opencv - 改进圆圈检测

Question

我正在尝试检测图像中的圆圈。我使用 EmguCV 在 C# 中编写了以下代码。大多数情况下它都有效，但在某些情况下，它会检测到稍微向一侧移动的较小或较大的圆圈。

这是我的代码:

        Thread.Sleep(1000);
        imgCrp.Save(DateTime.Now.ToString("yyMMddHHmmss") + ".jpg");

        imgCrpLab = imgCrp.Convert<Lab, Byte>();
        imgIsolatedCathNTipBW = new Image<Gray, Byte>(imgCrp.Size);
        CvInvoke.cvInRangeS(imgCrpLab.Split()[2], new MCvScalar(0), new MCvScalar(100), imgIsolatedCathNTipBW);

        imgCrpNoBgrnd = imgCrp.Copy(imgIsolatedCathNTipBW.Not());
        imgCrpNoBgrndGray = imgCrpNoBgrnd.Convert<Gray, Byte>().PyrUp().PyrDown();
        Thread.Sleep(1000);
        imgCrpNoBgrndGray.Save(DateTime.Now.ToString("yyMMddHHmmss") + ".jpg");

        Gray cannyThreshold = new Gray(150);
        Gray cannyThresholdLinking = new Gray(85);
        Gray circleAccumulatorThreshold = new Gray(15);

        imgCrpNoBgrndGrayCanny = imgCrpNoBgrndGray.Canny(cannyThreshold.Intensity, cannyThresholdLinking.Intensity);
        Thread.Sleep(1000);
        imgCrpNoBgrndGrayCanny.Save(DateTime.Now.ToString("yyMMddHHmmss") + ".jpg");

        circarrTip = imgCrpNoBgrndGrayCanny.HoughCircles(
            cannyThreshold,
            circleAccumulatorThreshold,
            1, //Resolution of the accumulator used to detect centers of the circles
            500, //min distance 
            15, //min radius
            42 //max radius
            )[0]; //Get the circles from the first channel

        imgCathNoTip = imgIsolatedCathNTipBW.Copy().Not();
        foreach (CircleF circle in circarrTip)
        {
            circLarger2RemTip = circle;
            circLarger2RemTip.Radius = circle.Radius;
            imgCathNoTip.Draw(circLarger2RemTip, new Gray(140), 1); // -1 IS TO FILL THE CIRCLE
        }
        Thread.Sleep(1000);
        imgCathNoTip.Save(DateTime.Now.ToString("yyMMddHHmmss") + ".jpg");

sleep 命令只是为了确保文件名不同，稍后将被删除。我还附上了在此过程中通过此代码保存的图像。最后一张图片显示了检测到的更大的圆，也向右移动。

任何人都可以检查我的代码并告诉我如何改进它以更准确地检测圆圈吗？

提前致谢。

Answer 1

类似于我在 Detect semi-circle in opencv 中的回答我看到一个问题:不要在 hough 圆检测之前提取 canny 边缘检测，因为 openCV houghCircle 本身会计算梯度和 canny。所以你要做的是从图像和边缘图像中提取精明的图像并检测其中的圆圈，导致(在最好的情况下)每条边周围有 2 个新边 => 错误的方式!

正如在 openCV 教程中所做的那样，您可以直接在灰度图像上计算 HoughCircles，为我提供以下结果:

输入:

参数:

cannyHigh = 100
cannyLow = 20
minSize = 0
maxSize = 100

代码:

int mainHough()
{
    cv::Mat input = cv::imread("../inputData/CircleDetectGray.jpg");

    // you could load as grayscale if you want, but I used it for (colored) output too
    cv::Mat gray;
    cv::cvtColor(input,gray,CV_BGR2GRAY);

    float canny1 = 100;
    float canny2 = 20;

    // canny here only for visualizing the chosen parameters
    //cv::Mat canny;
    //cv::Canny(gray, canny, canny1,canny2);
    //cv::imshow("canny",canny);

    std::vector<cv::Vec3f> circles;
    /// Apply the Hough Transform to find the circles
    cv::HoughCircles( gray, circles, CV_HOUGH_GRADIENT, 1, gray.cols/8, canny1,canny2,  0, 100 );

    std::cout << "found " << circles.size() << " circles" << std::endl;
    /// Draw the circles detected
    for( size_t i = 0; i < circles.size(); i++ ) 
    {
        cv::Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
        int radius = cvRound(circles[i][2]);
        cv::circle( input, center, 3, cv::Scalar(0,255,255), -1);
        cv::circle( input, center, radius, cv::Scalar(0,0,255), 1 );
    }

结果:

使用我在 Detect semi-circle in opencv 中发布的 RANSAC 方法(我的第二个答案但略有改变以搜索最完整的圆圈)(输入:canny edges)

代码:

float verifyCircle(cv::Mat dt, cv::Point2f center, float radius, std::vector<cv::Point2f> & inlierSet)
{
 unsigned int counter = 0;
 unsigned int inlier = 0;
 float minInlierDist = 2.0f;
 float maxInlierDistMax = 100.0f;
 float maxInlierDist = radius/25.0f;
 if(maxInlierDist<minInlierDist) maxInlierDist = minInlierDist;
 if(maxInlierDist>maxInlierDistMax) maxInlierDist = maxInlierDistMax;

 // choose samples along the circle and count inlier percentage
 for(float t =0; t<2*3.14159265359f; t+= 0.05f)
 {
     counter++;
     float cX = radius*cos(t) + center.x;
     float cY = radius*sin(t) + center.y;

     if(cX < dt.cols)
     if(cX >= 0)
     if(cY < dt.rows)
     if(cY >= 0)
     if(dt.at<float>(cY,cX) < maxInlierDist)
     {
        inlier++;
        inlierSet.push_back(cv::Point2f(cX,cY));
     }
 }

 return (float)inlier/float(counter);
}


inline void getCircle(cv::Point2f& p1,cv::Point2f& p2,cv::Point2f& p3, cv::Point2f& center, float& radius)
{
  float x1 = p1.x;
  float x2 = p2.x;
  float x3 = p3.x;

  float y1 = p1.y;
  float y2 = p2.y;
  float y3 = p3.y;

  // PLEASE CHECK FOR TYPOS IN THE FORMULA :)
  center.x = (x1*x1+y1*y1)*(y2-y3) + (x2*x2+y2*y2)*(y3-y1) + (x3*x3+y3*y3)*(y1-y2);
  center.x /= ( 2*(x1*(y2-y3) - y1*(x2-x3) + x2*y3 - x3*y2) );

  center.y = (x1*x1 + y1*y1)*(x3-x2) + (x2*x2+y2*y2)*(x1-x3) + (x3*x3 + y3*y3)*(x2-x1);
  center.y /= ( 2*(x1*(y2-y3) - y1*(x2-x3) + x2*y3 - x3*y2) );

  radius = sqrt((center.x-x1)*(center.x-x1) + (center.y-y1)*(center.y-y1));
}



std::vector<cv::Point2f> getPointPositions(cv::Mat binaryImage)
{
 std::vector<cv::Point2f> pointPositions;

 for(unsigned int y=0; y<binaryImage.rows; ++y)
 {
     //unsigned char* rowPtr = binaryImage.ptr<unsigned char>(y);
     for(unsigned int x=0; x<binaryImage.cols; ++x)
     {
         //if(rowPtr[x] > 0) pointPositions.push_back(cv::Point2i(x,y));
         if(binaryImage.at<unsigned char>(y,x) > 0) pointPositions.push_back(cv::Point2f(x,y));
     }
 }

 return pointPositions;
}



int mainRANSAC_circle()
{
    cv::Mat color = cv::imread("../inputData/CircleDetectGray.jpg");
    cv::Mat gray;

    // convert to grayscale
    // you could load as grayscale if you want, but I used it for (colored) output too
    cv::cvtColor(color, gray, CV_BGR2GRAY);


    cv::Mat mask;

    float canny1 = 100;
    float canny2 = 20;

    cv::Mat canny;
    cv::Canny(gray, canny, canny1,canny2);
    cv::imshow("canny",canny);

    mask = canny;



    std::vector<cv::Point2f> edgePositions;
    edgePositions = getPointPositions(mask);

    // create distance transform to efficiently evaluate distance to nearest edge
    cv::Mat dt;
    cv::distanceTransform(255-mask, dt,CV_DIST_L1, 3);

    //TODO: maybe seed random variable for real random numbers.

    unsigned int nIterations = 0;

    cv::Point2f bestCircleCenter;
    float bestCircleRadius;
    float bestCirclePercentage = 0;
    float minRadius = 10;   // TODO: ADJUST THIS PARAMETER TO YOUR NEEDS, otherwise smaller circles wont be detected or "small noise circles" will have a high percentage of completion

    //float minCirclePercentage = 0.2f;
    float minCirclePercentage = 0.05f;  // at least 5% of a circle must be present? maybe more...

    int maxNrOfIterations = edgePositions.size();   // TODO: adjust this parameter or include some real ransac criteria with inlier/outlier percentages to decide when to stop

    for(unsigned int its=0; its< maxNrOfIterations; ++its)
    {
        //RANSAC: randomly choose 3 point and create a circle:
        //TODO: choose randomly but more intelligent, 
        //so that it is more likely to choose three points of a circle. 
        //For example if there are many small circles, it is unlikely to randomly choose 3 points of the same circle.
        unsigned int idx1 = rand()%edgePositions.size();
        unsigned int idx2 = rand()%edgePositions.size();
        unsigned int idx3 = rand()%edgePositions.size();

        // we need 3 different samples:
        if(idx1 == idx2) continue;
        if(idx1 == idx3) continue;
        if(idx3 == idx2) continue;

        // create circle from 3 points:
        cv::Point2f center; float radius;
        getCircle(edgePositions[idx1],edgePositions[idx2],edgePositions[idx3],center,radius);

        // inlier set unused at the moment but could be used to approximate a (more robust) circle from alle inlier
        std::vector<cv::Point2f> inlierSet;

        //verify or falsify the circle by inlier counting:
        float cPerc = verifyCircle(dt,center,radius, inlierSet);

        // update best circle information if necessary
        if(cPerc >= bestCirclePercentage)
            if(radius >= minRadius)
            {
                bestCirclePercentage = cPerc;
                bestCircleRadius = radius;
                bestCircleCenter = center;
            }

    }

    std::cout << "bestCirclePerc: " << bestCirclePercentage << std::endl;
    std::cout << "bestCircleRadius: " << bestCircleRadius << std::endl;

    // draw if good circle was found
    if(bestCirclePercentage >= minCirclePercentage)
        if(bestCircleRadius >= minRadius);
    cv::circle(color, bestCircleCenter,bestCircleRadius, cv::Scalar(255,255,0),1);


    cv::imshow("output",color);
    cv::imshow("mask",mask);
    cv::imwrite("../outputData/1_circle_color.png", color);
    cv::imwrite("../outputData/1_circle_mask.png", mask);
    //cv::imwrite("../outputData/1_circle_normalized.png", normalized);
    cv::waitKey(0);

    return 0;
}

我反而取得了这个结果:

没有 C# 代码，抱歉。