Book Image

Raspberry Pi Computer Vision Programming - Second Edition

By : Ashwin Pajankar
5 (1)
Book Image

Raspberry Pi Computer Vision Programming - Second Edition

5 (1)
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)

Creating a negative of an image

In terms of pure mathematics, when we invert the colors of an image, it creates a negative of the image. This inversion operation can be computed by subtracting the color of a pixel from 255. If it is a color image, we invert the color of all the planes. For a grayscale image, we can directly compute the inversion by subtracting it from 255, as follows:

import cv2
img = cv2.imread('/home/pi/book/dataset/4.2.07.tiff', 0)
negative = abs(255 - img)
cv2.imshow('Grayscale', img)
cv2.imshow('Negative', negative)
cv2.waitKey(0)
cv2.destroyAllWindows()

The following is the output of this:

Figure 5.6 – A negative of an image

Figure 5.6 – A negative of an image

Try to find the negative of a color image, we just need to read the image in color mode in the preceding program.

Note:

The negative of a negative will be the original grayscale image. Try this on your own by computing the negative of the negative again for our...