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)

Questions

  1. Write a CUDA program to subtract two numbers. Pass parameters by value in the kernel function.
  2. Write a CUDA program to multiply two numbers. Pass parameters by reference in the kernel function.
  3. Suppose you want to launch 5,000 threads in parallel. Configure kernel parameters in three different ways to accomplish this. Maximum 512 threads are possible per block.
  4. True or false: The programmer can decide in which order blocks will execute on the device, and blocks will be assigned to which streaming multiprocessor?
  5. Write a CUDA program to find out that your system contains a GPU device that has a major-minor version of 5.0 or greater.
  1. Write a CUDA program to find a cube of a vector that contains numbers from 0 to 49.
  2. For the following applications, which communication pattern is useful?
    1. Image processing
    2. Moving average
    3. Sorting array in ascending order
    4. Finding cube of...