opencv-4.5.5-150400.1.28

- Remove the memoryperjob constraint which doesn't work as one
  would expect and breaks ppc64 builds.
- Use %limit_memory -m 1700 to set the number of concurrent
  jobs to a sane value and fix OOM errors when building in
  workers with many cores.
- Decrease the disk constraint to 9G which seems to be enough

- update to 4.5.5, highlights below, for details check
  https://github.com/opencv/opencv/wiki/ChangeLog#version455
  * Audio support as part of VideoCapture API: GStreamer #21264
  * Updated SOVERSION handling rules: #21178
  * DNN module patches:
    + Added tests to cover ONNX conformance test suite: #21088
    + Improved layers / activations / supported more models
    + Upgraded builtin protobuf from 3.5.2 to 3.19.1
    + More optimizations for RISC-V platform
    + Intel Inference Engine backend ( OpenVINO? ):
    added support for OpenVINO 2021.4.2 LTS release
  * G-API module:
    + G-API framework:
  - Fixed issue with accessing 1D data from cv::RMat: #21103
  - Restricted passing the G-API types to graph inputs/outputs
    for execution: #21041
  - Various fixes in G-API Doxygen reference: #20924
  - Renamed various internal structures for consistency #20836 #21040
    + Fluid backend:
  - Introduced a better vectorized version of Resize: #20664.
  - Added vectorized version of Multiply kernel: #21024
  - Added vectorized version of Divide kernel: #20914
  - Added vectorized version of AddC kernel: #21119
  - Added vectorized version of SubC kernel: #21158
  - Added vectorized version of MulC kernel: #21177
  - Added vectorized version of SubRC kernel: #21231
  - Enabled SIMD dispatching for AbsDiffC: #21204
    + OpenCL backend:
  - Fixed sporadic test failures in Multiply kernel running
    on GPU: #21205
    + Intel OpenVINO? inference backend:
  - Extended ie::Params to support static batch size as input
    to inference: #20856
  - Enabled 2D input tensor support in IE backend: #20925
  - Fixed various issues with imported (pre-compiled)
    networks: #20918
    + Media integration:
  - Introduced a GStreamer-based pipeline source for
    G-API: #20709
  - Completed the integration of Intel oneVPL as a pipeline
    source for G-API #20773 with device selection #20738,
  asynchronous execution #20901, intial demux support #21022,
  and GPU-side memory allocation via DirectX 11 #21049.
    + Samples:
  - Replaced custom kernels with now-standard G-API operations
    in several samples #21106
  - Moved API snippets from G-API samples to a dedicated
    place #20857
    + Other changes and fixes:
  - Fixed various static analysis issues for OpenVINO 2021.4
    release: #21083 and #21212
  - Fixed various build warnings introduced after OpenVINO
    update: #20937
  - Continued clean-up in the G-API test suite on GTest macros
    [#20922] and test data #20995
  - Added custom accuracy comparison functions to Fluid
    performance tests: #21150.
  * And many other contributions:
    + Added QRcode encoder: #17889
    + GSoC - OpenCV.js: Accelerate OpenCV.js DNN via WebNN: #20406
    + Add conventional Bayer naming: #20970
    + (opencv_contrib) Add Radon transform function to ximgproc: #3090
    + (opencv_contrib) New superpixel algorithm (F-DBSCAN): #3093
    + Created Stitching Tool: #21020
    + Improve CCL with new algorithms and tests: #21275
    + (opencv_contrib) Update ArUco tutorial: #3126
- Adjust memory constraints (mostly required for aarch64 on Leap)
- Add 0001-highgui-Fix-unresolved-OpenGL-functions-for-Qt-backe.patch

- update to 4.5.4:
  * 8-bit quantization in the dnn module
  * Improved Julia bindings
  * Speech recognition sample
  * dnn module optimizations for RISC-V
  * Tutorial about universal intrinsics and parallel_for usage
  * Improvements in the dnn module:
  - New layers and models support
  - Some existing layers have been fixed
  - Soft-NMS implementation
  - Supported OpenVINO 2021.4.1 LTS release

- Remove dependency on IlmBase, opencv never uses this directly.

- update to 4.5.2, highlights below, for details check
  https://github.com/opencv/opencv/wiki/ChangeLog#version452
  * core: added support for parallel backends.
  * imgproc: added IntelligentScissors implementation (JS demo).
  * videoio: improved hardware-accelerated video de-/encoding tasks.
  * DNN module:
    + Improved debugging of TensorFlow parsing errors: #19220
    + Improved layers / activations / supported more models:
  - optimized: NMS processing, DetectionOutput
  - fixed: Div with constant, MatMul, Reshape
    (TensorFlow behaviour)
  - added support: Mish ONNX subgraph, NormalizeL2 (ONNX),
    LeakyReLU (TensorFlow), TanH + SAM (Darknet), Exp
    + Intel Inference Engine backend ( OpenVINO? ):
    added support for OpenVINO 2021.3 release
  * G-API module:
    + Python support:
  - Introduced a new Python backend - now G-API can run custom
    kernels written in Python as part of the pipeline: #19351
  - Extended Inference support in the G-API bindings: #19318
  - Added more graph data types in the G-API bindings: #19319
    + Inference support:
  - Introduced dynamic input / CNN reshape functionality in the
    OpenVINO inference backend #18240
  - Introduced asynchronous execution support in the OpenVINO
    inference backend, now it can run in multiple parallel
  requests to increase stream density/throughput: #19487, #19425
  - Extended supported data types with INT64/INT32 in ONNX
    inference backend and with INT32 in the OpenVINO inference
  backend #19792
  - Introduced cv::GFrame / cv::MediaFrame and constant support
    in the ONNX backend: #19070
    + Media support:
  - Introduced cv::GFrame / cv::MediaFrame support in the
    drawing/rendering interface: #19516
  - Introduced multi-stream input support in Streaming mode
    and frame synchronization policies to support cases like
  Stereo: #19731
  - Added Y and UV operations to access NV12 data of cv::GFrame
    at the graph level; conversions are done on-the-fly if the
  media format is different: #19325
    + Operations and kernels:
  - Added performance tests for new operations (MorphologyEx,
    BoundingRect, FitLine, FindContours, KMeans, Kalman,
  BackgroundSubtractor)
  - Fixed RMat input support in the PlaidML backend: #19782
  - Added ARM NEON optimizations for Fluid AbsDiffC, AddWeighted,
    and bitwise operations: #18466, #19233
  - Other various static analysis and warning fixes
    + Documentation:
  - [GSoC] Added TF/PyTorch classification conversion: #17604
  - [GSoC] Added TF/PyTorch segmentation conversion: #17801
  - [GSoC] Added TF/PyTorch detection model conversion: #18237
  - Updated documentation to address Wide Universal Intrinsics
  (WUI) SIMD API: #18952
    + And many other great contributions from OpenCV community:
  - core: cuda::Stream constructor with stream flags: #19286
  - highgui: pollKey() implementation for w32 backend: #19411
  - imgcodecs: Added Exif parsing for PNG: #19439
  - imgcodecs: OpenEXR compression options: #19540
  - imgproc: connectedComponents optimizations: (Spaghetti
    Labeling): #19631
  - videoio: Android NDK camera support #19597
  - (contrib) WeChat QRCode module open source: #2821
  - (contrib) Implemented cv::cuda::inRange(): #2803
  - (contrib) Added algorithms from Edge Drawing Library: #2313
  - (contrib) Added Python bindings for Viz module: #2882
- Add libva build dependency for HW accelerated videoio
- Slight bump for memory constraints

- Enable aruco module (recognize markers to detect camera pose)

- update to 4.5.1, highlights below, for details check
  https://github.com/opencv/opencv/wiki/ChangeLog#version451
  * Continued merging of GSoC 2020 results:
    + Develop OpenCV.js DNN modules for promising web use cases
    together with their tutorials
    + OpenCV.js: WASM SIMD optimization 2.0
    + High Level API and Samples for Scene Text Detection and
    Recognition
    + SIFT: SIMD optimization of GaussianBlur 16U
  * DNN module:
    + Improved layers / activations / supported more models:
  - optimized: 1D convolution, 1D pool
  - fixed: Resize, ReduceMean, Gather with multiple outputs,
    importing of Faster RCNN ONNX model
  - added support: INT32 ONNX tensors
    + Intel Inference Engine backend (OpenVINO):
  - added support for OpenVINO 2021.2 release
  - added preview support for HDDL
    + Fixes and optimizations in DNN CUDA backend (thanks to
    @YashasSamaga)
  * G-API Framework:
    + Introduced serialization for cv::RMat, including
    serialization for user-defined memory adapters
    + Introduced desync, a new Operation for in-graph asynchronous
    execution - to allow different parts of the graph run with
    a different latency
    + Introduced a notion of "in-graph metadata", now various
    media-related information can be accessed in graph directly
    (currently only limited to timestamps and frame IDs)
    + Introduced a new generic task-based executor, based on
    Threading Building Blocks (TBB)
    + Extended infer<>() API to accept a new cv::GFrame data
    structure to allow handling of various media formats without
    changes in the graph structure
    + Made copy() an intrinsic where real copy may not happen
    (optimized out) based on graph structure, extended it to
    support cv::GFrame
    + Various fixes, including addressig static analysis,
    documentation, and test issues
  * G-API Operations:
    + Introduced new operations morphologyEx, boundingRect,
    fitLine, kmeans, Background Subtractor, Kalman filter
  * G-API Intel Inference Engine backend (OpenVINO):
    + Extended cv::gapi::ie::Params<> to import CNN networks (e.g.
    pre-compiled ones) instead of passing .XML and .BIN files;
    also enabled configuring Inference Engine plugins via
    this structure
    + Added a new overload to infer<>() to run inference over a
    single region of interest
    + Added support for cv::MediaFrame input data type (projected
    from cv::GFrame) and handling for NV12 input image format
  * G-API Python bindings:
    + Exposed G-API's Inference and Streaming APIs in the OpenCV
    Python bindings
    + Added initial Python support for cv::GArray data structure
  * Significant progress on RISC-V port.
- Updated constraints, bump memory to 5 GB
- Cleaned up spec file

- Split library package, move all libraries with external
  dependencies (Qt5, ffmpeg, gstreamer) into separate packages
- Move haar and LBP cascades into separate package, pull in from
  objdetect and face (detect) libraries.

- update to 4.5.0, see
  https://github.com/opencv/opencv/wiki/ChangeLog#version450
  for details, highlights:
  * OpenCV license has been changed to Apache 2 (OpenCV 3.x will
    keep using BSD)
  * GSoC is over, all projects were success and most of them have
    already been merged. Optimizations for RISC-V, bindings for
    Julia language, real-time single object tracking, improved SIFT
    and others
  * OpenJPEG is now used by default for JPEG2000
  * Supported multiple OpenCL contexts
  * Improvements in dnn module:
    + Support latest OpenVINO 2021.1 release
    + Tengine lite support for inference on ARM
    + Many fixes and optimizations in CUDA backend
  * Added Python bindings for G-API module
  * Multiple fixes and improvements in flann module
  * Added Robot-World/Hand-Eye calibration function

- update to 4.4.0:
  * SIFT (Scale-Invariant Feature Transform) algorithm has been
  moved to the main repository (patent on SIFT is expired)
  * DNN module:
  * State-of-art Yolo v4 Detector: #17148.
  * onnx: Add support for Resnet_backbone
  * EfficientDet models
  * add text recognition sample / demo
  * FlowNet2 optical flow
  * Intel Inference Engine backend
  * added support for OpenVINO 2020.3 LTS / 2020.4 releases
  * support of NN Builder API is planned for removal in the next release
  * Many fixes and optimizations in CUDA backend
  * Obj-C / Swift bindings: #17165
  * Julia bindings as part of ongoing GSoC project
  * BIMEF: A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement
  * Enable Otsu thresholding for CV_16UC1 images
  * Add Stroke Width Transform algorithm for Text Detection
  * Planned migration on Apache 2 license for next releases
- remove opencv-includedir.patch (obsolete)

- Add opencv-imgproc-fix-bounds-check.patch:
  make backport from commit 4ca89db to fix imgproc(hdr)
  bounds check in HdrDecoder::checkSignature ( bsc#1074312,
  CVE-2017-18009 ).

- 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 CVE-2019-15939.patch: add input check in HOG detector
  (boo#1149742 CVE-2019-15939).

- Add opencv-cmake-rel-deb-info.patch: rework MAP_IMPORTED_CONFIG
  to prevent unintended build failures (boo#1154091).
- Add opencv-check-macros.patch: Add "check" macros (boo#1144352
  boo#1144348).
- Add CVE-2019-14491.patch: objdetect: validate feature rectangle
  when reading (boo#1144352 boo#1144348 CVE-2019-14491
  CVE-2019-14492).

- 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

- Add CVE-2019-15939.patch: add input check in HOG detector
  (boo#1149742 CVE-2019-15939).

- Add opencv-cmake-rel-deb-info.patch: rework MAP_IMPORTED_CONFIG
  to prevent unintended build failures (boo#1154091).
- Add opencv-check-macros.patch: Add "check" macros (boo#1144352
  boo#1144348).
- Add CVE-2019-14491.patch: objdetect: validate feature rectangle
  when reading (boo#1144352 boo#1144348 CVE-2019-14491
  CVE-2019-14492).

- Add opencv-imgcodecs-refactoring.patch:
  make backport from commit 999f41fb to refactor imgcodecs for
  code quality and prevent attackers achieving remote code
  execution ( boo#1033150, CVE-2016-1517, boo#1033152,
  CVE-2016-1516 ).

- 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.