Learning Opencv

Learning OpenCV 3
by Adrian Kaehler, Gary Bradski

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to “see” and make decisions based on that data.

With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.

This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.

  • Learn OpenCV data types, array types, and array operations
  • Capture and store still and video images with HighGUI
  • Transform images to stretch, shrink, warp, remap, and repair
  • Explore pattern recognition, including face detection
  • Track objects and motion through the visual field
  • Reconstruct 3D images from stereo vision
  • Discover basic and advanced machine learning techniques in OpenCV

Learning OpenCV 3
by Adrian Kaehler, Gary Bradski

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to “see” and make decisions based on that data.

With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.

This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.

  • Learn OpenCV data types, array types, and array operations
  • Capture and store still and video images with HighGUI
  • Transform images to stretch, shrink, warp, remap, and repair
  • Explore pattern recognition, including face detection
  • Track objects and motion through the visual field
  • Reconstruct 3D images from stereo vision
  • Discover basic and advanced machine learning techniques in OpenCV

Learning OpenCV
by Gary Bradski, Adrian Kaehler

“This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised.”-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data.

Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.

Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:

  • A thorough introduction to OpenCV
  • Getting input from cameras
  • Transforming images
  • Segmenting images and shape matching
  • Pattern recognition, including face detection
  • Tracking and motion in 2 and 3 dimensions
  • 3D reconstruction from stereo vision
  • Machine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.


Machine Learning for OpenCV
by Michael Beyeler

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.

About This Book

  • Load, store, edit, and visualize data using OpenCV and Python
  • Grasp the fundamental concepts of classification, regression, and clustering
  • Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide
  • Evaluate, compare, and choose the right algorithm for any task

Who This Book Is For

This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.

What You Will Learn

  • Explore and make effective use of OpenCV’s machine learning module
  • Learn deep learning for computer vision with Python
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and pedestrians
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and Deep Learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
  • Get to grips with data pre-processing and feature engineering

In Detail

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today’s most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.

OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.

Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today’s hottest topic in the field: Deep Learning.

By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!

Style and approach

OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.


Learning OpenCV 3 Application Development
by Samyak Datta

Build, create, and deploy your own computer vision applications with the power of OpenCV

About This Book

  • This book provides hands-on examples that cover the major features that are part of any important Computer Vision application
  • It explores important algorithms that allow you to recognize faces, identify objects, extract features from images, help your system make meaningful predictions from visual data, and much more
  • All the code examples in the book are based on OpenCV 3.1 – the latest version

Who This Book Is For

This is the perfect book for anyone who wants to dive into the exciting world of image processing and computer vision. This book is aimed at programmers with a working knowledge of C++. Prior knowledge of OpenCV or Computer Vision/Machine Learning is not required.

What You Will Learn

  • Explore the steps involved in building a typical computer vision/machine learning application
  • Understand the relevance of OpenCV at every stage of building an application
  • Harness the vast amount of information that lies hidden in images into the apps you build
  • Incorporate visual information in your apps to create more appealing software
  • Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
  • Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
  • Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition

In Detail

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++.

At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations.

Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!

The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

Style and approach

This book takes a very hands-on approach to developing an end-to-end application with OpenCV. To avoid being too theoretical, the description of concepts are accompanied simultaneously by the development of applications. Throughout the course of the book, the projects and practical, real-life examples are explained and developed step by step in sync with the theory.


Learning OpenCV 3 Computer Vision with Python
by Joe Minichino, Joseph Howse

Unleash the power of computer vision with Python using OpenCV

About This Book

  • Create impressive applications with OpenCV and Python
  • Familiarize yourself with advanced machine learning concepts
  • Harness the power of computer vision with this easy-to-follow guide

Who This Book Is For

Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what’s new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view.

What You Will Learn

  • Install and familiarize yourself with OpenCV 3’s Python API
  • Grasp the basics of image processing and video analysis
  • Identify and recognize objects in images and videos
  • Detect and recognize faces using OpenCV
  • Train and use your own object classifiers
  • Learn about machine learning concepts in a computer vision context
  • Work with artificial neural networks using OpenCV
  • Develop your own computer vision real-life application

In Detail

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV’s API will enable the development of all sorts of real-world applications, including security and surveillance.

Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.

Style and approach

This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.


Learning Image Processing with OpenCV
by Gloria Bueno García, Oscar Deniz Suarez, José Luis Espinosa Aranda, Jesus Salido Tercero, Ismael Serrano Gracia, Noelia Vállez Enano

If you are a competent C++ programmer and want to learn the tricks of image processing with OpenCV, then this book is for you. A basic understanding of image processing is required.

Learning OpenCV
by Gary Bradski, Adrian Kaehler

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data.

The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface.

Computer vision is everywhere—in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.

This book includes:

  • A thorough introduction to OpenCV
  • Getting input from cameras
  • Transforming images
  • Segmenting images and shape matching
  • Pattern recognition, including face detection
  • Tracking and motion in 2 and 3 dimensions
  • 3D reconstruction from stereo vision
  • Machine learning algorithms

Learn Computer Vision Using OpenCV
by Sunila Gollapudi

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.
What You Will Learn

  • Understand what computer vision is, and its overall application in intelligent automation systems
  • Discover the deep learning techniques required to build computer vision applications
  • Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy
  • Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis


Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.



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