Book Image

Getting Started with Python for the Internet of Things

By : Tim Cox, Steven Lawrence Fernandes, Sai Yamanoor, Srihari Yamanoor, Prof. Diwakar Vaish
Book Image

Getting Started with Python for the Internet of Things

By: Tim Cox, Steven Lawrence Fernandes, Sai Yamanoor, Srihari Yamanoor, Prof. Diwakar Vaish

Overview of this book

This Learning Path takes you on a journey in the world of robotics and teaches you all that you can achieve with Raspberry Pi and Python. It teaches you to harness the power of Python with the Raspberry Pi 3 and the Raspberry Pi zero to build superlative automation systems that can transform your business. You will learn to create text classifiers, predict sentiment in words, and develop applications with the Tkinter library. Things will get more interesting when you build a human face detection and recognition system and a home automation system in Python, where different appliances are controlled using the Raspberry Pi. With such diverse robotics projects, you'll grasp the basics of robotics and its functions, and understand the integration of robotics with the IoT environment. By the end of this Learning Path, you will have covered everything from configuring a robotic controller, to creating a self-driven robotic vehicle using Python. • Raspberry Pi 3 Cookbook for Python Programmers - Third Edition by Tim Cox, Dr. Steven Lawrence Fernandes • Python Programming with Raspberry Pi by Sai Yamanoor, Srihari Yamanoor • Python Robotics Projects by Prof. Diwakar Vaish
Table of Contents (37 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Detecting edges in images


Edge detection is used to detect the borders in images. It provides the details regarding the shape and the region properties. This includes perimeter, major axis size, and minor axis size.

How to do it...

  1. Import the necessary packages:
import sys 
import cv2 
import numpy as np 
  1. Read the input image:
in_file = sys.argv[1] 
image = cv2.imread(in_file, cv2.IMREAD_GRAYSCALE) 
  1. Implement the Sobel edge detection scheme:
horizontal_sobel = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5) 
vertical_sobel = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=5) 
laplacian_img = cv2.Laplacian(image, cv2.CV_64F) 
canny_img = cv2.Canny(image, 30, 200) 
  1. Display the input image and its corresponding output:
cv2.imshow('Original', image) 
cv2.imshow('horizontal Sobel', horizontal_sobel) 
cv2.imshow('vertical Sobel', vertical_sobel) 
cv2.imshow('Laplacian image', laplacian_img) 
cv2.imshow('Canny image', canny_img) 
  1. Wait for the instruction from the operator:
cv2.waitKey() 
  1. Display the input image and the...