OpenCV找圆：SimpleBlobDetector源码，凸度和圆度等检测原理

青旅半醒 2022-09-02 08:27 93阅读 0赞

Blob是指图像中的一块连通区域，Blob分析就是对前景/背景分离后的二值图像，进行连通域提取和标记。知识点就是SimpleBlobDetector的使用，blob(斑点)筛选条件：斑点颜色、面积、圆度、惯性率、凸度。

void blobDetector()
    {
    	Mat img = imread("d:\\11.jpg");
    
    	SimpleBlobDetector::Params params;
        params.thresholdStep = 10;    //二值化的阈值步长
    	params.minThreshold = 20;
    	params.maxThreshold = 200;
    	params.filterByArea = true;
    	params.minArea = 10;
    	params.maxArea = 80000;
    	params.filterByCircularity = true;
    	params.minCircularity = 0.5;
    
    	Ptr<SimpleBlobDetector> detector = SimpleBlobDetector::create(params);
    	vector<KeyPoint> key_points;
    	detector->detect(img, key_points);
    	Mat output_img;
    	drawKeypoints(img, key_points, output_img, Scalar(0, 0, 255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
    
    	namedWindow("SimpleBlobDetector", WINDOW_NORMAL);
    	imshow("SimpleBlobDetector", output_img);
    	waitKey(0);
    }

*  阈值：通过使用以minThreshold开始的阈值对源图像进行阈值处理，将源图像转换为多个二进制图像。这些阈值以thresholdStep递增，直到maxThreshold。因此，第一个阈值为minThreshold，第二个阈值为minThreshold + thresholdStep，第三个阈值为minThreshold + 2 x thresholdStep，依此类推；
 *  按大小：可以通过设置参数filterByArea = 1以及minArea和maxArea的适当值来基于大小过滤blob。例如。设置minArea = 100将滤除所有少于100个像素的斑点。
 *  按圆度：这只是测量斑点距圆的距离。例如。正六边形的圆度比正方形高。要按圆度过滤，请设置filterByCircularity =1。然后为minCircularity和maxCircularity设置适当的值。圆度定义为（
    
    ![9801af59a11bc80cb9c58158c57a4221.png][]
    
    ）。圆的为圆度为1，正方形的圆度为PI/4，依此类推。
 *  [![9e804994ac17cf17acba92ad84549f53.png][]][9e804994ac17cf17acba92ad84549f53.png 1]

以下给出 opencv 对候选 Blob 筛查的部分源码：

[https://github.com/opencv/opencv/blob/4.2.0/modules/features2d/src/feature2d.cpp][https_github.com_opencv_opencv_blob_4.2.0_modules_features2d_src_feature2d.cpp]

[https://github.com/opencv/opencv/blob/4.2.0/modules/features2d/src/blobdetector.cpp][https_github.com_opencv_opencv_blob_4.2.0_modules_features2d_src_blobdetector.cpp]

[https://github.com/joostvanstuijvenberg/SimplerBlobDetector/blob/master/SimplerBlobDetector.cpp][https_github.com_joostvanstuijvenberg_SimplerBlobDetector_blob_master_SimplerBlobDetector.cpp]

//image为输入的灰度图像
    //binaryImage为二值图像
    //centers表示该二值图像的斑点
    void SimpleBlobDetector::findBlobs(const cv::Mat &image, const cv::Mat &binaryImage, vector<Center> ¢ers) const
    {
        (void)image;
        centers.clear();    //斑点变量清零
     
        vector < vector<Point> > contours;    //定义二值图像的斑点的边界像素变量
        Mat tmpBinaryImage = binaryImage.clone();    //复制二值图像
        //调用findContours函数，找到当前二值图像的所有斑点的边界
        findContours(tmpBinaryImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
     
    #ifdef DEBUG_BLOB_DETECTOR
        //  Mat keypointsImage;
        //  cvtColor( binaryImage, keypointsImage, CV_GRAY2RGB );
        //
        //  Mat contoursImage;
        //  cvtColor( binaryImage, contoursImage, CV_GRAY2RGB );
        //  drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
        //  imshow("contours", contoursImage );
    #endif
        //遍历当前二值图像的所有斑点
        for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
        {
            //结构类型Center代表着斑点，它包括斑点的中心位置，半径和权值
            Center center;    //斑点变量
            //初始化斑点中心的置信度，也就是该斑点的权值
            center.confidence = 1;
            //调用moments函数，得到当前斑点的矩
            Moments moms = moments(Mat(contours[contourIdx]));
            if (params.filterByArea)    //斑点面积的限制
            {
                double area = moms.m00;    //零阶矩即为二值图像的面积
                //如果面积超出了设定的范围，则不再考虑该斑点
                if (area < params.minArea || area >= params.maxArea)
                    continue;
            }
     
            if (params.filterByCircularity)    //斑点圆度的限制
            {
                double area = moms.m00;    //得到斑点的面积
                //计算斑点的周长
                double perimeter = arcLength(Mat(contours[contourIdx]), true);
                //由公式3得到斑点的圆度
                double ratio = 4 * CV_PI * area / (perimeter * perimeter);
                //如果圆度超出了设定的范围，则不再考虑该斑点
                if (ratio < params.minCircularity || ratio >= params.maxCircularity)
                    continue;
            }
     
            if (params.filterByInertia)    //斑点惯性率的限制
            {
                //计算公式13中最右侧等式中的开根号的值
                double denominator = sqrt(pow(2 * moms.mu11, 2) + pow(moms.mu20 - moms.mu02, 2));
                const double eps = 1e-2;    //定义一个极小值
                double ratio;
                if (denominator > eps)
                {
                    //cosmin和sinmin用于计算图像协方差矩阵中较小的那个特征值λ2
                    double cosmin = (moms.mu20 - moms.mu02) / denominator;
                    double sinmin = 2 * moms.mu11 / denominator;
                    //cosmin和sinmin用于计算图像协方差矩阵中较大的那个特征值λ1
                    double cosmax = -cosmin;
                    double sinmax = -sinmin;
                    //imin为λ2乘以零阶中心矩μ00
                    double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
                    //imax为λ1乘以零阶中心矩μ00
                    double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
                    ratio = imin / imax;    //得到斑点的惯性率
                }
                else
                {
                    ratio = 1;    //直接设置为1，即为圆
                }
                //如果惯性率超出了设定的范围，则不再考虑该斑点
                if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
                    continue;
                //斑点中心的权值定义为惯性率的平方
                center.confidence = ratio * ratio;
            }
     
            if (params.filterByConvexity)    //斑点凸度的限制
            {
                vector < Point > hull;    //定义凸壳变量
                //调用convexHull函数，得到该斑点的凸壳
                convexHull(Mat(contours[contourIdx]), hull);
                //分别得到斑点和凸壳的面积，contourArea函数本质上也是求图像的零阶矩
                double area = contourArea(Mat(contours[contourIdx]));
                double hullArea = contourArea(Mat(hull));
                double ratio = area / hullArea;    //公式5，计算斑点的凸度
                //如果凸度超出了设定的范围，则不再考虑该斑点
                if (ratio < params.minConvexity || ratio >= params.maxConvexity)
                    continue;
            }
     
            //根据公式7，计算斑点的质心
            center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
     
            if (params.filterByColor)    //斑点颜色的限制
            {
                //判断一下斑点的颜色是否正确
                if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
                    continue;
            }
     
            //compute blob radius
            {
                vector<double> dists;    //定义距离队列
                //遍历该斑点边界上的所有像素
                for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
                {
                    Point2d pt = contours[contourIdx][pointIdx];    //得到边界像素坐标
                    //计算该点坐标与斑点中心的距离，并放入距离队列中
                    dists.push_back(norm(center.location - pt));
                }
                std::sort(dists.begin(), dists.end());    //距离队列排序
                //计算斑点的半径，它等于距离队列中中间两个距离的平均值
                center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
            }
     
            centers.push_back(center);    //把center变量压入centers队列中
     
    #ifdef DEBUG_BLOB_DETECTOR
            //    circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
    #endif
        }
    #ifdef DEBUG_BLOB_DETECTOR
        //  imshow("bk", keypointsImage );
        //  waitKey();
    #endif
    }

\---

参考文献

OpenCV图像处理中“找圆技术”的使用

[https://www.cnblogs.com/jsxyhelu/p/14117304.html][https_www.cnblogs.com_jsxyhelu_p_14117304.html]

[9801af59a11bc80cb9c58158c57a4221.png]: /images/20220829/ad194277a3b444a0bf7d062fcedd69b3.png
[9e804994ac17cf17acba92ad84549f53.png]: /images/20220829/27126716897544ee983db3304e57ab58.png
[9e804994ac17cf17acba92ad84549f53.png 1]: https://img2020.cnblogs.com/blog/508489/202012/508489-20201213101946833-954290200.png
[https_github.com_opencv_opencv_blob_4.2.0_modules_features2d_src_feature2d.cpp]: https://github.com/opencv/opencv/blob/4.2.0/modules/features2d/src/feature2d.cpp
[https_github.com_opencv_opencv_blob_4.2.0_modules_features2d_src_blobdetector.cpp]: https://github.com/opencv/opencv/blob/4.2.0/modules/features2d/src/blobdetector.cpp
[https_github.com_joostvanstuijvenberg_SimplerBlobDetector_blob_master_SimplerBlobDetector.cpp]: https://github.com/joostvanstuijvenberg/SimplerBlobDetector/blob/master/SimplerBlobDetector.cpp
[https_www.cnblogs.com_jsxyhelu_p_14117304.html]: https://www.cnblogs.com/jsxyhelu/p/14117304.html