Learn Opencv 4 5 With Python 3 7 By Examples
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Learn Opencv 4 5 With Python 3 7 By Examples
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Author : James Chen
language : en
Publisher: James Chen
Release Date :
Learn Opencv 4 5 With Python 3 7 By Examples written by James Chen and has been published by James Chen this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
What This Book is About When you searched for this book, you have already known the importance of the OpenCV/Python in the fields of computer vision, image processing and machine learning. This book begins with step-by-step instructions of installation as well as a simple Hello World, then gets into the OpenCV Basics, Image Processing, Object Detection and finally Machine Learning. Key Features Example for every topic, all the source codes are available in Github. Line by line explanation of the source codes. Focus mainly on implementation of algorithms, rather than mathematical theories. Whom This Book Is For This book is for people with a variety of computer programming levels, from those with very limited knowledge of computer vision to the experienced ones. The readers do not need to have previous experiences of Python/OpenCV. No matter you are a beginner or experienced programmer, as long as you want to learn OpenCV with Python, you will benefit from this book. Table of Contents 1. Introduction 1.1 What Is OpenCV 1.2 Whom This Book Is For 1.3 How to Get the Source Codes for This Book 1.4 Hardware Requirements and Software Versions 1.5 How This Book Is Organized 2. Installation 2.1 Install on Windows 2.2 Install Python on Ubuntu 2.3 Configure PyCharm and Install OpenCV 3. OpenCV Basics 3.1 Load and Display Images 3.2 Load and Display Videos 3.3 Display Webcam 3.4 Play Youtube Video 3.5 Image Fundamentals 3.6 Draw Shapes 3.7 Draw Texts 3.8 Draw an OpenCV-like Icon 4. User Interaction 4.1 Mouse Operations 4.2 Draw Circles with Mouse 4.3 Draw Polygon with Mouse 4.4 Crop an Image with Mouse 4.5 Input Values with Trackbars 5. Image Processing 5.1 Change Color Spaces 5.2 Resize, Crop and Rotate an Image 5.3 Adjust Contrast and Brightness of an Image 5.4 Adjust Hue, Saturation and Value 5.5 Blend Image 5.6 Bitwise Operation 5.7 Warp Image 5.8 Blur Image 5.9 Histogram 6. Object Detection 6.1 Canny Edge Detection 6.2 Dilation and Erosion 6.3 Shape Detection 6.4 Color Detection 6.5 Text Recognition with Tesseract 6.6 Human Detection 6.7 Face and Eye Detection 6.8 Remove Background 6.9 Blur Background 7. Machine Learning 7.1 K-Means Clustering 7.2 K-Nearest Neighbors 7.3 Support Vector Machine 7.4 Artificial Neural Network (ANN) About the Author Index
Learn Opencv With Python By Examples
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Author : James Chen
language : en
Publisher: James Chen
Release Date : 2023-03-27
Learn Opencv With Python By Examples written by James Chen and has been published by James Chen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-27 with Computers categories.
This book is a comprehensive guide to learning the basics of computer vision and machine learning using the powerful OpenCV library and the Python programming language. The book offers a practical, hands-on approach to learning the concepts and techniques of computer vision through practical examples. All codes in this book are available on Github. Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning, and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets demonstrating how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image-processing applications from scratch. Whether you are a beginner or an experienced programmer, this book provides a valuable resource for learning computer vision with OpenCV and Python. The clear and concise writing style makes it easy for readers to follow along, and the numerous examples ensure that readers can practice and apply what they have learned. By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence. This book is an excellent resource for anyone looking to learn computer vision and machine learning using the OpenCV library and Python programming language. Table of Contents 1. Introduction 1.1 About OpenCV 1.2 Target Audients of This Book 1.3 Source Codes for This Book 1.4 Hardware Requirements and Software Versions 1.5 How This Book Is Organized 2. Installation 2.1 Install on Windows 2.2 Install Python on Ubuntu 2.3 Configure PyCharm and Install OpenCV 3. OpenCV Basics 3.1 Load and Display Images 3.2 Load and Display Videos 3.3 Display Webcam 3.4 Image Fundamentals 3.5 Draw Shapes 3.6 Draw Texts 3.7 Draw an OpenCV-like Icon 4. User Interaction 4.1 Mouse Operations 4.2 Draw Circles with Mouse 4.3 Draw Polygon with Mouse 4.4 Crop an Image with Mouse 4.5 Input Values with Trackbars 5. Image Processing 5.1 Conversion of Color Spaces 5.2 Resize, Crop and Rotate an Image 5.3 Adjust Contrast and Brightness of an Image 5.4 Adjust Hue, Saturation and Value 5.5 Blend Image 5.6 Bitwise Operation 5.7 Warp Image 5.8 Blur Image 5.9 Histogram 6. Object Detection 6.1 Canny Edge Detection 6.2 Dilation and Erosion 6.3 Shape Detection 6.4 Color Detection 6.5 Text Recognition with Tesseract 6.6 Human Detection 6.7 Face and Eye Detection 6.8 Remove Background 6.9 Blur Background 7. Machine Learning 7.1 K-Means Clustering 7.2 K-Nearest Neighbors 7.3 Support Vector Machine 7.4 Artificial Neural Network (ANN) 7.5 Convolutional Neural Network (CNN) References About the Author
Learning Opencv 4 Computer Vision With Python 3
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Author : Joseph Howse
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-02-20
Learning Opencv 4 Computer Vision With Python 3 written by Joseph Howse and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-20 with Computers categories.
Updated for OpenCV 4 and Python 3, this book covers the latest on depth cameras, 3D tracking, augmented reality, and deep neural networks, helping you solve real-world computer vision problems with practical code Key Features 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 Book 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. 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) Who this book is 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.
Machine Learning For Opencv 4
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Author : Aditya Sharma
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-09-06
Machine Learning For Opencv 4 written by Aditya Sharma and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-06 with Computers categories.
A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4 Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practicesBook Description OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4. What you will learnUnderstand the core machine learning concepts for image processingExplore the theory behind machine learning and deep learning algorithm designDiscover effective techniques to train your deep learning modelsEvaluate machine learning models to improve the performance of your modelsIntegrate algorithms such as support vector machines and Bayes classifier in your computer vision applicationsUse OpenVINO with OpenCV 4 to speed up model inferenceWho this book is for This book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.
Learning Opencv 3 Computer Vision With Python
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Author : Joe Minichino
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-29
Learning Opencv 3 Computer Vision With Python written by Joe Minichino and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-29 with Computers categories.
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.
Machine Learning For Opencv
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Author : Michael Beyeler
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-14
Machine Learning For Opencv written by Michael Beyeler and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-14 with Computers categories.
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.
The Fast Tutorial To Learn Database Programming Using Python Gui With Access And Sql Server
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2020-01-15
The Fast Tutorial To Learn Database Programming Using Python Gui With Access And Sql Server written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Computers categories.
This book covers microsoft acces and SQL Server based GUI programming using pyqt. Intentionally designed for various levels of interest and ability of learners, this book is suitable for students, engineers, and even researchers in a variety of disciplines. No advanced programming experience is needed, and only a few school-level programming skill are needed. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In third chapter, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In fourth chapter, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables as well.
Learn Pyqt By Example A Quick Start Guide To Mysql And Sqlite Driven Programming
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-11-24
Learn Pyqt By Example A Quick Start Guide To Mysql And Sqlite Driven Programming written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-24 with Computers categories.
This book explains relational theory in practice, and demonstrates through two projects how you can apply it to your use of MySQL and SQLite databases. This book covers the important requirements of teaching databases with a practical and progressive perspective. This book offers the straightforward, practical answers you need to help you do your job. This hands-on tutorial/reference/guide to MySQL and SQLite is not only perfect for students and beginners, but it also works for experienced developers who aren't getting the most from both databases. In designing a GUI and as an IDE, you will make use Qt Designer. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In chapter three, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In chapter four, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables.
Mastering Opencv 4 With Python
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Author : Alberto Fernández Villán
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-29
Mastering Opencv 4 With Python written by Alberto Fernández Villán and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with Computers categories.
Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality. Key FeaturesDevelop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4) and PythonApply machine learning and deep learning techniques with TensorFlow and KerasDiscover the modern design patterns you should avoid when developing efficient computer vision applicationsBook 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. What you will learnHandle files and images, and explore various image processing techniquesExplore image transformations, including translation, resizing, and croppingGain insights into building histogramsBrush up on contour detection, filtering, and drawingWork with Augmented Reality to build marker-based and markerless applicationsWork with the main machine learning algorithms in OpenCVExplore the deep learning Python libraries and OpenCV deep learning capabilitiesCreate computer vision and deep learning web applicationsWho this book is 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.
Learning Opencv 3
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Author : Adrian Kaehler
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-14
Learning Opencv 3 written by Adrian Kaehler and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-14 with Computers categories.
"This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of computer vision sufficient to help readers use OpenCV effectively."--Preface.