private Bitmap ProcessImage(SequenceReader reader)
{
// set up the face model
String root = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, @"..\..\");
var faceModel = new FaceModelParameters(root, false);
faceModel.optimiseForImages();
// set up a face detector and a landmark detector
var faceDetector = new FaceDetector();
var landmarkDetector = new CLNF(faceModel);
// read the image from the sequence reader
var frame = new RawImage(reader.GetNextImage());
var grayFrame = new RawImage(reader.GetCurrentFrameGray());
// detect faces
var faces = new List <Rect>();
var confidences = new List <double>();
faceDetector.DetectFacesHOG(faces, grayFrame, confidences);
// detect landmarks
var landmarks = new List <List <Tuple <double, double> > >();
foreach (var face in faces)
{
landmarkDetector.DetectFaceLandmarksInImage(grayFrame, face, faceModel);
var points = landmarkDetector.CalculateAllLandmarks();
landmarks.Add(points);
}
// draw rectangles and confidence values on image
var image = frame.ToBitmap();
using (Graphics g = Graphics.FromImage(image))
{
int index = 0;
var pen = new System.Drawing.Pen(System.Drawing.Color.LightGreen, 4);
var pen2 = new System.Drawing.Pen(System.Drawing.Color.Red, 4);
var font = new Font(FontFamily.GenericSansSerif, 30);
foreach (var face in faces)
{
g.DrawRectangle(pen, (int)face.X, (int)face.Y, (int)face.Width, (int)face.Height);
g.DrawString($"{confidences[index]:0.00}", font, Brushes.Black, (int)face.X + 36, (int)face.Y - 36);
// draw landmark points
foreach (var p in landmarks[index])
{
g.DrawRectangle(pen2, new Rectangle((int)p.Item1, (int)p.Item2, 4, 4));
}
index++;
}
}
return(image);
}
private Bitmap ProcessImage(SequenceReader reader)
{
// set up the face model
var root = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, @"..\..\");
var faceModel = new FaceModelParameters(root, false);
faceModel.optimiseForImages();
// set up a face detector, a landmark detector, and a face analyser
var faceDetector = new FaceDetector();
var landmarkDetector = new CLNF(faceModel);
var faceAnalyser = new FaceAnalyserManaged(root, true, 0);
// read the image from the sequence reader
var frame = new RawImage(reader.GetNextImage());
var grayFrame = new RawImage(reader.GetCurrentFrameGray());
// detect faces
var faces = new List <Rect>();
var confidences = new List <double>();
faceDetector.DetectFacesHOG(faces, grayFrame, confidences);
// detect landmarks
var image = frame.ToBitmap();
foreach (var face in faces)
{
landmarkDetector.DetectFaceLandmarksInImage(grayFrame, face, faceModel);
var points = landmarkDetector.CalculateAllLandmarks();
// calculate action units
var features = faceAnalyser.PredictStaticAUsAndComputeFeatures(grayFrame, points);
// find the action units
var actionUnits = (from au in features.Item2
where au.Value > 0
orderby au.Key
select au.Key);
// get top emotions
var topEmotions = GetTopEmotions(actionUnits);
// draw the emotion on the face
using (Graphics g = Graphics.FromImage(image))
{
string name = string.Join(Environment.NewLine, topEmotions);
Font fnt = new Font("Verdana", 15, GraphicsUnit.Pixel);
Brush brs = new SolidBrush(Color.Black);
var bump = 36;
System.Drawing.SizeF stringSize = g.MeasureString(name, fnt);
g.FillRectangle(new SolidBrush(Color.Yellow), (int)face.X + bump, (int)face.Y, stringSize.Width, stringSize.Height);
g.DrawString(name, fnt, brs, (int)face.X + bump, (int)face.Y);
}
}
return(image);
}
// 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;
}
// Capturing and processing the video frame by frame
private void VideoLoop(UtilitiesOF.SequenceReader reader)
{
Thread.CurrentThread.IsBackground = true;
String root = AppDomain.CurrentDomain.BaseDirectory;
FaceModelParameters model_params = new FaceModelParameters(root, true, false, false);
// Initialize the face detector
FaceDetector face_detector = new FaceDetector(model_params.GetHaarLocation(), model_params.GetMTCNNLocation());
// If MTCNN model not available, use HOG
if (!face_detector.IsMTCNNLoaded())
{
model_params.SetFaceDetector(false, true, false);
}
CLNF face_model = new CLNF(model_params);
GazeAnalyserManaged gaze_analyser = new GazeAnalyserManaged();
DateTime?startTime = CurrentTime;
var lastFrameTime = CurrentTime;
while (running)
{
//////////////////////////////////////////////
// CAPTURE FRAME AND DETECT LANDMARKS FOLLOWED BY THE REQUIRED IMAGE PROCESSING
//////////////////////////////////////////////
RawImage frame = reader.GetNextImage();
lastFrameTime = CurrentTime;
processing_fps.AddFrame();
var grayFrame = reader.GetCurrentFrameGray();
if (mirror_image)
{
frame.Mirror();
grayFrame.Mirror();
}
bool detectionSucceeding = ProcessFrame(face_model, gaze_analyser, model_params, frame, grayFrame, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
lock (recording_lock)
{
if (recording)
{
// Add objects to recording queues
List <float> pose = new List <float>();
face_model.GetPose(pose, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
RawImage image = new RawImage(frame);
recording_objects.Enqueue(new Tuple <RawImage, bool, List <float> >(image, detectionSucceeding, pose));
}
}
List <Tuple <System.Windows.Point, System.Windows.Point> > lines = null;
List <Tuple <float, float> > eye_landmarks = null;
List <System.Windows.Point> landmarks = new List <System.Windows.Point>();
List <Tuple <System.Windows.Point, System.Windows.Point> > gaze_lines = null;
Tuple <float, float> gaze_angle = new Tuple <float, float>(0, 0);
var visibilities = face_model.GetVisibilities();
double scale = face_model.GetRigidParams()[0];
if (detectionSucceeding)
{
List <Tuple <float, float> > landmarks_doubles = face_model.CalculateAllLandmarks();
foreach (var p in landmarks_doubles)
{
landmarks.Add(new System.Windows.Point(p.Item1, p.Item2));
}
eye_landmarks = face_model.CalculateVisibleEyeLandmarks();
gaze_lines = gaze_analyser.CalculateGazeLines(reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
gaze_angle = gaze_analyser.GetGazeAngle();
lines = face_model.CalculateBox(reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
}
if (reset)
{
face_model.Reset();
reset = false;
}
// Visualisation updating
try
{
Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() =>
{
if (latest_img == null)
{
latest_img = frame.CreateWriteableBitmap();
}
List <float> pose = new List <float>();
face_model.GetPose(pose, reader.GetFx(), reader.GetFy(), reader.GetCx(), reader.GetCy());
int yaw = (int)(pose[4] * 180 / Math.PI + 0.5);
int yaw_abs = Math.Abs(yaw);
int roll = (int)(pose[5] * 180 / Math.PI + 0.5);
int roll_abs = Math.Abs(roll);
int pitch = (int)(pose[3] * 180 / Math.PI + 0.5);
int pitch_abs = Math.Abs(pitch);
YawLabel.Content = yaw_abs + "°";
RollLabel.Content = roll_abs + "°";
PitchLabel.Content = pitch_abs + "°";
if (yaw > 0)
{
YawLabelDir.Content = "Right";
}
else if (yaw < 0)
{
YawLabelDir.Content = "Left";
}
else
{
YawLabelDir.Content = "Straight";
}
if (pitch > 0)
{
PitchLabelDir.Content = "Down";
}
else if (pitch < 0)
{
PitchLabelDir.Content = "Up";
}
else
{
PitchLabelDir.Content = "Straight";
}
if (roll > 0)
{
RollLabelDir.Content = "Left";
}
else if (roll < 0)
{
RollLabelDir.Content = "Right";
}
else
{
RollLabelDir.Content = "Straight";
}
XPoseLabel.Content = (int)pose[0] + " mm";
YPoseLabel.Content = (int)pose[1] + " mm";
ZPoseLabel.Content = (int)pose[2] + " mm";
String x_angle = String.Format("{0:F0}°", gaze_angle.Item1 * (180.0 / Math.PI));
String y_angle = String.Format("{0:F0}°", gaze_angle.Item2 * (180.0 / Math.PI));
YawLabelGaze.Content = x_angle;
PitchLabelGaze.Content = y_angle;
if (gaze_angle.Item1 > 0)
{
YawLabelGazeDir.Content = "Right";
}
else if (gaze_angle.Item1 < 0)
{
YawLabelGazeDir.Content = "Left";
}
else
{
YawLabelGazeDir.Content = "Straight";
}
if (gaze_angle.Item2 > 0)
{
PitchLabelGazeDir.Content = "Down";
}
else if (gaze_angle.Item2 < 0)
{
PitchLabelGazeDir.Content = "Up";
}
else
{
PitchLabelGazeDir.Content = "Straight";
}
double confidence = face_model.GetConfidence();
if (confidence < 0)
{
confidence = 0;
}
else if (confidence > 1)
{
confidence = 1;
}
frame.UpdateWriteableBitmap(latest_img);
webcam_img.Clear();
webcam_img.Source = latest_img;
webcam_img.Confidence.Add(confidence);
webcam_img.FPS = processing_fps.GetFPS();
if (detectionSucceeding)
{
webcam_img.OverlayLines.Add(lines);
webcam_img.OverlayPoints.Add(landmarks);
webcam_img.OverlayPointsVisibility.Add(visibilities);
webcam_img.FaceScale.Add(scale);
List <System.Windows.Point> eye_landmark_points = new List <System.Windows.Point>();
foreach (var p in eye_landmarks)
{
eye_landmark_points.Add(new System.Windows.Point(p.Item1, p.Item2));
}
webcam_img.OverlayEyePoints.Add(eye_landmark_points);
webcam_img.GazeLines.Add(gaze_lines);
// Publish the information for other applications
String str_head_pose = String.Format("{0}:{1:F2}, {2:F2}, {3:F2}, {4:F2}, {5:F2}, {6:F2}", "HeadPose", pose[0], pose[1], pose[2],
pose[3] * 180 / Math.PI, pose[4] * 180 / Math.PI, pose[5] * 180 / Math.PI);
zero_mq_socket.Send(new ZFrame(str_head_pose, Encoding.UTF8));
String str_gaze = String.Format("{0}:{1:F2}, {2:F2}", "GazeAngle", gaze_angle.Item1 * (180.0 / Math.PI), gaze_angle.Item2 * (180.0 / Math.PI));
zero_mq_socket.Send(new ZFrame(str_gaze, Encoding.UTF8));
}
}));
while (running & pause)
{
Thread.Sleep(10);
}
}
catch (TaskCanceledException)
{
// Quitting
break;
}
}
reader.Close();
System.Console.Out.WriteLine("Thread finished");
}