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)

Installing PyCUDA on Ubuntu

This section will describe the steps to install PyCUDA on Linux operating systems. Ubuntu is used for demonstration but the procedure will work on any recent Linux distribution. The steps are described below:

  1. If you have not installed the CUDA toolkit, as described in the first chapter, then download the latest CUDA toolkit from https://developer.nvidia.com/cuda-downloads. It will ask for your operating system, CPU architecture, and whether to install using the internet or to download the entire installer first. As can be seen from the following screenshot, we have chosen Ubuntu with the runfile (local) installer. You can choose values according to your settings:
  1. Run the sudo sh cuda_9.2.148_396.37_linux.run command on Command Prompt to install the CUDA toolkit.

  1. Anaconda distribution will be used as a Python interpreter so it can be downloaded...