Book Image

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By : Steven Lawrence Fernandes, Tim Cox
Book Image

Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

By: Steven Lawrence Fernandes, Tim Cox

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.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Index

Detecting corners in images


Corners are borders in images used to extract special features that infer the content of an image. Corner detection is frequently used in image registration, video tracking, image mosaics, motion detection, 3D modelling, panorama stitching, and object recognition.

How to do it...

  1. Import the necessary packages:
import sys 
import cv2 
import numpy as np 
  1. Load the input image:
in_file = sys.argv[1] 
image = cv2.imread(in_file) 
cv2.imshow('Input image', image) 
image_gray1 = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
image_gray2 = np.float32(image_gray1) 
  1. Implement the Harris corner detection scheme:
image_harris1 = cv2.cornerHarris(image_gray2, 7, 5, 0.04) 
  1. Dilate the input image and construct the corners:
image_harris2 = cv2.dilate(image_harris1, None) 
  1. Implement image thresholding:
image[image_harris2 > 0.01 * image_harris2.max()] = [0, 0, 0] 
  1. Display the input image:
cv2.imshow('Harris Corners', image) 
  1. Wait for the instruction from the operator:
cv2.waitKey() 
  1. The command...