Exemplo n.º 1
0
        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();
        }
Exemplo n.º 2
0
        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;
                }
            }
        }
Exemplo n.º 3
0
        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;
                }
            }
        }