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

Colorspace based tracking

Frame differencing gives us some useful information, but we cannot use it to build anything meaningful. In order to build a good object tracker, we need to understand what characteristics can be used to make our tracking robust and accurate. So, let's take a step in that direction and see how we can use color spaces to come up with a good tracker. As we have discussed in previous chapters, HSV color space is very informative when it comes to human perception. We can convert an image to the HSV space, and then use color space thresholding to track a given object.

Consider the following frame in the video:

If you run it through the color space filter and track the object, you will see something like this:

As we can see here, our tracker recognizes a particular object in the video, based on the color characteristics. In order to use this tracker, we need to know the color distribution of our target object. The following is the code:

import cv2 
import numpy as np