public CLNF(CLNF other) : this(LandmarkDetectorPINVOKE.new_CLNF__SWIG_2(CLNF.getCPtr(other)), true) { if (LandmarkDetectorPINVOKE.SWIGPendingException.Pending) { throw LandmarkDetectorPINVOKE.SWIGPendingException.Retrieve(); } }
static void Main() { // initialize application Application.EnableVisualStyles(); Application.SetCompatibleTextRenderingDefault(false); // set up the game form form = new GameForm(); form.OnGazeMove += new OnGazeMoveDelegate(OnGazeMove); // grab the first camera var baseFolder = AppDomain.CurrentDomain.BaseDirectory; var camera = SequenceReader.GetCameras(baseFolder).First(); // set up a sequence reader reader = new SequenceReader(camera.Item1, 640, 400); // set up the face model String root = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, @"..\..\"); faceModel = new FaceModelParameters(root, false); faceModel.optimiseForVideo(); // set up a face detector, a landmark detector, and a gaze analyser faceDetector = new FaceDetector(); landmarkDetector = new CLNF(faceModel); gazeAnalyser = new GazeAnalyserManaged(); // run the game Application.Run(form); }
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); }
public MainWindow() { InitializeComponent(); this.DataContext = this; // For WPF data binding // Set the icon Uri iconUri = new Uri("logo1.ico", UriKind.RelativeOrAbsolute); this.Icon = BitmapFrame.Create(iconUri); String root = AppDomain.CurrentDomain.BaseDirectory; face_model_params = new FaceModelParameters(root, LandmarkDetectorCECLM, LandmarkDetectorCLNF, LandmarkDetectorCLM); // Initialize the face detector face_detector = new FaceDetector(face_model_params.GetHaarLocation(), face_model_params.GetMTCNNLocation()); // If MTCNN model not available, use HOG if (!face_detector.IsMTCNNLoaded()) { FaceDetCNN.IsEnabled = false; DetectorCNN = false; DetectorHOG = true; } face_model_params.SetFaceDetector(DetectorHaar, DetectorHOG, DetectorCNN); landmark_detector = new CLNF(face_model_params); gaze_analyser = new GazeAnalyserManaged(); }
// If the landmark detector model changed need to reload it private void ReloadLandmarkDetector() { bool reload = false; if (face_model_params.IsCECLM() && !LandmarkDetectorCECLM) { reload = true; } else if (face_model_params.IsCLNF() && !LandmarkDetectorCLNF) { reload = true; } else if (face_model_params.IsCLM() && !LandmarkDetectorCLM) { reload = true; } if (reload) { String root = AppDomain.CurrentDomain.BaseDirectory; face_model_params = new FaceModelParameters(root, LandmarkDetectorCECLM, LandmarkDetectorCLNF, LandmarkDetectorCLM); landmark_detector = new CLNF(face_model_params); } }
public static void Draw(SWIGTYPE_p_cv__Mat img, CLNF clnf_model) { LandmarkDetectorPINVOKE.Draw__SWIG_2(SWIGTYPE_p_cv__Mat.getCPtr(img), CLNF.getCPtr(clnf_model)); if (LandmarkDetectorPINVOKE.SWIGPendingException.Pending) { throw LandmarkDetectorPINVOKE.SWIGPendingException.Retrieve(); } }
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); }
public static CVPoint2DList CalculateLandmarks(CLNF clnf_model) { CVPoint2DList ret = new CVPoint2DList(LandmarkDetectorPINVOKE.CalculateLandmarks__SWIG_1(CLNF.getCPtr(clnf_model)), true); if (LandmarkDetectorPINVOKE.SWIGPendingException.Pending) { throw LandmarkDetectorPINVOKE.SWIGPendingException.Retrieve(); } return(ret); }
unsafe void visualise_tracking(Mat captured_image, ref CLNF face_model, ref FaceModelParameters det_parameters, int frame_count, double fx, double fy, double cx, double cy) { //Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised double detection_certainty = face_model.detection_certainty; bool detection_success = face_model.detection_success; double visualisation_boundary = 0.2; // Only draw if the reliability is reasonable, the value is slightly ad-hoc if (detection_certainty < visualisation_boundary) { LandmarkDetector.Draw(new SWIGTYPE_p_cv__Mat(captured_image.CvPtr), face_model); double vis_certainty = detection_certainty; if (vis_certainty > 1) { vis_certainty = 1; } if (vis_certainty < -1) { vis_certainty = -1; } vis_certainty = (vis_certainty + 1) / (visualisation_boundary + 1); //A rough heuristic for box around the face width int thickness = (int)Math.Ceiling(2.0 * ((double)captured_image.Cols) / 640.0); Vec6d pose_estimate_to_draw = *((Vec6d *)LandmarkDetector.GetCorrectedPoseWorld(new SWIGTYPE_p_CLNF(CLNF.getCPtr(face_model)), fx, fy, cx, cy).Pointer); //Draw it in reddish if uncertain, blueish if certain var color = new Scalar((1 - vis_certainty) * 255.0, 0, vis_certainty * 255); LandmarkDetector.DrawBox(new SWIGTYPE_p_cv__Mat(captured_image.CvPtr), new SWIGTYPE_p_cv__Vec6d(new IntPtr(&pose_estimate_to_draw)), new SWIGTYPE_p_cv__Scalar(new IntPtr(&(color))), thickness, (float)fx, (float)fy, (float)cx, (float)cy); } //Work out the framerate if (frame_count % 10 == 0) { double t1 = Cv2.GetTickCount(); fps_tracker = 10.0 / ((double)(t1 - t0) / Cv2.GetTickFrequency()); t0 = (long)t1; } using (Mat resize = captured_image.Resize(new Size(Size.Width * SizeFactor, Size.Height * SizeFactor))) { string fpsSt = "FPS: " + Math.Round(fps_tracker).ToString("0.0"); Cv2.PutText(resize, fpsSt, new Point(10, 20), HersheyFonts.HersheyPlain, 1, new Scalar(0, 255, 0), 1, LineTypes.AntiAlias); Cv2.ImShow("tracking_result", resize); } }
public MainWindow() { InitializeComponent(); this.DataContext = this; // For WPF data binding // Set the icon Uri iconUri = new Uri("logo1.ico", UriKind.RelativeOrAbsolute); this.Icon = BitmapFrame.Create(iconUri); String root = AppDomain.CurrentDomain.BaseDirectory; face_model_params = new FaceModelParameters(root, false); landmark_detector = new CLNF(face_model_params); gaze_analyser = new GazeAnalyserManaged(); }
protected ExtractorBase(FaceModelParameters faceModelParameters) { if (_initialized) { return; } ModelParams = faceModelParameters; GazeAnalyzer = new GazeAnalyserManaged(); var face_detector = new FaceDetector(ModelParams.GetHaarLocation(), ModelParams.GetMTCNNLocation()); if (!face_detector.IsMTCNNLoaded()) // If MTCNN model not available, use HOG { ModelParams.SetFaceDetector(false, true, false); } FaceModel = new CLNF(ModelParams); _initialized = true; }
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(); }
public MainWindow() { InitializeComponent(); // Set the icon Uri iconUri = new Uri("logo1.ico", UriKind.RelativeOrAbsolute); this.Icon = BitmapFrame.Create(iconUri); String root = AppDomain.CurrentDomain.BaseDirectory; // TODO, create a demo version of parameters face_model_params = new FaceModelParameters(root, false); landmark_detector = new CLNF(face_model_params); face_analyser = new FaceAnalyserManaged(root, true, 112, true); gaze_analyser = new GazeAnalyserManaged(); Dispatcher.Invoke(DispatcherPriority.Render, new TimeSpan(0, 0, 0, 0, 200), (Action)(() => { headPosePlot.AssocColor(0, Colors.Blue); headPosePlot.AssocColor(1, Colors.Red); headPosePlot.AssocColor(2, Colors.Green); headPosePlot.AssocName(1, "Turn"); headPosePlot.AssocName(2, "Tilt"); headPosePlot.AssocName(0, "Up/Down"); headPosePlot.AssocThickness(0, 2); headPosePlot.AssocThickness(1, 2); headPosePlot.AssocThickness(2, 2); gazePlot.AssocColor(0, Colors.Red); gazePlot.AssocColor(1, Colors.Blue); gazePlot.AssocName(0, "Left-right"); gazePlot.AssocName(1, "Up-down"); gazePlot.AssocThickness(0, 2); gazePlot.AssocThickness(1, 2); smilePlot.AssocColor(0, Colors.Green); smilePlot.AssocColor(1, Colors.Red); smilePlot.AssocName(0, "Smile"); smilePlot.AssocName(1, "Frown"); smilePlot.AssocThickness(0, 2); smilePlot.AssocThickness(1, 2); browPlot.AssocColor(0, Colors.Green); browPlot.AssocColor(1, Colors.Red); browPlot.AssocName(0, "Raise"); browPlot.AssocName(1, "Furrow"); browPlot.AssocThickness(0, 2); browPlot.AssocThickness(1, 2); eyePlot.AssocColor(0, Colors.Green); eyePlot.AssocColor(1, Colors.Red); eyePlot.AssocName(0, "Eye widen"); eyePlot.AssocName(1, "Nose wrinkle"); eyePlot.AssocThickness(0, 2); eyePlot.AssocThickness(1, 2); })); }
// 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"); }
public override int Run() { //Convert arguments to more convenient vector form var arguments = new StringList { "./" }; // Search paths var configPath = "some/config/path"; var parentPath = arguments[0]; // Some initial parameters that can be overriden from command line StringList files = new StringList(), outputImages = new StringList(), outputLandmarkLocations = new StringList(), outputPoseLocations = new StringList(); // // Bounding boxes for a face in each image (optional) // vector<cv::Rect_<double>> bounding_boxes; CVDoubleRectList boundingBoxes = new CVDoubleRectList(); // LandmarkDetector::get_image_input_output_params(files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments); LandmarkDetector.get_image_input_output_params(files, outputLandmarkLocations, outputPoseLocations, outputImages, boundingBoxes, arguments); // LandmarkDetector::FaceModelParameters det_parameters(arguments); FaceModelParameters detParameters = new FaceModelParameters(arguments); // // No need to validate detections, as we're not doing tracking // det_parameters.validate_detections = false; detParameters.validate_detections = false; // // Grab camera parameters if provided (only used for pose and eye gaze and are quite important for accurate estimates) // float fx = 0, fy = 0, cx = 0, cy = 0; // int device = -1; // LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments); float fx = 0, fy = 0, cx = 0, cy = 0; int device = -1; LandmarkDetector.get_camera_params(out device, out fx, out fy, out cx, out cy, arguments); // // If cx (optical axis centre) is undefined will use the image size/2 as an estimate // bool cx_undefined = false; // bool fx_undefined = false; // if (cx == 0 || cy == 0) // { // cx_undefined = true; // } // if (fx == 0 || fy == 0) // { // fx_undefined = true; // } bool cx_undefined = false; bool fx_undefined = false; if (cx == 0 || cy == 0) { cx_undefined = true; } if (fx == 0 || fy == 0) { fx_undefined = true; } // // The modules that are being used for tracking // cout << "Loading the model" << endl; // LandmarkDetector::CLNF clnf_model(det_parameters.model_location); // cout << "Model loaded" << endl; INFO_STREAM("Loading the model"); CLNF clnfModel = new CLNF(detParameters.model_location); INFO_STREAM("Model loaded"); // TODO: dlib::frontal_face_detector ??? NANI ??? // cv::CascadeClassifier classifier(det_parameters.face_detector_location); // dlib::frontal_face_detector face_detector_hog = dlib::get_frontal_face_detector(); CascadeClassifier classifier = new CascadeClassifier(detParameters.face_detector_location); SWIGTYPE_p_dlib__frontal_face_detector faceDetectorHog; // // Loading the AU prediction models // string au_loc = "AU_predictors/AU_all_static.txt"; string auLoc = "AU_predictors/AU_all_static.txt"; // boost::filesystem::path au_loc_path = boost::filesystem::path(au_loc); // if (boost::filesystem::exists(au_loc_path)) // { // au_loc = au_loc_path.string(); // } // else if (boost::filesystem::exists(parent_path / au_loc_path)) // { // au_loc = (parent_path / au_loc_path).string(); // } // else if (boost::filesystem::exists(config_path / au_loc_path)) // { // au_loc = (config_path / au_loc_path).string(); // } // else // { // cout << "Can't find AU prediction files, exiting" << endl; // return 1; // } // // Used for image masking for AUs // string tri_loc; // boost::filesystem::path tri_loc_path = boost::filesystem::path("model/tris_68_full.txt"); // if (boost::filesystem::exists(tri_loc_path)) // { // tri_loc = tri_loc_path.string(); // } // else if (boost::filesystem::exists(parent_path / tri_loc_path)) // { // tri_loc = (parent_path / tri_loc_path).string(); // } // else if (boost::filesystem::exists(config_path / tri_loc_path)) // { // tri_loc = (config_path / tri_loc_path).string(); // } // else // { // cout << "Can't find triangulation files, exiting" << endl; // return 1; // } // FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc); // bool visualise = !det_parameters.quiet_mode; // // Do some image loading // for (size_t i = 0; i < files.size(); i++) // { // string file = files.at(i); // // Loading image // cv::Mat read_image = cv::imread(file, -1); // if (read_image.empty()) // { // cout << "Could not read the input image" << endl; // return 1; // } // // Making sure the image is in uchar grayscale // cv::Mat_<uchar> grayscale_image; // convert_to_grayscale(read_image, grayscale_image); // // If optical centers are not defined just use center of image // if (cx_undefined) // { // cx = grayscale_image.cols / 2.0f; // cy = grayscale_image.rows / 2.0f; // } // // Use a rough guess-timate of focal length // if (fx_undefined) // { // fx = 500 * (grayscale_image.cols / 640.0); // fy = 500 * (grayscale_image.rows / 480.0); // fx = (fx + fy) / 2.0; // fy = fx; // } // // if no pose defined we just use a face detector // if (bounding_boxes.empty()) // { // // Detect faces in an image // vector<cv::Rect_<double>> face_detections; // if (det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR) // { // vector<double> confidences; // LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_detector_hog, confidences); // } // else // { // LandmarkDetector::DetectFaces(face_detections, grayscale_image, classifier); // } // // Detect landmarks around detected faces // int face_det = 0; // // perform landmark detection for every face detected // for (size_t face = 0; face < face_detections.size(); ++face) // { // // if there are multiple detections go through them // bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_detections[face], clnf_model, det_parameters); // // Estimate head pose and eye gaze // cv::Vec6d headPose = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy); // // Gaze tracking, absolute gaze direction // cv::Point3f gazeDirection0(0, 0, -1); // cv::Point3f gazeDirection1(0, 0, -1); // if (success && det_parameters.track_gaze) // { // FaceAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true); // FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false); // } // auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model, false); // // Writing out the detected landmarks (in an OS independent manner) // if (!output_landmark_locations.empty()) // { // char name[100]; // // append detection number (in case multiple faces are detected) // sprintf(name, "_det_%d", face_det); // // Construct the output filename // boost::filesystem::path slash("/"); // std::string preferredSlash = slash.make_preferred().string(); // boost::filesystem::path out_feat_path(output_landmark_locations.at(i)); // boost::filesystem::path dir = out_feat_path.parent_path(); // boost::filesystem::path fname = out_feat_path.filename().replace_extension(""); // boost::filesystem::path ext = out_feat_path.extension(); // string outfeatures = dir.string() + preferredSlash + fname.string() + string(name) + ext.string(); // write_out_landmarks(outfeatures, clnf_model, headPose, gazeDirection0, gazeDirection1, ActionUnits.first, ActionUnits.second); // } // if (!output_pose_locations.empty()) // { // char name[100]; // // append detection number (in case multiple faces are detected) // sprintf(name, "_det_%d", face_det); // // Construct the output filename // boost::filesystem::path slash("/"); // std::string preferredSlash = slash.make_preferred().string(); // boost::filesystem::path out_pose_path(output_pose_locations.at(i)); // boost::filesystem::path dir = out_pose_path.parent_path(); // boost::filesystem::path fname = out_pose_path.filename().replace_extension(""); // boost::filesystem::path ext = out_pose_path.extension(); // string outfeatures = dir.string() + preferredSlash + fname.string() + string(name) + ext.string(); // write_out_pose_landmarks(outfeatures, clnf_model.GetShape(fx, fy, cx, cy), headPose, gazeDirection0, gazeDirection1); // } // if (det_parameters.track_gaze) // { // cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy); // // Draw it in reddish if uncertain, blueish if certain // LandmarkDetector::DrawBox(read_image, pose_estimate_to_draw, cv::Scalar(255.0, 0, 0), 3, fx, fy, cx, cy); // FaceAnalysis::DrawGaze(read_image, clnf_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy); // } // // displaying detected landmarks // cv::Mat display_image; // create_display_image(read_image, display_image, clnf_model); // if (visualise && success) // { // imshow("colour", display_image); // cv::waitKey(1); // } // // Saving the display images (in an OS independent manner) // if (!output_images.empty() && success) // { // string outimage = output_images.at(i); // if (!outimage.empty()) // { // char name[100]; // sprintf(name, "_det_%d", face_det); // boost::filesystem::path slash("/"); // std::string preferredSlash = slash.make_preferred().string(); // // append detection number // boost::filesystem::path out_feat_path(outimage); // boost::filesystem::path dir = out_feat_path.parent_path(); // boost::filesystem::path fname = out_feat_path.filename().replace_extension(""); // boost::filesystem::path ext = out_feat_path.extension(); // outimage = dir.string() + preferredSlash + fname.string() + string(name) + ext.string(); // create_directory_from_file(outimage); // bool write_success = cv::imwrite(outimage, display_image); // if (!write_success) // { // cout << "Could not output a processed image" << endl; // return 1; // } // } // } // if (success) // { // face_det++; // } // } // } // else // { // // Have provided bounding boxes // LandmarkDetector::DetectLandmarksInImage(grayscale_image, bounding_boxes[i], clnf_model, det_parameters); // // Estimate head pose and eye gaze // cv::Vec6d headPose = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy); // // Gaze tracking, absolute gaze direction // cv::Point3f gazeDirection0(0, 0, -1); // cv::Point3f gazeDirection1(0, 0, -1); // if (det_parameters.track_gaze) // { // FaceAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true); // FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false); // } //auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model, false); // // Writing out the detected landmarks // if(!output_landmark_locations.empty()) // { // string outfeatures = output_landmark_locations.at(i); // write_out_landmarks(outfeatures, clnf_model, headPose, gazeDirection0, gazeDirection1, ActionUnits.first, ActionUnits.second); // } // // Writing out the detected landmarks // if (!output_pose_locations.empty()) // { // string outfeatures = output_pose_locations.at(i); // write_out_pose_landmarks(outfeatures, clnf_model.GetShape(fx, fy, cx, cy), headPose, gazeDirection0, gazeDirection1); // } // // displaying detected stuff // cv::Mat display_image; // if (det_parameters.track_gaze) // { // cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy); //// Draw it in reddish if uncertain, blueish if certain //LandmarkDetector::DrawBox(read_image, pose_estimate_to_draw, cv::Scalar(255.0, 0, 0), 3, fx, fy, cx, cy); // FaceAnalysis::DrawGaze(read_image, clnf_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy); // } // create_display_image(read_image, display_image, clnf_model); // if(visualise) // { // imshow("colour", display_image); //cv::waitKey(1); // } // if(!output_images.empty()) // { // string outimage = output_images.at(i); // if(!outimage.empty()) // { // create_directory_from_file(outimage); //bool write_success = imwrite(outimage, display_image); // if (!write_success) // { // cout << "Could not output a processed image" << endl; // return 1; // } // } // } // } // } return(0); }
public MainWindow() { bool bPush = false; string configfile_name = "config.xml"; FileStream stream = new FileStream(configfile_name, FileMode.Open); XmlDocument document = new XmlDocument(); document.Load(stream); string serveraddress = "localhost"; int port = 5570; XmlNodeList list = document.GetElementsByTagName("Mode"); if (list.Count > 0 && ((XmlElement)list[0]).InnerText.ToLower().Equals("push")) { bPush = true; } list = document.GetElementsByTagName("IP"); if (list.Count > 0) { serveraddress = ((XmlElement)list[0]).InnerText; } list = document.GetElementsByTagName("Port"); if (list.Count > 0) { port = Int32.Parse(((XmlElement)list[0]).InnerText); } list = document.GetElementsByTagName("Topic"); if (list.Count > 0) { topic = ((XmlElement)list[0]).InnerText; } String hostName = Dns.GetHostName(); IPAddress[] addresses = Dns.GetHostAddresses(hostName); string myaddress = "localhost"; foreach (IPAddress address in addresses) { if (address.AddressFamily == System.Net.Sockets.AddressFamily.InterNetwork) { myaddress = address.ToString(); } } InitializeComponent(); this.DataContext = this; // For WPF data binding // Set the icon Uri iconUri = new Uri("logo1.ico", UriKind.RelativeOrAbsolute); this.Icon = BitmapFrame.Create(iconUri); String root = AppDomain.CurrentDomain.BaseDirectory; face_model_params = new FaceModelParameters(root, false); landmark_detector = new CLNF(face_model_params); gaze_analyser = new GazeAnalyserManaged(); frame_no = 0; // Added by Huang pubSocket = new PublisherSocket(); pubSocket.Options.SendHighWatermark = 1000; if (bPush) { pubSocket.Connect("tcp://" + serveraddress + ":" + port); } else { pubSocket.Bind("tcp://" + myaddress + ":" + port); } }
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(CLNF obj) { return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr); }
public LandmarkData(FrameData frame, CLNF faceModel, bool detectionSucceeded) { FrameData = frame; FaceModel = faceModel; DetectionSucceeded = detectionSucceeded; }
public override int Run() { int device = 0; var argument = new StringList { "./" }; FaceModelParameters det_parameters = new FaceModelParameters(argument); //vector<string> files, depth_directories, output_video_files, out_dummy; StringList files = new StringList(), output_video_files = new StringList(), out_dummy = new StringList(); bool u; string output_codec; LandmarkDetector.get_video_input_output_params(files, out_dummy, output_video_files, out u, out output_codec, argument); CLNF clnf_model = new CLNF(det_parameters.model_location); float fx = 0, fy = 0, cx = 0, cy = 0; LandmarkDetector.get_camera_params(out device, out fx, out fy, out cx, out cy, argument); // If cx (optical axis centre) is undefined will use the image size/2 as an estimate bool cx_undefined = false; bool fx_undefined = false; if (cx == 0 || cy == 0) { cx_undefined = true; } if (fx == 0 || fy == 0) { fx_undefined = true; } //// Do some grabbing INFO_STREAM("Attempting to capture from device: " + device); using (VideoCapture video_capture = new VideoCapture(device)) { using (Mat dummy = new Mat()) video_capture.Read(dummy); if (!video_capture.IsOpened()) { FATAL_STREAM("Failed to open video source"); return(1); } else { INFO_STREAM("Device or file opened"); } int frame_count = 0; Mat captured_image = new Mat(); video_capture.Read(captured_image); Size = new Size(captured_image.Width / SizeFactor, captured_image.Height / SizeFactor); using (var resized_image = captured_image.Resize(Size)) { // If optical centers are not defined just use center of image if (cx_undefined) { cx = resized_image.Cols / 2.0f; cy = resized_image.Rows / 2.0f; } // Use a rough guess-timate of focal length if (fx_undefined) { fx = (float)(500 * (resized_image.Cols / 640.0)); fy = (float)(500 * (resized_image.Rows / 480.0)); fx = (float)((fx + fy) / 2.0); fy = fx; } } // Use for timestamping if using a webcam long t_initial = Cv2.GetTickCount(); INFO_STREAM("Starting tracking"); while (video_capture.Read(captured_image)) { using (var resized_image = captured_image.Resize(Size)) { // Reading the images MatOfByte grayscale_image = new MatOfByte(); if (resized_image.Channels() == 3) { Cv2.CvtColor(resized_image, grayscale_image, ColorConversionCodes.BGR2GRAY); } else { grayscale_image = (MatOfByte)resized_image.Clone(); } // The actual facial landmark detection / tracking bool detection_success = LandmarkDetector.DetectLandmarksInVideo(new SWIGTYPE_p_cv__Mat_T_uchar_t(grayscale_image.CvPtr), new SWIGTYPE_p_CLNF(CLNF.getCPtr(clnf_model)), new SWIGTYPE_p_FaceModelParameters(FaceModelParameters.getCPtr(det_parameters))); // Visualising the results // Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised double detection_certainty = clnf_model.detection_certainty; visualise_tracking(resized_image, ref clnf_model, ref det_parameters, frame_count, fx, fy, cx, cy); // detect key presses char character_press = (char)Cv2.WaitKey(15); switch (character_press) { case 'r': clnf_model.Reset(); break; case 'q': return(0); } // Update the frame count frame_count++; grayscale_image.Dispose(); grayscale_image = null; } } } return(0); }