By : Itseez

( Open Source )

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  • License
    Open Source
  • Version
  • Requirements
    Microsoft Windows (All Versions)
  • Date added
    Oct 18, 2018
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The Open Source Computer Vision Library, or OpenCV in the event that you lean toward, houses more than 2500 calculations, broad documentation and test code for constant PC vision. 

OpenCV concentrates for the most part towards ongoing picture handling, all things considered, in the event that it discovers Intel's Integrated Performance Primitives on the framework, it will utilize these business enhanced schedules to quicken itself. 

OpenCV library supports: 

  • Constant catch. 
  • Video record import. 
  • Question discovery. 
  • Fundamental picture treatment: splendor, differentiate, edge. 
  • Blob discovery 

OpenCV can achieve various distinctive errands including fundamental picture preparing, for example, sifting, morphology, geometrical changes, histograms, and shading space changes. It can likewise perform propelled picture preparing like inpainting, watershed and meanshift division and so forth. OpenCV can likewise attempt more unpredictable assignments, for example, form preparing and computational geometry, different element locators and descriptors (these can extend from basic Harris finder to Hough change, SURF, or MSER) protest following, optical stream, question identification utilizing falls of supported haar classifiers, camera alignment, and machine learning instruments (information grouping and factual classifiers).

The application is cross-platform and works on Windows, Mac OS X, Linux, Android and iOS.

What’s new:

First of all, 4.0 alpha includes all the latest improvements, optimizations and bug fixes from 3.4 branch. In particular:

ONNX parser has been added to OpenCV DNN module. It supports various classification networks, such as AlexNet, Inception v2, Resnet, VGG etc. The tiny YOLO v2 object detection network is also partially supported.
A few other notable DNN improvements:
Mask RCNN support and the example
Faster object detection when using Intel Inference Engine (a part of Intel OpenVINO)
Several stability improvements in the OpenCL backend.
Fast QR code detector (~80FPS @ 640x480 resolution on Core i5 desktop). By 4.0 gold we plan to add the QR code decoder as well, so that we have a complete solution.
Constantly expanding set of SSE4-, AVX2- and NEON-optimized kernels via so called “wide universal intrinsics”.

Besides, OpenCV 4.0 alpha includes some exclusive features, such as:

OpenCV is C++11 library now and it requires C++11 compliant compiler. Therefore, some nice features like parallel_for with lambda functions, convenient iteration over cv::Mat, initialization of cv::Mat by listing its elements etc. are available by default.
The standard std::string and std::shared_ptr replaced hand-crafted cv::String and cv::Ptr. Our parallel_for can now use the pool of std::threads as the backend.
The legacy C API from OpenCV 1.x (using CvMat, IplImage, etc.) is partially excluded; the cleanup should mostly be finished by OpenCV 4.0 gold.
Added basic FP16 support (the new CV_16F type has been added).
CPU- and GPU-accelerated KinFu live 3d dense reconstruction algorithm has been included into opencv_contrib.
HPX parallel backend (thanks to Jakub Golinowski)
The new chessboard detector (thanks to Alexander Duda)

Overall, OpenCV 4.0 alpha release includes 85 patches, including 28 massive merge requests from 3.4 branch.

Please note that even though the release should be quite stable, quite a few changes in OpenCV API and implementation are yet to be done before 4.0 final.

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