private void Initialize(object sender, PipelineRunEventArgs e)
{
string rootDirectory = AppDomain.CurrentDomain.BaseDirectory;
faceModelParameters = new FaceModelParameters(rootDirectory, true, false, false);
faceModelParameters.optimiseForVideo();
faceDetector = new FaceDetector(faceModelParameters.GetHaarLocation(), faceModelParameters.GetMTCNNLocation());
if (!faceDetector.IsMTCNNLoaded())
{
faceModelParameters.SetFaceDetector(false, true, false);
}
landmarkDetector = new CLNF(faceModelParameters);
faceAnalyser = new FaceAnalyser(rootDirectory, dynamic: true, output_width: 112, mask_aligned: true);
gazeAnalyser = new GazeAnalyser();
landmarkDetector.Reset();
faceAnalyser.Reset();
}
private void ProcessLoop()
{
Thread.CurrentThread.IsBackground = true;
CLMParameters clmParams = new CLMParameters();
CLM clmModel = new CLM();
float fx = 500, fy = 500, cx = 0, cy = 0;
FaceAnalyser analyser = new FaceAnalyser();
DateTime?startTime = CurrentTime;
arousalPlot.AssocColor(0, Colors.Red);
valencePlot.AssocColor(0, Colors.Blue);
while (true)
{
var newFrames = frameQueue.Take();
var frame = new RawImage(newFrames.Item1);
var grayFrame = newFrames.Item2;
if (!startTime.HasValue)
{
startTime = CurrentTime;
}
if (cx == 0 && cy == 0)
{
cx = grayFrame.Width / 2f;
cy = grayFrame.Height / 2f;
}
if (reset)
{
clmModel.Reset();
analyser.Reset();
reset = false;
}
if (resetPoint.HasValue)
{
clmModel.Reset(resetPoint.Value.X, resetPoint.Value.Y);
analyser.Reset();
resetPoint = null;
}
detectionSucceeding = clmModel.DetectLandmarksInVideo(grayFrame, clmParams);
List <Tuple <Point, Point> > lines = null;
List <Point> landmarks = null;
if (detectionSucceeding)
{
landmarks = clmModel.CalculateLandmarks();
lines = clmModel.CalculateBox(fx, fy, cx, cy);
}
else
{
analyser.Reset();
}
//////////////////////////////////////////////
// Analyse frame and detect AUs
//////////////////////////////////////////////
analyser.AddNextFrame(grayFrame, clmModel, (CurrentTime - startTime.Value).TotalSeconds);
var alignedFace = analyser.GetLatestAlignedFace();
var hogDescriptor = analyser.GetLatestHOGDescriptorVisualisation();
trackingFps.AddFrame();
Dictionary <String, double> aus = analyser.GetCurrentAUs();
string emotion = analyser.GetCurrentCategoricalEmotion();
double arousal = analyser.GetCurrentArousal();
double valence = analyser.GetCurrentValence();
double confidence = analyser.GetConfidence();
try
{
Dispatcher.Invoke(() =>
{
if (latestAlignedFace == null)
{
latestAlignedFace = alignedFace.CreateWriteableBitmap();
}
if (latestHOGDescriptor == null)
{
latestHOGDescriptor = hogDescriptor.CreateWriteableBitmap();
}
confidenceBar.Value = confidence;
if (detectionSucceeding)
{
frame.UpdateWriteableBitmap(latestImg);
alignedFace.UpdateWriteableBitmap(latestAlignedFace);
hogDescriptor.UpdateWriteableBitmap(latestHOGDescriptor);
imgAlignedFace.Source = latestAlignedFace;
imgHOGDescriptor.Source = latestHOGDescriptor;
video.OverlayLines = lines;
video.OverlayPoints = landmarks;
video.Confidence = confidence;
video.Source = latestImg;
Dictionary <int, double> arousalDict = new Dictionary <int, double>();
arousalDict[0] = arousal * 0.5 + 0.5;
arousalPlot.AddDataPoint(new DataPoint()
{
Time = CurrentTime, values = arousalDict, Confidence = confidence
});
Dictionary <int, double> valenceDict = new Dictionary <int, double>();
valenceDict[0] = valence * 0.5 + 0.5;
valencePlot.AddDataPoint(new DataPoint()
{
Time = CurrentTime, values = valenceDict, Confidence = confidence
});
Dictionary <int, double> avDict = new Dictionary <int, double>();
avDict[0] = arousal;
avDict[1] = valence;
avPlot.AddDataPoint(new DataPoint()
{
Time = CurrentTime, values = avDict, Confidence = confidence
});
auGraph.Update(aus, confidence);
emotionLabelHistory.Enqueue(new Tuple <DateTime, string>(CurrentTime, emotion));
UpdateEmotionLabel();
}
else
{
foreach (var k in aus.Keys.ToArray())
{
aus[k] = 0;
}
auGraph.Update(aus, 0);
}
});
}
catch (TaskCanceledException)
{
// Quitting
break;
}
}
}
private void ProcessLoop()
{
Thread.CurrentThread.IsBackground = true;
CLMParameters clmParams = new CLMParameters();
CLM clmModel = new CLM();
float fx = 500, fy = 500, cx = 0, cy = 0;
FaceAnalyser analyser = new FaceAnalyser();
DateTime? startTime = CurrentTime;
arousalPlot.AssocColor(0, Colors.Red);
valencePlot.AssocColor(0, Colors.Blue);
while (true)
{
var newFrames = frameQueue.Take();
var frame = new RawImage(newFrames.Item1);
var grayFrame = newFrames.Item2;
if (!startTime.HasValue)
startTime = CurrentTime;
if (cx == 0 && cy == 0)
{
cx = grayFrame.Width / 2f;
cy = grayFrame.Height / 2f;
}
if (reset)
{
clmModel.Reset();
analyser.Reset();
reset = false;
}
if (resetPoint.HasValue)
{
clmModel.Reset(resetPoint.Value.X, resetPoint.Value.Y);
analyser.Reset();
resetPoint = null;
}
detectionSucceeding = clmModel.DetectLandmarksInVideo(grayFrame, clmParams);
List<Tuple<Point, Point>> lines = null;
List<Point> landmarks = null;
if (detectionSucceeding)
{
landmarks = clmModel.CalculateLandmarks();
lines = clmModel.CalculateBox(fx, fy, cx, cy);
}
else
{
analyser.Reset();
}
//////////////////////////////////////////////
// Analyse frame and detect AUs
//////////////////////////////////////////////
analyser.AddNextFrame(grayFrame, clmModel, (CurrentTime - startTime.Value).TotalSeconds);
var alignedFace = analyser.GetLatestAlignedFace();
var hogDescriptor = analyser.GetLatestHOGDescriptorVisualisation();
trackingFps.AddFrame();
Dictionary<String, double> aus = analyser.GetCurrentAUs();
string emotion = analyser.GetCurrentCategoricalEmotion();
double arousal = analyser.GetCurrentArousal();
double valence = analyser.GetCurrentValence();
double confidence = analyser.GetConfidence();
try
{
Dispatcher.Invoke(() =>
{
if (latestAlignedFace == null)
latestAlignedFace = alignedFace.CreateWriteableBitmap();
if (latestHOGDescriptor == null)
latestHOGDescriptor = hogDescriptor.CreateWriteableBitmap();
confidenceBar.Value = confidence;
if (detectionSucceeding)
{
frame.UpdateWriteableBitmap(latestImg);
alignedFace.UpdateWriteableBitmap(latestAlignedFace);
hogDescriptor.UpdateWriteableBitmap(latestHOGDescriptor);
imgAlignedFace.Source = latestAlignedFace;
imgHOGDescriptor.Source = latestHOGDescriptor;
video.OverlayLines = lines;
video.OverlayPoints = landmarks;
video.Confidence = confidence;
video.Source = latestImg;
Dictionary<int, double> arousalDict = new Dictionary<int, double>();
arousalDict[0] = arousal * 0.5 + 0.5;
arousalPlot.AddDataPoint(new DataPoint() { Time = CurrentTime, values = arousalDict, Confidence = confidence });
Dictionary<int, double> valenceDict = new Dictionary<int, double>();
valenceDict[0] = valence * 0.5 + 0.5;
valencePlot.AddDataPoint(new DataPoint() { Time = CurrentTime, values = valenceDict, Confidence = confidence });
Dictionary<int, double> avDict = new Dictionary<int, double>();
avDict[0] = arousal;
avDict[1] = valence;
avPlot.AddDataPoint(new DataPoint() { Time = CurrentTime, values = avDict, Confidence = confidence });
auGraph.Update(aus, confidence);
emotionLabelHistory.Enqueue(new Tuple<DateTime, string>(CurrentTime, emotion));
UpdateEmotionLabel();
}
else
{
foreach (var k in aus.Keys.ToArray())
aus[k] = 0;
auGraph.Update(aus, 0);
}
});
}
catch (TaskCanceledException)
{
// Quitting
break;
}
}
}
