Raspberry Pi 3 Cookbook for Python Programmers

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By : Steven Lawrence Fernandes, Tim Cox

Buy this Book

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By: Steven Lawrence Fernandes, Tim Cox

Buy this Book

Overview of this book

Raspberry Pi 3 Cookbook for Python Programmers – Third Edition begins by guiding you through setting up Raspberry Pi 3, performing tasks using Python 3.6, and introducing the first steps to interface with electronics. As you work through each chapter, you will build your skills and apply them as you progress. You will learn how to build text classifiers, predict sentiments in words, develop applications using the popular Tkinter library, and create games by controlling graphics on your screen. You will harness the power of a built in graphics processor using Pi3D to generate your own high-quality 3D graphics and environments. You will understand how to connect Raspberry Pi’s hardware pins directly to control electronics, from switching on LEDs and responding to push buttons to driving motors and servos. Get to grips with monitoring sensors to gather real-life data, using it to control other devices, and viewing the results over the internet. You will apply what you have learned by creating your own Pi-Rover or Pi-Hexipod robots. You will also learn about sentiment analysis, face recognition techniques, and building neural network modules for optical character recognition. Finally, you will learn to build movie recommendations system on Raspberry Pi 3.

Title Page

Dedication

Packt Upsell

Contributors

Preface

Free Chapter

Getting Started with a Raspberry Pi 3 Computer

Introduction

Connecting to Raspberry Pi

Using NOOBS to set up your Raspberry Pi SD card

Networking and connecting your Raspberry Pi to the internet via an Ethernet port, using a CAT6 Ethernet cable

Using built-in Wi-Fi and Bluetooth on Raspberry Pi

Configuring your network manually

Networking directly to a laptop or computer

Networking and connecting your Raspberry Pi to the internet via a USB Wi-Fi dongle

Connecting to the internet through a proxy server

Connecting remotely to Raspberry Pi over the network using VNC

Connecting remotely to Raspberry Pi over the network using SSH (and X11 forwarding)

Sharing the home folder of Raspberry Pi with SMB

Keeping Raspberry Pi up to date

Dividing Text Data and Building Text Classifiers

Introduction

Building a text classifier

Pre-processing data using tokenization

Stemming text data

Dividing text using chunking

Building a bag-of-words model

Applications of text classifiers

Using Python for Automation and Productivity

Introduction

Using Tkinter to create graphical user interfaces

Creating a graphical application – Start menu

Displaying photo information in an application

Organizing your photos automatically

Predicting Sentiments in Words

Building a Naive Bayes classifier

Logistic regression classifier

Splitting the dataset for training and testing

Evaluating the accuracy using cross-validation

Analyzing the sentiment of a sentence

Identifying patterns in text using topic modeling

Applications of sentiment analysis

Creating Games and Graphics

Introduction

Using IDLE3 to debug your programs

Drawing lines using a mouse on Tkinter Canvas

Creating a bat and ball game

Creating an overhead scrolling game

Detecting Edges and Contours in Images

Introduction

Loading, displaying, and saving images

Blurring and sharpening images

Detecting edges in images

Histogram equalization

Detecting corners in images

Creating 3D Graphics

Introduction

Getting started with 3D coordinates and vertices

Creating and importing 3D models

Creating a 3D world to explore

Building 3D maps and mazes

Building Face Detector and Face Recognition Applications

Introduction

Building a face detector application

Building a face recognition application

Applications of a face recognition system

Using Python to Drive Hardware

Introduction

Controlling an LED

Responding to a button

A controlled shutdown button

The GPIO keypad input

Multiplexed color LEDs

Writing messages using persistence of vision

Sensing and Displaying Real-World Data

Introduction

Using devices with the I2C bus

Reading analog data using an analog-to-digital converter

Logging and plotting data

Extending the Raspberry Pi GPIO with an I/O expander

Capturing data in an SQLite database

Viewing data from your own webserver

Sensing and sending data to online services

Building Neural Network Modules for Optical Character Recognition

Introduction

Visualizing optical characters

Building an optical character recognizer using neural networks

Applications of an OCR system

Building Robots

Introduction

Building a Rover-Pi robot with forward driving motors

Using advanced motor control

Building a six-legged Pi-Bug robot

Controlling servos directly with ServoBlaster

Using an infrared remote control with your Raspberry Pi

Avoiding objects and obstacles

Getting a sense of direction

Interfacing with Technology

Introduction

Automating your home with remotely controlled electrical sockets

Using SPI to control an LED matrix

Communicating using a serial interface

Controlling Raspberry Pi using Bluetooth

Controlling USB devices

Can I Recommend a Movie for You?

Introduction

Computing the Euclidean distance score

Computing a Pearson correlation score

Finding similar users in the dataset

Developing a movie recommendation module

Applications of recommender systems

Hardware and Software List

Introduction

General component sources

The hardware list

The software list

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Histogram equalization

Histogram equalization is used to enhance the visibility and the contrast of images. It is performed by varying the image intensities. These procedures are clearly described here.

How to do it…

Import the necessary packages:

import sys 
import cv2 
import numpy as np

Load the input image:

in_file = sys.argv[1] 
image = cv2.imread(in_file)

Convert the RGB image into grayscale:

image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
cv2.imshow('Input grayscale image', image_gray)

Regulate the histogram of the grayscale image:

image_gray_histoeq = cv2.equalizeHist(image_gray) 
cv2.imshow('Histogram equalized - grayscale image', image_gray_histoeq)

Regulate the histogram of the RGB image:

image_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV) 
image_yuv[:,:,0] = cv2.equalizeHist(image_yuv[:,:,0]) 
image_histoeq = cv2.cvtColor(image_yuv, cv2.COLOR_YUV2BGR)

Display the output image:

cv2.imshow('Input image', image) 
cv2.imshow('Histogram equalized - color image', image_histoeq) 
cv2.waitKey...

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By : Steven Lawrence Fernandes, Tim Cox

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By: Steven Lawrence Fernandes, Tim Cox

Overview of this book

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Histogram equalization

How to do it…