GpuCV: An OpenSource GPU-Accelerated Framework for Image Processing and Computer Vision Yannick Allusse

This paper presents GpuCV, an open source multi-platform library for easily developing GPU-accelerated image processing and Computer Vision operators and applications. It is meant for computer vision scientist not familiar with GPU technologies. It is designed to be compatible with Intel’s OpenCV library by offering GPU-accelerated operators that can be integrated into native OpenCV applications.
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GpuCV: An OpenSource GPU-Accelerated Framework for Image Processing and Computer Vision Yannick Allusse GpuCV: An OpenSource GPU-Accelerated Framework for Image Processing and Computer Vision Yannick Allusse Patrick Horain EPH, Telecom & Management EPH, Telecom & Management SudParis SudParis 9 Rue Charles Fourier 9 Rue Charles Fourier 91011 Évry Cedex,FRANCE 91011 Évry Cedex,FRANCE yannick.allusse@it- patrick.horain@it- sudparis.eu sudparis.eu Ankit Agarwal Cindula Saipriyadarshan EPH, Telecom & Management EPH, Telecom & Management SudParis SudParis 9 Rue Charles Fourier 9 Rue Charles Fourier 91011 Évry Cedex,FRANCE 91011 Évry Cedex,FRANCE ankit.agarwal@it- cindula.saipriyadarshan@it- sudparis.eu sudparis.euABSTRACT Nowadays, graphical processing units (GPUs) are power-This paper presents GpuCV, an open source multi-platform ful parallel processors mostly dedicated to image synthesislibrary for easily developing GPU-accelerated image process- and they have made their way to consumers PCs throughing and Computer Vision operators and applications. It is video games and multimedia. Recent graphics card genera-meant for computer vision scientist not familiar with GPU tion offers highly parallel architectures (hundreds of process-technologies. It is designed to be compatible with Intel’s ing units) and high memory bandwidth to reach peak perfor-OpenCV library by offering GPU-accelerated operators that mances close to the TeraFLOPS. In counter part, they suf-can be integrated into native OpenCV applications. The fer from complex integration and data manipulation proce-GpuCV framework transparently manages hardware capa- dures based on dedicated APIs compared to the well knownbilities, data synchronization, activation of low level GLSL CPUs, that barely reach 50 GigaFLOPS. While they haveand CUDA programs, on-the-fly benchmarking and switch- become the most powerful part of middle-end computers,ing to the most efficient implementation and finally offers they opened new gates to cheap General Purpose processinga set of image processing operators with GPU acceleration on GPU (GPGPU) that numerous public application couldavailable. use. In this paper, we present benefits and issues of usingCategories and Subject Descriptors GPGPU for image processing. Then we introduce our openI.4.0 [Image processing and computer vision]: Gen- source framework for image processing and computer vision, ˇeral—Image processing software which is an extension of IntelSs OpenCV[4] library, the pop- ular library for interactive computer vision applications.General Terms The GpuCV framework is meant to transparently manage hardware capabilities with different card generations, dataAlgorithms, Performance synchronization between central and graphics memory and activation of low level GLSL and CUDA programs. It per-Keywords forms on-the-fly benchmarking and switching t ...