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Learning OpenCV 5 Computer Vision with Python

You're reading from   Learning OpenCV 5 Computer Vision with Python Tackle computer vision and machine learning with the newest tools, techniques and algorithms

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803230221
Length
Edition 4th Edition
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
Author Profile Icon Joseph Howse
Joseph Howse
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Table of Contents (12) Chapters Close

1. Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning FREE CHAPTER
2. Setting Up OpenCV 3. Handling Files, Cameras, and GUIs 4. Processing Images with OpenCV 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. OpenCV Applications at Scale Appendix A: Bending Color Space with the Curves Filter

What are Containers?

We mentioned containers and Docker. Let’s see what they are and why they are so important in our day and age.

First of all, containers are a form of virtualization, that is, making a single physical machine run on or more “machines”, which are called virtual.

But while Virtual Machines (or VMs) are an abstraction of the hardware layer and simulate the entire operating system, in fact VM images are huge, in the order of GBs.

Containers however, are an abstraction at the application layer, and container images can be as small as tens of MBs, and they package code and dependencies into a single image that can be deployed in several ways.

The most popular technology for container deployment and orchestration is Kubernetes, and in AWS you can do this with a Kubernetes equivalent (Elastic Kubernetes Service or EKS), or three separate tools called ECS (Elastic Container Service), Fargate and AWS Lambda.

We will limit ourselves to the simplest and easiest...

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