open source pkg v1
This commit is contained in:
Vijay Yadev
10
pkg/OpenFace/exe/FaceLandmarkVidMulti/CMakeLists.txt
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pkg/OpenFace/exe/FaceLandmarkVidMulti/CMakeLists.txt
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# Local libraries
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include_directories(${LandmarkDetector_SOURCE_DIR}/include)
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add_executable(FaceLandmarkVidMulti FaceLandmarkVidMulti.cpp)
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target_link_libraries(FaceLandmarkVidMulti LandmarkDetector)
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target_link_libraries(FaceLandmarkVidMulti FaceAnalyser)
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target_link_libraries(FaceLandmarkVidMulti GazeAnalyser)
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target_link_libraries(FaceLandmarkVidMulti Utilities)
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install (TARGETS FaceLandmarkVidMulti DESTINATION bin)
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477
pkg/OpenFace/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp
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pkg/OpenFace/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp
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///////////////////////////////////////////////////////////////////////////////
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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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// 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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#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, 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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}
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return arguments;
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}
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double IOU(cv::Rect_<float> rect1, cv::Rect_<float> rect2)
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{
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double intersection_area = (rect1 & rect2).area();
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double union_area = rect1.area() + rect2.area() - intersection_area;
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return intersection_area / union_area;
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}
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void RemoveOverlapingModels(std::vector<LandmarkDetector::CLNF>& face_models, std::vector<bool>& active_models)
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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 model1 = 0; model1 < active_models.size(); ++model1)
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{
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if (active_models[model1])
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{
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// See if the detections intersect
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cv::Rect_<float> model1_rect = face_models[model1].GetBoundingBox();
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for (int model2 = model1 + 1; model2 < active_models.size(); ++model2)
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{
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if(active_models[model2])
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{
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cv::Rect_<float> model2_rect = face_models[model2].GetBoundingBox();
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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 (IOU(model1_rect, model2_rect) > 0.5)
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{
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active_models[model1] = false;
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face_models[model1].Reset();
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}
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}
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}
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}
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}
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}
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void NonOverlapingDetections(const std::vector<LandmarkDetector::CLNF>& clnf_models, std::vector<cv::Rect_<float> >& 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_<float> 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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// 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 (IOU(model_rect, face_detections[detection]) > 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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std::vector<std::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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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_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::MTCNN_DETECTOR;
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std::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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std::vector<LandmarkDetector::CLNF> face_models;
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std::vector<bool> active_models;
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int num_faces_max = 4;
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LandmarkDetector::CLNF face_model(det_parameters[0].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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// Loading the face detectors
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face_model.face_detector_HAAR.load(det_parameters[0].haar_face_detector_location);
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face_model.haar_face_detector_location = det_parameters[0].haar_face_detector_location;
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face_model.face_detector_MTCNN.Read(det_parameters[0].mtcnn_face_detector_location);
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face_model.mtcnn_face_detector_location = det_parameters[0].mtcnn_face_detector_location;
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// If can't find MTCNN face detector, default to HOG one
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if (det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::MTCNN_DETECTOR && face_model.face_detector_MTCNN.empty())
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{
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std::cout << "INFO: defaulting to HOG-SVM face detector" << std::endl;
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det_parameters[0].curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
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}
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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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std::cout << "WARNING: no eye model found" << std::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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std::cout << "WARNING: no Action Unit models 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(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 rgb_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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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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// For reporting progress
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double reported_completion = 0;
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INFO_STREAM("Starting tracking");
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while (!rgb_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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std::vector<cv::Rect_<float> > 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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std::vector<float> 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 if (det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::HAAR_DETECTOR)
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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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else
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{
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std::vector<float> confidences;
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LandmarkDetector::DetectFacesMTCNN(face_detections, rgb_image, face_models[0].face_detector_MTCNN, confidences);
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}
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}
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// Keep only non overlapping detections (so as not to start tracking where the face is already tracked)
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NonOverlapingDetections(face_models, face_detections);
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std::vector<bool> face_detections_used(face_detections.size(), false);
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// Go through every model and update the tracking
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for (unsigned int model = 0; model < face_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
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if (!face_detections_used[detection_ind])
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{
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face_detections_used[detection_ind] = true;
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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(rgb_image, face_detections[detection_ind], face_models[model], det_parameters[model], grayscale_image);
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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(rgb_image, face_models[model], det_parameters[model], grayscale_image);
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}
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}
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// Remove models that end up tracking overlapping faces
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// even if initial bounding boxes were not overlapping, they could have ended up converging to the same face
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RemoveOverlapingModels(face_models, active_models);
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// Keeping track of FPS
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fps_tracker.AddFrame();
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visualizer.SetImage(rgb_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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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 and recording 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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|
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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(rgb_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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// 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(LandmarkDetector::GetPose(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.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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// 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
|
||||
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());
|
||||
|
||||
// Record frame
|
||||
open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
|
||||
open_face_rec.WriteObservationTracked();
|
||||
|
||||
// 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;
|
||||
}
|
||||
|
||||
// Reporting progress
|
||||
if (sequence_reader.GetProgress() >= reported_completion / 10.0)
|
||||
{
|
||||
std::cout << reported_completion * 10 << "% ";
|
||||
if (reported_completion == 10)
|
||||
{
|
||||
std::cout << std::endl;
|
||||
}
|
||||
reported_completion = reported_completion + 1;
|
||||
}
|
||||
|
||||
// Update the frame count
|
||||
frame_count++;
|
||||
|
||||
// Grabbing the next frame in the sequence
|
||||
rgb_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;
|
||||
}
|
||||
|
||||
INFO_STREAM("Closing output recorder");
|
||||
open_face_rec.Close();
|
||||
INFO_STREAM("Closing input reader");
|
||||
sequence_reader.Close();
|
||||
INFO_STREAM("Closed successfully");
|
||||
|
||||
sequence_number++;
|
||||
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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Reference in New Issue
Block a user