c++ - 有没有办法使用opencv检测图像中的相邻圆

Question

我正在尝试检测图像中的相邻圆圈。这些可以是 4 或 5。有没有办法在 opencv 中检测它。我尝试了很多方法，包括霍夫圆法。但我也检测到额外的圆圈。如果在任何情况下我能够检测到圆圈，那么相同的参数将不适用于其他图像。

请告诉我任何可能实现这一目标的事情。

我使用霍夫圆的代码是:

Mat img, gray;
img = imread("/Users/Development/Desktop/Images/IMG_0297.jpg");
cvtColor(img, gray, CV_BGR2GRAY);
// smooth it, otherwise a lot of false circles may be detected
GaussianBlur( gray, gray, Size(9, 9), 2, 2 );
vector<Vec3f> circles;
HoughCircles(gray, circles, CV_HOUGH_GRADIENT, 2, gray.rows/16,80,100,30,50 );
for( size_t i = 0; i < circles.size(); i++ )
{
    Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
    int radius = cvRound(circles[i][2]);
    // draw the circle center
    circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 );
    // draw the circle outline
    circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
namedWindow( "circles", 1 );
imshow( "circles", img );
waitKey(0);
return 0;

示例图像是

我想检测其中彼此相邻的刻度盘

Answer 1

您可以使用partition将相邻圆的圆聚类，即中心距离与其半径的 sim 相似的圆。您只需定义适当的等价谓词，此处在CirclesOnSameLine中实现。您最终可以改进此谓词，仅将具有相似半径的圆视为相等。

此聚类的结果类似于(相同颜色表示相同的聚类):

通过这种方法，您可以安全地检测某些圆，因为您可以删除不属于超过 4-5 个圆的簇的圆。

代码:

#include <opencv2/opencv.hpp>
#include <vector>
using namespace cv;
using namespace std;

struct CirclesOnSameLine
{
    float _tolerance;

    CirclesOnSameLine(float tolerance) : _tolerance(tolerance) {};

    bool operator()(const Vec3f& lhs, const Vec3f& rhs)
    {
        // [0] = x
        // [1] = y
        // [2] = radius
        float center_distance = sqrt((lhs[0] - rhs[0])*(lhs[0] - rhs[0]) + (lhs[1] - rhs[1])*(lhs[1] - rhs[1]));
        float sum_radii = lhs[2] + rhs[2];

        if (sum_radii > center_distance)
        {
            return (sum_radii / center_distance) < _tolerance;
        }
        return (center_distance / sum_radii) < _tolerance;
    }
};


int main()
{
    Mat3b img = imread("path_to_image");

    Mat1b gray;
    cvtColor(img, gray, COLOR_BGR2GRAY);

    GaussianBlur(gray, gray, Size(9, 9), 2, 2);
    vector<Vec3f> circles;
    HoughCircles(gray, circles, CV_HOUGH_GRADIENT, 2, gray.rows / 16, 80, 100, 10, 100);

    // Cluster circles near each other
    vector<int> labels;
    int n_labels = partition(circles, labels, CirclesOnSameLine(1.1f));

    vector<Scalar> colors;
    for (int i = 0; i < n_labels; ++i)
    {
        Scalar color(rand() & 255, rand() & 255, rand() & 255);
        colors.push_back(color);
    }

    Mat3b adjacent = img.clone();
    for (size_t i = 0; i < circles.size(); i++)
    {
        Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
        int radius = cvRound(circles[i][2]);
        // draw the circle outline
        circle(adjacent, center, radius, colors[labels[i]], 3, 8, 0);
    }

    // Remove small clusters
    vector<int> count(labels.size(), 0);
    for (size_t i = 0; i < labels.size(); ++i)
    {
        count[labels[i]]++;
    }

    Mat3b big_clusters = img.clone();
    for (size_t i = 0; i < circles.size(); i++)
    {
        if (count[labels[i]] < 4) continue;

        Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
        int radius = cvRound(circles[i][2]);
        // draw the circle outline
        circle(big_clusters, center, radius, Scalar(0, 0, 255), 3, 8, 0);
    }



    imshow("Adjacent circles", adjacent);
    imshow("Adjacent circles", big_clusters);

    waitKey();

    return 0;
}