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

Accessing the webcam

We can build very interesting applications using the live video stream from the webcam. OpenCV provides a video capture object which handles everything related to the opening and closing of the webcam. All we need to do is create that object and keep reading frames from it.

The following code will open the webcam, capture the frames, scale them down by a factor of 2, and then display them in a window. You can press the Esc key to exit:

import cv2 
cap = cv2.VideoCapture(0)
# Check if the webcam is opened correctly 
if not cap.isOpened(): 
    raise IOError("Cannot open webcam") 
while True: 
    ret, frame = 
    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) 
    cv2.imshow('Input', frame) 
    c = cv2.waitKey(1) 
    if c == 27: 

Under the hood

As we can see in the preceding code, we use OpenCV's VideoCapture function to create the video capture object cap. Once it...