h1. OpenCV Change Logs

{{>toc}}

h2. version:2.4.5

__April, 2013__

* super resolution

* numerous OpenCL improvements and bug-fixes + new functionality (stereobm, stereobp)

* Experimental WinRT support

* CLAHE

* tutorial from MS on MSVS

* 78 p.r. (@git log --since 2013-03-02 --until 2013-04-05 --grep pull --oneline origin/2.4@)

* 25 bugs 

h2. version:2.4.4

__March, 2013__

p((((. !java_logo.svg.png!

* This is the biggest news in 2.4.4 - we've got full-featured OpenCV Java bindings on a desktop, not only Android! In fact you can use any JVM language, including functional Java or handy Groovy. Big thanks to "Eric Christiansen":https://plus.google.com/u/0/114834902961564956461 for the contribution! Check the "tutorial":http://docs.opencv.org/doc/tutorials/introduction/desktop_java/java_dev_intro.html for details and code samples.

p((((. !http://code.opencv.org/attachments/960/android_ocv_small.png!

* Android application framework, samples, tutorials, OpenCV Manager are updated, see [[Android Release Notes]] for details.

p((((. !CUDA.jpg!

* Numerous improvements in @gpu@ module and the following new functionality & optimizations:

** Optimizations for the NVIDIA Kepler architecture

** NVIDIA CARMA platform support

** HoughLinesP for line segments detection

** Lab/Luv <-> RGB conversions

p((((. !OpenCL.jpg!

* Let us be more verbose here. The openCL-based hardware acceleration (ocl) module is now mature, and, with numerous bug fixes, it is largely bug-free. Correct operation has been verified on all tested platforms, including discrete GPUs (tested on NVIDIA and AMD boards), as well as integrated GPUs (AMD APUs as well as Intel Ivy Bridge iGPUs). On the host side, there has been exhaustive testing on 32/64 bit, Windows/Linux systems, making the ocl module a very serious and robust cross-platform GPU hardware acceleration solution. While we currently do not test on other devices that implement OpenCL (e.g. FPGA, ARM or other processors), it is expected that the ocl module will work well on such devices as well (provided the minimum requirements explained in the user guide are met).

Here are specific highlights of the 2.4.4 release:

** The @ocl::Mat@ can now use “special” memory (e.g. pinned memory, host-local or device-local).

** The ocl module can detect if the underlying hardware supports “integrated memory,” and if so use “device-local” memory by default for all operations.

** New arithmetic operations for @ocl::Mat@, providing significant ease of use for simple numerical manipulations.

** Interop with OpenCL enables very easy integration of OpenCV in existing OpenCL applications, and vice versa.

** New algorithms include Hough circles, more color conversions (including YUV, YCrCb), and Hu Moments.

** Numerous bug fixes, and optimizations, including in: blendLinear, square samples, erode/dilate, Canny, convolution fixes with AMD FFT library, mean shift filtering, Stereo BM.

** Platform specific bug fixes: PyrLK, bruteForceMatcher, faceDetect now works also on Intel Ivy Bridge chips (as well as on AMD APUs/GPUs and NVIDIA GPUs); erode/dilate also works on NVIDIA GPUs (as well as AMD APUs/GPUs and Intel iGPUs).

p((((. !update.jpg!

* Many people contributed their code in the form of "pull requests":https://github.com/Itseez/opencv/pulls?direction=desc&page=1&sort=created&state=closed. Here are some of the most interesting contributions, that were included into 2.4 branch:

** ">100":http://code.opencv.org/projects/opencv/versions/11 reported problems have been resolved since 2.4.3

** Oscar Deniz "submitted":https://github.com/Itseez/opencv/pulls?direction=desc&page=1&sort=created&state=closed smile detector and sample.

** Alexander Smorkalov created a "tutorial":http://docs.opencv.org/doc/tutorials/introduction/crosscompilation/arm_crosscompile_with_cmake.html#arm-linux-cross-compile on cross-compilation of OpenCV for Linux on ARM platforms.

h2. version:2.4.3

__November, 2012__

p((((. !threads.jpg!

* Added universal @parallel_for@ implementation using various backends: TBB (cross-platform: http://threadingbuildingblocks.org), OpenMP (cross-platform: http://en.wikipedia.org/wiki/OpenMP), Grand Central Dispatch (iOS/MacOSX: http://en.wikipedia.org/wiki/Grand_Central_Dispatch), Concurrency (Visual Studio 2010 or later: http://msdn.microsoft.com/en-us/library/dd492418.aspx), C= (http://www.hoopoesnest.com/cstripes/cstripes-details.htm). Many exiting parallel OpenCV algorithms have been converted to the new primitive. Therefore, you can enjoy performance improvements even without having to install TBB and ship it with OpenCV-based applications.

p((((. !http://code.opencv.org/attachments/960/android_ocv_small.png!

* Improved OpenCV Manager, new Java samples framework, better camera support on Android, see [[Android Release Notes]] for details.

p((((. !ios6.jpg!

* *opencv2.framework* is now iOS6- and iPhone5- (armv7s) compatible. Thanks to the new threading mechanism several important OpenCV algorithms (e.g. face detection, bilateral filter, etc.) now run faster on A5 or newer dual-core chips. We also fixed bug in the framework build script and now the framework is built with "-O3" optimization instead of "-O0" in OpenCV 2.4.2. Finally, our GSoC students, Eduard Feicho and Charu Hans, have written detailed tutorials on how to add OpenCV to your iOS app, please, check http://docs.opencv.org/doc/tutorials/ios/table_of_content_ios/table_of_content_ios.html.

p((((. !python.png!

* Another our GSoC student, Alexander Mordvintsev, added several new exciting Python samples, including ASIFT, hand-written digit recognition, motion debluring and even the use of Amazon PiCloud. Check https://github.com/Itseez/opencv/tree/master/samples/python2

p((((. !profile.jpg!

* Yet another GSoC student Attila Novak has trained 2 new cascades, for silverware and profile faces. Check https://github.com/Itseez/opencv/tree/master/data/lbpcascades

p((((. !denoising.jpg!

* Another result from GSoC (that was a good year!): Victor Passichenko implemented non-local means denoising algorithm. It's not realtime, but quite decent. Please, check http://docs.opencv.org/trunk/modules/photo/doc/denoising.html

p((((. !optflow.jpg!

* Yet another result from GSoC (that was a good year indeed!): Yuri Zemlyansky implemented "simple flow" optical flow algorithm; check https://github.com/Itseez/opencv/tree/master/samples/cpp/simpleflow_demo.cpp.

p((((. !CUDA.jpg!

* Numerous improvements and new functionality in GPU module:

  - device layer opened for users; now users write CUDA device code using OpenCV.

  - LBP cascade support in cascade classifier

  - fast non local means image denoising

  - faster integral image calculation on Kepler

  - Hough circles, Hough lines and generalized Hough transform implementation

  - bilateral filter

  - generalized Flood Fill connected component labeling

  - background/foreground segmentation algorithms: Mixture of Gaussian, ViBe, GMG (A. Godbehere, A. Matsukawa, K. Goldberg) . 

  - added confidence calculation into HOG

p((((. !OpenCL.jpg!

* Technology-preview version of ocl - OpenCL-accelerated computer vision algorithms, contributed by the Chinese Academy of Science. It includes:

  - arithmetical operations

  - filtering

  - geometrical image transformations (resize, remap etc.)

  - cascade classifier (i.e. face detector)

  - optical flow

currently, it only runs on GPUs.

http://code.opencv.org/attachments/download/1147

p((((. !http://code.opencv.org/attachments/949/bug.png!

* ~130 reported problems have been resolved since 2.4.2

p((((. !github.jpg!

* OpenCV has never been that social. We are at github now: http://github.com/itseez/opencv. Grab the latest versions from there, submit pull requests.

p((((. !update.jpg!

* Since 2.4.3rc we fixed several build problems (OpenCV-based applications on Xcode 4.5 & iOS6, OpenCV+TBB on Windows etc.) and closed a few issues, reported at code.opencv.org.

h2. version:2.4.2

__July, 2012__

p((((. !http://code.opencv.org/attachments/960/android_ocv_small.png!

* Android package introduces a new service-based distribution model (see [[Android Release Notes]] for details).

p((((. !http://code.opencv.org/attachments/951/freak.jpg!

* New keypoint descriptor FREAK has been contributed by EPFL group: Kirell Benzi, Raphael Ortiz, Alexandre Alahi and Pierre Vandergheynst. It's claimed to be superior to ORB and SURF descriptors, yet it's very fast (comparable to ORB). Please, see source://trunk/opencv/samples/cpp/freak_demo.cpp.

p((((. !http://code.opencv.org/attachments/950/eigenfaces_opencv.png!

* Improved face recognizer and excellent tutorial on using it has been added by Philipp Wagner. Check the "face recognition tutorial":http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html.

p((((. !http://code.opencv.org/attachments/948/100px-Apple_iOS_icon.png!

* *opencv2.framework* for iOS has been created. You can either download the binary from SourceForge or build it yourself using the simple guide: http://docs.opencv.org/doc/tutorials/introduction/ios_install/ios_install.html#ios-installation. Also, you may be interested to look at some OpenCV on iOS samples, created by our GSoC 2012 students Eduard and Charu: gsoc2012:source:/ios/trunk and read the slides of the tutorial: gsoc2012:source:/ios/trunk/doc/CVPR2012_OpenCV4IOS_Tutorial.pdf

p((((. !http://code.opencv.org/attachments/949/bug.png!

* "Another 50 bugs":http://code.opencv.org/projects/opencv/issues?fixed_version_id=6&set_filter=1&status_id=5 have been fixed since 2.4.1 release.

p((((. !http://code.opencv.org/attachments/952/logo.png!

* It's not about the code, but now we have the brand-new user site, http://opencv.org and the new stack overflow-like site with answers to the most popular questions http://answers.opencv.org

p((((. !http://code.opencv.org/attachments/956/cuda.png!

* GPU module:

  - reimplemented CUDA accelerated gpu::PyrLKOpticalFlow for dense and sparse cases. New implementation up 1.5 - 2 times faster then previous GPU  optimized. Updated optical flow samples

  - implemented resize with area interpolation. CUDA optimized version for integer matrix types up 30 - 35 faster then not optimized OpenCV implementation and up 7 in worst case of 3 channel floating point matrix.

h2. 2.4.1

__June, 2012__

* The GPU module now supports CUDA 4.1 and CUDA 4.2 and can be compiled with CUDA 5.0 preview.

* Added API for storing OpenCV data structures in text string and reading them back:

<pre><code class="cpp">

//==== storing data ====

FileStorage fs(".xml", FileStorage::WRITE + FileStorage::MEMORY);

fs << "date" << date_string << "mymatrix" << mymatrix;

string buf = fs.releaseAndGetString();

//==== reading it back ====

FileStorage fs(buf, FileStorage::READ + FileStorage::MEMORY);

fs["date"] >> date_string;

fs["mymatrix"] >> mymatrix;

</code></pre>

* "@cv::calcOpticalFlowPyrLK@":http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowpyrlk now supports precomputed pyramids as input.

* Function signatures in documentation are made consistent with source code.

* Restored python wrappers for SURF and MSER.

* "45 more bugs":http://code.opencv.org/projects/opencv/issues?fixed_version_id=5&set_filter=1&status_id=5 in our bug tracker have been fixed

h2. version:2.4.0

__May, 2012__

The major changes since 2.4 beta

* OpenCV now provides pretty complete build information via cv::getBuildInformation().

* reading/writing video via ffmpeg finally works and is now available on MacOS X too.

note 1: we now demand reasonably fresh versions of ffmpeg/libav with libswscale included.

note 2: if possible, try to avoid reading or writing more than one video simultaneously (even within a single thread) with ffmpeg 0.7.x or earlier, since they seem to use some global structures that are destroyed by the codecs executed synchronously. Either build and install a newer ffmpeg (0.10.x is recommended), or serialize your video i/o, or use parallel processes instead of threads.

* MOG2 background subtraction by Zoran Zivkovic was optimized using TBB.

* The reference manual has been updated to match OpenCV 2.4.0.

* More than 20 bugs have been closed in our bug tracker. (http://code.opencv.org/projects/opencv/roadmap).

* Asus Xtion is now properly supported for HighGUI. For now, you have to manually specify this device by using VideoCapture(CV_CAP_OPENNI_ASUS) instead of VideoCapture(CV_CAP_OPENNI).

h2. 2.4 beta

__April, 2012__

As usual, we created 2.4 branch in our repository (http://code.opencv.org/svn/opencv/branches/2.4), where we will further stabilize the code. You can check this branch for changes periodically, before as well as after 2.4 release.

h3. Common changes

* At the age of 12, OpenCV got its own home! http://code.opencv.org is now the primary site for OpenCV development and http://opencv.org will be the official OpenCV user site.

* Some of the old functionality from the modules @imgproc@, @video@, @calib3d@, @features2d@, @objdetect@ has been moved to legacy.

* CMake scripts have been substantially modified. Now it's very easy to add new modules - just put the directory with include, src, doc and test sub-directories to the modules directory, create a very simple @CMakeLists.txt@ and your module will be built as a part of OpenCV. Also, it's possible to exclude certain modules from build (the CMake variables "@BUILD_opencv_<modulename>@" control that).

h3. New functionality

 * A new essential class @cv::Algorithm@ has been introduced. It's planned to be the fundamental part of all of the "non-trivial" OpenCV functionality. All Algorithm-based classes have the following features:

 ** "virtual constructor", i.e. an algorithm instance can be created by name;

 ** there is a list of available algorithms;

 ** one can retrieve and set algorithm parameters by name;

 ** one can save algorithm parameters to XML/YAML file and then load them.

* A new ffmpeg wrapper has been created that features multi-threaded decoding, more robust video positioning etc. It's used with ffmpeg starting with 0.7.x versions.

* @features2d@ API has been cleaned up. There are no more numerous classes with duplicated functionality. The base classes @FeatureDetector@ and @DescriptorExtractor@ are now derivatives of @cv::Algorithm@. There is also the base @Feature2D@, using which you can detect keypoints and compute the descriptors in a single call. This is also more efficient.

* SIFT and SURF have been moved to a separate module named @nonfree@ to indicate possible legal issues of using those algorithms in user applications. Also, SIFT performance has been substantially improved (by factor of 3-4x).

* The current state-of-art textureless detection algorithm, Line-Mod by S. Hinterstoisser, has been contributed by Patrick Mihelich. See @objdetect/objdetect.hpp@, class @Detector@.

* 3 face recognition algorithms have been contributed by Philipp Wagner. Please, check @opencv/contrib/contrib.hpp@, @FaceRecognizer@ class, and @opencv/samples/cpp/facerec_demo.cpp@.

* 2 algorithms for solving PnP problem have been added. Please, check @flags@ parameter in @solvePnP@ and @solvePnPRansac@ functions.

* Enhanced @LogPolar@ implementation (that uses Blind-Spot model) has been contributed by Fabio Solari and Manuela Chessa, see @opencv/contrib/contrib.hpp@, @LogPolar_*@ classes and @opencv/samples/cpp/logpolar_bsm.cpp@ sample.

* A stub module @photo@ has been created to support a quickly growing "computational photography" area. Currently, it only contains @inpainting@ algorithm, moved from @imgproc@, but it's planned to add much more functionality.

* Another module @videostab@ (beta version) has been added that solves a specific yet very important task of video stabilization. The module is under active development. Please, check @opencv/samples/cpp/videostab.cpp@ sample. 

* @findContours@ can now find contours on a 32-bit integer image of labels (not only on a black-and-white 8-bit image). This is a step towards more convenient connected component analysis.

* @Canny@ edge detector can now be run on color images, which results in better edge maps 

* Python bindings can now be used within python threads, so one can write multi-threaded computer vision applications in Python.

h3. OpenCV on GPU

* Different Optical Flow algorithms have been added:

** Brox (contributed by NVidia)

** PyrLK - both Dense and Sparse variations

** Farneback

* New feature detectors and descriptors:

** @GoodFeaturesToTrack@

** FAST/ORB which is patent free replacement of SURF.

* Overall GPU module enhancements:

** The module now requires CUDA 4.1 or later;

** Improved similarity of results between CPU and GPU;

** Added border extrapolation support for many functions;

** Improved performance.

* @pyrUp@/@pyrDown@ implementations.

* Matrix multiplication on GPU (wrapper for the CUBLAS library). This is optional, user need to compile OpenCV with CUBLAS support.

* @OpenGL back-end@ has been implemented for @highgui@  module, that allows to display @GpuMat@ directly without downloading them to CPU.

h3. OpenCV4Android

See the [[Android Release Notes]].

h3. Performance

* A few OpenCV functions, like color conversion, morphology, data type conversions, brute-force feature matcher have been optimized using TBB and/or SSE intrinisics.

* Along with regression tests, now many OpenCV functions have got performance tests. Now for most modules one can build @opencv_perf_<modulename>@ executables that run various functions from the particular module and produce a XML file. Note that if you want to run those tests, as well as the normal regression tests, you will need to get (a rather big) http://code.opencv.org/svn/opencv/trunk/opencv_extra directory and set environment variable @OPENCV_TEST_DATA_PATH@ to "@<your_copy_of_opencv_extra>/testdata@".

h3. Bug fixes

* In this version we fixed literally hundreds of bugs. Please, check http://code.opencv.org/projects/opencv/versions/1 for a list of fixed issues.

h3. Known issues

* When OpenCV is built statically, dynamically created classes (via @Algorithm::create@) can fail because linker excludes the "unused" object files. To avoid this problem, create classes explicitly, e.g

 <pre><code class="cpp">Ptr<DescriptorExtractor> d = new BriefDescriptorExtractor;</code></pre>

h2. 2.3.1

__August, 2011__

h3. OpenCV4Android

OpenCV Java bindings for Android platform are released in ''Beta 2'' quality. A lot of work is done to make them more stable and easier to use. Currently Java API has about 700 different OpenCV functions and covers 8 OpenCV modules including full port of features2d.

h3. Other New Functionality and Features

* Retina module has been contributed by Alexandre Benoit (in @opencv_contrib@ module). See the new retina sample and https://sites.google.com/site/benoitalexandrevision/.

* Support for Ximea cameras (http://www.ximea.com/) in @highgui@ has been contributed by Ximea developers.

* Planar subdivisions construction (Delaunay triangulation and Voronoi tessellation) 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.

h3. Documentation

* Quite a few new tutorials have been added. Check http://opencv.itseez.com/trunk for the up-to-date documentation.

h3. Optimization

* Performance of the sparse Lucas-Kanade optical flow has been greatly improved. On 4-core machine it is now 9x faster than the previous version.

h3. Bug Fixes

* Over 100 issues have been resolved since 2.3 release. Most of the issues (closed and still open) are listed at http://code.opencv.org/projects/opencv/issues?set_filter=1.

h3. Known issues

* TBB debug binaries are missed in the Windows installer. Here is a workaround:

** Download @tbb30_20110427oss_win.zip@ from the "TBB website":http://threadingbuildingblocks.org/ver.php?fid=171.

** Unzip and copy the @tbb*_debug.dll@ files from _bin/<ARCH>/<COMPILER>_ to the corresponding folder in the installed OpenCV location in _<OPENCVCV_ROOT>/build/common/tbb/<ARCH>/<COMPILER>_

h2. 2.3

__July, 2011__

h3. 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! The new reference manual combines information for C++ and C interfaces, the OpenCV 1.x-style Python bindings and the new C++-style Python bindings. It has also been thoroughly checked for grammar, style and integrity.

  Besides, there are new and updated tutorials.

  The up-to-date online documentation is available at http://opencv.itseez.com.

* The new binary package includes various pre-compiled libs:

   https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.3/

   Unfortunately, it's not a full-scale installation package, but simply a self-extracting archive with a @readme.txt@ supplied.

   The installation package is probably to come in the next version.

* VS2005 should build OpenCV 2.3 out of the box, including @DirectShow@ support.

* ffmpeg bindings are now available for all Windows users via compiler- and configuration- and

    version-independent @opencv_ffmpeg.dll@ (for 32-bit compilers) and @opencv_ffmpeg_64.dll@ (for 64-bit compilers).

h2. 2.3 beta

__June, 2011__

h3. General Modifications and Improvements

* Buildbot-based Continuous Integration system is now continuously testing OpenCV snapshots. The current status is available at http://buildbot.itseez.com

* OpenCV switched to Google Test (http://code.google.com/p/googletest/) engine for regression and correctness tests. Each module now has "test" sub-directory which includes the corresponding tests.

h3. 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. See http://opencv.itseez.com/modules/core/doc/intro.html#inputarray-and-outputarray for details.

* @core@

** @LAPACK@ is not used by OpenCV anymore. The change decreased the library footprint and the compile time. We now use our own implementation of Jacobi SVD. SVD performance on small matrices (2x2 to 10x10) has been considerably improved; on larger matrices it is still pretty good. SVD accuracy on poorly-conditioned matrices has also been polished.

** 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@

** A new calibration pattern, "circles grid":http://code.opencv.org/svn/opencv/branches/2.3/opencv/doc/acircles_pattern.png, has been added. See findCirclesGrid() function and the updated calibration.cpp sample. With the new pattern calibration accuracy is usually much higher.

* @highgui@

** [Windows] @videoInput@ is now a part of @highgui@. If there are any problems with compiling @highgui@, set @WITH_VIDEOINPUT=OFF@ in CMake.

* @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.

* @contrib@

** A new experimental variational stereo correspondence algorithm @StereoVar@ has been added.

* @gpu@

** the module now requires CUDA 4.0 or later; Many improvements and bug fixes have been made.

h3. Android port

* With support from NVIDIA, OpenCV Android port (which is actually not a separate branch of OpenCV, it's the same code tree with additional build scripts) has been greatly improved, a few demos developed. Camera support has been added as well.

  See [[Android Release Notes#231-beta1]] for details.

h3. Documentation

* OpenCV documentation is now written in @ReStructured Text@ and built using @Sphinx@ (http://sphinx.pocoo.org).

* It's not a single reference manual now, it's 4 reference manuals (OpenCV 2.x C++ API, OpenCV 2.x Python API, OpenCV 1.x C API, OpenCV 1.x Python API), the emerging user guide and a set of tutorials for beginners.

* Style and grammar of the main reference manual (OpenCV 2.x C++ API) have been thoroughly checked and fixed.

* Online up-to-date version of the manual is available at http://opencv.itseez.com

h3. Samples

* Several samples using the new Python bindings (cv2 module) have been added: http://code.opencv.org/svn/opencv/branches/2.3/opencv/samples/python2

h3. Optimization

* Several ML algorithms have been threaded using TBB.

h3. Bug Fixes

* Over 250 issues have been resolved. Most of the issues (closed and still open) are listed at http://code.opencv.org/projects/opencv/issues?set_filter=1.

h3. Known Problems/Limitations

* Documentation (especially on the new Python bindings) is still being updated. Watch http://opencv.itseez.com for updates.

* Android port does not provide Java interface for OpenCV. It is going to be added to "2.3 branch":http://code.opencv.org/svn/opencv/branches/2.3/opencv in a few weeks.

h2. 2.2

__December, 2010__

h3. General Modifications and Improvements

* The library has been reorganized. Instead of @cxcore@, @cv@, @cvaux@, @highgui@ and @ml@ we now have several smaller modules:

** @opencv_core@ - core functionality (basic structures, arithmetics and linear algebra, dft, XML and YAML I/O ...).

** @opencv_imgproc@ - image processing (@filter@, @GaussianBlur@, @erode@, @dilate@, @resize@, @remap@, @cvtColor@, @calcHist@ etc.)

** @opencv_highgui@ - GUI and image & video I/O

** @opencv_ml@ - statistical machine learning models (SVM, Decision Trees, Boosting etc.)

** @opencv_features2d@ - 2D feature detectors and descriptors (SURF, FAST etc., including the new feature detectors-descriptor-matcher framework)

** @opencv_video@ - motion analysis and object tracking (optical flow, motion templates, background subtraction)

** @opencv_objdetect@ - object detection in images (Haar & LBP face detectors, HOG people detector etc.)

** @opencv_calib3d@ - camera calibration, stereo correspondence and elements of 3D data processing

** @opencv_flann@ - the Fast Library for Approximate Nearest Neighbors (@FLANN@ 1.5) and the OpenCV wrappers

** @opencv_contrib@ - contributed code that is not mature enough

** @opencv_legacy@ - obsolete code, preserved for backward compatibility

** @opencv_gpu@ - acceleration of some OpenCV functionality using @CUDA@ (relatively unstable, yet very actively developed part of OpenCV)

If you detected OpenCV and configured your make scripts using CMake or pkg-config tool, your code will likely build fine without any changes. Otherwise, you will need to modify linker parameters (change the library names) and update the include paths.

It is still possible to use @#include <cv.h>@ etc. but the recommended notation is:

<pre><code class="cpp">

#include "opencv2/imgproc/imgproc.hpp"

...

</code></pre>

Please, check the new C and C++ samples (http://code.opencv.org/svn/opencv/trunk/opencv/samples), which now include the new-style headers.

* The new-style wrappers now cover much more of OpenCV 2.x API. The documentation and samples are to be added later. You will need numpy in order to use the extra functionality.

   SWIG-based Python wrappers are not included anymore.

* OpenCV can now be built for Android (GSoC 2010 project), thanks to Ethan Rublee; and there are some samples too. Please, check [[OpenCV4Android]]

* The completely new @opencv_gpu@ acceleration module has been created with support by NVIDIA. See below for details.

h3. New Functionality, Features

* @core@

** The new @cv::Matx<T, m, n>@ type for fixed-type fixed-size matrices has been added. @Vec<T, n>@ is now derived from @Matx<T, n, 1>@. The class can be used for very small matrices, where @cv::Mat@ use implies too much overhead. The operators to convert @Matx@ to @Mat@ and backwards are available.

** @cv::Mat@ and @cv::MatND@ are made the same type: @typedef cv::Mat cv::MatND@.

    Note that many functions do not check the matrix dimensionality yet, so be careful when processing 3-, 4- ... dimensional matrices using OpenCV.

** Experimental support for Eigen 2.x/3.x is added (@WITH_EIGEN2@ option in CMake). Again, there are convertors from Eigen2 matrices to @cv::Mat@ and backwards. See @modules/core/include/opencv2/core/eigen.hpp@.

** @cv::Mat@ can now be print with "@<<@" operator. See @opencv/samples/cpp/cout_mat.cpp@.

** @cv::exp@ and @cv::log@ are now much faster thanks to SSE2 optimization.

* @imgproc@

** color conversion functions have been rewritten;

** RGB->Lab & RGB->Luv performance has been noticeably improved. Now the functions assume sRGB input color space (e.g. gamma=2.2). If you want the original linear RGB->L** conversion (i.e. with gamma=1), use @CV_LBGR2LAB@ etc.

** VNG algorithm for Bayer->RGB conversion has been added. It's much slower than the simple interpolation algorithm, but returns significantly more detailed images

** The new flavors of RGB->HSV/HLS conversion functions have been added for 8-bit images. They use the whole 0..255 range for the H channel instead of 0..179. The conversion codes are @CV_RGB2HSV_FULL@ etc.

** special variant of @initUndistortRectifyMap@ for wide-angle cameras has been added: @initWideAngleProjMap()@

* @features2d@

** the unified framework for keypoint extraction, computing the descriptors and matching them has been introduced. The previously available and some new detectors and descriptors, like @SURF@, @FAST@, @StarDetector@ etc. have been wrapped to be used through the framework. The key advantage of the new framework (besides the uniform API for different detectors and descriptors) is that it also provides high-level tools for image matching and textured object detection. Please, see documentation http://opencv.itseez.com/modules/features2d/doc/common_interfaces_of_feature_detectors.html

      and the C++ samples:

*** @descriptor_extractor_matcher.cpp@ - finding object in a scene using keypoints and their descriptors.

*** @generic_descriptor_matcher.cpp@ - variation of the above sample where the descriptors do not have to be computed explicitly.

*** @bagofwords_classification.cpp@ - example of extending the framework and using it to process data from the VOC databases: http://pascallin.ecs.soton.ac.uk/challenges/VOC/

** the newest super-fast keypoint descriptor BRIEF by Michael Calonder has been integrated by Ethan Rublee. See the sample @opencv/samples/cpp/video_homography.cpp@

** SURF keypoint detector has been parallelized using TBB (the patch is by imahon and yvo2m)

* @objdetect@

** LatentSVM object detector, implementing P. Felzenszwalb algorithm, has been contributed by Nizhniy Novgorod State University (NNSU) team. See @opencv/samples/c/latentsvmdetect.cpp@

* @calib3d@

** The new rational distortion model:

    @x' = x * (1@ + @k@~1~ * @r@^2^ + @k@~2~ * @r@^4^ + @k@~3~ * @r@^6^@)/(1@ + @k@~4~ * @r@^2^ + @k@~5~ * @r@^4^ + @k@~6~ * @r@^6^@) +@ _<tangential_distortion for @x@>_,

    @y' = y * (1@ + @k@~1~ * @r@^2^ + @k@~2~ * @r@^4^ + @k@~3~ * @r@^6^@)/(1@ + @k@~4~ * @r@^2^ + @k@~5~ * @r@^4^ + @k@~6~ * @r@^6^@) +@ _<tangential_distortion for @y@>_

    has been introduced. It is useful for calibration of cameras with wide-angle lenses. 

    Because of the increased number of parameters to optimize you need to supply more data to robustly estimate all of them. Or, simply initialize the distortion vectors with zeros and pass @CV_CALIB_RATIONAL_MODEL@ to enable the new model @CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5@ or other such combinations to selectively enable or disable certain coefficients.

** rectification of trinocular camera setup, where all 3 heads are on the same line, is added. see @samples/cpp/3calibration.cpp@

* @ml@

** Gradient boosting trees model has been contributed by NNSU team.

* @highgui@

** Experimental Qt backend for OpenCV has been added as a result of GSoC 2010 project, completed by Yannick Verdie. The backend has a few extra features, not present in the other backends, like text rendering using TTF fonts, separate "control panel" with sliders, push-buttons, checkboxes and radio buttons, interactive zooming, panning of the images displayed in @highgui@ windows, "save as" etc. Please, check the youtube videos where Yannick demonstrates the new features: http://www.youtube.com/user/MrFrenchCookie#p/u

** The new API is described here: http://opencv.itseez.com/modules/highgui/doc/qt_new_functions.html To make use of the new API, you need to have Qt SDK (or @libqt4@ with development packages) installed on your machine, and build OpenCV with Qt support (pass @-DWITH_QT=ON@ to CMake; watch the output, make sure Qt is used as GUI backend)

** 16-bit and LZW-compressed TIFFs are now supported.

** You can now set the mode for IEEE1394 cameras on Linux.

* @contrib@

** Chamfer matching algorithm has been contributed by Marius Muja, Antonella Cascitelli, Marco Di Stefano and Stefano Fabri. See @samples/cpp/chamfer.cpp@

* @gpu@

    This is completely new part of OpenCV, created with the support by NVIDIA.

    Note that the package is at alpha, probably early beta state, so use it with care and check OpenCV SVN for updates.

    In order to use it, you need to have the latest NVidia CUDA SDK installed, and build OpenCV with CUDA support (@-DWITH_CUDA=ON@ CMake flag).

    All the functionality is put to @cv::gpu@ namespace. The full list of functions and classes can be found at

    @opencv/modules/gpu/include/opencv2/gpu/gpu.hpp@, and here are some major components of the API:

** image arithmetics, filtering operations, morphology, geometrical transformations, histograms

** 3 stereo correspondence algorithms: Block Matching, Belief Propagation and Constant-Space Belief Propagation.

** HOG-based object detector. It runs more than order of magnitude faster than the CPU version!

    See @opencv/samples/gpu@

* @python@ bindings

  A lot more of OpenCV 2.x functionality is now covered by Python bindings.

  These new wrappers require @numpy@ to be installed

  (see http://opencv.willowgarage.com/wiki/InstallGuide for details).

  Likewise the C++ API, in the new Python bindings you do not need to allocate output arrays.

  They will be automatically created by the functions.

  Here is a micro example:

  <pre><code class="python">

  import cv

  a=cv.imread("lena.jpg",0)

  b=cv.canny(a, 50, 100, apertureSize=3)

  cv.imshow("test",b)

  cv.waitKey(0)</code></pre>

  In the sample @a@ and @b@ are normal @numpy@ arrays, so the whole power of @numpy@ and @scipy@ can now be combined with OpenCV functionality.

h3. Documentation, Samples

* Links to wiki pages have been added to each function description, see http://opencv.willowgarage.com

* All the samples have been documented with default output ''(0 or incomplete number of parameters)'' set to print out "howto" run instructions; most samples have been converted to C++ to use the new OpenCV API.

h3. Bug Fixes

* Over 300 issues have been resolved. Most of the issues (closed and still open) are listed at http://code.opencv.org/projects/opencv/issues?set_filter=1

* The old bug tracker at https://sourceforge.net/projects/opencvlibrary/ is now closed for updates. As soon as all the still relevant bug reports will be moved to code.ros.org, the old bug tracker will be completely deleted. Please, use the new tracker from now on.

h3. Known Problems/Limitations

* Installation package for Windows is still 32-bit only and does not include TBB support. You can build parallel or 64-bit version of OpenCV from the source code.

h2. Previous versions

[[ChangeLog_v10-v21|ChangeLog v1.0 - v2.1]]