opencv SimpleBlobDetector blob分析斑点检测原理详解

概念

Blob是图像中具有某些共同属性(如灰度值、圆度等如下图所示属性)的一组连通像素。

一、SimpleBlobDetector算法原理

SimpleBlobDetector从图像中提取blobs的算法流程如下:

1. 根据阈值步距“thresholdStep”递增，从最小阈值‘minThreshold“(包含)到最大阈值maxThreshold(排除)计算几个阈值，第一个阈值minThreshold，第二个是minThreshold+ thresholdStep，…以此类推。将这些阈值分别应用于源图像转换为几张二值图像。
2. 通过findContours算子从每幅二值图像中提取连通分量并计算它们中心位置。
3. 由团块之间的最小距离minDistBetweenBlobs参数控制。将几个二值图像的团块中心坐标进行分组。闭合中心形成一组。
4. 从组中，估计斑点的最终中心和它们的半径，并返回点的位置和大小。
5. 最后对返回的blob执行特征过滤：
   （1）：颜色过滤：使用blobColor = 0提取亮色斑点，使用blobColor = 255提取暗色斑点。将二值化图像斑点中心的灰度值和blobColor比较 。如果它们不一致，则将该斑点过滤掉。
   （2）：面积过滤：提取面积在minArea(包含)和maxArea(不包含)之间的blob。
   （3）： 圆度过滤：提取的圆度介于minCircularity(包含)和maxCircularity(不包含)之间的Blob。
   （4）：惯性比过滤：提取惯量介于minInertiaRatio(包含)和maxInertiaRatio(不包含)之间的blob
   （5）: 凸性过滤：提取凸性介于minConvexity(包含)和maxConvexity(不包含)之间的blob。

二、opencv 类接口原型：

  class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
{
public:
  struct CV_EXPORTS_W_SIMPLE Params
  {
      CV_WRAP Params();
      CV_PROP_RW float thresholdStep;
      CV_PROP_RW float minThreshold;
      CV_PROP_RW float maxThreshold;
      CV_PROP_RW size_t minRepeatability;
      CV_PROP_RW float minDistBetweenBlobs;

      CV_PROP_RW bool filterByColor;
      CV_PROP_RW uchar blobColor;

      CV_PROP_RW bool filterByArea;
      CV_PROP_RW float minArea, maxArea;

      CV_PROP_RW bool filterByCircularity;
      CV_PROP_RW float minCircularity, maxCircularity;

      CV_PROP_RW bool filterByInertia;
      CV_PROP_RW float minInertiaRatio, maxInertiaRatio;

      CV_PROP_RW bool filterByConvexity;
      CV_PROP_RW float minConvexity, maxConvexity;

      void read( const FileNode& fn );
      void write( FileStorage& fs ) const;
  };

  CV_WRAP static Ptr<SimpleBlobDetector>
    create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
  CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
};

三、代码示例：

//SimpleBlobDetector
cv::SimpleBlobDetector::Params pBLOBDetector;
cv::Mat src;
int iThStep = 10;
int iMinth = 10;
int iMaxth = 200;
int iMinBt = 10;
int iMinar = 10;
int iMaxar = 1500;
int iMinCir = 0;
int iMinIne = 0;
int iMinCon = 0;

void detect(int ,void *)
{
    pBLOBDetector.thresholdStep = iThStep;
    pBLOBDetector.minThreshold = iMinth;
    pBLOBDetector.maxThreshold = iMaxth;
    pBLOBDetector.minRepeatability = 2;
    pBLOBDetector.minDistBetweenBlobs = iMinBt;
    pBLOBDetector.filterByColor = true;
    pBLOBDetector.blobColor = 0;
    //斑点面积
    pBLOBDetector.filterByArea = true;
    pBLOBDetector.minArea = iMinar;
    pBLOBDetector.maxArea = iMaxar;
    //斑点圆度
    pBLOBDetector.filterByCircularity = true;
    pBLOBDetector.minCircularity = iMinCir *0.01;
    pBLOBDetector.maxCircularity = (float)3.40282e+038;
    //斑点惯性率
    pBLOBDetector.filterByInertia = true;
    pBLOBDetector.minInertiaRatio = iMinIne * 0.01;
    pBLOBDetector.maxInertiaRatio = (float)3.40282e+038;
    //斑点凸度
    pBLOBDetector.filterByConvexity = true;
    pBLOBDetector.minConvexity = iMinCon * 0.01;
    pBLOBDetector.maxConvexity = (float)3.40282e+038;
    //*用参数创建对象
    cv::Ptr<cv::SimpleBlobDetector> blob = cv::SimpleBlobDetector::create(pBLOBDetector);
    //Ptr blob=SimpleBlobDetector::create();//默认参数创建
    //*blob检测
    vector<cv::KeyPoint> key_points;
    //Mat dst;
    //cvtColor(src, dst, COLOR_RGB2GRAY);

    blob->detect(src, key_points);
    cv::Mat outImg;
    //src.copyTo(outImg);

    //绘制结果
    cv::drawKeypoints(src, key_points, outImg, cv::Scalar(0, 0, 255));
    cv::imshow("blob", outImg);
}

void test_SimpleBlobDetector()
{
    //cv::Mat src;
    src = cv::imread("D:\\QtProject\\Opencv_Example\\SimpleBlobDetector\\blobs.png", cv::IMREAD_GRAYSCALE);
    if (src.empty()) {
        cout << "Cannot load image" << endl;
        return;
    }
    cv::imshow("src", src);
    cv::namedWindow("Detect window", cv::WINDOW_NORMAL);
    cv::createTrackbar("最小圆度", "Detect window", &iMinCir, 100, detect);
    cv::createTrackbar("最小惯性率", "Detect window", &iMinIne, 100, detect);
    cv::createTrackbar("最大凸度", "Detect window", &iMinCon, 100, detect);
    cv::createTrackbar("阈值步距", "Detect window", &iThStep, 100, detect);
    cv::createTrackbar("最小阈值", "Detect window", &iMinth, 255, detect);
    cv::createTrackbar("最大阈值", "Detect window", &iMaxth, 255, detect);
    cv::createTrackbar("最小距离", "Detect window", &iMinBt, 255, detect);
    cv::createTrackbar("最小面积", "Detect window", &iMinar, 1000, detect);
    cv::createTrackbar("最大面积", "Detect window", &iMaxar, 5000, detect);
}

四、运行效果：