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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

Alberto Fernández Villán

€18.99 per month

3.7 (12 Ratings)

Paperback Mar 2019 532 pages 1st Edition

€20.98 ~~€29.99~~

Renews at €18.99p/m

Alberto Fernández Villán

€18.99 per month

3.7 (12 Ratings)

Paperback Mar 2019 532 pages 1st Edition

€20.98 ~~€29.99~~

Renews at €18.99p/m

€20.98 ~~€29.99~~

Renews at €18.99p/m

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View table of contents

Preview Book

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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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Product Details

Publication date : Mar 29, 2019

Length: 532 pages

Edition : 1st

Language : English

ISBN-13 : 9781789344912

Vendor :

Intel

Category :

Data

Languages :

Concepts :

Augmented Reality

Tools :

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Table of Contents

19 Chapters

Section 1: Introduction to OpenCV 4 and Python

Section 1: Introduction to OpenCV 4 and Python

Setting Up OpenCV

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

Further reading

Image Basics in OpenCV

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

Further reading

Handling Files and Images

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

Further reading

Constructing Basic Shapes in OpenCV

Constructing Basic Shapes in OpenCV

Technical requirements

A theoretical introduction to drawing in OpenCV

Dynamic drawing with mouse events

Advanced drawing

Further reading

Section 2: Image Processing in OpenCV

Section 2: Image Processing in OpenCV

Image Processing Techniques

Image Processing Techniques

Technical requirements

Splitting and merging channels in OpenCV

Geometric transformations of images

Image filtering

Arithmetic with images

Morphological transformations

Further reading

Constructing and Building Histograms

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

Further reading

Thresholding Techniques

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

Further reading

Contour Detection, Filtering, and Drawing

Contour Detection, Filtering, and Drawing

Technical requirements

An introduction to contours

Compressing contours

More functionality related to contours

Filtering contours

Recognizing contours

Matching contours

Further reading

Augmented Reality

Augmented Reality

Technical requirements

An introduction to augmented reality

Markerless-based augmented reality

Marker-based augmented reality

Snapchat-based augmented reality

QR code detection

Further reading

Section 3: Machine Learning and Deep Learning in OpenCV

Section 3: Machine Learning and Deep Learning in OpenCV

Machine Learning with OpenCV

Machine Learning with OpenCV

Technical requirements

An introduction to machine learning

k-means clustering

k-nearest neighbor

Support vector machine

Further reading

Face Detection, Tracking, and Recognition

Face Detection, Tracking, and Recognition

Technical requirements

Face processing introduction

Detecting facial landmarks

Face recognition

Further reading

Introduction to Deep Learning

Introduction to Deep Learning

Technical requirements

Deep learning overview for computer vision tasks

Deep learning in OpenCV

The TensorFlow library

The Keras library

Further reading

Section 4: Mobile and Web Computer Vision

Section 4: Mobile and Web Computer Vision

Mobile and Web Computer Vision with Python and OpenCV

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

Further reading

Assessments

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About the author

Alberto Fernández Villán

Alberto Fernández Villán

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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