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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

By : Ashwin Pajankar
3.8 (4)
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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

3.8 (4)
By: Ashwin Pajankar

Overview of this book

Raspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects. At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you'll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You'll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you'll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision. By the end of this book, you'll be able to confidently build and deploy computer vision apps.
Table of Contents (15 chapters)
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Thresholding images

Thresholding is the simplest way to divide images into various parts, which are known as segments. Thresholding is the simplest form of segmentation operation. If we apply the thresholding operation to a grayscale image, it is usually (but not all the time) transformed into a binary image. A binary image is a strictly black and white image and it can either have a 0 (black) or 255 (white) value for a pixel. Many segmentation algorithms, advanced image processing operations, and computer vision applications use thresholding as the first step for processing images.

Thresholding is perhaps the simplest image processing operation. First, we must define a value for the threshold. If a pixel has a value greater than the threshold, then we assign 255 (white) to that pixel; otherwise, we assign 0 (black) to the pixel. This is the simplest way we can implement the thresholding operation on an image. There are other thresholding techniques too, and we will learn about...

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