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This commit is contained in:
Vijay Yadev
2020-08-04 19:12:31 -04:00
parent bef213dba9
commit c389fc2c47
3708 changed files with 1624220 additions and 1 deletions
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72
pkg/OpenFace/lib/local/CppInerop/AssemblyInfo.cpp Normal file
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///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
using namespace System;
using namespace System::Reflection;
using namespace System::Runtime::CompilerServices;
using namespace System::Runtime::InteropServices;
using namespace System::Security::Permissions;
//
// General Information about an assembly is controlled through the following
// set of attributes. Change these attribute values to modify the information
// associated with an assembly.
//
[assembly:AssemblyTitleAttribute("CppInterop")];
[assembly:AssemblyDescriptionAttribute("")];
[assembly:AssemblyConfigurationAttribute("")];
[assembly:AssemblyCompanyAttribute("")];
[assembly:AssemblyProductAttribute("CppInterop")];
[assembly:AssemblyCopyrightAttribute("Copyright (c) 2016")];
[assembly:AssemblyTrademarkAttribute("")];
[assembly:AssemblyCultureAttribute("")];
//
// Version information for an assembly consists of the following four values:
//
// Major Version
// Minor Version
// Build Number
// Revision
//
// You can specify all the value or you can default the Revision and Build Numbers
// by using the '*' as shown below:
[assembly:AssemblyVersionAttribute("1.0.*")];
[assembly:ComVisible(false)];
[assembly:CLSCompliantAttribute(true)];
[assembly:SecurityPermission(SecurityAction::RequestMinimum, UnmanagedCode = true)];

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pkg/OpenFace/lib/local/CppInerop/CppInerop.vcxproj Normal file
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pkg/OpenFace/lib/local/CppInerop/CppInerop.vcxproj.filters Normal file
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pkg/OpenFace/lib/local/CppInerop/CppInterop.cpp Normal file
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///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
// This is the main DLL file.
#include "GazeAnalyserInterop.h"
#include "LandmarkDetectorInterop.h"
#include "FaceAnalyserInterop.h"
#include "OpenCVWrappers.h"
#include "ImageReader.h"
#include "FaceDetectorInterop.h"
#include "RecorderInterop.h"
#include "VisualizerInterop.h"
#include "SequenceReader.h"

276
pkg/OpenFace/lib/local/CppInerop/FaceAnalyserInterop.h Normal file
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///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
// FaceAnalyser_Interop.h
#ifndef FACE_ANALYSER_INTEROP_H
#define FACE_ANALYSER_INTEROP_H
#pragma once
// Include all the unmanaged things we need.
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
#pragma unmanaged
#include <opencv2/opencv.hpp>
#include <OpenCVWrappers.h>
#include <Face_utils.h>
#include <FaceAnalyser.h>
#include <VisualizationUtils.h>
#pragma managed
using namespace System::Collections::Generic;
namespace FaceAnalyser_Interop {
public ref class FaceAnalyserManaged
{
private:
FaceAnalysis::FaceAnalyser* face_analyser;
// The actual descriptors (for visualisation and output)
cv::Mat_<float>* hog_features;
cv::Mat* aligned_face;
int* num_rows;
int* num_cols;
public:
FaceAnalyserManaged(System::String^ root, bool dynamic, int output_width, bool mask_aligned)
{
std::string root_std = msclr::interop::marshal_as<std::string>(root);
FaceAnalysis::FaceAnalyserParameters params(root_std);
if (!dynamic)
{
params.OptimizeForImages();
}
params.setAlignedOutput(output_width, -1.0, mask_aligned);
face_analyser = new FaceAnalysis::FaceAnalyser(params);
hog_features = new cv::Mat_<float>();
aligned_face = new cv::Mat();
num_rows = new int;
num_cols = new int;
}
void PostProcessOutputFile(System::String^ file)
{
face_analyser->PostprocessOutputFile(msclr::interop::marshal_as<std::string>(file));
}
void AddNextFrame(OpenCVWrappers::RawImage^ frame, List<System::Tuple<float, float>^>^ landmarks, bool success, bool online) {
// Construct an OpenCV matric from the landmarks
cv::Mat_<float> landmarks_mat(landmarks->Count * 2, 1, 0.0);
for (int i = 0; i < landmarks->Count; ++i)
{
landmarks_mat.at<float>(i, 0) = landmarks[i]->Item1;
landmarks_mat.at<float>(i + landmarks->Count, 0) = landmarks[i]->Item2;
}
//(captured_image, face_model.detected_landmarks, face_model.detection_success, sequence_reader.time_stamp, sequence_reader.IsWebcam());
face_analyser->AddNextFrame(frame->Mat, landmarks_mat, success, 0, online);
cv::Mat_<double> hog_d;
face_analyser->GetLatestHOG(hog_d, *num_rows, *num_cols);
hog_d.convertTo(*hog_features, CV_32F);
face_analyser->GetLatestAlignedFace(*aligned_face);
}
// Predicting AUs from a single image
System::Tuple<Dictionary<System::String^, double>^, Dictionary<System::String^, double>^>^
PredictStaticAUsAndComputeFeatures(OpenCVWrappers::RawImage^ frame, List<System::Tuple<float, float>^>^ landmarks)
{
// Construct an OpenCV matric from the landmarks
cv::Mat_<float> landmarks_mat(landmarks->Count * 2, 1, 0.0);
for (int i = 0; i < landmarks->Count; ++i)
{
landmarks_mat.at<float>(i, 0) = landmarks[i]->Item1;
landmarks_mat.at<float>(i + landmarks->Count, 0) = landmarks[i]->Item2;
}
face_analyser->PredictStaticAUsAndComputeFeatures(frame->Mat, landmarks_mat);
// Set the computed appearance features
cv::Mat_<double> hog_tmp;
face_analyser->GetLatestHOG(hog_tmp, *num_rows, *num_cols);
hog_tmp.convertTo(*hog_features, CV_32F);
face_analyser->GetLatestAlignedFace(*aligned_face);
// Set the computed AUs
auto AU_predictions_intensity = face_analyser->GetCurrentAUsReg();
auto AU_predictions_occurence = face_analyser->GetCurrentAUsClass();
auto au_intensities = gcnew Dictionary<System::String^, double>();
auto au_occurences = gcnew Dictionary<System::String^, double>();
for (auto p : AU_predictions_intensity)
{
au_intensities->Add(gcnew System::String(p.first.c_str()), p.second);
}
for (auto p : AU_predictions_occurence)
{
au_occurences->Add(gcnew System::String(p.first.c_str()), p.second);
}
return gcnew System::Tuple<Dictionary<System::String^, double>^, Dictionary<System::String^, double>^>(au_intensities, au_occurences);
}
List<System::String^>^ GetClassActionUnitsNames()
{
auto names = face_analyser->GetAUClassNames();
auto names_ret = gcnew List<System::String^>();
for(std::string name : names)
{
names_ret->Add(gcnew System::String(name.c_str()));
}
return names_ret;
}
List<System::String^>^ GetRegActionUnitsNames()
{
auto names = face_analyser->GetAURegNames();
auto names_ret = gcnew List<System::String^>();
for(std::string name : names)
{
names_ret->Add(gcnew System::String(name.c_str()));
}
return names_ret;
}
Dictionary<System::String^, double>^ GetCurrentAUsClass()
{
auto classes = face_analyser->GetCurrentAUsClass();
auto au_classes = gcnew Dictionary<System::String^, double>();
for(auto p: classes)
{
au_classes->Add(gcnew System::String(p.first.c_str()), p.second);
}
return au_classes;
}
Dictionary<System::String^, double>^ GetCurrentAUsReg()
{
auto preds = face_analyser->GetCurrentAUsReg();
auto au_preds = gcnew Dictionary<System::String^, double>();
for(auto p: preds)
{
au_preds->Add(gcnew System::String(p.first.c_str()), p.second);
}
return au_preds;
}
OpenCVWrappers::RawImage^ GetLatestAlignedFace() {
OpenCVWrappers::RawImage^ face_aligned_image = gcnew OpenCVWrappers::RawImage(*aligned_face);
return face_aligned_image;
}
OpenCVWrappers::RawImage^ GetLatestHOGFeature() {
OpenCVWrappers::RawImage^ HOG_feature = gcnew OpenCVWrappers::RawImage(*hog_features);
return HOG_feature;
}
// As the number of HOG rows and columns might not be known in advance, have methods for querying them
int GetHOGRows()
{
return *num_rows;
}
int GetHOGCols()
{
return *num_cols;
}
// The number of channels is always the same
int GetHOGChannels()
{
return 31;
}
void Reset()
{
face_analyser->Reset();
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!FaceAnalyserManaged()
{
delete hog_features;
delete aligned_face;
delete num_cols;
delete num_rows;
delete face_analyser;
}
// Destructor. Called on explicit Dispose() only.
~FaceAnalyserManaged()
{
this->!FaceAnalyserManaged();
}
};
}
#endif // FACE_ANALYSER_INTEROP_H

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Tadas Baltrusaitis,
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#ifndef FACE_DETECTOR_INTEROP_H
#define FACE_DETECTOR_INTEROP_H
#pragma once
// Include all the unmanaged things we need.
#pragma unmanaged
#undef _M_CEE
#include <opencv2/opencv.hpp>
#include <OpenCVWrappers.h>
#include <LandmarkCoreIncludes.h>
#define _M_CEE
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
using namespace System::Collections::Generic;
namespace FaceDetectorInterop {
public ref class FaceDetector
{
private:
// Where the face detectors are stored
dlib::frontal_face_detector* face_detector_hog;
LandmarkDetector::FaceDetectorMTCNN* face_detector_mtcnn;
cv::CascadeClassifier* face_detector_haar;
public:
// The constructor initializes the dlib face detector
FaceDetector(System::String^ haar_location, System::String^ mtcnn_location)
{
// Initialize all of the detectors (TODO should be done on need only basis)
face_detector_hog = new dlib::frontal_face_detector(dlib::get_frontal_face_detector());
face_detector_mtcnn = new LandmarkDetector::FaceDetectorMTCNN(msclr::interop::marshal_as<std::string>(mtcnn_location));
face_detector_haar = new cv::CascadeClassifier(msclr::interop::marshal_as<std::string>(haar_location));
}
// Face detection using HOG-SVM classifier
void DetectFacesHOG(List<System::Windows::Rect>^ o_regions, OpenCVWrappers::RawImage^ intensity, List<float>^ o_confidences)
{
std::vector<cv::Rect_<float> > regions_ocv;
std::vector<float> confidences_std;
::LandmarkDetector::DetectFacesHOG(regions_ocv, intensity->Mat, *face_detector_hog, confidences_std);
o_regions->Clear();
o_confidences->Clear();
for (size_t i = 0; i < regions_ocv.size(); ++i)
{
o_regions->Add(System::Windows::Rect(regions_ocv[i].x, regions_ocv[i].y, regions_ocv[i].width, regions_ocv[i].height));
o_confidences->Add(confidences_std[i]);
}
}
// Face detection using HOG-SVM classifier
void DetectFacesHaar(List<System::Windows::Rect>^ o_regions, OpenCVWrappers::RawImage^ intensity, List<float>^ o_confidences)
{
std::vector<cv::Rect_<float> > regions_ocv;
::LandmarkDetector::DetectFaces(regions_ocv, intensity->Mat, *face_detector_haar);
o_regions->Clear();
o_confidences->Clear();
for(size_t i = 0; i < regions_ocv.size(); ++i)
{
o_regions->Add(System::Windows::Rect(regions_ocv[i].x, regions_ocv[i].y, regions_ocv[i].width, regions_ocv[i].height));
// As Haar does not provide confidence, create a fake value
o_confidences->Add(1);
}
}
bool IsMTCNNLoaded()
{
return !face_detector_mtcnn->empty();
}
// Face detection using MTCNN face detector
void DetectFacesMTCNN(List<System::Windows::Rect>^ o_regions, OpenCVWrappers::RawImage^ rgb_image, List<float>^ o_confidences)
{
std::vector<cv::Rect_<float> > regions_ocv;
std::vector<float> confidences_std;
::LandmarkDetector::DetectFacesMTCNN(regions_ocv, rgb_image->Mat, *face_detector_mtcnn, confidences_std);
o_regions->Clear();
o_confidences->Clear();
for (size_t i = 0; i < regions_ocv.size(); ++i)
{
o_regions->Add(System::Windows::Rect(regions_ocv[i].x, regions_ocv[i].y, regions_ocv[i].width, regions_ocv[i].height));
o_confidences->Add(confidences_std[i]);
}
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!FaceDetector()
{
delete face_detector_hog;
delete face_detector_mtcnn;
delete face_detector_haar;
}
// Destructor. Called on explicit Dispose() only.
~FaceDetector()
{
this->!FaceDetector();
}
};
}
#endif // FACE_DETECTOR_INTEROP_H

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#pragma once
///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#ifndef GAZE_ANALYSER_INTEROP_H
#define GAZE_ANALYSER_INTEROP_H
#pragma once
// Include all the unmanaged things we need.
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
#pragma unmanaged
#include <opencv2/opencv.hpp>
#include <OpenCVWrappers.h>
#include <LandmarkDetectorInterop.h>
#include <GazeEstimation.h>
#pragma managed
namespace GazeAnalyser_Interop {
public ref class GazeAnalyserManaged
{
private:
// Variable storing gaze for recording
// Absolute gaze direction
cv::Point3f* gazeDirection0;
cv::Point3f* gazeDirection1;
cv::Vec2f* gazeAngle;
cv::Point3f* pupil_left;
cv::Point3f* pupil_right;
public:
GazeAnalyserManaged()
{
gazeDirection0 = new cv::Point3f();
gazeDirection1 = new cv::Point3f();
gazeAngle = new cv::Vec2f();
pupil_left = new cv::Point3f();
pupil_right = new cv::Point3f();
}
void AddNextFrame(CppInterop::LandmarkDetector::CLNF^ clnf, bool success, float fx, float fy, float cx, float cy) {
// After the AUs have been detected do some gaze estimation as well
GazeAnalysis::EstimateGaze(*clnf->getCLNF(), *gazeDirection0, fx, fy, cx, cy, true);
GazeAnalysis::EstimateGaze(*clnf->getCLNF(), *gazeDirection1, fx, fy, cx, cy, false);
// Estimate the gaze angle WRT to head pose here
System::Collections::Generic::List<float>^ pose_list = gcnew System::Collections::Generic::List<float>();
*gazeAngle = GazeAnalysis::GetGazeAngle(*gazeDirection0, *gazeDirection1);
// Grab pupil locations
int part_left = -1;
int part_right = -1;
for (size_t i = 0; i < clnf->getCLNF()->hierarchical_models.size(); ++i)
{
if (clnf->getCLNF()->hierarchical_model_names[i].compare("left_eye_28") == 0)
{
part_left = i;
}
if (clnf->getCLNF()->hierarchical_model_names[i].compare("right_eye_28") == 0)
{
part_right = i;
}
}
cv::Mat_<float> eyeLdmks3d_left = clnf->getCLNF()->hierarchical_models[part_left].GetShape(fx, fy, cx, cy);
cv::Point3f pupil_left_h = GazeAnalysis::GetPupilPosition(eyeLdmks3d_left);
pupil_left->x = pupil_left_h.x; pupil_left->y = pupil_left_h.y; pupil_left->z = pupil_left_h.z;
cv::Mat_<float> eyeLdmks3d_right = clnf->getCLNF()->hierarchical_models[part_right].GetShape(fx, fy, cx, cy);
cv::Point3f pupil_right_h = GazeAnalysis::GetPupilPosition(eyeLdmks3d_right);
pupil_right->x = pupil_right_h.x; pupil_right->y = pupil_right_h.y; pupil_right->z = pupil_right_h.z;
}
System::Tuple<System::Tuple<float, float, float>^, System::Tuple<float, float, float>^>^ GetGazeCamera()
{
auto gaze0 = gcnew System::Tuple<float, float, float>(gazeDirection0->x, gazeDirection0->y, gazeDirection0->z);
auto gaze1 = gcnew System::Tuple<float, float, float>(gazeDirection1->x, gazeDirection1->y, gazeDirection1->z);
return gcnew System::Tuple<System::Tuple<float, float, float>^, System::Tuple<float, float, float>^>(gaze0, gaze1);
}
System::Tuple<float, float>^ GetGazeAngle()
{
auto gaze_angle = gcnew System::Tuple<float, float>((*gazeAngle)[0], (*gazeAngle)[1]);
return gaze_angle;
}
System::Collections::Generic::List<System::Tuple<System::Windows::Point, System::Windows::Point>^>^ CalculateGazeLines(float fx, float fy, float cx, float cy)
{
std::vector<cv::Point3f> points_left;
points_left.push_back(cv::Point3f(*pupil_left));
points_left.push_back(cv::Point3f(*pupil_left + *gazeDirection0 * 40.0));
std::vector<cv::Point3f> points_right;
points_right.push_back(cv::Point3f(*pupil_right));
points_right.push_back(cv::Point3f(*pupil_right + *gazeDirection1 * 40.0));
// Perform manual projection of points
std::vector<cv::Point2f> imagePoints_left;
for (size_t i = 0; i < points_left.size(); ++i)
{
float x = points_left[i].x * fx / points_left[i].z + cx;
float y = points_left[i].y * fy / points_left[i].z + cy;
imagePoints_left.push_back(cv::Point2f(x, y));
}
std::vector<cv::Point2f> imagePoints_right;
for (size_t i = 0; i < points_right.size(); ++i)
{
float x = points_right[i].x * fx / points_right[i].z + cx;
float y = points_right[i].y * fy / points_right[i].z + cy;
imagePoints_right.push_back(cv::Point2f(x, y));
}
auto lines = gcnew System::Collections::Generic::List<System::Tuple<System::Windows::Point, System::Windows::Point>^>();
lines->Add(gcnew System::Tuple<System::Windows::Point, System::Windows::Point>(System::Windows::Point(imagePoints_left[0].x, imagePoints_left[0].y), System::Windows::Point(imagePoints_left[1].x, imagePoints_left[1].y)));
lines->Add(gcnew System::Tuple<System::Windows::Point, System::Windows::Point>(System::Windows::Point(imagePoints_right[0].x, imagePoints_right[0].y), System::Windows::Point(imagePoints_right[1].x, imagePoints_right[1].y)));
return lines;
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!GazeAnalyserManaged()
{
delete gazeDirection0;
delete gazeDirection1;
delete gazeAngle;
delete pupil_left;
delete pupil_right;
}
// Destructor. Called on explicit Dispose() only.
~GazeAnalyserManaged()
{
this->!GazeAnalyserManaged();
}
};
}
#endif // GAZE_ANALYSER_INTEROP_H

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Tadas Baltrusaitis, 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#pragma once
#pragma unmanaged
// Include all the unmanaged things we need.
#include <opencv2/opencv.hpp>
#include <vector>
#include <set>
#include <OpenCVWrappers.h>
#include <ImageCapture.h>
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
namespace UtilitiesOF {
public ref class ReadingFailedException : System::Exception
{
public:
ReadingFailedException(System::String^ message) : Exception(message) {}
};
public ref class ImageReader
{
private:
// OpenCV based video capture for reading from files
Utilities::ImageCapture* m_image_capture;
OpenCVWrappers::RawImage^ m_rgb_frame;
OpenCVWrappers::RawImage^ m_gray_frame;
bool* m_is_opened;
public:
// Can provide a directory, or a list of files
ImageReader(System::String^ image_directory, float fx, float fy, float cx, float cy)
{
m_image_capture = new Utilities::ImageCapture();
m_is_opened = new bool;
std::string image_dir_std = msclr::interop::marshal_as<std::string>(image_directory);
*m_is_opened = m_image_capture->OpenDirectory(image_dir_std, "", fx, fy, cx, cy);
if (!*m_is_opened)
{
throw gcnew ReadingFailedException("Failed to open a directory or an image");
}
}
// Can provide a directory, or a list of files
ImageReader(System::Collections::Generic::List<System::String^>^ image_files, float fx, float fy, float cx, float cy)
{
m_image_capture = new Utilities::ImageCapture();
m_is_opened = new bool;
std::vector<std::string> image_files_std;
for (int i = 0; i < image_files->Count; ++i)
{
std::string image_file = msclr::interop::marshal_as<std::string>(image_files[i]);
image_files_std.push_back(image_file);
}
*m_is_opened = m_image_capture->OpenImageFiles(image_files_std, fx, fy, cx, cy);
if (!*m_is_opened)
{
throw gcnew ReadingFailedException("Failed to open a directory or an image");
}
}
OpenCVWrappers::RawImage^ GetNextImage()
{
cv::Mat next_image = m_image_capture->GetNextImage();
m_rgb_frame = gcnew OpenCVWrappers::RawImage(next_image);
if (next_image.empty())
{
*m_is_opened = false;
}
return m_rgb_frame;
}
System::String^ GetName()
{
std::string filename = m_image_capture->name;
return gcnew System::String(filename.c_str());
}
double GetProgress()
{
return m_image_capture->GetProgress();
}
float GetFx()
{
return m_image_capture->fx;
}
float GetFy()
{
return m_image_capture->fy;
}
float GetCx()
{
return m_image_capture->cx;
}
float GetCy()
{
return m_image_capture->cy;
}
bool isOpened()
{
return *m_is_opened;
}
OpenCVWrappers::RawImage^ GetCurrentFrameGray() {
cv::Mat next_gray_image = m_image_capture->GetGrayFrame();
m_gray_frame = gcnew OpenCVWrappers::RawImage(next_gray_image);
return m_gray_frame;
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!ImageReader()
{
delete m_image_capture;
delete m_rgb_frame;
delete m_gray_frame;
delete m_is_opened;
}
// Destructor. Called on explicit Dispose() only.
~ImageReader()
{
this->!ImageReader();
}
};
}

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///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#ifndef LANDMARK_DETECTOR_INTEROP_H
#define LANDMARK_DETECTOR_INTEROP_H
#pragma once
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
#pragma unmanaged
// Include all the unmanaged things we need.
#include <opencv2/opencv.hpp>
#include <OpenCVWrappers.h>
#undef _M_CEE
#include <LandmarkCoreIncludes.h>
#include <Face_utils.h>
#include <FaceAnalyser.h>
#include <VisualizationUtils.h>
#define _M_CEE
using namespace System::Collections::Generic;
#pragma managed
namespace CppInterop {
namespace LandmarkDetector {
public ref class FaceModelParameters
{
public:
::LandmarkDetector::FaceModelParameters* params;
public:
// Initialise the parameters
FaceModelParameters(System::String^ root, bool ceclm, bool clnf, bool clm)
{
std::string root_std = msclr::interop::marshal_as<std::string>(root);
std::vector<std::string> args;
args.push_back(root_std);
std::string model_loc = "model/main_ceclm_general.txt";
if (ceclm)
{
model_loc = "model/main_ceclm_general.txt";
}
else if(clnf)
{
model_loc = "model/main_clnf_general.txt";
}
else if (clm)
{
model_loc = "model/main_clm_general.txt";
}
args.push_back("-mloc");
args.push_back(model_loc);
params = new ::LandmarkDetector::FaceModelParameters(args);
}
// TODO this could have optimize for demo mode (also could appropriately update sigma, reg_factor as well)
void optimiseForVideo()
{
params->window_sizes_small = std::vector<int>(4);
params->window_sizes_init = std::vector<int>(4);
// For fast tracking
params->window_sizes_small[0] = 0;
params->window_sizes_small[1] = 9;
params->window_sizes_small[2] = 7;
params->window_sizes_small[3] = 0;
// Just for initialisation
params->window_sizes_init.at(0) = 11;
params->window_sizes_init.at(1) = 9;
params->window_sizes_init.at(2) = 7;
params->window_sizes_init.at(3) = 5;
// For first frame use the initialisation
params->window_sizes_current = params->window_sizes_init;
params->multi_view = false;
params->num_optimisation_iteration = 5;
params->sigma = 1.5;
params->reg_factor = 25;
params->weight_factor = 0;
// Parameter optimizations for CE-CLM
if (params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CECLM_DETECTOR)
{
params->sigma = 1.5f * params->sigma;
params->reg_factor = 0.9f * params->reg_factor;
}
}
bool IsCECLM()
{
return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CECLM_DETECTOR;
}
bool IsCLNF()
{
return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CLNF_DETECTOR;
}
bool IsCLM()
{
return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CLM_DETECTOR;
}
System::String^ GetMTCNNLocation()
{
return gcnew System::String(params->mtcnn_face_detector_location.c_str());
}
System::String^ GetHaarLocation()
{
return gcnew System::String(params->haar_face_detector_location.c_str());
}
void SetFaceDetector(bool haar, bool hog, bool cnn)
{
if (cnn)
{
params->curr_face_detector = params->MTCNN_DETECTOR;
}
else if (hog)
{
params->curr_face_detector = params->HOG_SVM_DETECTOR;
}
else if (haar)
{
params->curr_face_detector = params->HAAR_DETECTOR;
}
}
void optimiseForImages()
{
params->window_sizes_init = std::vector<int>(4);
params->window_sizes_init[0] = 15;
params->window_sizes_init[1] = 13;
params->window_sizes_init[2] = 11;
params->window_sizes_init[3] = 11;
params->multi_view = true;
params->sigma = 1.25;
params->reg_factor = 35;
params->weight_factor = 2.5;
params->num_optimisation_iteration = 10;
// Parameter optimizations for CE-CLM
if (params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::MTCNN_DETECTOR)
{
params->sigma = 1.5f * params->sigma;
params->reg_factor = 0.9f * params->reg_factor;
}
}
::LandmarkDetector::FaceModelParameters* getParams() {
return params;
}
!FaceModelParameters()
{
delete params;
}
~FaceModelParameters()
{
this->!FaceModelParameters();
}
};
public ref class CLNF
{
public:
// A pointer to the CLNF landmark detector
::LandmarkDetector::CLNF* clnf;
public:
// Wrapper functions for the relevant CLNF functionality
CLNF() : clnf(new ::LandmarkDetector::CLNF()) { }
CLNF(FaceModelParameters^ params)
{
clnf = new ::LandmarkDetector::CLNF(params->getParams()->model_location);
}
!CLNF()
{
delete clnf;
}
~CLNF()
{
this->!CLNF();
}
bool isLoaded()
{
return clnf->loaded_successfully;
}
::LandmarkDetector::CLNF* getCLNF() {
return clnf;
}
void Reset() {
clnf->Reset();
}
void Reset(double x, double y) {
clnf->Reset(x, y);
}
bool HasEyeModel() {
return clnf->eye_model;
}
double GetConfidence()
{
return clnf->detection_certainty;
}
bool DetectLandmarksInVideo(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
return ::LandmarkDetector::DetectLandmarksInVideo(rgb_image->Mat, *clnf, *modelParams->getParams(), gray_image->Mat);
}
bool DetectLandmarksInVideo(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams) {
return ::LandmarkDetector::DetectLandmarksInVideo(rgb_image->Mat, *clnf, *modelParams->getParams(), cv::Mat());
}
bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, *clnf, *modelParams->getParams(), gray_image->Mat);
}
bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams) {
return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, *clnf, *modelParams->getParams(), cv::Mat());
}
bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, Rect^ bounding_box, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
cv::Rect_<float> bbox(bounding_box->Left, bounding_box->Top, bounding_box->Width, bounding_box->Height);
return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, bbox, *clnf, *modelParams->getParams(), gray_image->Mat);
}
bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, Rect^ bounding_box, FaceModelParameters^ modelParams) {
cv::Rect_<float> bbox(bounding_box->Left, bounding_box->Top, bounding_box->Width, bounding_box->Height);
return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, bbox, *clnf, *modelParams->getParams(), cv::Mat());
}
void GetPoseWRTCamera(List<float>^ pose, float fx, float fy, float cx, float cy) {
auto pose_vec = ::LandmarkDetector::GetPoseWRTCamera(*clnf, fx, fy, cx, cy);
pose->Clear();
for(int i = 0; i < 6; ++i)
{
pose->Add(pose_vec[i]);
}
}
void GetPose(List<float>^ pose, float fx, float fy, float cx, float cy) {
auto pose_vec = ::LandmarkDetector::GetPose(*clnf, fx, fy, cx, cy);
pose->Clear();
for(int i = 0; i < 6; ++i)
{
pose->Add(pose_vec[i]);
}
}
// Get the mask of which landmarks are currently visible (not self-occluded)
List<bool>^ GetVisibilities()
{
cv::Mat_<int> vis = clnf->GetVisibilities();
List<bool>^ visibilities = gcnew List<bool>();
for (auto vis_it = vis.begin(); vis_it != vis.end(); vis_it++)
{
visibilities->Add(*vis_it != 0);
}
return visibilities;
}
List<System::Tuple<float,float>^>^ CalculateVisibleLandmarks() {
std::vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateVisibleLandmarks(*clnf);
auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
for(cv::Point2f p : vecLandmarks) {
landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
}
return landmarks;
}
List<System::Tuple<float, float>^>^ CalculateAllLandmarks() {
std::vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateAllLandmarks(*clnf);
auto landmarks = gcnew List<System::Tuple<float, float>^>();
for (cv::Point2f p : vecLandmarks) {
landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
}
return landmarks;
}
List<System::Tuple<float, float>^>^ CalculateAllEyeLandmarks() {
std::vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateAllEyeLandmarks(*clnf);
auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
for (cv::Point2f p : vecLandmarks) {
landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
}
return landmarks;
}
List<System::Tuple<float, float, float>^>^ CalculateAllEyeLandmarks3D(float fx, float fy, float cx, float cy) {
std::vector<cv::Point3f> vecLandmarks = ::LandmarkDetector::Calculate3DEyeLandmarks(*clnf, fx, fy, cx, cy);
auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float, float>^>();
for (cv::Point3f p : vecLandmarks) {
landmarks->Add(gcnew System::Tuple<float, float, float>(p.x, p.y, p.z));
}
return landmarks;
}
List<System::Tuple<float, float>^>^ CalculateVisibleEyeLandmarks() {
std::vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateVisibleEyeLandmarks(*clnf);
auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
for (cv::Point2f p : vecLandmarks) {
landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
}
return landmarks;
}
List<System::Tuple<float, float, float>^>^ Calculate3DLandmarks(float fx, float fy, float cx, float cy) {
cv::Mat_<float> shape3D = clnf->GetShape(fx, fy, cx, cy);
auto landmarks_3D = gcnew List<System::Tuple<float, float, float>^>();
for(int i = 0; i < shape3D.cols; ++i)
{
landmarks_3D->Add(gcnew System::Tuple<float, float, float>(shape3D.at<float>(0, i), shape3D.at<float>(1, i), shape3D.at<float>(2, i)));
}
return landmarks_3D;
}
List<System::Tuple<System::Windows::Point, System::Windows::Point>^>^ CalculateBox(float fx, float fy, float cx, float cy) {
cv::Vec6f pose = ::LandmarkDetector::GetPose(*clnf, fx,fy, cx, cy);
std::vector<std::pair<cv::Point2f, cv::Point2f>> vecLines = ::Utilities::CalculateBox(pose, fx, fy, cx, cy);
auto lines = gcnew List<System::Tuple<System::Windows::Point,System::Windows::Point>^>();
for(std::pair<cv::Point2f, cv::Point2f> line : vecLines) {
lines->Add(gcnew System::Tuple<System::Windows::Point, System::Windows::Point>(System::Windows::Point(line.first.x, line.first.y), System::Windows::Point(line.second.x, line.second.y)));
}
return lines;
}
int GetNumPoints()
{
return clnf->pdm.NumberOfPoints();
}
int GetNumModes()
{
return clnf->pdm.NumberOfModes();
}
// Getting the non-rigid shape parameters describing the facial expression
List<float>^ GetNonRigidParams()
{
auto non_rigid_params = gcnew List<float>();
for (int i = 0; i < clnf->params_local.rows; ++i)
{
non_rigid_params->Add(clnf->params_local.at<float>(i));
}
return non_rigid_params;
}
// Getting the rigid shape parameters describing face scale rotation and translation (scale,rotx,roty,rotz,tx,ty)
List<float>^ GetRigidParams()
{
auto rigid_params = gcnew List<float>();
for (size_t i = 0; i < 6; ++i)
{
rigid_params->Add(clnf->params_global[i]);
}
return rigid_params;
}
// Rigid params followed by non-rigid ones
List<float>^ GetParams()
{
auto all_params = GetRigidParams();
all_params->AddRange(GetNonRigidParams());
return all_params;
}
};
}
}
#endif // LANDMARK_DETECTOR_INTEROP_H

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///////////////////////////////////////////////////////////////////////////////
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
// OpenCVWrappers.h
#pragma once
#pragma managed
#include <msclr\marshal_cppstd.h>
#pragma unmanaged
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#pragma managed
#pragma make_public(cv::Mat)
using namespace System::Windows;
using namespace System::Windows::Threading;
using namespace System::Windows::Media;
using namespace System::Windows::Media::Imaging;
namespace OpenCVWrappers {
public ref class RawImage : System::IDisposable
{
private:
cv::Mat* mat;
public:
static int PixelFormatToType(PixelFormat fmt)
{
if (fmt == PixelFormats::Gray8)
return CV_8UC1;
else if (fmt == PixelFormats::Bgr24)
return CV_8UC3;
else if (fmt == PixelFormats::Bgra32)
return CV_8UC4;
else if (fmt == PixelFormats::Gray32Float)
return CV_32FC1;
else
throw gcnew System::Exception("Unsupported pixel format");
}
static PixelFormat TypeToPixelFormat(int type)
{
switch (type) {
case CV_8UC1:
return PixelFormats::Gray8;
case CV_8UC3:
return PixelFormats::Bgr24;
case CV_8UC4:
return PixelFormats::Bgra32;
case CV_32FC1:
return PixelFormats::Gray32Float;
default:
throw gcnew System::Exception("Unsupported image type");
}
}
RawImage(const cv::Mat& m)
{
mat = new cv::Mat(m.clone());
}
// Constructing a raw image from bitmap
RawImage(System::Drawing::Bitmap^ bitmap)
{
auto fmt = bitmap->PixelFormat;
if (fmt == System::Drawing::Imaging::PixelFormat::Format24bppRgb)
{
mat = new cv::Mat(cv::Size(bitmap->Width, bitmap->Height), CV_8UC3);
}
else if (fmt == System::Drawing::Imaging::PixelFormat::Format32bppArgb)
{
mat = new cv::Mat(cv::Size(bitmap->Width, bitmap->Height), CV_8UC4);
}
else
{
throw gcnew System::Exception("Unsupported image type");
}
auto rect = System::Drawing::Rectangle(0, 0, bitmap->Width, bitmap->Height);
auto bitmap_data = bitmap->LockBits(rect, System::Drawing::Imaging::ImageLockMode::ReadOnly, bitmap->PixelFormat);
System::IntPtr source_data = bitmap_data->Scan0;
int bytes = bitmap_data->Stride * bitmap_data->Height;
memcpy(mat->data, source_data.ToPointer(), bytes);
//Unlock the bits.
bitmap->UnlockBits(bitmap_data);
if (fmt == System::Drawing::Imaging::PixelFormat::Format32bppArgb)
{
cv::cvtColor(*mat, *mat, cv::COLOR_RGBA2RGB);
}
}
void Mirror()
{
cv::flip(*mat, *mat, 1);
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!RawImage()
{
if (mat)
{
delete mat;
mat = NULL;
}
}
// Destructor. Called on explicit Dispose() only.
~RawImage()
{
this->!RawImage();
}
property int Width
{
int get()
{
return mat->cols;
}
}
property int Height
{
int get()
{
return mat->rows;
}
}
property int Stride
{
int get()
{
return (int) mat->step;
}
}
property PixelFormat Format
{
PixelFormat get()
{
return TypeToPixelFormat(mat->type());
}
}
property cv::Mat& Mat
{
cv::Mat& get()
{
return *mat;
}
}
property bool IsEmpty
{
bool get()
{
return !mat || mat->empty();
}
}
bool UpdateWriteableBitmap(WriteableBitmap^ bitmap)
{
if (bitmap == nullptr || bitmap->PixelWidth != Width || bitmap->PixelHeight != Height || bitmap->Format != Format)
return false;
else {
if (mat->data == NULL) {
cv::Mat zeros(bitmap->PixelHeight, bitmap->PixelWidth, PixelFormatToType(bitmap->Format), 0);
bitmap->WritePixels(Int32Rect(0, 0, Width, Height), System::IntPtr(zeros.data), Stride * Height * (Format.BitsPerPixel / 8), Stride, 0, 0);
}
else {
bitmap->WritePixels(Int32Rect(0, 0, Width, Height), System::IntPtr(mat->data), Stride * Height * (Format.BitsPerPixel / 8), Stride, 0, 0);
}
return true;
}
}
WriteableBitmap^ CreateWriteableBitmap()
{
return gcnew WriteableBitmap(Width, Height, 72, 72, Format, nullptr);
}
};
public ref class VideoWriter
{
private:
// OpenCV based video capture for reading from files
cv::VideoWriter* vc;
public:
VideoWriter(System::String^ location, int width, int height, double fps, bool colour)
{
msclr::interop::marshal_context context;
std::string location_std_string = context.marshal_as<std::string>(location);
vc = new cv::VideoWriter(location_std_string, cv::VideoWriter::fourcc('D', 'I', 'V', 'X'), fps, cv::Size(width, height), colour);
}
// Return success
bool Write(RawImage^ img)
{
if (vc != nullptr && vc->isOpened())
{
vc->write(img->Mat);
return true;
}
else
{
return false;
}
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!VideoWriter()
{
if (vc != nullptr)
{
vc->~VideoWriter();
}
}
// Destructor. Called on explicit Dispose() only.
~VideoWriter()
{
this->!VideoWriter();
}
};
}

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Tadas Baltrusaitis.
//
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#pragma once
#pragma unmanaged
// Include all the unmanaged things we need.
#include <RecorderOpenFace.h>
#include <OpenCVWrappers.h>
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
using namespace System::Collections::Generic;
namespace UtilitiesOF {
public ref class RecorderOpenFaceParameters
{
private:
Utilities::RecorderOpenFaceParameters *m_params;
public:
RecorderOpenFaceParameters(bool sequence, bool is_from_webcam, bool output_2D_landmarks, bool output_3D_landmarks,
bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze, bool output_hog, bool output_tracked,
bool output_aligned_faces, bool record_bad_aligned, float fx, float fy, float cx, float cy, double fps_vid_out)
{
m_params = new Utilities::RecorderOpenFaceParameters(sequence, is_from_webcam,
output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs,
output_gaze, output_hog, output_tracked, output_aligned_faces, record_bad_aligned, fx, fy, cx, cy, fps_vid_out);
}
Utilities::RecorderOpenFaceParameters * GetParams()
{
return m_params;
}
!RecorderOpenFaceParameters()
{
delete m_params;
}
// Destructor. Called on explicit Dispose() only.
~RecorderOpenFaceParameters()
{
this->!RecorderOpenFaceParameters();
}
};
public ref class RecorderOpenFace
{
private:
// OpenCV based video capture for reading from files
Utilities::RecorderOpenFace* m_recorder;
public:
// Can provide a directory, or a list of files
RecorderOpenFace(System::String^ in_filename, UtilitiesOF::RecorderOpenFaceParameters^ parameters, System::String^ output_directory)
{
std::string in_filename_std = msclr::interop::marshal_as<std::string>(in_filename);
std::string output_directory_std = msclr::interop::marshal_as<std::string>(output_directory);
m_recorder = new Utilities::RecorderOpenFace(in_filename_std, *parameters->GetParams(), output_directory_std);
}
void Close()
{
m_recorder->Close();
}
void WriteObservation()
{
m_recorder->WriteObservation();
}
void WriteObservationTracked()
{
m_recorder->WriteObservationTracked();
}
void SetObservationGaze(System::Tuple<float, float, float>^ gaze_direction0, System::Tuple<float, float, float>^ gaze_direction1, System::Tuple<float, float>^ gaze_angle,
List<System::Tuple<float, float>^>^ landmarks_2D, List<System::Tuple<float, float, float>^>^ landmarks_3D)
{
cv::Point3f gaze_direction0_cv(gaze_direction0->Item1, gaze_direction0->Item2, gaze_direction0->Item3);
cv::Point3f gaze_direction1_cv(gaze_direction1->Item1, gaze_direction1->Item2, gaze_direction1->Item3);
cv::Vec2f gaze_angle_cv(gaze_angle->Item1, gaze_angle->Item2);
// Construct an OpenCV matrix from the landmarks
std::vector<cv::Point2f> landmarks_2D_cv;
for (int i = 0; i < landmarks_2D->Count; ++i)
{
landmarks_2D_cv.push_back(cv::Point2f(landmarks_2D[i]->Item1, landmarks_2D[i]->Item2));
}
// Construct an OpenCV matrix from the landmarks
std::vector<cv::Point3f> landmarks_3D_cv;
for (int i = 0; i < landmarks_3D->Count; ++i)
{
landmarks_3D_cv.push_back(cv::Point3f(landmarks_3D[i]->Item1, landmarks_3D[i]->Item2, landmarks_3D[i]->Item3));
}
m_recorder->SetObservationGaze(gaze_direction0_cv, gaze_direction1_cv, gaze_angle_cv, landmarks_2D_cv, landmarks_3D_cv);
}
System::String^ GetCSVFile()
{
return gcnew System::String(m_recorder->GetCSVFile().c_str());
}
// Setting the observations
void SetObservationTimestamp(double timestamp)
{
m_recorder->SetObservationTimestamp(timestamp);
}
void SetObservationPose(List<float>^ pose)
{
cv::Vec6f pose_vec(pose[0], pose[1], pose[2], pose[3], pose[4], pose[5]);
m_recorder->SetObservationPose(pose_vec);
}
void SetObservationActionUnits(Dictionary<System::String^, double>^ au_regs, Dictionary<System::String^, double>^ au_class)
{
std::vector<std::pair<std::string, double> > au_regs_std;
auto enum_reg = au_regs->GetEnumerator();
while (enum_reg.MoveNext())
{
std::string au_name = msclr::interop::marshal_as<std::string>(enum_reg.Current.Key);
double value = (double)enum_reg.Current.Value;
au_regs_std.push_back(std::pair<std::string, double>(au_name, value));
}
std::vector<std::pair<std::string, double> > au_class_std;
auto enum_class = au_class->GetEnumerator();
while (enum_class.MoveNext())
{
std::string au_name = msclr::interop::marshal_as<std::string>(enum_class.Current.Key);
double value = (double)enum_class.Current.Value;
au_class_std.push_back(std::pair<std::string, double>(au_name, value));
}
m_recorder->SetObservationActionUnits(au_regs_std, au_class_std);
}
void SetObservationFaceAlign(OpenCVWrappers::RawImage^ aligned_face_image)
{
m_recorder->SetObservationFaceAlign(aligned_face_image->Mat);
}
void SetObservationVisualization(OpenCVWrappers::RawImage^ vis_image)
{
m_recorder->SetObservationVisualization(vis_image->Mat);
}
void SetObservationFaceID(int face_id)
{
m_recorder->SetObservationFaceID(face_id);
}
void SetObservationFrameNumber(int frame_number)
{
m_recorder->SetObservationFrameNumber(frame_number);
}
void SetObservationHOG(bool success, OpenCVWrappers::RawImage^ aligned_face_image, int num_cols, int num_rows, int num_channels)
{
m_recorder->SetObservationHOG(success, aligned_face_image->Mat, num_cols, num_rows, num_channels);
}
void SetObservationLandmarks(List<System::Tuple<float, float>^>^ landmarks_2D, List<System::Tuple<float, float, float>^>^ landmarks_3D, List<float>^ params_global, List<float>^ params_local, double confidence, bool success)
{
// Construct an OpenCV matrix from the landmarks
cv::Mat_<float> landmarks_2D_mat(landmarks_2D->Count * 2, 1, 0.0);
for (int i = 0; i < landmarks_2D->Count; ++i)
{
landmarks_2D_mat.at<float>(i, 0) = landmarks_2D[i]->Item1;
landmarks_2D_mat.at<float>(i + landmarks_2D->Count, 0) = landmarks_2D[i]->Item2;
}
// Construct an OpenCV matrix from the landmarks
cv::Mat_<float> landmarks_3D_mat(landmarks_3D->Count * 3, 1, 0.0);
for (int i = 0; i < landmarks_3D->Count; ++i)
{
landmarks_3D_mat.at<float>(i, 0) = landmarks_3D[i]->Item1;
landmarks_3D_mat.at<float>(i + landmarks_3D->Count, 0) = landmarks_3D[i]->Item2;
landmarks_3D_mat.at<float>(i + 2 * landmarks_3D->Count, 0) = landmarks_3D[i]->Item3;
}
cv::Vec6f params_global_vec(params_global[0], params_global[1], params_global[2], params_global[3], params_global[4], params_global[5]);
cv::Mat_<float> params_local_vec(params_local->Count, 1, 0.0);
for (int i = 0; i < params_local->Count; ++i)
{
params_local_vec.at<float>(i, 0) = params_local[i];
}
m_recorder->SetObservationLandmarks(landmarks_2D_mat, landmarks_3D_mat, params_global_vec, params_local_vec, confidence, success);
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!RecorderOpenFace()
{
delete m_recorder;
}
// Destructor. Called on explicit Dispose() only.
~RecorderOpenFace()
{
this->!RecorderOpenFace();
}
};
}

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Tadas Baltrusaitis, 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#pragma once
#pragma unmanaged
// Include all the unmanaged things we need.
#include <opencv2/opencv.hpp>
#include <vector>
#include <set>
#include <string>
#include <OpenCVWrappers.h>
#include <ImageReader.h>
#include <DeviceEnumerator.h>
#include <SequenceCapture.h>
#pragma managed
#include <msclr\marshal.h>
#include <msclr\marshal_cppstd.h>
using namespace System::Collections::Generic;
namespace UtilitiesOF {
public ref class SequenceReader
{
private:
// OpenCV based video capture for reading from files
Utilities::SequenceCapture* m_sequence_capture;
OpenCVWrappers::RawImage^ m_rgb_frame;
OpenCVWrappers::RawImage^ m_gray_frame;
public:
// Can provide a directory or a video filename, need to specify which
SequenceReader(System::String^ filename, bool directory)
{
m_sequence_capture = new Utilities::SequenceCapture();
std::string name_std = msclr::interop::marshal_as<std::string>(filename);
bool success;
if(directory)
{
success = m_sequence_capture->OpenImageSequence(name_std);
}
else
{
success = m_sequence_capture->OpenVideoFile(name_std);
}
if (!success)
{
throw gcnew ReadingFailedException("Failed to open an image sequence");
}
}
SequenceReader(System::String^ filename, bool directory, float fx, float fy, float cx, float cy)
{
m_sequence_capture = new Utilities::SequenceCapture();
std::string name_std = msclr::interop::marshal_as<std::string>(filename);
bool success;
if (directory)
{
success = m_sequence_capture->OpenImageSequence(name_std, fx, fy, cx, cy);
}
else
{
success = m_sequence_capture->OpenVideoFile(name_std, fx, fy, cx, cy);
}
if (!success)
{
throw gcnew ReadingFailedException("Failed to open an image sequence");
}
}
// Can provide a webcam id
SequenceReader(int webcam_id, int width, int height)
{
m_sequence_capture = new Utilities::SequenceCapture();
bool success = m_sequence_capture->OpenWebcam(webcam_id, width, height);
if (!success)
{
throw gcnew ReadingFailedException("Failed to open an image sequence");
}
}
SequenceReader(int webcam_id, int width, int height, float fx, float fy, float cx, float cy)
{
m_sequence_capture = new Utilities::SequenceCapture();
bool success = m_sequence_capture->OpenWebcam(webcam_id, width, height, fx, fy, cx, cy);
if (!success)
{
throw gcnew ReadingFailedException("Failed to open an image sequence");
}
}
OpenCVWrappers::RawImage^ GetNextImage()
{
cv::Mat next_image = m_sequence_capture->GetNextFrame();
m_rgb_frame = gcnew OpenCVWrappers::RawImage(next_image);
return m_rgb_frame;
}
OpenCVWrappers::RawImage^ GetCurrentFrameGray() {
cv::Mat_<uchar> next_gray_image = m_sequence_capture->GetGrayFrame();
m_gray_frame = gcnew OpenCVWrappers::RawImage(next_gray_image);
return m_gray_frame;
}
int GetFrameNumber()
{
return m_sequence_capture->GetFrameNumber();
}
double GetTimestamp()
{
return m_sequence_capture->time_stamp;
}
System::String^ GetName()
{
std::string filename = m_sequence_capture->name;
return gcnew System::String(filename.c_str());
}
double GetProgress()
{
return m_sequence_capture->GetProgress();
}
float GetFx()
{
return m_sequence_capture->fx;
}
float GetFy()
{
return m_sequence_capture->fy;
}
float GetCx()
{
return m_sequence_capture->cx;
}
float GetCy()
{
return m_sequence_capture->cy;
}
bool IsOpened()
{
return m_sequence_capture->IsOpened();
}
bool IsWebcam()
{
return m_sequence_capture->IsWebcam();
}
double GetFPS()
{
return m_sequence_capture->fps;
}
void Close() {
m_sequence_capture->Close();
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!SequenceReader()
{
delete m_sequence_capture;
delete m_rgb_frame;
delete m_gray_frame;
}
// Destructor. Called on explicit Dispose() only.
~SequenceReader()
{
this->!SequenceReader();
}
private:
// Static methods for listing cameras and their resolutions
static void split(const std::string &s, char delim, std::vector<std::string> &elems) {
std::stringstream ss;
ss.str(s);
std::string item;
while (std::getline(ss, item, delim)) {
elems.push_back(item);
}
}
// Camera listing is camera name and supported resolutions
static Dictionary<System::String^, List<System::Tuple<int, int>^>^>^ GetListingFromFile(std::string filename)
{
// Check what cameras have been written (using OpenCVs XML packages)
cv::FileStorage fs_read(filename, cv::FileStorage::READ);
auto managed_camera_list_initial = gcnew Dictionary<System::String^, List<System::Tuple<int, int>^>^>();
cv::FileNode camera_node_list = fs_read["cameras"];
// iterate through a sequence using FileNodeIterator
for (size_t idx = 0; idx < camera_node_list.size(); idx++)
{
std::string camera_name = (std::string)camera_node_list[idx]["name"];
cv::FileNode resolution_list = camera_node_list[idx]["resolutions"];
auto resolutions = gcnew System::Collections::Generic::List<System::Tuple<int, int>^>();
for (size_t r_idx = 0; r_idx < resolution_list.size(); r_idx++)
{
std::string res = resolution_list[r_idx]["res"];
std::vector<std::string> elems;
split(res, 'x', elems);
int x = stoi(elems[0]);
int y = stoi(elems[1]);
resolutions->Add(gcnew System::Tuple<int, int>(x, y));
}
managed_camera_list_initial[gcnew System::String(camera_name.c_str())] = resolutions;
}
fs_read.release();
return managed_camera_list_initial;
}
static void WriteCameraListingToFile(System::Collections::Generic::Dictionary<System::String^, System::Collections::Generic::List<System::Tuple<int, int>^>^>^ camera_list, std::string filename)
{
cv::FileStorage fs("camera_list.xml", cv::FileStorage::WRITE);
fs << "cameras" << "[";
for each(System::String^ name_m in camera_list->Keys)
{
std::string name = msclr::interop::marshal_as<std::string>(name_m);
fs << "{:" << "name" << name;
fs << "resolutions" << "[";
auto resolutions = camera_list[name_m];
for (int j = 0; j < resolutions->Count; j++)
{
std::stringstream ss;
ss << resolutions[j]->Item1 << "x" << resolutions[j]->Item2;
fs << "{:" << "res" << ss.str();
fs << "}";
}
fs << "]";
fs << "}";
}
fs << "]";
fs.release();
}
// A utility for listing the currently connected cameras together with their ID, name, subset of supported resolutions and a thumbnail
public:
static List<System::Tuple<int, System::String^, List<System::Tuple<int, int>^>^, OpenCVWrappers::RawImage^>^>^ GetCameras(System::String^ root_directory_m)
{
auto managed_camera_list = gcnew List<System::Tuple<int, System::String^, List<System::Tuple<int, int>^>^, OpenCVWrappers::RawImage^>^>();
DeviceEnumerator de;
// Get a listing of all connected video devices
std::map<int, Device> cameras = de.getVideoDevicesMap();
//std::cout << "Number of cameras found: " << cameras.size() << std::endl;
//// Print information about the devices
//for (auto const &device : cameras) {
// std::cout << "== VIDEO DEVICE (id:" << device.first << ") ==" << std::endl;
// std::cout << "Name: " << device.second.deviceName << std::endl;
// std::cout << "Path: " << device.second.devicePath << std::endl;
//}
size_t num_cameras = cameras.size();
// Pre-load supported camera resolutions if already computed
std::string root_directory = msclr::interop::marshal_as<std::string>(root_directory_m);
auto camera_resolution_list = GetListingFromFile(root_directory + "camera_list.xml");
for (size_t i = 0; i < num_cameras; ++i)
{
// Thumbnail to help with camera selection
cv::Mat sample_img;
auto resolutions = gcnew List<System::Tuple<int, int>^>();
// Before trying the resolutions, check if the resolutions have already been computed for the camera of interest
std::string device_name = cameras[i].deviceName;
System::String^ device_name_m = gcnew System::String(device_name.c_str());
if (camera_resolution_list->ContainsKey(device_name_m))
{
resolutions = camera_resolution_list[device_name_m];
// Grab a thumbnail from mid resolution
cv::VideoCapture cap1(i);
auto resolution = resolutions[(int)(resolutions->Count / 2)];
cap1.set(cv::CAP_PROP_FRAME_WIDTH, resolution->Item1);
cap1.set(cv::CAP_PROP_FRAME_HEIGHT, resolution->Item2);
// Read several frames, as the first one often is over-exposed
for (int k = 0; k < 2; ++k)
cap1.read(sample_img);
}
else
{
// A common set of resolutions for webcams
std::vector<std::pair<int, int>> common_resolutions;
common_resolutions.push_back(std::pair<int, int>(320, 240));
common_resolutions.push_back(std::pair<int, int>(640, 480));
common_resolutions.push_back(std::pair<int, int>(800, 600));
common_resolutions.push_back(std::pair<int, int>(960, 720));
common_resolutions.push_back(std::pair<int, int>(1280, 720));
common_resolutions.push_back(std::pair<int, int>(1280, 960));
common_resolutions.push_back(std::pair<int, int>(1920, 1080));
// Grab some sample images and confirm the resolutions
cv::VideoCapture cap1(i);
// Go through resolutions if they have not been identified
for (size_t i = 0; i < common_resolutions.size(); ++i)
{
auto resolution = gcnew System::Tuple<int, int>(common_resolutions[i].first, common_resolutions[i].second);
cap1.set(cv::CAP_PROP_FRAME_WIDTH, resolution->Item1);
cap1.set(cv::CAP_PROP_FRAME_HEIGHT, resolution->Item2);
// Add only valid resolutions as API sometimes provides wrong ones
int set_width = cap1.get(cv::CAP_PROP_FRAME_WIDTH);
int set_height = cap1.get(cv::CAP_PROP_FRAME_HEIGHT);
// Grab a thumbnail from mid resolution
if (i == (int)common_resolutions.size() / 2)
{
// Read several frames, as the first one often is over-exposed
for (int k = 0; k < 2; ++k)
cap1.read(sample_img);
}
resolution = gcnew System::Tuple<int, int>(set_width, set_height);
if (!resolutions->Contains(resolution))
{
resolutions->Add(resolution);
}
}
cap1.~VideoCapture();
// Ass the resolutions were not on the list, add them now
camera_resolution_list[device_name_m] = resolutions;
WriteCameraListingToFile(camera_resolution_list, root_directory + "camera_list.xml");
}
OpenCVWrappers::RawImage^ sample_img_managed = gcnew OpenCVWrappers::RawImage(sample_img);
managed_camera_list->Add(gcnew System::Tuple<int, System::String^, List<System::Tuple<int, int>^>^, OpenCVWrappers::RawImage^>(i, device_name_m, resolutions, sample_img_managed));
}
return managed_camera_list;
}
};
}

169
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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Tadas Baltrusaitis.
//
// 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 2.0: Facial Behavior Analysis Toolkit
// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
//
// Convolutional experts constrained local model for facial landmark detection.
// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
// in Computer Vision and Pattern Recognition Workshops, 2017.
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
///////////////////////////////////////////////////////////////////////////////
#pragma once
#pragma unmanaged
// Include all the unmanaged things we need.
#include <Visualizer.h>
#include <OpenCVWrappers.h>
#pragma managed
using System::Collections::Generic::List;
namespace UtilitiesOF {
public ref class Visualizer
{
private:
// OpenCV based video capture for reading from files
Utilities::Visualizer* m_visualizer;
public:
Visualizer(bool vis_track, bool vis_hog, bool vis_aligned, bool vis_aus)
{
m_visualizer = new Utilities::Visualizer(vis_track, vis_hog, vis_aligned, vis_aus);
}
void SetObservationGaze(System::Tuple<float, float, float>^ gaze_direction0, System::Tuple<float, float, float>^ gaze_direction1,
List<System::Tuple<float, float>^>^ landmarks_2D, List<System::Tuple<float, float, float>^>^ landmarks_3D,
double confidence)
{
cv::Point3f gaze_direction0_cv(gaze_direction0->Item1, gaze_direction0->Item2, gaze_direction0->Item3);
cv::Point3f gaze_direction1_cv(gaze_direction1->Item1, gaze_direction1->Item2, gaze_direction1->Item3);
// Construct an OpenCV matrix from the landmarks
std::vector<cv::Point2f> landmarks_2D_cv;
for (int i = 0; i < landmarks_2D->Count; ++i)
{
landmarks_2D_cv.push_back(cv::Point2f(landmarks_2D[i]->Item1, landmarks_2D[i]->Item2));
}
// Construct an OpenCV matrix from the landmarks
std::vector<cv::Point3f> landmarks_3D_cv;
for (int i = 0; i < landmarks_3D->Count; ++i)
{
landmarks_3D_cv.push_back(cv::Point3f(landmarks_3D[i]->Item1, landmarks_3D[i]->Item2, landmarks_3D[i]->Item3));
}
m_visualizer->SetObservationGaze(gaze_direction0_cv, gaze_direction1_cv, landmarks_2D_cv, landmarks_3D_cv, confidence);
}
// Setting the observations
void SetObservationPose(List<float>^ pose, double confidence)
{
cv::Vec6f pose_vec(pose[0], pose[1], pose[2], pose[3], pose[4], pose[5]);
m_visualizer->SetObservationPose(pose_vec, confidence);
}
void SetObservationFaceAlign(OpenCVWrappers::RawImage^ aligned_face_image)
{
m_visualizer->SetObservationFaceAlign(aligned_face_image->Mat);
}
void SetObservationHOG(OpenCVWrappers::RawImage^ observation_HOG, int num_cols, int num_rows)
{
m_visualizer->SetObservationHOG(observation_HOG->Mat, num_cols, num_rows);
}
void SetObservationLandmarks(List<System::Tuple<float, float>^>^ landmarks_2D, double confidence, List<bool>^ visibilities)
{
// Construct an OpenCV matrix from the landmarks
cv::Mat_<float> landmarks_2D_mat(landmarks_2D->Count * 2, 1, 0.0);
for (int i = 0; i < landmarks_2D->Count; ++i)
{
landmarks_2D_mat.at<float>(i, 0) = landmarks_2D[i]->Item1;
landmarks_2D_mat.at<float>(i + landmarks_2D->Count, 0) = landmarks_2D[i]->Item2;
}
// Construct an OpenCV matrix from the landmarks
cv::Mat_<int> visibilities_cv(visibilities->Count, 1, 0);
for (int i = 0; i < visibilities->Count; ++i)
{
if (visibilities[i])
{
visibilities_cv.at<int>(i, 0) = 1;
}
else
{
visibilities_cv.at<int>(i, 0) = 0;
}
}
m_visualizer->SetObservationLandmarks(landmarks_2D_mat, confidence, visibilities_cv);
}
void SetObservationLandmarks(List<System::Tuple<float, float>^>^ landmarks_2D, double confidence)
{
SetObservationLandmarks(landmarks_2D, confidence, gcnew List<bool>());
}
void SetImage(OpenCVWrappers::RawImage^ canvas, float fx, float fy, float cx, float cy)
{
m_visualizer->SetImage(canvas->Mat, fx, fy, cx, cy);
}
OpenCVWrappers::RawImage^ GetHOGVis()
{
OpenCVWrappers::RawImage^ hog_image = gcnew OpenCVWrappers::RawImage(m_visualizer->GetHOGVis());
return hog_image;
}
OpenCVWrappers::RawImage^ GetVisImage()
{
OpenCVWrappers::RawImage^ vis_image = gcnew OpenCVWrappers::RawImage(m_visualizer->GetVisImage());
return vis_image;
}
// Finalizer. Definitely called before Garbage Collection,
// but not automatically called on explicit Dispose().
// May be called multiple times.
!Visualizer()
{
// Automatically closes capture object before freeing memory.
delete m_visualizer;
}
// Destructor. Called on explicit Dispose() only.
~Visualizer()
{
this->!Visualizer();
}
};
}

24
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/AU_DISFA_best.txt Normal file
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svm_disfa/AU_1_dyn.dat AU01
svm_disfa/AU_2_dyn.dat AU02
svm_disfa/AU_4_stat.dat AU04
svm_disfa/AU_5_dyn.dat AU05
svm_disfa/AU_6_stat.dat AU06
svm_disfa/AU_9_dyn.dat AU09
svm_disfa/AU_12_dyn.dat AU12
svm_disfa/AU_15_dyn.dat AU15
svm_disfa/AU_17_dyn.dat AU17
svm_disfa/AU_20_dyn.dat AU20
svm_disfa/AU_25_dyn.dat AU25
svm_disfa/AU_26_dyn.dat AU26
svr_disfa/AU_1_dynamic_intensity.dat AU01
svr_disfa/AU_2_dynamic_intensity.dat AU02
svr_disfa/AU_4_dynamic_intensity.dat AU04
svr_disfa/AU_5_dynamic_intensity.dat AU05
svr_disfa/AU_6_dynamic_intensity.dat AU06
svr_disfa/AU_9_dynamic_intensity.dat AU09
svr_disfa/AU_12_dynamic_intensity.dat AU12
svr_disfa/AU_15_dynamic_intensity.dat AU15
svr_disfa/AU_17_dynamic_intensity.dat AU17
svr_disfa/AU_20_dynamic_intensity.dat AU20
svr_disfa/AU_25_dynamic_intensity.dat AU25
svr_disfa/AU_26_dynamic_intensity.dat AU26

24
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/AU_DISFA_static.txt Normal file
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@@ -0,0 +1,24 @@
svm_disfa/AU_1_stat.dat AU01
svm_disfa/AU_2_stat.dat AU02
svm_disfa/AU_4_stat.dat AU04
svm_disfa/AU_5_stat.dat AU05
svm_disfa/AU_6_stat.dat AU06
svm_disfa/AU_9_stat.dat AU09
svm_disfa/AU_12_stat.dat AU12
svm_disfa/AU_15_stat.dat AU15
svm_disfa/AU_17_stat.dat AU17
svm_disfa/AU_20_stat.dat AU20
svm_disfa/AU_25_stat.dat AU25
svm_disfa/AU_26_stat.dat AU26
svr_disfa/AU_1_static_intensity.dat AU01
svr_disfa/AU_2_static_intensity.dat AU02
svr_disfa/AU_4_static_intensity.dat AU04
svr_disfa/AU_5_static_intensity.dat AU05
svr_disfa/AU_6_static_intensity.dat AU06
svr_disfa/AU_9_static_intensity.dat AU09
svr_disfa/AU_12_static_intensity.dat AU12
svr_disfa/AU_15_static_intensity.dat AU15
svr_disfa/AU_17_static_intensity.dat AU17
svr_disfa/AU_20_static_intensity.dat AU20
svr_disfa/AU_25_static_intensity.dat AU25
svr_disfa/AU_26_static_intensity.dat AU26

35
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/AU_all_best.txt Normal file
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@@ -0,0 +1,35 @@
svm_combined/AU_1_dynamic.dat AU01
svm_combined/AU_2_dynamic.dat AU02
svm_combined/AU_4_static.dat AU04
svm_combined/AU_5_static.dat AU05
svm_combined/AU_6_static.dat AU06
svm_combined/AU_7_static.dat AU07
svm_combined/AU_9_dynamic.dat AU09
svm_combined/AU_10_static.dat AU10
svm_combined/AU_12_static.dat AU12
svm_combined/AU_14_static.dat AU14
svm_combined/AU_15_dynamic.dat AU15
svm_combined/AU_17_dynamic.dat AU17
svm_combined/AU_20_dynamic.dat AU20
svm_combined/AU_23_static.dat AU23
svm_combined/AU_25_dynamic.dat AU25
svm_combined/AU_26_dynamic.dat AU26
svm_combined/AU_28_dynamic.dat AU28
svm_combined/AU_45_dynamic.dat AU45
svr_combined/AU_1_dynamic_intensity_comb.dat AU01
svr_combined/AU_2_dynamic_intensity_comb.dat AU02
svr_combined/AU_4_static_intensity_comb.dat AU04
svr_combined/AU_5_dynamic_intensity.dat AU05
svr_combined/AU_6_static_intensity_comb.dat AU06
svr_combined/AU_7_static_intensity_comb.dat AU07
svr_combined/AU_9_dynamic_intensity.dat AU09
svr_combined/AU_10_static_intensity_comb.dat AU10
svr_combined/AU_12_static_intensity_comb.dat AU12
svr_combined/AU_14_static_intensity.dat AU14
svr_combined/AU_15_dynamic_intensity_comb.dat AU15
svr_combined/AU_17_dynamic_intensity_comb.dat AU17
svr_combined/AU_20_dynamic_intensity.dat AU20
svr_combined/AU_23_dynamic_intensity_comb.dat AU23
svr_combined/AU_25_dynamic_intensity_comb.dat AU25
svr_combined/AU_26_dynamic_intensity_comb.dat AU26
svr_combined/AU_45_dynamic_intensity_comb.dat AU45

35
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/AU_all_static.txt Normal file
View File

@@ -0,0 +1,35 @@
svm_combined/AU_1_static.dat AU01
svm_combined/AU_2_static.dat AU02
svm_combined/AU_4_static.dat AU04
svm_combined/AU_5_static.dat AU05
svm_combined/AU_6_static.dat AU06
svm_combined/AU_7_static.dat AU07
svm_combined/AU_9_static.dat AU09
svm_combined/AU_10_static.dat AU10
svm_combined/AU_12_static.dat AU12
svm_combined/AU_14_static.dat AU14
svm_combined/AU_15_static.dat AU15
svm_combined/AU_17_static.dat AU17
svm_combined/AU_20_static.dat AU20
svm_combined/AU_23_static.dat AU23
svm_combined/AU_25_static.dat AU25
svm_combined/AU_26_static.dat AU26
svm_combined/AU_28_static.dat AU28
svm_combined/AU_45_static.dat AU45
svr_combined/AU_1_static_intensity_comb.dat AU01
svr_combined/AU_2_static_intensity_comb.dat AU02
svr_combined/AU_4_static_intensity_comb.dat AU04
svr_combined/AU_5_static_intensity.dat AU05
svr_combined/AU_6_static_intensity_comb.dat AU06
svr_combined/AU_7_static_intensity_comb.dat AU07
svr_combined/AU_9_static_intensity.dat AU09
svr_combined/AU_10_static_intensity_comb.dat AU10
svr_combined/AU_12_static_intensity_comb.dat AU12
svr_combined/AU_14_static_intensity.dat AU14
svr_combined/AU_15_static_intensity_comb.dat AU15
svr_combined/AU_17_static_intensity_comb.dat AU17
svr_combined/AU_20_static_intensity.dat AU20
svr_combined/AU_23_static_intensity_comb.dat AU23
svr_combined/AU_25_static_intensity.dat AU25
svr_combined/AU_26_static_intensity_comb.dat AU26
svr_combined/AU_45_static_intensity_comb.dat AU45

3
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/DISFA_dynamic_svms.txt Normal file
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@@ -0,0 +1,3 @@
AUPredictor AU_DISFA_best.txt
PDM In-the-wild_aligned_PDM_68.txt
Triangulation tris_68_full.txt

3
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/DISFA_static_svms.txt Normal file
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@@ -0,0 +1,3 @@
AUPredictor AU_DISFA_static.txt
PDM In-the-wild_aligned_PDM_68.txt
Triangulation tris_68_full.txt

421
pkg/OpenFace/lib/local/FaceAnalyser/AU_predictors/In-the-wild_aligned_PDM_68.txt Normal file
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@@ -0,0 +1,421 @@
# The mean values of the components (in mm)
204
1
6
-73.393523
-72.775014
-70.533638
-66.850058
-59.790187
-48.368973
-34.121101
-17.875411
0.098749
17.477031
32.648966
46.372358
57.343480
64.388482
68.212038
70.486405
71.375822
-61.119406
-51.287588
-37.804800
-24.022754
-11.635713
12.056636
25.106256
38.338588
51.191007
60.053851
0.653940
0.804809
0.992204
1.226783
-14.772472
-7.180239
0.555920
8.272499
15.214351
-46.047290
-37.674688
-27.883856
-19.648268
-28.272965
-38.082418
19.265868
27.894191
37.437529
45.170805
38.196454
28.764989
-28.916267
-17.533194
-6.684590
0.381001
8.375443
18.876618
28.794412
19.057574
8.956375
0.381549
-7.428895
-18.160634
-24.377490
-6.897633
0.340663
8.444722
24.474473
8.449166
0.205322
-7.198266
-29.801432
-10.949766
7.929818
26.074280
42.564390
56.481080
67.246992
75.056892
77.061286
74.758448
66.929021
56.311389
42.419126
25.455880
6.990805
-11.666193
-30.365191
-49.361602
-58.769795
-61.996155
-61.033399
-56.686759
-57.391033
-61.902186
-62.777713
-59.302347
-50.190255
-42.193790
-30.993721
-19.944596
-8.414541
2.598255
4.751589
6.562900
4.661005
2.643046
-37.471411
-42.730510
-42.711517
-36.754742
-35.134493
-34.919043
-37.032306
-43.342445
-43.110822
-38.086515
-35.532024
-35.484289
28.612716
22.172187
19.029051
20.721118
19.035460
22.394109
28.079924
36.298248
39.634575
40.395647
39.836405
36.677899
28.677771
25.475976
26.014269
25.326198
28.323008
30.596216
31.408738
30.844876
47.667532
45.909403
44.842580
43.141114
38.635298
30.750622
18.456453
3.609035
-0.881698
5.181201
19.176563
30.770570
37.628629
40.886309
42.281449
44.142567
47.140426
14.254422
7.268147
0.442051
-6.606501
-11.967398
-12.051204
-7.315098
-1.022953
5.349435
11.615746
-13.380835
-21.150853
-29.284036
-36.948060
-20.132003
-23.536684
-25.944448
-23.695741
-20.858157
7.037989
3.021217
1.353629
-0.111088
-0.147273
1.476612
-0.665746
0.247660
1.696435
4.894163
0.282961
-1.172675
-2.240310
-15.934335
-22.611355
-23.748437
-22.721995
-15.610679
-3.217393
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-22.554245
-23.591626
-22.406106
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-4.785684
-20.893742
-22.220479
-21.025520
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# The principal components (eigenvectors) of identity or combined identity and expression model
204
34
6
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-0.002553 -0.075344 0.037127 0.201967 -0.069124 0.041645 -0.100939 0.018352 0.006598 0.081569 0.004894 0.038886 0.050631 0.056656 0.055353 -0.028457 -0.022518 -0.089693 -0.079196 0.010851 -0.017583 0.015095 -0.077834 -0.000944 -0.104255 -0.060338 0.106169 -0.065799 -0.067068 -0.051588 0.080490 0.063152 -0.047531 0.178216
-0.002097 -0.055385 0.025705 0.215785 -0.026274 0.053548 -0.086364 0.041926 0.035784 0.033485 -0.014602 0.049999 0.031950 0.125201 0.045372 -0.064200 0.070073 -0.074313 -0.083942 -0.007566 -0.003666 0.034226 -0.018354 -0.011639 -0.084633 -0.039431 0.064057 -0.056515 -0.046926 -0.074699 0.073477 0.074109 -0.054882 0.046675
-0.005533 -0.035821 0.019787 0.226957 0.030415 0.059508 -0.051058 0.057386 0.023240 -0.025487 -0.013088 0.043330 0.025141 0.113119 0.017370 -0.071064 0.108140 -0.049138 -0.043417 -0.065375 0.012959 0.070483 0.075536 -0.035685 -0.005847 0.012618 0.040297 -0.049743 -0.061354 -0.087164 0.033762 0.048771 -0.096563 -0.045189
-0.006914 0.011987 0.030022 0.205560 0.099633 0.072384 -0.008313 0.060244 -0.006584 -0.104943 -0.018036 0.032806 0.029879 0.068434 -0.043721 -0.052564 0.077505 -0.062192 0.026865 -0.122834 -0.001529 0.064443 0.160557 -0.036750 0.124476 0.072641 0.048609 -0.043443 -0.060729 -0.040443 -0.050540 0.050509 -0.118886 -0.146867
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0.016954 0.088125 0.034199 0.081242 0.170872 0.048964 0.063434 0.083842 -0.058188 -0.164769 -0.012135 0.002128 0.084787 -0.009691 -0.136231 0.068411 -0.091616 0.042046 0.053339 -0.068859 -0.141834 -0.040710 0.017020 0.034007 0.053187 0.096749 0.068416 0.075079 0.038728 0.063588 -0.109581 0.007737 0.033105 -0.152075
0.021733 0.081042 0.015310 0.029492 0.112212 0.012959 0.065003 0.095565 -0.047228 -0.120559 -0.022904 -0.051461 0.036411 -0.036990 -0.101765 0.150918 -0.102123 0.163128 0.047199 0.037063 -0.223718 -0.065303 -0.149191 -0.017823 -0.134138 0.084419 0.010718 0.087970 0.043030 0.040099 -0.068487 -0.008773 0.077035 -0.082281
0.024713 0.084952 0.002319 -0.002792 0.028194 -0.016856 0.036083 0.087381 -0.020743 -0.024386 -0.027268 -0.091654 -0.009025 -0.052124 -0.033407 0.168353 -0.061447 0.244847 0.024587 0.064221 -0.161138 0.020993 -0.164584 -0.068179 -0.181775 0.087509 -0.068202 0.059078 -0.004250 -0.001472 -0.031248 -0.049011 -0.019415 0.022163
0.025404 0.095704 -0.013447 -0.036864 -0.064015 -0.035962 0.001144 0.050101 0.006172 0.084003 -0.015860 -0.102295 -0.028308 -0.053076 0.017520 0.110370 -0.009494 0.248830 -0.017769 0.064655 -0.062487 0.133872 -0.077193 -0.077102 -0.104610 0.084923 -0.116710 -0.000424 -0.001453 -0.001504 -0.004986 -0.072792 -0.151917 0.063339
0.027234 0.088446 -0.037011 -0.090962 -0.124015 -0.052844 -0.020061 0.017974 0.031824 0.138013 -0.009523 -0.093892 -0.061812 -0.016350 0.043025 0.034106 0.036403 0.177560 -0.066137 0.039288 0.041972 0.145398 -0.004524 -0.021113 0.025045 0.065780 -0.102128 -0.061053 0.010043 -0.018688 0.018733 -0.002990 -0.240304 0.007274
0.033301 0.051083 -0.050161 -0.137239 -0.130410 -0.078352 -0.030467 -0.016581 0.042916 0.153230 -0.008295 -0.048519 -0.056020 -0.017449 0.051644 -0.021078 0.049456 0.042549 -0.075780 0.051205 0.057200 0.117965 0.058462 0.078890 0.121853 0.027311 -0.037081 -0.082100 0.013948 0.019188 0.029703 0.076102 -0.243643 -0.067711
0.034561 -0.024817 -0.037181 -0.171731 -0.091823 -0.080914 -0.018022 -0.019280 0.055534 0.112038 -0.006388 0.000380 -0.027767 -0.031689 0.027915 -0.012644 0.004351 -0.072270 -0.053990 0.028620 0.011428 0.077628 0.073278 0.159948 0.096141 0.006855 0.036568 -0.013290 -0.001587 0.033748 0.052795 0.164287 -0.166678 -0.098685
0.031311 -0.094373 -0.015266 -0.181991 -0.032285 -0.067979 0.020822 -0.028457 0.050194 0.060652 0.006169 0.060618 0.005717 -0.047184 0.006512 0.012425 -0.059877 -0.141325 -0.029145 -0.010906 -0.069894 0.032082 0.054382 0.151384 0.041519 0.026929 0.076049 0.067107 -0.027885 0.040313 0.041582 0.194576 -0.086531 -0.080338
0.030327 -0.127857 -0.008012 -0.166922 0.025066 -0.062660 0.063753 -0.029802 0.044933 0.028298 0.025152 0.109179 0.013585 -0.043385 0.003918 0.007538 -0.080941 -0.141969 0.011067 -0.055727 -0.099322 -0.018025 0.009874 0.065510 -0.019589 0.084273 0.059580 0.099232 -0.026102 0.043042 0.023322 0.151827 -0.031702 -0.023000
0.030239 -0.141766 -0.020490 -0.154724 0.073176 -0.066098 0.076339 -0.045120 0.049403 -0.014474 0.024782 0.109864 0.005943 -0.008921 0.001482 -0.008304 -0.060453 -0.100507 0.030769 -0.093919 -0.042422 -0.045051 -0.014060 -0.020183 -0.093279 0.134797 0.006048 0.040717 -0.038709 0.039630 0.006137 0.076303 0.021244 0.014680
0.030493 -0.158938 -0.031460 -0.156567 0.120023 -0.070626 0.084879 -0.065759 0.063746 -0.047728 -0.002344 0.094855 -0.013995 0.014159 0.011125 -0.034394 -0.064702 -0.055871 0.041269 -0.110361 0.045722 -0.064289 -0.013213 -0.104140 -0.133226 0.183607 -0.037284 0.002311 -0.041808 0.033510 -0.034315 0.012710 0.065609 0.046794
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-0.038458 0.020813 -0.004500 0.021759 -0.086203 0.017004 0.123118 -0.051398 -0.154096 0.048594 0.160783 0.035847 -0.105209 -0.131283 -0.034334 0.088202 -0.022053 0.065794 0.043514 -0.164773 0.038562 0.221823 -0.008980 0.076251 -0.067806 0.014541 0.027230 -0.014379 -0.016754 -0.010835 0.026126 -0.010414 0.029183 0.058614
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0.000867 0.042636 0.003104 0.029552 -0.036234 0.016402 0.123844 -0.126971 -0.075012 0.060293 0.148833 -0.081953 -0.173218 0.088994 -0.136465 0.063644 -0.092909 -0.034989 0.083554 0.107321 0.022452 0.121649 0.095422 0.064093 -0.044127 -0.041514 -0.001734 -0.007934 0.040275 -0.042178 -0.037676 0.021143 0.091871 -0.056272
0.024120 0.058528 0.006018 0.028610 -0.012243 0.008813 0.097121 -0.159602 -0.017056 0.077811 0.087002 -0.120528 -0.150197 0.184860 -0.180434 0.040369 -0.097894 -0.042266 0.075094 0.203623 0.039492 0.033806 0.132455 0.059185 -0.102173 -0.071020 -0.051542 0.002257 0.073697 -0.021845 -0.035627 0.009661 0.123071 -0.065039
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0.018946 -0.000145 0.011179 -0.029672 0.136914 -0.030086 -0.147824 -0.105721 0.066935 -0.079902 -0.036968 -0.110909 0.052283 0.004188 0.126896 0.134626 -0.006676 -0.052490 0.091685 0.063396 -0.013036 0.002970 0.053049 0.019284 0.038579 -0.025497 0.025340 -0.089926 0.050450 -0.006666 -0.013118 -0.030505 -0.058633 0.064143
0.026629 -0.059165 -0.000823 -0.058599 0.151849 -0.022073 -0.130501 -0.084385 0.071751 -0.134872 0.039773 -0.057794 -0.044027 0.076494 0.035989 0.101529 0.005315 -0.029779 0.032498 0.028803 0.005618 -0.063529 -0.004924 -0.022183 -0.113427 0.055922 -0.008186 -0.140830 0.090960 -0.114112 -0.009417 0.083947 -0.041032 -0.006371
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0.000399 0.075432 -0.010525 0.041532 -0.020281 -0.016042 -0.094246 0.065468 0.057754 -0.013075 0.035003 0.037001 -0.063322 0.051499 0.004328 -0.061380 -0.015256 0.001025 -0.051518 0.003395 -0.032991 0.024094 0.008757 0.054130 -0.053594 0.010905 -0.022832 -0.025114 -0.048340 0.149929 -0.021867 0.081009 -0.007931 -0.009067
0.011378 0.085467 -0.017111 0.041300 -0.001692 -0.039680 -0.104984 0.079709 0.063221 -0.028397 0.048561 0.041353 -0.084814 0.040411 0.000268 -0.038789 -0.000559 -0.008586 -0.044596 0.040237 -0.063474 -0.003204 -0.031149 0.016331 -0.050858 0.018453 -0.008099 -0.080392 -0.015774 0.091864 -0.016377 0.134277 0.049921 -0.027301
0.029766 0.102153 -0.038143 0.053924 0.038638 -0.061103 -0.101860 0.090831 0.056649 -0.017724 0.060045 0.040849 -0.108462 0.025297 0.013784 -0.048798 0.017389 -0.036169 -0.049864 0.021916 -0.066484 0.016243 -0.106255 -0.005066 -0.033200 -0.008221 0.050207 -0.137478 0.022810 0.034341 -0.021252 0.120671 0.042481 -0.025859
0.006889 0.087347 -0.040353 0.029840 -0.010245 -0.040820 -0.091202 0.072685 0.079209 -0.016224 0.043485 -0.001910 -0.084857 0.044870 -0.001342 -0.071705 -0.002282 -0.026485 -0.039688 0.033741 -0.055799 -0.013397 -0.029281 0.000374 -0.037317 -0.004296 0.042230 -0.104897 0.024080 0.125291 0.011644 0.079126 -0.013272 0.018850
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-0.086068 -0.007082 -0.045041 -0.060765 0.055270 0.132259 0.009008 -0.042025 0.050070 -0.002958 -0.051816 0.001503 -0.066612 -0.017554 -0.020320 -0.047269 0.007336 0.021029 -0.065278 0.045943 0.031788 0.044150 -0.060233 -0.015205 -0.047170 0.032557 0.110315 0.067668 0.142436 -0.112995 -0.095937 0.043696 -0.053263 0.018166
-0.089411 0.023741 -0.038912 -0.067964 0.042282 0.140245 0.010622 -0.036806 0.040610 0.006060 -0.017474 0.000163 -0.063926 0.009072 -0.059385 -0.064253 -0.040611 0.016892 -0.098455 0.064721 0.042317 0.071934 -0.068174 -0.023171 -0.006137 0.021292 0.136552 0.086574 0.159669 -0.085905 -0.090037 0.025543 -0.060574 -0.002428
-0.078575 0.034575 0.031263 -0.073552 0.040647 0.136747 -0.043737 -0.008796 0.032331 0.016926 -0.029196 0.043721 -0.033823 -0.069883 -0.054957 0.000382 0.001785 -0.066324 -0.031587 0.075610 0.002026 0.123617 -0.016946 -0.073108 0.082295 -0.003377 0.160760 0.045913 0.138259 -0.012841 -0.021037 0.019773 -0.081271 0.003220
-0.033366 0.059208 0.160210 -0.064990 0.028624 0.104300 -0.099598 0.045261 -0.039277 0.065556 -0.102912 0.108074 -0.081503 -0.190293 -0.052187 0.075107 0.008671 -0.121671 0.069010 0.122685 -0.035632 -0.012154 0.063158 -0.139158 0.053250 0.013035 0.078006 -0.006941 0.005533 0.101138 0.135668 0.005174 -0.004429 -0.021124
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-0.106284 -0.022433 0.016803 -0.009022 0.113956 -0.059454 0.115911 -0.012451 0.125340 0.015307 0.082184 -0.057654 0.037795 0.019407 0.065630 -0.026223 -0.104086 0.015953 -0.052260 -0.022874 -0.001840 -0.000797 -0.004680 0.054162 0.031667 -0.086636 -0.106973 -0.052172 -0.115387 -0.039377 0.098135 -0.063370 -0.010368 -0.058260
-0.105834 -0.044185 0.028207 -0.010833 0.109847 -0.041639 0.113964 -0.035423 0.117943 0.018785 0.073258 -0.043540 0.048715 0.037758 0.075421 -0.003877 -0.043429 0.013819 -0.039051 -0.084951 -0.018739 0.005618 -0.000400 0.028961 0.006799 -0.081430 -0.141147 -0.095855 -0.105823 -0.040548 0.071096 -0.022040 -0.010334 -0.072087
-0.085770 -0.066339 0.119689 -0.034493 0.098418 0.002001 0.025266 -0.023246 0.059661 0.027629 0.052453 0.022351 0.015313 -0.007107 -0.005840 0.096765 0.062508 -0.012640 -0.032725 -0.080090 -0.027756 -0.041996 0.055252 0.063300 -0.038983 -0.076443 -0.130005 -0.083137 -0.110099 -0.085295 0.021676 -0.004858 0.062427 -0.049960
-0.063490 -0.034197 0.223953 -0.071609 0.017273 0.072534 -0.070046 0.008472 -0.059151 0.024538 -0.057458 0.054908 -0.069500 -0.108298 -0.024305 0.086168 0.173404 0.023995 0.062833 0.058041 0.036625 -0.084243 -0.007661 0.049316 -0.069261 0.031241 -0.034048 -0.059035 -0.063512 -0.030367 -0.002864 -0.044970 0.061520 -0.067669
-0.080995 -0.029362 -0.020969 -0.037152 0.055714 0.092056 0.009933 -0.025303 0.020523 -0.004640 -0.020295 -0.009164 -0.005931 0.018848 0.033915 -0.030364 0.045067 0.004015 0.011173 -0.058507 0.056901 -0.042073 -0.006012 0.046935 -0.107241 0.032777 0.116424 0.038938 0.083070 0.044785 -0.024341 -0.040868 -0.054497 0.021228
-0.079570 -0.003619 -0.038468 -0.043009 0.061929 0.088254 0.002999 -0.017741 0.034379 -0.004900 -0.032478 -0.019353 -0.018212 -0.005055 0.014637 -0.048472 -0.001520 0.022877 0.006651 -0.017392 0.069312 -0.058844 -0.004646 0.065142 -0.096265 0.026598 0.135522 0.071861 0.091550 0.032257 -0.016765 -0.075337 -0.064651 0.050908
-0.077037 0.036024 -0.029906 -0.041175 0.045056 0.108172 0.001799 -0.013867 0.031473 0.010155 -0.003491 -0.014995 -0.021891 0.005419 -0.006514 -0.067131 -0.050711 0.002908 -0.017700 0.003424 0.063512 -0.037825 -0.008255 0.061508 -0.030670 0.015575 0.161325 0.090053 0.117788 0.091435 -0.010989 -0.096824 -0.085735 0.003260
-0.051962 0.043127 0.185753 -0.068488 0.030906 0.083935 -0.070252 0.031192 -0.032752 0.081860 -0.058415 0.100433 -0.075819 -0.159865 -0.059411 0.067034 0.026048 -0.117863 0.048451 0.078422 -0.022589 0.005932 0.109565 -0.086305 0.028208 0.027111 0.068306 -0.018619 -0.000395 0.065584 0.129299 -0.018649 -0.057046 -0.000423
-0.110771 0.026821 0.039896 -0.026400 0.073464 0.011366 0.088839 0.010905 0.059433 0.027762 0.038964 -0.069547 0.034358 0.017245 0.035428 -0.063296 -0.129262 0.026781 -0.092055 0.071498 -0.050266 0.045878 -0.036457 -0.040249 0.060855 -0.044906 -0.090430 -0.018843 -0.111822 -0.025148 0.069191 -0.000310 -0.018828 -0.067576
-0.109310 -0.015148 0.033856 -0.022173 0.084027 -0.011325 0.100709 0.004099 0.070556 0.012019 0.035921 -0.046262 0.047837 0.012906 0.059339 -0.023335 -0.082080 0.034241 -0.099980 0.038508 -0.057517 0.048531 -0.049704 -0.033707 0.035725 -0.049554 -0.119832 -0.033233 -0.116399 -0.076705 0.057450 0.017861 0.007758 -0.058416
-0.105002 -0.041286 0.052806 -0.020899 0.081333 0.007181 0.095687 -0.011183 0.063719 0.009967 0.029662 -0.037522 0.062980 0.038468 0.065127 -0.003635 -0.032995 0.016733 -0.100860 -0.006462 -0.072928 0.073195 -0.048581 -0.059580 0.018777 -0.048728 -0.138328 -0.072245 -0.108275 -0.063238 0.030578 0.066053 0.024802 -0.084883
# The variances of the components (eigenvalues) of identity or combined identity and expression model
1
34
6
826.213804 695.783596 380.584790 282.861398 209.814481 184.418561 113.076307 104.852653 81.857546 77.095662 71.081802 55.760367 49.883312 39.122009 37.668889 32.916377 31.165932 26.644275 24.233150 21.357573 17.029065 15.432317 13.812689 12.440567 10.213426 9.316734 5.327325 2.526152 1.659451 1.123615 1.015840 0.816193 0.718969 0.582660

3
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AUPredictor AU_all_best.txt
PDM In-the-wild_aligned_PDM_68.txt
Triangulation tris_68_full.txt

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AUPredictor AU_all_static.txt
PDM In-the-wild_aligned_PDM_68.txt
Triangulation tris_68_full.txt

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