What Is a Computer Vision Library?

OpenCV is a cross-platform computer vision library released under the BSD license (open source) and can run on Linux, Windows, Android and Mac OS operating systems. It is lightweight and efficient-it consists of a series of C functions and a small number of C ++ classes, and provides interfaces to languages such as Python, Ruby, MATLAB, etc., and implements many general algorithms in image processing and computer vision.

opencv

OpenCV is a cross-platform computer vision library released under the BSD license (open source) and can run on Linux, Windows, Android and Mac OS operating systems. It's lightweight and efficient-consists of a series of C functions and a small number of C ++ classes

OpenCV was founded in 1999 by

In January 1999, the CVL project was launched. The main goal is a human-machine interface, a real-time computer vision library that can be called by the UI.

1.Human-computer interaction

Object recognition

The list of authors can be found in the AUTOTHORS file.

In addition, many people have given suggestions, patches, bug reports, etc. There is an incomplete list of this in the file THANKS.

To learn about the new features of OpenCV, please refer to the OpenCV Change Log.

If there is a problem, enter "OpenCV" search in Google.

If you have problems installing / running / using OpenCV

1. Read the FAQ Chinese.

2. Search in the OpenCV mailing list.

3. Join the OpenCV mailing list on the yahoo group (refer to FAQs for how to join), and send your questions to the mailing list. (This mailing list may be migrated to OpenCV's SourceForge site)

4. Refer to the OpenCV sample code and read the reference manual.

OpenCV is written in C ++. Its main interface is also C ++, but it still retains a large number of C language interfaces. The library also has a large number of interfaces for Python, Java and MATLAB / OCTAVE (version 2.5). The API interface functions for these languages are available through the online documentation. Support for C #, Ch, Ruby is also available today.

All new developments and algorithms use the C ++ interface. A GPU interface using CUDA was also implemented in September 2010.

OpenCV can run on Windows, Android, Maemo, FreeBSD, OpenBSD, iOS, Linux and Mac OS. Users can get the official version at SourceForge or the development version from SVN. OpenCV also uses CMake.

When compiling the part related to camera input in OpenCV on Windows, some base classes in DirectShow SDK are needed. The SDK can be obtained from the subdirectory Samples \ Multimedia \ DirectShow \ BaseClasses of the pre-compiled Microsoft Platform SDK (or DirectX SDK 8.0 to 9.0c / DirectX Media SDK prior to 6.0).

On December 06, 2010, the official version of OpenCV 2.2.0 was released. ^[5]

On June 25, 2011, OpenCV-2.3.0rc was released. A stitching module was added. Android support is more convenient. The Google test framework is used. Other changes are mainly internal performance improvements.

On July 03, 2013, OpenCV 2.4.6 was released.

The main updates are on handheld devices:

First, OpenCV 2.3's Android build has finally become the officially supported NDK-Build method. The previous method is quite annoying. I used the non-Android official method to write the JNI interface. I did nt know why the tutorial followed.

Secondly, What's new says that the new Android support is being developed by NVidia. OpenCV originally supports CUDA graphics acceleration. ^[6]

On December 31, 2013, OpenCV 2.4.8 was released.

On April 25, 2014, OpenCV 2.4.9 was released.

On August 21, 2014, OpenCv 3.0 alpha version was released.

On November 11, 2014, the OpenCv 3.0 beta version was released.

On June 4, 2015, OpenCV 3.0 was released. ^[2]

On July 30, 2015, OpenCV 2.4.12 was released.

On December 21, 2015, OpenCV3.1 version was released.

On December 23, 2016, the OpenCV 3.2 version was released.

On August 3, 2017, OpenCV 3.3 was released. ^[7]

On July 4, 2018, OpenCV3.4.2 was released. ^[8]

On November 18, 2018, Opencv4.0.0 was released.