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,但我不知道哪个输入适合这张图片。我感谢任何帮助。
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();
原始图片:
成立圈子:
答案 0 :(得分:8)
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");
这是从左到右的结果:
答案 1 :(得分:4)
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 )。
我已经删除了Erode和Dilate步骤(在这种情况下只会破坏原始图像)。
我使用的是霍夫圆圈调用的循环(改变与累加器分辨率的反比),以确保我检测到多个圆圈,而不是我不感兴趣的圆圈。
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: