sustaining_gazes/exe/FeatureExtraction/FeatureExtraction.cpp

251 lines
9.7 KiB
C++

///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
//
// OpenFace: an open source facial behavior analysis toolkit
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
// in IEEE Winter Conference on Applications of Computer Vision, 2016
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// FeatureExtraction.cpp : Defines the entry point for the feature extraction console application.
// System includes
#include <fstream>
#include <sstream>
// OpenCV includes
#include <opencv2/videoio/videoio.hpp> // Video write
#include <opencv2/videoio/videoio_c.h> // Video write
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
// Boost includes
#include <filesystem.hpp>
#include <filesystem/fstream.hpp>
#include <boost/algorithm/string.hpp>
// Local includes
#include "LandmarkCoreIncludes.h"
#include <Face_utils.h>
#include <FaceAnalyser.h>
#include <GazeEstimation.h>
#include <RecorderOpenFace.h>
#include <RecorderOpenFaceParameters.h>
#include <SequenceCapture.h>
#include <Visualizer.h>
#include <VisualizationUtils.h>
#ifndef CONFIG_DIR
#define CONFIG_DIR "~"
#endif
#define INFO_STREAM( stream ) \
std::cout << stream << std::endl
#define WARN_STREAM( stream ) \
std::cout << "Warning: " << stream << std::endl
#define ERROR_STREAM( stream ) \
std::cout << "Error: " << stream << std::endl
static void printErrorAndAbort( const std::string & error )
{
std::cout << error << std::endl;
}
#define FATAL_STREAM( stream ) \
printErrorAndAbort( std::string( "Fatal error: " ) + stream )
using namespace std;
vector<string> get_arguments(int argc, char **argv)
{
vector<string> arguments;
// First argument is reserved for the name of the executable
for(int i = 0; i < argc; ++i)
{
arguments.push_back(string(argv[i]));
}
return arguments;
}
int main (int argc, char **argv)
{
vector<string> arguments = get_arguments(argc, argv);
// Load the modules that are being used for tracking and face analysis
// Load face landmark detector
LandmarkDetector::FaceModelParameters det_parameters(arguments);
// Always track gaze in feature extraction
LandmarkDetector::CLNF face_model(det_parameters.model_location);
// Load facial feature extractor and AU analyser
FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
Utilities::SequenceCapture sequence_reader;
// A utility for visualizing the results
Utilities::Visualizer visualizer(arguments);
// Tracking FPS for visualization
Utilities::FpsTracker fps_tracker;
fps_tracker.AddFrame();
while (true) // this is not a for loop as we might also be reading from a webcam
{
// The sequence reader chooses what to open based on command line arguments provided
if(!sequence_reader.Open(arguments))
break;
INFO_STREAM("Device or file opened");
if (sequence_reader.IsWebcam())
{
INFO_STREAM("WARNING: using a webcam in feature extraction, Action Unit predictions will not be as accurate in real-time webcam mode");
}
cv::Mat captured_image;
Utilities::RecorderOpenFaceParameters recording_params(arguments, true, sequence_reader.fps);
Utilities::RecorderOpenFace open_face_rec(sequence_reader.name, recording_params, arguments);
if (recording_params.outputGaze() && !face_model.eye_model)
cout << "WARNING: no eye model defined, but outputting gaze" << endl;
captured_image = sequence_reader.GetNextFrame();
// For reporting progress
double reported_completion = 0;
INFO_STREAM("Starting tracking");
while (!captured_image.empty())
{
// Converting to grayscale
cv::Mat_<uchar> grayscale_image = sequence_reader.GetGrayFrame();
// The actual facial landmark detection / tracking
bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_model, det_parameters);
// Gaze tracking, absolute gaze direction
cv::Point3f gazeDirection0(0, 0, -1); cv::Point3f gazeDirection1(0, 0, -1); cv::Vec2d gazeAngle(0, 0);
if (detection_success && face_model.eye_model)
{
GazeAnalysis::EstimateGaze(face_model, gazeDirection0, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, true);
GazeAnalysis::EstimateGaze(face_model, gazeDirection1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, false);
gazeAngle = GazeAnalysis::GetGazeAngle(gazeDirection0, gazeDirection1);
}
// Do face alignment
cv::Mat sim_warped_img;
cv::Mat_<double> hog_descriptor; int num_hog_rows = 0, num_hog_cols = 0;
// Perform AU detection and HOG feature extraction, as this can be expensive only compute it if needed by output or visualization
if (recording_params.outputAlignedFaces() || recording_params.outputHOG() || recording_params.outputAUs() || visualizer.vis_align || visualizer.vis_hog)
{
face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, sequence_reader.time_stamp, sequence_reader.IsWebcam());
face_analyser.GetLatestAlignedFace(sim_warped_img);
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
}
// Work out the pose of the head from the tracked model
cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
// Keeping track of FPS
fps_tracker.AddFrame();
// Displaying the tracking visualizations
visualizer.SetImage(captured_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
visualizer.SetObservationFaceAlign(sim_warped_img);
visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, detection_success);
visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
visualizer.SetObservationGaze(gazeDirection0, gazeDirection1, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_model.detection_certainty);
visualizer.SetFps(fps_tracker.GetFPS());
// detect key presses
char character_press = visualizer.ShowObservation();
// quit processing the current sequence (useful when in Webcam mode)
if (character_press == 'q')
{
break;
}
// Setting up the recorder output
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
open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
open_face_rec.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
open_face_rec.SetObservationLandmarks(face_model.detected_landmarks, face_model.GetShape(sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy),
face_model.params_global, face_model.params_local, face_model.detection_certainty, detection_success);
open_face_rec.SetObservationPose(pose_estimate);
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));
open_face_rec.SetObservationTimestamp(sequence_reader.time_stamp);
open_face_rec.SetObservationFaceAlign(sim_warped_img);
open_face_rec.WriteObservation();
// Reporting progress
if(sequence_reader.GetProgress() >= reported_completion / 10.0)
{
cout << reported_completion * 10 << "% ";
if (reported_completion == 10)
{
cout << endl;
}
reported_completion = reported_completion + 1;
}
// Grabbing the next frame in the sequence
captured_image = sequence_reader.GetNextFrame();
}
open_face_rec.Close();
if (recording_params.outputAUs())
{
INFO_STREAM("Postprocessing the Action Unit predictions");
face_analyser.PostprocessOutputFile(open_face_rec.GetCSVFile());
}
// Reset the models for the next video
face_analyser.Reset();
face_model.Reset();
}
return 0;
}