AoiTemplateMethod.cs
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using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Drawing2D;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using OpenCvSharp;
using OpenCvSharp.Blob;
using OpenCvSharp.XFeatures2D;
namespace AOI
{
/// <summary>
/// 模板匹配
/// </summary>
public class AoiTemplateMethod : AoiMethod
{
/// <summary>
/// 相似度百分比
/// </summary>
public double SamePercent = 50;
public override ResultBean Check(Image standardImage, Image imageToCheck)
{
ResultBean resultBean = new ResultBean(MethodName,4,SamePercent,SamePercent);
bool needCut = true;
Image standardRoiImg = GetRoiImage(standardImage, needCut);
resultBean.standardRoiImage = standardRoiImg;
double percent = GetTemplateMatchPercent(standardImage, imageToCheck, out Image cutImg);
bool result = false;
if(SamePercent > 100)
{
SamePercent = 100;
}
if(percent >= SamePercent)
{
result = true;
}
resultBean.currentRoiImage = cutImg;
resultBean.result = result;
resultBean.percentValue =Math.Round( percent,3);
return resultBean;
}
/// <summary>
/// 获取模板相似度
/// </summary>
/// <param name="standardImage"></param>
/// <param name="imageToCheck"></param>
/// <param name="cutImg"></param>
/// <returns></returns>
public double GetTemplateMatchPercent(Image standardImage, Image imageToCheck, out Image cutImg)
{
bool needCut = true;
//标准图中的Mart区域
Image templateImage = GetRoiImage(standardImage, RoiPath, needCut);
//搜索区域
Image searchImage = GetRoiImage(imageToCheck, RoiPath, needCut);
cutImg = searchImage;
try
{
//searchImage = imageToCheck;
if (templateImage != null && searchImage != null)
{
Mat searchMat = ImageUtil.ToMat(new Bitmap(searchImage));
Mat templateMat = ImageUtil.ToMat(new Bitmap(templateImage));
Mat result = new Mat(searchMat.Cols - templateMat.Cols + 1, searchMat.Rows - templateMat.Rows + 1, MatType.CV_32FC1);
//进行匹配(1母图,2模版子图,3返回的result，4匹配模式_这里的算法比opencv少，具体可以看opencv的相关资料说明)
Cv2.MatchTemplate(searchMat, templateMat, result, TemplateMatchModes.CCoeffNormed);
//对结果进行归一化(这里我测试的时候没有发现有什么用,但在opencv的书里有这个操作，应该有什么神秘加成，这里也加上)
//Cv2.Normalize(result, result, 1, 0, NormTypes.MinMax, -1);
/// 通过函数 minMaxLoc 定位最匹配的位置
/// (这个方法在opencv里有5个参数，这里我写的时候发现在有3个重载，看了下可以直接写成拿到起始坐标就不取最大值和最小值了)
/// minLocation和maxLocation根据匹配调用的模式取不同的点
Cv2.MinMaxLoc(result, out double minVal, out double maxVal, out OpenCvSharp.Point minLocation, out OpenCvSharp.Point maxLocation);
return maxVal * 100;
}
}catch(Exception ex)
{
Console.Write(ex.ToString());
}
return 0;
}
}
}