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)

Performance comparison of OpenCV applications with and without CUDA support

The performance of image processing algorithms can be measured in terms of the time it takes to process a single image. When algorithms work on video, performance is measured in terms of frames per second, which indicates the number of frames it can process in a second. When the algorithm can process more than 30 frames per second, it can be considered to work in real time. We can also measure the performance of our algorithms implemented in OpenCV, which will be discussed in this section.

As we discussed earlier, when OpenCV is built with CUDA compatibility, it can increase the performance of algorithms drastically. OpenCV functions in the CUDA module are optimized to utilize GPU parallel processing capability. OpenCV also provides similar functions that only run on CPU. In this section, we will compare...