Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- 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.
- 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.
- ~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
- 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
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
Add a python option (off by default).
2.3.1
(August, 2011)
New Functionality and Features
* Retina module has been contributed by Alexandre Benoit (in opencv_contrib
module).
* Planar subdivisions construction (Delaunay triangulation and Voronoi
tesselation) have been ported to C++. See the new delaunay2.cpp sample.
* Several new Python samples have been added.
* FLANN in OpenCV has been upgraded to v1.6. Also, added Python bindings
for FLANN.
* We now support the latest FFMPEG (0.8.x) that features multi-threaded
decoding. Reading videos in OpenCV has never been that fast.
* Over 100 issues have been resolved since 2.3 release.
2.3
(July, 2011)
Modifications and Improvements since 2.3rc
* A few more bugs reported in the OpenCV bug tracker have been fixed.
* Documentation has been improved a lot!
2.3rc
(June, 2011)
New Functionality, Features
* Many functions and methods now take InputArray/OutputArray instead of
"cv::Mat" references. It retains compatibility with the existing code and
yet brings more natural support for STL vectors and potentially other
"foreign" data structures to OpenCV.
core:
* LAPACK is not used by OpenCV anymore.
* Arithmetic operations now support mixed-type operands and arbitrary number
of channels.
features2d:
* Completely new patent-free BRIEF and ORB feature descriptors have been
added.
* Very fast LSH matcher for BRIEF and ORB descriptors will be added in 2.3.1.
calib3d:
* calibration.cpp sample. With the new pattern calibration accuracy is
usually much higher.
stitching:
* opencv_stitching is a beta version of new application that makes a panorama
out of a set of photos taken from the same point.
python:
* Now there are 2 extension modules: cv and cv2. cv2 includes wrappers for
OpenCV 2.x functionality. opencv/samples/python2 contain a few samples
demonstrating cv2 in use.
* Over 250 issues have been resolved.
