OpenCV with Qt: Difference between revisions

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'''Installation guides'''
'''Installation guides'''
Installers for Linux, Mac and Windows are readily available [http://opencv.org/downloads.html here] [opencv.org]
Installers for Linux, Mac and Windows are readily available [http://opencv.org/downloads.html here]{{dead link}} [opencv.org]
* [http://docs.opencv.org/doc/tutorials/introduction/linux_install/linux_install.html?highlight=installation Linux installation guide] [docs.opencv.org]
* [http://docs.opencv.org/doc/tutorials/introduction/linux_install/linux_install.html?highlight=installation Linux installation guide] [docs.opencv.org]
* [http://docs.opencv.org/doc/tutorials/introduction/windows_install/windows_install.html?highlight=installation Windows installation guide] [docs.opencv.org]
* [http://docs.opencv.org/doc/tutorials/introduction/windows_install/windows_install.html?highlight=installation Windows installation guide] [docs.opencv.org]

Latest revision as of 07:13, 30 April 2020

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OpenCV is a cross-platform, open-source, commonly used computer vision library. It uses C++ as its primary interface, but other languages such as C and Python can also be used. OpenCV was originally developed by Intel back in 1999 and is now maintained by Willow Garage and ItSeez.

OpenCV was originally written in C for maximum performance and portability. OpenCV v2.0 was released in October of 2009 and includes major improvements in C++ interface.

Latest releases introduced support to GPU calculation (CUDA, OpenCL), Android platform, iOS platform and Linux Embedded platforms.

OpenCV main strengths are computational efficiency with a focus on near-real-time applications. This makes OpenCV perfectly suitable for writing applications on mobile platforms such as robots and cell phones.

Face recognition, Object Identification and Augmented Reality are some of the examples of OpenCV usage.

Installation guides Installers for Linux, Mac and Windows are readily available hereTemplate:Dead link [opencv.org]

Documentation