Update to 2.2.0.

2.2

   (December, 2010)

   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:
          * #include "opencv2/imgproc/imgproc.hpp"
          * ..
     * Please, check the new C and C++ samples
       (https://code.ros.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 added 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
       http://opencv.willowgarage.com/wiki/Android
     * The completely new opencv_gpu acceleration module has been created
       with support by NVidia. See below for details.

   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.willowgarage.com/documentation/cpp/features2d_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 + k1*r2 + k2*r4 + k3*r6)/(1 + k4*r2 + k5*r4 +
                 k6*r6) + <tangential_distortion for x>,

                 y' = y*(1 + k1*r2 + k2*r4 + k3*r6)/(1 + k4*r2 + k5*r4 +
                 k6*r6) + <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.willowgarage.com/documentation/cpp/highgui_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/cpp/

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

   Documentation, Samples
     * Links to wiki pages (mostly empty) have been added to each function
       description, see http://opencv.willowgarage.com
     * All the samples have been documented; most samples have been converted
       to C++ to use the new OpenCV API.

   Bug Fixes
     * Over 300 issues have been resolved. Most of the issues (closed and
       still open) are listed at https://code.ros.org/trac/opencv/report/6.


(wiz)