k - person];
}
}
person = person + imagePerClass[i];
}
//int oo = 0;
for (int j = 0; j < Phi_w.GetLength(0); j++)
{
for (int k = 0; k < Phi_w.GetLength(1); k++)
{
Phi_w[j, k] = Phi_w[j, k] / Math.Sqrt(noOfImages);
}
}
one = new double[1, noOfSuspects];
for (int j = 0; j < noOfSuspects; j++)
{
one[0, j] = 1;
}
nm = new double[mean_i.GetLength(0) * one.GetLength(0), mean_c.GetLength(1) * one.GetLength(1)];
double[,] mi = new double[height * width, 1];
for (int j = 0; j < mi.GetLength(0); j++)
{
mi[j, 0] = mean_i[j];
}
k_t_p(ref mi, ref one, ref nm);
Phi_b = new double[mean_c.GetLength(0), mean_c.GetLength(1)];
for (int j = 0; j < mean_c.GetLength(0); j++)
{
for (int k = 0; k < mean_c.GetLength(1); k++)
{
Phi_b[j, k] = mean_c[j, k] - nm[j, k];
}
}
for (int i = 0; i < noOfSuspects; i++)
{
for (int j = 0; j < Phi_b.GetLength(0); j++)
{
Phi_b[j, i] = Phi_b[j, i] * Math.Sqrt((double)imagePerClass[i] / (double)noOfImages);
}
}
nm = new double[Phi_b.GetLength(1), Phi_b.GetLength(0)];
res = new double[nm.GetLength(0), Phi_b.GetLength(1)];
transpose(ref Phi_b, ref nm);
matrix_multiply(ref nm, ref Phi_b, ref res);
///calculation of Eigenvector of
MWArray c = null;
Eutilityclass Ev = new Eutilityclass();
c = Ev.Eigenvtr1((MWNumericArray)res);
MWNumericArray D = (MWNumericArray)c;
Array pA = D.ToVector(MWArrayComponent.Real);
double[] doubleArray1 = (double[])pA;
int icount = 0;
nm = new double[res.GetLength(0), res.GetLength(1)];//eigenvector
for (int i = 0; i < res.GetLength(0); i++)
{
for (int j = 0; j < res.GetLength(1); j++)
{
nm[j, i] = doubleArray1[icount];
icount++;
}
}
c = Ev.Eigenvlu1((MWNumericArray)res);
eigenval = new double[res.GetLength(0)];
D = (MWNumericArray)c;
pA = D.ToVector(MWArrayComponent.Real);
doubleArray1 = (double[])pA;
icount = 0;
int count = 0;
for (int i = 0; i < res.GetLength(0); i++)
{
for (int j = 0; j < res.GetLength(1); j++)
{
if (j == i)
{
eigenval[count] = Math.Abs(doubleArray1[icount]);
count++;
}
icount++;
}
}
eigenval_sbt = new double[res.GetLength(1)];
eigenval_sbt = (double[])eigenval.Clone();
Array.Sort(eigenval);
index = new int[res.GetLength(0)];
for (int i = 0; i < eigenval_sbt.GetLength(0); i++)
{
index[i] = eigenval_sbt.GetLength(0) - 1 - Array.IndexOf(eigenval, eigenval[i]);
eigenval_sbt[i] = eigenval[eigenval_sbt.GetLength(0) - i - 1];
}
aa = new double[res.GetLength(0)];
eigenvtr_sbt = new double[res.GetLength(0), res.GetLength(1)];
count = 0;
for (int i = 0; i < res.GetLength(0); i++)
{
for (int j = 0; j < res.GetLength(1); j++)
{
eigenvtr_sbt[j, (int)index[i]] = nm[j, i];
}
aa[i] = eigenval_sbt[i] / eigenval_sbt[0];
if (aa[i] < thresh_eigval_sb)
{
count++;
}
}
bb = new double[count];
m_b = (int)((Math.Round(((double)(noOfSuspects - 1)) * remain_eigvec)));
count = 0;
//double temp;
for (int i = 0; i < eigenval_sbt.GetLength(0); i++)
{
if (aa[i] < thresh_eigval_sb)
{
bb[count] = i;
count++;
}
//temp=ei
}
//MessageBox.Show(""+m_b);
eigenvtr = new double[eigenvtr_sbt.GetLength(0), m_b];
eigenval = new double[m_b];
D_b = new double[m_b, m_b];
for (int j = 0; j < m_b; j++)
{
for (int i = 0; i < eigenvtr_sbt.GetLength(0); i++)
{
eigenvtr[i, j] = eigenvtr_sbt[i, j];
if (i == j)
{
D_b[i, j] = Math.Pow(eigenval_sbt[j], -1);
}
}
eigenval[j] = eigenval_sbt[j];
}
eigenvtr_sbt = new double[Phi_b.GetLength(0), eigenvtr.GetLength(1)];
matrix_multiply(ref Phi_b, ref eigenvtr, ref eigenvtr_sbt);
nm = new double[eigenvtr_sbt.GetLength(0), D_b.GetLength(0)]; ///Z
matrix_multiply(ref eigenvtr_sbt, ref D_b, ref (nm));
res = new double[nm.GetLength(1), nm.GetLength(0)]; //Z'
transpose(ref nm, ref res);
mc = new double[res.GetLength(0), Phi_w.GetLength(1)]; //mT
matrix_multiply(ref res, ref Phi_w, ref mc);
mU_Sw_U = new double[mc.GetLength(0), mc.GetLength(0)];
res = new double[mc.GetLength(1), mc.GetLength(0)];//mT'
transpose(ref mc, ref res);
matrix_multiply(ref mc, ref res, ref mU_Sw_U);
Phi_w = null;
Phi_b = null;
D_b = null;
eigenvtr = null;
eigenvtr_sbt = null;
icount = 0;
c = Ev.Eigenvlu1((MWNumericArray)mU_Sw_U);
evl = new double[mU_Sw_U.GetLength(1)];
D = (MWNumericArray)c;
pA = D.ToVector(MWArrayComponent.Real);
doubleArray1 = (double[])pA;
for (int i = 0; i < mU_Sw_U.GetLength(0); i++)
{
for (int j = 0; j < mU_Sw_U.GetLength(1); j++)
{
if (i == j)
{
evl[j] = Math.Abs(doubleArray1[icount]);
}
icount++;
}
}
c = Ev.Eigenvtr1((MWNumericArray)mU_Sw_U);
evr = new double[mU_Sw_U.GetLength(0), mU_Sw_U.GetLength(1)];
D = (MWNumericArray)c;
pA = D.ToVector(MWArrayComponent.Real);
doubleArray1 = (double[])pA;
count = 0;
for (int i = 0; i < (mU_Sw_U.GetLength(0)); i++)
{
for (int j = 0; j < (mU_Sw_U.GetLength(1)); j++)
{
evr[j, i] = (doubleArray1[count]);
count++;
}
}
tmp = new double[evl.GetLength(0)];
Array.Sort(evl);
index = new int[evl.GetLength(0)];
for (int i = 0; i < evl.GetLength(0); i++)
{
index[i] = Array.IndexOf(evl, evl[i]);
//tmp[i] = evl[evl.GetLength(0) - i-1];
}
//evl = (double[])tmp.Clone();
eigenvtr = new double[evr.GetLength(0), evr.GetLength(1)];//U_Vec
for (int i = 0; i < evr.GetLength(0); i++)
{
for (int j = 0; j < evr.GetLength(1); j++)
{
eigenvtr[j, (int)index[i]] = evr[j, i];
}
}
res = new double[nm.GetLength(0), eigenvtr.GetLength(1)];
matrix_multiply(ref nm, ref eigenvtr, ref res);
nm = null;
A = new double[res.GetLength(1), res.GetLength(0)];
transpose(ref res, ref A);
res = null;
evr = null;
eigenvtr = null;
D_w = new double[evl.GetLength(0)];
for (int i = 0; i < D_w.GetLength(0); i++)
{
//D_w[i]=eta_sw+evl[i];
D_w[i] = Math.Pow(eta_sw + evl[i], -(1.0 / 2.0));
}
res = new double[D_w.GetLength(0), D_w.GetLength(0)];
for (int i = 0; i < D_w.GetLength(0); i++)
{
res[i, i] = D_w[i];
}
D_b = null;
D_w = null;
DFLD_Trans = new double[res.GetLength(0), A.GetLength(1)];
matrix_multiply(ref res, ref A, ref DFLD_Trans);
res = null;
A = null;
y = new double[DFLD_Trans.GetLength(0), mean_c.GetLength(1)];
matrix_multiply(ref DFLD_Trans, ref mean_c, ref y);
//int aaaa;
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
Mesh Rhino_mesh = new Mesh();
/// initialise parameters
double T_N = 0.0, T_C = 0.0, D = 0.0, M = 0.0, O = 0.0;
List <double> Rhino_indices = new List <double>();
///import data
if (!DA.GetData("Mesh", ref Rhino_mesh))
{
return;
}
if (!DA.GetData("ThresValN", ref T_N))
{
return;
}
if (!DA.GetData("ThresValC", ref T_C))
{
return;
}
if (!DA.GetData("MaxDist", ref D))
{
return;
}
if (!DA.GetData("Minsize", ref M))
{
return;
}
if (!DA.GetData("Offset", ref O))
{
return;
}
///get mesh data to Matlab
/// 1. decompose mesh into face centroids/normals (rhinocommon => .NET)
/// 1a. compute normals if not present
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
/// 1.
var Rhino_c = new Point3f();
var Rhino_n = new Vector3f();
var C_xyz = new float[Rhino_mesh.Faces.Count * 3];
var C_n = new float[Rhino_mesh.Faces.Count * 3];
///1a. check if normals are present
if (Rhino_mesh.FaceNormals.Count == 0)
{
Rhino_mesh.FaceNormals.ComputeFaceNormals();
}
/// 2.
for (int i = 0; i < Rhino_mesh.Faces.Count; i++)
{
Rhino_c = (Point3f)Rhino_mesh.Faces.GetFaceCenter(i);
C_xyz[i * 3] = Rhino_c.X;
C_xyz[i * 3 + 1] = Rhino_c.Y;
C_xyz[i * 3 + 2] = Rhino_c.Z;
Rhino_n = Rhino_mesh.FaceNormals[i];
C_n[i * 3] = Rhino_n.X;
C_n[i * 3 + 1] = Rhino_n.Y;
C_n[i * 3 + 2] = Rhino_n.Z;
}
var Matlab_xyz = new MWNumericArray(Rhino_mesh.Faces.Count, 3, C_xyz);
var Matlab_n = new MWNumericArray(Rhino_mesh.Faces.Count, 3, C_n);
/// 3.
Scan2BIM_Matlab.Segmentation segmentation = new Scan2BIM_Matlab.Segmentation();
MWArray cluster = new MWNumericArray();
cluster = segmentation.S2B_RegionGrowing_2(Matlab_xyz, Matlab_n, T_N, T_C, D, M, O);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.
Rhino_indices = new List <double>(dc);
/// 6.
DA.SetDataList(0, Rhino_indices);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
PointCloud Rhino_Cloud1 = new PointCloud();
PointCloud Rhino_Cloud2 = new PointCloud();
/// initialise parameters
double Metric = 0.0, InlierRatio = 1.0, MaxIterations = 60;
///import
if (!DA.GetData("pc_moving", ref Rhino_Cloud1))
{
return;
}
if (!DA.GetData("pc_fixed", ref Rhino_Cloud2))
{
return;
}
if (!DA.GetData("Metric", ref Metric))
{
return;
}
if (!DA.GetData("InlierRatio", ref InlierRatio))
{
return;
}
if (!DA.GetData("MaxIterations", ref MaxIterations))
{
return;
}
/// 1. decompose points into doubles (rhinocommon => .NET)
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
/// 1.
var Rhino_xyz1 = Rhino_Cloud1.GetPoints();
var Rhino_xyz2 = Rhino_Cloud2.GetPoints();
List <double> xyz1 = new List <double>();
List <double> xyz2 = new List <double>();
for (int i = 0; i < Rhino_Cloud1.Count; i++)
{
xyz1.Add(Rhino_xyz1[i].X);
xyz1.Add(Rhino_xyz1[i].Y);
xyz1.Add(Rhino_xyz1[i].Z);
}
var Matlab_xyz1 = new MWNumericArray(Rhino_Cloud1.Count, 3, xyz1.ToArray());
for (int j = 0; j < Rhino_Cloud2.Count; j++)
{
xyz2.Add(Rhino_xyz2[j].X);
xyz2.Add(Rhino_xyz2[j].Y);
xyz2.Add(Rhino_xyz2[j].Z);
}
var Matlab_xyz2 = new MWNumericArray(Rhino_Cloud2.Count, 3, xyz2.ToArray());
/// 3.
Scan2BIM_Matlab.General general = new Scan2BIM_Matlab.General();
//MWArray[] Result = new MWArray[2];
MWArray cluster = new MWNumericArray();
cluster = general.S2B_ICP2(Matlab_xyz1, Matlab_xyz2, Metric, InlierRatio, MaxIterations);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.
var Rhino_param = new List <double>(dc);
/// 6.
DA.SetDataList(0, Rhino_param);
}
/// <summary>
/// /////PCA Generation//////////
/// </summary>
public void pca()
{
for (int i = 0; i < height * width; i++)
{
float sum = 0;
for (int j = 0; j < trainData; j++)
{
sum += C[i, j];
}
mean[i] = (float)(sum / trainData);
}
//-------------deviation from mean----------------------
for (int i = 0; i < height * width; i++)
{
for (int j = 0; j < trainData; j++)
{
dC[i, j] = (C[i, j] - mean[i]);
}
}
//--------------multiply transpose(dC)*dC---------------
for (int i = 0; i < height * width; i++)
{
for (int j = 0; j < trainData; j++)
{
tdC[j, i] = dC[i, j];
}
}
for (int i = 0; i < trainData; i++)
{
for (int j = 0; j < trainData; j++)
{
float sum = 0;
for (int k = 0; k < height * width; k++)
{
sum += tdC[i, k] * dC[k, j];
}
L[i, j] = sum / (trainData);
}
}
//-------------------calculate Eigenvector of L----------------//
MWArray c = null;
Eutilityclass Ev = new Eutilityclass();
c = Ev.Eigenvtr1((MWNumericArray)L);
MWNumericArray D = (MWNumericArray)c;
Array pA = D.ToVector(MWArrayComponent.Real);
float[] doubleArray1 = (float[])pA;
int icount = 0;
for (int i = 0; i < trainData; i++)
{
for (int j = 0; j < trainData; j++)
{
V[j, i] = doubleArray1[icount];
sV[j, trainData - i - 1] = doubleArray1[icount];
icount++;
}
}
savp("realEig.txt", (float[, ])sV.Clone());
/*
* //Eigen value
* c = Ev.Eigenvlu1((MWNumericArray)L);
* float[] eigenval = new float[L.GetLength(0)];
* D = (MWNumericArray)c;
* pA = D.ToVector(MWArrayComponent.Real);
* doubleArray1 = (float[])pA;
* icount = 0;
* int count = 0;
* for (int i = 0; i < L.GetLength(0); i++)
* {
* for (int j = 0; j < L.GetLength(1); j++)
* {
* if (j == i)
* {
* eigenval[count] = Math.Abs(doubleArray1[icount]);
* count++;
* }
* icount++;
* }
* }
* Vd = (float[])eigenval;
* //----------------------V=dC*V----------------------------
*/
float[] sm = new float[trainData];
float[] ssm = new float[trainData];
temp = matmul(ref dC, ref V);
stemp = matmul(ref dC, ref sV);
for (int i = 0; i < height * width; i++)
{
for (int j = 0; j < trainData; j++)
{
sm[j] += temp[i, j] * temp[i, j];
ssm[j] += stemp[i, j] * stemp[i, j];
}
}
//Improving classification by dividing by length (i think mormalization)
/*
* for (int j = 0; j < trainData; j++)
* {
*
* sm[j] = (float)Math.Pow(sm[j], .5);
* ssm[j] = (float)Math.Pow(ssm[j], .5);
*
* }
* */
for (int i = 0; i < height * width; i++)
{
for (int j = 0; j < trainData; j++)
{
temp[i, j] = temp[i, j] / (float)Math.Pow(sm[j], .5);
stemp[i, j] = stemp[i, j] / (float)Math.Pow(ssm[j], .5);
}
}
// obtaining the value of 'r'=tDc*V
/*
* float summ = 0;
*
* for (int i = 0; i < trainData; i++)
* {
* for (int j = 0; j < trainData; j++)
* {
*
* for (int k = 0; k < height * width; k++)
* {
* summ += tdC[i, k] * stemp[k, j];
*
* }
* rc[i, j] = summ;
* summ = 0;
* }
* }
*
*/
rc = matmul(ref tdC, ref stemp);
}
/// <summary>
/// 预测
/// </summary>
/// <param name="spec"></param>
/// <param name="needFilter">是否需要前处理</param>
/// <returns></returns>
public PLS1Result Predict(Spectrum spec, bool needFilter = true, int numOfId = 5, int topK = 1)
{
if (spec == null)
{
throw new ArgumentNullException("");
}
var s = spec.Clone();
if (s.Data == null || s.Data.Y == null || s.Data.Y.Length == 0)
{
return(null);
}
var x = new MWNumericArray(s.Data.Lenght, 1, s.Data.Y);
//进行预处理
if (needFilter && this._filters != null)
{
x = Preprocesser.ProcessForPredict(this._filters, x);
s.Data.Y = (double[])x.ToVector(MWArrayComponent.Real);
}
var c = this._comp.Clone();
//定义变量
double[] SR, MD, ND, Ylast;
var handler = Tools.ModelHandler;
if (this._annType != PLSAnnEnum.None)
{
var r = handler.PLS1Predictor(5, x, this._Scores, this._Loads, this._Weights, this._Bias, this._Score_Length, this._centerSpecData, this._centerCompValue, (int)this._method);
var toolHandler = Tools.ToolHandler;
// SR,MD,nd,XScores
Ylast = (double[])((MWNumericArray)r[0]).ToVector(MWArrayComponent.Real);
SR = (double[])((MWNumericArray)r[1]).ToVector(MWArrayComponent.Real);
MD = (double[])((MWNumericArray)r[2]).ToVector(MWArrayComponent.Real);
ND = (double[])((MWNumericArray)r[3]).ToVector(MWArrayComponent.Real);
var mscores = (MWNumericArray)toolHandler.Centring(1, r[4], this._ScoresMean)[0];
var annp = (MWNumericArray)handler.annp(1,
mscores,
this._annModel.w1,
this._annModel.b1,
this._annModel.w2,
this._annModel.b2,
this._annArgus.F1.GetDescription(),
this._annArgus.F2.GetDescription())[0];
Ylast[this._factor - 1] = annp.ToScalarDouble() + this._centerCompValue;
}
else
{
var r = handler.PLS1Predictor(5, x, this._Scores, this._Loads, this._Weights, this._Bias, this._Score_Length, this._centerSpecData, this._centerCompValue, (int)this._method);
Ylast = (double[])((MWNumericArray)r[0]).ToVector(MWArrayComponent.Real);
SR = (double[])((MWNumericArray)r[1]).ToVector(MWArrayComponent.Real);
MD = (double[])((MWNumericArray)r[2]).ToVector(MWArrayComponent.Real);
ND = (double[])((MWNumericArray)r[3]).ToVector(MWArrayComponent.Real);
}
c.PredictedValue = Ylast[this.Factor - 1];
// c.PredictedValue = this._Nonnegative && c.PredictedValue < 0 ? 0 : c.PredictedValue;
c.PredictedValue = this._Nonnegative && c.PredictedValue < 0 ? Math.Pow(10, -c.Eps)
: c.PredictedValue;
if (s.Components != null && s.Components.Contains(c.Name))
{
c.ActualValue = s.Components[c.Name].ActualValue;
c.Error = c.PredictedValue - c.ActualValue;
}
//加入判定条件，设置样式
int errorcount = 0;
if (MD[this._factor - 1] > this._MDMin)
{
errorcount++;
}
else if (SR[this._factor - 1] > this._SRMin)
{
errorcount++;
}
else if (ND[this._factor - 1] > this._NNDMin)
{
errorcount++;
}
switch (errorcount)
{
case 0:
c.State = ComponentStatu.Pass;
break;
case 1:
c.State = ComponentStatu.Green;
break;
case 2:
c.State = ComponentStatu.Blue;
break;
default:
c.State = ComponentStatu.Red;
break;
}
PLS1Result item = new PLS1Result()
{
Spec = s,
MDMin = this._MDMin,
NDMin = this._NNDMin,
SRMin = this._SRMin,
Comp = c,
MahDist = MD,
ND = ND,
SR = SR,
YLast = Ylast,
Factor = this.Factor
};
return(item);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
List <double> nodeFeature1 = new List <double>();
List <double> nodeFeature2 = new List <double>();
List <double> edgeFeature1 = new List <double>();
List <double> edgeFeature2 = new List <double>();
List <int> Rhino_si = new List <int>();
List <int> Rhino_sj = new List <int>();
List <double> w = new List <double>();
List <int> Nstates = new List <int>();
///import data
if (!DA.GetDataList("nodeFeature1", nodeFeature1))
{
return;
}
if (!DA.GetDataList("nodeFeature2", nodeFeature2))
{
return;
}
if (!DA.GetDataList("edgeFeature1", edgeFeature1))
{
return;
}
if (!DA.GetDataList("edgeFeature2", edgeFeature2))
{
return;
}
if (!DA.GetDataList("Mesh_si", Rhino_si))
{
return;
}
if (!DA.GetDataList("Mesh_sj", Rhino_sj))
{
return;
}
if (!DA.GetDataList("Parameters", w))
{
return;
}
if (!DA.GetDataList("Nstates", Nstates))
{
return;
}
/// 0.cast list to MWarrays
/// 1.run decode function Matlab
/// 2.cast MWaray to list
/// 3.output list
///0.
var Matlab_fn1 = new MWNumericArray(nodeFeature1.Count, 1, nodeFeature1.ToArray());
var Matlab_fn2 = new MWNumericArray(nodeFeature2.Count, 1, nodeFeature2.ToArray());
var Matlab_fe1 = new MWNumericArray(edgeFeature1.Count, 1, edgeFeature1.ToArray());
var Matlab_fe2 = new MWNumericArray(edgeFeature2.Count, 1, edgeFeature2.ToArray());
var Matlab_si = new MWNumericArray(Rhino_si.Count, 1, Rhino_si.ToArray());
var Matlab_sj = new MWNumericArray(Rhino_sj.Count, 1, Rhino_sj.ToArray());
var Matlab_w = new MWNumericArray(w.Count, 1, w.ToArray());
var Matlab_states = new MWNumericArray(Nstates.Count, 1, Nstates.ToArray());
/// 1.
Clustering.cluster cluster_mesh = new Clustering.cluster();
MWArray cluster = new MWNumericArray();
cluster = cluster_mesh.G_CRF_wall_decoding(Matlab_states, Matlab_fn1, Matlab_fn2, Matlab_fe1, Matlab_fe2, Matlab_si, Matlab_sj, Matlab_w);
///2.
MWNumericArray na = (MWNumericArray)cluster;
var dc = (int[])na.ToVector(0);
var Rhino_indices = dc.ToList();
for (int i = 0; i < Rhino_indices.Count; i++)
{
Rhino_indices[i] = Rhino_indices[i] - 1;
}
/// 3.
DA.SetDataList(0, Rhino_indices);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
PointCloud Rhino_Cloud = new PointCloud();
double Samples = new double();
double Resolution = new double();
/// read inputs
if (!DA.GetData("Point_Cloud", ref Rhino_Cloud))
{
return;
}
if (!DA.GetData("Samples", ref Samples))
{
return;
}
/// interal parameters
var Rhino_xyz = Rhino_Cloud.GetPoints();
List <double> xyz = new List <double>();
List <double> x = new List <double>();
//if (x.Count != 0)
//{
// var temp = x.Max();
// if (temp != 0)
// {
// var result = x.Select(k => k / temp).ToList(); ;
// }
// else
// {
// var result = x;
// }
// A = result;
//}
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
xyz.Add(Rhino_xyz[i].X);
xyz.Add(Rhino_xyz[i].Y);
xyz.Add(Rhino_xyz[i].Z);
}
///2.
var Matlab_samples = new MWNumericArray(Samples);
var Matlab_xyz = new MWNumericArray(Rhino_Cloud.Count, 3, xyz.ToArray());
/// 3.
Scan2BIM_Matlab.General general = new General();
MWArray cluster = new MWNumericArray();
cluster = general.S2B_Resolution_PCD(Matlab_xyz, Matlab_samples);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.%£%/:
Resolution = dc[0];
/// 6.
DA.SetData(0, Resolution);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
PointCloud Rhino_Cloud = new PointCloud();
List <double> Rhino_indices = new List <double>();
/// initialise internal parameters
double T_N = 0.0, T_C = 0.0, D = 0.0, M = 0.0, O = 0.0, T_s = 0.0;
if (!DA.GetData("Point_Cloud", ref Rhino_Cloud))
{
return;
}
if (!DA.GetData("ThresValN", ref T_N))
{
return;
}
if (!DA.GetData("ThresValC", ref T_C))
{
return;
}
if (!DA.GetData("MaxDist", ref D))
{
return;
}
if (!DA.GetData("Minsize", ref M))
{
return;
}
if (!DA.GetData("Offset", ref O))
{
return;
}
if (!DA.GetData("Tilesize", ref T_s))
{
return;
}
/// 1. decompose PCD into vertices, normals (rhinocommon => .NET)
/// 1a. retrieve/construct colors
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
var Rhino_xyz = Rhino_Cloud.GetPoints();
var Rhino_n = Rhino_Cloud.GetNormals();
var Rhino_c = Rhino_Cloud.GetColors();
List <double> xyz = new List <double>();
List <double> n = new List <double>();
List <double> c = new List <double>();
MWNumericArray Matlab_n = new MWNumericArray();
MWNumericArray Matlab_c = new MWNumericArray();
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
xyz.Add(Rhino_xyz[i].X);
xyz.Add(Rhino_xyz[i].Y);
xyz.Add(Rhino_xyz[i].Z);
}
if (Rhino_Cloud.ContainsNormals == true)
{
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
n.Add(Rhino_n[i].X);
n.Add(Rhino_n[i].Y);
n.Add(Rhino_n[i].Z);
}
Matlab_n = new MWNumericArray(Rhino_Cloud.Count, 3, n.ToArray());
}
if (Rhino_Cloud.ContainsColors == true)
{
for (int i = 0; i < Rhino_Cloud.Count; i++)
{
c.Add(Rhino_c[i].R);
c.Add(Rhino_c[i].G);
c.Add(Rhino_c[i].B);
}
Matlab_c = new MWNumericArray(Rhino_Cloud.Count, 3, c.ToArray());
}
///2.
var Matlab_xyz = new MWNumericArray(Rhino_Cloud.Count, 3, xyz.ToArray());
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
MWArray cluster = new MWNumericArray();
cluster = segment_mesh.G_RegionGrowingNC2(Matlab_xyz, Matlab_n, Matlab_c, T_N, T_C, D, M, O, T_s);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.
Rhino_indices = new List <double>(dc);
/// 6.
DA.SetDataList(0, Rhino_indices);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
Mesh Rhino_mesh = new Mesh();
/// initialise parameters
double T_N = 0.0, T_C = 0.0, D = 0.0, M = 0.0, O = 0.0, T_s = 0.0;
List <double> Rhino_indices = new List <double>();
///import data
if (!DA.GetData("Mesh", ref Rhino_mesh))
{
return;
}
if (!DA.GetData("ThresValN", ref T_N))
{
return;
}
if (!DA.GetData("ThresValC", ref T_C))
{
return;
}
if (!DA.GetData("MaxDist", ref D))
{
return;
}
if (!DA.GetData("Minsize", ref M))
{
return;
}
if (!DA.GetData("Offset", ref O))
{
return;
}
if (!DA.GetData("Tilesize", ref T_s))
{
return;
}
///get mesh data to Matlab
/// 1. decompose mesh into face centroids/normals (rhinocommon => .NET)
/// 1a. compute normals if not present
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
/// 1.
var Rhino_c = new Point3f();
var Rhino_n = new Vector3f();
MeshFace Rhino_face = new MeshFace();
var C_xyz = new float[Rhino_mesh.Faces.Count * 3];
var C_n = new float[Rhino_mesh.Faces.Count * 3];
var C_c = new float[Rhino_mesh.Faces.Count * 3];
int r1, r2, r3, g1, g2, g3, b1, b2, b3, r, g, b;
///1a. check if normals are present
if (Rhino_mesh.FaceNormals.Count == 0)
{
Rhino_mesh.FaceNormals.ComputeFaceNormals();
}
/// 2.
for (int i = 0; i < Rhino_mesh.Faces.Count; i++)
{
Rhino_c = (Point3f)Rhino_mesh.Faces.GetFaceCenter(i);
C_xyz[i * 3] = Rhino_c.X;
C_xyz[i * 3 + 1] = Rhino_c.Y;
C_xyz[i * 3 + 2] = Rhino_c.Z;
Rhino_n = Rhino_mesh.FaceNormals[i];
C_n[i * 3] = Rhino_n.X;
C_n[i * 3 + 1] = Rhino_n.Y;
C_n[i * 3 + 2] = Rhino_n.Z;
Rhino_face = Rhino_mesh.Faces.GetFace(i);
if (Rhino_mesh.VertexColors.Count > 0)
{
r1 = Rhino_mesh.VertexColors[Rhino_face[0]].R;
g1 = Rhino_mesh.VertexColors[Rhino_face[0]].G;
b1 = Rhino_mesh.VertexColors[Rhino_face[0]].B;
r2 = Rhino_mesh.VertexColors[Rhino_face[1]].R;
g2 = Rhino_mesh.VertexColors[Rhino_face[1]].G;
b2 = Rhino_mesh.VertexColors[Rhino_face[1]].B;
r3 = Rhino_mesh.VertexColors[Rhino_face[2]].R;
g3 = Rhino_mesh.VertexColors[Rhino_face[2]].G;
b3 = Rhino_mesh.VertexColors[Rhino_face[2]].B;
r = (r1 + r2 + r3) / 3;
g = (g1 + g2 + g3) / 3;
b = (b1 + b2 + b3) / 3;
C_c[i * 3] = r;
C_c[i * 3 + 1] = g;
C_c[i * 3 + 2] = b;
}
}
var Matlab_xyz = new MWNumericArray(Rhino_mesh.Faces.Count, 3, C_xyz);
var Matlab_n = new MWNumericArray(Rhino_mesh.Faces.Count, 3, C_n);
var Matlab_c = new MWNumericArray(Rhino_mesh.Faces.Count, 3, C_c);
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
MWArray cluster = new MWNumericArray();
cluster = segment_mesh.G_RegionGrowingNC2(Matlab_xyz, Matlab_n, Matlab_c, T_N, T_C, D, M, O, T_s);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.
Rhino_indices = new List <double>(dc);
/// 6.
DA.SetDataList(0, Rhino_indices);
}
protected override void SolveInstance(IGH_DataAccess DA)
{
///input parameters
List <Point3d> Rhino_points = new List <Point3d>();
/// initialise parameters
double T_N = 0.0, D = 0.0, M = 0.0, O = 0.0, T_s = 0.0;
List <double> Rhino_indices = new List <double>();
List <double> Rhino_xyz = new List <double>();
///import data
if (!DA.GetDataList("Points", Rhino_points))
{
return;
}
if (!DA.GetData("ThresValN", ref T_N))
{
return;
}
if (!DA.GetData("MaxDist", ref D))
{
return;
}
if (!DA.GetData("Minsize", ref M))
{
return;
}
if (!DA.GetData("Offset", ref O))
{
return;
}
if (!DA.GetData("Tilesize", ref T_s))
{
return;
}
/// 1. decompose points into doubles (rhinocommon => .NET)
/// 2. create appropriete matlab arrays (.NET => Matlab)
/// 3. run matlab function (Matlab)
/// 4. convert function output (list with indices) to .Net array (Matlab => .Net)
/// 5. convert array to rhinocommon list. (.Net => Rhinocommon)
/// 6. output data
/// 1.
for (int i = 0; i < Rhino_points.Count; i++)
{
Rhino_xyz[i * 3] = Rhino_points[i].X;
Rhino_xyz[i * 3 + 1] = Rhino_points[i].Y;
Rhino_xyz[i * 3 + 2] = Rhino_points[i].Z;
}
/// 2.
var Matlab_xyz = new MWNumericArray(Rhino_points.Count, 3, Rhino_xyz.ToArray());
var Matlab_n = new MWNumericArray();
var Matlab_c = new MWNumericArray();
/// 3.
Segmentation.segment segment_mesh = new Segmentation.segment();
MWArray cluster = new MWNumericArray();
cluster = segment_mesh.G_RegionGrowingNC2(Matlab_xyz, Matlab_n, Matlab_c, T_N, D, M, O, T_s);
/// 4.
MWNumericArray na = (MWNumericArray)cluster;
double[] dc = (double[])na.ToVector(0);
/// 5.
Rhino_indices = new List <double>(dc);
/// 6.
DA.SetDataList(0, Rhino_indices);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
///define i/o parameters
List <double> GHArea = new List <double>();
List <double> GHNormalsimilarity = new List <double>();
List <double> GHNormalZ = new List <double>();
List <double> GHDiagonalXY = new List <double>();
List <double> GHHeight = new List <double>();
List <double> GHCoplanarity = new List <double>();
List <double> GHProximity = new List <double>();
List <double> GHConnections = new List <double>();
List <double> GHWallinlier = new List <double>();
List <double> GHDvBottom = new List <double>();
List <double> GHDvTop = new List <double>();
//List<double> GHProxXY = new List<double>();
List <double> GHColAbove = new List <double>();
List <double> GHColBelow = new List <double>();
List <double> GHColFarAbove = new List <double>();
List <double> GHVbot = new List <double>();
List <double> GHVtop = new List <double>();
List <double> GHRaytrace = new List <double>();
if (!DA.GetDataList <double>("Area", GHArea))
{
return;
}
if (!DA.GetDataList <double>("NormalSimilarity", GHNormalsimilarity))
{
return;
}
if (!DA.GetDataList <double>("NormalZ", GHNormalZ))
{
return;
}
if (!DA.GetDataList <double>("DiagonalXY", GHDiagonalXY))
{
return;
}
if (!DA.GetDataList <double>("Height", GHHeight))
{
return;
}
if (!DA.GetDataList <double>("Coplanarity", GHCoplanarity))
{
return;
}
if (!DA.GetDataList <double>("Proximity", GHProximity))
{
return;
}
if (!DA.GetDataList <double>("Connections", GHConnections))
{
return;
}
if (!DA.GetDataList <double>("Wallinlier", GHWallinlier))
{
return;
}
if (!DA.GetDataList <double>("DvBottom", GHDvBottom))
{
return;
}
if (!DA.GetDataList <double>("DvTop", GHDvTop))
{
return;
}
if (!DA.GetDataList <double>("ColAbove", GHColAbove))
{
return;
}
if (!DA.GetDataList <double>("ColBelow", GHColBelow))
{
return;
}
if (!DA.GetDataList <double>("ColFarAbove", GHColFarAbove))
{
return;
}
if (!DA.GetDataList <double>("Vbot", GHVbot))
{
return;
}
if (!DA.GetDataList <double>("Vtop", GHVtop))
{
return;
}
if (!DA.GetDataList <double>("Raytrace", GHRaytrace))
{
return;
}
/// initialise internal parameters
//convert C#List to C#Array and directly to MatlabArray
var Area = new MWNumericArray(GHArea.Count, 1, GHArea.ToArray());
var Normalsimilarity = new MWNumericArray(GHNormalsimilarity.Count, 1, GHNormalsimilarity.ToArray());
var NormalZ = new MWNumericArray(GHNormalZ.Count, 1, GHNormalZ.ToArray());
var DiagonalXY = new MWNumericArray(GHDiagonalXY.Count, 1, GHDiagonalXY.ToArray());
var Height = new MWNumericArray(GHHeight.Count, 1, GHHeight.ToArray());
var Coplanarity = new MWNumericArray(GHCoplanarity.Count, 1, GHCoplanarity.ToArray());
var Proximity = new MWNumericArray(GHProximity.Count, 1, GHProximity.ToArray());
var Connections = new MWNumericArray(GHConnections.Count, 1, GHConnections.ToArray());
var Wallinlier = new MWNumericArray(GHWallinlier.Count, 1, GHWallinlier.ToArray());
var DvBottom = new MWNumericArray(GHDvBottom.Count, 1, GHDvBottom.ToArray());
var DvTop = new MWNumericArray(GHDvTop.Count, 1, GHDvTop.ToArray());
var ColAbove = new MWNumericArray(GHColAbove.Count, 1, GHColAbove.ToArray());
var ColBelow = new MWNumericArray(GHColBelow.Count, 1, GHColBelow.ToArray());
var ColFarAbove = new MWNumericArray(GHColFarAbove.Count, 1, GHColFarAbove.ToArray());
var Vbot = new MWNumericArray(GHVbot.Count, 1, GHVbot.ToArray());
var Vtop = new MWNumericArray(GHVtop.Count, 1, GHVtop.ToArray());
var Raytrace = new MWNumericArray(GHRaytrace.Count, 1, GHRaytrace.ToArray());
Classification.Prediction predict = new Classification.Prediction();
MWArray c = new MWNumericArray();
c = predict.G_predictfunction(Area, Normalsimilarity, NormalZ, DiagonalXY, Height, Coplanarity, Proximity, Connections, Wallinlier, DvBottom, DvTop, ColAbove, ColBelow, ColFarAbove, Vbot, Vtop, Raytrace);
//convert MatlabArray to C#Array to C#List
MWNumericArray na = (MWNumericArray)c;
double[] dc = (double[])na.ToVector(0);
List <double> output = new List <double>(dc);
//// Finally assign the output to the output parameter.
DA.SetDataList(0, output);
}