updated docs: grabcut added, PDF updated, ChangeLog: removed duplicated section
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Vadim Pisarevsky
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@ -161,191 +161,7 @@ OpenCV 2.1 is basically a stabilized OpenCV 2.0, yet there are a few new feature
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</pre>
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<hr><h2>2.0 beta</h2>
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<p class="Blurb"><i>(September, 2009)</i></p>
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<pre>
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>>> New functionality, features: <<<
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- General:
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* The brand-new C++ interface for most of OpenCV functionality
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(cxcore, cv, highgui) has been introduced.
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Generally it means that you will need to do less coding to achieve the same results;
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it brings automatic memory management and many other advantages.
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See the C++ Reference section in opencv/doc/opencv.pdf and opencv/include/opencv/*.hpp.
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The previous interface is retained and still supported.
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* The source directory structure has been reogranized; now all the external headers are placed
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in the single directory on all platforms.
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* The primary build system is CMake, http://www.cmake.org (2.6.x is the preferable version).
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+ In Windows package the project files for Visual Studio, makefiles for MSVC,
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Borland C++ or MinGW are note supplied anymore; please, generate them using CMake.
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+ In MacOSX the users can generate project files for Xcode.
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+ In Linux and any other platform the users can generate project files for
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cross-platform IDEs, such as Eclipse or Code Blocks,
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or makefiles for building OpenCV from a command line.
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* OpenCV repository has been converted to Subversion, hosted at SourceForge:
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http://opencvlibrary.svn.sourceforge.net/svnroot/opencvlibrary
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where the very latest snapshot is at
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http://opencvlibrary.svn.sourceforge.net/svnroot/opencvlibrary/trunk,
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and the more or less stable version can be found at
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http://opencvlibrary.svn.sourceforge.net/svnroot/opencvlibrary/tags/latest_tested_snapshot
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- CXCORE, CV, CVAUX:
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* CXCORE now uses Lapack (CLapack 3.1.1.1 in OpenCV 2.0) in its various linear algebra functions
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(such as solve, invert, SVD, determinant, eigen etc.) and the corresponding old-style functions
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(cvSolve, cvInvert etc.)
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* Lots of new feature and object detectors and descriptors have been added
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(there is no documentation on them yet), see cv.hpp and cvaux.hpp:
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+ FAST - the fast corner detector, submitted by Edward Rosten
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+ MSER - maximally stable extremal regions, submitted by Liu Liu
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+ LDetector - fast circle-based feature detector by V. Lepetit (a.k.a. YAPE)
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+ Fern-based point classifier and the planar object detector -
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based on the works by M. Ozuysal and V. Lepetit
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+ One-way descriptor - a powerful PCA-based feature descriptor,
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(S. Hinterstoisser, O. Kutter, N. Navab, P. Fua, and V. Lepetit,
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"Real-Time Learning of Accurate Patch Rectification").
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Contributed by Victor Eruhimov
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+ Spin Images 3D feature descriptor - based on the A. Johnson PhD thesis;
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implemented by Anatoly Baksheev
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+ Self-similarity features - contributed by Rainer Leinhart
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+ HOG people and object detector - the reimplementation of Navneet Dalal framework
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(http://pascal.inrialpes.fr/soft/olt/). Currently, only the detection part is ported,
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but it is fully compatible with the original training code.
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See cvaux.hpp and opencv/samples/c/peopledetect.cpp.
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+ Extended variant of the Haar feature-based object detector - implemented by Maria Dimashova.
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It now supports Haar features and LBPs (local binary patterns);
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other features can be more or less easily added
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+ Adaptive skin detector and the fuzzy meanshift tracker - contributed by Farhad Dadgostar,
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see cvaux.hpp and opencv/samples/c/adaptiveskindetector.cpp
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* The new traincascade application complementing the new-style HAAR+LBP object detector has been added.
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See opencv/apps/traincascade.
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* The powerful library for approximate nearest neighbor search FLANN by Marius Muja
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is now shipped with OpenCV, and the OpenCV-style interface to the library
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is included into cxcore. See cxcore.hpp and opencv/samples/c/find_obj.cpp
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* The bundle adjustment engine has been contributed by PhaseSpace; see cvaux.hpp
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* Added dense optical flow estimation function (based on the paper
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"Two-Frame Motion Estimation Based on Polynomial Expansion" by G. Farnerback).
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See cv::calcOpticalFlowFarneback and the C++ documentation
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* Image warping operations (resize, remap, warpAffine, warpPerspective)
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now all support bicubic and Lanczos interpolation.
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* Most of the new linear and non-linear filtering operations (filter2D, sepFilter2D, erode, dilate ...)
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support arbitrary border modes and can use the valid image pixels outside of the ROI
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(i.e. the ROIs are not "isolated" anymore), see the C++ documentation.
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* The data can now be saved to and loaded from GZIP-compressed XML/YML files, e.g.:
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cvSave("a.xml.gz", my_huge_matrix);
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- MLL:
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* Added the Extremely Random Trees that train super-fast,
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comparing to Boosting or Random Trees (by Maria Dimashova).
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* The decision tree engine and based on it classes
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(Decision Tree itself, Boost, Random Trees)
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have been reworked and now:
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+ they consume much less memory (up to 200% savings)
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+ the training can be run in multiple threads (when OpenCV is built with OpenMP support)
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+ the boosting classification on numerical variables is especially
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fast because of the specialized low-overhead branch.
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* mltest has been added. While far from being complete,
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it contains correctness tests for some of the MLL classes.
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- HighGUI:
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* [Linux] The support for stereo cameras (currently Videre only) has been added.
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There is now uniform interface for capturing video from two-, three- ... n-head cameras.
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* Images can now be compressed to or decompressed from buffers in the memory,
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see the C++ HighGUI reference manual
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- Documentation:
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* The reference manual has been converted from HTML to LaTeX (by James Bowman and Caroline Pantofaru),
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so there is now:
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+ opencv.pdf for reading offline
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+ and the online up-to-date documentation
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(as the result of LaTeX->Sphinx->HTML conversion) available at
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http://opencv.willowgarage.com/documentation/index.html
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- Samples, misc.:
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* Better eye detector has been contributed by Shiqi Yu,
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see opencv/data/haarcascades/*[lefteye|righteye]*.xml
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* sample LBP cascade for the frontal face detection
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has been created by Maria Dimashova,
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see opencv/data/lbpcascades/lbpcascade_frontalface.xml
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* Several high-quality body parts and facial feature detectors
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have been contributed by Modesto Castrillon-Santana,
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see opencv/data/haarcascades/haarcascade_mcs*.xml
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>>> Optimization:
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* Many of the basic functions and the image processing operations
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(like arithmetic operations, geometric image transformations, filtering etc.)
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have got SSE2 optimization, so they are several times faster.
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- The model of IPP support has been changed. Now IPP is supposed to be
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detected by CMake at the configuration stage and linked against OpenCV.
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(In the beta it is not implemented yet though).
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* PNG encoder performance improved by factor of 4 by tuning the parameters
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>>> Bug fixes: <<<
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TBD
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(see http://sourceforge.net/tracker/?group_id=22870&atid=376677 of the list
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of the closed and still opened bugs).
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Many thanks to everybody who submitted bug reports and/or provided the patches!
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>>> Known issues:
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* configure+autotools based build is currently broken.
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Please, use CMake.
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* OpenCV bug tracker at SF still lists about 150 open bugs.
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Some of them may be actually fixed already, and most of the remaining bugs
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are going to be fixed by OpenCV 2.0 gold.
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* IPP is not supported. As the new OpenCV includes a lot of SSE2 code,
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it may be not such a serious problem, though.
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The support (at least for most important functions that do not have
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SSE2 optimization) will be returned in 2.0 gold.
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* The documentation has been updated and improved a lot, but it still
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needs quite a bit of work:
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- some of the new functionality in cvaux is not described yet.
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- the bibliography part is broken
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- there are quite a few known bugs and typos there
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- many of the hyperlinks are not working.
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* The existing tests partly cover the new functionality
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(via the old backward-compatibility OpenCV 1.x API), but the coverage is
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not sufficient of course.
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* The new-style Python interface is not included yet
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Many of the problems will be addressed in 2.0 gold.
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If you have found some specific problem, please, put the record to the bug tracker:
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http://sourceforge.net/tracker/?group_id=22870
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Better if the bug reports will include a small code sample in C++/python +
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all the necessary data files needed to reproduce the problem.
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</pre>
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<h2>2.0</h2>
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<hr><h2>2.0</h2>
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<p class="Blurb"><i>(September, 2009)</i></p>
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<pre>
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@ -1205,4 +1205,32 @@ can be found in OpenCV samples directory; see \texttt{watershed.cpp} demo.
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See also: \cvCppCross{findContours}
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\cvCppFunc{grabCut}
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Runs GrabCut algorithm
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\cvdefCpp{void grabCut(const Mat\& image, Mat\& mask, Rect rect,\par
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Mat\& bgdModel, Mat\& fgdModel,\par int iterCount, int mode );}
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\begin{description}
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\cvarg{image}{The input 8-bit 3-channel image.}
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\cvarg{mask}{The input/output 8-bit single-channel mask. Its elements may have one of four values. The mask is initialize when \texttt{mode==GC\_INIT\_WITH\_RECT}}
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\begin{description}
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\cvarg{GC\_BGD}{Certainly a background pixel}
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\cvarg{GC\_FGD}{Certainly a foreground (object) pixel}
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\cvarg{GC\_PR\_BGD}{Likely a background pixel}
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\cvarg{GC\_PR\_BGD}{Likely a foreground pixel}
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\end{description}
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\cvarg{rect}{The ROI containing the segmented object. The pixels outside of the ROI are marked as "certainly a background". The parameter is only used when \texttt{mode==GC\_INIT\_WITH\_RECT}}
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\cvarg{bgdModel, fgdModel}{Temporary arrays used for segmentation. Do not modify them while you are processing the same image}
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\cvarg{iterCount}{The number of iterations the algorithm should do before returning the result. Note that the result can be refined with further calls with the \texttt{mode==GC\_INIT\_WITH\_MASK} or \texttt{mode==GC\_EVAL}}
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\cvarg{mode}{The operation mode}
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\begin{description}
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\cvarg{GC\_INIT\_WITH\_RECT}{The function initializes the state and the mask using the provided rectangle. After that it runs \texttt{iterCount} iterations of the algorithm}
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\cvarg{GC\_INIT\_WITH\_MASK}{The function initializes the state using the provided mask. Note that \texttt{GC\_INIT\_WITH\_RECT} and \texttt{GC\_INIT\_WITH\_MASK} can be combined, then all the pixels outside of the ROI are automatically initialized with \texttt{GC\_BGD}}.
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\cvarg{GC\_EVAL}{The value means that algorithm should just resume.}
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\end{description}
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\end{description}
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The function implements the \href{http://en.wikipedia.org/wiki/GrabCut}{GrabCut} image segmentation algorithm.
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See the sample grabcut.cpp on how to use the function.
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\fi
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doc/opencv.pdf
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