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README.md

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# H264Sharp
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Cisco's OpenH264 Native wrapper for .Net with optimised image format conversion support. It is very suitable for realtime streaming over network.
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This is the only open source C# library with full feature wrapper.
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Image format converters are faster than OpenCV implementation.
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Cisco's OpenH264 Native wrapper for .Net with optimised color format conversion support. It is very suitable for realtime streaming over network.
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This is the only open source .Net library with full feature wrapper, supported for windows and linux.
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Arm platforms are work in progress.
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SIMD color format converters are faster than OpenCV implementation.
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- Cross Platform
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- Plug&Play
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- Tested on .NetFramework and Net(up to 8).
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- Tested on .NetFramework and Net(up to 8), Windows & Linux.
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- Compatible with OpenCV.(i.e. OpenCVsharp)
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- Tested on WPF application with camera and screen capture.
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- No memory leaks or GC pressure.
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- Simple console application example and WPF application is provided as an example.
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Library consist of native dll which acts as OpenH264 wrapper and image format converter (YUV420p <-> RGB,BGR,RGBA,BGRA)
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Library consist of native dll which acts as OpenH264 wrapper and color format converter (YUV420p <-> RGB,BGR,RGBA,BGRA)
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<br/>Converters are vectorised(AVX2 and SSE) and can be configured for parallelisation for high performance.
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C# library is .Net standard wrapper library for this dll and performs PInvoke to handle transcoding.
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## Nuget
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Install the nuget package and its ready to go. All native dependencies are automatically installed and will apepear on your executable directory.
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linux binaries are provided on releases, Nuget release coming soon.
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[![NuGet](https://img.shields.io/nuget/v/H264Sharp)](https://www.nuget.org/packages/H264Sharp/1.2.0)
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decoder.Initialize(decParam);
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```
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Image format conversion (RGB <-> YUV420) has optional configuration where you can provide number of threads on parallelisation.
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Color format conversion (RGB <-> YUV420) has optional configuration where you can provide number of threads on parallelisation.
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<br/>Using 1 thread gives consumes least cpu cycles and most efficient but it takes more time.
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Beyond 4 threads you start to get diminishing returns.
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<br/>Fastest performance is achieved when threadcount is same as your phyical threads on your machine.

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