Detection of hamming markers for OpenCV written in python.

Originally written by Max Brauer: github. All I did was clean up some little stuff and package it for use on pypi.

This package is able to read and create hamming markers, described in this blogpost.

This project was for a robotics/computer vision class I taught Spring 2018. I wanted something simple enough we could go through the code and they could understood how it worked. I also taught them OpenCV, so I wanted something written in that. Eventually we made "street signs" and the students drove Roomba robots around on these "strees" (ok, really it was 3 inch wide black tape for the roads). When they detected an intersection, they used a camera to read the street sign (ar marker) and it told them to: go straight, turn left, or turn right.

Sometimes it isn't as robust as I would like, so you may have to move the target around before it gets recognized.

The simplest way to install is:

pip install ar_markers

You will also need OpenCV 3.x as a minimum. On macOS you can do:

brew install opencv

There are two helper scripts:

• ar_markers_generate.py to generate the markers. Do ar_markers_generate.py --help to see the options
• ar_markers_scan.py to scan the marker. Once you have created and printed out a marker, hold the marker in front of your camera. You will see a blue border around the marker, (if detected) and a green number, showing the ID the marker represents.

or use in a program like:

#!/usr/bin/env python

from __future__ import print_function
import cv2
from ar_markers import detect_markers


if __name__ == '__main__':
    print('Press "q" to quit')
    capture = cv2.VideoCapture(0)

    if capture.isOpened():  # try to get the first frame
        frame_captured, frame = capture.read()
    else:
        frame_captured = False

    while frame_captured:
        markers = detect_markers(frame)
        for marker in markers:
            marker.highlite_marker(frame)
        cv2.imshow('Test Frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        frame_captured, frame = capture.read()

    # When everything done, release the capture
    capture.release()
    cv2.destroyAllWindows()

Copyright (c) 2007, Max Brauer All rights reserved.

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