SamSharp

Feature

Models

How to use

dotnet add package IntptrMax.SamSharp

Note

Please add one of libtorch-cpu, libtorch-cuda-12.1, libtorch-cuda-12.1-win-x64 or libtorch-cuda-12.1-linux-x64 version 2.5.1.0 to execute.

Predict

string checkpointPath = @"..\..\..\Assets\Weights\MobileSam.pt";
SamDevice device = SamDevice.CUDA; // or SamDevice.Cpu if you want to run on CPU
SamScalarType dtype = SamScalarType.Float32;
int imageSize = 512; // The maximum size of the image to process, can be adjusted based on your GPU memory

SKBitmap image = SKBitmap.Decode(@"..\..\..\Assets\Images\truck.jpg");

// Use predictor
SamSharp.Utils.SamPredictor predictor = new SamSharp.Utils.SamPredictor(checkpointPath, device, dtype);
List<SamPoint> points = new List<SamPoint>
{
	new SamPoint(500, 375, false),
	new SamPoint(1524,675, false),
};
List<SamBox> boxes = new List<SamBox>
{
	new SamBox(75, 275, 1725, 850),
	new SamBox(425, 600, 700, 875),
	new SamBox(1375, 550, 1650, 800),
	new SamBox(1240, 675, 1400, 750),
};

predictor.SetImage(image);
List<PredictOutput> outputs = predictor.Predict(null, boxes);

Console.WriteLine("The predictions are :");

using (SKCanvas canvas = new SKCanvas(image))
{
	canvas.Clear(SKColors.Transparent);
	var random = new Random();

	for (int i = 0; i < outputs.Count; i++)
	{
		PredictOutput output = outputs[i];
		Console.WriteLine($"Mask {i}: Precision: {output.Precision * 100:F2}%");
		bool[,] mask = output.Mask;

		SKColor color = new SKColor((byte)random.Next(256), (byte)random.Next(256), (byte)random.Next(256));
		using (var paint = new SKPaint { Color = color, BlendMode = SKBlendMode.Src })
		{
			int width = mask.GetLength(0);
			int height = mask.GetLength(1);

			using (var path = new SKPath())
			{
				for (int y = 0; y < height; y++)
				{
					for (int x = 0; x < width; x++)
					{
						if (mask[x, y])
						{
							path.AddRect(new SKRect(x, y, x + 1, y + 1));
						}
					}
				}
				canvas.DrawPath(path, paint);
			}
		}
	}
}
SKData data = image.Encode(SKEncodedImageFormat.Png, 100);
using (var stream = File.OpenWrite($"mask.png"))
{
	data.SaveTo(stream);
}

Automatic Mask Generator

string checkpointPath = @".\Assets\sam_vit_h_4b8939.pth";
SamDevice device = SamDevice.CUDA; // or SamDevice.Cpu if you want to run on CPU
SamScalarType dtype = SamScalarType.Float32;
int imageSize = 512; // The maximum size of the image to process, can be adjusted based on your GPU memory

SKBitmap image = SKBitmap.Decode(@"..\..\..\Assets\dog.jpg");

// Use Automatic Mask Generator
SamSharp.Utils.SamAutomaticMaskGenerator generator = new SamSharp.Utils.SamAutomaticMaskGenerator(checkpointPath, device: device, dtype: dtype);
List<PredictOutput> outputs = generator.generate(image, maxImageSize: imageSize);

Console.WriteLine("The predictions are :");

using (SKCanvas canvas = new SKCanvas(image))
{
	canvas.Clear(SKColors.Transparent);
	var random = new Random();

	for (int i = 0; i < outputs.Count; i++)
	{
		PredictOutput output = outputs[i];
		Console.WriteLine($"Mask {i}: Precision: {output.Precision * 100:F2}%");
		bool[,] mask = output.Mask;

		SKColor color = new SKColor((byte)random.Next(256), (byte)random.Next(256), (byte)random.Next(256));
		using (var paint = new SKPaint { Color = color, BlendMode = SKBlendMode.Src })
		{
			int width = mask.GetLength(0);
			int height = mask.GetLength(1);

			using (var path = new SKPath())
			{
				for (int y = 0; y < height; y++)
				{
					for (int x = 0; x < width; x++)
					{
						if (mask[x, y])
						{
							path.AddRect(new SKRect(x, y, x + 1, y + 1));
						}
					}
				}
				canvas.DrawPath(path, paint);
			}
		}
	}
}
SKData data = image.Encode(SKEncodedImageFormat.Png, 100);
using (var stream = File.OpenWrite($"mask.png"))
{
	data.SaveTo(stream);
}

Work to do