252 lines
9.8 KiB
C++
252 lines
9.8 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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// FeatureExtraction.cpp : Defines the entry point for the feature extraction console application.
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// System includes
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#include <fstream>
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#include <sstream>
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// OpenCV includes
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#include <opencv2/videoio/videoio.hpp> // Video write
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#include <opencv2/videoio/videoio_c.h> // Video write
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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// Boost includes
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#include <filesystem.hpp>
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#include <filesystem/fstream.hpp>
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#include <boost/algorithm/string.hpp>
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// Local includes
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#include "LandmarkCoreIncludes.h"
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#include <Face_utils.h>
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#include <FaceAnalyser.h>
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#include <GazeEstimation.h>
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#include <RecorderOpenFace.h>
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#include <RecorderOpenFaceParameters.h>
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#include <SequenceCapture.h>
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#include <Visualizer.h>
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#ifndef CONFIG_DIR
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#define CONFIG_DIR "~"
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#endif
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#define INFO_STREAM( stream ) \
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std::cout << stream << std::endl
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#define WARN_STREAM( stream ) \
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std::cout << "Warning: " << stream << std::endl
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#define ERROR_STREAM( stream ) \
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std::cout << "Error: " << stream << std::endl
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static void printErrorAndAbort( const std::string & error )
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{
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std::cout << error << std::endl;
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}
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#define FATAL_STREAM( stream ) \
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printErrorAndAbort( std::string( "Fatal error: " ) + stream )
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using namespace std;
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vector<string> get_arguments(int argc, char **argv)
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{
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vector<string> arguments;
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// First argument is reserved for the name of the executable
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for(int i = 0; i < argc; ++i)
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{
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arguments.push_back(string(argv[i]));
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}
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return arguments;
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}
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// Some globals for tracking timing information for visualisation (TODO bit ugly)
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double fps_tracker = -1.0;
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int64 t0 = 0;
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int frame_count = 0;
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// Visualising the results TODO separate class
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void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, double fx, double fy, double cx, double cy)
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{
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// Work out the framerate TODO
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if (frame_count % 10 == 0)
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{
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double t1 = cv::getTickCount();
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fps_tracker = 10.0 / (double(t1 - t0) / cv::getTickFrequency());
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t0 = t1;
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}
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// Write out the framerate on the image before displaying it
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char fpsC[255];
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std::sprintf(fpsC, "%d", (int)fps_tracker);
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string fpsSt("FPS:");
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fpsSt += fpsC;
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cv::putText(captured_image, fpsSt, cv::Point(10, 20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255, 0, 0), 1, CV_AA);
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frame_count++;
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}
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int main (int argc, char **argv)
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{
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vector<string> arguments = get_arguments(argc, argv);
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// Load the modules that are being used for tracking and face analysis
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// Load face landmark detector
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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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// Always track gaze in feature extraction
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det_parameters.track_gaze = true;
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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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// Load facial feature extractor and AU analyser
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FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
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FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
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Utilities::SequenceCapture sequence_reader;
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// A utility for visualizing the results
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Utilities::Visualizer visualizer(arguments);
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while (true) // this is not a for loop as we might also be reading from a webcam
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{
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// The sequence reader chooses what to open based on command line arguments provided
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if(!sequence_reader.Open(arguments))
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break;
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INFO_STREAM("Device or file opened");
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cv::Mat captured_image;
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Utilities::RecorderOpenFaceParameters recording_params(arguments, true, sequence_reader.fps);
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Utilities::RecorderOpenFace open_face_rec(sequence_reader.name, recording_params, arguments);
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captured_image = sequence_reader.GetNextFrame();
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// For reporting progress
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double reported_completion = 0;
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INFO_STREAM("Starting tracking");
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while (!captured_image.empty())
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{
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// Converting to grayscale
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cv::Mat_<uchar> grayscale_image = sequence_reader.GetGrayFrame();
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// The actual facial landmark detection / tracking
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bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_model, det_parameters);
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// Gaze tracking, absolute gaze direction
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cv::Point3f gazeDirection0(0, 0, -1); cv::Point3f gazeDirection1(0, 0, -1); cv::Vec2d gazeAngle(0, 0);
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if (det_parameters.track_gaze && detection_success && face_model.eye_model)
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{
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GazeAnalysis::EstimateGaze(face_model, gazeDirection0, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, true);
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GazeAnalysis::EstimateGaze(face_model, gazeDirection1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, false);
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gazeAngle = GazeAnalysis::GetGazeAngle(gazeDirection0, gazeDirection1);
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}
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// Do face alignment
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cv::Mat sim_warped_img;
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cv::Mat_<double> hog_descriptor; int num_hog_rows = 0, num_hog_cols = 0;
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// Perform AU detection and HOG feature extraction, as this can be expensive only compute it if needed by output or visualization
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if (recording_params.outputAlignedFaces() || recording_params.outputHOG() || recording_params.outputAUs() || visualizer.vis_align || visualizer.vis_hog)
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{
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face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, sequence_reader.time_stamp, false);
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face_analyser.GetLatestAlignedFace(sim_warped_img);
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face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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}
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// Work out the pose of the head from the tracked model
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cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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// Displaying the tracking visualizations
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visualizer.SetImage(captured_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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visualizer.SetObservationFaceAlign(sim_warped_img);
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visualizer.SetObservationGaze(gazeDirection0, gazeDirection1, gazeAngle, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy));
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visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, detection_success);
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visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
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visualizer.ShowObservation();
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// Setting up the recorder output
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open_face_rec.SetObservationHOG(detection_success, hog_descriptor, num_hog_rows, num_hog_cols, 31); // The number of channels in HOG is fixed at the moment, as using FHOG
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open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
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open_face_rec.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
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open_face_rec.SetObservationGaze(gazeDirection0, gazeDirection1, gazeAngle, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy));
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open_face_rec.SetObservationLandmarks(face_model.detected_landmarks, face_model.GetShape(sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy),
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face_model.params_global, face_model.params_local, face_model.detection_certainty, detection_success);
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open_face_rec.SetObservationPose(pose_estimate);
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open_face_rec.SetObservationTimestamp(sequence_reader.time_stamp);
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open_face_rec.SetObservationFaceAlign(sim_warped_img);
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open_face_rec.WriteObservation();
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// Reporting progress
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if(sequence_reader.GetProgress() >= reported_completion / 10.0)
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{
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cout << reported_completion * 10 << "% ";
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reported_completion = reported_completion + 1;
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}
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// Grabbing the next frame in the sequence
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captured_image = sequence_reader.GetNextFrame();
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}
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open_face_rec.Close();
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if (recording_params.outputAUs())
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{
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INFO_STREAM("Postprocessing the Action Unit predictions");
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face_analyser.PostprocessOutputFile(open_face_rec.GetCSVFile());
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}
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// Reset the models for the next video
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face_analyser.Reset();
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face_model.Reset();
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}
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return 0;
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}
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