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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

Writing the first program in PyCUDA

This section describes the procedure for writing a simple "Hello, PyCUDA!" program using PyCUDA. It will demonstrate the workflow for writing any PyCUDA programs. As Python is an interpreted language, the code can also be run line by line from the Python terminal, or it can be saved with the .py extension and executed as a file.

The program for displaying a simple string from the kernel using PyCUDA is shown as follows:

import pycuda.driver as drv
import pycuda.autoinit
from pycuda.compiler import SourceModule

mod = SourceModule("""
#include <stdio.h>

__global__ void myfirst_kernel()
{
printf("Hello,PyCUDA!!!");
}
""")

function = mod.get_function("myfirst_kernel")
function(block=(1,1,1))

The first step while developing PyCUDA code is to include all libraries needed for the...

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