using HalconDotNet;
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using LB_VisionControl;
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using LB_VisionProcesses.Alogrithms.Halcon;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using static LB_VisionProcesses.Alogrithms.Halcon.HFindCode2dTool;
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namespace LB_VisionProcesses.Alogrithms.Halcon
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{
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public enum Transition { Positive, Negative, Ignore }
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public enum Selector { First, Last, Best }
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[Process("Halcon2D找线工具", Category = "Halcon2D工具", Description = "创建找线工具")]
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public class HFindLineTool : TAlgorithm
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{
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public HFindLineTool()
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{
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strProcessClass = "LB_VisionProcesses.Alogrithms.Halcon.HFindLineTool";
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strProcessName = "Halcon2D找线工具";
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Params.Inputs.Add("卡尺数量", 6);
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Params.Inputs.Add("卡尺长度", 30);
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Params.Inputs.Add("卡尺宽度", 10);
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Params.Inputs.Add("过滤一半像素", 2);
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Params.Inputs.Add("对比度阈值", 5);
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Params.Inputs.Add("极性", "Ignore");
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Params.Inputs.Add("边缘位置", "Best");
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Params.Inputs.Add("忽略点数", 0);
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Params.Outputs.Add("X", new List<double>());
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Params.Outputs.Add("Y", new List<double>());
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Params.Outputs.Add("CenterX", 0.0);
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Params.Outputs.Add("CenterY", 0.0);
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Params.Outputs.Add("Phi", 0.0);
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Params.Outputs.Add("Angle", 0.0);
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Params.Outputs.Add("Count", 0);
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Params.Outputs.Add("Segment", new HSegment());
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Params.ROI = new HSegment(0, 0, 250, 250);
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}
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/// <summary>
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/// 算子逻辑
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/// </summary>
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public override void TAlgorithmMain()
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{
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#region 初始化变量
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HObject ho_Regions, ho_LineXld;
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HOperatorSet.GenEmptyObj(out ho_Regions);
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HOperatorSet.GenEmptyObj(out ho_LineXld);
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#endregion
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try
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{
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if (InputImage == null)
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{
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Msg = "输入图片为空";
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Result = false;
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return;
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}
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#region 裁剪区域
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if (!(Params.ROI is HSegment))
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{
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Msg = "ROI类型错误,必须为HSegment类型";
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Result = false;
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return;
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}
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if (!(InputImage is HObject))
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{
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Msg = "输入图片类型错误,必须为HObject类型";
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Result = false;
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return;
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}
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HObject DomainImage = ((HObject)InputImage)?.CopyObj(1, -1);
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#endregion
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#region 算子逻辑
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Record = new ObjectRecord();
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HObject hoDomainImage = DomainImage as HObject;
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HTuple hv_Channels = new HTuple();
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//判断是否为灰度图
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using (HDevDisposeHelper dh = new HDevDisposeHelper())
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{
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try
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{
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HOperatorSet.CountChannels(hoDomainImage, out hv_Channels);
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if (hv_Channels.TupleInt() != 1)
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HOperatorSet.Rgb1ToGray(hoDomainImage, out hoDomainImage);
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//转换后再次检查是否为灰度图
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HOperatorSet.CountChannels(hoDomainImage, out hv_Channels);
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if (hv_Channels.TupleInt() != 1)
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{
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HOperatorSet.Rgb1ToGray(hoDomainImage, out hoDomainImage);
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Msg = "输入图片不为灰度图";
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Result = false;
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return;
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}
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}
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catch
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{
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Msg = "输入图片不为灰度图且转换失败";
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Result = false;
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return;
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}
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}
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int hv_Elements = Convert.ToInt16(Params.Inputs["卡尺数量"]);
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double hv_DetectHeight = Convert.ToDouble(Params.Inputs["卡尺长度"]);
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double hv_DetectWidth = Convert.ToDouble(Params.Inputs["卡尺宽度"]);
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int hv_Sigma = Convert.ToInt16(Params.Inputs["过滤一半像素"]);
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int hv_Threshold = Convert.ToInt16(Params.Inputs["对比度阈值"]);
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int hv_IgnoreNum = Convert.ToInt16(Params.Inputs["忽略点数"]);
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Enum.TryParse(Params.Inputs["极性"]?.ToString(), out Transition transition);
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string hv_Transition = "ignore";
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switch (transition)
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{
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case Transition.Positive:
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hv_Transition = "positive";
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break;
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case Transition.Negative:
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hv_Transition = "negative";
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break;
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case Transition.Ignore:
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default:
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hv_Transition = "ignore";
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break;
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}
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Enum.TryParse(Params.Inputs["边缘位置"]?.ToString(), out Selector selector);
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string hv_Select = "best";
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switch (selector)
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{
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case Selector.First:
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hv_Select = "first";
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break;
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case Selector.Last:
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hv_Select = "last";
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break;
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case Selector.Best:
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default:
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hv_Select = "best";
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break;
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}
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double hv_Row1 = Convert.ToDouble(((HSegment)Params.ROI).BeginRow + Params.Fixture.Row);
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double hv_Column1 = Convert.ToDouble(((HSegment)Params.ROI).BeginColumn + Params.Fixture.Column);
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double hv_Row2 = Convert.ToDouble(((HSegment)Params.ROI).EndRow + Params.Fixture.Row);
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double hv_Column2 = Convert.ToDouble(((HSegment)Params.ROI).EndColumn + Params.Fixture.Column);
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Rake(hoDomainImage, out ho_Regions, hv_Elements, hv_DetectHeight, hv_DetectWidth
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, hv_Sigma, hv_Threshold, hv_Transition, hv_Select
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, hv_Row1, hv_Column1, hv_Row2, hv_Column2
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, out HTuple hv_ResultRow, out HTuple hv_ResultColumn);
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#endregion
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#region 结果处理
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List<double> X = new List<double>();
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List<double> Y = new List<double>();
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double CenterX = 0;
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double CenterY = 0;
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double Phi = 0;
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HSegment HSegment = new HSegment();
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if (hv_ResultRow.Type != HTupleType.EMPTY && hv_ResultRow.Length > 0)
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{
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for (int k = 0; k < hv_ResultRow.Length; k++)
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{
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Y.Add(hv_ResultRow[k].D);
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X.Add(hv_ResultColumn[k].D);
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}
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if (X.Count >= 3)
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{
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pts_to_best_line(out ho_LineXld, hv_ResultRow, hv_ResultColumn, hv_IgnoreNum
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, out HTuple hv_Row11, out HTuple hv_Column11, out HTuple hv_Row21, out HTuple hv_Column21);
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((ObjectRecord)Record).AddXld(ho_LineXld);
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CenterX = (hv_Column11.D + hv_Column21.D) / 2;
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CenterY = (hv_Row11.D + hv_Row21.D) / 2;
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//计算直线与x轴的夹角，逆时针方向为正向
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HOperatorSet.AngleLx(hv_Row11, hv_Column11, hv_Row21, hv_Column21, out HTuple hv_ATan);
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Phi = hv_ATan;
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HSegment = new HSegment(hv_Column11.D, hv_Row11.D, hv_Column21.D, hv_Row21.D);
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}
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else
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{
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Msg = "找线失败";
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Result = false;
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return;
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}
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}
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else
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{
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Msg = "找线失败";
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Result = false;
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return;
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}
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Params.Outputs["X"] = X;
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Params.Outputs["Y"] = Y;
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Params.Outputs["CenterX"] = CenterX;
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Params.Outputs["CenterY"] = CenterY;
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Params.Outputs["Phi"] = Phi;
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Params.Outputs["Angle"] = Phi * 180.0 / Math.PI;
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Params.Outputs["Count"] = X.Count;
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Params.Outputs["Segment"] = HSegment;
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#endregion
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#region 生成OutputImage给后续处理
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try
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{
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OutputImage = hoDomainImage;
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}
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catch (Exception ex)
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{
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Msg = "生成OutputImage失败,原因是:" + ex.ToString();
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Result = false;
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return;
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}
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#endregion
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if (Msg == "运行超时")
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{
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Result = false;
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return;
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}
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Msg = "运行成功";
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Result = true;
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return;
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}
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catch (Exception ex)
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{
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Msg = "运行失败,原因是:" + ex.ToString().TrimEnd();
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OutputImage = null;
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Result = false;
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return;
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}
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finally
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{
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if (!Result)
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{
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Params.Outputs.Add("X", new List<double>());
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Params.Outputs.Add("Y", new List<double>());
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Params.Outputs.Add("CenterX", 0.0);
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Params.Outputs.Add("CenterY", 0.0);
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Params.Outputs.Add("Phi", 0.0);
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Params.Outputs.Add("Angle", 0.0);
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Params.Outputs.Add("Count", 0);
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Params.Outputs.Add("Segment", new HSegment());
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}
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bCompleted = true;
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#region 内存释放
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ho_Regions.Dispose();
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#endregion
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}
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}
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}
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}
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