public async Task <emotion_ferplusOutput> EvaluateAsync(emotion_ferplusInput input)
{
binding.Bind("Input338", input.Input338);
var result = await session.EvaluateAsync(binding, "0");
var output = new emotion_ferplusOutput();
output.Plus692_Output_0 = result.Outputs["Plus692_Output_0"] as TensorFloat;
return(output);
}
// get frame and analyze
private async void Preview_FrameArrived(object sender, FrameEventArgs e)
{
if (!alarmOn)
{
return;
}
var bitmap = e.VideoFrame.SoftwareBitmap;
if (bitmap == null)
{
return;
}
// faceDector requires Gray8 or Nv12
var convertedBitmap = SoftwareBitmap.Convert(bitmap, BitmapPixelFormat.Gray8);
var faces = await faceDetector.DetectFacesAsync(convertedBitmap);
// if there is a face in the frame, evaluate the emotion
var detectedFace = faces.FirstOrDefault();
if (detectedFace != null)
{
var boundingBox = new Rect(detectedFace.FaceBox.X,
detectedFace.FaceBox.Y,
detectedFace.FaceBox.Width,
detectedFace.FaceBox.Height);
var croppedFace = Crop(convertedBitmap, boundingBox);
emotion_ferplusInput input = new emotion_ferplusInput();
input.Input338 = VideoFrame.CreateWithSoftwareBitmap(croppedFace);
var emotionResults = await model.EvaluateAsync(input);
// to get percentages, you'd need to run the output through a softmax function
// we don't need percentages, we just need max value
var emotionIndex = emotionResults.Plus692_Output_0.IndexOf(emotionResults.Plus692_Output_0.Max());
if (emotionIndex == currentEmotionIndex)
{
// if the user has been dooing the same emotion for over 3 seconds - turn off alarm
if (lastTimeEmotionMatched != null && DateTime.Now - lastTimeEmotionMatched >= TimeSpan.FromSeconds(3))
{
alarmOn = false;
}
if (lastTimeEmotionMatched == null)
{
lastTimeEmotionMatched = DateTime.Now;
}
}
else
{
lastTimeEmotionMatched = null;
}
}
else
{
// can't find face
lastTimeEmotionMatched = null;
}
}
