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:

1. Some basic knowledge of Python programming is assumed as all the scripts and examples in this book are in Python.
2. 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

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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.

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