Book Image

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

By : Bhaumik Vaidya
Book Image

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

By: Bhaumik 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)

Introducing CUDA and Getting Started with CUDA

This chapter gives you a brief introduction to CUDA architecture and how it has redefined the parallel processing capabilities of GPUs. The application of CUDA architecture in real-life scenarios will be demonstrated. This chapter will serve as a starting guide for software developers who want to accelerate their applications by using general-purpose GPUs and CUDA. The chapter describes development environments used for CUDA application development and how the CUDA toolkit can be installed on all operating systems. It covers how basic code can be developed using CUDA C and executed on Windows and Ubuntu operating systems.

The following topics will be covered in this chapter:

  • Introducing CUDA
  • Applications of CUDA
  • CUDA development environments
  • Installing CUDA toolkit on Windows, Linux, and macOS
  • Developing simple code, using CUDA C