Blob Detection

Circular Blob Detection

This example shows how to find circular blobs in an grayscale image. The evaluation of the circularity of a blob is done using the area and the perimeter (arc length) of the contour. The center point gets evaluated using the moments of the contour.

#include "opencv/cv.h"
#include "opencv/highgui.h"
#include "opencv/cxcore.h"

using namespace cv;

int main(int argc, char** argv)
{
    Mat img = imread("image.jpg", CV_LOAD_IMAGE_GRAYSCALE);
    Mat resultImg;
    cvtColor(img, resultImg, CV_GRAY2BGR);

    // threshold the image with gray value of 100
   Mat binImg;
   threshold(img, binImg, 100, 255, THRESH_BINARY);

    // find the contours
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(binImg, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);

    if(contours.size() <= 0)
    {
        printf("no contours found");
        return 0;
    }
    // filter the contours
    vector<vector<Point>> filteredBlobs;
    Mat centers = Mat::zeros(0,2,CV_64FC1);
    for(int i = 0; i < contours.size(); i++)
    {
        // calculate circularity
        double area = contourArea(contours[i]);
        double arclength = arcLength(contours[i], true);
        double circularity = 4 * CV_PI * area / (arclength * arclength);
        if(circularity > 0.8)
        {
            filteredBlobs.push_back(contours[i]);
        
            //calculate center
            Moments mu = moments(contours[i], false);
            Mat centerpoint = Mat(1,2,CV_64FC1);
            centerpoint.at<double>(i,0) = mu.m10 / mu.m00; // x-coordinate
            centerpoint.at<double>(i,1) = mu.m01 / mu.m00; // y-coordinate
            centers.push_back(centerpoint);
        }
    }

    if(filteredBlobs.size() <= 0)
    {
        printf("no circular blobs found");
        return 0;
    }
    drawContours(resultImg, filteredBlobs, -1, Scalar(0,0,255), CV_FILLED, 8);

    imshow("Blobs",resultImg);
    waitKey(0);
    return 0;
}