feat: initial working POC
This commit is contained in:
Stevan Freeborn
@@ -0,0 +1 @@
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*.onnx filter=lfs diff=lfs merge=lfs -text
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+484
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@@ -0,0 +1,21 @@
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<Project Sdk="Microsoft.NET.Sdk">
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||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
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<TargetFramework>net9.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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||||
<ItemGroup>
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||||
<PackageReference Include="Microsoft.ML.OnnxRuntime" Version="1.20.1" />
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||||
<PackageReference Include="SixLabors.ImageSharp" Version="3.1.6" />
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</ItemGroup>
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||||
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||||
<ItemGroup>
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||||
<EmbeddedResource Include="modnet.onnx" />
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<EmbeddedResource Include="rmbg.onnx" />
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||||
<EmbeddedResource Include="u2net.onnx" />
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||||
</ItemGroup>
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||||
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||||
</Project>
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||||
@@ -0,0 +1,207 @@
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||||
using System.Reflection;
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||||
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||||
using Microsoft.ML.OnnxRuntime;
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||||
using Microsoft.ML.OnnxRuntime.Tensors;
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||||
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||||
using SixLabors.ImageSharp;
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||||
using SixLabors.ImageSharp.Formats.Png;
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||||
using SixLabors.ImageSharp.PixelFormats;
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||||
using SixLabors.ImageSharp.Processing;
|
||||
|
||||
if (args.Length < 1)
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||||
{
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||||
Console.WriteLine("Usage: BackgroundRemover <input_image_path>");
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||||
return;
|
||||
}
|
||||
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||||
var inputImagePath = args[0];
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||||
var maskImagePath = Path.ChangeExtension(inputImagePath, null) + "_mask.png";
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||||
var outputImagePath = Path.ChangeExtension(inputImagePath, null) + "_no_bg.png";
|
||||
|
||||
try
|
||||
{
|
||||
var assembly = Assembly.GetExecutingAssembly();
|
||||
var resourceName = "BGR.Console.u2net.onnx";
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||||
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||||
using var stream = assembly.GetManifestResourceStream(resourceName) ?? throw new FileNotFoundException("Model not found in embedded resources.");
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||||
var modelBytes = new byte[stream.Length];
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||||
stream.ReadExactly(modelBytes);
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||||
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||||
using var image = Image.Load<Rgba32>(inputImagePath);
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||||
var inputTensor = CreateTensorInput(image);
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||||
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||||
using var options = new SessionOptions() { LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR };
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||||
using InferenceSession session = new(modelBytes, options);
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||||
var inputs = new List<NamedOnnxValue>()
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||||
{
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||||
NamedOnnxValue.CreateFromTensor(session.InputNames[0], inputTensor),
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||||
};
|
||||
|
||||
using var results = session.Run(inputs);
|
||||
var outputTensor = results[0].AsTensor<float>();
|
||||
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||||
using var mask = GenerateMask(outputTensor, image.Width, image.Height);
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||||
|
||||
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<float> CreateTensorInput(Image<Rgba32> 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<float> 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<Rgba32> GenerateMask(Tensor<float> maskTensor, int width, int height)
|
||||
{
|
||||
var mask = new Image<Rgba32>(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<Rgba32> 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<Rgba32> GetImageWithBackgroundRemoved(Image<Rgba32> image, Image<Rgba32> mask)
|
||||
{
|
||||
Image<Rgba32> 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<int, int> action)
|
||||
{
|
||||
for (var y = 0; y < height; y++)
|
||||
{
|
||||
for (var x = 0; x < width; x++)
|
||||
{
|
||||
action(x, y);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:07c308cf0fc7e6e8b2065a12ed7fc07e1de8febb7dc7839d7b7f15dd66584df9
|
||||
size 25888640
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fcea23951a378f92634834888896cc1eec54655366ae6e949282646ce17c5420
|
||||
size 366087549
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8d10d2f3bb75ae3b6d527c77944fc5e7dcd94b29809d47a739a7a728a912b491
|
||||
size 175997641
|
||||
Reference in New Issue
Block a user