//像素去震动函数：pixelWithVibrationcorrection()
public void pixelWithVibrationcorrection(OpticMeasureClass opticFlowFunc)
{
int InReferencePictureIndex = 0;//在母板系照片灰度中的索引
Mt = new CSharpAlgorithm.Algorithm.MatrixOfAlgorithm();
//call读识标记点函数，将母板的标记点识取出来
functionFun.ReadMarkPointsFunc();
//将第i张母板标记点存到PictureOpticflowDataResultArray[ReferencePictureDataArray[i].ReferencePictureIndex].PictureMarkpointsArray
opticFlowFunc.PictureOpticflowDataResultArray[opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].ReferencePictureIndex].PictureMarkpointsArray = functionFun.MarkPointsArray;
CreatArray.creatInReferencePicturePixXYArray(RefPictureWidth * RefPictureHight, ref InReferencePicturePixXYArray);//像素经过旋转平移后在母板坐标系下的坐标数组
//=============待匹配照片循环================
for (int k = 0; k < length; k++)
{
CurPictureIndex = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray[k]; //待匹配照片在结果数组中的索引
CreatArray.creatPixelResultArray(opticFlowFunc, CurPictureIndex, RefPictureHight, RefPictureWidth); //创建像素光流结果数组
//装待匹配照片灰度数组
CurrentPictureBitmap = (Bitmap)Image.FromFile(opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].PictureLujin);
CreatArray.creatPictureGreyArray(CurrentPictureBitmap, ref CurrentPictureGreyArray); //创建待匹配照片灰度数组
functionFun.LoadgrayValuesArrayForMatch(CurrentPictureBitmap, ref CurrentPictureGreyArray); //装载待匹配照片灰度数组
functionFun.ReadMarkPointsFunc(); //call识标记点函数
//将第K张待匹配照片标记点到光流结果数组中
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].PictureMarkpointsArray = functionFun.MarkPointsArray;
// call 求旋转平移矩阵函数，得到母板照片旋转平移到当前待匹配照片上的旋转平移矩阵函数
functionFun.GetRotationMatrixAndTranslationFunc(ref Mt);
//=============待匹配照片像素循环 像素流计算（去震动像素直接相减）==================
for (int j = 0; j < CurrentPictureGreyArray.Length; j++)
{
// 得到旋转平移后的坐标
functionFun.Get2DzuobiaoInReferencePictureFunc(opticFlowFunc, CurrentPictureBitmap, Mt, T, InReferencePictureDataArrayIndex, j, RefPictureWidth * RefPictureHight, ref InReferencePicturePixXYArray);
//计算编号
functionFun.CalculateIndexOfInReferencePictureFunc(RefPictureHight, RefPictureWidth, j, InReferencePicturePixXYArray, ref InReferencePictureIndex);
//求灰度差 直接相减
Greyvalue = CurrentPictureGreyArray[j].OrigingrayValue - ReferencePictureGreyArray[InReferencePictureIndex].OrigingrayValue;
//将结果存到结果数组中
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPixelArray[j].Greyvalue = Greyvalue;
}
//清空待匹配照片灰度数组
Array.Resize(ref CurrentPictureGreyArray, 0);
}
//销毁对象
functionFun = null;
//清空母版照片灰度数组
Array.Resize(ref ReferencePictureGreyArray, 0);
}
//将母板点坐标(x,y)存到母板点数组中函数: packedPiontImageCoorditionArrayFunction（）
public void packedPiontImageCoorditionArrayFunction(OpticMeasureClass opticFlowFunc, int PointArraylength)
{
//判断母板点数组是否建立
if (opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray == null)
{
opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray = new Vector2[PointArraylength];
}
else
{
Array.Resize(ref opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray, PointArraylength);
}
//判断母板点索引数组是否建立
if (opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray == null)
{
opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray = new int[PointArraylength];
}
else
{
Array.Resize(ref opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray, PointArraylength);
}
//将母板点坐标(x,y)存到母板点数组中
for (int n = 0; n < PointArraylength; n++)
{
opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[n].X = trian.TriangleVertexArray[n].D2V1.X;
opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[n].Y = trian.TriangleVertexArray[n].D2V1.Y;
opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray[n] = trian.TriangleVertexArray[n].Index;
}
}
//像素不去震动函数：pixelNoVibrationcorrection()
public void pixelNoVibrationcorrection(OpticMeasureClass opticFlowFunc)
{
//=============待匹配照片循环==================
for (int k = 0; k < length; k++)
{
CurPictureIndex = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray[k];//待匹配照片在结果数组中的索引
CurrentPictureBitmap = (Bitmap)Image.FromFile(opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].PictureLujin);
CreatArray.creatPictureGreyArray(CurrentPictureBitmap, ref CurrentPictureGreyArray); // 创建匹配照片的灰度数组
functionFun.LoadgrayValuesArrayForMatch(CurrentPictureBitmap, ref CurrentPictureGreyArray); // 装待匹配照片的灰度数组
CreatArray.creatPixelResultArray(opticFlowFunc, CurPictureIndex, RefPictureHight, RefPictureWidth); //像素结果数组创建
//=============照片像素循环 像素流计算（像素直接相减）==================
for (int j = 0; j < CurrentPictureGreyArray.Length; j++)
{
//Greyvalue = CurrentPictureGreyArray[j].OrigingrayValue - ReferencePictureGreyArray[j].OrigingrayValue;
//Greyvalue = CurrentPictureGreyArray[j].OrigingrayValue / ReferencePictureGreyArray[j].OrigingrayValue - ReferencePictureGreyArray[j].OrigingrayValue/ReferencePictureGreyArray[j].OrigingrayValue;
//Greyvalue = CurrentPictureGreyArray[j].OrigingrayValue / ReferencePictureGreyArray[j].OrigingrayValue - CurrentPictureGreyArray[j+1].OrigingrayValue / ReferencePictureGreyArray[j].OrigingrayValue;
//将结果存到结果数组中
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPixelArray[j].Greyvalue = Greyvalue;
}
//清空数组
Array.Resize(ref CurrentPictureGreyArray, 0);
}
//清数数组
Array.Resize(ref ReferencePictureGreyArray, 0);
// 销毁functionFun
functionFun = null;
}
CreatArray CreatArray = new CreatArray(); //创建数组对象
public void Flow_Diagnostics_Run_Refine(OpticMeasureClass currentOMC)
{
//------定义局部变量-------------------------------
int[] window_shifting = new int[4]; //装载矩形窗的左上角和右下角点的坐标 数组
int size_average = 3; // 卷积核大小，即邻域大小
byte thresholding = 20; //得到阈值
byte dwtSeries = 2; //得到小波分解级数
byte flagFilter = 0; //得到应用何种小波基和何种阈值法（0-5）
//------装待匹配照片灰度数组-------------------------------------------
for (int i = 0; i < currentOMC.readFileNames.Length - 1; i++)
{
//模板照片灰度数组
ReferencePictureBitmap = (Bitmap)Image.FromFile(currentOMC.readFileNames[i]);
CreatArray.creatPictureGreyArray(ReferencePictureBitmap, ref ReferencePictureGreyArray);
functionFun.LoadgrayValuesArrayForMatch(ReferencePictureBitmap, ref ReferencePictureGreyArray);
//带匹配照片灰度数组
CurrentPictureBitmap = (Bitmap)Image.FromFile(currentOMC.readFileNames[i + 1]);
CreatArray.creatPictureGreyArray(CurrentPictureBitmap, ref CurrentPictureGreyArray); //创建待匹配照片灰度数组
functionFun.LoadgrayValuesArrayForMatch(CurrentPictureBitmap, ref CurrentPictureGreyArray); //装载待匹配照片灰度数组
}
//-------------选择需要处理的区域(功能空缺)-------------------------------------
//---------------------光强修正-------------------------------------------------
//先将原始数组拷贝
Im1 = new Apicturegrayinformation[ReferencePictureGreyArray.Length];
Im2 = new Apicturegrayinformation[CurrentPictureGreyArray.Length];
Array.Copy(ReferencePictureGreyArray, Im1, ReferencePictureGreyArray.Length);
Array.Copy(CurrentPictureGreyArray, Im2, CurrentPictureGreyArray.Length);
//functionFun.correction_illumination(ReferencePictureBitmap, ReferencePictureGreyArray, CurrentPictureGreyArray, window_shifting, size_average, ref Im2);
//------------图片预处理（小波变换、高斯低通滤波）-----------------------------------
//---先 小波变换-------------
Apicturegrayinformation[] Im1WaveletArray = new Apicturegrayinformation[Im1.Length]; //Im1 小波变换后的数组（大小减半）
Apicturegrayinformation[] Im2WaveletArray = new Apicturegrayinformation[Im2.Length]; //Im2 小波变换后的数组（大小减半）
functionFun.WaveletFun(ReferencePictureBitmap, Im1, thresholding, dwtSeries, flagFilter, ref Im1WaveletArray); // 模板照片小波变换
functionFun.WaveletFun(CurrentPictureBitmap, Im2, thresholding, dwtSeries, flagFilter, ref Im2WaveletArray); // 匹配照片小波变换
//--再 高斯低通滤波-----------------
double sigma = 1.5; //均方值
Apicturegrayinformation[] Im1GaussrasultGrayArray = new Apicturegrayinformation[Im1.Length]; //Im1 高斯滤波后数组
Apicturegrayinformation[] Im2GaussrasultGrayArray = new Apicturegrayinformation[Im2.Length]; //Im2 高斯滤波后数组
//调用高斯滤波函数
functionFun.GaussFunc(Im1WaveletArray, sigma, ref Im1GaussrasultGrayArray);
functionFun.GaussFunc(Im2WaveletArray, sigma, ref Im2GaussrasultGrayArray);
//------------光流计算-----------------------------
}
//创建点光流结果数组
public void creatPointsResultArray(OpticMeasureClass opticFlowFunc, int CurPictureIndex, int RefPictureHight, int RefPictureWidth)
{
if (opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray == null)
{
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray = new OpticflowResultForPointsGrey[RefPictureWidth * RefPictureHight];
}
else
{
Array.Resize(ref opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray, RefPictureWidth * RefPictureHight);
}
}
public void Init(OpticMeasureClass _opmc, GetOpticflowResultOfSingleReferencePicture _singleOpticFlow)
{
compVector2 = _singleOpticFlow;
currentEdit = _opmc;
pictureVector2.Size = panelVector2.Size;
drawVector2.CanvasSize = new Point(this.panelVector2.Width, panelVector2.Height);
bool succ;
succ = pamater.GetDefaultSetting(ref pamater.zbDrawParamater);
if (succ == false)
{
pamater.zbDrawParamater.Init();
}
// 如果参数表读取错误，则：取参数的初始值
viewImage = new Image2DView(pictureVector2, panelVector2, pamater);
TreeNode node;
string filename;
int ppindex = -1;
if (currentEdit.readFileNames != null)
{
for (int i = 0; i < currentEdit.readFileNames.Length; i++)
{
ppindex = i;//Convert.ToInt32(currentEdit.WaitMatchPictureIndexArray[i].ToString());
filename = currentEdit.readFileNames[ppindex];
node = new TreeNode();
node.Name = filename;
node.Text = imageCommon.getFileNameOnly(filename);
node.Tag = ppindex.ToString(); // 该照片 在数组中的序号
filetree.Nodes.Add(node);
}
if (filetree.Nodes.Count > 0)
{
filetree.SelectedNode = filetree.Nodes[0];
}
}
}
bool IsVibrationcorrection; //是否去震动
//单母板光流计算函数
public void GetOpticflowResultOfSingleReferencePictureFunc(OpticMeasureClass opticFlowFunc, int _InReferencePictureDataArrayIndex)
{
int CalcuateModer; //计算模式
InReferencePictureDataArrayIndex = _InReferencePictureDataArrayIndex; //第i张母板索引号为输入量
length = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray.Length; //第i张母板待匹配照片数组长度
StepValue = opticFlowFunc.StepValue; //像素点间间隔
IsStep = opticFlowFunc.IsStep; //=true表示需要跳点，跳点间隔=StepValue
IsVibrationcorrection = opticFlowFunc.IsVibrationcorrection; //是否去震动
//----修改--switch case 判断四种情况---
//判断计算模型
if (IsStep == false & IsVibrationcorrection == false)
{
CalcuateModer = 0;
}
else if (IsStep == false & IsVibrationcorrection == true)
{
CalcuateModer = 1;
}
else if (IsStep == true & IsVibrationcorrection == false)
{
CalcuateModer = 2;
}
else
{
CalcuateModer = 3;
}
if (opticFlowFunc.ReferencePictureDataArray == null)
{
MessageBox.Show("还没有读入照片！");
}
else
{
//装母板的灰度数组
functionFun = new FunctionFuncCalss();
ReferencePictureBitmap = (Bitmap)Image.FromFile(opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].ReferencePictureLuJin);//得到母板点的Bitmap
RefPictureHight = ReferencePictureBitmap.Height;
RefPictureWidth = ReferencePictureBitmap.Width;
CreatArray.creatPictureGreyArray(ReferencePictureBitmap, ref ReferencePictureGreyArray); //创建母板灰度数组
functionFun.LoadgrayValuesArrayForMatch(ReferencePictureBitmap, ref ReferencePictureGreyArray); //装载母板灰度数组
//根据计算模板选择相应的光流计算
switch (CalcuateModer)
{
case 0: //逐像素不去震动
pixelNoVibrationcorrection(opticFlowFunc);
break;
case 1: //逐像素去震动
pixelWithVibrationcorrection(opticFlowFunc);
break;
case 2: //跳点不去震动
piontsNoVibrationcorrection(opticFlowFunc);
break;
case 3: //跳点去震动
piontsWithVibrationcorrection(opticFlowFunc);
break;
}
}
}
//跳点去震动函数：piontsWithVibrationcorrection()
public void piontsWithVibrationcorrection(OpticMeasureClass opticFlowFunc)
{
int PointArraylength; //第i张母板点坐标数组长度
int InReferencePictureIndex = 0; //在母板系照片灰度中的索引
int s = 0; //点序号
Mt = new CSharpAlgorithm.Algorithm.MatrixOfAlgorithm();
trian = new Triangulation(); // 创建三角剖分对象
CalculateWidthAndHightOfPointImage(); //计算母板点数组的长和宽
//call装顶点表函数，装母板顶点表
trian.GetTrianglerVertexArrayFunc(ReferencePictureBitmap.Height, ReferencePictureBitmap.Width, StepValue);
PointArraylength = trian.TriangleVertexArray.Length; //母板点数组长度
//PointArraylength = RefPicturePointArrayWidth * RefPicturePointArrayHight;//母板点数组长度
packedPiontImageCoorditionArrayFunction(opticFlowFunc, PointArraylength); //装载到母板点数组中
//销毁trian对象
trian = null; //可以这么销毁
functionFun.ReadMarkPointsFunc(); //call读识标记点函数，将母板的标记点识取出来
//将第i张母板标记点存到PictureOpticflowDataResultArray[ReferencePictureDataArray[i].ReferencePictureIndex].PictureMarkpointsArray
opticFlowFunc.PictureOpticflowDataResultArray[opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].ReferencePictureIndex].PictureMarkpointsArray = functionFun.MarkPointsArray;
CreatArray.creatInReferencePicturePixXYArray(RefPictureHight * RefPictureWidth, ref InReferencePicturePixXYArray); //创建像素经过旋转平移后在母板坐标系下的坐标数组
//=======匹配照片 循环计算=====
for (int k = 0; k < length; k++)
{
CurPictureIndex = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray[k]; //待匹配照片在结果数组中的索引
CreatArray.creatPointsResultArray(opticFlowFunc, CurPictureIndex, RefPicturePointArrayHight, RefPicturePointArrayWidth); //创建点光流结果数组
CurrentPictureBitmap = (Bitmap)Image.FromFile(opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].PictureLujin);
CreatArray.creatPictureGreyArray(CurrentPictureBitmap, ref CurrentPictureGreyArray); //创建 匹配照片灰度数组
functionFun.LoadgrayValuesArrayForMatch(CurrentPictureBitmap, ref CurrentPictureGreyArray); //装待匹配照片灰度数组
CreatArray.creatPictureDlateGreyArray(CurrentPictureBitmap, ref PictureDlateGreyArray); //创建灰度差数组
CreatArray.creatHomologousPointsDisplacementsArray(PointArraylength, ref HomologousPointsDisplacementsArray); //创建位移场数组
functionFun.ReadMarkPointsFunc(); //call识标记点函数
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].PictureMarkpointsArray = functionFun.MarkPointsArray; //将第K张待匹配照片标记点到光流结果数组中
// call 求旋转平移矩阵函数，得到母板照片旋转平移到当前待匹配照片上的旋转平移矩阵函数
functionFun.GetRotationMatrixAndTranslationFunc(ref Mt);
// 求母板照片和待匹配照片灰度差
for (int j = 0; j < CurrentPictureGreyArray.Length; j++)
{
// 得到旋转平移后的坐标
functionFun.Get2DzuobiaoInReferencePictureFunc(opticFlowFunc, CurrentPictureBitmap, Mt, T, InReferencePictureDataArrayIndex, j, CurrentPictureGreyArray.Length, ref InReferencePicturePixXYArray);
//计算编号
functionFun.CalculateIndexOfInReferencePictureFunc(RefPictureHight, RefPictureWidth, j, InReferencePicturePixXYArray, ref InReferencePictureIndex);
//求灰度差 直接相减
PictureDlateGreyArray[j] = CurrentPictureGreyArray[j].OrigingrayValue - ReferencePictureGreyArray[InReferencePictureIndex].OrigingrayValue;
}
//清数组
Array.Resize(ref CurrentPictureGreyArray, 0);
//=============待匹配照片 点循环 计算（去震动）==================
//for (int j = 0; j < PointArraylength; j++)
//{
// //将点坐标存起
// opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].pointCoordinate.X = opticFlowFunc.ReferencePictureDataArray[i].PiontImageCoorditionArray[j].X;
// opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].pointCoordinate.Y = opticFlowFunc.ReferencePictureDataArray[i].PiontImageCoorditionArray[j].Y;
// // call求灰度差函数
// functionFun.racepossibleEdgePointIndexFunc(RefPicturePointArrayHight, RefPicturePointArrayWidth, opticFlowFunc.ReferencePictureDataArray[i].IndexArray[j], ref CurrentPixtNeighbourArray);
// //判断 八邻域数组长度是否为8，不为8的话 梯度赋0
// if (CurrentPixtNeighbourArray.Count == 8)
// {
// //soble算子
// Grad.X = 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[2]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[6]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
// Grad.Y = -2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[0]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[4]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
// }
// else
// {
// Grad.X = 0;
// Grad.Y = 0;
// }
// //将灰度差存到结果数组里面
// opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].Grad = Grad;
//}
//=====存到赵涛数组里===========
//装载显示点的坐标
for (int j = 0; j < PointArraylength; j++)
{
HomologousPointsDisplacementsArray[j].pointCoordinate.X = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[j].X;
HomologousPointsDisplacementsArray[j].pointCoordinate.Y = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[j].Y;
}
//====母板点数组循环计算=========
for (int j = 0; j < PointArraylength; j++)
{
//装八邻域数组
functionFun.racepossibleEdgePointIndexFunc(ReferencePictureBitmap.Height, ReferencePictureBitmap.Width, opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray[j], ref CurrentPixtNeighbourArray);
//判断 八邻域数组长度是否为8，不为8的话 梯度赋0
if (CurrentPixtNeighbourArray.Count == 8)
{
//soble算子
HomologousPointsDisplacementsArray[s].deltaX = 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[2]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[6]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
HomologousPointsDisplacementsArray[s].deltaY = -2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[0]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[4]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
}
else
{
HomologousPointsDisplacementsArray[s].deltaX = 0;
HomologousPointsDisplacementsArray[s].deltaY = 0;
}
s++;
}
//销毁对象
functionFun = null;
// 清除数组
Array.Resize(ref PictureDlateGreyArray, 0);
CurrentPixtNeighbourArray.Clear();
}
// 清除数组
Array.Resize(ref ReferencePictureGreyArray, 0);
}
// 跳点不去震动函数：piontsNoVibrationcorrection()
public void piontsNoVibrationcorrection(OpticMeasureClass opticFlowFunc)
{
int PointArraylength; //第i张母板点坐标数组长度
CalculateWidthAndHightOfPointImage(); //计算母板点数组的长和宽
trian = new Triangulation(); // 创建三角剖分对象
//call装顶点表函数，装母板顶点表
trian.GetTrianglerVertexArrayFunc(ReferencePictureBitmap.Height, ReferencePictureBitmap.Width, StepValue);
PointArraylength = trian.TriangleVertexArray.Length;
packedPiontImageCoorditionArrayFunction(opticFlowFunc, PointArraylength);//装母板点数组
//销毁trian对象
trian.Destroy();
GC.Collect();
//=============待匹配照片循环 计算==================
for (int k = 0; k < length; k++)
{
CurPictureIndex = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray[k];//待匹配照片在结果数组中的索引
CurrentPictureBitmap = (Bitmap)Image.FromFile(opticFlowFunc.PictureOpticflowDataResultArray[opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexOfPictureForMachArray[k]].PictureLujin);
CreatArray.creatPictureGreyArray(CurrentPictureBitmap, ref CurrentPictureGreyArray); //创建 匹配照片灰度数组
CreatArray.creatPictureDlateGreyArray(CurrentPictureBitmap, ref PictureDlateGreyArray); //创建灰度差值数组
functionFun.LoadgrayValuesArrayForMatch(CurrentPictureBitmap, ref CurrentPictureGreyArray); //装载 匹配照片灰度数组
//CreatArray.creatHomologousPointsDisplacementsArray(PointArraylength,ref HomologousPointsDisplacementsArray);//创建位移场数组
CreatArray.creatPointsResultArray(opticFlowFunc, CurPictureIndex, RefPicturePointArrayHight, RefPicturePointArrayWidth);//创建结果数组
// 求母板照片和待匹配照片灰度差
for (int j = 0; j < CurrentPictureGreyArray.Length; j++)
{
PictureDlateGreyArray[j] = CurrentPictureGreyArray[j].OrigingrayValue - ReferencePictureGreyArray[j].OrigingrayValue;
}
//清数组
Array.Resize(ref CurrentPictureGreyArray, 0);
//====母板点数组循环计算=========
for (int j = 0; j < PointArraylength; j++)
{
//装载点坐标数组
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].pointCoordinate.X = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[j].X;
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].pointCoordinate.Y = opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].PiontImageCoorditionArray[j].Y;
//装八邻域数组
functionFun.racepossibleEdgePointIndexFunc(ReferencePictureBitmap.Height, ReferencePictureBitmap.Width, opticFlowFunc.ReferencePictureDataArray[InReferencePictureDataArrayIndex].IndexArray[j], ref CurrentPixtNeighbourArray);
//soble算子
Grad.X = 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[2]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[6]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
Grad.Y = -2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[0]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] + 2 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[4]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
//Prewitt算子
//Grad.X = 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[2]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[6]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
//Grad.Y = -1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[0]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[1]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[3]] +1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[4]] + 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[5]] - 1 * PictureDlateGreyArray[(int)CurrentPixtNeighbourArray[7]];
opticFlowFunc.PictureOpticflowDataResultArray[CurPictureIndex].OpticflowResultFOfPointsArray[j].Grad = Grad;
}
// 清除数组
Array.Resize(ref PictureDlateGreyArray, 0);
CurrentPixtNeighbourArray.Clear();
}
// 清除数组
Array.Resize(ref ReferencePictureGreyArray, 0);
}
public void Init(OpticMeasureClass _currentOMC, int _primitivePictureIndex)
{
currentOMC = _currentOMC;
primitivePictureIndex = _primitivePictureIndex;
}
public DrawVector2Frm(OpticMeasureClass _opmc, GetOpticflowResultOfSingleReferencePicture _singleOpticFlow)
{
InitializeComponent();
currentEdit = _opmc;
compVector2 = _singleOpticFlow;
}
public GrayAddPicture(OpticMeasureClass _opmc)
{
InitializeComponent();
opmc = _opmc;
}