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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

Product type Book
Published in Sep 2018
Publisher Packt
ISBN-13 9781789348293
Pages 380 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Bhaumik Vaidya Bhaumik Vaidya
Profile icon Bhaumik Vaidya
LinkedIn
Bhaumik Vaidya is an experienced computer vision engineer and mentor. He has worked extensively on OpenCV Library in solving computer vision problems. He is a University gold medalist in masters and is now doing a PhD in the acceleration of computer vision algorithms built using OpenCV and deep learning libraries on GPUs. He has a background in teaching and has guided many projects in computer vision and VLSI(Very-large-scale integration). He has worked in the VLSI domain previously as an ASIC verification engineer, so he has very good knowledge of hardware architectures also. He has published many research papers in reputable journals to his credit. He, along with his PhD mentor, has also received an NVIDIA Jetson TX1 embedded development platform as a research grant from NVIDIA.
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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 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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Authors (1)

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Profile icon Bhaumik Vaidya
LinkedIn
Bhaumik Vaidya is an experienced computer vision engineer and mentor. He has worked extensively on OpenCV Library in solving computer vision problems. He is a University gold medalist in masters and is now doing a PhD in the acceleration of computer vision algorithms built using OpenCV and deep learning libraries on GPUs. He has a background in teaching and has guided many projects in computer vision and VLSI(Very-large-scale integration). He has worked in the VLSI domain previously as an ASIC verification engineer, so he has very good knowledge of hardware architectures also. He has published many research papers in reputable journals to his credit. He, along with his PhD mentor, has also received an NVIDIA Jetson TX1 embedded development platform as a research grant from NVIDIA.
Read more
See other products by Bhaumik Vaidya
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