436 lines
16 KiB
C++
436 lines
16 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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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// FaceTrackingVidMulti.cpp : Defines the entry point for the multiple face tracking console application.
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#include "LandmarkCoreIncludes.h"
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#include "VisualizationUtils.h"
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#include "Visualizer.h"
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#include "SequenceCapture.h"
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#include <RecorderOpenFace.h>
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#include <RecorderOpenFaceParameters.h>
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#include <GazeEstimation.h>
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#include <FaceAnalyser.h>
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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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#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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abort();
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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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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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void NonOverlapingDetections(const vector<LandmarkDetector::CLNF>& clnf_models, vector<cv::Rect_<double> >& face_detections)
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{
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// Go over the model and eliminate detections that are not informative (there already is a tracker there)
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for(size_t model = 0; model < clnf_models.size(); ++model)
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{
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// See if the detections intersect
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cv::Rect_<double> model_rect = clnf_models[model].GetBoundingBox();
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for(int detection = face_detections.size()-1; detection >=0; --detection)
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{
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double intersection_area = (model_rect & face_detections[detection]).area();
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double union_area = model_rect.area() + face_detections[detection].area() - 2 * intersection_area;
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// If the model is already tracking what we're detecting ignore the detection, this is determined by amount of overlap
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if( intersection_area/union_area > 0.5)
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{
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face_detections.erase(face_detections.begin() + detection);
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}
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}
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}
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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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// no arguments: output usage
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if (arguments.size() == 1)
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{
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cout << "For command line arguments see:" << endl;
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cout << " https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments";
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return 0;
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}
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LandmarkDetector::FaceModelParameters det_params(arguments);
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// This is so that the model would not try re-initialising itself
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det_params.reinit_video_every = -1;
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det_params.curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
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vector<LandmarkDetector::FaceModelParameters> det_parameters;
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det_parameters.push_back(det_params);
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// The modules that are being used for tracking
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vector<LandmarkDetector::CLNF> face_models;
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vector<bool> active_models;
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int num_faces_max = 15;
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LandmarkDetector::CLNF face_model(det_parameters[0].model_location);
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face_model.face_detector_HAAR.load(det_parameters[0].face_detector_location);
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face_model.face_detector_location = det_parameters[0].face_detector_location;
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face_models.reserve(num_faces_max);
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face_models.push_back(face_model);
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active_models.push_back(false);
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for (int i = 1; i < num_faces_max; ++i)
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{
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face_models.push_back(face_model);
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active_models.push_back(false);
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det_parameters.push_back(det_params);
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}
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// Load facial feature extractor and AU analyser (make sure it is static, as we don't reidentify faces)
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FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
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face_analysis_params.OptimizeForImages();
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FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
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if (!face_model.eye_model)
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{
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cout << "WARNING: no eye model found" << endl;
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}
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if (face_analyser.GetAUClassNames().size() == 0 && face_analyser.GetAUClassNames().size() == 0)
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{
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cout << "WARNING: no Action Unit models found" << endl;
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}
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// Open a sequence
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Utilities::SequenceCapture sequence_reader;
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// A utility for visualizing the results (show just the tracks)
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Utilities::Visualizer visualizer(arguments);
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// Tracking FPS for visualization
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Utilities::FpsTracker fps_tracker;
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fps_tracker.AddFrame();
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int sequence_number = 0;
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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 = sequence_reader.GetNextFrame();
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int frame_count = 0;
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Utilities::RecorderOpenFaceParameters recording_params(arguments, true, sequence_reader.IsWebcam(),
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sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, sequence_reader.fps);
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// for some reason not accepted as cli parameter, as we don't need it: disable it anyway
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recording_params.setOutputAUs(false);
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recording_params.setOutputHOG(false);
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recording_params.setOutputAlignedFaces(false);
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recording_params.setOutputTracked(false);
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if (!face_model.eye_model)
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{
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recording_params.setOutputGaze(false);
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}
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Utilities::RecorderOpenFace open_face_rec(sequence_reader.name, recording_params, arguments);
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if (sequence_reader.IsWebcam())
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{
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INFO_STREAM("WARNING: using a webcam in feature extraction, forcing visualization of tracking to allow quitting the application (press q)");
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visualizer.vis_track = true;
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}
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if (recording_params.outputAUs())
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{
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INFO_STREAM("WARNING: using a AU detection in multiple face mode, it might not be as accurate and is experimental");
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}
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INFO_STREAM( "Starting tracking");
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while(!captured_image.empty())
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{
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// Reading the images
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cv::Mat_<uchar> grayscale_image = sequence_reader.GetGrayFrame();
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vector<cv::Rect_<double> > face_detections;
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bool all_models_active = true;
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for(unsigned int model = 0; model < face_models.size(); ++model)
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{
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if(!active_models[model])
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{
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all_models_active = false;
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}
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}
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// Get the detections (every 8th frame and when there are free models available for tracking)
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if(frame_count % 8 == 0 && !all_models_active)
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{
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if(det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
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{
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vector<double> confidences;
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LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_models[0].face_detector_HOG, confidences);
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}
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else
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{
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LandmarkDetector::DetectFaces(face_detections, grayscale_image, face_models[0].face_detector_HAAR);
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}
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}
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// Keep only non overlapping detections (also convert to a concurrent vector
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NonOverlapingDetections(face_models, face_detections);
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vector<tbb::atomic<bool> > face_detections_used(face_detections.size());
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// Go through every model and update the tracking
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tbb::parallel_for(0, (int)face_models.size(), [&](int model){
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//for(unsigned int model = 0; model < clnf_models.size(); ++model)
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//{
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bool detection_success = false;
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// If the current model has failed more than 4 times in a row, remove it
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if(face_models[model].failures_in_a_row > 4)
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{
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active_models[model] = false;
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face_models[model].Reset();
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}
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// If the model is inactive reactivate it with new detections
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if(!active_models[model])
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{
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for(size_t detection_ind = 0; detection_ind < face_detections.size(); ++detection_ind)
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{
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// 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)
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if(face_detections_used[detection_ind].compare_and_swap(true, false) == false)
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{
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// Reinitialise the model
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face_models[model].Reset();
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// This ensures that a wider window is used for the initial landmark localisation
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face_models[model].detection_success = false;
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], face_models[model], det_parameters[model]);
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// This activates the model
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active_models[model] = true;
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// break out of the loop as the tracker has been reinitialised
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break;
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}
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}
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}
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else
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{
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// The actual facial landmark detection / tracking
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_models[model], det_parameters[model]);
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}
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});
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// Keeping track of FPS
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fps_tracker.AddFrame();
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visualizer.SetImage(captured_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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std::stringstream jsonOutput;
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jsonOutput << "[";
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int jsonFaceId = 0;
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// Go through every model and detect eye gaze, record results and visualise the results
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for(size_t model = 0; model < face_models.size(); ++model)
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{
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// Visualising the results
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if(active_models[model])
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{
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// Estimate head pose and eye gaze
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cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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cv::Point3f gaze_direction0(0, 0, 0); cv::Point3f gaze_direction1(0, 0, 0); cv::Vec2d gaze_angle(0, 0);
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// Detect eye gazes
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if (face_models[model].detection_success && face_model.eye_model)
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{
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GazeAnalysis::EstimateGaze(face_models[model], gaze_direction0, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, true);
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GazeAnalysis::EstimateGaze(face_models[model], gaze_direction1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, false);
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gaze_angle = GazeAnalysis::GetGazeAngle(gaze_direction0, gaze_direction1);
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}
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// Face analysis step
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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.PredictStaticAUsAndComputeFeatures(captured_image, face_models[model].detected_landmarks);
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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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cv::Vec6d head_pose = LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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// Visualize the features
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visualizer.SetObservationFaceAlign(sim_warped_img);
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visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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visualizer.SetObservationLandmarks(face_models[model].detected_landmarks, face_models[model].detection_certainty);
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visualizer.SetObservationPose(head_pose, face_models[model].detection_certainty);
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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);
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visualizer.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
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if(face_models[model].detection_success && face_model.eye_model) {
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if(jsonFaceId > 0){
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jsonOutput << ",";
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}
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jsonFaceId++;
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jsonOutput << "{\"fid\":";
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jsonOutput << model << ", \"confidence\":" << face_models[model].detection_certainty;
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// gaze_angle_x, gaze_angle_y Eye gaze direction in radians in world coordinates averaged for both eyes and converted into more easy to use format than gaze vectors. If a person is looking left-right this will results in the change of gaze_angle_x and, if a person is looking up-down this will result in change of gaze_angle_y, if a person is looking straight ahead both of the angles will be close to 0 (within measurement error)
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jsonOutput << ", \"gaze_angle\": [" << gaze_angle[0] << ", " << gaze_angle[1] << "]";
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jsonOutput << ", head_pos: [" << head_pose[0] << ", " << head_pose[1] << ", " << head_pose[2] << "]";
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jsonOutput << ", head_rot: [" << head_pose[3] << ", " << head_pose[4] << ", " << head_pose[5] << "]";
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jsonOutput << "}";
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}
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// Output features
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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
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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.SetObservationLandmarks(face_models[model].detected_landmarks, face_models[model].GetShape(sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy),
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face_models[model].params_global, face_models[model].params_local, face_models[model].detection_certainty, face_models[model].detection_success);
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open_face_rec.SetObservationPose(pose_estimate);
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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));
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open_face_rec.SetObservationFaceAlign(sim_warped_img);
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open_face_rec.SetObservationFaceID(model);
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open_face_rec.SetObservationTimestamp(sequence_reader.time_stamp);
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open_face_rec.SetObservationFrameNumber(sequence_reader.GetFrameNumber());
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open_face_rec.WriteObservation();
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}
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}
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visualizer.SetFps(fps_tracker.GetFPS());
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jsonOutput << "]";
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std::cout << jsonOutput.str() << std::endl;
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// show visualization and detect key presses
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char character_press = visualizer.ShowObservation();
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// restart the trackers
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if(character_press == 'r')
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{
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for(size_t i=0; i < face_models.size(); ++i)
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{
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face_models[i].Reset();
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active_models[i] = false;
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}
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}
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// quit the application
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else if(character_press=='q')
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{
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return 0;
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}
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// Update the frame count
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frame_count++;
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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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frame_count = 0;
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// Reset the model, for the next video
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for(size_t model=0; model < face_models.size(); ++model)
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{
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face_models[model].Reset();
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active_models[model] = false;
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}
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sequence_number++;
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}
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return 0;
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}
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