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)

Basic operations on images

Let's perform a few basic operations, such as splitting and combining the channels of a color image and adding a border to an image. We will continue this demonstration in interactive mode. Let's import OpenCV and read a color image, as follows:

>>> import cv2
>>> img = cv2.imread('/home/pi/book/dataset/4.1.01.tiff', 1)

For any image, the origin—the (0, 0) pixel—is the pixel at the upper-left corner. We can retrieve the intensity values for all the channels by running the following statement:

>>> print(img[10, 10])
[34 38 44]

These are the intensity values of the blue, green, and red channels, respectively, for pixel (10, 10). If you only want to access an individual channel for a pixel, then run the following statement:

>>> print(img[10, 10, 0])
34

The preceding output, 34, is the intensity of the blue channel. Similarly, we can access the green and red channels with...