Are you looking for the best books on OpenCV?… If yes, then this article is for you. In this article, you will find the Best OpenCV Books for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best OpenCV Books for Beginners and find the Best OpenCV Books for Beginners to Advanced according to your need.
In the previous article, I shared the Best Machine Learning Books for beginners to Advanced to read in 2022, you can go through the list and enjoy reading.
Best OpenCV Books for Beginners to Advanced to know in 2022
Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning
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.
- 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)
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
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.
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
Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection
This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you’ll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos.
In later chapters, you’ll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules.
By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
- Stay up-to-date with algorithmic design approaches for complex computer vision tasks
- Work with OpenCV’s most up-to-date API through various projects
- Understand 3D scene reconstruction and Structure from Motion (SfM)
- Study camera calibration and overlay augmented reality (AR) using the ArUco module
- Create CMake scripts to compile your C++ application
- Explore segmentation and feature extraction techniques
- Remove backgrounds from static scenes to identify moving objects for surveillance
- Work with new OpenCV functions to detect and recognize text with Tesseract
OpenCV 4 with Python Blueprints: Build creative computer vision projects with the latest version of OpenCV 4 and Python 3
This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks.
You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks.
By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.
- Generate real-time visual effects using filters and image manipulation techniques such as dodging and burning
- Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
- Learn feature extraction and feature matching to track arbitrary objects of interest
- Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques
- Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons
- Classify, localize, and detect objects with deep neural networks
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
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.
- 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
Machine Learning for OpenCV 4: Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn
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.
- Understand the core machine learning concepts for image processing
- Explore the theory behind machine learning and deep learning algorithm design
- Discover effective techniques to train your deep learning models
- Evaluate machine learning models to improve the performance of your models
- Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications
- Use OpenVINO with OpenCV 4 to speed up model inference
OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision applications with OpenCV and C++
OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you’ll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers.
ach recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs.
This book begins by setting up OpenCV, and explains how to manipulate pixels. You’ll understand how you can process images with classes and count pixels with histograms. You’ll also learn detecting, describing, and matching interest points.
As you advance through the chapters, you’ll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you’ll cover deep learning concepts such as face and object detection.
By the end of the book, you’ll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects
- Install and create a program using the OpenCV library
- Segment images into homogenous regions and extract meaningful objects
- Apply image filters to enhance image content
- Exploit image geometry to relay different views of a pictured scene
- Calibrate the camera from different image observations
- Detect people and objects in images using machine learning techniques
- Reconstruct a 3D scene from images
- Explore face detection using deep learning
Mastering OpenCV with Practical Computer Vision Projects
Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV’s new C++ interface before migrating from the C API to the C++ API.
Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you’re most interested in.
Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on.
Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.
- Perform Face Analysis including simple Face & Eye & Skin Detection, Fisherfaces Face Recognition, 3D Head Orientation, complex Facial Feature Tracking.
- Do Number Plate Detection and Optical Character Recognition (OCR) using Artificial Intelligence (AI) methods including SVMs and Neural Networks
- Learn Augmented Reality for desktop and iPhone or iPad using simple artificial markers or complex markerless natural images
- Generate a 3D object model by moving a plain 2D camera, using 3D Structure from Motion (SfM) camera reprojection methods
- Redesign desktop real-time computer vision applications to more suitable Android & iOS mobile apps
- Use simple image filter effects including cartoon, sketch, paint, and alien effects
- Execute Human-Computer Interaction with an XBox Kinect sensor using the whole body as a dynamic input
And here the list ends. So, these are the Best OpenCV Books for Beginners to Advanced. I will keep adding more Best OpenCV Books for Beginners to advanced to this list.
Conclusion
I hope these Best OpenCV Books for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.