396 lines
15 KiB
C++
396 lines
15 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.
|
|
//
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
// FaceTrackingVidMulti.cpp : Defines the entry point for the multiple face tracking console application.
|
|
#include "LandmarkCoreIncludes.h"
|
|
|
|
#include "VisualizationUtils.h"
|
|
#include "Visualizer.h"
|
|
#include "SequenceCapture.h"
|
|
#include <RecorderOpenFace.h>
|
|
#include <RecorderOpenFaceParameters.h>
|
|
#include <GazeEstimation.h>
|
|
#include <FaceAnalyser.h>
|
|
|
|
#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>
|
|
|
|
#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;
|
|
abort();
|
|
}
|
|
|
|
#define FATAL_STREAM( stream ) \
|
|
printErrorAndAbort( std::string( "Fatal error: " ) + stream )
|
|
|
|
using namespace std;
|
|
|
|
vector<string> get_arguments(int argc, char **argv)
|
|
{
|
|
|
|
vector<string> arguments;
|
|
|
|
for(int i = 0; i < argc; ++i)
|
|
{
|
|
arguments.push_back(string(argv[i]));
|
|
}
|
|
return arguments;
|
|
}
|
|
|
|
void NonOverlapingDetections(const vector<LandmarkDetector::CLNF>& clnf_models, vector<cv::Rect_<double> >& face_detections)
|
|
{
|
|
|
|
// Go over the model and eliminate detections that are not informative (there already is a tracker there)
|
|
for(size_t model = 0; model < clnf_models.size(); ++model)
|
|
{
|
|
|
|
// See if the detections intersect
|
|
cv::Rect_<double> model_rect = clnf_models[model].GetBoundingBox();
|
|
|
|
for(int detection = face_detections.size()-1; detection >=0; --detection)
|
|
{
|
|
double intersection_area = (model_rect & face_detections[detection]).area();
|
|
double union_area = model_rect.area() + face_detections[detection].area() - 2 * intersection_area;
|
|
|
|
// If the model is already tracking what we're detecting ignore the detection, this is determined by amount of overlap
|
|
if( intersection_area/union_area > 0.5)
|
|
{
|
|
face_detections.erase(face_detections.begin() + detection);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
int main (int argc, char **argv)
|
|
{
|
|
|
|
vector<string> arguments = get_arguments(argc, argv);
|
|
|
|
// no arguments: output usage
|
|
if (arguments.size() == 1)
|
|
{
|
|
cout << "For command line arguments see:" << endl;
|
|
cout << " https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments";
|
|
return 0;
|
|
}
|
|
|
|
LandmarkDetector::FaceModelParameters det_params(arguments);
|
|
// This is so that the model would not try re-initialising itself
|
|
det_params.reinit_video_every = -1;
|
|
|
|
det_params.curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
|
|
|
|
vector<LandmarkDetector::FaceModelParameters> det_parameters;
|
|
det_parameters.push_back(det_params);
|
|
|
|
// The modules that are being used for tracking
|
|
vector<LandmarkDetector::CLNF> face_models;
|
|
vector<bool> active_models;
|
|
|
|
int num_faces_max = 4;
|
|
|
|
LandmarkDetector::CLNF face_model(det_parameters[0].model_location);
|
|
face_model.face_detector_HAAR.load(det_parameters[0].face_detector_location);
|
|
face_model.face_detector_location = det_parameters[0].face_detector_location;
|
|
|
|
face_models.reserve(num_faces_max);
|
|
|
|
face_models.push_back(face_model);
|
|
active_models.push_back(false);
|
|
|
|
for (int i = 1; i < num_faces_max; ++i)
|
|
{
|
|
face_models.push_back(face_model);
|
|
active_models.push_back(false);
|
|
det_parameters.push_back(det_params);
|
|
}
|
|
|
|
// Load facial feature extractor and AU analyser (make sure it is static, as we don't reidentify faces)
|
|
FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
|
|
face_analysis_params.OptimizeForImages();
|
|
FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
|
|
|
|
// Open a sequence
|
|
Utilities::SequenceCapture sequence_reader;
|
|
|
|
// A utility for visualizing the results (show just the tracks)
|
|
Utilities::Visualizer visualizer(arguments);
|
|
|
|
// Tracking FPS for visualization
|
|
Utilities::FpsTracker fps_tracker;
|
|
fps_tracker.AddFrame();
|
|
|
|
int sequence_number = 0;
|
|
|
|
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");
|
|
|
|
cv::Mat captured_image = sequence_reader.GetNextFrame();
|
|
|
|
int frame_count = 0;
|
|
|
|
Utilities::RecorderOpenFaceParameters recording_params(arguments, true, sequence_reader.IsWebcam(),
|
|
sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, sequence_reader.fps);
|
|
// Do not do AU detection on multi-face case as it is not supported
|
|
recording_params.setOutputAUs(false);
|
|
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;
|
|
|
|
if (sequence_reader.IsWebcam())
|
|
{
|
|
INFO_STREAM("WARNING: using a webcam in feature extraction, forcing visualization of tracking to allow quitting the application (press q)");
|
|
visualizer.vis_track = true;
|
|
}
|
|
|
|
if (recording_params.outputAUs())
|
|
{
|
|
INFO_STREAM("WARNING: using a AU detection in multiple face mode, it might not be as accurate and is experimental");
|
|
}
|
|
|
|
|
|
INFO_STREAM( "Starting tracking");
|
|
while(!captured_image.empty())
|
|
{
|
|
|
|
// Reading the images
|
|
cv::Mat_<uchar> grayscale_image = sequence_reader.GetGrayFrame();
|
|
|
|
vector<cv::Rect_<double> > face_detections;
|
|
|
|
bool all_models_active = true;
|
|
for(unsigned int model = 0; model < face_models.size(); ++model)
|
|
{
|
|
if(!active_models[model])
|
|
{
|
|
all_models_active = false;
|
|
}
|
|
}
|
|
|
|
// Get the detections (every 8th frame and when there are free models available for tracking)
|
|
if(frame_count % 8 == 0 && !all_models_active)
|
|
{
|
|
if(det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
|
|
{
|
|
vector<double> confidences;
|
|
LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_models[0].face_detector_HOG, confidences);
|
|
}
|
|
else
|
|
{
|
|
LandmarkDetector::DetectFaces(face_detections, grayscale_image, face_models[0].face_detector_HAAR);
|
|
}
|
|
|
|
}
|
|
|
|
// Keep only non overlapping detections (also convert to a concurrent vector
|
|
NonOverlapingDetections(face_models, face_detections);
|
|
|
|
vector<tbb::atomic<bool> > face_detections_used(face_detections.size());
|
|
|
|
// Go through every model and update the tracking
|
|
tbb::parallel_for(0, (int)face_models.size(), [&](int model){
|
|
//for(unsigned int model = 0; model < clnf_models.size(); ++model)
|
|
//{
|
|
|
|
bool detection_success = false;
|
|
|
|
// If the current model has failed more than 4 times in a row, remove it
|
|
if(face_models[model].failures_in_a_row > 4)
|
|
{
|
|
active_models[model] = false;
|
|
face_models[model].Reset();
|
|
}
|
|
|
|
// If the model is inactive reactivate it with new detections
|
|
if(!active_models[model])
|
|
{
|
|
|
|
for(size_t detection_ind = 0; detection_ind < face_detections.size(); ++detection_ind)
|
|
{
|
|
// if it was not taken by another tracker take it (if it is false swap it to true and enter detection, this makes it parallel safe)
|
|
if(face_detections_used[detection_ind].compare_and_swap(true, false) == false)
|
|
{
|
|
|
|
// Reinitialise the model
|
|
face_models[model].Reset();
|
|
|
|
// This ensures that a wider window is used for the initial landmark localisation
|
|
face_models[model].detection_success = false;
|
|
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], face_models[model], det_parameters[model]);
|
|
|
|
// This activates the model
|
|
active_models[model] = true;
|
|
|
|
// break out of the loop as the tracker has been reinitialised
|
|
break;
|
|
}
|
|
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// The actual facial landmark detection / tracking
|
|
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_models[model], det_parameters[model]);
|
|
}
|
|
});
|
|
|
|
// Keeping track of FPS
|
|
fps_tracker.AddFrame();
|
|
|
|
visualizer.SetImage(captured_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
|
|
|
|
// Go through every model and detect eye gaze, record results and visualise the results
|
|
for(size_t model = 0; model < face_models.size(); ++model)
|
|
{
|
|
// Visualising the results
|
|
if(active_models[model])
|
|
{
|
|
|
|
// Estimate head pose and eye gaze
|
|
cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
|
|
|
|
cv::Point3f gaze_direction0(0, 0, 0); cv::Point3f gaze_direction1(0, 0, 0); cv::Vec2d gaze_angle(0, 0);
|
|
|
|
// Detect eye gazes
|
|
if (face_models[model].detection_success && face_model.eye_model)
|
|
{
|
|
GazeAnalysis::EstimateGaze(face_models[model], gaze_direction0, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, true);
|
|
GazeAnalysis::EstimateGaze(face_models[model], gaze_direction1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, false);
|
|
gaze_angle = GazeAnalysis::GetGazeAngle(gaze_direction0, gaze_direction1);
|
|
}
|
|
|
|
// Face analysis step
|
|
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.PredictStaticAUsAndComputeFeatures(captured_image, face_models[model].detected_landmarks);
|
|
face_analyser.GetLatestAlignedFace(sim_warped_img);
|
|
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
|
|
}
|
|
|
|
// Visualize the features
|
|
visualizer.SetObservationFaceAlign(sim_warped_img);
|
|
visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
|
|
visualizer.SetObservationLandmarks(face_models[model].detected_landmarks, face_models[model].detection_certainty);
|
|
visualizer.SetObservationPose(LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_models[model].detection_certainty);
|
|
visualizer.SetObservationGaze(gaze_direction0, gaze_direction1, LandmarkDetector::CalculateAllEyeLandmarks(face_models[model]), LandmarkDetector::Calculate3DEyeLandmarks(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_models[model].detection_certainty);
|
|
|
|
// Output features
|
|
open_face_rec.SetObservationHOG(face_models[model].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_models[model].detected_landmarks, face_models[model].GetShape(sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy),
|
|
face_models[model].params_global, face_models[model].params_local, face_models[model].detection_certainty, face_models[model].detection_success);
|
|
open_face_rec.SetObservationPose(pose_estimate);
|
|
open_face_rec.SetObservationGaze(gaze_direction0, gaze_direction1, gaze_angle, LandmarkDetector::CalculateAllEyeLandmarks(face_models[model]), LandmarkDetector::Calculate3DEyeLandmarks(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy));
|
|
open_face_rec.SetObservationFaceAlign(sim_warped_img);
|
|
open_face_rec.SetObservationFaceID(model);
|
|
open_face_rec.SetObservationTimestamp(sequence_reader.time_stamp);
|
|
open_face_rec.SetObservationFrameNumber(sequence_reader.GetFrameNumber());
|
|
open_face_rec.WriteObservation();
|
|
|
|
|
|
}
|
|
}
|
|
visualizer.SetFps(fps_tracker.GetFPS());
|
|
|
|
// show visualization and detect key presses
|
|
char character_press = visualizer.ShowObservation();
|
|
|
|
// restart the trackers
|
|
if(character_press == 'r')
|
|
{
|
|
for(size_t i=0; i < face_models.size(); ++i)
|
|
{
|
|
face_models[i].Reset();
|
|
active_models[i] = false;
|
|
}
|
|
}
|
|
// quit the application
|
|
else if(character_press=='q')
|
|
{
|
|
return 0;
|
|
}
|
|
|
|
// Update the frame count
|
|
frame_count++;
|
|
|
|
// Grabbing the next frame in the sequence
|
|
captured_image = sequence_reader.GetNextFrame();
|
|
|
|
}
|
|
|
|
frame_count = 0;
|
|
|
|
// Reset the model, for the next video
|
|
for(size_t model=0; model < face_models.size(); ++model)
|
|
{
|
|
face_models[model].Reset();
|
|
active_models[model] = false;
|
|
}
|
|
|
|
sequence_number++;
|
|
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|