Learn OpenCV 2021 – Best OpenCV courses & Best OpenCV books

Best OpenCV Courses 2021


Best OpenCV Tutorials 2021

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

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 2021.

Master Computer Vision™ OpenCV4 in Python with Deep Learning

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

Python For Vision and Detection : OpenCv Python

Computer vision is a technology based on artificial intelligence, that is, artificial intelligence that allows computers to understand and label images. It is now used in convenience stores, driverless car testing, security access mechanisms, police and investigative surveillance, daily medical diagnosis monitoring the health of crops and live animals, etc.

A common example is the face detection and unlocking mechanism that you use in your mobile phone. We use this daily. It is also a great application of Computer Vision. And today, big tech companies like Amazon, Google, Microsoft, Facebook, etc. invest millions and millions of dollars in the research and development of products based on computer vision.

Computer vision allows us to analyze and exploit image and video data, with applications in a variety of industries, including self-driving cars, social media applications, medical diagnostics and more. other.

As the fastest growing language in popularity, Python is well suited for leveraging the power of existing computer vision libraries to learn from all that image and video data.

In this course, we’ll teach you everything you need to know to become a computer vision expert! This $ 20 billion industry will be one of the most important job markets for years to come.

You will learn

Use OpenCV to work with image files
Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphology.
Create face detection software
Detect objects, including corner, edge and grid detection techniques with OpenCV and Python
Use Python and Deep Learning to Create Image Classifiers
Use Python and OpenCV to draw shapes on images and videos
Create color histograms with OpenCV
Study from MIT Notes and Get Interview Questions
Crack the limits of image processing by developing applications.
Latest 2021 project ideas with source code and link

Best OpenCV Books 2021

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

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
  • O\'Reilly Media
  • Kaehler, Adrian (Author)
  • English (Publication Language)
  • 1024 Pages - 01/24/2017 (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

Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms...
  • 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

OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision...
  • Millan Escriva, David (Author)
  • English (Publication Language)
  • 494 Pages - 05/03/2019 (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.

SaleBestseller No. 1
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
  • O\'Reilly Media
  • Kaehler, Adrian (Author)
  • English (Publication Language)
  • 1024 Pages - 01/24/2017 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 2
OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision...
  • Millan Escriva, David (Author)
  • English (Publication Language)
  • 494 Pages - 05/03/2019 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 3
Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms...
  • Howse, Joseph (Author)
  • English (Publication Language)
  • 372 Pages - 02/20/2020 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 4
Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision...
  • Millan Escriva, David (Author)
  • English (Publication Language)
  • 538 Pages - 03/26/2019 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 5
Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented...
  • Villan, Alberto Fernandez (Author)
  • English (Publication Language)
  • 532 Pages - 03/29/2019 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 6
OpenCV 4 with Python Blueprints: Build creative computer vision projects with the latest version of...
  • Gevorgyan, Dr. Menua (Author)
  • English (Publication Language)
  • 366 Pages - 03/20/2020 (Publication Date) - Packt Publishing (Publisher)
SaleBestseller No. 7
Programming Computer Vision with Python: Tools and algorithms for analyzing images
  • O Reilly Media
  • Solem, Jan Erik (Author)
  • English (Publication Language)
  • 264 Pages - 07/10/2012 (Publication Date) - O'Reilly Media (Publisher)
SaleBestseller No. 8
Learning OpenCV: Computer Vision with the OpenCV Library
  • ISBN13: 9780596516130
  • Condition: New
  • Notes: BRAND NEW FROM PUBLISHER! 100% Satisfaction Guarantee. Tracking provided on most orders. Buy with Confidence! Millions of books sold!
  • Bradski, Gary (Author)
  • English (Publication Language)
Bestseller No. 9
Beginning Robotics with Raspberry Pi and Arduino: Using Python and OpenCV
  • Cicolani, Jeff (Author)
  • English (Publication Language)
  • 376 Pages - 03/04/2021 (Publication Date) - Apress (Publisher)
Bestseller No. 10
Raspberry Pi Computer Vision Programming: Design and implement computer vision applications with...
  • Pajankar, Ashwin (Author)
  • English (Publication Language)
  • 306 Pages - 06/29/2020 (Publication Date) - Packt Publishing (Publisher)
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