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)

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...