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)

Introduction to the PyCUDA module

In the last section, we saw many advantages of using the Python programming language. It is also mentioned that Python is much slower than C or C++. So, it will be beneficial if it can leverage the parallel processing capability of a GPU. Python provides a PyCUDA wrapper that can utilize the parallel computing capability of a GPU by using the Nvidia CUDA API. Python also has a PyOpenCL module that can be used for parallel computation on any GPU.

Then, one question you might ask is why you have to use PyCUDA, which is specific to Nvidia GPUs. There are many advantages of using PyCUDA over other similar modules; the following are the reasons:

  • It provides an easy interface with CUDA API for Python developers and has good documentation, which make it easy to learn.
  • The full power of CUDA API provided by Nvidia can be used within Python code using...