C#中OpenCvSharp 通過特征點匹配圖片的方法

來源：本站原創(chuàng)|時間：2020-01-10|欄目：C#教程|點擊：次

現(xiàn)在的手游基本都是重復操作,一個動作要等好久,結束之后繼續(xù)另一個動作.很麻煩,所以動起了自己寫一個游戲輔助的心思.

這個輔助本身沒什么難度,就是通過不斷的截圖,然后從這個截圖中找出預先截好的能代表相應動作的按鈕或者觸發(fā)條件的小圖.

找到之后獲取該子區(qū)域的左上角坐標,然后通過windows API調用鼠標或者鍵盤做操作就行了.

這里面最難的也就是找圖了,因為要精準找圖,而且最好能適應不同的分辨率下找圖,所以在模板匹配的基礎上,就有了SIFT和SURF的特征點找圖方式.

在寫的過程中查找資料,大都是C++ 或者python的, 很少有原生的C#實現(xiàn), 所以我就直接拿來翻譯過來了(稍作改動).

SIFT算法

public static Bitmap MatchPicBySift(Bitmap imgSrc, Bitmap imgSub)
    {
      using (Mat matSrc = imgSrc.ToMat())
      using (Mat matTo = imgSub.ToMat())
      using (Mat matSrcRet = new Mat())
      using (Mat matToRet = new Mat())
      {
        KeyPoint[] keyPointsSrc, keyPointsTo;
        using (var sift = OpenCvSharp.XFeatures2D.SIFT.Create())
        {
          sift.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
          sift.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
        }
        using (var bfMatcher = new OpenCvSharp.BFMatcher())
        {
          var matches = bfMatcher.KnnMatch(matSrcRet, matToRet, k: 2);
          var pointsSrc = new List<Point2f>();
          var pointsDst = new List<Point2f>();
          var goodMatches = new List<DMatch>();
          foreach (DMatch[] items in matches.Where(x => x.Length > 1))
          {
            if (items[0].Distance < 0.5 * items[1].Distance)
            {
              pointsSrc.Add(keyPointsSrc[items[0].QueryIdx].Pt);
              pointsDst.Add(keyPointsTo[items[0].TrainIdx].Pt);
              goodMatches.Add(items[0]);
              Console.WriteLine($"{keyPointsSrc[items[0].QueryIdx].Pt.X}, {keyPointsSrc[items[0].QueryIdx].Pt.Y}");
            }
          }
          var outMat = new Mat();
          // 算法RANSAC對匹配的結果做過濾
          var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
          var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
          var outMask = new Mat();
          // 如果原始的匹配結果為空, 則跳過過濾步驟
          if (pSrc.Count > 0 && pDst.Count > 0)
            Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
          // 如果通過RANSAC處理后的匹配點大于10個,才應用過濾. 否則使用原始的匹配點結果(匹配點過少的時候通過RANSAC處理后,可能會得到0個匹配點的結果).
          if (outMask.Rows > 10)
          {
            byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
            outMask.GetArray(0, 0, maskBytes);
            Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
          }
          else
            Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
          return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
        }
      }
    }

SURF算法

public static Bitmap MatchPicBySurf(Bitmap imgSrc, Bitmap imgSub, double threshold = 400)
    {
      using (Mat matSrc = imgSrc.ToMat())
      using (Mat matTo = imgSub.ToMat())
      using (Mat matSrcRet = new Mat())
      using (Mat matToRet = new Mat())
      {
        KeyPoint[] keyPointsSrc, keyPointsTo;
        using (var surf = OpenCvSharp.XFeatures2D.SURF.Create(threshold,4,3,true,true))
        {
          surf.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
          surf.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
        }
        using (var flnMatcher = new OpenCvSharp.FlannBasedMatcher())
        {
          var matches = flnMatcher.Match(matSrcRet, matToRet);
          //求最小最大距離
          double minDistance = 1000;//反向逼近
          double maxDistance = 0;
          for (int i = 0; i < matSrcRet.Rows; i++)
          {
            double distance = matches[i].Distance;
            if (distance > maxDistance)
            {
              maxDistance = distance;
            }
            if (distance < minDistance)
            {
              minDistance = distance;
            }
          }
          Console.WriteLine($"max distance : {maxDistance}");
          Console.WriteLine($"min distance : {minDistance}");
          var pointsSrc = new List<Point2f>();
          var pointsDst = new List<Point2f>();
          //篩選較好的匹配點
          var goodMatches = new List<DMatch>();
          for (int i = 0; i < matSrcRet.Rows; i++)
          {
            double distance = matches[i].Distance;
            if (distance < Math.Max(minDistance * 2, 0.02))
            {
              pointsSrc.Add(keyPointsSrc[matches[i].QueryIdx].Pt);
              pointsDst.Add(keyPointsTo[matches[i].TrainIdx].Pt);
              //距離小于范圍的壓入新的DMatch
              goodMatches.Add(matches[i]);
            }
          }
          var outMat = new Mat();
          // 算法RANSAC對匹配的結果做過濾
          var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
          var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
          var outMask = new Mat();
          // 如果原始的匹配結果為空, 則跳過過濾步驟
          if (pSrc.Count > 0 && pDst.Count > 0)
            Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
          // 如果通過RANSAC處理后的匹配點大于10個,才應用過濾. 否則使用原始的匹配點結果(匹配點過少的時候通過RANSAC處理后,可能會得到0個匹配點的結果).
          if (outMask.Rows > 10)
          {
            byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
            outMask.GetArray(0, 0, maskBytes);
            Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
          }
          else
            Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
          return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
        }
      }
    }

模板匹配

 public static System.Drawing.Point FindPicFromImage(Bitmap imgSrc, Bitmap imgSub, double threshold = 0.9)
    {
      OpenCvSharp.Mat srcMat = null;
      OpenCvSharp.Mat dstMat = null;
      OpenCvSharp.OutputArray outArray = null;
      try
      {
        srcMat = imgSrc.ToMat();
        dstMat = imgSub.ToMat();
        outArray = OpenCvSharp.OutputArray.Create(srcMat);
        OpenCvSharp.Cv2.MatchTemplate(srcMat, dstMat, outArray, Common.templateMatchModes);
        double minValue, maxValue;
        OpenCvSharp.Point location, point;
        OpenCvSharp.Cv2.MinMaxLoc(OpenCvSharp.InputArray.Create(outArray.GetMat()), out minValue, out maxValue, out location, out point);
        Console.WriteLine(maxValue);
        if (maxValue >= threshold)
          return new System.Drawing.Point(point.X, point.Y);
        return System.Drawing.Point.Empty;
      }
      catch(Exception ex)
      {
        return System.Drawing.Point.Empty;
      }
      finally
      {
        if (srcMat != null)
          srcMat.Dispose();
        if (dstMat != null)
          dstMat.Dispose();
        if (outArray != null)
          outArray.Dispose();
      }
    }

總結

以上所述是小編給大家介紹的C#中OpenCvSharp 通過特征點匹配圖片,希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回復大家的。在此也非常感謝大家對我們網站的支持！
如果你覺得本文對你有幫助，歡迎轉載，煩請注明出處，謝謝！

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