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)

CUDA program structure

We have seen a very simple Hello, CUDA! program earlier, that showcased some important concepts related to CUDA programs. A CUDA program is a combination of functions that are executed either on the host or on the GPU device. The functions that do not exhibit parallelism are executed on the CPU, and the functions that exhibit data parallelism are executed on the GPU. The GPU compiler segregates these functions during compilation. As seen in the previous chapter, functions meant for execution on the device are defined using the __global__ keyword and compiled by the NVCC compiler, while normal C host code is compiled by the C compiler. A CUDA code is basically the same ANSI C code with the addition of some keywords needed for exploiting data parallelism.

So, in this section, a simple two-variable addition program is taken to explain important concepts related...