Best OpenCV books &#038; Best OpenCV courses in 2024

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Building Computer Vision Projects with OpenCV 4 and C++

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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

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Best OpenCV Courses 2022

Computer Vision Basics

Master Computer Vision OpenCV4 in Python with Deep Learning

Python for Computer Vision with OpenCV and Deep Learning

Best OpenCV Tutorials 2022

Computer Vision Basics

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By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.

This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).

Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.

Computer Vision

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Learn cutting-edge computer vision and deep learning techniques—from basic image processing, to building and customizing convolutional neural networks. Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects.

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

Master Computer Vision OpenCV4 in Python with Deep Learning

You have certainly heard of AI and Deep Learning. But when you ask yourself where do I stand in relation to this new industrial revolution, it might lead you to another fundamental question: am I a consumer or a designer? For most people these days, the answer would be, a consumer.

But what if you could also become a creator?

What if there was a way for you to easily enter the world of artificial intelligence and create amazing apps that take advantage of the latest technology to make the world a better place?

Sounds too good to be true, doesn’t it?

But there is actually a way.

Computer vision is by far the easiest way to become a designer.

And it’s not only the easiest way, it’s also the branch of AI where there is the most to build.

Why? You will ask.

It is because computer vision is applied everywhere. From healthcare to retail to entertainment, the list goes on. Computer vision is already an $ 18 billion market and growing exponentially.

Just think of tumor detection in MRI brain scans of patients. How many lives are saved every day just because a computer can analyze 10,000 times more images than a human?

What if you found an industry where computer vision is not yet applied? Well fine! This means that there is a business opportunity that you can take advantage of.

So now this begs the question: how do you enter the world of computer vision?

Until now, computer vision has been for the most part a maze. A growing labyrinth.

As the number of codes, libraries and tools in CV increases, it becomes more and more difficult not to get lost.

In addition to that, you should not only know how to use it, but also know how it works to maximize the benefits of computer vision.

To this problem we want to bring …

Computer vision A-Z.

With this brand new course, you will not only learn how the most popular computer vision methods work, but you will also learn how to apply them in practice!

You will learn:

Have a toolbox of the most powerful computer vision models
Understand the theory behind computer vision
OpenCV Master
Master object detection
Primary facial recognition
Create powerful computer vision applications

This is the best OpenCV course in 2022.

Master Computer Vision™ OpenCV4 in Python with Deep Learning

Python for Computer Vision with OpenCV and Deep Learning

Building Computer Vision Projects with OpenCV 4 and C++

Why learn computer vision in Python with OpenCV?

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of technology, billion dollar apps like Pokémon GO, Snapchat and upcoming apps like MSQRD and PRISMA.

Even Facebook, Google, Microsoft, Apple, Amazon and Tesla all use computer vision extensively for face and object recognition, image search and especially in self-driving cars!

As a result, the demand for computer vision expertise is increasing exponentially!

However, learning computer vision is difficult! Existing online tutorials, manuals, and free MOOCs are often outdated, use older incompatible libraries, or are too theoretical, making them difficult to understand.

This was my problem when learning computer vision and it got incredibly frustrating. Even just running sample code that I found online proved difficult because libraries and functions were often out of date.

I’ve created this course to teach you all the key concepts without the heavy math theory while using the most up-to-date methods.

I take a very hands-on approach, using over 50 code examples.

By the end of the course, you will be able to create 12 awesome computer vision applications using OpenCV in Python.

I am using OpenCV which is the best supported open source computer vision library out there today! Using it in Python is just fantastic because Python allows us to focus on the problem at hand without getting bogged down in complex code.

If you are a university or college student, I always point you in the right direction if you want to learn more by linking the research papers on the techniques we use.

So, if you want to get a great foundation in computer vision, look no further.

This is the course for you!

In this course, you will experience the power of OpenCV in Python and gain skills to dramatically increase your career prospects as a computer vision developer.

You benefit from over 3 hours of in-depth computer vision learning with Keras, which includes:

A free virtual machine with all Python Deep Learning libraries such as Keras and TensorFlow preinstalled

Detailed Explanation of Neural Networks and Convolutional Neural Networks

Understand how Keras works and how to use and create image datasets

Create a handwritten digit classifier

Create a multi-image classifier

Building a cats vs dogs classifier

Understand how to improve CNN performance using data augmentation

Extract and sort credit card numbers

You will learn

Understand and use OpenCV4 in Python
How to use Deep Learning with Keras and TensorFlow in Python
Create face detectors and recognizers and create your own advanced face permutations using DLIB
Object detection, motion tracking and analysis
Create augmented reality applications
Programming skills such as basic Python and Numpy
How to Use Computer Vision to Execute Cool Startup Ideas
Understanding Neural and Convolutional Neural Networks
Learn how to create simple image classifiers in Python
Learn how to create an OCR reader for credit cards
Learn How To Perform Neural Style Transfer Using OpenCV
Learn how to do multi-object detection in OpenCV (up to 90 objects!) Using SSD (single shot detector)
Learn how to convert black and white images to color using Caffe
Learn how to create Automatic License Plate Recognition (ALPR)
Learn the basics of computer vision and image processing

Best OpenCV Books 2022

Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection

Escrivá, David Millán (Author)
English (Publication Language)
538 Pages - 03/22/2019 (Publication Date) - Packt Publishing (Publisher)

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.

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.

What you will learn:

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

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Kaehler, Adrian (Author)
English (Publication Language)
1022 Pages - 02/07/2017 (Publication Date) - O'Reilly Media (Publisher)

Get started with the fast expanding field of computer philosophy with this simple guide. Written by Adrian Kehler and Gary Bradsky, creators of the open source OpenCV Library, the book provides a wide range of roles for developers, educators, robotics and hobbyists. You can learn what it takes to create applications that allow computers to “see” and make decisions based on that data. With more than 500 activities across many fields of philosophy, OpenCV is used for commercial applications such as protection, medical imaging, face and shape recognition, robotics and factory product inspections. This book gives you a solid foundation in computer vision and openness for creating simple or sophisticated vision applications. The practical exercises in each chapter help you apply what you have learned. This modern sculpture with machine learning tools for computer vision covers the entire library in its modern C ++ implementation.

Learn about OpenCV data types, array types and array activities
Capture and store still images and videos with HighGUI
Convert images to enlarge, shrink, distort, rebuild, and repair
Explore pattern recognition with face recognition
Track objects and movements through visual fields
Reconstruct 3D images from stereo vision
Discover OpenCV’s basic and advanced machine learning techniques

Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition

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OpenCV 4 Computer Vision Application Programming Cookbook

Howse, Joseph (Author)
English (Publication Language)
372 Pages - 02/20/2020 (Publication Date) - Packt Publishing (Publisher)

Updated to OpenCV4 and Python 3, the book covers the latest in depth cameras, 3D tracking, advanced reality and deep neural networks, a practical code to help you solve real-world computer vision problems. Computer vision is a rapidly evolving science that includes a variety of applications and techniques. This book will help not only those who are new to computer vision, but also experts in the field. You will be able to put the theory into practice by creating applications with OpenCV4 and Python3. Computer vision is a rapidly evolving science that encompasses various applications and techniques. This book will help not only those new to computer vision, but also experts in the field. You can put the theory into practice by building applications with OpenCV 4 and Python 3.

You will start by understanding OpenCV 4 and how to configure it with Python 3 on different platforms. Next, you will learn to perform basic operations such as reading, writing, manipulating, and viewing still images, videos, and camera streams. Whether it’s to guide you through image processing, video analysis, depth estimation, and segmentation, or to help you get some practice building a GUI application, this book has you covered with hands-on activities. Next, you will tackle two popular challenges: Face Detection and Face Recognition. You will also learn about the concepts of object classification and machine learning, allowing you to create and use object detectors and classifiers, and even track objects in film or video camera broadcasts. Subsequently, you will develop your skills in 3D tracking and augmented reality. Finally, you will tackle ANN and DNN, and learn how to develop applications to recognize handwritten numbers and classify a person’s gender and age.

By the end of this book, you will have the skills you need to do real-world computer vision projects.
What you are going to learn

Install and familiarize yourself with the OpenCV 4 Python 3 bindings
Understand the basics of image processing and video analysis.
Use a depth camera to distinguish the foreground and background regions
Detect and identify objects and track their movement in videos
Train and use your own models to match pictures and classify objects
Detect and recognize faces and classify their gender and age.
Create an augmented reality application to follow a 3D image
Work with machine learning models including SVM, artificial neural networks (ANN), and deep neural networks (DNN).

OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision applications with OpenCV and C++, 4th Edition

Escrivá, David Millán (Author)
English (Publication Language)
494 Pages - 05/02/2019 (Publication Date) - Packt Publishing (Publisher)

Discover great recipes to help you understand the concepts of object detection, image processing and facial recognition. Look for the latest features and APIs of OpenCV4 and create computer vision algorithms. Create an efficient, powerful, and secure vision for your applications. Create computer vision algorithms with machine learning capabilities. OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you will work on recipes that perform a variety of tasks, such as facial recognition and identification. With 70 tutorials alone, this book examines common weakness points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven solution and best practice with information on how it works, so you can copy the code and configuration files and modify them according to your needs.

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