private bool FitLine(List <HTuple> hv_RowEdges, List <HTuple> hv_ColumnEdges)
{
if (hv_RowEdges.Count < 2 || hv_RowEdges.Count != hv_ColumnEdges.Count)
{
//MessageBox.Show("样本点数据错误");
return(false);
}
HTuple hv_Mx = null;
HTuple hv_My = null;
for (int i = 0; i < hv_RowEdges.Count; i++)
{
//HOperatorSet.TupleInsert(hv_Mx,i, hv_RowEdges[i],out hv_Mx);
//HOperatorSet.TupleInsert(hv_My, i, hv_ColumnEdges[i], out hv_My);
HOperatorSet.TupleInsert(hv_My, i, hv_RowEdges[i], out hv_My);
HOperatorSet.TupleInsert(hv_Mx, i, hv_ColumnEdges[i], out hv_Mx);
}
HTuple hv_x = null;
HTuple hv_y = null;
HOperatorSet.CreateMatrix(hv_RowEdges.Count, 2, 1, out hv_x);
HOperatorSet.CreateMatrix(hv_RowEdges.Count, 1, hv_My, out hv_y);
HOperatorSet.SetValueMatrix(hv_x, HTuple.TupleGenSequence(0, hv_RowEdges.Count - 1, 1), HTuple.TupleGenConst(hv_RowEdges.Count, 0), hv_Mx);
HTuple hv_xtx = null;
HTuple hv_xty = null;
HOperatorSet.MultMatrix(hv_x, hv_x, "ATB", out hv_xtx);
HOperatorSet.MultMatrix(hv_x, hv_y, "ATB", out hv_xty);
HTuple hv_invxtx = null;
HOperatorSet.InvertMatrix(hv_xtx, "general", 0, out hv_invxtx);
HTuple hv_beta = null;
HOperatorSet.MultMatrix(hv_invxtx, hv_xty, "AB", out hv_beta);
HTuple hv_Values = null;
HOperatorSet.GetFullMatrix(hv_beta, out hv_Values);
HTuple hv_Newy = null;
hv_Newy = ((hv_Values.TupleSelect(0)) * (new HTuple(10)).TupleConcat(800)) + (hv_Values.TupleSelect(1));
HObject ho_Contour;
HOperatorSet.GenEmptyObj(out ho_Contour);
HOperatorSet.GenContourPolygonXld(out ho_Contour, hv_Newy, (new HTuple(10)).TupleConcat(800));
HTuple hv_XldHomMat2D;
HOperatorSet.HomMat2dIdentity(out hv_XldHomMat2D);
HOperatorSet.HomMat2dScale(hv_XldHomMat2D, hv_ZoomFactor, hv_ZoomFactor, 0, 0, out hv_XldHomMat2D);
HOperatorSet.AffineTransContourXld(ho_Contour, out ho_Contour, hv_XldHomMat2D);
HOperatorSet.DispObj(ho_Contour, hv_ImageWindow);
//HOperatorSet.AffineTransPoint2d();
return(true);
}
// Main procedure
private void action()
{
// Local iconic variables
HObject ho_Image, ho_ModelRegion, ho_TemplateImage;
HObject ho_ModelContours, ho_RectifiedImage = null, ho_Regions = null;
HObject ho_ConnectedRegions = null, ho_SelectedRegions = null;
HObject ho_Contours = null, ho_Regions1 = null, ho_RegionLines = null;
// Local control variables
HTuple hv_ModelID = new HTuple(), hv_ModelRegionArea = new HTuple();
HTuple hv_RefRow = new HTuple(), hv_RefColumn = new HTuple();
HTuple hv_TestImages = new HTuple(), hv_T = new HTuple();
HTuple hv_Row = new HTuple(), hv_Column = new HTuple();
HTuple hv_Angle = new HTuple(), hv_Score = new HTuple();
HTuple hv_I = new HTuple(), hv_RectificationHomMat2D = new HTuple();
HTuple hv_Row1 = new HTuple(), hv_Column1 = new HTuple();
HTuple hv_Radius = new HTuple(), hv_MetrologyHandle = new HTuple();
HTuple hv_Width = new HTuple(), hv_Height = new HTuple();
HTuple hv_Index = new HTuple(), hv_Row3 = new HTuple();
HTuple hv_Column3 = new HTuple(), hv_Parameter = new HTuple();
HTuple hv_Row2 = new HTuple(), hv_Column2 = new HTuple();
HTuple hv_Radius1 = new HTuple(), hv_MetrologyHandle1 = new HTuple();
HTuple hv_Index1 = new HTuple(), hv_Parameter1 = new HTuple();
HTuple hv_SmlC = new HTuple(), hv_SR = new HTuple(), hv_SC = new HTuple();
HTuple hv_BR = new HTuple(), hv_BC = new HTuple(), hv_Distance = new HTuple();
// Initialize local and output iconic variables
HOperatorSet.GenEmptyObj(out ho_Image);
HOperatorSet.GenEmptyObj(out ho_ModelRegion);
HOperatorSet.GenEmptyObj(out ho_TemplateImage);
HOperatorSet.GenEmptyObj(out ho_ModelContours);
HOperatorSet.GenEmptyObj(out ho_RectifiedImage);
HOperatorSet.GenEmptyObj(out ho_Regions);
HOperatorSet.GenEmptyObj(out ho_ConnectedRegions);
HOperatorSet.GenEmptyObj(out ho_SelectedRegions);
HOperatorSet.GenEmptyObj(out ho_Contours);
HOperatorSet.GenEmptyObj(out ho_Regions1);
HOperatorSet.GenEmptyObj(out ho_RegionLines);
//
//Matching 01: ************************************************
//Matching 01: BEGIN of generated code for model initialization
//Matching 01: ************************************************
HOperatorSet.SetSystem("border_shape_models", "false");
//
//Matching 01: Obtain the model image
ho_Image.Dispose();
HOperatorSet.ReadImage(out ho_Image, "C:/Users/Chanru/Desktop/Class/bd/bd1.png");
//
//Matching 01: Build the ROI from basic regions
ho_ModelRegion.Dispose();
HOperatorSet.GenRectangle1(out ho_ModelRegion, 445.809, 637.592, 801.311, 1070.62);
//
//Matching 01: Reduce the model template
ho_TemplateImage.Dispose();
HOperatorSet.ReduceDomain(ho_Image, ho_ModelRegion, out ho_TemplateImage);
//
//Matching 01: Create the shape model
hv_ModelID.Dispose();
HOperatorSet.CreateShapeModel(ho_TemplateImage, 6, (new HTuple(0)).TupleRad()
, (new HTuple(360)).TupleRad(), (new HTuple(0.4233)).TupleRad(), (new HTuple("point_reduction_medium")).TupleConcat(
"no_pregeneration"), "use_polarity", ((new HTuple(10)).TupleConcat(14)).TupleConcat(
35), 3, out hv_ModelID);
//
//Matching 01: Get the model contour for transforming it later into the image
ho_ModelContours.Dispose();
HOperatorSet.GetShapeModelContours(out ho_ModelContours, hv_ModelID, 1);
//
//Matching 01: Get the reference position
hv_ModelRegionArea.Dispose(); hv_RefRow.Dispose(); hv_RefColumn.Dispose();
HOperatorSet.AreaCenter(ho_ModelRegion, out hv_ModelRegionArea, out hv_RefRow,
out hv_RefColumn);
//
//Matching 01: END of generated code for model initialization
//Matching 01: * * * * * * * * * * * * * * * * * * * * * * *
//Matching 01: BEGIN of generated code for model application
//
//Matching 01: Loop over all specified test images
hv_TestImages.Dispose();
hv_TestImages = new HTuple();
hv_TestImages[0] = "C:/Users/Chanru/Desktop/Class/bd/bd1.png";
hv_TestImages[1] = "C:/Users/Chanru/Desktop/Class/bd/bd10.png";
hv_TestImages[2] = "C:/Users/Chanru/Desktop/Class/bd/bd2.png";
hv_TestImages[3] = "C:/Users/Chanru/Desktop/Class/bd/bd3.png";
hv_TestImages[4] = "C:/Users/Chanru/Desktop/Class/bd/bd4.png";
hv_TestImages[5] = "C:/Users/Chanru/Desktop/Class/bd/bd5.png";
hv_TestImages[6] = "C:/Users/Chanru/Desktop/Class/bd/bd6.png";
hv_TestImages[7] = "C:/Users/Chanru/Desktop/Class/bd/bd7.png";
hv_TestImages[8] = "C:/Users/Chanru/Desktop/Class/bd/bd8.png";
hv_TestImages[9] = "C:/Users/Chanru/Desktop/Class/bd/bd9.png";
for (hv_T = 0; (int)hv_T <= 9; hv_T = (int)hv_T + 1)
{
//
//Matching 01: Obtain the test image
ho_Image.Dispose();
HOperatorSet.ReadImage(out ho_Image, hv_TestImages.TupleSelect(hv_T));
//
//Matching 01: Find the model
hv_Row.Dispose(); hv_Column.Dispose(); hv_Angle.Dispose(); hv_Score.Dispose();
HOperatorSet.FindShapeModel(ho_Image, hv_ModelID, (new HTuple(0)).TupleRad()
, (new HTuple(360)).TupleRad(), 0.5, 0, 0.5, "least_squares", (new HTuple(6)).TupleConcat(
1), 0.75, out hv_Row, out hv_Column, out hv_Angle, out hv_Score);
//
//Matching 01: Code for rectification of the image
//Matching 01: Calculate an inverse hom_mat2d for each of the matching results
for (hv_I = 0; (int)hv_I <= (int)((new HTuple(hv_Score.TupleLength())) - 1); hv_I = (int)hv_I + 1)
{
hv_RectificationHomMat2D.Dispose();
HOperatorSet.HomMat2dIdentity(out hv_RectificationHomMat2D);
{
HTuple ExpTmpOutVar_0;
HOperatorSet.HomMat2dTranslate(hv_RectificationHomMat2D, hv_RefRow - (hv_Row.TupleSelect(
hv_I)), hv_RefColumn - (hv_Column.TupleSelect(hv_I)), out ExpTmpOutVar_0);
hv_RectificationHomMat2D.Dispose();
hv_RectificationHomMat2D = ExpTmpOutVar_0;
}
{
HTuple ExpTmpOutVar_0;
HOperatorSet.HomMat2dRotate(hv_RectificationHomMat2D, -(hv_Angle.TupleSelect(
hv_I)), hv_RefRow, hv_RefColumn, out ExpTmpOutVar_0);
hv_RectificationHomMat2D.Dispose();
hv_RectificationHomMat2D = ExpTmpOutVar_0;
}
ho_RectifiedImage.Dispose();
HOperatorSet.AffineTransImage(ho_Image, out ho_RectifiedImage, hv_RectificationHomMat2D,
"constant", "false");
//
//Matching 01: Insert your code using the rectified image here
ho_Regions.Dispose();
HOperatorSet.Threshold(ho_Image, out ho_Regions, 99, 255);
ho_ConnectedRegions.Dispose();
HOperatorSet.Connection(ho_Regions, out ho_ConnectedRegions);
ho_SelectedRegions.Dispose();
HOperatorSet.SelectShape(ho_ConnectedRegions, out ho_SelectedRegions, "area",
"and", 150, 800);
hv_Row1.Dispose(); hv_Column1.Dispose(); hv_Radius.Dispose();
HOperatorSet.SmallestCircle(ho_SelectedRegions, out hv_Row1, out hv_Column1,
out hv_Radius);
hv_MetrologyHandle.Dispose();
HOperatorSet.CreateMetrologyModel(out hv_MetrologyHandle);
hv_Width.Dispose(); hv_Height.Dispose();
HOperatorSet.GetImageSize(ho_RectifiedImage, out hv_Width, out hv_Height);
HOperatorSet.SetMetrologyModelImageSize(hv_MetrologyHandle, hv_Width, hv_Height);
hv_Index.Dispose();
HOperatorSet.AddMetrologyObjectCircleMeasure(hv_MetrologyHandle, hv_Row1,
hv_Column1, hv_Radius, 20, 5, 1, 30, "num_measures", 30, out hv_Index);
HOperatorSet.ApplyMetrologyModel(ho_RectifiedImage, hv_MetrologyHandle);
ho_Contours.Dispose(); hv_Row3.Dispose(); hv_Column3.Dispose();
HOperatorSet.GetMetrologyObjectMeasures(out ho_Contours, hv_MetrologyHandle,
"all", "all", out hv_Row3, out hv_Column3);
hv_Parameter.Dispose();
HOperatorSet.GetMetrologyObjectResult(hv_MetrologyHandle, "all", "all", "result_type",
"all_param", out hv_Parameter);
//*********
ho_Regions1.Dispose();
HOperatorSet.Threshold(ho_Image, out ho_Regions1, 22, 78);
hv_Row2.Dispose(); hv_Column2.Dispose(); hv_Radius1.Dispose();
HOperatorSet.SmallestCircle(ho_Regions1, out hv_Row2, out hv_Column2, out hv_Radius1);
hv_MetrologyHandle1.Dispose();
HOperatorSet.CreateMetrologyModel(out hv_MetrologyHandle1);
HOperatorSet.SetMetrologyModelImageSize(hv_MetrologyHandle1, hv_Width, hv_Height);
hv_Index1.Dispose();
HOperatorSet.AddMetrologyObjectCircleMeasure(hv_MetrologyHandle1, hv_Row2,
hv_Column2, hv_Radius1, 20, 5, 1, 30, new HTuple(), new HTuple(), out hv_Index1);
HOperatorSet.ApplyMetrologyModel(ho_RectifiedImage, hv_MetrologyHandle1);
hv_Parameter1.Dispose();
HOperatorSet.GetMetrologyObjectResult(hv_MetrologyHandle1, "all", "all",
"result_type", "all_param", out hv_Parameter1);
//*******
hv_SmlC.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_SmlC = HTuple.TupleGenSequence(
0, (new HTuple(hv_Parameter.TupleLength())) - 1, 3);
}
hv_SR.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_SR = hv_Parameter.TupleSelect(
hv_SmlC);
}
hv_SC.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_SC = hv_Parameter.TupleSelect(
hv_SmlC + 1);
}
hv_BR.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BR = (hv_SR * 0) + (hv_Parameter1.TupleSelect(
0));
}
hv_BC.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BC = (hv_SC * 0) + (hv_Parameter1.TupleSelect(
1));
}
hv_Distance.Dispose();
HOperatorSet.DistancePp(hv_SR, hv_SC, hv_BR, hv_BC, out hv_Distance);
if (HDevWindowStack.IsOpen())
{
HOperatorSet.DispObj(ho_RectifiedImage, HDevWindowStack.GetActive());
}
ho_RegionLines.Dispose();
HOperatorSet.GenRegionLine(out ho_RegionLines, hv_SR, hv_SC, hv_BR, hv_BC);
// stop(...); only in hdevelop
}
}
//
//Matching 01: *******************************************
//Matching 01: END of generated code for model application
//Matching 01: *******************************************
//
ho_Image.Dispose();
ho_ModelRegion.Dispose();
ho_TemplateImage.Dispose();
ho_ModelContours.Dispose();
ho_RectifiedImage.Dispose();
ho_Regions.Dispose();
ho_ConnectedRegions.Dispose();
ho_SelectedRegions.Dispose();
ho_Contours.Dispose();
ho_Regions1.Dispose();
ho_RegionLines.Dispose();
hv_ModelID.Dispose();
hv_ModelRegionArea.Dispose();
hv_RefRow.Dispose();
hv_RefColumn.Dispose();
hv_TestImages.Dispose();
hv_T.Dispose();
hv_Row.Dispose();
hv_Column.Dispose();
hv_Angle.Dispose();
hv_Score.Dispose();
hv_I.Dispose();
hv_RectificationHomMat2D.Dispose();
hv_Row1.Dispose();
hv_Column1.Dispose();
hv_Radius.Dispose();
hv_MetrologyHandle.Dispose();
hv_Width.Dispose();
hv_Height.Dispose();
hv_Index.Dispose();
hv_Row3.Dispose();
hv_Column3.Dispose();
hv_Parameter.Dispose();
hv_Row2.Dispose();
hv_Column2.Dispose();
hv_Radius1.Dispose();
hv_MetrologyHandle1.Dispose();
hv_Index1.Dispose();
hv_Parameter1.Dispose();
hv_SmlC.Dispose();
hv_SR.Dispose();
hv_SC.Dispose();
hv_BR.Dispose();
hv_BC.Dispose();
hv_Distance.Dispose();
}
//加载
public void LoadXML(string xmlPath)
{
// 1216 lw 此处保存产品会出错
/*await Task.Run(() =>
* {
* while (htWindow.HTWindow.HalconWindow.Handle == (IntPtr)0xffffffffffffffff)
* {
* Thread.Sleep(500);
* }
* });*/
IsLoadXML = true;
try
{
XElement root = XElement.Load(xmlPath);
XElement wireParameterNode = root.Element("WireParameter");
if (wireParameterNode == null)
{
return;
}
XMLHelper.ReadParameters(wireParameterNode, wireParameter);
//从金线自动生成区域加载参考信息
(wireAddAutoRegion as WireAddAutoRegion).LoadReferenceData();
//-----------------------------------------------------------------------------------------------------------------------------
XElement wireRegionsGroupNode = root.Element("WireRegionsGroup");
if (wireRegionsGroupNode == null)
{
return;
}
wireRegionsGroup.Clear();
foreach (var groupNode in wireRegionsGroupNode.Elements("Group"))
{
WireRegionsGroup group = new WireRegionsGroup();
group.BondOnICUserRegions = UserRegion.FromXElement(groupNode.Element("BondOnICUserRegions"), false, wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, false, false);
group.BondOnFrameUserRegions = UserRegion.FromXElement(groupNode.Element("BondOnFrameUserRegions"), false, wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, false, false);
//group.RefLineUserRegions = UserRegion.FromXElement(groupNode.Element("RefLineUserRegions"), false, wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset);
XMLHelper.ReadRegion(groupNode, group.LineUserRegions, "LineUserRegions", wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, true, false, false);
//1212 金线检测区域的通道值设置-生成线
for (int i = 0; i < group.LineUserRegions.Count(); i++)
{
if (group.LineUserRegions.ElementAt(i).ChannelNames.Count() == 0)
{
group.LineUserRegions.ElementAt(i).ChannelNames = wireParameter.ChannelNames; // currentChannelName
int tmp_ind;
tmp_ind = group.LineUserRegions.ElementAt(i).ImageIndex;
}
}
wireRegionsGroup.Add(group);
group.Index = wireRegionsGroup.Count;
}
(wireAddRegion as WireAddRegion).GroupsCount = wireRegionsGroup.Count;
//-----加载自动生成金线界面
//加载起始焊点区域归属
startBondOnRecipes.Clear();
XMLHelper.ReadOnRecipes(root, startBondOnRecipes, "StartBondOnRecipes");
XMLHelper.ReadRegion(root, startBallAutoUserRegion, "StartBallAutoUserRegion", wireParameter.DieImageRowOffset,
wireParameter.DieImageColumnOffset, false, false, false, false);
// add lw
HTuple hv__filePath = new HTuple(), hv_FileExists = new HTuple();
HTuple Startreg_index_after_sort = new HTuple();
hv__filePath = ModelsDirectory + "Startreg_index_after_sort.tup";
HOperatorSet.FileExists(hv__filePath, out hv_FileExists);
if ((int)(hv_FileExists) != 0)
{
HOperatorSet.ReadTuple(hv__filePath, out Startreg_index_after_sort);
}
else
{
Startreg_index_after_sort = HTuple.TupleGenSequence(1, startBallAutoUserRegion.Count, 1);
}
for (int i = 0; i < Startreg_index_after_sort.Length; i++)
{
startBallAutoUserRegion[i].Index_ini = Startreg_index_after_sort[i];
}
if (startBondOnRecipes.Count == 0)
{
IsLoadXML = false;
if (wireParameter.IsEnableStartVirtualBond == true)
{
wireParameter.IsEnableStartVirtualBond = false;
wireParameter.IsEnableStartVirtualBond = true;
}
if (wireParameter.IsEnableStartVirtualBond == false)
{
wireParameter.IsEnableStartVirtualBond = true;
wireParameter.IsEnableStartVirtualBond = false;
}
}
//加载结束焊点归属
endBondOnRecipes.Clear();
XMLHelper.ReadOnRecipes(root, endBondOnRecipes, "EndBondOnRecipes");
XMLHelper.ReadRegion(root, stopBallAutoUserRegion, "StopBallAutoUserRegion", wireParameter.DieImageRowOffset,
wireParameter.DieImageColumnOffset, false, false, false, false);
// add lw
HTuple Stopreg_index_after_sort = new HTuple();
hv__filePath = ModelsDirectory + "Stopreg_index_after_sort.tup";
HOperatorSet.FileExists(hv__filePath, out hv_FileExists);
if ((int)(hv_FileExists) != 0)
{
HOperatorSet.ReadTuple(hv__filePath, out Stopreg_index_after_sort);
}
else
{
Stopreg_index_after_sort = HTuple.TupleGenSequence(1, stopBallAutoUserRegion.Count, 1);
}
for (int i = 0; i < Stopreg_index_after_sort.Length; i++)
{
stopBallAutoUserRegion[i].Index_ini = Stopreg_index_after_sort[i];
}
if (endBondOnRecipes.Count == 0)
{
IsLoadXML = false;
if (wireParameter.IsEnableEndVirtualBond == true)
{
wireParameter.IsEnableEndVirtualBond = false;
wireParameter.IsEnableEndVirtualBond = true;
}
if (wireParameter.IsEnableEndVirtualBond == false)
{
wireParameter.IsEnableEndVirtualBond = true;
wireParameter.IsEnableEndVirtualBond = false;
}
}
//---------------生成界面中金线
(wireAddAutoRegion as WireAddAutoRegion).UpdateStartandStopLineRegions(true);
//金线模板Model
XElement wireRegionsModelGroupNode = root.Element("WireRegionsModelGroup");
if (wireRegionsModelGroupNode == null)
{
return;
}
wireRegionsModelGroup.Clear();
foreach (var modelGroupNode in wireRegionsModelGroupNode.Elements("ModelGroup"))
{
WireAutoRegionGroup modelGroup = new WireAutoRegionGroup();
modelGroup.ModelStartUserRegions = UserRegion.FromXElement(modelGroupNode.Element("ModelStartUserRegions"),
false, wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, false, false);
modelGroup.ModelStopUserRegions = UserRegion.FromXElement(modelGroupNode.Element("ModelStopUserRegions"),
false, wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, false, false);
modelGroup.RefLineModelRegions = UserRegion.FromXElement(modelGroupNode.Element("RefLineModelRegions"), false,
wireParameter.DieImageRowOffset, wireParameter.DieImageColumnOffset, false, false, false);
XMLHelper.ReadRegion(modelGroupNode, modelGroup.LineModelRegions, "LineModelRegions", wireParameter.DieImageRowOffset,
wireParameter.DieImageColumnOffset, false, false, true, false);
//1212 金线检测区域的通道值设置-模板线
for (int i = 0; i < modelGroup.LineModelRegions.Count(); i++)
{
if (modelGroup.LineModelRegions.ElementAt(i).ChannelNames.Count() == 0)
{
modelGroup.LineModelRegions.ElementAt(i).ChannelNames = wireParameter.ChannelNames;
int tmp_ind;
tmp_ind = modelGroup.LineModelRegions.ElementAt(i).ImageIndex;
}
}
wireRegionsModelGroup.Add(modelGroup);
modelGroup.Index = wireRegionsModelGroup.Count;
modelGroup.SelectModelNumber = (int)modelGroupNode.FirstAttribute.NextAttribute;
}
(wireAddAutoRegion as WireAddAutoRegion).ModelGroupsCount = wireRegionsModelGroup.Count;
//恢复自动生成金线起始区域金线ModelType add by wj
for (int i = 0; i < wireParameter.WireRegModelType.Length; i++)
{
startBallAutoUserRegion[i].ModelGroups = wireRegionsModelGroup;
startBallAutoUserRegion[i].CurrentModelGroup = wireRegionsModelGroup.ElementAt(wireParameter.WireRegModelType[i] - 1);
}
//1109 赋值初始排序
if (wireParameter.WireAutoIndex_sorted_start.Length == startBallAutoUserRegion.Count)
{
for (int i = 0; i < wireParameter.WireAutoIndex_sorted_start.Length; i++)
{
startBallAutoUserRegion[i].Index_ini = wireParameter.WireAutoIndex_sorted_start[i];
}
}
if (wireParameter.WireAutoIndex_sorted_stop.Length == stopBallAutoUserRegion.Count)
{
for (int i = 0; i < wireParameter.WireAutoIndex_sorted_stop.Length; i++)
{
stopBallAutoUserRegion[i].Index_ini = wireParameter.WireAutoIndex_sorted_stop[i];
}
}
//1029 default model setting
if (wireRegionsModelGroup.Count > 0)
{
(wireAddAutoRegion as WireAddAutoRegion).CurrentModelGroup = (wireAddAutoRegion as WireAddAutoRegion).ModelGroups.ElementAt(0);
}
if ((wireAddAutoRegion as WireAddAutoRegion).StartBallAutoUserRegion.Count > 0)
{
(wireAddAutoRegion as WireAddAutoRegion).WireParameter.IsStartPickUp = true;
}
if ((wireAddAutoRegion as WireAddAutoRegion).StopBallAutoUserRegion.Count > 0)
{
(wireAddAutoRegion as WireAddAutoRegion).WireParameter.IsStopPickUp = true;
}
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
}
private void train_classifier(HTuple hv_DLClassifierHandle, HTuple hv_FileName,
HTuple hv_NumEpochs, HTuple hv_TrainingImages, HTuple hv_TrainingLabels, HTuple hv_ValidationImages,
HTuple hv_ValidationLabels, HTuple hv_LearningRateStepEveryNthEpoch, HTuple hv_LearningRateStepRatio,
HTuple hv_PlotEveryNthEpoch, HTuple hv_WindowHandle)
{
// Stack for temporary objects
HObject[] OTemp = new HObject[20];
// Local iconic variables
HObject ho_BatchImages = null;
// Local control variables
HTuple hv_TrainingErrors = new HTuple(), hv_ValidationErrors = new HTuple();
HTuple hv_LearningRates = new HTuple(), hv_Epochs = new HTuple();
HTuple hv_LossByIteration = new HTuple(), hv_BatchSize = new HTuple();
HTuple hv_MinValidationError = new HTuple(), hv_NumBatchesInEpoch = new HTuple();
HTuple hv_NumTotalIterations = new HTuple(), hv_PlottedIterations = new HTuple();
HTuple hv_TrainSequence = new HTuple(), hv_SelectPercentageTrainingImages = new HTuple();
HTuple hv_TrainingImagesSelected = new HTuple(), hv_TrainingLabelsSelected = new HTuple();
HTuple hv_Epoch = new HTuple(), hv_Iteration = new HTuple();
HTuple hv_BatchStart = new HTuple(), hv_BatchEnd = new HTuple();
HTuple hv_BatchIndices = new HTuple(), hv_BatchImageFiles = new HTuple();
HTuple hv_BatchLabels = new HTuple(), hv_GenParamName = new HTuple();
HTuple hv_GenParamValue = new HTuple(), hv_DLClassifierTrainResultHandle = new HTuple();
HTuple hv_Loss = new HTuple(), hv_CurrentIteration = new HTuple();
HTuple hv_TrainingDLClassifierResultIDs = new HTuple();
HTuple hv_ValidationDLClassifierResultIDs = new HTuple();
HTuple hv_TrainingTop1Error = new HTuple(), hv_ValidationTop1Error = new HTuple();
HTuple hv_LearningRate = new HTuple();
HTupleVector hvec_TrainingPredictedLabels = new HTupleVector(1);
HTupleVector hvec_TrainingConfidences = new HTupleVector(1);
HTupleVector hvec_ValidationPredictedLabels = new HTupleVector(1);
HTupleVector hvec_ValidationConfidences = new HTupleVector(1);
// Initialize local and output iconic variables
HOperatorSet.GenEmptyObj(out ho_BatchImages);
try
{
//For the plot during training,
//we need to concatenate some intermediate results.
hv_TrainingErrors.Dispose();
hv_TrainingErrors = new HTuple();
hv_ValidationErrors.Dispose();
hv_ValidationErrors = new HTuple();
hv_LearningRates.Dispose();
hv_LearningRates = new HTuple();
hv_Epochs.Dispose();
hv_Epochs = new HTuple();
hv_LossByIteration.Dispose();
hv_LossByIteration = new HTuple();
hv_BatchSize.Dispose();
HOperatorSet.GetDlClassifierParam(hv_DLClassifierHandle, "batch_size", out hv_BatchSize);
hv_MinValidationError.Dispose();
hv_MinValidationError = 1;
//
//Create a tuple that includes all the iterations
//where the plot should be computed (including the last ieration).
hv_NumBatchesInEpoch.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_NumBatchesInEpoch = (((((new HTuple(hv_TrainingImages.TupleLength()
)) / (hv_BatchSize.TupleReal()))).TupleFloor())).TupleInt();
}
hv_NumTotalIterations.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_NumTotalIterations = (hv_NumBatchesInEpoch * hv_NumEpochs) - 1;
}
hv_PlottedIterations.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_PlottedIterations = ((((hv_NumBatchesInEpoch * HTuple.TupleGenSequence(
0, hv_NumEpochs - 1, hv_PlotEveryNthEpoch))).TupleConcat(hv_NumTotalIterations))).TupleRound()
;
}
//
//TrainSequence is used for easier indexing of the training data.
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_TrainSequence.Dispose();
HOperatorSet.TupleGenSequence(0, (new HTuple(hv_TrainingImages.TupleLength()
)) - 1, 1, out hv_TrainSequence);
}
//
//Select a subset of the training data set
//in order to obtain a fast approximation
//of the training error during training (plotting).
hv_SelectPercentageTrainingImages.Dispose();
hv_SelectPercentageTrainingImages = 100;
hv_TrainingImagesSelected.Dispose(); hv_TrainingLabelsSelected.Dispose();
select_percentage_dl_classifier_data(hv_TrainingImages, hv_TrainingLabels,
hv_SelectPercentageTrainingImages, out hv_TrainingImagesSelected, out hv_TrainingLabelsSelected);
//
HTuple end_val25 = hv_NumEpochs - 1;
HTuple step_val25 = 1;
#region
for (hv_Epoch = 0; hv_Epoch.Continue(end_val25, step_val25); hv_Epoch = hv_Epoch.TupleAdd(step_val25))
{
//In order to get randomness in each epoch,
//the training set is shuffled every epoch.
{
HTuple ExpTmpOutVar_0;
tuple_shuffle(hv_TrainSequence, out ExpTmpOutVar_0);
hv_TrainSequence.Dispose();
hv_TrainSequence = ExpTmpOutVar_0;
}
HTuple end_val29 = hv_NumBatchesInEpoch - 1;
HTuple step_val29 = 1;
for (hv_Iteration = 0; hv_Iteration.Continue(end_val29, step_val29); hv_Iteration = hv_Iteration.TupleAdd(step_val29))
{
//Select a batch from the training data set.
hv_BatchStart.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BatchStart = hv_Iteration * hv_BatchSize;
}
hv_BatchEnd.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BatchEnd = hv_BatchStart + (hv_BatchSize - 1);
}
hv_BatchIndices.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BatchIndices = hv_TrainSequence.TupleSelectRange(
hv_BatchStart, hv_BatchEnd);
}
hv_BatchImageFiles.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BatchImageFiles = hv_TrainingImages.TupleSelect(
hv_BatchIndices);
}
hv_BatchLabels.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_BatchLabels = hv_TrainingLabels.TupleSelect(
hv_BatchIndices);
}
//
//Read the image of the current batch.
ho_BatchImages.Dispose();
HOperatorSet.ReadImage(out ho_BatchImages, hv_BatchImageFiles);
//Augment the images to get a better variety of training images.
hv_GenParamName.Dispose();
hv_GenParamName = "mirror";
hv_GenParamValue.Dispose();
hv_GenParamValue = "rc";
{
HObject ExpTmpOutVar_0;
augment_images(ho_BatchImages, out ExpTmpOutVar_0, hv_GenParamName, hv_GenParamValue);
ho_BatchImages.Dispose();
ho_BatchImages = ExpTmpOutVar_0;
}
//
//Train the network with these images and ground truth labels.
hv_DLClassifierTrainResultHandle.Dispose();
HOperatorSet.TrainDlClassifierBatch(ho_BatchImages, hv_DLClassifierHandle,
hv_BatchLabels, out hv_DLClassifierTrainResultHandle);
//You can access the current value of the loss function,
//which should decrease during the training.
hv_Loss.Dispose();
HOperatorSet.GetDlClassifierTrainResult(hv_DLClassifierTrainResultHandle,
"loss", out hv_Loss);
//Store the loss in a tuple .
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
{
HTuple
ExpTmpLocalVar_LossByIteration = hv_LossByIteration.TupleConcat(
hv_Loss);
hv_LossByIteration.Dispose();
hv_LossByIteration = ExpTmpLocalVar_LossByIteration;
}
}
//
//In regular intervals, we want to evaluate
//how well our classifier performs.
hv_CurrentIteration.Dispose();
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
hv_CurrentIteration = ((hv_Iteration + (hv_NumBatchesInEpoch * hv_Epoch))).TupleInt()
;
}
if ((int)(((hv_CurrentIteration.TupleEqualElem(hv_PlottedIterations))).TupleSum()
) != 0)
{
//Plot the progress regularly.
//Evaluate the current classifier on the training and validation set.
hv_TrainingDLClassifierResultIDs.Dispose(); hvec_TrainingPredictedLabels.Dispose(); hvec_TrainingConfidences.Dispose();
apply_dl_classifier_batchwise(hv_TrainingImagesSelected, hv_DLClassifierHandle,
out hv_TrainingDLClassifierResultIDs, out hvec_TrainingPredictedLabels,
out hvec_TrainingConfidences);
hv_ValidationDLClassifierResultIDs.Dispose(); hvec_ValidationPredictedLabels.Dispose(); hvec_ValidationConfidences.Dispose();
apply_dl_classifier_batchwise(hv_ValidationImages, hv_DLClassifierHandle,
out hv_ValidationDLClassifierResultIDs, out hvec_ValidationPredictedLabels,
out hvec_ValidationConfidences);
//Evaluate the top-1 error on each dataset.
hv_TrainingTop1Error.Dispose();
evaluate_dl_classifier(hv_TrainingLabelsSelected, hv_DLClassifierHandle,
hv_TrainingDLClassifierResultIDs, "top1_error", "global", out hv_TrainingTop1Error);
hv_ValidationTop1Error.Dispose();
evaluate_dl_classifier(hv_ValidationLabels, hv_DLClassifierHandle, hv_ValidationDLClassifierResultIDs,
"top1_error", "global", out hv_ValidationTop1Error);
//Concatenate the values for the plot.
hv_LearningRate.Dispose();
HOperatorSet.GetDlClassifierParam(hv_DLClassifierHandle, "learning_rate",
out hv_LearningRate);
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
{
HTuple
ExpTmpLocalVar_TrainingErrors = hv_TrainingErrors.TupleConcat(
hv_TrainingTop1Error);
hv_TrainingErrors.Dispose();
hv_TrainingErrors = ExpTmpLocalVar_TrainingErrors;
}
}
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
{
HTuple
ExpTmpLocalVar_ValidationErrors = hv_ValidationErrors.TupleConcat(
hv_ValidationTop1Error);
hv_ValidationErrors.Dispose();
hv_ValidationErrors = ExpTmpLocalVar_ValidationErrors;
}
}
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
{
HTuple
ExpTmpLocalVar_LearningRates = hv_LearningRates.TupleConcat(
hv_LearningRate);
hv_LearningRates.Dispose();
hv_LearningRates = ExpTmpLocalVar_LearningRates;
}
}
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
{
HTuple
ExpTmpLocalVar_Epochs = hv_Epochs.TupleConcat(
(hv_PlottedIterations.TupleSelect(new HTuple(hv_Epochs.TupleLength()
))) / (hv_NumBatchesInEpoch.TupleReal()));
hv_Epochs.Dispose();
hv_Epochs = ExpTmpLocalVar_Epochs;
}
}
//Plot validation and error against the epochs in order to
//observe the progress of the training.
plot_dl_classifier_training_progress(hv_TrainingErrors, hv_ValidationErrors, hv_LearningRates, hv_Epochs, hv_NumEpochs, hv_WindowHandle);
//如果上面绘制坐标系到窗体过程报错，搞不定的话就用下面这句代替，直接显示训练数据到窗体
//disp_training_progress_datas(hv_TrainingErrors, hv_ValidationErrors, hv_LearningRates, hv_Epochs, hv_NumEpochs, hv_WindowHandle);
if ((int)(new HTuple(hv_ValidationTop1Error.TupleLessEqual(hv_MinValidationError))) != 0)
{
HOperatorSet.WriteDlClassifier(hv_DLClassifierHandle, hv_FileName);
hv_MinValidationError.Dispose();
hv_MinValidationError = new HTuple(hv_ValidationTop1Error);
}
}
}
//Reduce the learning rate every nth epoch.
if ((int)(new HTuple((((hv_Epoch + 1) % hv_LearningRateStepEveryNthEpoch)).TupleEqual(
0))) != 0)
{
using (HDevDisposeHelper dh = new HDevDisposeHelper())
{
HOperatorSet.SetDlClassifierParam(hv_DLClassifierHandle, "learning_rate",
hv_LearningRate * hv_LearningRateStepRatio);
}
hv_LearningRate.Dispose();
HOperatorSet.GetDlClassifierParam(hv_DLClassifierHandle, "learning_rate",
out hv_LearningRate);
}
}
#endregion
// stop(...); only in hdevelop
ho_BatchImages.Dispose();
hv_TrainingErrors.Dispose();
hv_ValidationErrors.Dispose();
hv_LearningRates.Dispose();
hv_Epochs.Dispose();
hv_LossByIteration.Dispose();
hv_BatchSize.Dispose();
hv_MinValidationError.Dispose();
hv_NumBatchesInEpoch.Dispose();
hv_NumTotalIterations.Dispose();
hv_PlottedIterations.Dispose();
hv_TrainSequence.Dispose();
hv_SelectPercentageTrainingImages.Dispose();
hv_TrainingImagesSelected.Dispose();
hv_TrainingLabelsSelected.Dispose();
hv_Epoch.Dispose();
hv_Iteration.Dispose();
hv_BatchStart.Dispose();
hv_BatchEnd.Dispose();
hv_BatchIndices.Dispose();
hv_BatchImageFiles.Dispose();
hv_BatchLabels.Dispose();
hv_GenParamName.Dispose();
hv_GenParamValue.Dispose();
hv_DLClassifierTrainResultHandle.Dispose();
hv_Loss.Dispose();
hv_CurrentIteration.Dispose();
hv_TrainingDLClassifierResultIDs.Dispose();
hv_ValidationDLClassifierResultIDs.Dispose();
hv_TrainingTop1Error.Dispose();
hv_ValidationTop1Error.Dispose();
hv_LearningRate.Dispose();
hvec_TrainingPredictedLabels.Dispose();
hvec_TrainingConfidences.Dispose();
hvec_ValidationPredictedLabels.Dispose();
hvec_ValidationConfidences.Dispose();
return;
}
catch (HalconException HDevExpDefaultException)
{
ho_BatchImages.Dispose();
hv_TrainingErrors.Dispose();
hv_ValidationErrors.Dispose();
hv_LearningRates.Dispose();
hv_Epochs.Dispose();
hv_LossByIteration.Dispose();
hv_BatchSize.Dispose();
hv_MinValidationError.Dispose();
hv_NumBatchesInEpoch.Dispose();
hv_NumTotalIterations.Dispose();
hv_PlottedIterations.Dispose();
hv_TrainSequence.Dispose();
hv_SelectPercentageTrainingImages.Dispose();
hv_TrainingImagesSelected.Dispose();
hv_TrainingLabelsSelected.Dispose();
hv_Epoch.Dispose();
hv_Iteration.Dispose();
hv_BatchStart.Dispose();
hv_BatchEnd.Dispose();
hv_BatchIndices.Dispose();
hv_BatchImageFiles.Dispose();
hv_BatchLabels.Dispose();
hv_GenParamName.Dispose();
hv_GenParamValue.Dispose();
hv_DLClassifierTrainResultHandle.Dispose();
hv_Loss.Dispose();
hv_CurrentIteration.Dispose();
hv_TrainingDLClassifierResultIDs.Dispose();
hv_ValidationDLClassifierResultIDs.Dispose();
hv_TrainingTop1Error.Dispose();
hv_ValidationTop1Error.Dispose();
hv_LearningRate.Dispose();
hvec_TrainingPredictedLabels.Dispose();
hvec_TrainingConfidences.Dispose();
hvec_ValidationPredictedLabels.Dispose();
hvec_ValidationConfidences.Dispose();
throw HDevExpDefaultException;
}
}
