Detect markers position in 2D images

edit savecancel

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;
}

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

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;
}