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

You're reading from   Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA Effective techniques for processing complex image data in real time using GPUs

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789348293
Length 380 pages
Edition 1st Edition
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Author (1):
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Bhaumik Vaidya Bhaumik Vaidya
Author Profile Icon Bhaumik Vaidya
Bhaumik Vaidya
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Table of Contents (15) Chapters Close

Preface 1. Introducing CUDA and Getting Started with CUDA 2. Parallel Programming using CUDA C FREE CHAPTER 3. Threads, Synchronization, and Memory 4. Advanced Concepts in CUDA 5. Getting Started with OpenCV with CUDA Support 6. Basic Computer Vision Operations Using OpenCV and CUDA 7. Object Detection and Tracking Using OpenCV and CUDA 8. Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1 9. Deploying Computer Vision Applications on Jetson TX1 10. Getting Started with PyCUDA 11. Working with PyCUDA 12. Basic Computer Vision Applications Using PyCUDA 13. Assessments 14. Other Books You May Enjoy

Summary

To summarize, this chapter gave an introduction to the Python programming language and how it is used in various domains for a vast range of applications. It is lightweight but slow compared to the C or C++ languages. So, if it can leverage the advantages of the parallel computing capability of the GPU then it will be very useful. PyCUDA is a Python wrapper that allows the Python code to take advantage of Nvidia CUDA APIs. The advantage of PyCUDA over other parallel processing modules available in Python is explained in detail. PyCUDA uses an Nvidia CUDA runtime API and Python interpreter. Anaconda, which is a famous Python distribution, comes with many useful Python libraries and IDEs installed, along with the CUDA toolkit. The detailed steps to install PyCUDA on Windows and Ubuntu operating systems are discussed in this chapter.

We will see how to use PyCUDA to accelerate...

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