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)

Thread and block execution in PyCUDA

We saw in the A kernel call section that we can start multiple blocks and multiple threads in parallel. So, in which order do these blocks and threads start and finish their execution? It is important to know this if we want to use the output of one thread in other threads. To understand this, we have modified the kernel in the hello,PyCUDA! program, seen in the earlier section, by including a print statement in a kernel call, which prints the block number. The modified code 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("I am in block no: %d \\n", blockIdx.x);
}
""")

function = mod.get_function("myfirst_kernel")
function(grid=(4,1),block...