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What is a Python's AprilTag?

  • An example of a fiducial marker is an April tag. Fiducials, or simply "markers," are items that serve as references positioned in the camera's field of vision when an image or video frame is taken.

    The fiducial marker is then detected in the input picture by the computer vision software, which then applies specific operations dependent on the kind of marker and its location in the input image. As seen in the graphic at the top of this lesson, April Tags is a special fiducial marker consisting of a black square with a white foreground created in a certain pattern.

    Computer vision and image processing algorithms may more easily recognize April Tags thanks to the black border encircling the marker under a range of settings, such as changes in rotation, size, illumination, etc.

    An April Tag is comparable to a QR code in that it is a 2D binary pattern that can be recognized by computer vision techniques. However, compared to a QR code, which typically can contain up to 3KB of data, an April Tag only stores 4–12 bits of information. So why even try to use April Tags? If April Tags can only store so little info, why not use QR codes instead?

    Fewer data storage by April Tags is a feature, not a flaw or restriction. According to the official April Tag datasheet, because April Tag payloads are so tiny, they may be more readily discovered, recognized, and detected at extended ranges.

    Utility QR codes if you wish to store data in a 2D barcode. However, April Tags should be used if you want to employ markers that are simpler to identify in your machine vision pipeline.

    Fiducial markers like April Tags are a crucial component of many computer vision systems, including but not limited to:

    • camera averaging
    • Estimation of object size
    • determining the distance between a subject and the camera
    • 3D placement
    • orientation to objects
    • Robotics, or autonomous navigation to a predetermined landmark
    • etc.

    One of their main advantages is that April Tags can be made with simple software and a printer. Python tools are available to automatically identify the April tags for you, so all you have to do is produce the April tag on your machine, print it out, and add it to your image processing pipeline!

    I'll demonstrate how to identify April tags with Python and OpenCV in the remaining sections of this tutorial.

    Python's AprilTag

    The definitions of April tags and fiducial markers will be covered in the first section of this lesson. The Python library we'll use to find April Tags in input photos, April tag, will then be installed.

    After reviewing the organization of our project's directories, we'll put our Python script for finding and identifying April tags into use.

    The lesson will come to a close with a recap of our findings and a discussion of some of the shortcomings (and annoyances) of April Tags in particular.

    What are fiducial markers and April tags?

    An example of a fiducial marker is an April tag. Fiducials are unique markers that we place in the camera's field of vision to make them visible and easy to identify.

    For instance, in each of the following lessons, fiducial markers were used to calculate the size of an object in an image or the separation of two distinct things:

    • Using Python and OpenCV, calculate the distance from the camera to an object or marker
    • Using OpenCV to measure the size of items in a picture
    • Using OpenCV to measure the separation between items in an image

    These projects could only be completed successfully because a marker or reference item was positioned in the camera's field of vision. When I found the article, I could determine its width and height because I already knew how big the reference object was.

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    In this lesson, You learned about April Tags, a collection of fiducial markers used often in robotics, calibration, and 3D computer vision projects.

    Because they are frequently quite simple to identify in real-time, we employ April Tags (as well as the closely related A r U co tags) in these circumstances. In almost every programming language used to do computer vision, including Python, Java, C++, etc., libraries are available to recognize April Tags and Ar U co tags.

    We applied the Python package April-tag in our situation. This package can be installed via pip and allows us to pass in pictures loaded by OpenCV, which makes it useful and efficient in many Python-based computer vision pipelines. I'll demonstrate real-world applications utilizing April Tags and A r U co tags later this year or at the beginning of 2021. Still, I wanted to present them now so you could get to know them.