OpenCV3.0: How to detect normal marker using ArUco library

Question

I implemented ArUco module in opencv3.0 it works completely fine while detecting aruco markers.

For aruco marker detection i am using this image

But is it possible to detect normal markers like this below image using aruco module?

Here is some snippets of my code:

aruco::DetectorParameters detectorParams;
if (parser.has("dp")) {
bool readOk = readDetectorParameters(parser.get<string>("dp"), detectorParams);
    if (!readOk) {
        cerr << "Invalid detector parameters file" << endl;
        return 0;
    }
}

aruco::Dictionary dictionary =
    aruco::getPredefinedDictionary(aruco::PREDEFINED_DICTIONARY_NAME(dictionaryId));

Mat camMatrix, distCoeffs;
if (estimatePose) {
    bool readOk = readCameraParameters(parser.get<string>("c"), camMatrix, distCoeffs);
    if (!readOk) {
        cerr << "Invalid camera file" << endl;
        return 0;
    }
}

// detect markers and estimate pose
    aruco::detectMarkers(image, dictionary, corners, ids, detectorParams, rejected);
    if (estimatePose && ids.size() > 0)
        aruco::estimatePoseSingleMarkers(corners, markerLength, camMatrix, distCoeffs, rvecs,
            tvecs);

// draw results
    image.copyTo(imageCopy);
    if (ids.size() > 0) {
        aruco::drawDetectedMarkers(imageCopy, corners, ids);

        if (estimatePose) {
            for (unsigned int i = 0; i < ids.size(); i++)
                aruco::drawAxis(imageCopy, camMatrix, distCoeffs, rvecs[i], tvecs[i],
                    markerLength * 0.5f);
        }
    }

    if (showRejected && rejected.size() > 0)
        aruco::drawDetectedMarkers(imageCopy, rejected, noArray(), Scalar(100, 0, 255));

    imshow("out", imageCopy);
    char key = (char)waitKey(waitTime);
    if (key == 27) break;
}

How can i make this code to detect normal markers?

Answer 1

In the FAQ

Should I use a predefined dictionary or generate my own dictionary?

In general, it is easier to use one of the predefined dictionaries. However, if you need a bigger dictionary (in terms of number of markers or number of bits) you should generate your own dictionary. Dictionary generation is also useful if you want to maximize the inter-marker distance to achieve a better error correction during the identification step.

I think this is exactly your case, you want to use something that isn't in the standard ArUco Dictionary. A dictionary is simply a class, you need to create one and fill it with the correct data.