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)

Geometric transformation on images

Sometimes, there is a need for resizing an image, the translation of images, and the rotation of images for larger computer vision applications. These kinds of geometric transformation is explained in this section.

Image resizing

images need to be of specific sizes in some computer vision applications. So there is a need to convert the image of arbitrary size into the specific size. OpenCV provides a function to resize an image. The code for image resizing is as follows:

#include <iostream>
#include "opencv2/opencv.hpp"
int main ()
{
cv::Mat h_img1 = cv::imread("images/cameraman.tif",0);
cv::cuda::GpuMat d_img1,d_result1,d_result2;
d_img1.upload(h_img1);
int width...