313 lines
11 KiB
C++
313 lines
11 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 2.0: Facial Behavior Analysis Toolkit
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// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
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// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
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//
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// Convolutional experts constrained local model for facial landmark detection.
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// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
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// in Computer Vision and Pattern Recognition Workshops, 2017.
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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-specific 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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///////////////////////////////////////////////////////////////////////////////
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// FaceTrackingVid.cpp : Defines the entry point for the console application for tracking faces in videos.
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// Libraries for landmark detection (includes CLNF and CLM modules)
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#include "LandmarkCoreIncludes.h"
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#include "GazeEstimation.h"
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#include <FaceAnalyser.h>
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#include <SequenceCapture.h>
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#include <Visualizer.h>
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#include <VisualizationUtils.h>
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#include <RecorderOpenFace.h>
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#include <RecorderOpenFaceParameters.h>
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#include <iostream>
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#include <fstream>
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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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std::vector<std::string> get_arguments(int argc, std::string *out_dir, char **argv)
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{
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std::vector<std::string> arguments;
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for (int i = 0; i < argc; ++i)
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{
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arguments.push_back(std::string(argv[i]));
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if (std::string(argv[i]).compare("-out_dir") == 0)
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{
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*out_dir = std::string(argv[i+1]);
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}
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}
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return arguments;
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}
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int main(int argc, char **argv)
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{
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std::string out_dir = ".";
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std::vector<std::string> arguments = get_arguments(argc, &out_dir, argv);
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std::cout<< "out_dir:" << out_dir <<std::endl;
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// no arguments: output usage
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if (arguments.size() == 1)
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{
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std::cout << "For command line arguments see:" << std::endl;
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std::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_parameters(arguments);
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// The modules that are being used for tracking
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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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if (!face_model.loaded_successfully)
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{
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std::cout << "ERROR: Could not load the landmark detector" << std::endl;
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return 1;
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}
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if (!face_model.eye_model)
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{
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std::cout << "WARNING: no eye model found" << std::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(true, false, false, false);
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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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std::string ext = ".mp4";
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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 rgb_image = sequence_reader.GetNextFrame();
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INFO_STREAM("Starting tracking");
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std::ofstream results;
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std::ofstream confidence;
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std::string path = sequence_reader.name;
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std::string base_filename = path.substr(path.find_last_of("/\\") + 1);
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base_filename = base_filename.replace(base_filename.find(ext),sizeof(ext)-1,"");
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results.open(out_dir + '/' + base_filename + "_landmark_output.csv");
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confidence.open(out_dir + '/' + base_filename + "_landmark_likelihoods.csv");
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int lx = 0;
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int ly = 0;
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for(lx = 0; lx < 2; lx++){
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for(ly = 0; ly < 68; ly++){
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if (lx == 0){
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results << "l" << ly << "_x,";
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confidence << "c" << ly <<",";
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}
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if (lx == 1){
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results << "l" << ly << "_y,";
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}
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}
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}
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results << "pose_Tx,pose_Ty,pose_Tz,pose_Rx,pose_Ry,pose_Rz" ;
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results << std::endl;
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confidence << std::endl;
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int counter = 0;
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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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while (!rgb_image.empty()) // this is not a for loop as we might also be reading from a webcam
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{
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// Added lines
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// Utilities::RecorderOpenFaceParameters recording_params(arguments, false, false,
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// sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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// std::string stem = sequence_reader.name;
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// stem = stem.replace(stem.find(ext),sizeof(ext)-1,"_");
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// Utilities::RecorderOpenFace open_face_rec(stem+std::to_string(counter)+ext, recording_params, arguments);
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// Reading the images
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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(rgb_image, face_model, det_parameters, grayscale_image);
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// Gaze tracking, absolute gaze direction
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cv::Point3f gazeDirection0(0, 0, -1);
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cv::Point3f gazeDirection1(0, 0, -1);
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// If tracking succeeded and we have an eye model, estimate gaze
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if (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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}
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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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// cv::Mat sim_warped_img;
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// face_analyser.PredictStaticAUsAndComputeFeatures(rgb_image, face_model.detected_landmarks);
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// face_analyser.GetLatestAlignedFace(sim_warped_img);
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// Keeping track of FPS
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fps_tracker.AddFrame();
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// Displaying the tracking visualizations
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// std::cout<< "setting observation landmarks"<<std::endl;
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visualizer.SetImage(rgb_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, face_model.GetVisibilities());
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visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
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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);
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visualizer.SetFps(fps_tracker.GetFPS());
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// std::cout << "openfacerec set obs landmarks"<<std::endl;
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// std::cout<< fps_tracker.GetFPS() <<std::endl;
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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, face_model.detection_success);
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// open_face_rec.SetObservationPose(pose_estimate);
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// open_face_rec.SetObservationFaceAlign(sim_warped_img);
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int i;
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for (i=0;i< 136;i++){
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results << face_model.detected_landmarks[0][i] << ",";
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}
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for (i=0;i< 6;i++){
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if (i==5){
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results << pose_estimate[i];
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}
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else{
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results << pose_estimate[i] << ",";
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}
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}
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results <<std::endl;
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for(i=0;i<68;i++){
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if (i==67){
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confidence << face_model.landmark_likelihoods[0][i];
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}
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else{
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confidence << face_model.landmark_likelihoods[0][i] << ",";
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}
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}
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confidence <<std::endl;
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// detect key presses (due to pecularities of OpenCV, you can get it when displaying images)
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//char character_press = visualizer.ShowObservation();
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char character_press = 't';
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// restart the tracker
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if (character_press == 'r')
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{
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face_model.Reset();
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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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// added lines
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// open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
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// open_face_rec.WriteObservationTracked();
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// open_face_rec.Close();
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// Grabbing the next frame in the sequence
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rgb_image = sequence_reader.GetNextFrame();
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counter++;
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}
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// Reset the model, for the next video
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face_model.Reset();
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sequence_reader.Close();
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sequence_number++;
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results.close();
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}
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return 0;
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}
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