public cFeedBackMessage Run(c3DNewWorld _3DWorld)
{
if (base.Start() == false)
{
base.FeedBackMessage.IsSucceed = false;
return base.FeedBackMessage;
}
#region Properties Management
object _firstValue = base.ListProperties.FindByName("Thresold");
double Thresold = 0.5;
if (_firstValue == null)
{
base.GenerateError("-Thresold- not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
Thresold = (double)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("-Thresold- cast didn't work");
return base.FeedBackMessage;
}
_firstValue = base.ListProperties.FindByName("Split objects ?");
bool IsSplit = false;
if (_firstValue == null)
{
base.GenerateError("-Split objects ?- not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
IsSplit = (bool)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("-Split objects ?- cast didn't work");
return base.FeedBackMessage;
}
#endregion
ListObjects = new cListGeometric3DObject("");
if (IsSplit == false)
{
c3DMeshObject MyMesh = null;
if (this.VTKInput == null)
{
MyMesh = new c3DMeshObject(this.Input, Thresold);
}
else
{
MyMesh = new c3DMeshObject(this.VTKInput, (int)Thresold);
}
MyMesh.Create(Color.Aqua, this.Pos );
ListObjects.Name = MyMesh.GetName() + " metaobject";
ListObjects.Add(MyMesh);
}
else
{
// ok that's a little bit more complicated now
// first we need to binarize the image
cImageSegmentationThreshold IST = new cImageSegmentationThreshold();
IST.ListProperties.FindByName("Threshold").SetNewValue(Thresold);
cImage SourceImage = new cImage(this.Input, false);
IST.SetInputData(SourceImage);
IST.Run();
// now perform image labeling
cImage BinImage = IST.GetOutPut();
cImage LabeledImage = new cImage(BinImage, false);
ConnectedComponentSet CCS = new ConnectedComponentSet(BinImage,LabeledImage,SourceImage, 0, eConnectivity.THREED_6, 0, float.MaxValue);
Random RD = new Random();
int IdxObj = 1;
// loop over each object
int NumObj = CCS.Count;
ListObjects.Name = "T_" + Thresold + " [" + SourceImage.Name + " Metaobject";
for(int i = 0;i<NumObj;i++)
{
ConnectedVoxels item = CCS[i];
// if (item.Volume <= 1) continue;
List<cPoint3D> ExtremePts = item.GetExtremaPoints();
// crop the labeled image
cImage TmpIm = LabeledImage.Crop(ExtremePts[0], ExtremePts[1]);
// we have to clean the cropped image to prevent any overlapping object to be segmented
for (int Pix = 0; Pix < TmpIm.ImageSize; Pix++)
{
if ((TmpIm.SingleChannelImage[0].Data[Pix] > 0) && (TmpIm.SingleChannelImage[0].Data[Pix] != IdxObj)) TmpIm.SingleChannelImage[0].Data[Pix] = 0;
}
// update the position of the object
cPoint3D NewPos = ExtremePts[0]*SourceImage.Resolution;
c3DMeshObject MyMesh = new c3DMeshObject(TmpIm.SingleChannelImage[0], 0.5);
MyMesh.Create(Color.FromArgb(RD.Next(255), RD.Next(255), RD.Next(255)), NewPos);
MyMesh.SetName(MyMesh.GetName() + "_" + IdxObj);
MyMesh.AssociatedConnectedComponent = item;
ListObjects.AddObject(MyMesh);
IdxObj++;
}
}
return base.FeedBackMessage;
}
private void ToolStripMenuItem_SegmentationThreshold(object sender, EventArgs e)
{
cDisplaySingleImage NewView = new cDisplaySingleImage();
cImageSegmentationThreshold ME = new cImageSegmentationThreshold();
ME.SetInputData(this.AssociatedImage);
ME.ListProperties.FindByName("Threshold").SetNewValue((double)100);
ME.ListProperties.FindByName("Threshold").IsGUIforValue = true;
ME.IsInvert = true;
ME.Run();
NewView.SetInputData(ME.GetOutPut());
NewView.Run();
}
private void buttonApply_Click(object sender, EventArgs e)
{
if (DensityMaps.GetNumChannels() > cGlobalInfo.ListCellularPhenotypes.Count - 2)
{
MessageBox.Show("This process cannot manage more than " + (cGlobalInfo.ListCellularPhenotypes.Count - 2) + " different classes !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
cImageSegmentationThreshold ST = new cImageSegmentationThreshold();
ST.SetInputData(DensityMaps);
ST.ListProperties.UpdateValueByName("Threshold", (double)this.numericUpDown1.Value);
ST.MaxValue = 1;
ST.Run();
cImage SegmentedImages = ST.GetOutPut();
// let's create an image containing the different non overlapping populations
// Values:
// 0 : outliers
// 1 : Overlapping population
// then population specific areas
ResultingImage = new cImage(SegmentedImages.Width, SegmentedImages.Height, SegmentedImages.Depth, 1);
ResultingImage.Name = "Overlapping Populations Analysis";
for (int i = 0; i < ResultingImage.Width * ResultingImage.Height * ResultingImage.Depth; i++)
{
int IdxPop = 0;
float PixTotal = 0;
float CurrentValue = 0;
for (int IdxChannel = 0; IdxChannel < SegmentedImages.GetNumChannels(); IdxChannel++)
{
CurrentValue = SegmentedImages.SingleChannelImage[IdxChannel].Data[i];
PixTotal += CurrentValue;
if (CurrentValue == 1)
IdxPop = IdxChannel;
}
if (PixTotal > 1)
ResultingImage.SingleChannelImage[0].Data[i] = 1;
else if (PixTotal == 1)
{
ResultingImage.SingleChannelImage[0].Data[i] = IdxPop + 2;
}
//float Value0 = SegmentedImages.SingleChannelImage[PositiveBand].Data[i];
//float Value1 = SegmentedImages.SingleChannelImage[NegativeBand].Data[i];
//if ((Value0 > 0) && (Value1 > 0)) // overlapping
// ResultingImage.SingleChannelImage[0].Data[i] = 1;
//else if ((Value0 > 0) && (Value1 <= 0))
// ResultingImage.SingleChannelImage[0].Data[i] = 2;// DensityMaps.SingleChannelImage[PositiveBand].Data[i];
//else if ((Value0 <= 0) && (Value1 > 0))
// ResultingImage.SingleChannelImage[0].Data[i] = 3;// -DensityMaps.SingleChannelImage[NegativeBand].Data[i];
}
if (checkBoxUpdatePhenotypeName.Checked)
{
cGlobalInfo.ListCellularPhenotypes[0].Name = "Outliers";
cGlobalInfo.ListCellularPhenotypes[1].Name = "Overlapping Populations";
for (int IdxChannel = 0; IdxChannel < DensityMaps.GetNumChannels(); IdxChannel++)
{
cGlobalInfo.ListCellularPhenotypes[IdxChannel + 2].Name = DensityMaps.SingleChannelImage[IdxChannel].Name + " Specific";
}
}
if (checkBoxDisplayResultingImage.Checked)
{
// cImageViewer IV2 = new cImageViewer();
// IV2.SetImage(ResultingImage/*SegmentedImages*/);
// IV2.Show();
List<byte[][]> ListLUTs = new List<byte[][]>();
cLUT LUT = new cLUT();
ListLUTs.Add(LUT.LUT_FIRE);
pictureBoxForImage.Image = ResultingImage.GetBitmap(1, null, ListLUTs);
}
// return;
// now apply the result to the points
for (int j = 0; j < Parent.InputTable[0].Count/* dt.Rows.Count*/; j++)
{
double TmpValueX = (ImageWidth * (ListX[j] - MinX)) / (MaxX - MinX);
double TmpValueY = ImageHeight - (ImageHeight * (ListY[j] - MinY)) / (MaxY - MinY);
int PosX = (int)TmpValueX;
int PosY = (int)TmpValueY;
if (PosX >= ResultingImage.Width) PosX = ResultingImage.Width - 1;
if (PosY >= ResultingImage.Height) PosY = ResultingImage.Height - 1;
if (PosX < 0) PosX = 0;
if (PosY < 0) PosY = 0;
double Value = ResultingImage.SingleChannelImage[0].Data[PosX + PosY * ResultingImage.Width];
if (Value > 0)
{
Parent.MachineLearning.Classes[j] = Value;
}
else
Parent.MachineLearning.Classes[j] = 0;
//double currentClass = MachineLearning.Classes[j];
//ListX.Add(double.Parse(dt.Rows[j][this.comboBoxAxeX.SelectedIndex].ToString()));
//ListY.Add(double.Parse(dt.Rows[j][this.comboBoxAxeY.SelectedIndex].ToString()));
}
Parent.MachineLearning.UpDateNumberOfCluster();
Parent.ReDraw();
//(int)MachineLearning.Classes[j]
}