Raspberry Pi was developed as a low-cost single-board computer with the intention of promoting computer science education in schools. It also represents a welcome return to a simple and fun yet effective way to learn computer science and programming.
You can use Raspberry Pi to learn and implement concepts in computer vision.
With a $35 Raspberry Pi computer and a USB webcam, anyone can afford to become a pro in computer vision in no time and build a real-life computer vision application to impress friends and colleagues.
Chapter 1, Introduction to Computer Vision and Raspberry Pi, takes you through the introduction and initial setup of Raspberry Pi and computer vision.
Chapter 2, Working with Images, Webcams, and GUI, teaches you how to work with images, videos, and various cameras.
Chapter 3, Basic Image Processing, explores arithmetic and logical operations on images.
Chapter 4, Colorspaces, Transformations, and Thresholds, introduces you to colorspaces and conversions, which are then followed by a simple project. This chapter also explores geometric transformations and segmentation by thresholding.
Chapter 5, Let's Make Some Noise, teaches the basics of noise in digital images and low-pass filters. It also discussed their usage in the removal of noise from images.
Chapter 6, Edges, Circles, and Lines' Detection, explores high-pass filters and their applications. It also explores the detection of features like edges, circles, and lines.
Chapter 7, Image Restoration, Quantization, and Depth Map, explores image restoration by inpainting. It also teaches image segmentation, quantization, and depth maps.
Chapter 8, Histograms, Contours, Morphological Transformations, and Performance Measurement, introduces the readers to histograms and plotting. It explores the concepts of contours and morphological transformations on an image. It concludes with the basics of performance measurement and improvement.
Chapter 9, Real-life Computer Vision Applications, implements various real-life applications of computer vision using Raspberry Pi and a webcam.
Chapter 10, Introduction to SimpleCV, teaches the installation and usage of SimpleCV, a powerful yet simple computer vision library, and concludes with a few real-life projects.
The following hardware is recommended for maximum enjoyment:
The Raspberry Pi computer (Model B, B+, or Pi 2)
SD card (8 GB minimum)
5V 1A power supply
HDMI or VGA monitor
HDMI to VGA converter if a VGA monitor is used
Wired Internet connection
A keyboard and a mouse
A good quality webcam
A Pi Camera
A Windows computer/laptop with an embedded or external card reader
This book is intended for novices as well as seasoned Raspberry Pi and Python enthusiasts who would like to explore the area of computer vision. Readers with very little programming or coding/scripting experience can create wonderful image processing and computer vision applications with relatively few lines of code in Python.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: " We are going to learn about the linspace() function now."
A block of code is set as follows:
import picamera
import time
with picamera.PiCamera() as cam:
cam.resolution=(1024,768)
cam.start_preview()
time.sleep(5)
cam.capture('/home/pi/book/output/still.jpg')Any command-line input or output is written as follows:
>>> a**2 array([ 1, 9, 36, 81])
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Go to Enable Boot to Desktop/Scratch | Desktop."
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