opencv-4.5.5-150400.1.28 >  A bwUvp9|1DR'4{ڙi# ߨQG?"8Qv6tPh _ΕCͲ7?tVSmT㌟Ab!EkpA?d   H  9Z   @ `  0 @ D  ( 18 889 8:8BFGHXIXYZ[\],^Pbc@defluv wxyCz 04:|Copencv4.5.5150400.1.28Collection of algorithms for computer visionOpenCV means Intel Open Source Computer Vision Library. It is a collection of C functions and a few C++ classes that implement some popular Image Processing and Computer Vision algorithms.bwTibs-power9-11SUSE Linux Enterprise 15SUSE LLC BSD-3-Clause AND GPL-2.0-only AND Apache-2.0https://www.suse.com/Development/Libraries/C and C++https://opencv.org/linuxppc64le  ,^,^DA큤AbwSbwSbwSbwSbwSbwSaƕbwOaƕaƕaƕaƕaƕaƕaƕ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-4.5.5-150400.1.28.src.rpmopencvopencv(ppc-64)opencv-qt5@@@@@@@@@@@@@@@@@@@@@    libc.so.6()(64bit)libc.so.6(GLIBC_2.17)(64bit)libgcc_s.so.1()(64bit)libgcc_s.so.1(GCC_3.0)(64bit)libm.so.6()(64bit)libm.so.6(GLIBC_2.17)(64bit)libopencv_aruco.so.405()(64bit)libopencv_calib3d.so.405()(64bit)libopencv_core.so.405()(64bit)libopencv_dnn.so.405()(64bit)libopencv_features2d.so.405()(64bit)libopencv_highgui.so.405()(64bit)libopencv_imgcodecs.so.405()(64bit)libopencv_imgproc.so.405()(64bit)libopencv_videoio.so.405()(64bit)libstdc++.so.6()(64bit)libstdc++.so.6(CXXABI_1.3)(64bit)libstdc++.so.6(GLIBCXX_3.4)(64bit)libstdc++.so.6(GLIBCXX_3.4.11)(64bit)libstdc++.so.6(GLIBCXX_3.4.21)(64bit)libstdc++.so.6(GLIBCXX_3.4.9)(64bit)rpmlib(CompressedFileNames)rpmlib(FileDigests)rpmlib(PayloadFilesHavePrefix)rpmlib(PayloadIsXz)3.0.4-14.6.0-14.0-15.2-14.14.3aaa)@a,@`B`%@_`@__]@_^ _>e^^n@^&^%@^O@]@]@]fl]e@]Z@]N]M`@]G@[@[ @[ @ZkZS]@Z[@Z @ZYYZ@YYdYjY5GY5GY*W@WV@WEV@V@V'@V@VяVVzV^@VBU(U@U@T,@alarrosa@suse.comstefan.bruens@rwth-aachen.dedmueller@suse.comjengelh@inai.destefan.bruens@rwth-aachen.dealoisio@gmx.comstefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.dedmueller@suse.commliska@suse.czstefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.dedimstar@opensuse.orgstefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.destefan.bruens@rwth-aachen.demarius.kittler@suse.comjengelh@inai.dejbrielmaier@suse.dechristophe@krop.frchristophe@krop.frstefan.bruens@rwth-aachen.dechristophe@krop.frfabian@ritter-vogt.defvogt@suse.comschwab@suse.detchvatal@suse.comstefan.bruens@rwth-aachen.dempluskal@suse.comecsos@opensuse.orgecsos@opensuse.orgmpluskal@suse.commpluskal@suse.comkah0922@gmail.comstefan.bruens@rwth-aachen.detchvatal@suse.comtchvatal@suse.comstefan.bruens@rwth-aachen.deolaf@aepfle.dedimstar@opensuse.orgmartin.liska@suse.comtoddrme2178@gmail.comtoddrme2178@gmail.comtittiatcoke@gmail.comjoerg.lorenzen@ki.tng.dejoerg.lorenzen@ki.tng.deolaf@aepfle.dealarrosa@suse.comohering@suse.deolaf@aepfle.demlin@suse.comcoolo@suse.comcoolo@suse.comtittiatcoke@gmail.com- 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)- Use memoryperjob constraint instead of %limit_build macro.- Update to 4.3.0 * DNN module: + Improved layers / activations / supported more models: - ONNX: LSTM, Broadcasting, Algebra over constants, Slice with multiple inputs - DarkNet: grouped convolutions, sigmoid, swish, scale_channels - MobileNet-SSD v3: #16760 + New samples / demos: - Clothes parts segmentation and CP-VTON - DaSiamRPN tracker Intel® Inference Engine backend (OpenVINO™): - added support for custom layers through nGraph OpenVINO API: #16628 - nGraph OpenVINO API is used by default: #16746 + Many fixes and optimizations in CUDA backend (thanks to @YashasSamaga) + OPEN AI LAB team submitted the patch that accelerates OpenCV DNN on ARM using their Tengine library * G-API module: + Introduced a new graph-level data type GOpaque. This type can be used to pass arbitrary user data types between G-API nodes in the graph (supported for CPU/OpenCV backend only). + Introduced a way to declare G-API CPU (OpenCV) kernels in-place + Added a new sample "Privacy masking camera", combining Deep Learning with traditional Image Processing (link) + Added more operations in the default library: WarpAffine, WarpPerspective, NV12toGray. * Performance improvements: + IPP-ICV library with CPU optimizations has been updated to version 2020.0.0 Gold + SIMD intrinsics: integral, resize, (opencv_contrib) RLOF implementation #2476 * And many other great contributions from OpenCV community: + (opencv_contrib) Computer Vision based Alpha Matting (GSoC 2019) #2306 + calib3d: findChessboardCornersSB improvements: #16625 + calib3d: updated documentation for RT matrices: #16860 + core: improved getNumberOfCPUs(): #16268 + imgproc: new algorithm HOUGH_GRADIENT_ALT is added to HoughCircles() function #16561. It has much better recall and precision + imgcodecs: added initial support for OpenJPEG library (version 2+): #16494 + highgui(Qt): added Copy to clipboard: #16677 + dnn: TensorFlow, Darknet and ONNX importers improvements by @ashishkrshrivastava + (opencv_contrib) added rapid module for silhouette based 3D object tracking: #2356 + (opencv_contrib) SIFT detector is enabled by default due patents expiration (without requirement of NONFREE build option) + help materials: OpenCV Cheat Sheet in Python: #4875 * Changes that can potentially break compatibility: + image filtering functions throws exception on empty input (voting results) - Packaging changes: * Stop mangling CMake diagnostic output, no dependency versions end up in the packages anyway, drop opencv-build-compare.patch * Set empty OPENCV_DOWNLOAD_TRIES_LIST, skip downloads even when network is available during builds (e.g. local build). * Drop upstream GLES patches: + 0001-Do-not-include-glx.h-when-using-GLES.patch + opencv-gles.patch- Disable Python 2 bindings for Tumbleweed.- 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). openSUSE follows other distros in dropping JasPer now (much later than most others, incl. Debian).- Add webp build dependency to use system libwebp instead of bundled one. - Enable dispatch of AVX512 optimized code.- Update to 4.2.0 * DNN module: + Integrated GSoC project with CUDA backend: #14827 + Intel® Inference Engine backend ( OpenVINO™ ): - support for nGraph OpenVINO API (preview / experimental): #15537 * G-API module: + Enabled in-graph inference: #15090. Now G-API can express more complex hybrid CV/DL algorithms; - Intel® Inference Engine backend is the only available now, support for DNN module will be added in the future releases. + Extended execution model with streaming support: #15216. Decoding, image processing, inference, and post-processing are now pipelined efficiently when processing a video stream with G-API. + Added tutorials covering these new features: Face analytics pipeline and a sample Face beautification algorithm. * Performance improvements: + SIMD intrinsics: StereoBM/StereoSGBM algorithms, resize, integral, flip, accumulate with mask, HOG, demosaic, moments + Muti-threading: pyrDown * And many other great patches from OpenCV community: + VideoCapture: video stream extraction (demuxing) through FFmpeg backend. + VideoCapture: waitAny() API for camera input multiplexing (Video4Linux through poll() calls). + (opencv_contrib) new algorithm Rapid Frequency Selective Reconstruction (FSR): #2296 + tutorial. + (opencv_contrib) RIC method for sparse match interpolation: #2367. + (opencv_contrib) LOGOS features matching strategy: #2383. * Breaking changes: + Disabled constructors for legacy C API structures. + Implementation of Thread Local Storage (TLS) has been improved to release data from terminated threads. API has been changed. + Don't define unsafe CV_XADD implementation by default. + Python conversion rules of passed arguments will be updated in next releases: #15915.- Limit build parallelism with limit_build, some ARM and PPC workers have a high SMP/memory ratio and run out of memory otherwise. - Apply memory constraints (3GB) to all architectures, avoid being scheduled on very weak workers.- Update to 4.1.2 * DNN module: + Intel Inference Engine backend (OpenVINO): - 2019R3 has been supported - Support modern IE Core API - New approach for custom layers management. Now all the OpenCV layers fallbacks are implemented as IE custom layers which helps to improve efficiency due less graph partitioning. - High-level API which introduces dnn::Model class and set of task-specific classes such dnn::ClassificationModel, dnn::DetectionModel, dnn::SegmentationModel. It supports automatic pre- and post-processing for deep learning networks. * Performance improvements and platforms support: + MSA SIMD implementation has been contributed for MIPS platforms: https://github.com/opencv/opencv/pull/15422 + OpenCV.js optimization (threading and SIMD as part of GSoC project): https://github.com/opencv/opencv/pull/15371 + More optimizations using SIMD intrinsics: dotProd, FAST corners, HOG, LK pyramid (VSX), norm, warpPerspective, etc + Fixed detection of Cascade Lake CPUs * And many other great patches from OpenCV community: + GUI: support topmost window mode (Win32/COCOA): https://github.com/opencv/opencv/pull/14872 + Java: fix Mat.toString() for higher dimensions: https://github.com/opencv/opencv/pull/15181 + Implementation of colormap "Turbo" https://github.com/opencv/opencv/pull/15388 + QR-Code detection accuracy improvement: https://github.com/opencv/opencv/pull/15356 + GSoC: Add learning-based super-resolution module: https://github.com/opencv/opencv_contrib/pull/2229 and https://github.com/opencv/opencv_contrib/pull/2231 + Detection accuracy improvement of the white marker aruco corners: https://github.com/opencv/opencv_contrib/pull/2236 + Added pattern generator tool for aruco: https://github.com/opencv/opencv_contrib/pull/2250 + and special thanks to @sturkmen72 for improvind and cleaning up code of samples/tutorials * Breaking changes: + fixed values thresholding accuracy in calcHist() * Security fixes: CVE-2019-15939 (boo#1149742). - Enable Graph API (G-API) - Minor spec file cleanup- Include pkg-config file in opencv-devel package * Add opencv-includedir.patch- Avoid use of ®/™ signs in specfiles as per guidelines.- Disable LTO on ppc64le for now, as it fails to build when enabled (boo#1146096).- Increase the disk space needed to build opencv.- Update to 4.1.1 * DNN module: * 3D convolution networks initial support * A lot of improvements for ONNX and TenforFlow importers * Performance improvements * Added IPPE method for planar pose estimation in solvePnP * Added solvePnPRefineLM and solvePnPRefineVVS * Security fixes: CVE-2019-14491 (boo#1144352), CVE-2019-14492 (boo#1144348). - Check https://github.com/opencv/opencv/wiki/ChangeLog#version411 for the complete list of changes. - Drop fix_processor_detection_for_32bit_on_64bit.patch. Fixed upstream - Drop 0001-Handle-absolute-OPENCV_INCLUDE_INSTALL_PATH-correctl.patch Fixed upstream - Refresh 0001-Do-not-include-glx.h-when-using-GLES.patch and opencv-build-compare.patch.- 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 * Fix cmake mapping of RelWithDebInfo (boo#1154091). * 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= …” 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.opencv-qt5ibs-power9-11 1651987457 4.5.5-150400.1.284.5.5-150400.1.284.5.54.5.5opencv_annotationopencv_interactive-calibrationopencv_model_diagnosticsopencv_versionopencv_visualisationopencvLICENSELICENSE.contribSoftFloat-COPYING.txtittnotify-LICENSE.BSDittnotify-LICENSE.GPLopencl-headers-LICENSE.txtprotobuf-LICENSEprotobuf-README.mdquirc-LICENSEopencv4/usr/bin//usr/share/licenses//usr/share/licenses/opencv//usr/share/-fmessage-length=0 -grecord-gcc-switches -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector-strong -funwind-tables -fasynchronous-unwind-tables -fstack-clash-protection -gobs://build.suse.de/SUSE:SLE-15-SP4:GA/standard/02d6483556ad3bbd8c0ab597e65ba131-opencvcpioxz5ppc64le-suse-linuxELF 64-bit LSB shared object, 64-bit PowerPC or cisco 7500, version 1 (SYSV), dynamically linked, interpreter /lib64/ld64.so.2, BuildID[sha1]=d99742a940b1d170f69cd294a627ed2c9f26a14b, for GNU/Linux 3.10.0, strippedELF 64-bit LSB shared object, 64-bit PowerPC or cisco 7500, version 1 (SYSV), dynamically linked, interpreter /lib64/ld64.so.2, BuildID[sha1]=5bfcef3f3b3554d721ab661e2ead21bfbedaa241, for GNU/Linux 3.10.0, strippedELF 64-bit LSB shared object, 64-bit PowerPC or cisco 7500, version 1 (SYSV), dynamically linked, interpreter /lib64/ld64.so.2, BuildID[sha1]=f6e953a5a537aaa2f92715989e7c63cd998d7349, for GNU/Linux 3.10.0, strippedELF 64-bit LSB shared object, 64-bit PowerPC or cisco 7500, version 1 (SYSV), dynamically linked, interpreter /lib64/ld64.so.2, BuildID[sha1]=43bce3ee47fc66da22af1bfa417b05e1460cc717, for GNU/Linux 3.10.0, strippedELF 64-bit LSB shared object, 64-bit PowerPC or cisco 7500, version 1 (SYSV), dynamically linked, interpreter /lib64/ld64.so.2, BuildID[sha1]=8d3e78d975aaa244be0eba07ec38e38dd6e1b011, for GNU/Linux 3.10.0, strippeddirectoryASCII textNon-ISO extended-ASCII text, with very long linesASCII text, with very long lines *5  RRRRRRR R R RRRRRRRRRRRRR RRRRR R RRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRR RR R RRRRƄ|Mt (Zutf-8c4e4e3954b42ca7144cef7fc9335917cd1b3ff06c07c098a823b71656b180dec?7zXZ !t/A ] crv9w!AXĤQ-EgC^]79Pl[䛣!=EurK/%B1OOV 4kK{N(>8A/Gq\LkgA{c;E3e^t#CVFॣU{cK5+NP_er} U%5C|=>e4\CKh(2"ӕڀ)*s"Ѡ \Fc0qIJMC< Ng4T D3ڻd9`!Ѿi0yt54N{hcbi| W.劮d3&ὠXй#S~Fnjq$n~,j#Cҩ|#8UӲz\a1LWp?o=Zs~vM!1q?6CQQ֩?j;ʭ1߱[؅" .5U0|Xk)7[é  K[F*݂J|gOUTG݄jGo>F %PHtj _am.aqaGvK#Ys^ |[gUsʕ*Rc=Bܽ]hA?T'"Lfgˌa{rݸ5,Hp]z`kP7ơ97 /oİ{LYB7Aۙs>JoČ *$+ aQWT:΀6:?Y12g]$zs,x ņtw:Q줟lt0:E3<Ll@;ChA0+դ.j{ 3/;al}MĄ&Q!Q<a_Hi\}ѕauLsYj~z=(Ǭ)SQlW˝UW}]TakVh uWc.,H嗗JahHѷmJ/p_hshHr'1 ~MY%_Žoq%b0Qx-KO}I&h6njKt1?7|GtXs*hSKĀlVܞ'e nQv4_j0g6-<7]/4u'6w޾}ːdT4Ə{oVeNrA-͑df`s Soi,%Rt/,\8^ظhgqpJ~pmd:-.G)%eKkQl0ˆ-_XT[s[&vgAgkyN4682Wy /?w¥mv| E0|R|`8u/>;>iLj'r0,CV/cX"bvWX~[';e~m.=%[m.kUwHNw냷.KF 7u@)p֜ [8A}SPWqmiB[HW1ڝ{A f)Q>)Jܸ쵴uTz( D?0K >^QsW-VL0fك)Jw] oDfr9zLX&- cʶķ3JS_rEVI?H>jan"H^p\Q[ 9IYFN"O>uQ)Rω+ iz3.q"M?Y]Q"Uѹl3w {^\Ve kgEPkѵ]ע(ʒC̍J5%` n(|lͨk K=&抐Zr0҇< ,*y;:ZwƸ\|?'2Ru陋9[nQX*ޚV1 O|`4)Ms71IjmXǥ8/Yz[ơDFzCBDsKNWJg#c>T PJоq65!3 Jy7i%AlAF%.9 /\d7v<8Jְ:;@HgyUmej1Oۀdw׋{JA|D>q.c:@|sWLJ2%AZB@ 3%yRzN ph{a̾d.;_zS-P#`ܹOgu=KO:7 m JRiR?H FP | )\q zN.De@ $X 4&`ͫA^NƯq2p4w.: (8Ј/T]^!SJED=mw#(xA~1z;Whx{p.cRM(B(h5!MA,f ½q?W$j"[Ko!E|X9]Ӹopz47icNA6{ELM&; M^!fVag-nL.=svgc\ŤS|_s;*{ywɍ[T,f,hN x;@%Q3@TndS%as6Q@e =ZPT@K'?ZO] _,|4K:( a8R®}dBp[lUWn:jDtXgH`iJ&I|P[Q?T?Fcf#zuI%{+>^vYFՖbx)H֕AxKf.rac,6*p.>E<?p+L,{a evEo8|\{_&g׫H,+0 ,mDIZٝ;@|:n<7-8yܲe?rվedQNR)VhcjcX387 UD610<$f$9YiP>Y3u&xloIcH@&-~,QxԶl -SvYlxǩ/H҇ކΒgyU.{%2Z ƪ<MʽOÃrPo&󵉱5"e]]48K%B|`Z&[H`hdY$n%xd2%-C+^ td)Ss~vm/_ԃ5-lF49zqL+j,'l2mUZbX P|0'ʲx$3ƽ HZڨ:AS„m9]"w8td$Wq>d+8}KVWծ ZlV;:4Z gcV֤/cMo|)P82xHJc"" te J >-߹*VwoT9 Qm'DAg 8fW N; \I/ NBeNEZv*ClTsa4X 'e8w,fg[Zl3zp~CbXķgug[,㍊HBN ʁL>޹r%A 9 OX(QaG͖ܰe̫k7"a: |GlsN_2Wpy7o7_=J3 tԷaUd^DA-ťjp R]#cWr݆(+= a>W FXa3#T3nl'dEw@-Euz>0 Z\d2g t͜pZ?ekEsNvFH#l菋shRZB-NH~ܖ !!"I d=lngOP1Nq-_$g"?nG öm5KrKQq>>ЗCp2Q(!ԃ0a.^鹷a'. 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