C#Halcon差异分类模型

缺陷检测抛开blob分析，尺寸判定与深度学习之外还有一种常用的方式，个人称之为差异分类模型，简称差分模型

个人对其应用过程为：

准备阶段：图像预处理，创建模型，查找模型定位，创建差分模型

应用阶段：查找模型定位，差分模型分类

Demo效果（由于限制5M动图，图像压缩的有些失真，下面进行详细介绍时会以单图呈现）

一，图像预处理

1，加载图像

2，绘制目标区域大致ROI

鉴于图像情况个人建议可以采用

create_drawing_object_rectangle2  ||  draw_rectangle2   ||   draw_rectangle2_mod

进行封装

3，锁定抠出目标区域

二值化提取区域然后进行区域膨胀（这一步主要是扣取模板图像，个人采用 threshold 算子提取，shape_trans生成多边形，dilation_circle进行区域膨胀）

二，创建create_shape_model用以定位

num_levels        # 金字塔的层数
angle_start        # 模板旋转的起始角度
angle_extent     # 模板旋转角度范围
angle_step        # 旋转角度的步长
optimization      # 模板优化和模板创建方法
metric               # 匹配方法设置
contrast            # 设置对比度
min_contrast    # 设置最小对比度

三，验证find_shape_model定位效果

四，创建差分模型

1，选择图像文件夹路径

FolderBrowserDialog folderBrowserDialog = new FolderBrowserDialog();

2，创建差分模型（CreateVariationModel参数最好是开放，小编Demo案例直接写死）

 private void btn_create_variation_model_Click(object sender, EventArgs e)
 {
     try
     {
         hv_VarModelID.Dispose();
         HOperatorSet.CreateVariationModel(imageWidth, imageHeight, "byte", "standard",
             out hv_VarModelID);
         laTicps2.ForeColor = Color.Green;
         laTicps2.Text = "创建差异模型成功";
     }
     catch
     {
         laTicps2.ForeColor = Color.Red;
         laTicps2.Text = "创建差异模型失败";
     }

 }

3，训练差分模型（养成良好的内存管理习惯很重要）

 private void btn_train_variation_model_Click(object sender, EventArgs e)
 {

     try
     {
         DirectoryInfo TheFolder = new DirectoryInfo(fileName);//文件路径

         for (int i = 0; i < TheFolder.GetFiles().Length; i++)//遍历文件夹
         {

             if (TheFolder.GetFiles()[i].Length > 0 && (TheFolder.GetFiles()[i].Extension == ".png" || TheFolder.GetFiles()[i].Extension == ".bmp"))//或者jpg,png 文件大小要大于0且是图片文件
             {
                 Application.DoEvents();

                 HOperatorSet.ReadImage(out HIMage, fileName + "\\" + tifNames[i]);
                 FitImage(HIMage, hWC);

                 HTuple angle_start2, angle_extent2, MinScore, matchNum, MaxOverlap, SubPixel, numLeave, greediness;

                 angle_start2 = (new HTuple(Convert.ToDouble(tbx_angle_start2.Text))).TupleRad();
                 angle_extent2 = (new HTuple(Convert.ToDouble(tbx_angle_extent2.Text))).TupleRad();
                 MinScore = Convert.ToDouble(tbx_MinScore.Text);
                 matchNum = Convert.ToDouble(tbx_matchNum.Text);
                 MaxOverlap = Convert.ToDouble(tbx_MaxOverlap.Text);
                 SubPixel = cbx_SubPixel.Text;
                 numLeave = Convert.ToDouble(tbx_numLeave.Text);
                 greediness = Convert.ToDouble(tbx_greediness.Text);
                 HTuple hv_Column = new HTuple(), hv_Angle = new HTuple();
                 HTuple hv_Score = new HTuple(), hv_Row = new HTuple();

                 hv_Row.Dispose(); hv_Column.Dispose(); hv_Angle.Dispose(); hv_Score.Dispose();
                 HOperatorSet.FindShapeModel(HIMage, hv_ModelID, angle_start2, angle_extent2, MinScore, matchNum, MaxOverlap, SubPixel, numLeave, greediness, out hv_Row, out hv_Column, out hv_Angle, out hv_Score);



                 HTuple hv_HomMat2D = new HTuple();
                 HObject ho_TraingImage = null, ho_ImageAffineTrans = null;
                 HOperatorSet.GenEmptyObj(out ho_ImageAffineTrans);
                 HOperatorSet.GenEmptyObj(out ho_TraingImage);
                 //创建一个矩阵将当前图像变换到模板位置
                 hv_HomMat2D.Dispose();

                 HOperatorSet.VectorAngleToRigid(hv_Row, hv_Column, hv_Angle, hv_SRow, hv_SColumn,
                     0, out hv_HomMat2D);
                 //把当前图像变换到参照位置
                 ho_ImageAffineTrans.Dispose();
                 HOperatorSet.AffineTransImage(HIMage, out ho_ImageAffineTrans, hv_HomMat2D,
                     "constant", "false");
                 //把训练模型图像锁定出来
                 ho_TraingImage.Dispose();
                 HOperatorSet.ReduceDomain(ho_ImageAffineTrans, ho_RegionDilation, out ho_TraingImage
                     );
                 //训练模板
                 HOperatorSet.TrainVariationModel(ho_TraingImage, hv_VarModelID);

                 //操作提示
                 laTicps.Text = "当前正在训练" + (i + 1) + "/" + (TheFolder.GetFiles().Length) + "张图像";
                 Thread.Sleep(1000);
             }
         }

         laTicps2.ForeColor = Color.Green;
         laTicps2.Text = "训练模型成功";
     }
     catch
     {
         laTicps2.ForeColor = Color.Red;
         laTicps2.Text = "训练模型失败";
     }


 }

4，确定最终差分模型

PrepareVariationModel

5，查看差分模型效果区域图像

五，应用验证

private void btn_getResult_Click(object sender, EventArgs e)
{
    HTuple angle_start2, angle_extent2, MinScore, matchNum, MaxOverlap, SubPixel, numLeave, greediness;

    angle_start2 = (new HTuple(Convert.ToDouble(tbx_angle_start2.Text))).TupleRad();
    angle_extent2 = (new HTuple(Convert.ToDouble(tbx_angle_extent2.Text))).TupleRad();
    MinScore = Convert.ToDouble(tbx_MinScore.Text);
    matchNum = Convert.ToDouble(tbx_matchNum.Text);
    MaxOverlap = Convert.ToDouble(tbx_MaxOverlap.Text);
    SubPixel = cbx_SubPixel.Text;
    numLeave = Convert.ToDouble(tbx_numLeave.Text);
    greediness = Convert.ToDouble(tbx_greediness.Text);
    HTuple hv_Column = new HTuple(), hv_Angle = new HTuple();
    HTuple hv_Score = new HTuple(), hv_Row = new HTuple();

    hv_Row.Dispose(); hv_Column.Dispose(); hv_Angle.Dispose(); hv_Score.Dispose();
    HOperatorSet.FindShapeModel(HIMage, hv_ModelID, angle_start2, angle_extent2, MinScore, matchNum, MaxOverlap, SubPixel, numLeave, greediness, out hv_Row, out hv_Column, out hv_Angle, out hv_Score);
    //创建一个矩阵将当前图像变换到模板位置

    HTuple hv_HomMat2D = new HTuple();
    HTuple hv_Number = new HTuple();
    HObject ho_ImageAffineTrans = null, ho_CheckImage = null;
    HObject ho_CheckRegion = null, ho_ConnectedRegions = null, ho_SelectedRegions = null;
    HOperatorSet.GenEmptyObj(out ho_ImageAffineTrans);
    HOperatorSet.GenEmptyObj(out ho_CheckImage);
    HOperatorSet.GenEmptyObj(out ho_CheckRegion);
    HOperatorSet.GenEmptyObj(out ho_ConnectedRegions);
    HOperatorSet.GenEmptyObj(out ho_SelectedRegions);

    hv_HomMat2D.Dispose();
    HOperatorSet.VectorAngleToRigid(hv_Row, hv_Column, hv_Angle, hv_SRow, hv_SColumn,
        0, out hv_HomMat2D);

    //把当前图像变换到参照位置
    ho_ImageAffineTrans.Dispose();
    HOperatorSet.AffineTransImage(HIMage, out ho_ImageAffineTrans, hv_HomMat2D,
        "constant", "false");


    //把训练模型图像锁定出来
    ho_CheckImage.Dispose();
    HOperatorSet.ReduceDomain(ho_ImageAffineTrans, ho_RegionDilation, out ho_CheckImage
        );
    //获取模板图像与差异化图像的差分区域
    ho_CheckRegion.Dispose();
    HOperatorSet.CompareVariationModel(ho_CheckImage, out ho_CheckRegion, hv_VarModelID);
    //联通区域断开
    ho_ConnectedRegions.Dispose();
    HOperatorSet.Connection(ho_CheckRegion, out ho_ConnectedRegions);
    //根据要求筛选区域
    ho_SelectedRegions.Dispose();
    HOperatorSet.SelectShape(ho_ConnectedRegions, out ho_SelectedRegions, "area",
        "and", 100, 99999);
    //判断及输出缺陷区域
    hv_Number.Dispose();
    HOperatorSet.CountObj(ho_SelectedRegions, out hv_Number);

    HOperatorSet.DispObj(ho_ImageAffineTrans, HW);
    HObject ho_ModelContours;
    HOperatorSet.GenEmptyObj(out ho_ModelContours);
    ho_ModelContours.Dispose();
    HOperatorSet.GetShapeModelContours(out ho_ModelContours, hv_ModelID, 1);
    HOperatorSet.VectorAngleToRigid(0, 0, 0, hv_SRow, hv_SColumn,0, out HTuple homMat2D);
    HOperatorSet.AffineTransContourXld(ho_ModelContours, out HObject ho_ModelContours1, homMat2D);
    HW.SetColor("green");
    HW.DispObj(ho_ModelContours1);
    if (hv_Number>0)
     {

        HW.SetDraw("fill");
        HW.SetLineWidth(2);
        HW.SetColor("red");
        //显示ROI
        HOperatorSet.DispObj(ho_SelectedRegions, HW);

        ticps3.ForeColor = Color.Red;
        ticps3.Text = "NG";
    }
    else
    {
        ticps3.ForeColor = Color.Green;
        ticps3.Text = "OK";

    }


}