Detect markers position in 2D images

edit

Another approach would be to use the SimpleBlobDetector() class, and playing with the parameters extract (have a look here for a nice explanation of the parameters) the desired result, plus that it can be implemented with much less code and personally I find it a bit neater as an approach. Have a look in the example bellow:

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    Mat src = imread("markers.png");

    // resizing for practical reasons
    Mat rsz;
    Size size(800, 900);
    resize(src, rsz, size);

    imshow("rsz", rsz);

    vector<KeyPoint> keypoints;
    // Change parameters
    SimpleBlobDetector::Params params;

    // Specify how close you want to be to a square, if you check  
    // the link you can make this work for other shapes as well
    params.filterByCircularity = true;
    params.maxCircularity = .85;
    params.minCircularity = .785;

    Ptr<FeatureDetector> blobsDetector = SimpleBlobDetector::create(params);
    blobsDetector->detect(rsz, keypoints);

    Mat drawImage = rsz.clone();
    for (size_t i = 0; i < keypoints.size(); ++i)
        circle(drawImage, keypoints[i].pt, 16, Scalar(0, 0, 255), 2);
//    imwrite("result.png", drawImage);
    imshow("result", drawImage);

    waitKey(0);
    return 0;
}

As already answered by FooBar, yes you can do it.

Just an example code here.

The image to find the templates:

The template image.

#include <iostream>

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


//See: http://docs.opencv.org/doc/tutorials/imgproc/histograms/template_matching/template_matching.html
//See: http://answers.opencv.org/question/60382/detect-markers-position-in-2d-images/
int main() {
  cv::Mat img, templateImg, result;
  cv::VideoCapture capture("http://answers.opencv.org/upfiles/14297307634571599.png");
  if(capture.isOpened()) {
    capture >> img;
  } else {
    return -1;
  }

  capture = cv::VideoCapture("http://answers.opencv.org/upfiles/14297308125543022.png");
  if(capture.isOpened()) {
    capture >> templateImg;
  } else {
    return -1;
  }

  /// Reduce the size of the image to display it on my screen
  cv::resize(img, img, cv::Size(), 0.5, 0.5);
  /// Reduce the size of the template image
  /// (first to fit the size used to create the image test, second to fit the size of the reduced image)
  cv::resize(templateImg, templateImg, cv::Size(), 0.25, 0.25);

  cv::Mat img_display;
  img.copyTo(img_display);

  // Create the result matrix
  int result_cols =  img.cols - templateImg.cols + 1;
  int result_rows = img.rows - templateImg.rows + 1;

  result.create(result_rows, result_cols, CV_32FC1);

  /// Do the Matching and Normalize
  cv::matchTemplate(img, templateImg, result, CV_TM_CCORR_NORMED);
  cv::normalize(result, result, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());

  /// Localizing the best match with minMaxLoc
  double minVal; double maxVal; cv::Point minLoc; cv::Point maxLoc;
  cv::Point matchLoc;

  for(;;) {
    cv::minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, cv::Mat());
    matchLoc = maxLoc;
    std::cout << "Max correlation=" << maxVal << std::endl;
    if(maxVal < 0.8) {
      break;
    }

    /// Show me what you got
    cv::rectangle(img_display, matchLoc, cv::Point(matchLoc.x + templateImg.cols , matchLoc.y + templateImg.rows),
        cv::Scalar::all(0), 2, 8, 0);
    cv::rectangle(result, cv::Point(matchLoc.x - templateImg.cols/2 , matchLoc.y - templateImg.rows/2),
        cv::Point(matchLoc.x + templateImg.cols/2 , matchLoc.y + templateImg.rows/2 ), cv::Scalar::all(0), 2, 8, 0);

    cv::imshow("result", result);
    cv::waitKey(0);

    /// Fill the detected location with a rectangle of zero
    cv::rectangle(result, cv::Point( matchLoc.x - templateImg.cols/2 , matchLoc.y - templateImg.rows/2),
        cv::Point(matchLoc.x + templateImg.cols/2 , matchLoc.y + templateImg.rows/2 ), cv::Scalar::all(0), -1);
  } while (maxVal > 0.9);


  cv::imshow("result", result);
  cv::imshow("img_display", img_display);
  cv::waitKey(0);

  return 0;
}

You could find the maximum, set the area around this position to zero and iterate.