OpenCvSharp4.Windows
4.10.0.20241108
dotnet add package OpenCvSharp4.Windows --version 4.10.0.20241108
NuGet\Install-Package OpenCvSharp4.Windows -Version 4.10.0.20241108
<PackageReference Include="OpenCvSharp4.Windows" Version="4.10.0.20241108" />
paket add OpenCvSharp4.Windows --version 4.10.0.20241108
#r "nuget: OpenCvSharp4.Windows, 4.10.0.20241108"
// Install OpenCvSharp4.Windows as a Cake Addin #addin nuget:?package=OpenCvSharp4.Windows&version=4.10.0.20241108 // Install OpenCvSharp4.Windows as a Cake Tool #tool nuget:?package=OpenCvSharp4.Windows&version=4.10.0.20241108
OpenCV 4.x wrapper. All-in-one package for Windows users.
Learn more about Target Frameworks and .NET Standard.
-
.NETCoreApp 3.1
- OpenCvSharp4 (>= 4.10.0.20241108)
- OpenCvSharp4.runtime.win (>= 4.10.0.20241108)
- OpenCvSharp4.WpfExtensions (>= 4.10.0.20241108)
-
.NETFramework 4.8
- OpenCvSharp4 (>= 4.10.0.20241108)
- OpenCvSharp4.runtime.win (>= 4.10.0.20241108)
- OpenCvSharp4.WpfExtensions (>= 4.10.0.20241108)
-
.NETStandard 2.0
- OpenCvSharp4 (>= 4.10.0.20241108)
- OpenCvSharp4.runtime.win (>= 4.10.0.20241108)
-
.NETStandard 2.1
- OpenCvSharp4 (>= 4.10.0.20241108)
- OpenCvSharp4.runtime.win (>= 4.10.0.20241108)
-
net6.0
- OpenCvSharp4 (>= 4.10.0.20241108)
- OpenCvSharp4.runtime.win (>= 4.10.0.20241108)
NuGet packages (38)
Showing the top 5 NuGet packages that depend on OpenCvSharp4.Windows:
Package | Downloads |
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our1314.work
Package Description |
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RatEye
Image processing library for Escape from Tarkov |
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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 (1)
Showing the top 1 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 - UI Automation Testing Tools For Genshin Impact
|
Version | Downloads | Last updated |
---|---|---|
4.10.0.20241108 | 3,365 | 11/8/2024 |
4.10.0.20241107 | 486 | 11/7/2024 |
4.10.0.20240616 | 45,146 | 6/16/2024 |
4.10.0.20240615 | 396 | 6/15/2024 |
4.9.0.20240103 | 72,692 | 1/3/2024 |
4.8.0.20230708 | 97,833 | 7/10/2023 |
4.7.0.20230115 | 170,419 | 1/15/2023 |
4.6.0.20220608 | 155,149 | 6/8/2022 |
4.5.5.20211231 | 96,813 | 12/31/2021 |
4.5.3.20211228 | 26,832 | 12/28/2021 |
4.5.3.20211207 | 10,084 | 12/7/2021 |
4.5.3.20211204 | 804 | 12/5/2021 |
4.5.3.20210817 | 49,658 | 8/21/2021 |
4.5.3.20210725 | 11,007 | 7/25/2021 |
4.5.2.20210404 | 83,023 | 4/4/2021 |
4.5.1.20210210 | 19,851 | 2/10/2021 |
4.5.1.20210208 | 1,263 | 2/8/2021 |
4.5.1.20210206 | 643 | 2/6/2021 |
4.5.1.20210123 | 5,058 | 1/24/2021 |
4.5.1.20201229 | 38,782 | 12/29/2020 |
4.5.1.20201226 | 6,246 | 12/26/2020 |
4.5.0.20201013 | 28,526 | 10/13/2020 |
4.4.0.20200915 | 15,383 | 9/16/2020 |
4.4.0.20200725 | 14,603 | 7/25/2020 |
4.3.0.20200701 | 35,332 | 7/8/2020 |
4.3.0.20200524 | 23,734 | 5/27/2020 |
4.3.0.20200421 | 32,059 | 4/21/2020 |
4.3.0.20200405 | 11,697 | 4/5/2020 |
4.2.0.20200208 | 31,069 | 2/8/2020 |
4.2.0.20200108 | 16,042 | 1/8/2020 |
4.2.0.20191223 | 5,474 | 12/23/2019 |
4.1.1.20191216 | 6,416 | 12/17/2019 |
4.1.1.20191110 | 46,587 | 11/10/2019 |
4.1.1.20191026 | 5,683 | 10/27/2019 |
4.1.1.20191025 | 705 | 10/25/2019 |
4.1.1.20191021 | 817 | 10/23/2019 |
4.1.1.20191017 | 1,561 | 10/18/2019 |
4.1.0.20190416 | 106,895 | 4/16/2019 |
4.0.1.20190326 | 11,460 | 3/26/2019 |
4.0.0.20190108 | 5,463 | 1/8/2019 |
4.0.0.20181225 | 3,115 | 12/25/2018 |