sustaining_gazes/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp

///////////////////////////////////////////////////////////////////////////////
// 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 = 15;

	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);

	if (!face_model.eye_model)
	{
		cout << "WARNING: no eye model found" << endl;
	}

	if (face_analyser.GetAUClassNames().size() == 0 && face_analyser.GetAUClassNames().size() == 0)
	{
		cout << "WARNING: no Action Unit models found" << endl;
	}

	// 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);
      // for some reason not accepted as cli parameter, as we don't need it: disable it anyway
    recording_params.setOutputAUs(false);
    recording_params.setOutputHOG(false);
    recording_params.setOutputAlignedFaces(false);
    recording_params.setOutputTracked(false);
		if (!face_model.eye_model)
		{
			recording_params.setOutputGaze(false);
    }


		Utilities::RecorderOpenFace open_face_rec(sequence_reader.name, recording_params, arguments);


		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);

      std::stringstream jsonOutput;
      jsonOutput << "[";
      int jsonFaceId = 0;
			// 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);
					}

          cv::Vec6d head_pose = LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);

					// 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(head_pose, 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);
					visualizer.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());

          if(face_models[model].detection_success && face_model.eye_model) {
              if(jsonFaceId > 0){
                jsonOutput << ",";
              }
              jsonFaceId++;
            		jsonOutput << "{\"fid\":";
            		jsonOutput << model << ", \"confidence\":" << face_models[model].detection_certainty;
                // 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)
                jsonOutput << ", \"gaze_angle\": [" << gaze_angle[0] << ", " << gaze_angle[1] << "]";
                jsonOutput << ", head_pos: [" << head_pose[0] << ", " << head_pose[1] << ", " << head_pose[2] << "]";
                jsonOutput << ", head_rot: [" << head_pose[3] << ", " << head_pose[4] << ", " << head_pose[5] << "]";
                jsonOutput << "}";
          }

					// 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());

      jsonOutput << "]";
      std::cout << jsonOutput.str() << std::endl;

			// 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;
}