From 651869c27ff9ed390d78bf7ae6b81b3aa1acefb3 Mon Sep 17 00:00:00 2001 From: Stevan Freeborn <65925598+StevanFreeborn@users.noreply.github.com> Date: Thu, 6 Feb 2025 23:26:58 -0600 Subject: [PATCH] feat: initial working POC --- .gitattributes | 1 + .gitignore | 484 +++++++++++++++++++++++++++++++++ BGR.Console/BGR.Console.csproj | 21 ++ BGR.Console/Program.cs | 207 ++++++++++++++ BGR.Console/modnet.onnx | 3 + BGR.Console/rmbg.onnx | 3 + BGR.Console/u2net.onnx | 3 + 7 files changed, 722 insertions(+) create mode 100644 .gitattributes create mode 100644 .gitignore create mode 100644 BGR.Console/BGR.Console.csproj create mode 100644 BGR.Console/Program.cs create mode 100644 BGR.Console/modnet.onnx create mode 100644 BGR.Console/rmbg.onnx create mode 100644 BGR.Console/u2net.onnx diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..0bb75f7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.onnx filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bc78471 --- /dev/null +++ b/.gitignore @@ -0,0 +1,484 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from `dotnet new gitignore` + +# dotenv files +.env + +# User-specific files +*.rsuser +*.suo +*.user +*.userosscache +*.sln.docstates + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Mono auto generated files +mono_crash.* + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +[Ww][Ii][Nn]32/ +[Aa][Rr][Mm]/ +[Aa][Rr][Mm]64/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ +[Ll]ogs/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUnit +*.VisualState.xml +TestResult.xml +nunit-*.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET +project.lock.json +project.fragment.lock.json +artifacts/ + +# Tye +.tye/ + +# ASP.NET Scaffolding +ScaffoldingReadMe.txt + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_h.h +*.ilk +*.meta +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*_wpftmp.csproj +*.log +*.tlog +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Coverlet is a free, cross platform Code Coverage Tool +coverage*.json +coverage*.xml +coverage*.info + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. 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+ +using Microsoft.ML.OnnxRuntime; +using Microsoft.ML.OnnxRuntime.Tensors; + +using SixLabors.ImageSharp; +using SixLabors.ImageSharp.Formats.Png; +using SixLabors.ImageSharp.PixelFormats; +using SixLabors.ImageSharp.Processing; + +if (args.Length < 1) +{ + Console.WriteLine("Usage: BackgroundRemover "); + return; +} + +var inputImagePath = args[0]; +var maskImagePath = Path.ChangeExtension(inputImagePath, null) + "_mask.png"; +var outputImagePath = Path.ChangeExtension(inputImagePath, null) + "_no_bg.png"; + +try +{ + var assembly = Assembly.GetExecutingAssembly(); + var resourceName = "BGR.Console.u2net.onnx"; + + using var stream = assembly.GetManifestResourceStream(resourceName) ?? throw new FileNotFoundException("Model not found in embedded resources."); + var modelBytes = new byte[stream.Length]; + stream.ReadExactly(modelBytes); + + using var image = Image.Load(inputImagePath); + var inputTensor = CreateTensorInput(image); + + using var options = new SessionOptions() { LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR }; + using InferenceSession session = new(modelBytes, options); + var inputs = new List() + { + NamedOnnxValue.CreateFromTensor(session.InputNames[0], inputTensor), + }; + + using var results = session.Run(inputs); + var outputTensor = results[0].AsTensor(); + + using var mask = GenerateMask(outputTensor, image.Width, image.Height); + + using var bgRemoved = GetImageWithBackgroundRemoved(image, mask); + + var encoder = new PngEncoder { CompressionLevel = PngCompressionLevel.BestCompression }; + + mask.Save(maskImagePath, encoder); + bgRemoved.Save(outputImagePath, encoder); + + Console.WriteLine($"Background removed and saved to {outputImagePath}"); +} +catch (Exception ex) +{ + Console.WriteLine($"Error: {ex.Message}"); + throw; +} + +static DenseTensor CreateTensorInput(Image image) +{ + // U2Net expects input images to be 320x320. This is dependent on the model. + const int targetWidth = 320; + const int targetHeight = 320; + + // ImageNet normalization parameters + // source: + // - https://www.image-net.org/ + // - https://pytorch.org calculated these values from the ImageNet dataset + // and they are commonly used for models trained on ImageNet so we use them here + // to normalize the input image to better match the distribution of the data the model was trained on + // NOTE: These values are not universal and may vary for different models + const float rMean = 0.485f; // Mean value for Red channel + const float gMean = 0.456f; // Mean value for Green channel + const float bMean = 0.406f; // Mean value for Blue channel + const float rStd = 0.229f; // Standard deviation for Red channel + const float gStd = 0.224f; // Standard deviation for Green channel + const float bStd = 0.225f; // Standard deviation for Blue channel + const float pixelMax = 255f; // Maximum pixel intensity for normalization + + // Create a temporary image for preprocessing + using var resized = image.Clone(); + resized.Mutate(x => x.Resize(targetWidth, targetHeight)); + + // Create tensor of shape (1, 3, 320, 320) + // 1 for batch size, 3 for RGB channels, 320x320 for image dimensions + DenseTensor tensor = new([1, 3, targetHeight, targetWidth]); + + // Normalize pixel values and copy to tensor + WalkImage(resized.Height, resized.Width, (x, y) => + { + var pixel = resized[x, y]; + + // u2net expects expect input images to be normalized using ImageNet mean and std + // to better match the distribution of the data the model was trained on + // Normalize to range [0, 1] and standardize using ImageNet mean/std + // The tensor is filled with normalized pixel values + tensor[0, 0, y, x] = ((pixel.R / pixelMax) - rMean) / rStd; // Red channel + tensor[0, 1, y, x] = ((pixel.G / pixelMax) - gMean) / gStd; // Green channel + tensor[0, 2, y, x] = ((pixel.B / pixelMax) - bMean) / bStd; // Blue channel + }); + + return tensor; +} + +static Image GenerateMask(Tensor maskTensor, int width, int height) +{ + var mask = new Image(width, height); + + var sourceHeight = maskTensor.Dimensions[2]; // Height of the original tensor mask + var sourceWidth = maskTensor.Dimensions[3]; // Width of the original tensor mask + + using Image tempMask = new(sourceWidth, sourceHeight); + + // Sigmoid function parameters + const float sigmoidScale = 1f; // Scaling factor for sigmoid activation + const float sigmoidShift = 1f; // Shift factor in the denominator of the sigmoid function + const float sigmoidDivisor = -1f; // Multiplier for the exponent in the sigmoid function + + static float CalculateSigmoid(float x) + { + return sigmoidScale / (sigmoidShift + MathF.Exp(sigmoidDivisor * x)); + } + + const float binarizationThreshold = 0.5f; // Threshold to determine foreground vs. background + const float normalizationFactor = 2f; // Scales the thresholded value to enhance contrast + + // Pixel intensity values + const byte maxIntensity = 255; // Maximum grayscale intensity + const byte opaqueAlpha = 255; // Fully opaque alpha value + + + WalkImage(sourceHeight, sourceWidth, (x, y) => + { + // a sigmoid function is a function that produces an S-shaped curve + // it is often used in machine learning and statistics to model probabilities + // the sigmoid function is defined as: + // f(x) = 1 / (1 + e^(-x)) + // where e is the base of the natural logarithm and x is the input value + + // the raw tensor values for our mask are going to be real unbounded numbers + // i.e. -1.5, 0.5, 2.0, etc. + // the sigmoid function will map these values to a range between 0 and 1 + // this allows us to say that value closer to 0 is background and value + // closer to 1 is foreground + var sigmoidValue = CalculateSigmoid(maskTensor[0, 0, y, x]); + + // now we want to threshold the sigmoid value to determine if it is foreground or background + // we are arbitrarily choosing 0.5 as the threshold. so if the sigmoid value is greater than + // 0.5 we will consider it foreground and if it is less than 0.5 we will consider it background + + // when a sigmoid value is greater than 0.5 we will subtract the threshold from it + // and multiply it by 2 this way the intensity value will be larger for values closer to 1 + // and create more contrast in the mask + var normalizedValue = sigmoidValue > binarizationThreshold + ? (sigmoidValue - binarizationThreshold) * normalizationFactor + : 0f; + + // Convert to an 8-bit grayscale intensity + var intensity = (byte)(normalizedValue * maxIntensity); + + // Store the pixel with full opacity + tempMask[x, y] = new Rgba32(intensity, intensity, intensity, opaqueAlpha); + }); + + // Resize the mask to match the target dimensions + tempMask.Mutate(x => x.Resize(width, height)); + + // Copy the resized mask to the final output image + WalkImage(height, width, (x, y) => mask[x, y] = tempMask[x, y]); + + return mask; +} + +static Image GetImageWithBackgroundRemoved(Image image, Image mask) +{ + Image result = new(image.Width, image.Height); + + const byte alphaThreshold = 20; + Rgba32 transparentPixel = new(0, 0, 0, 0); + + WalkImage(image.Height, image.Width, (x, y) => + { + var sourcePixel = image[x, y]; + var maskPixel = mask[x, y]; + + var alpha = maskPixel.R; + + result[x, y] = alpha > alphaThreshold + ? new Rgba32(sourcePixel.R, sourcePixel.G, sourcePixel.B, sourcePixel.A) + : transparentPixel; + }); + + return result; +} + +static void WalkImage(int height, int width, Action action) +{ + for (var y = 0; y < height; y++) + { + for (var x = 0; x < width; x++) + { + action(x, y); + } + } +} + diff --git a/BGR.Console/modnet.onnx b/BGR.Console/modnet.onnx new file mode 100644 index 0000000..000bf52 --- /dev/null +++ b/BGR.Console/modnet.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07c308cf0fc7e6e8b2065a12ed7fc07e1de8febb7dc7839d7b7f15dd66584df9 +size 25888640 diff --git a/BGR.Console/rmbg.onnx b/BGR.Console/rmbg.onnx new file mode 100644 index 0000000..14d566a --- /dev/null +++ b/BGR.Console/rmbg.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fcea23951a378f92634834888896cc1eec54655366ae6e949282646ce17c5420 +size 366087549 diff --git a/BGR.Console/u2net.onnx b/BGR.Console/u2net.onnx new file mode 100644 index 0000000..d5e2c4d --- /dev/null +++ b/BGR.Console/u2net.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d10d2f3bb75ae3b6d527c77944fc5e7dcd94b29809d47a739a7a728a912b491 +size 175997641