Preface

Now in its third edition, this is the original book on OpenCV's Python bindings. Readers will learn a great range of techniques and algorithms, from the classics to the state-of-the-art, and from geometry to machine learning. All of this is in aid of solving practical computer vision problems in well-built applications. Using OpenCV 4 and Python 3, we adopt an approach that is accessible to computer vision novices, yet also informative for experts who want to expand and update their skills.

We start with an introduction to OpenCV 4 and explain how to set it up with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. You'll learn about image processing and video analysis, along with depth estimation and segmentation, and you'll gain practice by building a simple GUI application. Next, you'll tackle two popular problems: face detection and face recognition.

As we advance, we'll explore concepts of object classification and machine learning, enabling you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Then, we'll extend our work into 3D tracking and augmented reality. Finally, we'll learn about artificial neural networks (ANNs) and deep neural networks (DNNs) as we develop applications to recognize handwritten digits, and to classify a person's gender and age.

By the end of this book, you will have acquired the right knowledge and skills to embark on your own real-world computer vision projects.

Who this book is for

This book is intended for people interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications. The book will appeal to computer vision novices as well as experts who want to get up to date with OpenCV 4 and Python 3. Readers should be familiar with basic Python programming, but no prior knowledge of image processing, computer vision, or machine learning is required.

What this book covers

Chapter 1, Setting Up OpenCV, explains how to set up OpenCV 4 with Python 3 on various platforms. It also provides troubleshooting steps for common problems.

Chapter 2, Handling Files, Cameras, and GUIs, introduces OpenCV's I/O functionalities. It also discusses an object-oriented design for a GUI project that we will develop further in other chapters.

Chapter 3, Processing Images with OpenCV, presents some techniques required to alter images, such as manipulating colors, sharpening an image, marking contours of objects, and detecting geometric shapes.

Chapter 4, Depth Estimation and Segmentation, shows you how to use data from a depth camera to identify foreground and background regions, such that we can limit an effect to only the foreground or background.

Chapter 5, Detecting and Recognizing Faces, introduces some of OpenCV's functionality for face detection and recognition, along with the data files that define particular types of detectable objects.

Chapter 6, Retrieving Images and Searching Using Image Descriptors, shows how to describe the features of an image with the help of OpenCV, and how to make use of features to match and search for images.

Chapter 7, Building Custom Object Detectors, applies a combination of computer vision and machine learning algorithms to locate and classify objects in an image. It shows how to implement this combination of algorithms with OpenCV.

Chapter 8, Tracking Objects, demonstrates ways to track and predict the motion of people and objects in a video or live camera feed.

Chapter 9, Camera Models and Augmented Reality, enables you to build an augmented reality application that uses information about cameras, objects, and motion to superimpose 3D graphics atop tracked objects in real time.

Chapter 10, Introduction to Neural Networks with OpenCV, introduces you to artificial neural networks (ANNs) and deep neural networks (DNNs) in OpenCV, and illustrates their usage in real-world applications.

Appendix A, Bending Color Space with a Curves Filter, describes the concept of color curves and our implementation of them using SciPy.

To get the most out of this book

The reader is expected to have at least basic proficiency in the Python programming language.

A Windows, macOS, or Linux development machine is recommended. You can refer to Chapter 1, Setting Up OpenCV, for instructions about setting up OpenCV 4, Python 3, and other dependencies.

This book takes a hands-on approach to learning and includes 77 sample scripts, along with sample data. Working through these examples as you read the book will help enforce the concepts.

The code for this book is released under the BSD 3-Clause open source license, which is the same as the license used by OpenCV itself. The reader is encouraged to use, modify, improve, and even publish their changes to these example programs.

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.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. 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/Learning-OpenCV-4-Computer-Vision-with-Python-Third-Edition. 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!

Code in Action

Code in Action videos for this book can be viewed at http://bit.ly/2STXnRN.

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://static.packt-cdn.com/downloads/9781789531619_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: "OpenCV provides the VideoCapture and VideoWriter classes, which support various video file formats."

A block of code is set as follows:

import cv2

grayImage = cv2.imread('MyPic.png', cv2.IMREAD_GRAYSCALE)
cv2.imwrite('MyPicGray.png', grayImage)

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

import cv2

cameraCapture = cv2.VideoCapture(0)
fps = 30  # An assumption
size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
        int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
videoWriter = cv2.VideoWriter(
    'MyOutputVid.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)

In general, command-line input or output is written as follows:

$ pip install opencv-contrib-python

Alternatively, for Windows, command-line input or output may be written as follows:

> pip install opencv-contrib-python

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Now, under System variables, select Path and click on the Edit... button."

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 [e-mail].

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