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Home> Data> Augmented Reality> Mastering OpenCV 4 with Python
Mastering OpenCV 4 with Python
Mastering OpenCV 4 with Python

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

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Profile Icon Alberto Fernández Villán
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€36.99
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.4(10 Ratings)
PaperbackMar 2019532 pages1st Edition
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€8.98 €29.99
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Arrow left icon
Profile Icon Alberto Fernández Villán
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€36.99
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.4(10 Ratings)
PaperbackMar 2019532 pages1st Edition
eBook
€8.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.98 €29.99
Paperback
€36.99
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Renews at €18.99p/m

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Mastering OpenCV 4 with Python

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:

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Key benefits

  • Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4) and Python
  • Apply machine learning and deep learning techniques with TensorFlow and Keras
  • Discover the modern design patterns you should avoid when developing efficient computer vision applications

Description

OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language.In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras.By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.

Who is this book for?

This book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.

What you will learn

  • Handle files and images, and explore various image processing techniques
  • Explore image transformations, including translation, resizing, and cropping
  • Gain insights into building histograms
  • Brush up on contour detection, filtering, and drawing
  • Work with Augmented Reality to build marker-based and markerless applications
  • Work with the main machine learning algorithms in OpenCV
  • Explore the deep learning Python libraries and OpenCV deep learning capabilities
  • Create computer vision and deep learning web applications
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Publication date, Length, Edition, Language, ISBN-13
Publication date :Mar 29, 2019
Length:532 pages
Edition :1st
Language :English
ISBN-13 :9781789344912
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Table of Contents

19 Chapters
Section 1: Introduction to OpenCV 4 and PythonChevron down iconChevron up icon
Section 1: Introduction to OpenCV 4 and Python
Setting Up OpenCVChevron down iconChevron up icon
Setting Up OpenCV
Technical requirements
Understanding Python
A theoretical introduction to the OpenCV library
Installing OpenCV, Python, and other packages
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
The OpenCV and Python project structure
Our first Python and OpenCV project
Summary
Questions
Further reading
Image Basics in OpenCVChevron down iconChevron up icon
Image Basics in OpenCV
Technical requirements
A theoretical introduction to image basics
Concepts of pixels, colors, channels, images, and color spaces
The coordinate system in OpenCV
Accessing and manipulating pixels in OpenCV
BGR order in OpenCV
Summary
Questions
Further reading
Handling Files and ImagesChevron down iconChevron up icon
Handling Files and Images
Technical requirements
An introduction to handling files and images
Reading and writing images
Reading camera frames and video files
Writing a video file
Playing with video capture properties
Summary
Questions
Further reading
Constructing Basic Shapes in OpenCVChevron down iconChevron up icon
Constructing Basic Shapes in OpenCV
Technical requirements
A theoretical introduction to drawing in OpenCV
Drawing shapes
Writing text
Dynamic drawing with mouse events
Advanced drawing
Summary
Questions
Further reading
Section 2: Image Processing in OpenCVChevron down iconChevron up icon
Section 2: Image Processing in OpenCV
Image Processing TechniquesChevron down iconChevron up icon
Image Processing Techniques
Technical requirements
Splitting and merging channels in OpenCV
Geometric transformations of images
Image filtering
Arithmetic with images
Morphological transformations
Color spaces
Color maps
Summary
Questions
Further reading
Constructing and Building HistogramsChevron down iconChevron up icon
Constructing and Building Histograms
Technical requirements
A theoretical introduction to histograms
Grayscale histograms
Color histograms
Custom visualizations of histograms
Comparing OpenCV, NumPy, and Matplotlib histograms
Histogram equalization
Contrast Limited Adaptive Histogram Equalization
Comparing CLAHE and histogram equalization
Histogram comparison
Summary
Questions
Further reading
Thresholding TechniquesChevron down iconChevron up icon
Thresholding Techniques
Technical requirements
Introducing thresholding techniques
Simple thresholding
Adaptive thresholding
Otsu's thresholding algorithm
The triangle binarization algorithm
Thresholding color images
Thresholding algorithms using scikit-image
Summary
Questions
Further reading
Contour Detection, Filtering, and DrawingChevron down iconChevron up icon
Contour Detection, Filtering, and Drawing
Technical requirements
An introduction to contours
Compressing contours
Image moments
More functionality related to contours
Filtering contours
Recognizing contours
Matching contours
Summary
Questions
Further reading
Augmented RealityChevron down iconChevron up icon
Augmented Reality
Technical requirements
An introduction to augmented reality
Markerless-based augmented reality
Marker-based augmented reality
Snapchat-based augmented reality
QR code detection
Summary
Questions
Further reading
Section 3: Machine Learning and Deep Learning in OpenCVChevron down iconChevron up icon
Section 3: Machine Learning and Deep Learning in OpenCV
Machine Learning with OpenCVChevron down iconChevron up icon
Machine Learning with OpenCV
Technical requirements
An introduction to machine learning
k-means clustering
k-nearest neighbor
Support vector machine
Summary
Questions
Further reading
Face Detection, Tracking, and RecognitionChevron down iconChevron up icon
Face Detection, Tracking, and Recognition
Technical requirements
Face processing introduction
Face detection
Detecting facial landmarks
Face tracking
Face recognition
Summary
Questions
Further reading
Introduction to Deep LearningChevron down iconChevron up icon
Introduction to Deep Learning
Technical requirements
Deep learning overview for computer vision tasks
Deep learning in OpenCV
The TensorFlow library
The Keras library
Summary
Questions
Further reading
Section 4: Mobile and Web Computer VisionChevron down iconChevron up icon
Section 4: Mobile and Web Computer Vision
Mobile and Web Computer Vision with Python and OpenCVChevron down iconChevron up icon
Mobile and Web Computer Vision with Python and OpenCV
Technical requirements
Introduction to Python web frameworks
Introduction to Flask
Web computer vision applications using OpenCV and Flask
Deep learning API using Keras and Flask
Deploying a Flask application to the cloud
Summary
Questions
Further reading
AssessmentsChevron down iconChevron up icon
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
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Customer reviews

Top Reviews
Rating distribution
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.4
(10 Ratings)
5 star40%
4 star20%
3 star0%
2 star20%
1 star20%
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Top Reviews

Filter reviews by




ManésSep 18, 2020
Full star iconFull star iconFull star iconFull star iconFull star icon5
I am an Electronic Engineer but not a specialist in image processing. I purchased the book because know I need to work on image processing applied to some branches of research in electrical Engineering. I did really like the book since it is practical and accessible. It is possible to get introduced in the field of image processing as well as to dive into more complex applications including machine learning. The use of Python combined with examples makes the learning curve fast, and the reader can choose the level of the depth or the field of application more suitable to his/her needs.Therefore, I think this book can be used by anyone with a very basic knowledge of image processing or someone with expertise in the field that wants to get practical information for any specific field. I do recommend it.
Amazon Verified reviewAmazon
XolmaticJun 25, 2019
Full star iconFull star iconFull star iconFull star iconFull star icon5
Queria tener un libro que hablase de OpenCV 4.0 y este es sencillamente genial, esta estructurado de una manera correcta, con un avance en el temario que hace que sea muy facil su lectura. Ne gusta su maquetacion, contenido... espero que su autor se aventure a escribir mas obras relacionadas. Me parece muy recomendable.
Amazon Verified reviewAmazon
Valentine DvorovkinJul 31, 2020
Full star iconFull star iconFull star iconFull star iconFull star icon5
Very good book. I immediately downloaded all code, so I can see how it works.Very good explanation inside book text + WORKING CODE, that shows in OpenCV and Matplotlib - Images, Histograms - RGB and Gray, binarization, Thresholding methods and results + face detecting and deep learning with OpenCV, TensorFlow etc. If you are starter - highly recommend.
Amazon Verified reviewAmazon
Cody E.Mar 12, 2020
Full star iconFull star iconFull star iconFull star iconFull star icon5
I highly recommend this book especially if you have no experience with OpenCV. I bought this book to help with my capstone project for Computer Science. It has tons of insight and tries to work into ideas based on previous chapters. I was disappointed that it only uses PreTrained models for deep learning, but you can't expect everything in one book.
Amazon Verified reviewAmazon
Amazon CustomerMay 06, 2019
Full star iconFull star iconFull star iconFull star iconEmpty star icon4
One of the best
Amazon Verified reviewAmazon
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About the author

Profile icon Alberto Fernández Villán
Alberto Fernández Villán
Github icon
Alberto Fernndez Villn 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.
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