Install the new interchangeable BLAS system created by Thomas Orgis,
currently supporting Netlib BLAS/LAPACK, OpenBLAS, cblas, lapacke, and
Apple's Accelerate.framework. This system allows the user to select any
BLAS implementation without modifying packages or using package options, by
setting PKGSRC_BLAS_TYPES in mk.conf. See mk/blas.buildlink3.mk for details.
This commit should not alter behavior of existing packages as the system
defaults to Netlib BLAS/LAPACK, which until now has been the only supported
implementation.
Details:
Add new mk/blas.buildlink3.mk for inclusion in dependent packages
Install compatible Netlib math/blas and math/lapack packages
Update math/blas and math/lapack MAINTAINER approved by adam@
OpenBLAS, cblas, and lapacke will follow in separate commits
Update direct dependents to use mk/blas.buildlink3.mk
Perform recursive revbump
version:3.4.8
OpenCV 3.4.8 has been released. Bug fixes, optimizations and other enhancements are propagated into OpenCV 4.1.2.
version:3.4.7
OpenCV 3.4.7 has been released. Bug fixes, optimizations and other enhancements are propagated into OpenCV 4.1.1.
version:3.4.6
OpenCV 3.4.6 has been released. Bug fixes, optimizations and other enhancements are propagated into OpenCV 4.1.0.
version:3.4.5
OpenCV 3.4.5 has been released. Bug fixes, optimizations and other enhancements are propagated into OpenCV 4.0.1.
version:3.4.4
OpenCV 3.4.4 has been released. This is a mantenance release. New features are landed in OpenCV 4.0.
version:3.4.3
OpenCV 3.4.3 has been released, with further extended dnn module, documentation improvements, some other new functionality and bug fixes.
version:3.4.2
OpenCV 3.4.2 has been released, with further extended dnn module, documentation improvements, some other new functionality and bug fixes.
OpenCV 3.4.x development is switched from "master" to "3.4" branch. "master" branch is used for development of upcoming OpenCV 4.x releases.
Bugfixes / optimizations / small improvemets should go into "3.4" branch. We will merge changes from "3.4" into "master" regularly (weekly/bi-weekly).
== OpenCV 3.4.1
dnn
- 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, such
as convolution and fully-connected layers
- SSD-based models trained and retrained in TensorFlow Object
Detection API can be easier imported by a single invocation of
python script making a text graph representation
- Performance of pthreads backend of cv::parallel_for_() has been
greatly improved on many core machines
- OpenCL backend has been expanded to cover more layers
- Several bugs in various layers have been fixed
OpenCL
- On-disk caching of precompiled OpenCL kernels has been fixed to
comply with OpenCL standard
- Certain cases with UMat deadlock when copying UMats in different
threads has been fixed
Android
- Supported Android NDK16
- Added build.gradle into OpenCV 4 Android SDK
- Added initial support of Camera2 API via JavaCamera2View interface
C++
- C++11: added support of multi-dimentional cv::Mat creation via C++
initializers lists
- C++17: OpenCV source code and tests comply C++17 standard
Misc
- opencv_contrib: added GMS matching
- opencv_contrib: added CSR-DCF tracker
- opencv_contrib: several improvements in OVIS module
== OpenCV 3.4
- New background subtraction algorithms have been integrated.
dnn
- Added faster R-CNN support
- Javascript bindings have been extended to cover DNN module
- DNN has been further accelerated for iGPU using OpenCL
OpenCL
- On-disk caching of precompiled OpenCL kernels has been finally
implemented
- It's now possible to load and run pre-compiled OpenCL kernels via
T-API
- Bit-exact 8-bit and 16-bit resize has been implemented
Sync opencv-contrib-face too.
Main changes:
- DNN module from opencv_contrib was promoted to the main repository,
improved and accelerated it a lot. An external BLAS implementation is
not needed anymore. For GPU there is experimental DNN acceleration using
Halide (http://halide-lang.org).
- OpenCV can now be built as C++ 11 library using the flag ENABLE_CXX11.
Some cool features for C++ 11 programmers have been added.
- We've also enabled quite a few AVX/AVX2 and SSE4.x optimizations in
the default build of OpenCV thanks to the feature called 'dynamic
dispatching'. The DNN module also has some AVX/AVX2 optimizations.
- Intel Media SDK can now be utilized by our videoio module to do
hardware-accelerated video encoding/decoding. MPEG1/2, as well as
H.264 are supported.
- Embedded into OpenCV Intel IPP subset has been upgraded from 2015.12
to 2017.2 version, resulting in ~15% speed improvement in our core &
imgproc perf tests.
Full release notes:
https://github.com/opencv/opencv/wiki/ChangeLog
Many Darwin library handling patches removed because of commit 912592de4ce
Remove "INSTALL_NAME_DIR lib" target property
Full changelog at
https://github.com/opencv/opencv/wiki/ChangeLog
Highlights:
* Results from 11 GSoC 2016 projects have been submitted to the
library, 9 of them have been integrated already, 2 still pending
(the numbers below are the id's of the Pull Requests in opencv or
opencv_contrib repository):
+ Ambroise Moreau (Delia Passalacqua) - sinusoidal patterns for
structured light and phase unwrapping module (711)
+ Alexander Bokov (Maksim Shabunin) - 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 (689, 708, 722, 736, 745, 747)
+ Tyan Vladimir (Antonella Cascitelli) - CNN based tracking
algorithm (GOTURN) (718, 899)
+ Vladislav Samsonov (Ethan Rublee) - PCAFlow and Global Patch
Collider algorithms implementation (710, 752)
+ Jo o Cartucho (Vincent Rabaud) - Multi-language OpenCV
Tutorials in Python, C++ and Java (7041)
+ Jiri Horner (Bo Li) - New camera model and parallel processing
for stitching pipeline (6933)
+ Vitaliy Lyudvichenko (Anatoly Baksheev) - Optimizations and
improvements of dnn module (707, 750)
+ Iric Wu (Vadim Pisarevsky) - Base64 and JSON support for file
storage (6697, 6949, 7088). Use names like
`"myfilestorage.xml?base64"` when writing file storage to
store big chunks of numerical data in base64-encoded form.
+ Edgar Riba (Manuele Tamburrano, Stefano Fabri) - tiny_dnn
improvements and integration (720: pending)
+ Yida Wang (Manuele Tamburrano, Stefano Fabri) - Quantization
and semantic saliency detection with tiny_dnn
+ Anguelos Nicolaou (Lluis Gomez) - Word-spotting CNN based
algorithm (761: pending)
