Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Mastering OpenCV 4 with Python

A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Alberto Fernández Villán

BIRMINGHAM - MUMBAI

Mastering OpenCV 4 with Python

Copyright © 2019 Packt Publishing

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 author, 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 Tripathi

Acquisition Editor: Alok Dhuri

Content Development Editor: Manjusha Mantri

Technical Editor: Riddesh Dawne

Copy Editor: Safis Editing

Project Coordinator: Prajakta Naik

Proofreader: Safis Editing

Indexer: Rekha Nair

Graphics: Jisha Chirayil

Production Coordinator: Shraddha Falebhai

First published: March 2019

Production reference: 1280319

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-78934-491-2

www.packtpub.com

mapt.io

Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.

Why subscribe?

Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals

Improve your learning with Skill Plans built especially for you

Get a free eBook or video every month

Mapt is fully searchable

Copy and paste, print, and bookmark content

Packt.com

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.packt.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at [email protected] for more details.

At www.packt.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks. 

Contributors

About the author

Alberto Fernández Villán is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various projects related to monitoring systems for industrial plants, applying both Internet of Things (IoT) and big data technologies. He has a Ph.D. in computer vision (2017), a deep learning certification (2018), and several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. As of 2013, he is a registered and active user (albertofernandez) on the Q&A OpenCV forum.

About the reviewers

Wilson Choo is a computer vision engineer working on validating computer vision and deep learning algorithms on many different hardware configurations. His strongest skills include algorithm benchmarking, integration, app development, and test automation. 

He is also a machine learning and computer vision enthusiast. He often researches trending CVDL algorithms and applies them to solve modern-day problems. Besides that, Wilson likes to participate in hackathons, where he showcases his ideas and competes with other developers. His favorite programming languages are Python and C++.

Vincent Kok is a maker and a software platform application engineer in the transportation industry. He graduated from USM with a MSc in embedded system engineering. Vincent actively involves himself with the developer community, as well as attending Maker Faire events held around the world, such as in Shenzhen in 2014, and in Singapore and Tokyo in 2015. Designing electronics hardware kits and giving soldering/Arduino classes for beginners are some of his favorite ways to spend his free time. Currently, his focus is in computer vision technology, software test automation, deep learning, and constantly keeping himself up to date with the latest technology.

Rubén Usamentiaga is a tenured associate professor in the department of computer science and engineering at the University of Oviedo. He received his M.S. and Ph.D. degrees in computer science from the University of Oviedo in 1999 and 2005, respectively. He has participated in 4 European projects, 3 projects of the National R&D Plan, 2 projects of the Regional Plan of the Principado of Asturias, and 14 contracts with companies. He is the author of more than 60 publications in JCR journals (25 of Q1) and more than 50 publications in international conferences. In addition, he has completed a 6-month research stay at the Aeronautical Technology Center and a 3-month research stay at the University of Laval in Quebec. 

Arun Ponnusamy, works as a computer vision research engineer at an AI start-up in India. He is a lifelong learner, passionate about image processing, computer vision, and machine learning. He is an engineering graduate from PSG College of Technology, Coimbatore. He started his career at MulticoreWare Inc., where he spent most of his time on image processing, OpenCV, software optimization, and GPU computing.

Arun loves to understand computer vision concepts clearly and explain them in an intuitive way in his blog and in meetups. He has created an open source Python library for computer vision, named cvlib, which is aimed at simplicity and user friendliness. He is currently working on object detection, action recognition, and generative networks.

Packt is searching for authors like you

If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.

Table of Contents

Title Page

Copyright and Credits

Mastering OpenCV 4 with Python

About Packt

Why subscribe?

Packt.com

Contributors

About the author

About the reviewers

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

Get in touch

Reviews

Section 1: Introduction to OpenCV 4 and Python

Setting Up OpenCV

Technical requirements

Code testing specifications

Hardware specifications

Understanding Python

Introducing OpenCV

Contextualizing the reader

A theoretical introduction to the OpenCV library

OpenCV modules

OpenCV users

OpenCV applications

Why citing OpenCV in your research work

Installing OpenCV, Python, and other packages

Installing Python, OpenCV, and other packages globally

Installing Python

Installing Python on Linux

Installing Python on Windows

Installing OpenCV

Installing OpenCV on Linux

Installing OpenCV on Windows

Testing the installation

Installing Python, OpenCV, and other packages with virtualenv

Python IDEs to create virtual environments with virtualenv

Anaconda/Miniconda distributions and conda package–and environment-management system 

Packages for scientific computing, data science, machine learning, deep learning, and computer vision

Jupyter Notebook

Trying Jupiter Notebook online 

Installing the Jupyter Notebook

Installing Jupyter using Anaconda

Installing Jupyter with pip

The OpenCV and Python project structure

Our first Python and OpenCV project

Summary

Questions

Further reading

Image Basics in OpenCV

Technical requirements

A theoretical introduction to image basics

Main problems in image processing

Image-processing steps

Images formulation

Concepts of pixels, colors, channels, images, and color spaces

File extensions

The coordinate system in OpenCV

Accessing and manipulating pixels in OpenCV

Accessing and manipulating pixels in OpenCV with BGR images

Accessing and manipulating pixels in OpenCV with grayscale images

BGR order in OpenCV

Summary

Questions

Further reading

Handling Files and Images

Technical requirements

An introduction to handling files and images

sys.argv

Argparse – command-line option and argument parsing

Reading and writing images

Reading images in OpenCV

Reading and writing images in OpenCV

Reading camera frames and video files

Reading camera frames

Accessing some properties of the capture object

Saving camera frames

Reading a video file

Reading from an IP camera

Writing a video file

Calculating frames per second

Considerations for writing a video file

Playing with video capture properties

Getting all the properties from the video capture object

Using the properties – playing a video backwards

Summary

Questions

Further reading

Constructing Basic Shapes in OpenCV

Technical requirements

A theoretical introduction to drawing in OpenCV

Drawing shapes

Basic shapes – lines, rectangles, and circles

Drawing lines

Drawing rectangles

Drawing circles

Understanding advanced shapes

Drawing a clip line

Drawing arrows

Drawing ellipses

Drawing polygons

Shift parameter in drawing functions

lineType parameter in drawing functions

Writing text

Drawing text

Using all OpenCV text fonts

More functions related to text

Dynamic drawing with mouse events

Drawing dynamic shapes

Drawing both text and shapes

Event handling with Matplotlib

Advanced drawing

Summary

Questions

Further reading

Section 2: Image Processing in OpenCV

Image Processing Techniques

Technical requirements

Splitting and merging channels in OpenCV

Geometric transformations of images

Scaling an image

Translating an image

Rotating an image

Affine transformation of an image

Perspective transformation of an image

Cropping an image

Image filtering

Applying arbitrary kernels

Smoothing images

Averaging filter

Gaussian filtering

Median filtering

Bilateral filtering

Sharpening images

Common kernels in image processing

Creating cartoonized images

Arithmetic with images

Saturation arithmetic

Image addition and subtraction

Image blending

Bitwise operations

Morphological transformations

Dilation operation

Erosion operation

Opening operation

Closing operation

Morphological gradient operation

Top hat operation

Black hat operation

Structuring element

Applying morphological transformations to images

Color spaces

Showing color spaces

Skin segmentation in different color spaces

Color maps

Color maps in OpenCV

Custom color maps

Showing the legend for the custom color map

Summary

Questions

Further reading

Constructing and Building Histograms

Technical requirements

A theoretical introduction to histograms

Histogram terminology

Grayscale histograms

Grayscale histograms without a mask

Grayscale histograms with a mask

Color histograms

Custom visualizations of histograms

Comparing OpenCV, NumPy, and Matplotlib histograms

Histogram equalization

Grayscale histogram equalization

Color histogram equalization

Contrast Limited Adaptive Histogram Equalization 

Comparing CLAHE and histogram equalization

Histogram comparison

Summary

Questions

Further reading

Thresholding Techniques

Technical requirements

Installing scikit-image

Installing SciPy

Introducing thresholding techniques

Simple thresholding 

Thresholding types

Simple thresholding applied to a real image

Adaptive thresholding

Otsu's thresholding algorithm

The triangle binarization algorithm

Thresholding color images

Thresholding algorithms using scikit-image

Introducing thresholding with scikit-image

Trying out more thresholding techniques with scikit-image

Summary

Questions

Further reading

Contour Detection, Filtering, and Drawing

Technical requirements

An introduction to contours

Compressing contours

Image moments

Some object features based on moments

Hu moment invariants

Zernike moments

More functionality related to contours

Filtering contours

Recognizing contours

Matching contours

Summary

Questions

Further reading

Augmented Reality

Technical requirements

An introduction to augmented reality

Markerless-based augmented reality

Feature detection

Feature matching

Feature matching and homography computation to find objects

Marker-based augmented reality

Creating markers and dictionaries

Detecting markers

Camera calibration

Camera pose estimation

Camera pose estimation and basic augmentation

Camera pose estimation and more advanced augmentation

Snapchat-based augmented reality

Snapchat-based augmented reality OpenCV moustache overlay

Snapchat-based augmented reality OpenCV glasses overlay

QR code detection

Summary

Questions

Further reading

Section 3: Machine Learning and Deep Learning in OpenCV

Machine Learning with OpenCV

Technical requirements

An introduction to machine learning

Supervised machine learning

Unsupervised machine learning

Semi-supervised machine learning

k-means clustering

Understanding k-means clustering

Color quantization using k-means clustering

k-nearest neighbor

Understanding k-nearest neighbors

Recognizing handwritten digits using k-nearest neighbor 

Support vector machine

Understanding SVM

Handwritten digit recognition using SVM

Summary

Questions

Further reading

Face Detection, Tracking, and Recognition

Technical requirements

Installing dlib

Installing the face_recognition package

Installing the cvlib package

Face processing introduction

Face detection

Face detection with OpenCV

Face detection with dlib

Face detection with face_recognition

Face detection with cvlib

Detecting facial landmarks

Detecting facial landmarks with OpenCV

Detecting facial landmarks with dlib

Detecting facial landmarks with face_recognition

Face tracking

Face tracking with the dlib DCF-based tracker

Object tracking with the dlib DCF-based tracker

Face recognition

Face recognition with OpenCV

Face recognition with dlib

Face recognition with face_recognition

Summary

Questions

Further reading

Introduction to Deep Learning

Technical requirements

Installing TensorFlow

Installing Keras

Deep learning overview for computer vision tasks

Deep learning characteristics

Deep learning explosion

Deep learning for image classification

Deep learning for object detection 

Deep learning in OpenCV

Understanding cv2.dnn.blobFromImage()

Complete examples using the OpenCV DNN face detector

OpenCV deep learning classification

AlexNet for image classification

GoogLeNet for image classification

ResNet for image classification

SqueezeNet for image classification

OpenCV deep learning object detection

MobileNet-SSD for object detection

YOLO for object detection

The TensorFlow library

Introduction example to TensorFlow

Linear regression in TensorFlow

Handwritten digits recognition using TensorFlow

The Keras library

Linear regression in Keras

Handwritten digit recognition in Keras

Summary

Questions

Further reading

Section 4: Mobile and Web Computer Vision

Mobile and Web Computer Vision with Python and OpenCV

Technical requirements

Installing the packages

Introduction to Python web frameworks

Introduction to Flask

Web computer vision applications using OpenCV and Flask

A minimal example to introduce OpenCV and Flask 

Minimal face API using OpenCV

Deep learning cat detection API using OpenCV

Deep learning API using Keras and Flask

Keras applications

Deep learning REST API using Keras Applications

Deploying a Flask application to the cloud

Summary

Questions

Further reading

Assessments

Chapter 1

Chapter 2 

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Other Books You May Enjoy

Leave a review - let other readers know what you think

Preface

In a nutshell, this book is about computer vision using OpenCV, which is a computer vision (and also machine learning) library, and the Python programming language. You may be wondering why OpenCV and Python? That is really a good question, which we address in the first chapter of this book. To summarize, OpenCV is the best open source computer vision library (BSD license—it is free for both academic and commercial use), offering more than 2,500 optimized algorithms, including state-of-the-art computer vision algorithms, and it also has machine learning and deep learning support. OpenCV is written in optimized C/C++, but it provides Python wrappers. Therefore, this library can be used in your Python programs. In this sense, Python is considered the ideal language for scientific computing because it stimulates rapid prototyping and has a lot of prebuilt libraries for every aspect of your computer vision projects. 

As introduced in the previous paragraph, there are many prebuilt libraries you can use in your projects. Indeed, in this book, we use lots of them, showing you that it's really easy to install and use new libraries. Libraries such as Matplotlib, scikit-image, SciPy, dlib, face-recognition, Pillow, cvlib, Keras, TensorFlow, and Flask will be used in this book to show you the potential of the Python ecosystem. If this is the first time that you're reading about these libraries, don't worry, because we introduce hello world examples for almost all of these libraries.

This book is a complete resource for creating advanced applications with Python and OpenCV using various techniques, such as facial recognition, target tracking, augmented reality, object detection, and classification, among others. In addition, this book

explores the potential of machine learning and deep learning techniques in computer vision applications using the Python ecosystem.

It's time to dive deeper into the content of this book. We are going to introduce you to what this book covers, including a short paragraph talking about each chapter of the book. So, let's get started!

Who this book is for

This book is great for students, researchers, and developers with basic Python programming knowledge who are new to computer vision and who would like to dive deeper into this world. It's assumed that readers have some previous experience with Python. A basic understanding of image data (for example, pixels and color channels) would also be helpful, but is not necessary, because these concepts are covered in the book. Finally, standard mathematical skills are required. 

What this book covers

Chapter 1, Setting Up OpenCV, shows how to install everything you need to start programming with Python and OpenCV. You'll also be introduced to general terminology and concepts to contextualize what you will learn, establishing and setting the bases in relation to the main concepts of computer vision using OpenCV.

Chapter 2, Image Basics in OpenCV, demonstrates how to start writing your first scripts, in order to introduce you to the OpenCV library.

Chapter 3, Handling Files and Images, shows you how to cope with files and images, which are necessary for building your computer vision applications.

Chapter 4, Constructing Basic Shapes in OpenCV, covers how to draw shapes—from basic ones to some that are more advanced—using the OpenCV library.

Chapter 5, Image Processing Techniques, introduces most of the common image processing techniques you will need for your computer vision projects.

Chapter 6, Constructing and Building Histograms, shows how to both create and understand histograms, which are a powerful tool for understanding image content.

Chapter 7, Thresholding Techniques, introduces the main thresholding techniques you will need for your computer vision applications as a key process of image segmentation.

Chapter 8, Contour Detection, Filtering, and Drawing, shows how to deal with contours, which are used for shape analysis and for both object detection and recognition.

Chapter 9, Augmented Reality, teaches you how to build your first augmented reality application.

Chapter 10, Machine Learning with OpenCV, introduces you to the world of machine learning. You will see how machine learning can be used in your computer vision projects.

Chapter 11, Face Detection, Tracking, and Recognition, demonstrates how to create face processing projects using state-of-the-art algorithms, in connection with face detection, tracking, and recognition.

Chapter 12, Introduction to Deep Learning, introduces you to the world of deep learning with OpenCV and also some deep learning Python libraries (TensorFlow and Keras).

Chapter 13, Mobile and Web Computer Vision with Python and OpenCV, shows how to create computer vision and deep learning web applications using Flask.

To get the most out of this book

With the aim of making the most of this book, you have to take into account two simple but key considerations:

Some basic knowledge of Python programming is assumed as all the scripts and examples in this book are in Python.

The NumPy and OpenCV-Python packages are highly interconnected (you will learn why in this book). In spite of NumPy examples being fully explained, the learning curve can be softened if some NumPy knowledge is acquired before starting this book.

Download the example code files

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

You can download the code files by following these steps:

Log in or register at www.packt.com.

Select the SUPPORT tab.

Click on Code Downloads & Errata.

Enter the name of the book in the Search box and follow the onscreen instructions.

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

WinRAR/7-Zip for Windows

Zipeg/iZip/UnRarX for Mac

7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mastering-OpenCV-4-with-Python. 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 at https://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/9781789344912_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: The code for build_sample_image() is provided next.

A block of code is set as follows:

channels = cv2.split(img)

    eq_channels = []

for ch in channels:

        eq_channels.append(cv2.equalizeHist(ch))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

Hu moments (original): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 2.06595472e-11]'

Hu moments (rotation): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 2.06595472e-11]'

Hu moments (reflection): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 -2.06595472e-11]'

Any command-line input or output is written as follows:

$ mkdir opencv-project

$ cd opencv-project

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: Select System info from the Administration panel.

Warnings or important notes appear like this.

Tips and tricks appear like this.

Get in touch

Feedback from our readers is always welcome.

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

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packt.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.

Reviews

Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!

For more information about Packt, please visit packt.com.

Section 1: Introduction to OpenCV 4 and Python

In this first section of the book, you will be introduced to the OpenCV library. You will learn how to install everything you need to start programming with Python and OpenCV. Also, you will familiarize yourself with the general terminology and concepts to contextualize what you will learn, establishing the foundations you will need in order to grasp the main concepts of this book. Additionally, you will start writing your first scripts in order to get to grips with the OpenCV library, and you will also learn how to work with files and images, which are necessary for building your computer vision applications. Finally, you will see how to draw basic and advanced shapes using the OpenCV library.

The following chapters will be covered in this section:

Chapter 1, Setting Up OpenCV

Chapter 2, Image Basics in OpenCV

Chapter 3, Handling Files and Images

Chapter 4, Constructing Basic Shapes in OpenCV

Setting Up OpenCV

Mastering OpenCV 4 with Python will give you the knowledge to build projects involving Open Source Computer Vision Library (OpenCV) and Python. These two technologies (the first one is a programming language, while the second one is a computer vision and machine learning library) will be introduced. Also, you will learn why the combination of OpenCV and Python has the potential to build every kind of computer application. Finally, an introduction about the main concepts related to the content of this book will be provided. 

In this chapter, you will be given step-by-step instructions to install everything you need to start programming with Python and OpenCV. This first chapter is quite long, but do not worry, because it is divided into easily assimilated sections, starting with general terminology and concepts, which assumes that the reader is new to this information. At the end of this chapter, you will be able to build your first project involving Python and OpenCV. 

The following topics will be covered in this chapter:

A theoretical introduction to the OpenCV library

Installing Python OpenCV and other packages

Running samples, documentation, help, and updates

Python and OpenCV project structure

First Python and OpenCV project

Technical requirements

This chapter and subsequent chapters are focused on Python (a programming language) and OpenCV (a computer vision library) concepts in connection with computer vision, machine learning, and deep learning techniques (among others). Therefore, Python (https://www.python.org/) and OpenCV (https://opencv.org/) should be installed on your computer. Moreover, some Python packages related to scientific computing and data science should also be installed (for example, NumPy (http://www.numpy.org/) or Matplotlib (https://matplotlib.org/)). 

Additionally, it is recommended that you install an integrated development environment (IDE) software package because it facilitates computer programmers with software development. In this sense, a Python-specific IDE is recommended. The de facto Python IDE is PyCharm, which can be downloaded from https://www.jetbrains.com/pycharm/. 

Finally, in order to facilitate GitHub activities (for example, cloning a repository), you should install a Git client. In this sense, GitHub provides desktop clients that include the most common repository actions. For an introduction to Git commands, check out https://education.github.com/git-cheat-sheet-education.pdf, where commonly used Git command-line instructions are summarized. Additionally, instructions for installing a Git client on your operating system are included. 

The GitHub repository for this book, which contains all the supporting project files necessary to work through the book from the first chapter to the last, can be accessed at https://github.com/PacktPublishing/Mastering-OpenCV-4-with-Python.

Finally, it should be noted that the README file of the GitHub repository for Mastering OpenCV with Python includes the following, which is also attached here for the sake of completeness:

Code testing specifications

Hardware specifications

Related books and products

Code testing specifications

Mastering OpenCV 4 with Python requires some installed packages, which you can see here:

Chapter 1, Setting Up OpenCV:opencv-contrib-python

Chapter 2, Image Basics in OpenCV:opencv-contrib-python and matplotlib

Chapter 3, Handling Files and Images:opencv-contrib-python and matplotlib

Chapter 4, Constructing Basic Shapes in OpenCV:opencv-contrib-python and matplotlib

Chapter 5, Image Processing Techniques:opencv-contrib-python and matplotlib

Chapter 6, Constructing and Building Histograms:opencv-contrib-python and matplotlib

Chapter 7, Thresholding Techniques: opencv-contrib-python, matplotlib, scikit-image, and scipy

Chapter 8, Contours Detection, Filtering, and Drawing:opencv-contrib-python and matplotlib

Chapter 9, Augmented Reality:opencv-contrib-python and matplotlib

Chapter 10, Machine Learning with OpenCV:opencv-contrib-python and matplotlib

Chapter 11, Face Detection, Tracking, and Recognition:opencv-contrib-python, matplotlib, dlib, face-recognition, cvlib, requests, progressbar, keras, and tensorflow

Chapter 12, Introduction to Deep Learning:opencv-contrib-python, matplotlib, tensorflow, and keras

Chapter 13, Mobile and Web Computer Vision with Python and OpenCV:opencv-contrib-python, matplotlib, flask, tensorflow, keras, requests, and pillow

Make sure that the version numbers of your installed packages are equal to, or greater than, versions specified here to ensure that the code examples run correctly.

If you want to install the exact versions this book was tested on, include the version when installing from pip, which is indicated as follows.

Run the following command to install the both main and contrib modules:

Install opencv-contrib-python:

pip install opencv-contrib-python==4.0.0.21

It should be noted that OpenCV requires numpy. numpy-1.16.1 has been installed when installing opencv-contrib-python==4.0.0.21.

Run the following command to install Matplotlib library:

Install matplotlib:

pip install matplotlib==3.0.2

It should be noted that matplotlib requires kiwisolver, pyparsing, six, cycler, and python-dateutil.

cycler-0.10.0, kiwisolver-1.0.1, pyparsing-2.3.1, python-dateutil-2.8.0, and six-1.12.0 have been installed when installing matplotlib==3.0.2.

Run the following command to install library which contains collections of algorithm for image processing:

Install scikit-image:

pip install scikit-image==0.14.2

It should be noted that scikit-image requires cloudpickle, decorator, networkx, numpy, toolz, dask, pillow, PyWavelets, and six.

PyWavelets-1.0.1, cloudpickle-0.8.0, dask-1.1.1, decorator-4.3.2, networkx-2.2, numpy-1.16.1, pillow-5.4.1, six-1.12.0, and toolz-0.9.0 have been installed when installing scikit-image==0.14.2.

If you need SciPy, you can install it with the following command:

Install scipy:

pip install scipy==1.2.1

It should be noted that scipy requires numpy.

numpy-1.16.1 has been installed when installing scipy==1.2.1.

Run the following command to install dlib library: 

Install dlib:

pip install dlib==19.8.1

To install the face recognition library, run the following command:

Install face-recognition:

pip install face-recognition==1.2.3

It should be noted that face-recognition requires dlib, Click, numpy, face-recognition-models, and pillow.

dlib-19.8.1, Click-7.0, face-recognition-models-0.3.0, and pillow-5.4.1 have been installed when installing face-recognition==1.2.3.

Run the following command to install open source computer vision library:

Install cvlib:

pip install cvlib==0.1.8

To install requests library run the following command:

Install requests:

pip install requests==2.21.0

It should be noted that requests requires urllib3, chardet, certifi, and idna.

urllib3-1.24.1, chardet-3.0.4, certifi-2018.11.29, and idna-2.8 have been installed when installing requests==2.21.0.

Run the following command to install text progress bar library:

Install progressbar:

pip install progressbar==2.5

Run the following command to install Keras library for deep learning:

Install keras:

pip install keras==2.2.4

It should be noted that keras requires numpy, six, h5py, keras-applications, scipy, keras-preprocessing, and pyyaml.

h5py-2.9.0, keras-applications-1.0.7, keras-preprocessing-1.0.9, numpy-1.16.1 pyyaml-3.13, and scipy-1.2.1 six-1.12.0 have been installed when installing keras==2.2.4.

Run the following command to install TensorFlow library: 

Install tensorflow:

pip install tensorflow==1.12.0

It should be noted that TensorFlow requires termcolor, numpy, wheel, gast, six, setuptools, protobuf, markdown, grpcio, werkzeug, tensorboard, absl-py, h5py, keras-applications, keras-preprocessing, and astor.

termcolor-1.1.0, numpy-1.16.1, wheel-0.33.1, gast-0.2.2, six-1.12.0, setuptools-40.8.0, protobuf-3.6.1, markdown-3.0.1, grpcio-1.18.0, werkzeug-0.14.1, tensorboard-1.12.2, absl-py-0.7.0, h5py-2.9.0, keras-applications-1.0.7, keras-preprocessing-1.0.9, and astor-0.7.1 have been installed when installing tensorflow==1.12.0.

Run the following command to install Flask library:

Install flask:

pip install flask==1.0.2

It should be noted that flask requires Werkzeug, click, itsdangerous, and MarkupSafe Jinja2.

Jinja2-2.10, MarkupSafe-1.1.1, Werkzeug-0.14.1, click-7.0, and itsdangerous-1.1.0 have been installed when installing flask==1.0.2.

Hardware specifications

The hardware specifications are as follows:

32-bit or 64-bit architecture

2+ GHz CPU

4 GB RAM

At least 10 GB of hard disk space available

Understanding Python

Python is an interpreted high-level and general-purpose programming language with a dynamic type system and automatic memory management. The official home of the Python programming language is https://www.python.org/. The popularity of Python has risen steadily over the