mirror of
https://gitcode.com/gh_mirrors/ope/OpenFace.git
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466 lines
13 KiB
C++
466 lines
13 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace 2.0: Facial Behavior Analysis Toolkit
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// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
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// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
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//
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// Convolutional experts constrained local model for facial landmark detection.
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// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
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// in Computer Vision and Pattern Recognition Workshops, 2017.
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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///////////////////////////////////////////////////////////////////////////////
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#ifndef __LANDMARK_DETECTOR_INTEROP_h_
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#define __LANDMARK_DETECTOR_INTEROP_h_
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#pragma once
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#pragma managed
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#include <msclr\marshal.h>
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#include <msclr\marshal_cppstd.h>
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#pragma unmanaged
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// Include all the unmanaged things we need.
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#include <opencv2/core/core.hpp>
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#include "opencv2/objdetect.hpp"
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#include "opencv2/calib3d.hpp"
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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// Allows to overcome boost name clash stuff with C++ CLI
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#ifdef __cplusplus_cli
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#define generic __identifier(generic)
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#endif
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#include <OpenCVWrappers.h>
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#include <LandmarkCoreIncludes.h>
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#include <Face_utils.h>
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#include <FaceAnalyser.h>
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#include <VisualizationUtils.h>
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#ifdef __cplusplus_cli
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#undef generic
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#endif
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using namespace System::Collections::Generic;
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#pragma managed
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namespace CppInterop {
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namespace LandmarkDetector {
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public ref class FaceModelParameters
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{
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public:
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::LandmarkDetector::FaceModelParameters* params;
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public:
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// Initialise the parameters
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FaceModelParameters(System::String^ root, bool ceclm, bool clnf, bool clm)
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{
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std::string root_std = msclr::interop::marshal_as<std::string>(root);
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vector<std::string> args;
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args.push_back(root_std);
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std::string model_loc = "model/main_ceclm_general.txt";
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if (ceclm)
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{
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model_loc = "model/main_ceclm_general.txt";
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}
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else if(clnf)
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{
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model_loc = "model/main_clnf_general.txt";
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}
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else if (clm)
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{
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model_loc = "model/main_clm_general.txt";
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}
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args.push_back("-mloc");
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args.push_back(model_loc);
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params = new ::LandmarkDetector::FaceModelParameters(args);
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}
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// TODO this could have optimize for demo mode (also could appropriately update sigma, reg_factor as well)
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void optimiseForVideo()
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{
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params->window_sizes_small = vector<int>(4);
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params->window_sizes_init = vector<int>(4);
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// For fast tracking
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params->window_sizes_small[0] = 0;
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params->window_sizes_small[1] = 9;
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params->window_sizes_small[2] = 7;
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params->window_sizes_small[3] = 0;
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// Just for initialisation
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params->window_sizes_init.at(0) = 11;
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params->window_sizes_init.at(1) = 9;
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params->window_sizes_init.at(2) = 7;
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params->window_sizes_init.at(3) = 5;
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// For first frame use the initialisation
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params->window_sizes_current = params->window_sizes_init;
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params->multi_view = false;
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params->num_optimisation_iteration = 5;
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params->sigma = 1.5;
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params->reg_factor = 25;
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params->weight_factor = 0;
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// Parameter optimizations for CE-CLM
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if (params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CECLM_DETECTOR)
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{
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params->sigma = 1.5f * params->sigma;
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params->reg_factor = 0.9f * params->reg_factor;
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}
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}
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bool IsCECLM()
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{
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return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CECLM_DETECTOR;
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}
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bool IsCLNF()
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{
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return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CLNF_DETECTOR;
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}
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bool IsCLM()
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{
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return params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::CLM_DETECTOR;
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}
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System::String^ GetMTCNNLocation()
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{
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return gcnew System::String(params->mtcnn_face_detector_location.c_str());
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}
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System::String^ GetHaarLocation()
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{
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return gcnew System::String(params->haar_face_detector_location.c_str());
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}
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void SetFaceDetector(bool haar, bool hog, bool cnn)
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{
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if (cnn)
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{
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params->curr_face_detector = params->MTCNN_DETECTOR;
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}
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else if (hog)
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{
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params->curr_face_detector = params->HOG_SVM_DETECTOR;
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}
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else if (haar)
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{
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params->curr_face_detector = params->HAAR_DETECTOR;
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}
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}
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void optimiseForImages()
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{
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params->window_sizes_init = vector<int>(4);
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params->window_sizes_init[0] = 15;
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params->window_sizes_init[1] = 13;
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params->window_sizes_init[2] = 11;
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params->window_sizes_init[3] = 11;
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params->multi_view = true;
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params->sigma = 1.25;
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params->reg_factor = 35;
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params->weight_factor = 2.5;
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params->num_optimisation_iteration = 10;
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// Parameter optimizations for CE-CLM
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if (params->curr_landmark_detector == ::LandmarkDetector::FaceModelParameters::MTCNN_DETECTOR)
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{
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params->sigma = 1.5f * params->sigma;
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params->reg_factor = 0.9f * params->reg_factor;
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}
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}
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::LandmarkDetector::FaceModelParameters* getParams() {
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return params;
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}
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!FaceModelParameters()
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{
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delete params;
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}
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~FaceModelParameters()
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{
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this->!FaceModelParameters();
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}
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};
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public ref class CLNF
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{
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public:
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// A pointer to the CLNF landmark detector
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::LandmarkDetector::CLNF* clnf;
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public:
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// Wrapper functions for the relevant CLNF functionality
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CLNF() : clnf(new ::LandmarkDetector::CLNF()) { }
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CLNF(FaceModelParameters^ params)
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{
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clnf = new ::LandmarkDetector::CLNF(params->getParams()->model_location);
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}
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!CLNF()
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{
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delete clnf;
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}
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~CLNF()
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{
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this->!CLNF();
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}
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bool isLoaded()
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{
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return clnf->loaded_successfully;
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}
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::LandmarkDetector::CLNF* getCLNF() {
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return clnf;
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}
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void Reset() {
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clnf->Reset();
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}
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void Reset(double x, double y) {
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clnf->Reset(x, y);
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}
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bool HasEyeModel() {
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return clnf->eye_model;
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}
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double GetConfidence()
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{
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return clnf->detection_certainty;
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}
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bool DetectLandmarksInVideo(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
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return ::LandmarkDetector::DetectLandmarksInVideo(rgb_image->Mat, *clnf, *modelParams->getParams(), gray_image->Mat);
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}
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bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
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return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, *clnf, *modelParams->getParams(), gray_image->Mat);
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}
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bool DetectFaceLandmarksInImage(OpenCVWrappers::RawImage^ rgb_image, Rect^ bounding_box, FaceModelParameters^ modelParams, OpenCVWrappers::RawImage^ gray_image) {
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cv::Rect_<float> bbox(bounding_box->Left, bounding_box->Top, bounding_box->Width, bounding_box->Height);
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return ::LandmarkDetector::DetectLandmarksInImage(rgb_image->Mat, bbox, *clnf, *modelParams->getParams(), gray_image->Mat);
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}
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void GetPoseWRTCamera(List<float>^ pose, float fx, float fy, float cx, float cy) {
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auto pose_vec = ::LandmarkDetector::GetPoseWRTCamera(*clnf, fx, fy, cx, cy);
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pose->Clear();
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for(int i = 0; i < 6; ++i)
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{
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pose->Add(pose_vec[i]);
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}
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}
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void GetPose(List<float>^ pose, float fx, float fy, float cx, float cy) {
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auto pose_vec = ::LandmarkDetector::GetPose(*clnf, fx, fy, cx, cy);
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pose->Clear();
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for(int i = 0; i < 6; ++i)
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{
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pose->Add(pose_vec[i]);
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}
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}
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// Get the mask of which landmarks are currently visible (not self-occluded)
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List<bool>^ GetVisibilities()
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{
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cv::Mat_<int> vis = clnf->GetVisibilities();
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List<bool>^ visibilities = gcnew List<bool>();
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for (auto vis_it = vis.begin(); vis_it != vis.end(); vis_it++)
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{
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visibilities->Add(*vis_it != 0);
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}
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return visibilities;
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}
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List<System::Tuple<float,float>^>^ CalculateVisibleLandmarks() {
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vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateVisibleLandmarks(*clnf);
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auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
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for(cv::Point2f p : vecLandmarks) {
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landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
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}
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return landmarks;
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}
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List<System::Tuple<float, float>^>^ CalculateAllLandmarks() {
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vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateAllLandmarks(*clnf);
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auto landmarks = gcnew List<System::Tuple<float, float>^>();
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for (cv::Point2f p : vecLandmarks) {
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landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
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}
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return landmarks;
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}
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List<System::Tuple<float, float>^>^ CalculateAllEyeLandmarks() {
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vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateAllEyeLandmarks(*clnf);
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auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
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for (cv::Point2f p : vecLandmarks) {
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landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
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}
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return landmarks;
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}
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List<System::Tuple<float, float, float>^>^ CalculateAllEyeLandmarks3D(float fx, float fy, float cx, float cy) {
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vector<cv::Point3f> vecLandmarks = ::LandmarkDetector::Calculate3DEyeLandmarks(*clnf, fx, fy, cx, cy);
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auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float, float>^>();
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for (cv::Point3f p : vecLandmarks) {
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landmarks->Add(gcnew System::Tuple<float, float, float>(p.x, p.y, p.z));
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}
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return landmarks;
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}
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List<System::Tuple<float, float>^>^ CalculateVisibleEyeLandmarks() {
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vector<cv::Point2f> vecLandmarks = ::LandmarkDetector::CalculateVisibleEyeLandmarks(*clnf);
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auto landmarks = gcnew System::Collections::Generic::List<System::Tuple<float, float>^>();
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for (cv::Point2f p : vecLandmarks) {
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landmarks->Add(gcnew System::Tuple<float, float>(p.x, p.y));
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}
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return landmarks;
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}
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List<System::Tuple<float, float, float>^>^ Calculate3DLandmarks(float fx, float fy, float cx, float cy) {
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cv::Mat_<float> shape3D = clnf->GetShape(fx, fy, cx, cy);
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auto landmarks_3D = gcnew List<System::Tuple<float, float, float>^>();
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for(int i = 0; i < shape3D.cols; ++i)
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{
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landmarks_3D->Add(gcnew System::Tuple<float, float, float>(shape3D.at<float>(0, i), shape3D.at<float>(1, i), shape3D.at<float>(2, i)));
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}
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return landmarks_3D;
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}
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List<System::Tuple<System::Windows::Point, System::Windows::Point>^>^ CalculateBox(float fx, float fy, float cx, float cy) {
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cv::Vec6f pose = ::LandmarkDetector::GetPose(*clnf, fx,fy, cx, cy);
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vector<pair<cv::Point2f, cv::Point2f>> vecLines = ::Utilities::CalculateBox(pose, fx, fy, cx, cy);
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auto lines = gcnew List<System::Tuple<System::Windows::Point,System::Windows::Point>^>();
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for(pair<cv::Point2f, cv::Point2f> line : vecLines) {
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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)));
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}
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return lines;
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}
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int GetNumPoints()
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{
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return clnf->pdm.NumberOfPoints();
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}
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int GetNumModes()
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{
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return clnf->pdm.NumberOfModes();
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}
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// Getting the non-rigid shape parameters describing the facial expression
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List<float>^ GetNonRigidParams()
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{
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auto non_rigid_params = gcnew List<float>();
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for (int i = 0; i < clnf->params_local.rows; ++i)
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{
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non_rigid_params->Add(clnf->params_local.at<float>(i));
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}
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return non_rigid_params;
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}
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// Getting the rigid shape parameters describing face scale rotation and translation (scale,rotx,roty,rotz,tx,ty)
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List<float>^ GetRigidParams()
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{
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auto rigid_params = gcnew List<float>();
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for (size_t i = 0; i < 6; ++i)
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{
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rigid_params->Add(clnf->params_global[i]);
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}
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return rigid_params;
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}
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// Rigid params followed by non-rigid ones
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List<float>^ GetParams()
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{
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auto all_params = GetRigidParams();
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all_params->AddRange(GetNonRigidParams());
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return all_params;
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}
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};
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}
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}
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#endif |