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IMPORTANT INSTALLATION NOTE

pip install gift-grab currently works only with --install-option’s. Please see this guideline on the GIFT-Grab repository.

GIFT-Grab

GIFT-Grab is an open-source C++ and Python API for acquiring, processing and encoding video streams in real time.

GIFT-Grab supports several frame-grabber cards, standard-compliant network streams and video files.

The Python API is compatible with NumPy and SciPy. Please note that currently only Python 2 is supported.

Features

  • Capturing video streams using frame-grabber hardware, the following cards are supported:
  • Capturing standard-compliant network video streams

  • Reading (decoding) video files

  • Writing (encoding) video files (including real-time encoding), the following formats are supported:
  • Video data as NumPy arrays to facilitate processing with NumPy-compatible Python libraries

Getting started

Citing GIFT-Grab

If you use GIFT-Grab in your work, please cite Shakir et al. (2017):

Shakir DI, García-Peraza-Herrera LC, Daga P, Doel T, Clarkson MJ, Ourselin S, and Vercauteren T. GIFT-Grab: Real-time C++ and Python multi-channel video capture, processing and encoding API. Journal of Open Research Software. 2017;5(1):27. DOI: http://doi.org/10.5334/jors.169

BibTeX entry:

@article{giftgrab17,
  author = {Dzhoshkun Ismail Shakir and Luis Carlos Garc\'{i}a-Peraza-Herrera and Pankaj Daga and Tom Doel and Matthew J. Clarkson and S\'{e}bastien Ourselin and Tom Vercauteren},
  title = {{GIFT-Grab: Real-time C++ and Python multi-channel video capture, processing and encoding API}},
  journal = {{Journal of Open Research Software}},
  year = {2017},
  number = {1},
  pages = {27},
  month = {10},
  day = {9},
  volume = {5},
  url = {http://doi.org/10.5334/jors.169},
  doi = {http://doi.org/10.5334/jors.169},
}

Support and contributing

Please see the contribution guide for bug reports, feature requests, and if you would like to contribute to GIFT-Grab.

Acknowledgements

GIFT-Grab was developed as part of the GIFT-Surg project at the Translational Imaging Group in the Centre for Medical Image Computing at University College London (UCL).

This work was supported through an Innovative Engineering for Health award by the Wellcome Trust [WT101957], the Engineering and Physical Sciences Research Council [NS/A000027/1] and a National Institute for Health Research Biomedical Research Centre UCLH / UCL High Impact Initiative.

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