Loading...

Follow OpenCV library on Feedspot

Continue with Google
Continue with Facebook
or

Valid

Note: If the form below does not work, please use this link: https://goo.gl/forms/NvgMtNSfedZf5mpg1

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

We've just released a very close approximation of OpenCV 4.0, the release candidate.

OpenCV 4.0 RC includes ~60 new patches since OpenCV 4.0 beta:

  • Experimental Vulkan backend have been added to the dnn module, thanks to Zhiwen Wu and Juan J. Zhao.
  • opencv_stitching module interface has been refactored, thanks to Jiri Horner.
  • More accurate camera calibraion method have been implemented, thanks to Wenfeng CAI.
  • Persistence part of the core module (storing data in XML/YML/JSON formats) has been rewritten in C++ and consumes less memory when reading FileStorage. The C API (CvFileStorage) has been removed.
  • Graph API module has been extended: it recieved initial heterogenity support, OpenCL support, better documentation, ability to build it standalone. Thanks to Dmitry Matveev, Dmitry Budnikov and other G-API folks!
  • Some more of obsolete C-API has been removed in the following modules: photo, video, imgcodecs, videoio
  • Dropped obsolete videoio and highgui backends: QuickTime, QTKit, Unicap, Video for Windows, libv4l, DC1394_V1, Carbon
  • shape, superres, videostab, viz modules and TVL1 optical flow algorithm were moved to opencv_contrib
  • DIS optical flow algorithm, implemented by Alexander Bokov, has been moved to the main repository from opencv_contrib.
  • Some latest changes in 3.4 branch have been merged to the master, including updated libpng (security fixes), XCode 10 support, Turing GPU support, many optimizations and bug fixes
Contributors

Big thanks to everybody who contributed (here is the incomplete list of patch authors; please, report if you contributed but do not see your name here):

opencv

Alexander Alekhin, Vadim Pisarevsky, Dmitry Kurtaev, berak, catree, Wu Zhiwen, Alexander Nesterov, Dmitry Matveev, LaurentBerger, Michał Janiszewski, Ruslan Garnov, Vitaly Tuzov, AsyaPronina, Dmitry Budnikov, Maksim Shabunin, Mansoo Kim, Pavel Rojtberg, Rostislav Vasilikhin, Sayed Adel, Sean McBride, Suleyman TURKMEN, Wenfeng CAI, fegorsch, Alexey Nikolaev, Ali Yasin Eser, Antonio Borondo, Apoorv, Apoorv Goel, Diego Barrios Romero, Emanuele Ruffaldi, Evgeny Latkin, Henry, Ilari Venäläinen, Ivan Pozdeev, Jean Carass, Jiri Horner, Karpushin Vladislav, Marco A. Gutierrez, Matt Bennett, Paul Shin, Tomoaki Teshima, WuZhiwen, gineshidalgo99, huangqinjin, jasjuang, kamino410, lqy123000, maver1, tompollok, unknown, wanghanmin

opencv_contrib

Alexander Alekhin, Vadim Pisarevsky, Pavel Rojtberg, Jukka Komulainen, LaurentBerger, tompollok, Mohammad Haghighat, Suleyman TURKMEN, Tomoaki Teshima, Varvrar, YTY, berak, kartoffelsalat

Download

Documentation

Sources

Win pack

iOS pack

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

Dear OpenCV Community,

We are glad to announce that OpenCV 4.0 Beta is now available, which includes many new features and enhancements. Along with this new library, are new open source tools to help fast-track high performance computer vision development and deep learning inference in OpenVINO™ toolkit (Open Visual Inference and Neural Network Optimization). More details about these releases and where to download follow.

OpenCV 4.0 Beta—What’s New

OpenCV 4.0 Beta includes 29 new patches, including massive merges from 3.4 branch, since OpenCV 4.0 alpha (https://opencv.org/opencv-4-0-0-alpha.html):

  • ONNX* (Open Neural Network Exchange) importer has been further improved to support more topologies.
  • OpenCV DNN sample object_detection.py has been improved to fill in the right model parameters, so it’s much easier to use now.
  • G-API (Graph API) - super-efficient image processing pipeline engine has been integrated as opencv_gapi module
  • Fast QR code decoder, based on free QUirc (https://github.com/dlbeer/quirc) library has been integrated, so now we have a complete QR-code detection and decoding pipeline (that runs ~20-80FPS @ 640x480 resolution).
  • 18 functions, over 60 kernels have been accelerated for AVX2 using “wide universal intrinsics.”
  • Kinect Fusion algorithm has been accelerated for iGPU, which resulted in ~3x speedup over parallel CPU version on high-resolution volume (512x512x512).
The release should be quite stable, but more changes are expected by OpenCV 4.0 gold. Contributors

Big thanks to everybody who contributed (here is the incomplete list of patch authors; please, report if you contributed but do not see your name here):

opencv

Alexander Alekhin, Hamdi Sahloul, Dmitry Kurtaev, Suleyman TURKMEN, Tomoaki Teshima, Vitaly Tuzov, Maksim Shabunin, Dmitry Matveev, Sayed Adel, Adam Radomski, Apoorv Goel, Pavel Rojtberg, Rostislav Vasilikhin, Alexander Duda, Alexander Nesterov, Andrew Mroczkowski, Antonio Borondo, Anush Elangovan, Georgy Mironov, Loic Devulder, Loic Petit, Lubov Batanina, Menghui Xie, Peter Rekdal Sunde, Peter Whidden, Reid Kleckner, Sam Radhakrishnan, berak, chacha21, drkoller, soonbro, take1014, tellowkrinkle, tompollok

opencv_contrib

Hamdi Sahloul, Tomoaki Teshima, Alexander Alekhin, Pavel Rojtberg, Maksim Shabunin, berak, soyer, Lubos, Rostislav Vasilikhin, Antonio Borondo, Jeff Bail, LaurentBerger, Sayed Adel, Suleyman TURKMEN, tompollok

Download

Documentation

Sources

Win pack

iOS pack

Intel Releases Open Source Tools to Accelerate Computer Vision & Deep Learning

Intel announces that OpenVINO™ toolkit is now open sourced. This developer toolkit provides flexibility and availability to the developer community to accelerate development of vision capabilities and AI end-to-end across device to network and cloud. The toolkit enables high performance computer vision and deep learning inference with easy heterogeneous execution across multiple types of Intel® platforms. It includes:

  • The Deep Learning Deployment Toolkit helps enable fast, heterogeneous deep learning inferencing for Intel® processors (CPU and GPU/ Intel® Processor Graphics), and supports more than 100 public and custom models.
  • Open Model Zoo includes 20+ pre-trained deep learning models to expedite development and improve deep learning inference on Intel® processors, along with many samples to easily get started.

Learn more at https://01.org/openvinotoolkit

Note: The Intel® Distribution of OpenVINO™ toolkit will still be available as free commercial product and includes additional, proprietary support for Intel® FPGAs, Intel® Movidius™ Neural Compute Stick, and traditional computer vision functions and libraries

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

As many of you know, Intel is now one of the biggest sponsors of OpenCV, and gets really serious about AI and CV. OpenCV community, i.e. you, are invited to attend Intel AI DevCon 2018 in San Francisco on May 23-24th:

“Connect with top minds in data science, machine and deep learning, application development, and research to hear the latest perspectives and see practical implementations that break barriers between theory and real-world function. Get two full days of hands-on training on TensorFlow, MXNet, AWS DeepLens and more, see real-world demos by GE, Lenovo, NASA FDL, Ferrari Challenge and others, and hear leading-edge research from experts including Professor Yisong Yue from Caltech and Matt Zeiler, Founder and CEO of Clarifai.”

OpenCV and Intel Computer Vision SDK (https://software.intel.com/en-us/computer-vision-sdk), which includes OpenCV, will be mentioned in the keynotes and during the demos.

More details and the registration information are available at https://www.intel.com/content/www/us/en/events/ai/devcon.html.

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

Right before the Christmas and New Year holidays, we are glad to present the latest and the greatest OpenCV 3.4.

What's new
  • Further improvements in the DNN module include faster R-CNN support, Javascript bindings and acceleration of OpenCL implementation.
  • We've implemented a disk cache and manual loading for precompiled OpenCL kernel binaries, it can greatly reduce initialization time of many applications and allow to run OpenCL implementations on embedded platforms without JIT compiler.
  • One more bit-exact algorithm has been implemented. New 8-bit bilinear resize will lay stable base for complex computer vision pipelines across variety of platforms.
  • One more GSoC project has been integrated - it adds implementations of new background subtraction algorithms.
  • ~250 patches have been merged since OpenCV 3.3.1 and over 200 issues have been closed

More details about the changes and new functionality in OpenCV 3.4 can be found at https://github.com/opencv/opencv/wiki/ChangeLog.

Contributors

Here is the list of contributors, based on git logs:

opencv

Alexander Alekhin, Dmitry Kurtaev, Maksim Shabunin, Li Peng, elenagvo, Vitaly Tuzov, Suleyman TURKMEN, catree, Tomoaki Teshima, Sayed Adel, LaurentBerger, Vladislav Sovrasov, Wu Zhiwen, Pavel Rojtberg, Rostislav Vasilikhin, Vadim Pisarevsky, tribta, Bhanudutta, Fakabbir Amin, Mattia Rizzolo, Ryan Fox, Shinya Ishikawa, berak, dtmoodie, Akhilesh Kumar, Alexander Nesterov, Alexander Rybnikov, Amro, Andrey Smorodov, Arthur Pastel, Cartucho, Christof Kaufmann, David Geldreich, Elena Gvozdeva, Florian Echtler, Hamdi Sahloul, Haritha, Iago Suárez, Igor Wodiany, Ivan Pozdeev, Jacob MacRitchie, James Perkins, Jcrist99, Jiri Horner, Jonathan Viney, Juha Reunanen, KUANG Fangjun, Mikhail Paulyshka, Muhammad Abdullah, Nickola, Pushkal Katara, Riyuzakii, Roman Cattaneo, Shresth Verma, Simon Guo, Wei Hao, Wu, Zhiwen, alessandro faria, gdkessler, klchang, woody.chow, wxzs5, zhijackchen, zhongwuzw

opencv_contrib

sghoshcvc, Vladislav Sovrasov, Alexander Alekhin, Vitaly Tuzov, berak, Hamdi Sahloul, Maksim Shabunin, Pavel Rojtberg, Suman Ghosh, LaurentBerger, Leonardo Brás, Suleyman TURKMEN, Adam Gradzki, Anup Parikh, Dmitry Kurtaev, Egor Pugin, Leonardo lontra, Oleg Kalachev, Vladislav Samsonov, cDc, fiammante, klchang, kurnianggoro, kushalvyaskv, sukhad-app

Download

Documentation

Sources

Win pack

iOS pack

Android pack

Sincerely yours and with the best wishes to you for 2018,
OpenCV dev team

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

OpenCV is de-facto standard framework for CV developers, with 16+ year-long history, ~1M lines of code, 1000s of algorithms and tens of 1000s unit tests. While OpenCV delivers decent performance out-of-the-box for classical algorithms on desktops, it does not provide sufficient performance for modern CV algorithms, such as a suite of deep learning algorithms, as well as lack on performance on embedded platforms. We offer a short-term solution for the performance problem using T-API (transparent API) - OpenCL-based acceleration layer in OpenCV. We'll demonstrate how to use this technology using a popular CV problem - pedestrian detection. OpenCL is a standard tool to program modern parallel hardware, from CPU and GPU to specialized DSPs and even FPGA. In search of a long-term solution to performance problem we experiment with alternative approaches, such as Halide. We expect Halide to address existing OpenCL issues, namely the lack of performance-portability and complexity of use for non-experts because Halide provides higher abstraction level and convenience of use. We will demonstrate early experimental results of using Halide. Should this approach prove successful, we will include support for Halide into the next versions of OpenCV. Importantly, OpenCV now includes support for OpenVX, which we use to accelerate some image processing pipelines as well as deep nets execution.

Links:

Read Full Article
  • Show original
  • .
  • Share
  • .
  • Favorite
  • .
  • Email
  • .
  • Add Tags 

Separate tags by commas
To access this feature, please upgrade your account.
Start your free month
Free Preview