- State true or false: The use of the d_out[i]++ line instead of the atomicadd operation will yield an accurate result in histogram calculation.
- What is the advantage of using shared memory with atomic operations?
- What is the modification in the kernel call function when shared memory is used in the kernel?
- Which information can be obtained by calculating the histogram of an image?
- State true or false: The kernel function developed in this chapter for BGR into grayscale conversion will also work for RGB into grayscale conversion.
- Why is the image flattened in all of the examples shown in this chapter? Is it a compulsory step?
- Why is the image converted into the uint8 data type from the numpy library before being displayed?
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Book Overview & Buying
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Table Of Contents
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.
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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