public async Task <ArcFaceOutput> EvaluateAsync(ArcFaceInput input)
{
binding.Bind("data", input.data);
var result = await session.EvaluateAsync(binding, "0");
var output = new ArcFaceOutput();
output.fc1 = result.Outputs["fc1"] as TensorFloat;
return(output);
}
private async Task <List <float> > ArcFace(SoftwareBitmap softwareBitmap)
{
// Encapsulate the image within a VideoFrame to be bound and evaluated
VideoFrame inputImage = VideoFrame.CreateWithSoftwareBitmap(softwareBitmap);
int height = inputImage.SoftwareBitmap.PixelHeight;
int width = inputImage.SoftwareBitmap.PixelWidth;
float[] data = new float[1 * 3 * ARC_FACE_INPUT * ARC_FACE_INPUT];
byte[] imageBytes = new byte[4 * height * width];
inputImage.SoftwareBitmap.CopyToBuffer(imageBytes.AsBuffer());
int id = 0;
for (int i = 0; i < data.Length; i += 4)
{
float blue = (float)imageBytes[i];
float green = (float)imageBytes[i + 1];
float red = (float)imageBytes[i + 2];
data[id++] = blue;
data[id++] = green;
data[id++] = red;
}
_arcFaceInput.data = TensorFloat.CreateFromArray(new List <long> {
1, 3, ARC_FACE_INPUT, ARC_FACE_INPUT
}, data);
// Process the frame with the model
_arcFaceOutput = await _arcFaceModel.EvaluateAsync(_arcFaceInput);
IReadOnlyList <float> vectorImage = _arcFaceOutput.fc1.GetAsVectorView();
return(vectorImage.ToList());
}
