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)

Chapter 3

  1. The best method to choose the number of threads and number of blocks is as follows:
gpuAdd << <512, 512 >> >(d_a, d_b, d_c);

There is a limit to the number of threads that can be launched per block which is 512 or 1024 for the latest processors. The same way there is a limit to the number of blocks per grid. So if there are a large number of threads then it is better to launch kernel by a small number of blocks and threads as described.

  1. Following is the CUDA program to find the cube of 50000 number:
#include "stdio.h"
#include<iostream>
#include <cuda.h>
#include <cuda_runtime.h>
#define N 50000
__global__ void gpuCube(float *d_in, float *d_out)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
while (tid < N)
{
float temp = d_in[tid];
d_out[tid] = temp*temp*temp;
tid += blockDim.x * gridDim.x;
}
}
int main...