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)

Introduction to Jetson TX1

When high-end visual computing and computer vision applications need to be deployed in real-life scenarios, then embedded development platforms are required, which can do computationally intensive tasks efficiently. Platforms such as Raspberry Pi can use OpenCV for computer vision applications and camera-interfacing capability, but it is very slow for real-time applications. Nvidia, which specializes in GPU manufacturing, has developed modules that use GPUs for computationally intensive tasks. These modules can be used to deploy computer vision applications on embedded platforms and include Jetson TK1, Jetson TX1, and Jetson TX2.

Jetson TK1 is the preliminary board and contains 192 CUDA cores with the Nvidia Kepler GPU. It is the cheapest of the three. Jetson TX1 is intermediate in terms of processing speed, with 256 CUDA cores with Maxwell architecture...