public static async Task <u2netModel> CreateFromStreamAsync(IRandomAccessStreamReference stream)
{
u2netModel learningModel = new u2netModel();
learningModel.model = await LearningModel.LoadFromStreamAsync(stream);
learningModel.session = new LearningModelSession(learningModel.model);
learningModel.binding = new LearningModelBinding(learningModel.session);
return(learningModel);
}
private async void Button_Click(object sender, RoutedEventArgs e)
{
// Use Picket to get file
var file = await GetImageFile();
SoftwareBitmap softwareBitmap;
byte[] bytes;
// Load image & scale to tensor input dimensions
using (IRandomAccessStream stream = await file.OpenAsync(FileAccessMode.Read))
{
bytes = await GetImageAsByteArrayAsync(stream, 320, 320, BitmapPixelFormat.Rgba8);
softwareBitmap = await GetImageAsSoftwareBitmapAsync(stream, 320, 320, BitmapPixelFormat.Bgra8);
}
// Display source image
var source = new SoftwareBitmapSource();
await source.SetBitmapAsync(softwareBitmap);
sourceImage.Source = source;
// Convert rgba-rgba-...-rgba to bb...b-rr...r-gg...g as colour weighted tensor (0..1)
TensorFloat input = TensorFloat.CreateFromIterable(new long[] { 1, 3, 320, 320 }, TensorBrg(bytes));
// Load model & perform inference
StorageFile modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/u2net.onnx"));
u2netModel model = await u2netModel.CreateFromStreamAsync(modelFile);
Stopwatch sw = new Stopwatch();
sw.Start();
u2netOutput output = await model.EvaluateAsync(new u2netInput { input = input });
sw.Stop();
await ToImage(output.o6, o6);
await ToImage(output.o5, o5);
await ToImage(output.o4, o4);
await ToImage(output.o3, o3);
await ToImage(output.o2, o2);
await ToImage(output.o1, o1);
await ToBlendedImage(bytes, output.o0, targetImage);
}