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GIFT-Grab: Real-time C++ and Python multi-channel video capture, processing and encoding API Cover

GIFT-Grab: Real-time C++ and Python multi-channel video capture, processing and encoding API

Open Access
|Oct 2017

Abstract

GIFT-Grab is an open-source API for acquiring, processing and encoding video streams in real time. GIFT-Grab supports video acquisition using various frame-grabber hardware as well as from standard-compliant network streams and video files. The current GIFT-Grab release allows for multi-channel video acquisition and encoding at the maximum frame rate of supported hardware – 60 frames per second (fps). GIFT-Grab builds on well-established highly configurable multimedia libraries including FFmpeg and OpenCV. GIFT-Grab exposes a simplified high-level API, aimed at facilitating integration into client applications with minimal coding effort. The core implementation of GIFT-Grab is in C++11. GIFT-Grab also features a Python API compatible with the widely used scientific computing packages NumPy and SciPy.

GIFT-Grab was developed for capturing multiple simultaneous intra-operative video streams from medical imaging devices. Yet due to the ubiquity of video processing in research, GIFT-Grab can be used in many other areas. GIFT-Grab is hosted and managed on the software repository of the Centre for Medical Image Computing (CMIC) at University College London, and is also mirrored on GitHub. In addition it is available for installation from the Python Package Index (PyPI) via the pip installation tool.

 

Funding statement: This work was supported through an Innovative Engineering for Health award by the Wellcome Trust [WT101957], the Engineering and Physical Sciences Research Council (EPSRC) [NS/A000027/1] and a National Institute for Health Research Biomedical Research Centre UCLH/UCL High Impact Initiative. Sébastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278) and the MRC (MR/J01107X/1). Luis C. García-Peraza-Herrera is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1).

DOI: https://doi.org/10.5334/jors.169 | Journal eISSN: 2049-9647
Language: English
Submitted on: Mar 16, 2017
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Accepted on: Aug 24, 2017
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Published on: Oct 9, 2017
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2017 Dzhoshkun Ismail Shakir, Luis Carlos García-Peraza-Herrera, Pankaj Daga, Tom Doel, Matthew J. Clarkson, Sébastien Ourselin, Tom Vercauteren, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.