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Uchiyama did a Paper on his so called "Random dot markers" where he searches for black blobs (inverse of your binary-image..) before applying the LLAH to identify the markers. I'm not entirely sure whether I used parts of his algorithm (Source available at http://hvrl.ics.keio.ac.jp/uchiyama/me/code/UCHIYAMARKERS/index.html ) or was unsatisfied and implemented it on my own, at least my comment in the header says it's somewhere grabbed from there.

However, I found a pretty nice implementation I did a year ago - not really tested but working. The output of it is the following;

clusters_result.png

I hope that works for you as well.

#include <iostream>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "keyExtraction.h"

using namespace cv;
using namespace std;

int main() 
{

    Mat m = imread("/home/tbergmueller/clusters.png", CV_LOAD_IMAGE_GRAYSCALE);

    Mat debug; // for output only
    cvtColor(m,debug, CV_GRAY2BGR); // convert to BGR to allow red numbers printed to this mat


    bitwise_not(m,m); // Invert because my algorithm is for searching black regions

    CvMat cMat = m;
    avPoint* clusters = NULL; // important: init with NULL, otherwise algo crashes

    int nrOfPoints = avExtractKeys(&cMat,&clusters);

    cout << "Found " << nrOfPoints << " points" << endl;

    for(int i=0; i<nrOfPoints; i++)
    {
        stringstream ss;
        ss << i;

        Point p(clusters[i].center.x, clusters[i].center.y);
        putText(debug,ss.str(),p,CV_FONT_HERSHEY_COMPLEX, 0.5, CV_RGB(255,0,0), 1, CV_AA);
    }

    imshow("Debug", debug);
    waitKey();

    return 0;
}

I uploaded the complete source if you want; http://thomasbergmueller.com/share/src.zip

Uchiyama did a Paper on his so called "Random dot markers" where he searches for black blobs (inverse of your binary-image..) before applying the LLAH to identify the markers. I'm not entirely sure whether I used parts of his algorithm (Source available at http://hvrl.ics.keio.ac.jp/uchiyama/me/code/UCHIYAMARKERS/index.html ) or was unsatisfied and implemented it on my own, at least my comment in the header says it's somewhere grabbed from there.

However, I found a pretty nice implementation I did a year ago - not really tested but working. The output of it is the following;

clusters_result.png

I hope that works for you as well.

#include <iostream>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "keyExtraction.h"

using namespace cv;
using namespace std;

int main() 
{

    Mat m = imread("/home/tbergmueller/clusters.png", CV_LOAD_IMAGE_GRAYSCALE);

    Mat debug; // for output only
    cvtColor(m,debug, CV_GRAY2BGR); // convert to BGR to allow red numbers printed to this mat


    bitwise_not(m,m); // Invert because my algorithm is for searching black regions

    CvMat cMat = m;
    avPoint* clusters = NULL; // important: init with NULL, otherwise algo crashes

    int nrOfPoints = avExtractKeys(&cMat,&clusters);

    cout << "Found " << nrOfPoints << " points" << endl;

    for(int i=0; i<nrOfPoints; i++)
    {
        stringstream ss;
        ss << i;

        Point p(clusters[i].center.x, clusters[i].center.y);
        putText(debug,ss.str(),p,CV_FONT_HERSHEY_COMPLEX, 0.5, CV_RGB(255,0,0), 1, CV_AA);
    }

    imshow("Debug", debug);
    waitKey();

    return 0;
}

I uploaded the complete source if you want; http://thomasbergmueller.com/share/src.zip

Uchiyama did a Paper on his so called "Random dot markers" where he searches for black blobs (inverse of your binary-image..) before applying the LLAH to identify the markers. I'm not entirely sure whether I used parts of his algorithm (Source available at http://hvrl.ics.keio.ac.jp/uchiyama/me/code/UCHIYAMARKERS/index.html ) or was unsatisfied and implemented it on my own, at least my comment in the header says it's somewhere grabbed from there.

However, I found a pretty nice implementation I did a year ago - not really tested but working. The output of it is the following;

clusters_result.png

I hope that works for you as well.

#include <iostream>

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "keyExtraction.h"

using namespace cv;
using namespace std;

int main() 
{

    Mat m = imread("/home/tbergmueller/clusters.png", CV_LOAD_IMAGE_GRAYSCALE);

    Mat debug; // for output only
    cvtColor(m,debug, CV_GRAY2BGR); // convert to BGR to allow red numbers printed to this mat


    bitwise_not(m,m); // Invert because my algorithm is for searching black regions

    CvMat cMat = m;
    avPoint* clusters = NULL; // important: init with NULL, otherwise algo crashes

    int nrOfPoints = avExtractKeys(&cMat,&clusters);

    cout << "Found " << nrOfPoints << " points" << endl;

    for(int i=0; i<nrOfPoints; i++)
    {
        stringstream ss;
        ss << i;

        Point p(clusters[i].center.x, clusters[i].center.y);
        putText(debug,ss.str(),p,CV_FONT_HERSHEY_COMPLEX, 0.5, CV_RGB(255,0,0), 1, CV_AA);
    }

    imshow("Debug", debug);
    waitKey();

    return 0;
}

I uploaded the complete source if you want; http://thomasbergmueller.com/share/src.zip