OpenCV2.2出来了！

核心库结构重新进行了调整，针对具体领域进行细化，方便调用、生成更苗条的程序。

支持GPU加速（基于 NViDIA CUDA SDK）、支持Android应用开发！

修改了摄像头标定模型，加强对广角镜头的畸变校正。新增共线三摄像头的位姿标定。

立体匹配算法方面增加了Block Matching, Belief Propagation and Constant-Space Belief Propagation 的 GPU API。

加入了若干局部特征（SIFT，SURF，FAST），加入了LatentSVM object detector，GUI变成了QT等等。

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 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 type for fixed-type fixed-size matrices has been added. Vec is now derived from Matx . 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) + , y’ = y*(1 + k₁*r² + k₂*r⁴ + k₃*r⁶)/(1 + k₄*r² + k₅*r⁴ + k₆*r⁶) +
    • 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 + 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.
    • 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:

      •       import cv
        
              a=cv.imread("lena.jpg",0)
              b=cv.canny(a, 50, 100, apertureSize=3)
              cv.imshow("test",b)
              cv.waitKey(0)

        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.

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.
• 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.

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.
• The list of the other open bugs can be found at http://code.ros.org/trac/opencv/report/1.