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)

Accessing the individual pixel intensities of an image

Sometimes there is a need to access pixel intensity value at a particular location when we are working with images. This is very useful when we want to change the brightness or contrast of a group of pixels or we want to perform some other pixel-level operations. For an 8-bit grayscale image, this intensity value at a point will be in a range of 0 to 255, while for a color image there will be three different intensity values for the blue, green, and red channels with all having values between 0 to 255.

OpenCV provides a cv::Mat::at<> method for accessing intensity values at a particular location for any channel images. It needs one argument, which is the location of the point at which the intensity is to be accessed. The point is passed using the Point class with row and column values as arguments. For a grayscale image...