HoughCircles 在 OpenCV 中无法正确检测圆

Question

我正在使用 Visual Studio 2015、OpenCV.3 和 EmguCV.3。 我的代码如下所示，结果如图所示。我知道问题是 HoughCircles 函数的输入值，但我不知道哪些输入适合这张图片。感谢您的帮助。

                Image<Gray, byte> OriginalImage = new Image<Gray, byte>(Openfile.FileName);
                Image<Gray, byte> ResizedImage = OriginalImage.Resize(OriginalImage.Width / 2, OriginalImage.Height / 2, Emgu.CV.CvEnum.Inter.Cubic);

                //********** Convert Image to Binary
                Image<Gray, byte> smoothImg = 
                ResizedImage.SmoothGaussian(5);
                smoothImg._Erode(5);
                smoothImg._Dilate(5);
                Image<Gray, byte> BinaryImage = 
                smoothImg.ThresholdBinary(new Gray(20), new Gray(255));

                //********** Find Circles
                Image<Rgb, byte> ROIImgScaledCircles = ROIImgScaled.Convert<Rgb, byte>();
                CircleF[] circles = smoothImg.HoughCircles(
                    new Gray(180),//cannyThreshold
                    new Gray(60),//circleAccumulatorThreshold
                    2.0, //dp:Resolution of the accumulator used to detect centers of the circles
                    10.0, //min distance 
                    10, //min radius
                    128 //max radius
                    )[0]; //Get the circles from the first channel
                foreach (CircleF cir in circles)
                {
                    ROIImgScaledCircles.Draw(cir, new Rgb(235, 20, 30), 1);
                }                   
                pbxCircles.Image = ROIImgScaledCircles.ToBitmap();

原图：

创建的圈子：

Answer 1

使用完整的形状，您可能会发现检测边缘然后找到轮廓更容易。这是一个例子：

    Image<Bgr, byte> original = new Image<Bgr, byte>(@"E:\Downloads\original.jpg");
    UMat grayscale = new UMat();            
    UMat pyrdown = new UMat();
    UMat canny = new UMat();

    double cannyThreshold = 128;

    CvInvoke.CvtColor(original, grayscale, ColorConversion.Bgr2Gray);        
    // remove noise and run edge detection
    CvInvoke.PyrDown(grayscale, pyrdown);
    CvInvoke.PyrUp(pyrdown, grayscale);
    CvInvoke.Canny(grayscale, canny, cannyThreshold, cannyThreshold * 2);

    Image<Bgr, byte> result = original.Copy();
    // find and draw circles   
    VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
    CvInvoke.FindContours(canny, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
    //CvInvoke.DrawContours(result, contours, -1, new MCvScalar(0, 0, 255));
    for (int i = 0; i < contours.Size; i++)
    {
        Ellipse ellipse = new Ellipse(CvInvoke.FitEllipse(contours[i]));
        result.Draw(ellipse, new Bgr(Color.Red), 1);
    }

    result.Save(@"E:\Downloads\circles.jpg");

这是结果，从左到右：

1. 原图
2. 模糊图像（使用 pyrdown/pyrup）
3. canny 边缘检测结果
4. 从轮廓重建圆

Answer 2

这是一个解决方案（基于OpenCvSharp，而不是基于 emgucv，它允许 C# 代码非常接近您可以在 C++ 或 Python 中找到的所有 OpenCV 代码，但您可以轻松地将其转换回 emgucv）。

我删除了 Erode 和 Dilate 步骤（在这种情况下，这只会过多地破坏原始图像）。

我使用的是一个关于霍夫圆调用的循环（改变累加器分辨率的反比），以确保我检测到多个圆，而不是我不感兴趣的圆。

  int blurSize = 5;
  using (var src = new Mat("2Okrv.jpg"))
  using (var gray = src.CvtColor(ColorConversionCodes.BGR2GRAY))
  using (var blur = gray.GaussianBlur(new Size(blurSize, blurSize), 0))
  using (var dst = src.Clone())
  {
      // this hashset will automatically store all "unique" detected circles
      // circles are stored modulo some "espilon" value, set to 5 here (half of min size of hough circles below)
      var allCircles = new HashSet<CircleSegment>(new CircleEqualityComparer { Epsilon = 5 });

      // vary inverse ratio of accumulator resolution
      // depending on image, you may vary start/end/step
      for (double dp = 1; dp < 5; dp += 0.2)
      {
          // we use min dist = 1, to make sure we can detect concentric circles
          // we use standard values for other parameters (canny, ...)
          // we use your min max values (the max may be important when dp varies)
          var circles = Cv2.HoughCircles(blur, HoughMethods.Gradient, dp, 1, 100, 100, 10, 128);
          foreach (var circle in circles)
          {
              allCircles.Add(circle);
          }
      }

      // draw final list of unique circles
      foreach (var circle in allCircles)
      {
          Cv2.Circle(dst, circle.Center, (int)circle.Radius, Scalar.FromRgb(235, 20, 30), 1);
      }

      // display images
      using (new Window("src image", src))
      using (new Window("dst image", dst))
      {
          Cv2.WaitKey();
      }
  }

  public class CircleEqualityComparer : IEqualityComparer<CircleSegment>
  {
      public double Epsilon { get; set; }

      public bool Equals(CircleSegment x, CircleSegment y) => x.Center.DistanceTo(y.Center) <= Epsilon && Math.Abs(x.Radius - y.Radius) <= Epsilon;

      // bit of a hack... we return a constant so only Equals is used to compare two circles
      // since we have only few circles that's ok, we don't play with millions...
      public int GetHashCode(CircleSegment obj) => 0;
  }

结果如下：