History

WikiStart »

« Previous - Version 100/258 (diff) - Next » - Current version
Vadim Pisarevsky, 2015-12-22 07:08 pm

OpenCV Change Logs

Contents

2.4.4

March, 2013

  • Application framework, samples, tutorials, OpenCV4Android Manager are updated, see Android Release Notes for details.

  • Numerous improvements in gpu module and the following new functionality & optimizations:
    - Kepler optimizations
    - HoughLinesP for line segments detection
    - CARMA platform support
    - Lab/Luv <-> RGB conversions

  • 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).

  • >100 reported problems have been resolved since 2.4.3

version:2.4.3

November, 2012

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

  • 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.

  • 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

  • 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.

  • ~130 reported problems have been resolved since 2.4.2

  • 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.

2.4.2

July, 2012

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

  • 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.

  • Improved face recognizer and excellent tutorial on using it has been added by Philipp Wagner. Check the face recognition tutorial.

  • 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.

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:
     1//==== storing data ====
 2FileStorage fs(".xml", FileStorage::WRITE + FileStorage::MEMORY);
 3fs << "date" << date_string << "mymatrix" << mymatrix;
 4string buf = fs.releaseAndGetString();
 5
 6//==== reading it back ====
 7FileStorage fs(buf, FileStorage::READ + FileStorage::MEMORY);
 8fs["date"] >> date_string;
 9fs["mymatrix"] >> mymatrix;
  • Function signatures in documentation are made consistent with source code.
  • Restored python wrappers for SURF and MSER.

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.
  • 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).

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.

Common changes

  • 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).

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.

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.

OpenCV4Android

See the Android Release Notes.

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".

Bug fixes

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 
    1Ptr<DescriptorExtractor> d = new BriefDescriptorExtractor;

2.3.1

August, 2011

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.

Other New Functionality and Features

  • 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.

Documentation

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.

Bug Fixes

Known issues

  • TBB debug binaries are missed in the Windows installer. Here is a workaround:
    • Download tbb30_20110427oss_win.zip from the TBB website.
    • 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>

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! 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.

  • 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).

2.3 beta

June, 2011

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.

New Functionality, Features

  • 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, 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.

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 for details.

Documentation

  • 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.

Samples

Optimization

  • Several ML algorithms have been threaded using TBB.

Bug Fixes

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 in a few weeks.

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:

1#include "opencv2/imgproc/imgproc.hpp" 
2...

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.

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 + 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.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:

    1  import cv
2
3  a=cv.imread("lena.jpg",0)
4  b=cv.canny(a, 50, 100, apertureSize=3)
5  cv.imshow("test",b)
6  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

  • 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.

Bug Fixes

  • 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.

Previous versions

ChangeLog v1.0 - v2.1

ios6.jpg (7.9 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

threads.jpg (15 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

python.png (13.6 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

profile.jpg (9.9 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

denoising.jpg (17.9 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

optflow.jpg (14.2 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

github.jpg (14.5 kB) Vadim Pisarevsky, 2012-10-24 01:41 pm

CUDA.jpg (7.1 kB) Vadim Pisarevsky, 2012-10-25 01:33 pm

OpenCL.jpg (21.9 kB) Vadim Pisarevsky, 2012-10-25 01:33 pm

update.jpg (6.9 kB) Vadim Pisarevsky, 2012-11-02 09:47 am

Java_logo.svg.png (9.8 kB) Vadim Pisarevsky, 2013-02-08 11:05 am

superres.jpg (14.5 kB) Vadim Pisarevsky, 2013-04-09 02:00 pm

clahe.jpg (11.6 kB) Vadim Pisarevsky, 2013-04-09 02:00 pm

winrt_opencv.jpg (9.1 kB) Vadim Pisarevsky, 2013-04-09 03:17 pm

visual_studio_image_watch.png (18.2 kB) Vadim Pisarevsky, 2013-04-09 03:22 pm

qt.png (8.4 kB) Andrey Pavlenko, 2013-07-01 03:56 pm

github2.png (6.6 kB) Andrey Pavlenko, 2013-11-08 10:30 am

opencl2.png (5 kB) Andrey Pavlenko, 2013-11-08 10:54 am

viz.jpg - viz_logo (16.1 kB) Anatoly Baksheev, 2014-04-16 03:17 pm

Intel_IPP_logo.png (29.9 kB) Sergey Sivolgin, 2014-08-11 11:39 am

contrib.jpg (7.1 kB) Vadim Pisarevsky, 2014-08-18 12:06 pm

gsoc2013.jpg (18 kB) Vadim Pisarevsky, 2014-08-18 12:06 pm

gsoc2014.jpg (14 kB) Vadim Pisarevsky, 2014-08-18 12:06 pm

buildbot.png (23.1 kB) Vadim Pisarevsky, 2014-08-18 01:07 pm

attention.png (11.9 kB) Vadim Pisarevsky, 2014-08-18 01:08 pm

thank_you.jpg (28.9 kB) Vadim Pisarevsky, 2014-08-18 01:36 pm

neon.png (10 kB) Vadim Pisarevsky, 2014-11-10 11:54 am

hal.jpg (21 kB) Vadim Pisarevsky, 2015-04-22 03:49 pm

compatibility.jpg (9.5 kB) Vadim Pisarevsky, 2015-04-22 03:49 pm

mjpeg.jpg (8.5 kB) Vadim Pisarevsky, 2015-04-22 03:50 pm

daisy.jpg (11.6 kB) Vadim Pisarevsky, 2015-06-02 02:50 pm

win10.png (9.2 kB) Vadim Pisarevsky, 2015-12-11 02:43 pm

elcapitan.png (10.2 kB) Vadim Pisarevsky, 2015-12-11 02:43 pm

gsoc2015.jpg (16.7 kB) Vadim Pisarevsky, 2015-12-11 03:27 pm