public async Task <hangulOutput> EvaluateAsync(hangulInput input)
{
binding.Bind("input:0", input.input00);
binding.Bind("keep_prob:0", input.keep_prob);
var result = await session.EvaluateAsync(binding, "0");
var output = new hangulOutput();
output.output00 = result.Outputs["output:0"] as TensorFloat;
return(output);
}
private async void PredictHangul(VideoFrame inputimage)
{
Stopwatch sw = new Stopwatch();
sw.Start();
// convert to bgr8
SoftwareBitmap bitgray8 = SoftwareBitmap.Convert(inputimage.SoftwareBitmap, BitmapPixelFormat.Gray8);
var buff = new byte[64 * 64];
bitgray8.CopyToBuffer(buff.AsBuffer());
var fbuff = new float[4096];
for (int i = 0; i < 4096; i++)
{
fbuff[i] = (float)buff[i] / 255;
}
long[] shape = { 1, 4096 };
charInput.input00 = TensorFloat.CreateFromArray(shape, fbuff);
var dummy = new float[1];
long[] dummy_shape = { };
charInput.keep_prob = TensorFloat.CreateFromArray(dummy_shape, dummy);
//Evaluate the model
charOuput = await charModel.EvaluateAsync(charInput);
//Convert output to datatype
IReadOnlyList <float> VectorImage = charOuput.output00.GetAsVectorView();
IList <float> ImageList = VectorImage.ToList();
//Display top results
var topPred = ImageList.Select((value, index) => new { index, value })
.ToDictionary(pair => pair.index, pair => pair.value)
.OrderByDescending(key => key.Value)
.ToArray();
string topLabeltxt = "";
for (int i = 1; i < 6; i++)
{
var item = topPred[i];
Debug.WriteLine($"{item.Key}, {item.Value}, {charLabel[item.Key]}");
topLabeltxt += $"{charLabel[item.Key]} ";
}
numberLabel.Text = charLabel[topPred[0].Key];
topLabel.Text = topLabeltxt;
Debug.WriteLine($"process time = {sw.Elapsed}");
}
