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Key benefits

Build powerful computer vision applications in concise code with OpenCV 4 and Python 3

Learn the fundamental concepts of image processing, object classification, and 2D and 3D tracking

Train, use, and understand machine learning models such as Support Vector Machines (SVMs) and neural networks

Description

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and 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. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.

Who is this book for?

If you are interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications, then this book is for you. This OpenCV book will also be useful for anyone getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Although no prior knowledge of image processing, computer vision or machine learning is required, familiarity with basic Python programming is a must.

What you will learn

Install and familiarize yourself with OpenCV 4 s Python 3 bindings

Understand image processing and video analysis basics

Use a depth camera to distinguish foreground and background regions

Detect and identify objects, and track their motion in videos

Train and use your own models to match images and classify objects

Detect and recognize faces, and classify their gender and age

Build an augmented reality application to track an image in 3D

Work with machine learning models, including SVMs, artificial neural networks (ANNs), and deep neural networks (DNNs)

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Amazon verified reviews

Adam H Jones Dec 05, 2023

This book is good for an overall review, but if lacks detail about values it passes to function calls and why using one over another. I can read the help for the cv2, and this does not do much than more than that.

Amazon Verified review

TimeTraveller Mar 22, 2023

This is a good book. It really does engage the reader in a few pages. As basics are important, I want to focus on Chapter 2 and the pros and cons of the teaching approach. I do see, that the author has made it clear that familiarity in Python is required, but that still is not a reason to leave subtle but important things out.I will state a couple of examples here because I feel we may have to be prepared for this pattern of teaching in subsequent chapters.So, in Chapter 2 the lesson does a great job of explaining 8-bit vs. 24-bit, 3 channel images, but then when talking about converting images to and from from byteArrays, the lesson fails to mention certain detail.For example: When reshaping an image, the height comes first, before the widthAnother example: When creating images from random bytes, a 120000 random numbers are chosen. Why? Why not 12000? There is no justification. And further down the line, the array is reshaped back to a 400 x 300 Grayscale image, or a 400 x 100 color image, there is no explanation on why these numbers were chosen.I had to figure that out by running into a few failures. Then I could see that 400 x 300 is chosen because the product of these 2 numbers is 120000, and 400 x 100 was chosen because it stores 3 channel colors and therefore it had to be 1/3rd of the size of the Grayscale image (400 x 100 x 3 = 120000).Inference: You can reshape the array back to an image of any width x height as long as the two equations above work out. Trying to reshape a 120000 byte array into a 300 x 500 will not work!This section also misses out on bringing the image.shape method that would give the user insight into what the shape (rows x cols) of an array is so that it can be used in determining how to work with this array. Maybe it's discussed further in the book, but all of the above bits of information would have definitely helped new users learn the basics better.We may know Python or numpy, but that does not mean we know how images as bytes are processed. Which is one of the reasons why we purchased this book I suppose.That said, the overall approach is engaging and good. But you have to discover and infer a few basics yourself.

royland Apr 15, 2022

Thank you for the car detection information.Perhaps the License Plate detection and recognition section could be added?What do you think?

Amazon Customer Oct 12, 2021

extremely outdated

NORBERT VOSSIEK Aug 03, 2021

Das Buch ist kein OpenCV-4-Lehrbuch. Vielmehr entwickelt es nicht viel mehr als eine kleine Anwendung rund um das Face Tracking. Wie sich OpenCV 4 von der Version 3 unterscheidet wird auf einer Seite (Seite 9) von 351 Seiten erklärt.

Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning , Third Edition

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