private bool ProcessFrame(CLNF landmark_detector, GazeAnalyserManaged gaze_analyser, FaceModelParameters model_params, RawImage frame, RawImage grayscale_frame, float fx, float fy, float cx, float cy)
{
bool detection_succeeding = landmark_detector.DetectLandmarksInVideo(frame, model_params, grayscale_frame);
gaze_analyser.AddNextFrame(landmark_detector, detection_succeeding, fx, fy, cx, cy);
return(detection_succeeding);
}
/// <summary>
/// Calculate the current gaze point.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
static void OnGazeMove(object sender, GazeEventArgs e)
{
// read the image from the sequence reader
var frame = new RawImage(reader.GetNextImage());
var grayFrame = new RawImage(reader.GetCurrentFrameGray());
// only detect faces every 5 frames
if (currentFace == default(Rect) || frameIndex % 5 == 0)
{
// detect faces
var faces = new List <System.Windows.Rect>();
var confidences = new List <double>();
faceDetector.DetectFacesHOG(faces, grayFrame, confidences);
// abort if we couldn't find a face
if (currentFace == default(Rect) && faces.Count() == 0)
{
return;
}
currentFace = faces.First();
}
// detect landmarks
var success = landmarkDetector.DetectLandmarksInVideo(grayFrame, faceModel);
// calculate gaze angle
gazeAnalyser.AddNextFrame(landmarkDetector, success, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
var gaze = gazeAnalyser.GetGazeAngle();
// convert gaze to a point coordinate
e.Point.X = (int)(5000 * Math.Tan(-gaze.Item1) + form.Width / 2);
e.Point.Y = form.Height / 2;
// update frame counter
frameIndex++;
}
// The main function call for processing sequences
private void ProcessSequence(SequenceReader reader)
{
Thread.CurrentThread.Priority = ThreadPriority.Highest;
SetupFeatureExtractionMode();
thread_running = true;
// Reload the face landmark detector if needed
ReloadLandmarkDetector();
if (!landmark_detector.isLoaded())
{
DetectorNotFoundWarning();
EndMode();
thread_running = false;
return;
}
// Set the face detector
face_model_params.SetFaceDetector(DetectorHaar, DetectorHOG, DetectorCNN);
face_model_params.optimiseForVideo();
// Setup the visualization
Visualizer visualizer_of = new Visualizer(ShowTrackedVideo || RecordTracked, ShowAppearance, ShowAppearance, false);
// Initialize the face analyser
face_analyser = new FaceAnalyserManaged(AppDomain.CurrentDomain.BaseDirectory, DynamicAUModels, image_output_size, MaskAligned);
// Reset the tracker
landmark_detector.Reset();
// Loading an image file
var frame = new RawImage(reader.GetNextImage());
var gray_frame = new RawImage(reader.GetCurrentFrameGray());
// Setup recording
RecorderOpenFaceParameters rec_params = new RecorderOpenFaceParameters(true, reader.IsWebcam(),
Record2DLandmarks, Record3DLandmarks, RecordModelParameters, RecordPose, RecordAUs,
RecordGaze, RecordHOG, RecordTracked, RecordAligned, false,
reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), reader.GetFPS());
RecorderOpenFace recorder = new RecorderOpenFace(reader.GetName(), rec_params, record_root);
// For FPS tracking
DateTime?startTime = CurrentTime;
var lastFrameTime = CurrentTime;
// Empty image would indicate that the stream is over
while (!gray_frame.IsEmpty)
{
if (!thread_running)
{
break;
}
double progress = reader.GetProgress();
bool detection_succeeding = landmark_detector.DetectLandmarksInVideo(frame, face_model_params, gray_frame);
// The face analysis step (for AUs and eye gaze)
face_analyser.AddNextFrame(frame, landmark_detector.CalculateAllLandmarks(), detection_succeeding, false);
gaze_analyser.AddNextFrame(landmark_detector, detection_succeeding, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
// Only the final face will contain the details
VisualizeFeatures(frame, visualizer_of, landmark_detector.CalculateAllLandmarks(), landmark_detector.GetVisibilities(), detection_succeeding, true, false, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), progress);
// Record an observation
RecordObservation(recorder, visualizer_of.GetVisImage(), 0, detection_succeeding, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy(), reader.GetTimestamp(), reader.GetFrameNumber());
if (RecordTracked)
{
recorder.WriteObservationTracked();
}
while (thread_running & thread_paused && skip_frames == 0)
{
Thread.Sleep(10);
}
if (skip_frames > 0)
{
skip_frames--;
}
frame = new RawImage(reader.GetNextImage());
gray_frame = new RawImage(reader.GetCurrentFrameGray());
lastFrameTime = CurrentTime;
processing_fps.AddFrame();
}
// Finalize the recording and flush to disk
recorder.Close();
// Post-process the AU recordings
if (RecordAUs)
{
face_analyser.PostProcessOutputFile(recorder.GetCSVFile());
}
// Close the open video/webcam
reader.Close();
EndMode();
}
// The main function call for processing the webcam feed
private void ProcessingLoop(SequenceReader reader)
{
thread_running = true;
Thread.CurrentThread.IsBackground = true;
DateTime?startTime = CurrentTime;
var lastFrameTime = CurrentTime;
landmark_detector.Reset();
face_analyser.Reset();
int frame_id = 0;
double old_gaze_x = 0;
double old_gaze_y = 0;
double smile_cumm = 0;
double frown_cumm = 0;
double brow_up_cumm = 0;
double brow_down_cumm = 0;
double widen_cumm = 0;
double wrinkle_cumm = 0;
while (thread_running)
{
// Loading an image file
RawImage frame = new RawImage(reader.GetNextImage());
RawImage gray_frame = new RawImage(reader.GetCurrentFrameGray());
lastFrameTime = CurrentTime;
processing_fps.AddFrame();
bool detection_succeeding = landmark_detector.DetectLandmarksInVideo(gray_frame, face_model_params);
// The face analysis step (only done if recording AUs, HOGs or video)
face_analyser.AddNextFrame(frame, landmark_detector.CalculateAllLandmarks(), detection_succeeding, true);
gaze_analyser.AddNextFrame(landmark_detector, detection_succeeding, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
double confidence = landmark_detector.GetConfidence();
if (confidence < 0)
{
confidence = 0;
}
else if (confidence > 1)
{
confidence = 1;
}
List <double> pose = new List <double>();
landmark_detector.GetPose(pose, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
List <double> non_rigid_params = landmark_detector.GetNonRigidParams();
double scale = landmark_detector.GetRigidParams()[0];
double time_stamp = (DateTime.Now - (DateTime)startTime).TotalMilliseconds;
List <Tuple <Point, Point> > lines = null;
List <Tuple <double, double> > landmarks = null;
List <Tuple <double, double> > eye_landmarks = null;
List <Tuple <Point, Point> > gaze_lines = null;
Tuple <double, double> gaze_angle = gaze_analyser.GetGazeAngle();
if (detection_succeeding)
{
landmarks = landmark_detector.CalculateVisibleLandmarks();
eye_landmarks = landmark_detector.CalculateVisibleEyeLandmarks();
lines = landmark_detector.CalculateBox(reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
gaze_lines = gaze_analyser.CalculateGazeLines(reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
}
// Visualisation
Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() =>
{
var au_regs = face_analyser.GetCurrentAUsReg();
if (au_regs.Count > 0)
{
double smile = (au_regs["AU12"] + au_regs["AU06"] + au_regs["AU25"]) / 13.0;
double frown = (au_regs["AU15"] + au_regs["AU17"]) / 12.0;
double brow_up = (au_regs["AU01"] + au_regs["AU02"]) / 10.0;
double brow_down = au_regs["AU04"] / 5.0;
double eye_widen = au_regs["AU05"] / 3.0;
double nose_wrinkle = au_regs["AU09"] / 4.0;
Dictionary <int, double> smileDict = new Dictionary <int, double>();
smileDict[0] = 0.7 * smile_cumm + 0.3 * smile;
smileDict[1] = 0.7 * frown_cumm + 0.3 * frown;
smilePlot.AddDataPoint(new DataPointGraph()
{
Time = CurrentTime, values = smileDict, Confidence = confidence
});
Dictionary <int, double> browDict = new Dictionary <int, double>();
browDict[0] = 0.7 * brow_up_cumm + 0.3 * brow_up;
browDict[1] = 0.7 * brow_down_cumm + 0.3 * brow_down;
browPlot.AddDataPoint(new DataPointGraph()
{
Time = CurrentTime, values = browDict, Confidence = confidence
});
Dictionary <int, double> eyeDict = new Dictionary <int, double>();
eyeDict[0] = 0.7 * widen_cumm + 0.3 * eye_widen;
eyeDict[1] = 0.7 * wrinkle_cumm + 0.3 * nose_wrinkle;
eyePlot.AddDataPoint(new DataPointGraph()
{
Time = CurrentTime, values = eyeDict, Confidence = confidence
});
smile_cumm = smileDict[0];
frown_cumm = smileDict[1];
brow_up_cumm = browDict[0];
brow_down_cumm = browDict[1];
widen_cumm = eyeDict[0];
wrinkle_cumm = eyeDict[1];
}
else
{
// If no AUs present disable the AU visualization
MainGrid.ColumnDefinitions[2].Width = new GridLength(0);
eyePlot.Visibility = Visibility.Collapsed;
browPlot.Visibility = Visibility.Collapsed;
smilePlot.Visibility = Visibility.Collapsed;
}
Dictionary <int, double> poseDict = new Dictionary <int, double>();
poseDict[0] = -pose[3];
poseDict[1] = pose[4];
poseDict[2] = pose[5];
headPosePlot.AddDataPoint(new DataPointGraph()
{
Time = CurrentTime, values = poseDict, Confidence = confidence
});
Dictionary <int, double> gazeDict = new Dictionary <int, double>();
gazeDict[0] = gaze_angle.Item1 * (180.0 / Math.PI);
gazeDict[0] = 0.5 * old_gaze_x + 0.5 * gazeDict[0];
gazeDict[1] = -gaze_angle.Item2 * (180.0 / Math.PI);
gazeDict[1] = 0.5 * old_gaze_y + 0.5 * gazeDict[1];
gazePlot.AddDataPoint(new DataPointGraph()
{
Time = CurrentTime, values = gazeDict, Confidence = confidence
});
old_gaze_x = gazeDict[0];
old_gaze_y = gazeDict[1];
if (latest_img == null)
{
latest_img = frame.CreateWriteableBitmap();
}
frame.UpdateWriteableBitmap(latest_img);
video.Source = latest_img;
video.Confidence = confidence;
video.FPS = processing_fps.GetFPS();
if (!detection_succeeding)
{
video.OverlayLines.Clear();
video.OverlayPoints.Clear();
video.OverlayEyePoints.Clear();
video.GazeLines.Clear();
}
else
{
video.OverlayLines = lines;
List <Point> landmark_points = new List <Point>();
foreach (var p in landmarks)
{
landmark_points.Add(new Point(p.Item1, p.Item2));
}
List <Point> eye_landmark_points = new List <Point>();
foreach (var p in eye_landmarks)
{
eye_landmark_points.Add(new Point(p.Item1, p.Item2));
}
video.OverlayPoints = landmark_points;
video.OverlayEyePoints = eye_landmark_points;
video.GazeLines = gaze_lines;
}
}));
if (reset)
{
if (resetPoint.HasValue)
{
landmark_detector.Reset(resetPoint.Value.X, resetPoint.Value.Y);
resetPoint = null;
}
else
{
landmark_detector.Reset();
}
face_analyser.Reset();
reset = false;
Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() =>
{
headPosePlot.ClearDataPoints();
headPosePlot.ClearDataPoints();
gazePlot.ClearDataPoints();
smilePlot.ClearDataPoints();
browPlot.ClearDataPoints();
eyePlot.ClearDataPoints();
}));
}
frame_id++;
}
reader.Close();
latest_img = null;
}