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

Loading and saving an image

OpenCV provides multiple ways of loading an image. Let's say we want to load a color image in grayscale mode, we can do that using the following piece of code:

import cv2
gray_img = cv2.imread('images/input.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow('Grayscale', gray_img)

Here, we are using the ImreadFlag, as cv2.IMREAD_GRAYSCALE, and loading the image in grayscale mode, although you may find more read modes in the official documentation.

You can see the image displayed in the new window. Here is the input image:

Following is the corresponding grayscale image:

We can save this image as a file as well:

cv2.imwrite('images/output.jpg', gray_img)

This will save the grayscale image as an output file named output.jpg. Make sure you get comfortable with reading, displaying, and saving images in OpenCV, because we will be doing this quite a bit during the course of this book.

Changing image format

We can save this image as a file as well, and change the original image format to PNG:

import cv2
img = cv2.imread('images/input.jpg')
cv2.imwrite('images/output.png', img, [cv2.IMWRITE_PNG_COMPRESSION])

The imwrite() method will save the grayscale image as an output file named output.png. This is done using PNG compression with the help of ImwriteFlag and cv2.IMWRITE_PNG_COMPRESSION. The ImwriteFlag allows the output image to change the format, or even the image quality.