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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

By : Vaidya
4.4 (5)
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

4.4 (5)
By: Vaidya

Overview of this book

Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples. Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python. By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach.
Table of Contents (15 chapters)
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Parallel Programming using CUDA C

In the last chapter, we saw how easy it is to install CUDA and write a program using it. Though the example was not impressive, it was shown to convince you that it is very easy to get started with CUDA. In this chapter, we will build upon this concept. It teaches you to write advance programs using CUDA for GPUs in detail. It starts with a variable addition program and then incrementally builds towards complex vector manipulation examples in CUDA C. It also covers how the kernel works and how to use device properties in CUDA programs. The chapter discusses how vectors are operated upon in CUDA programs and how CUDA can accelerate vector operations compared to CPU processing. It also discusses terminologies associated with CUDA programming.

The following topics will be covered in this chapter:

  • The concept of the kernel call
  • Creating kernel functions...
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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