Skip to main content

scikit-surgeryarucotracker is a simple tracking interface using ARuCo markers

Project description


GitHub CI test status Test coverage Documentation Status The SciKit-Surgery paper Software DOI

Author: Stephen Thompson

scikit-surgeryarucotracker provides a simple Python interface between OpenCV’s ARuCo marker tracking libraries and other Python packages designed around scikit-surgerytrackers. It allows you to treat an object tracked using ARuCo markers in the same way as an object tracked using other tracking hardware (e.g. aruco - scikit-surgerynditracker).

scikit-surgeryarucotracker is part of the SciKit-Surgery software project, developed at the Wellcome EPSRC Centre for Interventional and Surgical Sciences, part of University College London (UCL).

scikit-surgeryarucotracker is tested with Python 3.6 and may support other Python versions.


pip install scikit-surgeryarucotracker


Configuration is done using Python libraries. Tracking data is returned in NumPy arrays.

from sksurgeryarucotracker.arucotracker import ArUcoTracker
config = {
    "video source" : 0
tracker = ArUcoTracker(config)




You can clone the repository using the following command:

git clone

Running the tests

You can run the unit tests by installing and running tox:

pip install tox


Please see the contributing guidelines.


Supported by Wellcome and EPSRC.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-surgeryarucotracker-0.2.7.tar.gz (26.9 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page