TensorStack.Video.Windows 0.1.9

Prefix Reserved

There is a newer version of this package available.
See the version list below for details.

dotnet add package TensorStack.Video.Windows --version 0.1.9

NuGet\Install-Package TensorStack.Video.Windows -Version 0.1.9

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="TensorStack.Video.Windows" Version="0.1.9" />

For projects that support PackageReference, copy this XML node into the project file to reference the package.

<PackageVersion Include="TensorStack.Video.Windows" Version="0.1.9" />
                    

                            Directory.Packages.props

<PackageReference Include="TensorStack.Video.Windows" />
                    

                            Project file

For projects that support Central Package Management (CPM), copy this XML node into the solution Directory.Packages.props file to version the package.

paket add TensorStack.Video.Windows --version 0.1.9

The NuGet Team does not provide support for this client. Please contact its maintainers for support.

#r "nuget: TensorStack.Video.Windows, 0.1.9"

#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.

#:package TensorStack.Video.Windows@0.1.9

#:package directive can be used in C# file-based apps starting in .NET 10 preview 4. Copy this into a .cs file before any lines of code to reference the package.

#addin nuget:?package=TensorStack.Video.Windows&version=0.1.9
                    

                            Install as a Cake Addin

#tool nuget:?package=TensorStack.Video.Windows&version=0.1.9
                    

                            Install as a Cake Tool

The NuGet Team does not provide support for this client. Please contact its maintainers for support.

TensorStack.Video.Windows

TensorStack.Video.Windows provides Windows-specific support for reading and writing video.
It facilitates efficient access to video streams, enabling frames to be read, processed, and saved back to common formats using the Windows media stack.

Frame Interpolation

The Interpolation Pipeline uses RIFE (Real-Time Intermediate Flow Estimation) RIFE analyzes motion between consecutive frames and predicts new intermediate frames, producing smoother motion and higher frame rates without traditional frame blending artifacts.
It’s designed for both speed and quality, making it ideal for upscaling or enhancing AI-generated and low-FPS video content.

Quick Start

This minimal example demonstrates how to perform video frame interpolation using TensorStack.Video.Windows.

[nuget: TensorStack.Video.Windows]
[nuget: TensorStack.Providers.DML]

async Task QuickStartAsync()
{
    var provider = Provider.GetProvider();

    // Create the interpolation pipeline
    using (var pipeline = InterpolationPipeline.Create(provider))
    {
        // Read video stream
        var inputStream = new VideoInputStream("Input.mp4");

        // Interpolate the stream (e.g., 3x frame rate)
        var outputStream = pipeline.RunAsync(new InterpolationStreamOptions
        {
            Multiplier = 3,
            Stream = inputStream.GetAsync()
        });

        // Save the output video
        await outputStream.SaveAync("Output.mp4");
    }
}

Multiplier — Defines how many new frames are generated between existing ones.
For example, a value of 3 triples the frame rate (turning 30 FPS into 90 FPS).

Product	Compatible and additional computed target framework versions.
.NET	net9.0-windows10.0.17763 is compatible. net10.0-windows was computed.

Compatible target framework(s)

Included target framework(s) (in package)

Learn more about Target Frameworks and .NET Standard.

net9.0-windows10.0.17763
- OpenCvSharp4 (>= 4.11.0.20250507)
- OpenCvSharp4.runtime.win (>= 4.11.0.20250507)
- System.Linq.Async (>= 6.0.3)
- TensorStack.Video (>= 0.1.9)

NuGet packages (1)

Showing the top 1 NuGet packages that depend on TensorStack.Video.Windows:

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