OpenCV

Key takeaways

OpenCV belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect OpenCV to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of OpenCV from memory before moving on to harder problems.

Reference excerpt

OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel). The library is cross-platform and licensed as free and open-source software under Apache License 2. Starting in 2011, OpenCV features GPU acceleration for real-time operations.

History Officially launched in 1999, the OpenCV project was initially an Intel Research initiative to advance CPU-intensive applications, part of a series of projects including real-time ray tracing and 3D display walls. The main contributors to the project included a number of optimization experts in Intel Russia, as well as Intel's Performance Library Team. In the early days of OpenCV, the goals of the project were described as:

Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel. Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable. Advance vision-based commercial applications by making portable, performance-optimized code available for free – with a license that did not require code to be open or free itself.

The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-release" was released in October 2008. The second major release of the OpenCV was in October 2009. OpenCV 2 includes major changes to the C++ interface, aiming at easier, more type-safe patterns, new functions, and better implementations for existing ones in terms of performance (especially on multi-core systems). Official releases now occur every six months and development is now done by an independent Russian team supported by commercial corporations. In August 2012, support for OpenCV was taken over by a non-profit foundation, OpenCV.org, which maintains a developer and user site. In May 2016, Intel signed an agreement to acquire Itseez, a leading developer of OpenCV.

Applications

OpenCV's application areas include:

2D and 3D feature toolkits Egomotion estimation Facial recognition system Gesture recognition Human–computer interaction (HCI) Mobile robotics Motion understanding Object detection Segmentation and recognition Stereopsis stereo vision: depth perception from 2 cameras Structure from motion (SFM) Motion video tracking Augmented reality To support some of the above areas, OpenCV includes a statistical machine learning library that contains:

Boosting Decision tree learning Gradient boosting trees Expectation-maximization algorithm k-nearest neighbor algorithm Naive Bayes classifier Artificial neural networks Random forest Support vector machine (SVM) Deep neural networks (DNN)

Programming language OpenCV is written in the programming language C++, as is its primary interface. There are language bindings in Python, Java, and MATLAB/Octave. The application programming interface (API) for these interfaces can be found in the online documentation. Wrapper libraries in several languages have been developed to encourage adoption by a wider audience. In version 3.4, JavaScript bindings for a selected subset of OpenCV functions were released as OpenCV.js, to be used for web platforms.

Hardware acceleration If the library finds Intel's Integrated Performance Primitives on the system, it will use these proprietary optimized routines to accelerate itself. A Compute Unified Device Architecture (CUDA) based graphics processing unit (GPU) interface has been in progress since September 2010. An OpenCL-based GPU interface has been in progress since October 2012, documentation for version 2.4.13.3 can be found at docs.opencv.org. An IPU may also utilise OpenCV hardware acceleration.

Illustrations

Worked examples

Example 1 — a first encounter with OpenCV

Start with the simplest possible case. Write down what OpenCV claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to OpenCV before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about OpenCV ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of OpenCV

In research: OpenCV appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses OpenCV in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: OpenCV is common in secondary-school and first-year university syllabi. It links to neighbouring topics C++ libraries, Computer vision software, Gesture recognition, so understanding it makes those chapters shorter.
In everyday life: Look for OpenCV outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study OpenCV in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what OpenCV means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain OpenCV out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is OpenCV in simple terms?

OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel).

Why does OpenCV matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study OpenCV?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on OpenCV.