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

Frame differencing

This is, possibly, the simplest technique we can use to see what parts of the video are moving. When we consider a live video stream, the difference between successive frames gives us a lot of information. The concept is fairly straightforward! We just take the difference between successive frames and display the differences.

If we move rapidly from left to right, we will see something like this:

As you can see from the previous image, only the moving parts in the video get highlighted. This gives us a good starting point to see what areas are moving in the video. Here is the code to do this:

import cv2 

# Compute the frame difference 
def frame_diff(prev_frame, cur_frame, next_frame): 
    # Absolute difference between current frame and next frame 
    diff_frames1 = cv2.absdiff(next_frame, cur_frame) 

    # Absolute difference between current frame and 
     # previous frame 
    diff_frames2 = cv2.absdiff(cur_frame, prev_frame) 

    # Return the result of bitwise 'AND...