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Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
of charge, free of licensing fees, for commercial and non-commerical
use.
- T-API (transparent API) has been introduced, this is transparent GPU
acceleration layer using OpenCL. It does not add any compile-time or
runtime dependency of OpenCL. When OpenCL is available, it's detected
and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
because these are mostly basic functions, some higher-level functions
got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
of NEON-optimized code and that should form a base for the open-source
and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
objdetect etc. They now follow the uniform "abstract interface - hidden
implementation" pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
have been integrated in opencv 3.0 and opencv_contrib (earlier results
are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware
filters, superpixels,...)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new
tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
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* under clang, C-style cast from nullptr_t to enum are not allowed.
Ok@ wiz
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via PLIST_VARS
Reviewed by wiz@
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From Mansour Moufid in private mail.
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whilst here to correctly find the preferred zlib.
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Changelog:
2.4.9
April, 2014
Several improvements in OpenCL optimizations (ocl::sum, ocl::countNonZero, ocl::minMax, bitwise operationss, Haar face detector, etc)
Multiple fixes in Naitve Camera (NativeCameraView, cv::VideoCapture);
Improved CUDA support for all CUDA-enabled SoCs.
New VTK-based 3D visualization module viz stabilized and back-ported to 2.4 branch.
The module provides a very convenient way to display and position clouds, meshes, cameras and trajectories, and simple widgets (cube, line, circle, etc.).
Full demo video can be found at Itseez Youtube channel
Numerous bugfixes in code and docs from community
156 pull requests have been merged since 2.4.8
55 reported bugs have been closed since 2.4.8
2.4.8
December, 2013
User provided OpenCL context can be used by OpenCV ( ocl::initializeContext )
A separate OpenCL command queue is created for every CPU thread (allows concurrent kernels execution)
Some new OpenCL optimizations and bug-fixes
NVidia CUDA support on CUDA capable SoCs;
Android 4.4 support, including native camera;
Java wrappers for GPU-detection functions from core module were added;
New sample with CUDA on Android was added;
OpenCV Manager and apps hanging were fixed on Samsung devices with Android 4.3 (#3368, #3372, #3403, #3414, #3436).
Static linkage support for native C++ libraries;
139 pull requests have been merged since version:2.4.7!
32 reported bugs have been closed since version:2.4.7
2.4.7
November, 2013
Now 'ocl' module can be built without installing OpenCL SDK (Khronos headers in OpenCV tree);
Dynamic dependency on OpenCL runtime (allows run-time branching between OCL and non-OCL implementation);
Changing default OpenCL device via OPENCV_OPENCL_DEVICE environment variable (without app re-build);
Refactoring/extending/bug-fixing of existing OpenCL optimizations, updated documentation;
New OpenCL optimizations of SVM, MOG/MOG2, KalmanFilter and more;
New optimization for histograms, TV-L1 optical flow and resize;
Updated multi gpu sample for stereo matching;
Fixed BGR<->YUV color conversion and bitwize operations;
Fixed several build issues;
Android NDK-r9 (x86, x86_64) support;
Android 4.3 support: hardware detector (Bugs #3124, #3265, #3270) and native camera (Bug #3185);
MediaRecorder hint enabled for all Android devices with API level 14 and above;
Fixed JavaCameraView slowdown (Bugs #3033, #3238);
Fixed MS Certification test issues for all algorithmical modules and highgui, except OpenEXR and Media Foundation code for camera;
Implemented XAML-based sample for video processing using OpenCV;
Fixed issue in Media Foundation back-end for VideoCapture (#3189);
382 pull requests have been merged since 2.4.6!
54 reported bugs have been fixed since 2.4.6 (issue tracker query).
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Fix PR pkg/48777
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From Tobias Nygren in PR 48544.
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Changes in 2.4.6.1:
* Hotfix for camera pipeline for Linux (V4L).
Changes in 2.4.6:
* Windows RT: added video file i/o and sample application using camera,
enabled parallelization with TBB or MS Concurrency
* CUDA 5.5: added support for desktop and ARM
* Added Qt 5 support
* Binary compatiblility with both OpenCL 1.1/1.2 platforms. Now the binaries
compiled with any of AMD/Intel/Nvidia's SDK can run on all other platforms.
* New functions ported, CLAHE, GoodFeaturesToTrack, TVL1 optical flow and more
* Performance optimizations, HOG and more.
* More kernel binary cache options though setBinaryDiskCache interface.
* OpenCL binaries are now included into the superpack for Windows (for VS2010
and VS2012 only)
* Switched all the remaining parallel loops from TBB-only
'tbb::parallel_for()' to universal 'cv::parallel_for_()' with many possible
backends (MS Concurrency, Apple's GDC, OpenMP, Intel TBB etc.)
* iOS build scripts (together with Android ones) moved to 'opencv/platforms'
directory
* Fixed bug with incorrect saved video from camera through CvVideoCamera
* Added 'rotateVideo' flag to the CvVideoCamera class to control camera
preview rotation on device rotation
* Added functions to convert between UIImage and cv::Mat (just include
opencv2/highgui/ios.h)
* Numerous bug-fixes across all the library
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2.4.5
April, 2013
Experimental WinRT support (build for WindowsRT guide)
the new video super-resolution module has been added that
implements the following papers:
- S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and robust
Super-Resolution. Proc 2003 IEEE Int Conf on Image Process,
pp. 291â294, 2003.
- D. Mitzel, T. Pock, T. Schoenemann, D. Cremers. Video super
resolution using duality based TV-L1 optical flow. DAGM, 2009.
CLAHE (adaptive histogram equalization) algorithm has been
implemented, both CPU and GPU-accelerated versions (in imgproc
and gpu modules, respectively)
there are further improvements and extensions in ocl module:
- 2 stereo correspondence algorithms: stereobm (block matching)
and stereobp (belief propagation) have been added
- many bugs fixed, including some crashes on Intel HD4000
The tutorial on displaying cv::Mat inside Visual Studio 2012
debugger has been contributed by Wolf Kienzle from Microsoft
Research. See
http://opencv.org/image-debugger-plug-in-for-visual-studio.html
78 pull requests have been merged. Big thanks to everybody who
contributed!
At least 25 bugs have been fixed since 2.4.4 (see
http://code.opencv.org/projects/opencv/issues select closed
issues with target version set to "2.4.5").
2.4.4
March, 2013
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 for the contribution!
Check the tutorial for details and code samples.
Android application framework, samples, tutorials, OpenCV
Manager are updated, see Android Release Notes for details.
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
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 âspecialâ memory (e.g. pinned
memory, host-local or device-local).
The ocl module can detect if the underlying hardware supports
âintegrated memory,â and if so use âdevice-localâ 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).
Many people contributed their code in the form of pull requests.
Here are some of the most interesting contributions, that were
included into 2.4 branch:
>100 reported problems have been resolved since 2.4.3
Oscar Deniz submitted smile detector and sample.
Alexander Smorkalov created a tutorial on cross-compilation
of OpenCV for Linux on ARM platforms.
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NetBSD 6, requested by tron.
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Recursively bump package revisions again after the "freetype2" and
"fontconfig" handling was fixed.
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to address issues with NetBSD-6(and earlier)'s fontconfig not being
new enough for pango.
While doing that, also bump freetype2 dependency to current pkgsrc
version.
Suggested by tron in PR 47882
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Changelog:
* Add universal parallell mechianism support
* Add sample codes
* Add some new algorithms
* Many improvements in GPU support
* Many bugfixes
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Suggested by SAITOH Masanobu <msaitoh@execsw.org> in PR 47051.
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requested by Thomas Klausner.
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(additionaly, reset PKGREVISION of qt4-* sub packages from base qt4 update)
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- New keypoint descriptor FREAK contributed by EPFL group
- Improved face recognizer class and tutorial added by Philipp Wagner
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installation on Mac OS X
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