OpenCV, the Open Source Computer Vision Library includes state of the art computer vision and deep learning algorithms (including running deep networks) and apps. It is professionally coded and optimized. It can be used in C++, Python, javascipt, Cuda, OpenCL and Matlab. It runs on: Android, iOS, Windows, Linux and MacOS and many embedded implementations such as Raspberry Pi.

• The user site is at https://opencv.org/
• The developer site is at: https://github.com/opencv/opencv/wiki
• Nightly builds of the documentation are at: https://docs.opencv.org/master
• Code is at:
  • OpenCV (the core data structures, optimized algorithms, sample and tutorial code): https://github.com/opencv/opencv
  • opencv_contrib (new algorithms, applications and GSoC contributions and related tutorial and sample code): https://github.com/opencv/opencv_contrib.git
  • opencv_extra (extra data and code samples): https://github.com/opencv/opencv_extra
  • downloads for various OS and mobile devices: https://opencv.org/releases.html

It is also useful to look at the change log: https://github.com/opencv/opencv/wiki/ChangeLog and instructions to install on various platforms: https://docs.opencv.org/3.3.1/df/d65/tutorial_table_of_content_introduction.html

Please see our videos for the past several years of GSoC contributions: (2017: https://docs.opencv.org/master/da/d9d/tutorial_dnn_yolo.html) (2015: https://youtu.be/OUbUFn71S4s) (2014: https://youtu.be/3f76HCHJJRA) (2013: https://youtu.be/_TTtN4frMEA).