- Update to version 4.1.0
* DNN module:
+ Reduced peak memory consumption for some models up to 30%.
+ Inference Engine
- Inference Engine 2018R3 is now a minimal supported version of IE.
- Myriad X (Intel® Neural Compute Stick 2) is now supported and tested.
- Automatic IR network reshaping for different inputs.
- Improved samples to work with models from OpenVINO Open Model Zoo
+ New networks from TensorFlow Object Detection API: Faster-RCNNs, SSDs
and Mask-RCNN with dilated convolutions, FPN SSD
* Performance improvements:
+ More optimization using AVX2 instruction set.
+ Automatic runtime dispatching is available for large set of functions
from core and imgproc modules.
* Other improvements:
+ Matplotlib Perceptually Uniform Sequential colormaps
+ Add keypoints matching visualization for real-time pose estimation tutorial
+ Add Hand-Eye calibration methods
+ Java: improved support for multidimensional arrays (Mat)
+ Dynamically loaded videoio backends (FFmpeg, GStreamer)
+ opencv_contrib: Robust local optical flow (RLOF) implementations
+ opencv_contrib: Implementation of Quasi Dense Stereo algorithm
+ opencv_contrib: New module: Image Quality Analysis (IQA) API
+ opencv_contrib: BRISQUE No Reference Image Quality Assessment (IQA) API
Check https://github.com/opencv/opencv/wiki/ChangeLog#version410
- Update to version 4.0.0
* A lot of C API from OpenCV 1.x has been removed. The affected modules are
objdetect, photo, video, videoio, imgcodecs, calib3d.
* Persistence (storing and loading structured data to/from XML, YAML or JSON)
in the core module has been completely reimplemented.
* OpenCV is now C++11 library and requires C++11-compliant compiler.
Thanks to the extended C++11 standard library, we could get rid of hand-crafted
cv::String and cv::Ptr. Now cv::String == std::string and cv::Ptr is a thin
wrapper on top of std::shared_ptr. Also, on Linux/BSD for cv::parallel_for_
we now use std::thread's instead of pthreads.
* DNN improvements
* Completely new module opencv_gapi has been added. It is the engine for very
efficient image processing, based on lazy evaluation and on-fly construction.
* Performance improvements
A few hundreds of basic kernels in OpenCV have been rewritten using so-called
"wide universal intrinsics". Those intrinsics map to SSE2, SSE4, AVX2, NEON or
VSX intrinsics, depending on the target platform and the compile flags.
* QR code detector and decoder have been added to opencv/objdetect module.
* The popular Kinect Fusion algorithm has been implemented, optimized for CPU and
GPU (OpenCL), and integrated into opencv_contrib/rgbd module.
* Very efficient and yet high-quality DIS dense optical flow algorithm has been
moved from opencv_contrib to opencv, video module. See the example.
* The slower TV L1 optical flow algorithm has been moved to opencv_contrib.
Check https://github.com/opencv/opencv/wiki/ChangeLog#version400
- Drop obsolete opencv-lib_suffix.patch
- Add 0001-Handle-absolute-OPENCV_INCLUDE_INSTALL_PATH-correctl.patch
- As this is a major version upgrade, the old 3.4.x package is still
available as opencv3
- Update to 3.4.3
* Compatibility fixes with python 3.7
* Added a new computational target DNN_TARGET_OPENCL_FP16
* Extended support of Intel's Inference Engine backend
* Enabled import of Intel's OpenVINO pre-trained networks from
intermediate representation (IR).
* tutorials improvements
Check https://github.com/opencv/opencv/wiki/ChangeLog#version343
for the complete changelog.
- Drop fix-build-i386-nosse.patch, build-workaround-issues-with-c.patch
(fixed upstream)
- Refresh patches
- Add patch to fix use of headers from C:
* build-workaround-issues-with-c.patch
- Update to 3.4.1:
* Added support for quantized TensorFlow networks
* OpenCV is now able to use Intel DL inference engine as DNN
acceleration backend
* Added AVX-512 acceleration to the performance-critical kernels
* For more information, read
https://github.com/opencv/opencv/wiki/ChangeLog#version341
- Update contrib modules to 3.4.1:
* No changelog available
- Change mechanism the contrib modules are built
- Include LICENSE of contrib tarball as well
- Build with python3 on >= 15
- Add patch to fix build on i386 without SSE:
* fix-build-i386-nosse.patch
- Refresh patches:
* fix_processor_detection_for_32bit_on_64bit.patch
* opencv-build-compare.patch
- Mention all libs explicitly
- Rebase 3.4.0 update from i@marguerite.su
- update to 3.4.0
* Added faster R-CNN support
* Javascript bindings have been extended to
cover DNN module
* DNN has been further accelerated for iGPU
using OpenCL
* On-disk caching of precompiled OpenCL
kernels has been finally implemented
* possible to load and run pre-compiled
OpenCL kernels via T-API
* Bit-exact 8-bit and 16-bit resize has been
implemented (currently supported only
bilinear interpolation)
- update face module to 3.4.0
- add opencv-lib_suffix.patch, remove LIB_SUFFIX
from OPENCV_LIB_INSTALL_PATH, as CMAKE_INSTALL
_LIBDIR is arch dependent.
- Add option to build without openblas
- Add conditionals for python2 and python3 to allow us enabling
only desired python variants when needed
- Do not depend on sphinx as py2 and py3 seem to collide there
- Readd opencv-gles.patch, it is *not* included upstream; otherwise
build breaks on all GLES Qt5 platforms (armv6l, armv7l, aarch64)
- add fix_processor_detection_for_32bit_on_64bit.patch
- Correctly set optimizations and dynamic dispatch on ARM, use
OpenCV 3.3 syntax on x86.
- Update licensing information
- change requires of python-numpy-devel to build in Leap and
to not break factory in future
- fix build error/unresolvable for Leap 42.2 and 42.3
- Update to version 3.3.1:
* Lots of various bugfixes
- Update source url
- Rename python subpackage to python2
- Do not explicitly require python-base for python subpackages
- Update to 3.3
- Dropped obsolete patches
* opencv-gcc6-fix-pch-support-PR8345.patch
* opencv-gles.patch
- Updated opencv-build-compare.patch
- Add 0001-Do-not-include-glx.h-when-using-GLES.patch
Fix build for 32bit ARM, including both GLES and desktop GL headers
causes incompatible pointer type errors
- Add conditional for the qt5/qt4 integration
* This is used only for gui tools, library is not affected
- Add provides/obsoletes for the qt5 packages to allow migration
- Drop patch opencv-qt5-sobump.diff
* Used only by the obsoleted qt5 variant
- Cleanup a bit with spec-cleaner
- Use %cmake macros
- Remove the conditions that are not really needed
- Add tests conditional disabled by default
* Many tests fail and there are missing testdata
- Switch to pkgconfig style dependencies
- Update to OpenCV 3.2.0
- Results from 11 GSoC 2016 projects have been submitted to the library:
+ sinusoidal patterns for structured light and phase unwrapping module
[Ambroise Moreau (Delia Passalacqua)]
+ DIS optical flow (excellent dense optical flow algorithm that is both
significantly better and significantly faster than Farneback’s algorithm –
our baseline), and learning-based color constancy algorithms implementation
[Alexander Bokov (Maksim Shabunin)]
+ CNN based tracking algorithm (GOTURN) [Tyan Vladimir (Antonella Cascitelli)]
+ PCAFlow and Global Patch Collider algorithms implementation
[Vladislav Samsonov (Ethan Rublee)]
+ Multi-language OpenCV Tutorials in Python, C++ and Java
[João Cartucho (Vincent Rabaud)]
+ New camera model and parallel processing for stitching pipeline
[Jiri Horner (Bo Li)]
+ Optimizations and improvements of dnn module
[Vitaliy Lyudvichenko (Anatoly Baksheev)]
+ Base64 and JSON support for file storage. Use names like
“myfilestorage.xml?base64” when writing file storage to store big chunks of
numerical data in base64-encoded form. [Iric Wu (Vadim Pisarevsky)]
+ tiny_dnn improvements and integration
[Edgar Riba (Manuele Tamburrano, Stefano Fabri)]
+ Quantization and semantic saliency detection with tiny_dnn
[Yida Wang (Manuele Tamburrano, Stefano Fabri)]
+ Word-spotting CNN based algorithm
[Anguelos Nicolaou (Lluis Gomez)]
- Contributions besides GSoC:
+ Greatly improved and accelerated dnn module in opencv_contrib:
- Many new layers, including deconvolution, LSTM etc.
- Support for semantic segmentation and SSD networks with samples.
- TensorFlow importer + sample that runs Inception net by Google.
+ More image formats and camera backends supported
+ Interactive camera calibration app
+ Multiple algorithms implemented in opencv_contrib
+ Supported latest OSes, including Ubuntu 16.04 LTS and OSX 10.12
+ Lot’s of optimizations for IA and ARM archs using parallelism, vector
instructions and new OpenCL kernels.
+ OpenCV now can use vendor-provided OpenVX and LAPACK/BLAS (including Intel MKL,
Apple’s Accelerate, OpenBLAS and Atlas) for acceleration
- Refreshed opencv-build-compare.patch
- Dropped upstream opencv-gcc5.patch
- Replace opencv-gcc6-disable-pch.patch with upstream patch
opencv-gcc6-fix-pch-support-PR8345.patch
- Enable TBB support (C++ threading library)
- Add dependency on openBLAS
- Enable ffmpeg support unconditional
- In case we build using GCC6 (or newer), add -mlra to CFLAGS to
workaround gcc bug
https://gcc.gnu.org/bugzilla/show_bug.cgi?id=71294.
- Apply upstream patch opencv-gcc6-disable-pch.patch to disable
PCH for GCC6.
- Test for python versions greater than or equal to the current
version.
- Add python 3 support
- Added opencv_contrib_face-3.1.0.tar.bz2
* This tarball is created to take only the face module from the
contrib package. The Face module is required by libkface, which
in its turn is required by digikam.
- Added _constraints file to avoid random failures on small workers
(at least for builds on PMBS)
- Update to OpenCV 3.1.0
- A lot of new functionality has been introduced during Google
Summer of Code 2015:
+ “Omnidirectional Cameras Calibration and Stereo 3D
Reconstruction” – opencv_contrib/ccalib module
(Baisheng Lai, Bo Li)
+ “Structure From Motion” – opencv_contrib/sfm module
(Edgar Riba, Vincent Rabaud)
+ “Improved Deformable Part-based Models” – opencv_contrib/dpm
module (Jiaolong Xu, Bence Magyar)
+ “Real-time Multi-object Tracking using Kernelized Correlation
Filter” – opencv_contrib/tracking module
(Laksono Kurnianggoro, Fernando J. Iglesias Garcia)
+ “Improved and expanded Scene Text Detection” –
opencv_contrib/text module (Lluis Gomez, Vadim Pisarevsky)
+ “Stereo correspondence improvements” – opencv_contrib/stereo
module (Mircea Paul Muresan, Sergei Nosov)
+ “Structured-Light System Calibration” –
opencv_contrib/structured_light (Roberta Ravanelli,
Delia Passalacqua, Stefano Fabri, Claudia Rapuano)
+ “Chessboard+ArUco for camera calibration” –
opencv_contrib/aruco (Sergio Garrido, Prasanna, Gary Bradski)
+ “Implementation of universal interface for deep neural
network frameworks” – opencv_contrib/dnn module
(Vitaliy Lyudvichenko, Anatoly Baksheev)
+ “Recent advances in edge-aware filtering, improved SGBM
stereo algorithm” – opencv/calib3d and opencv_contrib/ximgproc
(Alexander Bokov, Maksim Shabunin)
+ “Improved ICF detector, waldboost implementation” –
opencv_contrib/xobjdetect (Vlad Shakhuro, Alexander Bovyrin)
+ “Multi-target TLD tracking” – opencv_contrib/tracking module
(Vladimir Tyan, Antonella Cascitelli)
+ “3D pose estimation using CNNs” – opencv_contrib/cnn_3dobj
(Yida Wang, Manuele Tamburrano, Stefano Fabri)
- Many great contributions made by the community, such as:
+ Support for HDF5 format
+ New/Improved optical flow algorithms
+ Multiple new image processing algorithms for filtering,
segmentation and feature detection
+ Superpixel segmentation and much more
- IPPICV is now based on IPP 9.0.1, which should make OpenCV
even faster on modern Intel chips
- opencv_contrib modules can now be included into the
opencv2.framework for iOS
- Newest operating systems are supported: Windows 10 and
OSX 10.11 (Visual Studio 2015 and XCode 7.1.1)
- Interoperability between T-API and OpenCL, OpenGL, DirectX and
Video Acceleration API on Linux, as well as Android 5 camera.
- HAL (Hardware Acceleration Layer) module functionality has been
moved into corresponding basic modules; the HAL replacement
mechanism has been implemented along with the examples
- Removed improve-sphinx-search.diff, opencv-altivec-vector.patch,
opencv-pkgconfig.patch and opencv-samples.patch, fixed upstream.
- Fixed opencv-qt5-sobump.diff, opencv-build-compare.patch,
opencv-gcc5.patch and opencv-gles.patch.
- Version OpenCV 3.0.0
+ ~1500 patches, submitted as PR @ github. All our patches go
the same route.
+ opencv_contrib (http://github.com/itseez/opencv_contrib)
repository has been added. A lot of new functionality is there
already! opencv_contrib is only compatible with 3.0/master,
not 2.4. Clone the repository and use “cmake …
-D OPENCV_EXTRA_MODULES_PATH=<path_to opencv_contrib/modules> …”
to build opencv and opencv_contrib together.
+ 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. It’s used by default in x86 and x64 builds
on Windows, Linux and Mac.
+ 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. It covers ~100 OpenCV functions.
This work has been done by contract and with generous support
from AMD and Intel companies.
+ ~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 (still in alpha stage).
+ 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
- etc.
+ Besides Google, we enjoyed (and hope that you will enjoy too)
many useful contributions from community, like:
- biologically inspired vision module
- DAISY features, LATCH descriptor, improved BRIEF
- image registration module
- etc.
- Reduce build-compare noise
opencv-build-compare.patch
- Remove BuildRequirement for python-sphinx in SLE12, since it's
not available there and it's not a mandatory requirement.
- Reduce differences between two spec files
- Use pkgconfig for ffmpeg BuildRequires
- Update improve-sphinx-search.diff for new python-Sphinx(1.3.1)
* now that sphinx-build disallow executing without arguments and
give you "Insufficient arguments" error, use "sphinx-build -h"
instead
* the default usages output ie. sphinx-build(or --help) no longer
are standard error but standard output, drop OUTPUT_QUIET and
add OUTPUT_VARIABLE throws the output to SPHINX_OUTPUT as well
- support gcc 5 (i.e. gcc versions without minor version):
opencv-gcc5.patch
- Update to OpenCV 2.4.11 - can't find NEWS or Changelog
merely collecting bug fixes while 3.0 is in the making, 2.4.11
didn't even make it on their web page, it's only on download
server
- remove opencv-underlinking.patch as obsolete
- remove upstream patch bomb_commit_gstreamer-1x-support.patch
- commenting out opencv-pkgconfig.patch - possibly it requires a rebase,
but the problem it tries to solve is unclear
- Add specific buildrequires for libpng15, so that we are
building against the system provided libpng.
- Update to OpenCV 2.4.9
More info at:
http://opencv.org/opencv-2-4-9-is-out.html
The brief list of changes:
* new 3D visualization module ‘viz’;
* performance fixes in ‘ocl’ module;
* fixes in Android Camera;
* improved CUDA support for mobile platforms;
* bugfixes from community;
* 55 reported bugs have been closed;
* 156 pull requests have been merged.
- Drop the BuildRequires on libucil and libunicap for Factory. This
stops us from getting ride of Gstreamer 0.10 and besides these two
libraries seem to be unmaintained upstream as that the latest
actions are from 2010
- Add upstream patch (3.0 version) to support Gstreamer 1.x
* bomb_commit_gstreamer-1x-support.patch
- Upstream now provides tarballs with source only as git tags
from github so update Source0 path.
- Add requires on various X extensions linked to opencv_ts module.
As those are present in the .pc file we need it anyway.
- Update to OpenCV 2.4.8
More info at:
http://opencv.org/opencv-2-4-8.html
The brief list of changes:
* NVidia CUDA support on Android devices with CUDA capable SoC and
CUDA sample;
* Concurrent kernel execution and user defined context support for
OpenCL;
* Integration with Intel Perceptual SDK and new depth sensors support
for Windows;
* 32 reported bugs have been closed;
* 139 pull requests have been merged;
- Fix build with altivec:
opencv-altivec-vector.patch
- Added opencv-pkgconfig.patch: make sure to provide link flags in
OpenCV pc file (bnc#853036)
- Update to OpenCV 2.4.7
More info at:
http://opencv.org/opencv-2-4-7-is-out.html
The brief list of changes:
* dynamic OpenCL runtime loading, setting default OpenCL device
via env var, many bug-fixes and some new optimization with OpenCL
* bug-fixes and new optimizations in CUDA stuff
* latest NDK and Android OS support, Native Android Camera tuning
* minor fixes, XAML sample and MS Certification compatibility
in WinRT stuff
* 382 pull requests have been merged
* 54 reported bugs have been fixed
- Added pkgconfig(glu) Requires to devel package, as per .pc file
- Make devel package provides also devel-static one
- Drop assume-Sphinx-is-there.diff, and add improve-sphinx-search.diff,
for properly finding sphinx with alphabetic chars in version
- Add patch assume-Sphinx-is-there.diff to fix building with
Sphinx versions that have alphanumeric characters in the version
(Only for factory builds at the moment)
- Use eigen3 instead of eigen2 as build requirement for openSUSE > 12.3.
- Enable compilation with libucil and libunicap.
- Removed dos2unix build requirement (not needed anymore).
- Update to OpenCV 2.4.6.1
More info at:
http://opencv.org/opencv-2-4-6-is-out.html
The brief list of changes:
* added video file i/o Windows RT and sample application using
camera, enabled parallelization with TBB or MS Concurrency
* added CUDA 5.5 support for desktop and ARM systems
* added Qt 5 support
* added many new OpenCL algorithms ports, included OpenCL binaries
into the Windows superpack
* iOS build scripts (together with Android ones) moved to
‘opencv/platforms’ directory
* added functions for UIImage <-> cv::Mat conversion
* correct front/back camera selection in Android app framework
* added Linaro NDK support and fixes for MIPS to Android CMake
toolchain
* stability has been improved by a lot, numerous bug-fixes across
all the library
- build with LFS_CFLAGS in 32 bit archs.
- Disable SSE3 for all architectures (bnc#814333)
- Disable SSE(2) on non x86_64 architectures, causes crashing
kde#276923, bnc#789173
- Update to OpenCV 2.4.5
More info at:
http://opencv.org/opencv-2-4-5-is-out.html
The brief list of changes:
* experimental WinRT support
* new video super-resolution module
* CLAHE (adaptive histogram equalization) algorithm on both CPU
and GPU
* further improvements and extensions in ocl module
(stereo block matching and belief propagation have been added,
fixed crashes on Intel HD4000)
* Visual Studio 2012 cv::Mat visualizer plugin debugger tutorial
from Microsoft Research
* OpenCV4Android SDK improvements
(NDK r8e support, native activity sample using OpenCV Manager,
bug-fixes)
* ~25 reported problems have been resolved since 2.4.4, ~78 pull
requests have been merged, thanks everybody who participated!
- Update to OpenCV 2.4.4
More info at:
http://opencv.org/opencv-2-4-4-is-out.html
The brief list of changes:
* OpenCV Java bindings are ported from Android to desktop Java!
Actually any JVM language will work, see Tutorial for details,
and Java or Scala code samples.
* Android application framework, samples, tutorials, and OpenCV
Manager are improved.
* Optimizations for the new NVIDIA Kepler architecture, CARMA
platform support and other new optimizations in CUDA.
* OpenCL module now builds successfully with various SDKs (from
AMD, NVIDIA, Intel and Apple) and runs well on different GPUs
(AMD, NVidia, Intel HD4000). A lot of new functionality has been
added, tons of bugs fixed, performance of many functions has
been significantly improved.
* 100+ reported problems have been resolved since 2.4.3, thanks
everybody who participated!
- Drop the buildrequire for libxine
- Update to OpenCV 2.4.3
More info at:
http://opencv.org/opencv-2-4-3-released.html
The nicely formatted changelog can be seen here:
http://code.opencv.org/projects/opencv/wiki/ChangeLog;
here are the highlights:
* A lot of good stuff from the Google Summer of Code 2012 has been
integrated; this was a very productive summer!
* Significantly improved and optimized Android and iOS ports.
* Greatly extended GPU (i.e. CUDA-based) module.
* The brand new ocl (OpenCL-based) module that unleashes GPU power
also for AMD and Intel GPU users. It’s not included into the
binary package, since there are different SDKs, and it’s not
turned on by default. You need to run CMake and turn on
“WITH_OPENCL”. Also, please note that this is very first version
of the module, so it may be not very stable and not very
functional.
* Much better performance on many-core systems out of the box. You
do not need TBB anymore on MacOSX, iOS and Windows. BTW, the
binary package for Windows is now built without TBB support.
Libraries and DLLs for Visual Studio 2010 use the Concurrency
framework.
* About 130 bugs have been fixed since 2.4.2.
* Since 2.4.3rc we fixed several more problems, in particular some
compile problems with iOS 6 SDK.
- buildrequire glu
- Update to OpenCV 2.4.2
More info at:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
- Drop opencv-datadir.patch to comply with upstream directory layout
- Update to OpenCV 2.4.1
More info at:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
- Update to OpenCV 2.4.0
More info at:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
- Add opencv-gcc47.patch: Fix build with gcc 4.7.
- Use Explicit Buildrequires on several needed libraries
future dependency cleanups may/will cause build to fail otherwise.
- Add upstream r6881 to fix clang compatibility
- uncomment libraries not in 12.1 for now
- Changed groups (fix for RPMLINT warning)
- Added check for duplicate files (fix for RPMLINT warning)
- Added py_requires macros and python-base dependencies (fix for RPMLINT warning)
- Escaped macros (fix for RPMLINT warning)
- Fixed end-of-line encoding problems (fix for RPMLINT warning)
- Added libeigen2-devel buildrequires
- Added libunicap and libucil buildrequires (libunicap supports requires libucil)
- Cleaned up spec file formatting
- Dropped opencv-2.3-ffmpeg.patch, applied upstream
- Revive opencv-2.3-ffmpeg.patch, needs rebase
- Tag all patches according to openSUSE packaging guidelines
- Removed opencv-2.3-cmake.patch, old cmake cannot be used any more.
- Python bindings cannot be built without NumPy any more.
- Update to OpenCV 2.3.1
- Update and readd opencv-2.3-underlinking.patch since it is still
necessary.
- Fix support for new ffmpeg versions
- Removed unnecessary patches
- Enable Python NumPy support on openSUSE 11.2
- Build Qt instead of Gtk GUI
- Fix cmake files for openSUSE 11.1
- No GStreamer support on openSUSE 11.1
- Update to OpenCV 2.3.0.
More info at:
http://opencv.willowgarage.com/wiki/OpenCV%20Change%20Logs
- Fix build on openSUSE 11.2
- Add opencv-v4l-2.6.38.patch: use the new libv4l2 interface
- Enable Python NumPy support
- SWIG is not required any more
- Enable OpenEXR support
- Update to OpenCV 2.2.0.
More info at:
http://opencv.willowgarage.com/wiki/OpenCV%20Change%20Logs
- Use system zlib, oh, and do not export ZLIB symbols to
other applications, clashes ensued.
- fix build with gcc 4.6
- add -underlinking patch
- devel package renamed to opencv-devel, so that switching between
OBS and packman opencv packages is easier
- fix gstreamer support
- fix xine support
- fix some rpmlint warnings
- fix shared libraries permissions
- Do not waste resources building the tests as we do not run them
- Do not disable SSE,SSE2,etc. According to OpenCV changelog,
it should be safe to leave these enabled.
- fix build on openSUSE 11.0
- Update to OpenCV 2.1.0:
* The whole OpenCV is now using exceptions instead of the old
libc-style mechanism
* Experimental "static" OpenCV configuration in CMake was
contributed by Jose Luis Blanco.
Pass "BUILD_SHARED_LIBS=OFF" to CMake to build OpenCV statically.
* new improved version of one-way descriptor is added
* User can now control the image areas visible after the stereo
rectification
* Fullscreen has been added (thanks to Yannick Verdie).
* Further info at:
http://opencv.willowgarage.com/wiki/OpenCV%20Change%20Logs
- fix build with libpng14
- small spec file cleanup
- Moved to the KDE repositories to enable inclusion in kipi-plugins
- Initial package
OBS-URL: https://build.opensuse.org/request/show/714221
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/opencv?expand=0&rev=73
- Add patch to fix use of headers from C:
* build-workaround-issues-with-c.patch
- Update to 3.4.1:
* Added support for quantized TensorFlow networks
* OpenCV is now able to use Intel DL inference engine as DNN
acceleration backend
* Added AVX-512 acceleration to the performance-critical kernels
* For more information, read
https://github.com/opencv/opencv/wiki/ChangeLog#version341
- Update contrib modules to 3.4.1:
* No changelog available
- Change mechanism the contrib modules are built
- Include LICENSE of contrib tarball as well
- Build with python3 on >= 15
- Add patch to fix build on i386 without SSE:
* fix-build-i386-nosse.patch
- Refresh patches:
* fix_processor_detection_for_32bit_on_64bit.patch
* opencv-build-compare.patch
- Mention all libs explicitly
- Rebase 3.4.0 update from i@marguerite.su
- update to 3.4.0
* Added faster R-CNN support
* Javascript bindings have been extended to
cover DNN module
* DNN has been further accelerated for iGPU
using OpenCL
* On-disk caching of precompiled OpenCL
kernels has been finally implemented
OBS-URL: https://build.opensuse.org/request/show/612803
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/opencv?expand=0&rev=71
- Upstream now provides tarballs with source only as git tags
from github so update Source0 path.
- Add requires on various X extensions linked to opencv_ts module.
As those are present in the .pc file we need it anyway.
- Update to OpenCV 2.4.8
More info at:
http://opencv.org/opencv-2-4-8.html
The brief list of changes:
* NVidia CUDA support on Android devices with CUDA capable SoC and
CUDA sample;
* Concurrent kernel execution and user defined context support for
OpenCL;
* Integration with Intel Perceptual SDK and new depth sensors support
for Windows;
* 32 reported bugs have been closed;
* 139 pull requests have been merged;
OBS-URL: https://build.opensuse.org/request/show/225779
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/opencv?expand=0&rev=49
