scikit-surgeryarucotracker is a simple tracking interface using ARuCo markers
Project description
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 usinng other tracking hardware (e.g. NDI - scikit-surgerynditracker).
scikit-surgeryarucotracker is part of the SNAPPY software project, developed at the Wellcome EPSRC Centre for Interventional and Surgical Sciences, part of University College London (UCL).
scikit-surgeryarucotracker supports Python 2.7 and Python 3.6.
Installing
pip install scikit-surgeryarucotracker
Using
Configuration is done using Python libraries. Tracking data is returned in NumPy arrays.
from sksurgerarucotracker.tracker import ARuCoTracker SETTINGS = { "video source" : 0 } TRACKER = ARuCo() TRACKER.connect(SETTINGS) TRACKER.start_tracking() print(TRACKER.get_frame() TRACKER.stop_tracking() TRACKER.close()
Developing
Cloning
You can clone the repository using the following command:
git clone https://weisslab.cs.ucl.ac.uk/WEISS/SoftwareRepositories/SNAPPY/scikit-surgeryarucotracker
Running the tests
You can run the unit tests by installing and running tox:
pip install tox tox
Contributing
Please see the contributing guidelines.
Useful links
Licensing and copyright
Copyright 2019 University College London. scikit-surgerytracker is released under the BSD-3 license. Please see the license file for details.
Acknowledgements
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