Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

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...