Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
OpenCV 3.x with Python By Example

You're reading from   OpenCV 3.x with Python By Example Make the most of OpenCV and Python to build applications for object recognition and augmented reality

Arrow left icon
Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788396905
Length 268 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Gabriel Garrido Calvo Gabriel Garrido Calvo
Author Profile Icon Gabriel Garrido Calvo
Gabriel Garrido Calvo
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Contributors
Packt Upsell
Preface
1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Seam Carving 7. Detecting Shapes and Segmenting an Image 8. Object Tracking 9. Object Recognition 10. Augmented Reality 11. Machine Learning by an Artificial Neural Network 1. Other Books You May Enjoy

Approximating a contour


A lot of contours that we encounter in real life are noisy. This means that the contours don't look smooth, and hence our analysis takes a hit. So, how do we deal with this? One way to go about this would be to get all the points on the contour and then approximate it with a smooth polygon.

Let's consider the boomerang image again. If you approximate the contours using various thresholds, you will see the contours changing their shapes. Let's start with a factor of 0.05:

If you reduce this factor, the contours will get smoother. Let's make it 0.01:

If you make it really small, say 0.00001, then it will look like the original image:

The following code represents how to convert those contours into approximate smoothing of polygons:

import sys 
import cv2 
import numpy as np 

if __name__=='__main__': 
    # Input image containing all the different shapes 
    img1 = cv2.imread(sys.argv[1]) 
    # Extract all the contours from the input image 
    input_contours = get_all_contours...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image