颜色识别器(如何用LabVIEW做颜色识别)

C#调用NI的库函数实现颜色识别检测（在halcon环境下）

一直使用C#+halcon进行视觉算法的开发，但是遇到了一个非常普遍的需求，对物体进行颜色识别。在halcon中颜色识别主要分两种方式，一种为进行色域转化，由RGB转换为HSV后根据颜色表在H或者其他通道中对不同的颜色值进行区分，此种方式缺点是在进行建模时必须知道目标ROI的H通道值，且与其他ROI的值差别较大，不然非常容易误报。另一种方法即建立分类器，使用mlp或者gmm进行训练，然后将要识别的区域给分类器让其判断，这其中有一个缺点为，在建立分类器时必须知道当前有几种颜色，然后建立起对应输出的分类器，并且再有样本添加进入时也必须按同时将这几种颜色都加入进去（即使当前状态只有一种颜色出现差异需要再训练），同时，也不能再追加一种新的颜色。

在LabView的Vision模块中，有直接的颜色匹配模式，即将选定的ROI区域划分为16个向量再与检测的ROI作比较，识别较为准确。故本文介绍在C#环境下调用LV中的颜色识别函数，显示窗口依然使用halcon的HWindowControl（毕竟主要的开发算法还是在halcon下写的，并且个人感觉LV的图像显示窗口做的并不好，杂乱！）。

首先，调用LV需要先安装labview并且安装vision assistan模块,安装好后在其安装路径下有两个dll，分别为NationlInstryments.Vision.dll 和 NationlInstryments.Vision.Common.dll，同时引用halcondonet.dll(halcon的dll)，找不到在哪的可以使用软件everything进行搜索。在自己的工程中引用这两个dll，同时引用namespace,添加halcon图像显示窗口，使用该文章中https\";

imageDialog.Filter = "All Files(*.*)";

if (imageDialog.ShowDialog() == DialogResult.OK)

{

string imagePath = imageDialog.FileName;

LoadSelectedImage(imagePath); // 使用LV读取图像

}

}

private void LoadSelectedImage(string imagePath)

{

myVisionImage.ReadFile(imagePath);

myVisionImage.Type = ImageType.Rgb32; // 次句一定要加上，不然在进行识别时报错，默认读取进入后是U8单通道格式

}

在halcon窗口上进行roi的划定

private HObject GetModelDrawRegion(HObject drawImage, ref HTuple hv_Row1, ref HTuple hv_Column1, ref HTuple hv_Row2, ref HTuple hv_Column2)

{

HObject ho_ModelRegion, ho_TemplateImage, ho_RegionSelect, ho_RegionUnion, ho_RegionModel;

HObject ho_ModelContours, ho_TransContours = null;

HTuple hv_TempHomMat2D = new HTuple();

HTuple hv_HomMat = new HTuple();

// 初始化本地变量值

HOperatorSet.GenEmptyObj(out ho_ModelRegion);

HOperatorSet.GenEmptyObj(out ho_TemplateImage);

HOperatorSet.GenEmptyObj(out ho_ModelContours);

HOperatorSet.GenEmptyObj(out ho_TransContours);

HOperatorSet.GenEmptyObj(out ho_RegionSelect);

HOperatorSet.GenEmptyObj(out ho_RegionUnion);

HOperatorSet.GenEmptyObj(out ho_RegionModel);

try

{

HObject ho_temp_brush = new HObject();

hWindow_Final1.DrawModel = true; // 缩放功能禁用

HOperatorSet.SetSystem("border_shape_models", "false");

ho_ModelRegion.Dispose();

HalconToolClass.set_display_font(hWindow_Final1.hWindowControl.HalconWindow, 10, "mono", new HTuple("true"), new HTuple("false"));

HalconToolClass.disp_message(hWindow_Final1.hWindowControl.HalconWindow, "在窗口中将MARK1点位置框出,点击右键完成", "window", 20, 20, "red", "false");

hWindow_Final1.Focus();

HOperatorSet.SetColor(hWindow_Final1.hWindowControl.HalconWindow, "red");

HOperatorSet.DrawRectangle1(hWindow_Final1.hWindowControl.HalconWindow, out hv_Row1, out hv_Column1, out hv_Row2, out hv_Column2);

HOperatorSet.GenRectangle1(out ho_ModelRegion, hv_Row1, hv_Column1, hv_Row2, hv_Column2);

hWindow_Final1.DrawModel = false;

if (hv_Row1.D != 0)

{

brush_region.Dispose();

brush_region = ho_ModelRegion;

}

else

{

hWindow_Final1.HobjectToHimage(drawImage);

HalconToolClass.set_display_font(hWindow_Final1.hWindowControl.HalconWindow, 20, "mono", new HTuple("true"), new HTuple("false"));

HalconToolClass.disp_message(hWindow_Final1.hWindowControl.HalconWindow, "未画出有效区域", "window", 20, 20, "red", "false");

}

HalconToolClass.set_display_font(hWindow_Final1.hWindowControl.HalconWindow, 20, "mono", new HTuple("true"), new HTuple("false"));

hWindow_Final1.DispObj(ho_ModelRegion, "yellow");

ho_TemplateImage.Dispose();

HOperatorSet.ReduceDomain(drawImage, ho_ModelRegion, out ho_TemplateImage);

}

catch

{

MessageBox.Show("划定模板框出错！");

}

finally

{

ho_ModelRegion.Dispose();

}

return ho_TemplateImage;

}

划定好ROI后进行颜色的学习，并将学习完毕的颜色向量存入数据库

private void buttonRecColor_Click(object sender, EventArgs e)

{

HTuple hv_Row1 = null, hv_Column1 = null, hv_Row2 = null, hv_Column2 = null;

HObject ho_ModelRegion;

ho_ModelRegion = GetModelDrawRegion(halconImage, ref hv_Row1, ref hv_Column1, ref hv_Row2, ref hv_Column2);

double []lvRoi = ConvertHalconToLV(hv_Row1, hv_Column1, hv_Row2, hv_Column2); // 在halcon中矩形的存储为左上行列坐标，右下行列坐标；

// 而在LV中，矩形存储方式为中心行列坐标，weight和height长

// 查询插入语言

sqlCommand = "INSERT INTO roi_rec_inf(id, left_top_row, left_top_column, right_bottom_row, right_bottom_column) SELECT (SELECT MAX(id) FROM roi_rec_inf)+1, "" + hv_Row1 + "", "" + hv_Column1 + "", "" + hv_Row2 + "", "" + hv_Column2 + "";";

mySqlClass.UsualSqlCommand(sqlCommand);

RectangleContour rectangle = new RectangleContour(lvRoi[0], lvRoi[1], lvRoi[2], lvRoi[3]); // 矩形

Roi rectangleRoi = rectangle.ConvertToRoi();

// 该函数为调用的LV中学习颜色的函数，ROI使用halcon窗口中画出的ROI，若此时不存入数据库，也可直接使用colorInformation进行颜色识别

ColorInformation colorInformation = Algorithms.LearnColor(myVisionImage, rectangleRoi, ColorSensitivity.Low, (int)80);

sqlCommand = @"INSERT INTO color_match(

rec_id, color1, color2, color3, color4, color5, color6, color7, color8, color9, color10, color11, color12, color13, color14, color15, color16)

SELECT (SELECT MAX(id) from roi_rec_inf),

"" + colorInformation.Information[0] + "", "" + colorInformation.Information[1] + "", "" + colorInformation.Information[2] + "", "" + colorInformation.Information[3] + "", "" + colorInformation.Information[4] + "", "" + colorInformation.Information[5] + "", "" + colorInformation.Information[6] + "", "" + colorInformation.Information[7] + "", "" + colorInformation.Information[8] + "", "" + colorInformation.Information[9] + "", "" + colorInformation.Information[10] + "", "" + colorInformation.Information[11] + "", "" + colorInformation.Information[12] + "", "" + colorInformation.Information[13] + "", "" + colorInformation.Information[14] + "", "" + colorInformation.Information[15] + """;

mySqlClass.UsualSqlCommand(sqlCommand); // 插入颜色数据

}

private double[] ConvertHalconToLV(HTuple hv_Row1, HTuple hv_Column1, HTuple hv_Row2, HTuple hv_Column2)

{

double width = 0, height = 0;

if (hv_Row2 > hv_Row1)

{

width = hv_Row2 - hv_Row1;

}

if (hv_Column2 > hv_Column1)

{

height = hv_Column2 - hv_Column1;

}

double[] lvRoi = { hv_Column1, hv_Row1, width, height };// 需要传出的左上横纵坐标及宽，长信息

return lvRoi;

}

现在进行图像颜色识别，给定要识别的ROI区域及对应的图像和之前保存的颜色向量，函数返回匹配分值

private void MatchColor(HObject imageMatch)

{

VisionImage myImage = new VisionImage();

myImage.Type = ImageType.Rgb32;

LoadSelectedImage("F:\tempImage.jpeg", ref myImage);

double[] lvROI = ConvertHalconToLV(Convert.ToDouble(dtSelect.Rows[0]["left_top_row"].ToString()), Convert.ToDouble(dtSelect.Rows[0]["left_top_column"].ToString()), Convert.ToDouble(dtSelect.Rows[0]["right_bottom_row"].ToString()), Convert.ToDouble(dtSelect.Rows[0]["right_bottom_column"].ToString()));

Roi rectangleRoi = new Roi(new RectangleContour(lvROI[0], lvROI[1], lvROI[2], lvROI[3])); // 矩形

qlCommand = "SELECT color1, color2, color3, color4, color5, color6, color7, color8, color9, color10, color11, color12, color13, color14, color15, color16 FROM color_match WHERE rec_id = "" + Convert.ToInt32(dtSelect.Rows[0]["id"].ToString()) + "";";

DataTable dtColor = mySqlClass.SelectDataUsual(sqlCommand);

double []colorValue = DTConvertToDouble(dtColor);

ColorInformation myColorInformation = new ColorInformation(new Collection<double>(colorValue));

Collection<int> scores = Algorithms.MatchColor(myImage, myColorInformation, rectangleRoi);

if (scores[0] < 700)

{

DoNGSomething(Convert.ToInt32(dtSelect.Rows[0]["id"].ToString()));

richTextBox1.Text = "NG";

}

else

{

DoOKSomething(Convert.ToInt32(dtSelect.Rows[0]["id"].ToString()));

richTextBox1.Text = "OK";

}

}

总结：

先在halcon窗口上划定ROI区域，将此ROI转换为LV中Roi类型，然后调用ColorInformation = Algorithms.LearnColor(image,roi,low,threshold)方法，该函数返回16行向量值 ColorInformation即为该区域的颜色分布

给定ROI区域（同样在halcon中划定并进行转换)，调用Algorithms.MatchColor(image, ColorInformation, roi)进行指定区域的颜色识别，该方法返回一个匹配分值

在给定image值时，一定要将其typeImage类型设定为RGB32