- State the difference between terms computer vision and image processing
- Why is OpenCV ideal for deploying computer vision applications on embedded systems
- Write an OpenCV command to initialize 1960 x 1960 color image with red color
- Write a program to capture frames from a webcam and save it to disk
- Which color format is used by OpenCV to read and display a color image
- Write a program to capture video from webcam, convert it to grayscale and display on the screen
- Write a program to measure the performance of add and subtract operation on GPU
- Write a program for bitwise AND and OR operation on images and explain how it can be used for masking
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
By :
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
By:
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.
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Current Title:
Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
Table of Contents (15 chapters)
Preface
Free Chapter
Introducing CUDA and Getting Started with CUDA
Parallel Programming using CUDA C
Threads, Synchronization, and Memory
Advanced Concepts in CUDA
Getting Started with OpenCV with CUDA Support
Basic Computer Vision Operations Using OpenCV and CUDA
Object Detection and Tracking Using OpenCV and CUDA
Introduction to the Jetson TX1 Development Board and Installing OpenCV on Jetson TX1
Deploying Computer Vision Applications on Jetson TX1
Getting Started with PyCUDA
Working with PyCUDA
Basic Computer Vision Applications Using PyCUDA
Assessments
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