Apriltag python

Apriltag

AprilTag is a visual fiducial system, useful for a wide variety of tasks including augmented reality, robotics, and camera calibration. Targets can be created from an ordinary printer, and the AprilTag detection software computes the precise 3D position, orientation, and identity of the tags relative to the camera. Implementations are available in Java, as well as in C. Notably, the C implementation has no external dependencies and is designed to be easily included in other applications, as well as portable to embedded devices. Real-time performance can be achieved even on cell-phone grade processors.

The two main papers to refer to understand the apriltag algorithm are the following:

1. IROS 2016 - AprilTag 2: Efficient and robust fiducial detection: this is the paper which refers to the version of the algorithm that we want to use

2. ICRA 2011 - AprilTag: A robust and flexible visual fiducial system: this is the paper which explains how the first version of the algorithm works

Apriltag python

This program is base on apriltag without any c extra plugin.And this program encompass tag36h11 tag25h9 and tag16h5.If you han any idea about enchance this program`s function,you can modify whatever you want. Just support tag36h11,tag25h9,tag16h5.

Installation and Usage

You can use pip or uv to install the dependencies and run the project.

Using pip

1. Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

2. Install dependencies:

   pip install .

   For development (including tests):

   pip install ".[dev]"

3. Run tests:

   pytest

Using uv (Recommended)

uv is a fast Python package installer and resolver.

1. Install uv:

   pip install uv

2. Create a virtual environment and install dependencies:

   uv venv
   source .venv/bin/activate
   uv pip install ".[dev]"

3. Run tests:

   uv run pytest

Running the detection on an image

To run the AprilTag detection on the sample image (tests/tag.png), you can execute the test_detection.py test. This will generate an output image with the detection results in test_outputs/detection_result.png.

Using pytest:

pytest tests/test_detection.py

Or using uv:

uv run pytest tests/test_detection.py