OpenCV 3.0 版正式发布，史上功能最全，速度最快的版本
OpenCV 3.0 发布，史上功能最全，速度最快的版本。在Windows, Linux and Mac, x64 和 ARM 平台做了充分测试，变得相当稳定。OpenCV是一个基于（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows和Mac OS操作系统上。它轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法。
6月4日，OpenCV 官网宣布 OpenCV 3.0 版正式发布（详细更新日志）。
With a great pleasure and great relief OpenCV team finally announces OpenCV 3.0 gold release, the most functional and the fastest OpenCV ever. And yet it’s very stable too – all the thousands of tests that we created during the project + many new tests pass successfully on Windows, Linux and Mac, x64 and ARM.
The changes since 3.0-rc, as well previous changes, can be found at http://code.opencv.org/projects/opencv/wiki/ChangeLog. This is a short executive summary on what’s new in 3.0 vs 2.4:
- ~1500 patches, submitted as PR @ github. All our patches go the same route.
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository has been added. A lot of new functionality is there already! opencv_contrib is only compatible with 3.0/master, not 2.4. Clone the repository and use “cmake … -D OPENCV_EXTRA_MODULES_PATH=<path_to opencv_contrib/modules> …” to build opencv and opencv_contrib together.
- a subset of Intel IPP (IPPCV) is given to us and our users free of charge, free of licensing fees, for commercial and non-commerical use. It’s used by default in x86 and x64 builds on Windows, Linux and Mac.
- T-API (transparent API) has been introduced, this is transparent GPU acceleration layer using OpenCL. It does not add any compile-time or runtime dependency of OpenCL. When OpenCL is available, it’s detected and used, but it can be disabled at compile time or at runtime. It covers ~100 OpenCV functions. This work has been done by contract and with generous support from AMD and Intel companies.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and because these are mostly basic functions, some higher-level functions got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation of NEON-optimized code and that should form a base for the open-source and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d, objdetect etc. They now follow the uniform “abstract interface – hidden implementation” pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on the Python bindings), newly introduced Matlab bindings (still in alpha stage).
- Improved Android support – now OpenCV Manager is in Java and supports both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and let OpenCV in. The results of many successful GSoC 2013 and 2014 projects have been integrated in opencv 3.0 and opencv_contrib (earlier results are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware filters, superpixels, …)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
- Besides Google, we enjoyed (and hope that you will enjoy too) many useful contributions from community, like:
- biologically inspired vision module
- DAISY features, LATCH descriptor, improved BRIEF
- image registration module
(note: if anything is missing here, please, mail to us and we will update the announcement and the changelog).