Computer Vision

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

A .NET library for computer vision and image processing.

Provides a sample implementation of a ContentPage that shows how to use Microsoft ComputerVision (Cognitive Services API) to analyse an image to detect: - text - faces - emotion - ages - adult content - racy content - celebrities - tags - sentiments - language

General availability for AWS Panorama. AWS SDK for Panorama includes APIs to manage your devices and nodes, and deploy computer vision applications to the edge. For more information, see the AWS Panorama documentation at http://docs.aws.amazon.com/panorama

SortCS is a 'Multiple Object Tracker'

Computer vision features for IronOCR, an advanced OCR (Optical Character Recognition) library for C# and .NET

Windows redistributables for the ODModelBuilderTF library with TensorFlow 2.6.0 (second lib part)

Interface for low-effort image pixelizing using cloud technology.

SimpleLPR is a professional-grade license plate recognition (LPR/ANPR) library that provides automatic detection and reading of vehicle license plates in images and video streams. Developed over 10+ years and deployed in thousands of production systems worldwide, SimpleLPR delivers enterprise-level reliability with a simple, developer-friendly API. **Proven Performance** Achieves 85-95% recognition accuracy under typical conditions (plate text height ≥20 pixels, reasonable image quality). The specialized OCR engine is trained specifically for license plate fonts and formats, handling challenging real-world scenarios including varying lighting, camera angles up to ±30°, and vehicle motion. **Comprehensive Input Support** • Static images: JPEG, PNG, TIFF, BMP from files or memory buffers • Video files: AVI, MP4, MKV and other common formats • Live streams: RTSP, HTTP, and other network protocols • Configurable resolution limits for performance optimization **Advanced Features** • Multi-threaded processor pools enable concurrent analysis of multiple streams • Intelligent plate tracking correlates detections across video frames, reducing false positives • Plate region detection provides exact coordinates and confidence scores • Character-level analysis with individual confidence metrics • Contrast sensitivity adjustment for shadow and glare compensation • Automatic perspective correction for angled plate views • Support for approximately 90 countries with region-specific templates **Developer-Focused Design** SimpleLPR offers native APIs for C++, .NET (C#, VB.NET, F#), and Python, each following platform conventions. The library handles all complexity internally - developers simply provide an image and receive structured results containing the plate text, country, confidence scores, and location coordinates. **Video Processing Capabilities** The video API includes frame extraction, automatic buffering, stream reconnection for network sources, and synchronized result delivery. The plate tracker maintains temporal consistency across frames, eliminating transient misreads common in frame-by-frame processing. **System Requirements** • Windows 10/11 (x64) or Linux Ubuntu 20.04+ (x64) • .NET Standard 2.0 or higher for .NET integration • Python 3.8, 3.9, 3.10, 3.11, or 3.12 for Python integration • Optional: CUDA-capable GPU for performance boost (SDK version) **Licensing** 60-day evaluation included. Production license is one-time purchase with unlimited, royalty-free redistribution rights. Includes one year of technical support and updates. **Resources** • Project Site: https://www.warelogi.com • Documentation: https://www.warelogic.com/doc/SimpleLPR3.pdf • .NET API Reference: https://www.warelogic.com/doc/SimpleLPR.chm • Python API Reference: https://www.warelogic.com/doc/simplelpr_python_api_reference.htm • Python Quick Start: https://www.warelogic.com/doc/simplelpr_python_quickstart_guide.htm • Sample Code: https://github.com/xgirones/SimpleLPR-samples • Support: support@warelogic.com See the project README for complete documentation and code examples.

This package provides operators for real-time computer vision and image processing.

Redistributable components for package 'opencv.win.native.static'. This package should only be installed as a dependency. (This is not the package you are looking for).

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This package contains just FaceAiSharp's managed code and does not include any ONNX models. Take a look at FaceAiSharp.Bundle for a batteries-included package with everything you need to get started.

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This is a bundle package that installs FaceAiSharp's managed code and multiple AI models in the ONNX format.

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This package contains just FaceAiSharp's managed code and does not include any ONNX models. Take a look at FaceAiSharp.Bundle for a batteries-included package with everything you need to get started.

OpenCVSharp port for Xamarin

Recommended Google client library to access the Google Document AI API (v1), which is a service to parse structured information from unstructured or semi-structured documents using state-of-the-art Google AI such as natural language, computer vision, translation, and AutoML.

The Basler pylon .NET API provides a convenient object-oriented programming interface to access Basler cameras. This package allows you to build projects that use pylon .NET API features. To run the resulting program, the pylon Camera Software Suite has to be installed.

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 an Apache 2 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.

Windows redistributables for the ODModelBuilderTF library with TensorFlow 2.6.0 (first lib part)

Pre-built OpenCV 3.x binaries on Windows (UWP). This package contains native builds of OpenCV3 - C++ (x86, x64, arm). Note that VideoIO, and all dependent modules, require UWP Desktop extensions.

An automation black-box testing framework based on image recognition.

A lightweight library that allows to easily analyze images using Custom Vision, with the ability to automatically resize the source before sending it to the service.

Windows redistributables for the ODModelBuilderTF library with object detection 2.6.0

Windows redistributables for the ODModelBuilderTF library with TensorFlow 2.6.0 compiled for CUDA 10.1

Framework for machine learning

Pre-built OpenCV binaries on Windows. This package contains native builds of OpenCV3 - C++ for Visual Studio 2015. (Supports x86 and x64.)

OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it's free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 14 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. Note: This package contains extra modules which are not necessarily licensed under the same terms as OpenCV itself.

Symbols for package 'opencv.win.native.static'. This package should not likely be installed. (This is not the package you are looking for).

This library is a port of Qualcomm's FastCV library to C# for Computer Vision developement in Windows Phone 8

OpenCV (Open Source Computer Vision) is a library of programming functions for realtime computer vision. OpenCV is released under a BSD license and hence it's free for both academic and commercial use. It has C++, C, Python and Java (Android) interfaces and supports Windows, Linux, Android, iOS and Mac OS. It has more than 2500 optimized algorithms. Adopted all around the world, OpenCV has more than 7 million downloads growing by nearly 200K/month. Usage ranges from interactive art, to mines inspection, stitching maps on the web on through advanced robotics.

Symbols for package 'wys.OpenCV4.5.2'. This package should not likely be installed. (This is not the package you are looking for).

A port of Qualcomms FastCV library to C# for computer vision development in Windows Phone.

Assorted Computer Vision functions for use within Bonsai.