OpenCvSharp is a cross-platform .NET wrapper for OpenCV, providing a rich set of image processing and computer vision functionality. It supports .NET 8+, .NET Standard 2.0/2.1, and .NET Framework 4.6.1+.

Quick Start

Windows

dotnet add package OpenCvSharp4.Windows

Linux / Ubuntu

dotnet add package OpenCvSharp4
dotnet add package OpenCvSharp4.official.runtime.linux-x64
# optional slim profile (smaller native dependency surface)
# dotnet add package OpenCvSharp4.official.runtime.linux-x64.slim

For more installation options, see the Installation section below.

Features

• OpenCvSharp is modeled on the native OpenCV C/C++ API style as much as possible.
• Many classes of OpenCvSharp implement IDisposable. Unsafe resources are managed automatically.
• OpenCvSharp does not force object-oriented programming style on you. You can also call native-style OpenCV functions.
• OpenCvSharp provides functions for converting from Mat to Bitmap (GDI+) or WriteableBitmap (WPF).

Target OpenCV

• OpenCV 4.13.0 with opencv_contrib

Requirements

• .NET 8 or later / .NET Standard 2.0 / .NET Standard 2.1
• .NET Framework 4.6.1 or later is supported via the .NET Standard 2.0 target (WpfExtensions also directly targets .NET Framework 4.8)
• (Windows) Visual C++ 2022 Redistributable Package
• (Windows Server) Media Foundation

PS1> Install-WindowsFeature Server-Media-Foundation

• (Linux) The official OpenCvSharp4.official.runtime.linux-x64 package is built on manylinux_2_28 and works on Ubuntu 20.04+, Debian 10+, RHEL/AlmaLinux 8+, and other Linux distributions with glibc 2.28+. The full package includes FFmpeg (LGPL v2.1) and Tesseract statically linked.
  • The full package uses GTK3 for highgui support (Cv2.ImShow, Cv2.WaitKey, etc.). GTK3 is pre-installed on standard Ubuntu/Debian/RHEL environments. In minimal or container environments where it is absent, install it manually (apt-get install libgtk-3-0 or dnf install gtk3), or use the slim profile instead.
  • The slim package (OpenCvSharp4.official.runtime.linux-x64.slim) disables highgui and has no GUI dependencies — suitable for headless and container use.

OpenCvSharp won't work on Unity and Xamarin platforms. For Unity, please consider using OpenCV for Unity or some other solutions.

OpenCvSharp does not support CUDA. If you want to use CUDA features, you need to customize the native bindings yourself.

Installation

Windows (except UWP)

Add OpenCvSharp4 and OpenCvSharp4.runtime.win NuGet packages to your project. Alternatively, you can use the OpenCvSharp4.Windows all-in-one package. For a smaller feature profile, pair OpenCvSharp4 with OpenCvSharp4.runtime.win.slim, or use the all-in-one OpenCvSharp4.Windows.Slim package.

⚠️ OpenCvSharp4.runtime.uwp is deprecated and no longer maintained. The last published version targets OpenCV 4.9.0. New UWP projects are not recommended; consider migrating to WinUI 3.

Linux (Ubuntu and other distributions)

Add OpenCvSharp4 and OpenCvSharp4.official.runtime.linux-x64 NuGet packages to your project. This package uses the portable linux-x64 RID and works with .NET 8+ publish/deploy workflows out of the box.

dotnet new console -n ConsoleApp01
cd ConsoleApp01
dotnet add package OpenCvSharp4
dotnet add package OpenCvSharp4.official.runtime.linux-x64
# or slim profile:
# dotnet add package OpenCvSharp4.official.runtime.linux-x64.slim
# -- edit Program.cs --- # 
dotnet run

⚠️ The distro-specific OpenCvSharp4.official.runtime.ubuntu.* packages are no longer maintained. Use OpenCvSharp4.official.runtime.linux-x64 instead.

Slim profile module coverage

The slim runtime packages keep a practical subset while reducing runtime dependencies.

• Enabled modules: core, imgproc, imgcodecs, calib3d, features2d, flann, objdetect, photo, ml, video, stitching, barcode
• Disabled modules: contrib, dnn, videoio, highgui

This profile is used by:

• OpenCvSharp4.runtime.win.slim
• OpenCvSharp4.Windows.Slim
• OpenCvSharp4.official.runtime.linux-x64.slim

Usage

For more details, refer to the samples and Wiki pages.

Always remember to release Mat and other IDisposable resources using the using syntax:

// Edge detection by Canny algorithm
using OpenCvSharp;

using var src = new Mat("lenna.png", ImreadModes.Grayscale);
using var dst = new Mat();

Cv2.Canny(src, dst, 50, 200);
using (new Window("src image", src))
using (new Window("dst image", dst))
{
    Cv2.WaitKey();
}

Advanced: Using ResourcesTracker for automatic resource management

As mentioned above, objects of classes such as Mat and MatExpr have unmanaged resources and need to be manually released by calling the Dispose() method. Additionally, the +, -, *, and other operators create new objects each time, and these objects need to be disposed to prevent memory leaks. Despite having the using syntax, the code can still look verbose.

Therefore, a ResourcesTracker class is provided. The ResourcesTracker implements the IDisposable interface, and when the Dispose() method is called, all resources tracked by the ResourcesTracker are disposed. The T() method of ResourcesTracker can track an object or an array of objects, and the NewMat() method is equivalent to T(new Mat(...)). All objects that need to be released can be wrapped with T(). For example: t.T(255 - t.T(picMat * 0.8)). Example code is as follows:

using (var t = new ResourcesTracker())
{
    Mat mat1 = t.NewMat(new Size(100, 100), MatType.CV_8UC3, new Scalar(0));
    Mat mat3 = t.T(255-t.T(mat1*0.8));
    Mat[] mats1 = t.T(mat3.Split());
    Mat mat4 = t.NewMat();
    Cv2.Merge(new Mat[] { mats1[0], mats1[1], mats1[2] }, mat4);
}

using (var t = new ResourcesTracker())
{
    var src = t.T(new Mat(@"lenna.png", ImreadModes.Grayscale));
    var dst = t.NewMat();
    Cv2.Canny(src, dst, 50, 200);
    var blurredDst = t.T(dst.Blur(new Size(3, 3)));
    t.T(new Window("src image", src));
    t.T(new Window("dst image", blurredDst));
    Cv2.WaitKey();
}      

Code samples

https://github.com/shimat/opencvsharp_samples/

API Documents

http://shimat.github.io/opencvsharp/api/OpenCvSharp.html

NuGet

Managed libraries

Package	Description
OpenCvSharp4	OpenCvSharp core libraries
OpenCvSharp4.Extensions	GDI+ Extensions
OpenCvSharp4.WpfExtensions	WPF Extensions
OpenCvSharp4.Windows	All-in-one package for Windows (except UWP)
OpenCvSharp4.Windows.Slim	All-in-one slim package for Windows (except UWP)

Native bindings

Package	Description
OpenCvSharp4.runtime.win	Native bindings for Windows x64 (except UWP)
OpenCvSharp4.runtime.win.slim	Slim native bindings for Windows x64 (except UWP), with core,imgproc,imgcodecs,calib3d,features2d,flann,objdetect,photo,ml,video,stitching,barcode enabled
OpenCvSharp4.official.runtime.linux-x64	Native bindings for Linux x64 (portable RID, recommended). Built on manylinux_2_28. Includes FFmpeg and Tesseract statically linked. Requires GTK3 runtime (libgtk-3.so.0) for highgui (Cv2.ImShow etc.).
OpenCvSharp4.official.runtime.linux-x64.slim	Slim native bindings for Linux x64 (portable RID), with core,imgproc,imgcodecs,calib3d,features2d,flann,objdetect,photo,ml,video,stitching,barcode enabled. No external runtime dependencies.
OpenCvSharp4.runtime.linux-arm	Native bindings for Linux Arm
OpenCvSharp4.runtime.wasm	Native bindings for WebAssembly

Note: Windows x86 (32-bit) support has been dropped as of the OpenCV 4.13.0 release series. The OpenCvSharp4.runtime.win and OpenCvSharp4.runtime.win.slim packages now ship x64-only native binaries. Users requiring x86 Windows support should stay on the last OpenCV 4.12.x-based packages.

Native binding (OpenCvSharpExtern.dll / libOpenCvSharpExtern.so) is required for OpenCvSharp to work. To use OpenCvSharp, you should add both OpenCvSharp4 and OpenCvSharp4.runtime.* packages to your project. Currently, native bindings for Windows, Linux x64/ARM, and WebAssembly are available.

Packages named OpenCvSharp3-* and OpenCvSharp-* are deprecated.

OpenCvSharp3-AnyCPU / OpenCvSharp3-WithoutDll / OpenCvSharp-AnyCPU / OpenCvSharp-WithoutDll

Downloads

If you are not using NuGet, you can download the DLL files from the release page.

Docker images

https://github.com/shimat?tab=packages

OpenCvSharp Build Instructions

Windows

Prerequisites

• Visual Studio 2022 / 2026, or the corresponding Build Tools, with the Desktop development with C++ workload
• CMake 3.20+ in PATH (winget install Kitware.CMake)
• Git in PATH
• vcpkg in PATH or VCPKG_INSTALLATION_ROOT set

  git clone https://github.com/microsoft/vcpkg C:\vcpkg
  C:\vcpkg\bootstrap-vcpkg.bat
  # Add C:\vcpkg to PATH, or set $env:VCPKG_INSTALLATION_ROOT = 'C:\vcpkg'

Steps

1. Clone the repository with submodules (opencv + opencv_contrib are included as submodules):

   git clone --recursive https://github.com/shimat/opencvsharp.git
   cd opencvsharp

   If you already cloned without submodules:

   git submodule update --init --recursive

2. Build OpenCV from source (Tesseract and other native dependencies are automatically installed via vcpkg):

   # Install vcpkg dependencies (only needed once, or after vcpkg.json changes)
   C:\vcpkg\vcpkg.exe install --triplet x64-windows-static --overlay-triplets cmake\triplets --x-install-root vcpkg_installed

   .\build_opencv_windows.ps1
   # Use -Jobs N to control parallel build (default: 4)
   .\build_opencv_windows.ps1 -Jobs 8

   Output is installed to opencv_artifacts/.

3. Build the native wrapper OpenCvSharpExtern:

   cmake -S src -B src\build -G "Visual Studio 17 2022" -A x64 `
         -D "CMAKE_PREFIX_PATH=$PWD\opencv_artifacts" `
         -D CMAKE_TOOLCHAIN_FILE="C:\vcpkg\scripts\buildsystems\vcpkg.cmake" `
         -D VCPKG_TARGET_TRIPLET=x64-windows-static `
         -D "VCPKG_INSTALLED_DIR=$PWD\vcpkg_installed" `
         -D "VCPKG_OVERLAY_TRIPLETS=$PWD\cmake\triplets"
   cmake --build src\build --config Release

4. Build the managed OpenCvSharp library:

   • Open OpenCvSharp.sln and build, or:

   dotnet build src/OpenCvSharp/OpenCvSharp.csproj -c Release

   Note on the native DLL: Step 3 automatically copies OpenCvSharpExtern.dll into test/OpenCvSharp.Tests/ as a post-build step, so dotnet test works out of the box. For your own application, either add src\build\OpenCvSharpExtern\Release to PATH, or copy OpenCvSharpExtern.dll alongside your app's output directory.

Ubuntu

Tesseract and image libraries (libjpeg-turbo, libpng, libtiff, libwebp) can be managed either via vcpkg (recommended, consistent with Windows) or via the system package manager.

Option A: vcpkg (recommended)

Prerequisites: cmake, git, g++, vcpkg installed.

git clone --recursive https://github.com/shimat/opencvsharp.git
cd opencvsharp
git submodule update --init --recursive

# Install dependencies via vcpkg
/path/to/vcpkg/vcpkg install \
  --triplet x64-linux-static \
  --overlay-triplets cmake/triplets

Build OpenCV and OpenCvSharpExtern pointing to vcpkg:

cmake -C cmake/opencv_build_options.cmake \
      -S opencv -B opencv/build \
      -D OPENCV_EXTRA_MODULES_PATH=$PWD/opencv_contrib/modules \
      -D CMAKE_INSTALL_PREFIX=$PWD/opencv_artifacts \
      -D CMAKE_TOOLCHAIN_FILE=/path/to/vcpkg/scripts/buildsystems/vcpkg.cmake \
      -D VCPKG_TARGET_TRIPLET=x64-linux-static
cmake --build opencv/build -j$(nproc)
cmake --install opencv/build

cmake -S src -B src/build \
      -D CMAKE_BUILD_TYPE=Release \
      -D CMAKE_PREFIX_PATH=$PWD/opencv_artifacts \
      -D CMAKE_TOOLCHAIN_FILE=/path/to/vcpkg/scripts/buildsystems/vcpkg.cmake \
      -D VCPKG_TARGET_TRIPLET=x64-linux-static
cmake --build src/build -j$(nproc)

Option B: system packages (apt)

sudo apt-get install -y g++ cmake libtesseract-dev \
  libjpeg-dev libpng-dev libtiff-dev libwebp-dev \
  libavcodec-dev libavformat-dev libswscale-dev

Build OpenCV from source: https://docs.opencv.org/4.x/d7/d9f/tutorial_linux_install.html

Then build the native wrapper:

cmake -S src -B src/build \
      -D CMAKE_BUILD_TYPE=Release \
      -D CMAKE_PREFIX_PATH=${YOUR_OPENCV_INSTALL_PATH}
cmake --build src/build -j$(nproc)

Add a reference to the built shared library:

export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/path/to/opencvsharp/src/build/OpenCvSharpExtern"

Build managed library (both options)

dotnet build src/OpenCvSharp/OpenCvSharp.csproj -c Release

Customizing OpenCV and OpenCvSharp for embedded (ARM) Platforms

If you want to use OpenCV and OpenCvSharp on an embedded platform like a Raspberry Pi with a 64-bit OS, you have to build both libraries manually, and it's easiest to build it right on your target hardware to avoid cross-compiler toolchain challenges. The instructions for this build and install are here.

Donations

If you find the OpenCvSharp library useful and would like to show your gratitude by donating, here are some donation options. Thank you.

https://github.com/sponsors/shimat