private async void Current_SoftwareBitmapFrameCaptured(object sender, SoftwareBitmapEventArgs e)
{
Debug.WriteLine("FrameCaptured");
Debug.WriteLine($"Frame evaluation started {DateTime.Now}");
if (e.SoftwareBitmap != null)
{
BitmapPixelFormat bpf = e.SoftwareBitmap.BitmapPixelFormat;
var uncroppedBitmap = SoftwareBitmap.Convert(e.SoftwareBitmap, BitmapPixelFormat.Nv12);
var faces = await _faceDetector.DetectFacesAsync(uncroppedBitmap);
if (faces.Count > 0)
{
//crop image to focus on face portion
var faceBox = faces[0].FaceBox;
VideoFrame inputFrame = VideoFrame.CreateWithSoftwareBitmap(e.SoftwareBitmap);
VideoFrame tmp = null;
tmp = new VideoFrame(e.SoftwareBitmap.BitmapPixelFormat, (int)(faceBox.Width + faceBox.Width % 2) - 2, (int)(faceBox.Height + faceBox.Height % 2) - 2);
await inputFrame.CopyToAsync(tmp, faceBox, null);
//crop image to fit model input requirements
VideoFrame croppedInputImage = new VideoFrame(BitmapPixelFormat.Gray8, (int)_inputImageDescriptor.Shape[3], (int)_inputImageDescriptor.Shape[2]);
var srcBounds = GetCropBounds(
tmp.SoftwareBitmap.PixelWidth,
tmp.SoftwareBitmap.PixelHeight,
croppedInputImage.SoftwareBitmap.PixelWidth,
croppedInputImage.SoftwareBitmap.PixelHeight);
await tmp.CopyToAsync(croppedInputImage, srcBounds, null);
ImageFeatureValue imageTensor = ImageFeatureValue.CreateFromVideoFrame(croppedInputImage);
_binding = new LearningModelBinding(_session);
TensorFloat outputTensor = TensorFloat.Create(_outputTensorDescriptor.Shape);
List <float> _outputVariableList = new List <float>();
// Bind inputs + outputs
_binding.Bind(_inputImageDescriptor.Name, imageTensor);
_binding.Bind(_outputTensorDescriptor.Name, outputTensor);
// Evaluate results
var results = await _session.EvaluateAsync(_binding, new Guid().ToString());
Debug.WriteLine("ResultsEvaluated: " + results.ToString());
var outputTensorList = outputTensor.GetAsVectorView();
var resultsList = new List <float>(outputTensorList.Count);
for (int i = 0; i < outputTensorList.Count; i++)
{
resultsList.Add(outputTensorList[i]);
}
var softMaxexOutputs = SoftMax(resultsList);
double maxProb = 0;
int maxIndex = 0;
// Comb through the evaluation results
for (int i = 0; i < Constants.POTENTIAL_EMOJI_NAME_LIST.Count(); i++)
{
// Record the dominant emotion probability & its location
if (softMaxexOutputs[i] > maxProb)
{
maxIndex = i;
maxProb = softMaxexOutputs[i];
}
//for evaluations run on the EmotionPage, record info about single specific emotion of interest
if (CurrentEmojis._currentEmoji != null && Constants.POTENTIAL_EMOJI_NAME_LIST[i].Equals(CurrentEmojis._currentEmoji.Name))
{
SoftwareBitmap potentialBestPic;
try
{
potentialBestPic = SoftwareBitmap.Convert(uncroppedBitmap, BitmapPixelFormat.Bgra8);
}
catch (Exception ex)
{
Debug.WriteLine($"Error converting SoftwareBitmap. Details:{ex.Message}. Attempting to continue...");
return;
}
await Windows.ApplicationModel.Core.CoreApplication.MainView.CoreWindow.Dispatcher.RunAsync(CoreDispatcherPriority.Normal,
async() =>
{
// Give user immediate visual feedback by updating success gauge
ScoreUpdated?.Invoke(this, new EmotionPageGaugeScoreEventArgs()
{
Score = softMaxexOutputs[i]
});
// Save original pic for each emotion no matter how bad it is (and record its associated info)
double bestScore = CurrentEmojis._emojis.Emojis[CurrentEmojis._currentEmojiIndex].BestScore;
if (softMaxexOutputs[i] > bestScore)
{
CurrentEmojis._emojis.Emojis[CurrentEmojis._currentEmojiIndex].BestScore = softMaxexOutputs[i];
var source = new SoftwareBitmapSource();
await source.SetBitmapAsync(potentialBestPic);
// Create format of potentialBestPic to be displayed in a gif later
SoftwareBitmap tmpBitmap = potentialBestPic;
WriteableBitmap wb = new WriteableBitmap(tmpBitmap.PixelWidth, tmpBitmap.PixelHeight);
tmpBitmap.CopyToBuffer(wb.PixelBuffer);
CurrentEmojis._emojis.Emojis[CurrentEmojis._currentEmojiIndex].BestPic = source;
CurrentEmojis._emojis.Emojis[CurrentEmojis._currentEmojiIndex].ShowOopsIcon = false;
CurrentEmojis._emojis.Emojis[CurrentEmojis._currentEmojiIndex].BestPicWB = wb;
}
}
);
}
}
Debug.WriteLine($"Probability = {maxProb}, Threshold set to = {Constants.CLASSIFICATION_CERTAINTY_THRESHOLD}, Emotion = {Constants.POTENTIAL_EMOJI_NAME_LIST[maxIndex]}");
// For evaluations run on the MainPage, update the emoji carousel
if (maxProb >= Constants.CLASSIFICATION_CERTAINTY_THRESHOLD)
{
Debug.WriteLine("first page emoji should start to update");
IntelligenceServiceEmotionClassified?.Invoke(this, new ClassifiedEmojiEventArgs(CurrentEmojis._emojis.Emojis[maxIndex]));
}
// Dispose of resources
if (e.SoftwareBitmap != null)
{
e.SoftwareBitmap.Dispose();
e.SoftwareBitmap = null;
}
}
}
IntelligenceServiceProcessingCompleted?.Invoke(this, null);
Debug.WriteLine($"Frame evaluation finished {DateTime.Now}");
}
private async void Current_SoftwareBitmapFrameCaptured(object sender, SoftwareBitmapEventArgs e)
{
Debug.WriteLine("FrameCaptured");
Debug.WriteLine($"Frame evaluation started {DateTime.Now}");
if (e.SoftwareBitmap != null)
{
BitmapPixelFormat bpf = e.SoftwareBitmap.BitmapPixelFormat;
var uncroppedBitmap = SoftwareBitmap.Convert(e.SoftwareBitmap, BitmapPixelFormat.Nv12);
var faces = await _faceDetector.DetectFacesAsync(uncroppedBitmap);
if (faces.Count > 0)
{
//crop image to focus on face portion
var faceBox = faces[0].FaceBox;
VideoFrame inputFrame = VideoFrame.CreateWithSoftwareBitmap(e.SoftwareBitmap);
VideoFrame tmp = null;
tmp = new VideoFrame(e.SoftwareBitmap.BitmapPixelFormat, (int)(faceBox.Width + faceBox.Width % 2) - 2, (int)(faceBox.Height + faceBox.Height % 2) - 2);
await inputFrame.CopyToAsync(tmp, faceBox, null);
//crop image to fit model input requirements
VideoFrame croppedInputImage = new VideoFrame(BitmapPixelFormat.Gray8, (int)_inputImageDescriptor.Shape[3], (int)_inputImageDescriptor.Shape[2]);
var srcBounds = GetCropBounds(
tmp.SoftwareBitmap.PixelWidth,
tmp.SoftwareBitmap.PixelHeight,
croppedInputImage.SoftwareBitmap.PixelWidth,
croppedInputImage.SoftwareBitmap.PixelHeight);
await tmp.CopyToAsync(croppedInputImage, srcBounds, null);
ImageFeatureValue imageTensor = ImageFeatureValue.CreateFromVideoFrame(croppedInputImage);
_binding = new LearningModelBinding(_session);
TensorFloat outputTensor = TensorFloat.Create(_outputTensorDescriptor.Shape);
List <float> _outputVariableList = new List <float>();
// Bind inputs + outputs
_binding.Bind(_inputImageDescriptor.Name, imageTensor);
_binding.Bind(_outputTensorDescriptor.Name, outputTensor);
// Evaluate results
var results = await _session.EvaluateAsync(_binding, new Guid().ToString());
Debug.WriteLine("ResultsEvaluated: " + results.ToString());
var outputTensorList = outputTensor.GetAsVectorView();
var resultsList = new List <float>(outputTensorList.Count);
for (int i = 0; i < outputTensorList.Count; i++)
{
resultsList.Add(outputTensorList[i]);
}
var softMaxexOutputs = SoftMax(resultsList);
double maxProb = 0;
int maxIndex = 0;
// Comb through the evaluation results
for (int i = 0; i < Constants.POTENTIAL_EMOJI_NAME_LIST.Count(); i++)
{
// Record the dominant emotion probability & its location
if (softMaxexOutputs[i] > maxProb)
{
maxIndex = i;
maxProb = softMaxexOutputs[i];
}
}
Debug.WriteLine($"Probability = {maxProb}, Threshold set to = {Constants.CLASSIFICATION_CERTAINTY_THRESHOLD}, Emotion = {Constants.POTENTIAL_EMOJI_NAME_LIST[maxIndex]}");
// For evaluations run on the MainPage, update the emoji carousel
if (maxProb >= Constants.CLASSIFICATION_CERTAINTY_THRESHOLD)
{
Debug.WriteLine("first page emoji should start to update");
IntelligenceServiceEmotionClassified?.Invoke(this, new ClassifiedEmojiEventArgs(CurrentEmojis._emojis.Emojis[maxIndex]));
}
// Dispose of resources
if (e.SoftwareBitmap != null)
{
e.SoftwareBitmap.Dispose();
e.SoftwareBitmap = null;
}
}
}
IntelligenceServiceProcessingCompleted?.Invoke(this, null);
Debug.WriteLine($"Frame evaluation finished {DateTime.Now}");
}
