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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

This chapter demonstrated the concepts of programming in PyCUDA. It started with the development of a simple Hello, PyCUDA program using PyCUDA. The concepts of kernel definition in C or C++ and calling it from Python code and the API for accessing GPU device properties from a PyCUDA program were discussed in detail. The execution mechanism for multiple threads and blocks in a PyCUDA program was explained with a simple program. The basic structure of a PyCUDA program was described with a simple example of an array addition. The simplification of PyCUDA code was described by using directives from a driver class. The use of CUDA events to measure the performance of the PyCUDA programs was explained in detail. The functionality of the inout directive of the driver class and the gpuarray class was explained using an element-wise squaring example. The gpuarray class was used...

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