OpenCvSharp4.Windows 4.5.1.20210123

There is a newer version of this package available.
See the version list below for details. 
dotnet add package OpenCvSharp4.Windows --version 4.5.1.20210123                

                    
                

            
NuGet\Install-Package OpenCvSharp4.Windows -Version 4.5.1.20210123
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package. 
<PackageReference Include="OpenCvSharp4.Windows" Version="4.5.1.20210123" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add OpenCvSharp4.Windows --version 4.5.1.20210123                

                    
                

            
#r "nuget: OpenCvSharp4.Windows, 4.5.1.20210123"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package. 
// Install OpenCvSharp4.Windows as a Cake Addin
#addin nuget:?package=OpenCvSharp4.Windows&version=4.5.1.20210123

// Install OpenCvSharp4.Windows as a Cake Tool
#tool nuget:?package=OpenCvSharp4.Windows&version=4.5.1.20210123

OpenCV 4.x wrapper. All-in-one package for Windows users.

There are no supported framework assets in this package.

Learn more about Target Frameworks and .NET Standard.

NuGet packages (38)

Showing the top 5 NuGet packages that depend on OpenCvSharp4.Windows:

Package Downloads
our1314.work

Package Description
RatEye

Image processing library for Escape from Tarkov
UnionSoft.UiAuto.Automation

Library to use UnionSoft automaion project.
OpenVinoSharp.win

基于C#平台调用OpenVINO套件部署深度学习模型。 Based on the C # platform, call the OpenVINO suite to deploy a deep learning model.
MxNet.Sharp

C# Binding for the Apache MxNet library. NDArray, Symbolic and Gluon Supported MxNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

GitHub repositories (14)

Showing the top 5 popular GitHub repositories that depend on OpenCvSharp4.Windows:

Repository Stars
babalae/better-genshin-impact
📦BetterGI · 更好的原神 - 自动拾取 | 自动剧情 | 全自动钓鱼(AI) | 全自动七圣召唤 | 自动伐木 | 自动刷本 | 自动采集/挖矿/锄地 | 一条龙 - UI Automation Testing Tools For Genshin Impact
dorisoy/Dorisoy.Pan
Dorisoy.Pan 是基于.net core8 的跨平台文档管理系统，使用 MS SQL 2012 / MySql8.0（或更高版本）后端数据库，您可以在 Windows、Linux 或 Mac 上运行它,项目中的所有方法都是异步的,支持令牌基身份验证,项目体系结构遵循著名的软件模式和最佳安全实践。源代码是完全可定制的,热插拔且清晰的体系结构,使开发定制功能和遵循任何业务需求变得容易。 系统使用最新的 Microsoft 技术，高性能稳定性和安全性
Keboo/MaterialDesignInXaml.Examples
A collection of small samples using MaterialDesignInXaml.
MediaPortal/MediaPortal-2
Development of MediaPortal 2
dengqizhou30/AIAssist
GameAssist是一个AI游戏助手，结合OpenCv、OpenCvSharp4、ssd_mobilenet_v3等技术，对游戏对象进行识别，支持自动瞄准/自动开枪等功能，提升玩家的游戏体验
Version Downloads Last updated
4.10.0.20241108 14,244 11/8/2024
4.10.0.20241107 548 11/7/2024
4.10.0.20240616 51,000 6/16/2024
4.10.0.20240615 420 6/15/2024
4.9.0.20240103 77,584 1/3/2024
4.8.0.20230708 116,790 7/10/2023
4.7.0.20230115 176,732 1/15/2023
4.6.0.20220608 158,835 6/8/2022
4.5.5.20211231 97,866 12/31/2021
4.5.3.20211228 28,150 12/28/2021
4.5.3.20211207 10,150 12/7/2021
4.5.3.20211204 815 12/5/2021
4.5.3.20210817 50,853 8/21/2021
4.5.3.20210725 11,058 7/25/2021
4.5.2.20210404 84,512 4/4/2021
4.5.1.20210210 19,937 2/10/2021
4.5.1.20210208 1,274 2/8/2021
4.5.1.20210206 652 2/6/2021
4.5.1.20210123 5,107 1/24/2021
4.5.1.20201229 39,032 12/29/2020
4.5.1.20201226 6,350 12/26/2020
4.5.0.20201013 28,649 10/13/2020
4.4.0.20200915 15,706 9/16/2020
4.4.0.20200725 14,680 7/25/2020
4.3.0.20200701 35,651 7/8/2020
4.3.0.20200524 23,779 5/27/2020
4.3.0.20200421 32,283 4/21/2020
4.3.0.20200405 11,801 4/5/2020
4.2.0.20200208 31,300 2/8/2020
4.2.0.20200108 16,237 1/8/2020
4.2.0.20191223 5,507 12/23/2019
4.1.1.20191216 6,469 12/17/2019
4.1.1.20191110 48,503 11/10/2019
4.1.1.20191026 5,708 10/27/2019
4.1.1.20191025 733 10/25/2019
4.1.1.20191021 826 10/23/2019
4.1.1.20191017 1,576 10/18/2019
4.1.0.20190416 107,347 4/16/2019
4.0.1.20190326 11,579 3/26/2019
4.0.0.20190108 5,539 1/8/2019
4.0.0.20181225 3,321 12/25/2018