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

Good features to track

The Harris Corner Detector performs well in many cases, but it misses out on a few things. Around six years after the original paper by Harris and Stephens, Shi and Tomasi came up with a better corner detector. You can read the original paper at J. Shi and C.Tomasi used a different scoring function to improve the overall quality. Using this method, we can find the N strongest corners in the given image. This is very useful when we don't want to use every single corner to extract information from the image.

If you apply the Shi-Tomasi Corner Detector to the image shown earlier, you will see something like this:

The following is the code:

import cv2 
import numpy as np 

img = cv2.imread('images/box.png') 
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 

corners = cv2.goodFeaturesToTrack(gray, maxCorners=7, qualityLevel=0.05, minDistance=25) 
corners = np.float32(corners) 

for item in corners: 
    x, y = item[0...