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|File - Download OpenCV v4.5.2|
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OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies.
Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan.
It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.
Documentation and Tutorials.
Spring update for OpenCV 4.x has been released.
Highlights of this release:
core: added support for parallel backends. Special OpenCV builds allow to select parallel backend and/or load it dynamically through plugins
imgproc: added IntelligentScissors implementation (JS demo). The feature is integrated into CVAT annotation tool and you can try it online on https://cvat.org
videoio: improved hardware-accelerated video decoding/encoding tasks.
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 (Darknet), SAM (Darknet), Exp
Intel® Inference Engine backend ( OpenVINO™ ):
added support for OpenVINO 2021.3 release
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 Python bindings: #19318;
Added more graph data types support in the G-API Python bindings: #19319;
Introduced dynamic input / CNN reshape functionality in the OpenVINO inference backend #18240;
Introduced asynchronous execution support in the OpenVINO inference backend, now inference can run in multiple requests in parallel 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;
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;
[GSoC] Added TF and PyTorch classification conversion cases: #17604
[GSoC] Added TF and PyTorch segmentation conversion cases: #17801
[GSoC] Added TF and PyTorch detection model conversion cases: #18237
Updated documentation to address Wide Universal Intrinsics (WUI) SIMD API: #18952
And many other great contributions from OpenCV community:
core: add cuda::Stream constructor with cuda stream flags: #19286
highgui: expose VSYNC window property for OpenGL on Win32: #19408
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
(opencv_contrib) WeChat QRCode module open source: #2821
(opencv_contrib) Implemented cv::cuda::inRange(): #2803
(opencv_contrib) Added algorithms from Edge Drawing Library: #2313
(opencv_contrib) Added Python bindings for Viz module: #2882
opencv (92 contributors)
opencv_contrib (30 contributors)
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|1,881||2,682||opencv.org <img src="https://www.oldergeeks.com/downloads/gallery/thumbs/OpenCV1_th.png"border="0">||Apr 09, 2021 - 18:01||4.5.2||213.39MB||EXE||, out of 28 Votes.|
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