Book Image

Raspberry Pi By Example

By : Arush Kakkar
Book Image

Raspberry Pi By Example

By: Arush Kakkar

Overview of this book

Want to put your Raspberry Pi through its paces right out of the box? This tutorial guide is designed to get you learning all the tricks of the Raspberry Pi through building complete, hands-on hardware projects. Speed through the basics and then dive right in to development! Discover that you can do almost anything with your Raspberry Pi with a taste of almost everything. Get started with Pi Gaming as you learn how to set up Minecraft, and then program your own game with the help of Pygame. Turn the Pi into your own home security system with complete guidance on setting up a webcam spy camera and OpenCV computer vision for image recognition capabilities. Get to grips with GPIO programming to make a Pi-based glowing LED system, build a complete functioning motion tracker, and more. Finally, get ready to tackle projects that push your Pi to its limits. Construct a complete Internet of Things home automation system with the Raspberry Pi to control your house via Twitter; turn your Pi into a super-computer through linking multiple boards into a cluster and then add in advanced network capabilities for super speedy processing!
Table of Contents (22 chapters)
Raspberry Pi By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Splitting and merging image color channels


On several occasions, we might be interested in working separately with the red, green, and blue channels. For example, we might want to build a histogram for each channel of an image.

The cv2.split() method is used to split an image into three different intensity arrays for each color channel, whereas cv2.merge() is used to merge different arrays into a single multichannel array, that is, a color image. Let's take a look at an example:

import cv2
img = cv2.imread('4.2.03.tiff',1)
b,g,r = cv2.split (img)
cv2.imshow('Blue Channel',b)
cv2.imshow('Green Channel',g)
cv2.imshow('Red Channel',r)
img=cv2.merge((b,g,r))
cv2.imshow('Merged Output',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

The preceding program first splits the image into three channels (blue, green, and red) and then displays each one of them. The separate channels will only hold the intensity values of that color, and they will be essentially displayed as grayscale intensity images. Then...