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OpenCV 3 Computer Vision with Python Cookbook E-Book

Aleksei Spizhevoi

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Herausgeber: Packt Publishing
Kategorie: Fachliteratur
Sprache: Englisch

Beschreibung

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications.

In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis.

Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks.

By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.

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Seitenzahl: 249

Veröffentlichungsjahr: 2018

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OpenCV 3 Computer Vision with Python Cookbook

Leverage the power of OpenCV 3 and Python to build computer vision applications

Alexey Spizhevoy

Aleksandr Rybnikov

BIRMINGHAM - MUMBAI

OpenCV 3 Computer Vision with Python Cookbook

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Richa TripathiAcquisition Editor: Alok DhuriContent Development Editor: Rohit Kumar SinghTechnical Editor: Ketan KambleCopy Editor:Safis EditingProject Coordinator: Vaidehi SawantProofreader: Safis EditingIndexer: Tejal Daruwale SoniGraphics: Jason MonteiroProduction Coordinator: Aparna Bhagat

First published: March 2018

Production reference: 1200318

Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.

ISBN 978-1-78847-444-3

www.packtpub.com

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Contributors

About the authors

Alexey Spizhevoy has over 7 years of experience in computer vision R&D. He has worked for 5 years at Itseez, the main OpenCV contributor, before it was acquired by Intel. He has contributed to video stabilization and photo stitching modules into OpenCV library. He has successfully participated in numerous Computer Vision projects in such areas as 3D reconstruction, video conferencing, object detection and tracking, semantic segmentation, driving assistance, and others. He holds a master's degree in computer science, and he is currently pursuing PhD.

Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

Without the great teachers I had, this book wouldn't have been possible. So I want to express gratitude to my colleagues who guided and advised me on my path in Computer Vision. Writing a book, especially when you want to make it useful and practical, isn't simple in any way and takes a lot of time. Many thanks and love to my wife, Daria Tikhonova, who has supported me while working on the book.

About the reviewer

Joseph Howse lives in a Canadian fishing village with four cats; the cats like fish, but they prefer chicken.

Joseph provides computer vision expertise through his company, Nummist Media. He is a Packt author whose books include OpenCV for Secret Agents, OpenCV 3 Blueprints, Android Application Programming with OpenCV 3, iOS Application Development with OpenCV 3, Learning OpenCV 3 Computer Vision with Python, and Python Game Programming by Example.

I wish to congratulate this book's authors, who have contributed so much code and wisdom to the OpenCV community. I am also thankful to the whole team at Packt for our continued collaboration. As always, I dedicate my work to Sam, Jan, Bob, Bunny, and the cats.

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Title Page

OpenCV 3 Computer Vision with Python Cookbook

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the authors

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Sections

Getting ready

How to do it...

How it works...

There's more...

Preface

Computer Vision is a broad topic comprising a lot of different areas. If you want to start using Computer Vision algorithms in your projects, it may be ambiguous where the entry point is. Even if you're an experienced Computer Vision engineer, undoubtedly there are some technologies that you would want to explore in depth or get familiar with. In both cases, a practical approach works best. Only through applying methods to real problems, tuning existing methods to meet your requirements, and playing with samples can you fully understand the possibilities and limitations of any Computer Vision algorithm. This book is specifically designed to get your hands dirty with solving real computer vision tasks. Recipes in this book use OpenCV—the most popular, functionally rich, and widely used open source Computer Vision library. This book progresses from the simplest samples to the most complicated ones, so you will be able to find some useful and information which is easy to understand.

Who this book is for

This book is for developers who have basic knowledge of Python. If you are aware of the basics of OpenCV and are ready to build computer vision systems that are smarter, faster, more complex, and more practical than the competition, then this book is for you.

What this book covers

Chapter 1, I/O and GUI, teaches the basic operations with images and video: loading, saving and displaying.

Chapter 2, Matrices, Colors, and Filters, covers operations to manipulate with matrices: accessing regions of an image, channels, and pixels. Conversions between various color spaces and usage of filters are also described.

Chapter 3, Contours and Segmentation, shows how to create image masks, find contours, and segment images.

Chapter 4, Object Detection and Machine Learning, describes ways of detecting and tracking different types of objects, from specially constructed (QR codes and ArUCo markers) to ones that can be met in natural scenes.

Chapter 5, Deep Learning, outlines new functionality in OpenCV connected with Deep Neural Nets. It provides examples of loading Deep Learning models and applying them to Computer Vision tasks.

Chapter 6, Linear Algebra, dives into useful mathematical methods for solving linear algebra problems and provides examples of applying these methods in Computer Vision.

Chapter 7, Detectors and Descriptors, contains information about how to work with image feature descriptors: how to compute them with different methods, how to display them, and how to match them for object detection and tracking purposes.

Chapter 8, Image and Video Processing, shows readers how to work with image sequences and get results based on correlations among the sequence.

Chapter 9, Multiple View Geometry, describes how to use cameras to retrieve information about 3D geometry of the scene.

To get the most out of this book

All the required information to get started with the respective recipes is mentioned in the recipes.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

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Select the

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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/OpenCV-3-Computer-Vision-with-Python-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available athttps://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/OpenCV3ComputerVisionwithPythonCookbook_ColorImages.pdf.

Sections

In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).

To give clear instructions on how to complete a recipe, use these sections as follows:

Getting ready

This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.

How to do it...

This section contains the steps required to follow the recipe.

How it works...

This section usually consists of a detailed explanation of what happened in the previous section.

There's more...

This section consists of additional information about the recipe in order to make you more knowledgeable about the recipe.

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].

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I/O and GUI

In this chapter, we will cover the following recipes:

Reading images from file

Simple image transformations—resizing and flipping

Saving images using lossy and lossless compression

Showing images in an OpenCV window

Working with UI elements, such as buttons and trackbars, in an OpenCV window

Drawing 2D primitives—markers, lines, ellipses, rectangles, and text

Handling user input from a keyboard

Making your app interactive through handling user input from a mouse

Capturing and showing frames from a camera

Playing frame stream from video

Obtaining a frame stream properties

Writing a frame stream into video

Jumping between frames in video files

Introduction

Computer vision algorithms consume and produce data—they usually take images as an input and generate features of the input, such as contours, points or regions of interest, bounding boxes for objects, or another images. So dealing with the input and output of graphical information is an essential part of any computer vision algorithm. This means not only reading and saving images, but also displaying them with additional information about their features.

In this chapter, we will cover basic OpenCV functionality related to I/O functions. From the recipes, you will learn how to obtain images from different sources (either filesystem or camera), display them, and save images and videos. Also, the chapter covers the topic of working with the OpenCV UI system; for instance, in creating windows and trackbars.

Reading images from files

In this recipe, we will learn how to read images from files. OpenCV supports reading images in different formats, such as PNG, JPEG, and TIFF. Let's write a program that takes the path to an image as its first parameter, reads the image, and prints its shape and size.

Getting ready

You need to have OpenCV 3.x installed with Python API support.

How it works...

The loaded image is represented as a NumPy array. The same representation is used in OpenCV for matrices. NumPy arrays have such properties as shape, which is an image's size and number of color channels, and dtype, which is the underlying data type (for example, uint8 or float32). Note that OpenCV loads images in BGR, not RGB, format.

The shape tuple in this case should be interpreted as follows: image height, image width, color channels count.

The cv.imread function also supports optional flags, where users can specify whether conversion to uint8 type should be performed, and whether the image is grayscale or color.

Having run the code with the default parameters, you should see the following output:

read ../data/Lena.png

shape: (512, 512, 3)

dtype: uint8

read ../data/Lena.png as grayscale

shape: (512, 512)

dtype: uint8

Simple image transformations—resizing and flipping

Now we're able to load an image, it's time to do some simple image processing. The operations we're going to review—resize and flip—are basic and usually used as preliminary steps of complex computer vision algorithms.