Howse / Minichino Learning OpenCV 5 Computer Vision with Python

Build practical computer vision and machine learning applications with OpenCV 5 and Python 3
1. Auflage 2026
ISBN: 978-1-80323-425-0
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 534 Seiten

Computer vision is a fast-moving field of artificial intelligence, powering applications that need to understand images, video, objects, faces, motion, depth, and 3D environments. Learning OpenCV 5 Computer Vision with Python gives you a practical path to building these systems with OpenCV 5 and Python 3.

You will begin by setting up OpenCV, understanding what is new in OpenCV 5, and working with images, video, camera feeds, files, and graphical interfaces. From there, you will learn essential image-processing techniques, depth estimation and segmentation, face detection and recognition, and image retrieval using visual descriptors. As your skills develop, you will build custom object detectors, classify and track moving objects, and explore camera models, augmented reality, 3D reconstruction, and navigation. You will also work with machine learning and neural networks, applying them to practical vision tasks before learning how computer vision solutions can be deployed to the cloud and scaled beyond local experimentation.

Throughout the book, hands-on Python examples help you connect core concepts with working implementations. By the end, you will have the practical skills to design, build, and extend real-world computer vision applications with OpenCV 5 across desktop and cloud-based workflows.
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Autoren/Hrsg.

Howse, Joseph

Minichino, Joe

Leseproben

Preface

Now in its fourth 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 5 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 5 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 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 5 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

explains how to set up OpenCV 5 with Python 3 on various platforms. It also provides troubleshooting steps for common problems.

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.

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

shows you how to use data from a depth camera to identify foreground and background regions, so that we can limit an effect to only the foreground or background.

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

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.

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.

demonstrates ways to track and predict the motion of people and objects in a video or live camera feed.

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.

shows how to measure motion using input from a depth camera. It also introduces the use of point clouds and segmentation techniques to represent objects in 3D scenes.

introduces you to artificial neural networks (ANNs) and deep neural networks (DNNs) in OpenCV, and illustrates their usage in real-world applications.

shows how to package OpenCV applications and deploy them to a server, making them available to the public even at a large scale.

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 , , for instructions about setting up OpenCV 5, Python 3, and other dependencies.

This book takes a hands-on approach to learning and includes 104 sample scripts, along with sample data. Working through these examples as you read the book will help reinforce the concepts.

The code for this book is released under the BSD 3-Clause open source license. The reader is encouraged to use, modify, improve, and even publish their changes to these example programs. However, be aware that our third-party dependencies such as libraries, datasets, and AI/ML models may have different licenses than our sample code itself.

Download the example code files

This book includes a complete downloadable code bundle containing all the example projects and files used throughout the chapters. We recommend downloading the bundle so you can follow along smoothly and experiment with the examples.

Use the bundle as a practical starting point. Modify it, extend it, and apply what you learn by creating your own variations as you progress through the chapters.

Get the code bundle

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3. Find this book and click Download Code

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Usage note: You're free to use and modify this code for personal learning and non-commercial projects.

Disclaimer on images

Some images in this title are presented for contextual purposes, and the readability of the graphic is not crucial to the discussion. Please refer to our free graphic bundle to download the images.

Download the color images

Your purchase includes a color, DRM-free PDF copy of this book, ideal for viewing color images, screenshots, and diagrams. Refer to at the end of the to unlock your PDF copy.

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781803230221.

Conventions used

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

: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: "Note that we allow kernel sizes to be specified as arguments for . The argument is used as for , while is used as for ."

A block of code is set as follows:

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

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: "NumPy has a fast Fourier transform (FFT) package, which contains the method. This method allows us to compute a discrete Fourier transform (DFT) of the image."

or important notes appear like this.

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