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libopencv3_4-3.4.16-150400.1.9 RPM for aarch64

From OpenSuSE Leap 15.5 for aarch64

Name: libopencv3_4 Distribution: SUSE Linux Enterprise 15
Version: 3.4.16 Vendor: SUSE LLC <>
Release: 150400.1.9 Build date: Sun May 8 12:23:59 2022
Group: System/Libraries Build host: ibs-centriq-5
Size: 18842264 Source RPM: opencv3-3.4.16-150400.1.9.src.rpm
Summary: Libraries to use OpenCV computer vision
The Open Computer Vision Library is a collection of algorithms and sample code
for various computer vision problems. The library is compatible with IPL and
utilizes Intel Integrated Performance Primitives for better performance.






* Mon Nov 01 2021
  - Update to 3.4.16
    * For details see
    * Bug fixes, optimizations and other enhancements are propagated
      into OpenCV 4.x
  - Add opencv3-pr19384-tbb2021.patch for support of TBB 2021
    * gh#opencv/opencv#19384
* Tue Apr 13 2021
  - do not require pkgconfig(IlmBase), the correct dependency
    [pkgconfig(IlmBase) for openexr < 3.0.0, pkgconfig(Imath) for
    openexr >= 3.0.0] is pulled by pkgconfig(OpenEXR)
* Thu Jan 21 2021
  - Use the python2/python3 macros only when the corresponding
    binaries are installed, needed by new python-rpm-macros
* Fri Jun 19 2020
  - Update to 3.4.10
    For details, see
  - Remove obsolete patches:
    * opencv-gles.patch
    * 0001-Do-not-include-glx.h-when-using-GLES.patch
    * opencv-gcc10.patch
  - Disable Python 2 bindings for Tumbleweed
* Wed Jun 03 2020
  - Fix build with GCC10:
    * opencv-gcc10.patch
* Wed Feb 19 2020
  - Drop Jasper (i.e jpeg2k) support (boo#1130404, boo#1144260)
    JasPer is unmaintained, CVEs are not being addressed (some issues
    received patches submitted to the upstream github project, but are
    not being merged, other CVEs are considered unfixable).
* Wed Dec 25 2019
  - Update to 3.4.9
    For details, see
* Sun Dec 08 2019
  - Update to 3.4.8
    For details, see
    Drop upstream CVE-2019-15939.patch
  - Adjust _constraints, fix out-of-disk-space failures
* Thu Oct 17 2019
  - Update to 3.4.7
    Maintenance release, no changelog provided
    * Security fixes: CVE-2019-14491 (boo#1144352), CVE-2019-14492
  - Drop fix_processor_detection_for_32bit_on_64bit.patch: fixed
  - Add CVE-2019-15939.patch: add input check in HOG detector
    (boo#1149742 CVE-2019-15939).
* Fri Sep 13 2019
  - Disable LTO on ppc64le for now, as it fails to build when enabled
* Tue Aug 27 2019
  - Avoid use of ®/™ signs in specfiles as per guidelines.
* Tue Jul 30 2019
  - Reenable LTO again, fixed in GCC upstream:
* Wed Jul 24 2019
  - Disable lto to work around an internal compiler error (boo#1142656)
* Fri Jun 28 2019
  - Update to 3.4.6
    Maintenance release, no changelog provided
  - Update to 3.4.5
    Maintenance release, no changelog provided
  - Update to 3.4.4
    OpenVINO™ toolkit components were updated to the R4 baseline
  - Drop obsolete opencv-lib_suffix.patch
* Mon Oct 29 2018
  - 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
    for the complete changelog.
  - Drop fix-build-i386-nosse.patch, build-workaround-issues-with-c.patch
    (fixed upstream)
  - Refresh patches
* Tue May 29 2018
  - Add patch to fix use of headers from C:
    * build-workaround-issues-with-c.patch
* Mon May 28 2018
  - 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
  - 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
  - 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
    _LIBDIR is arch dependent.
* Mon Mar 12 2018
  - Add option to build without openblas
* Mon Jan 08 2018
  - 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
* Sat Nov 25 2017
  - 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.
* Mon Nov 13 2017
  - Update licensing information
* Wed Nov 08 2017
  - change requires of python-numpy-devel to build in Leap and
    to not break factory in future
* Sat Nov 04 2017
  - fix build error/unresolvable for Leap 42.2 and 42.3
* Fri Nov 03 2017
  - Update to version 3.3.1:
    * Lots of various bugfixes
  - Update source url
* Thu Nov 02 2017
  - Rename python subpackage to python2
  - Do not explicitly require python-base for python subpackages
* Mon Oct 09 2017
  - Update to 3.3
  - Dropped obsolete patches
    * opencv-gcc6-fix-pch-support-PR8345.patch
    * opencv-gles.patch
  - Updated opencv-build-compare.patch
* Sat Jul 15 2017
  - 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
* Mon Jun 05 2017
  - 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
* Mon Jun 05 2017
  - 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
* Sun May 28 2017
  - 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
  - Enable TBB support (C++ threading library)
  - Add dependency on openBLAS
* Thu Jul 21 2016
  - Enable ffmpeg support unconditional
* Tue Jun 07 2016
  - In case we build using GCC6 (or newer), add -mlra to CFLAGS to
    workaround gcc bug
* Wed May 25 2016
  - Apply upstream patch opencv-gcc6-disable-pch.patch to disable
    PCH for GCC6.
* Mon Mar 21 2016
  - Test for python versions greater than or equal to the current
* Wed Mar 09 2016
  - Add python 3 support
* Thu Mar 03 2016
  - 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.
* Sun Feb 28 2016
  - Added _constraints file to avoid random failures on small workers
    (at least for builds on PMBS)
* Sat Feb 27 2016
  - 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 (
      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:
    + 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.
* Fri Jan 22 2016
  - Reduce build-compare noise
* Wed Dec 23 2015
  - Remove BuildRequirement for python-sphinx in SLE12, since it's
    not available there and it's not a mandatory requirement.
* Wed Dec 02 2015
  - Reduce differences between two spec files
* Tue Sep 22 2015
  - Use pkgconfig for ffmpeg BuildRequires
* Fri Jul 24 2015
  - 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"
    * 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
* Wed Apr 29 2015
  - support gcc 5 (i.e. gcc versions without minor version):
* Wed Apr 29 2015
  - 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
  - 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
* Mon Jan 26 2015
  - Add specific buildrequires for libpng15, so that we are
    building against the system provided libpng.



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Fabrice Bellet, Tue Jul 9 18:17:49 2024