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)
* A lot of new functionality has been introduced during GSoC 2015:
- "Omnidirectional Cameras Calibration and Stereo 3D Reconstruction"
opencv_contrib/ccalib module
- "Structure From Motion" - opencv_contrib/sfm module
- "Improved Deformable Part-based Models" - opencv_contrib/dpm module
- "Real-time Multi-object Tracking using Kernelized Correlation Filter"
- opencv_contrib/tracking module
- "Improved and expanded Scene Text Detection" - opencv_contrib/text
module
- "Stereo correspondence improvements" - opencv_contrib/stereo module
- "Structured-Light System Calibration" - opencv_contrib/structured_light
- "Chessboard+ArUco for camera calibration" - opencv_contrib/aruco
- "Implementation of universal interface for deep neural network
frameworks" - opencv_contrib/dnn module
- "Recent advances in edge-aware filtering, improved SGBM stereo
algorithm" - opencv/calib3d and opencv_contrib/ximgproc
- "Improved ICF detector, waldboost implementation"
- opencv_contrib/xobjdetect
- "Multi-target TLD tracking" - opencv_contrib/tracking module
- "3D pose estimation using CNNs" - opencv_contrib/cnn_3dobj
* 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
* 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
See full changelog:
https://github.com/Itseez/opencv/wiki/ChangeLog
Problems found with existing digests:
Package fotoxx distfile fotoxx-14.03.1.tar.gz
ac2033f87de2c23941261f7c50160cddf872c110 [recorded]
118e98a8cc0414676b3c4d37b8df407c28a1407c [calculated]
Package ploticus-examples distfile ploticus-2.00/plnode200.tar.gz
34274a03d0c41fae5690633663e3d4114b9d7a6d [recorded]
da39a3ee5e6b4b0d3255bfef95601890afd80709 [calculated]
Problems found locating distfiles:
Package AfterShotPro: missing distfile AfterShotPro-1.1.0.30/AfterShotPro_i386.deb
Package pgraf: missing distfile pgraf-20010131.tar.gz
Package qvplay: missing distfile qvplay-0.95.tar.gz
Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden). All existing
SHA1 digests retained for now as an audit trail.
Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- 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.
- 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.
- ~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
- 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
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
