1174 lines
32 KiB
C++
1174 lines
32 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: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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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-speci?c 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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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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// FeatureExtraction.cpp : Defines the entry point for the feature extraction console application.
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// System includes
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#include <fstream>
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#include <sstream>
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// OpenCV includes
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#include <opencv2/videoio/videoio.hpp> // Video write
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#include <opencv2/videoio/videoio_c.h> // Video write
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui/highgui.hpp>
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// Boost includes
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#include <filesystem.hpp>
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#include <filesystem/fstream.hpp>
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#include <boost/algorithm/string.hpp>
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// Local includes
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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 <GazeEstimation.h>
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#ifndef CONFIG_DIR
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#define CONFIG_DIR "~"
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#endif
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#define INFO_STREAM( stream ) \
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std::cout << stream << std::endl
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#define WARN_STREAM( stream ) \
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std::cout << "Warning: " << stream << std::endl
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#define ERROR_STREAM( stream ) \
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std::cout << "Error: " << stream << std::endl
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static void printErrorAndAbort( const std::string & error )
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{
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std::cout << error << std::endl;
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}
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#define FATAL_STREAM( stream ) \
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printErrorAndAbort( std::string( "Fatal error: " ) + stream )
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using namespace std;
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using namespace boost::filesystem;
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vector<string> get_arguments(int argc, char **argv)
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{
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vector<string> arguments;
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// First argument is reserved for the name of the executable
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for(int i = 0; i < argc; ++i)
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{
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arguments.push_back(string(argv[i]));
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}
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return arguments;
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}
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// Useful utility for creating directories for storing the output files
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void create_directory_from_file(string output_path)
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{
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// Creating the right directory structure
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// First get rid of the file
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auto p = path(path(output_path).parent_path());
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if(!p.empty() && !boost::filesystem::exists(p))
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{
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bool success = boost::filesystem::create_directories(p);
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if(!success)
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{
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cout << "Failed to create a directory... " << p.string() << endl;
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}
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}
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}
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void create_directory(string output_path)
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{
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// Creating the right directory structure
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auto p = path(output_path);
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if(!boost::filesystem::exists(p))
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{
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bool success = boost::filesystem::create_directories(p);
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if(!success)
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{
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cout << "Failed to create a directory..." << p.string() << endl;
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}
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}
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}
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void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
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int &similarity_size, bool &grayscale, bool& verbose, bool& dynamic, bool &output_2D_landmarks, bool &output_3D_landmarks,
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bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
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void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments);
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void output_HOG_frame(std::ofstream* hog_file, bool good_frame, const cv::Mat_<double>& hog_descriptor, int num_rows, int num_cols);
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// Some globals for tracking timing information for visualisation
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double fps_tracker = -1.0;
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int64 t0 = 0;
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// Visualising the results
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void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy)
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{
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// Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised
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double detection_certainty = face_model.detection_certainty;
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bool detection_success = face_model.detection_success;
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double visualisation_boundary = 0.2;
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// Only draw if the reliability is reasonable, the value is slightly ad-hoc
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if (detection_certainty < visualisation_boundary)
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{
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LandmarkDetector::Draw(captured_image, face_model);
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double vis_certainty = detection_certainty;
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if (vis_certainty > 1)
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vis_certainty = 1;
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if (vis_certainty < -1)
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vis_certainty = -1;
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vis_certainty = (vis_certainty + 1) / (visualisation_boundary + 1);
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// A rough heuristic for box around the face width
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int thickness = (int)std::ceil(2.0* ((double)captured_image.cols) / 640.0);
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cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(face_model, fx, fy, cx, cy);
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// Draw it in reddish if uncertain, blueish if certain
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LandmarkDetector::DrawBox(captured_image, pose_estimate_to_draw, cv::Scalar((1 - vis_certainty)*255.0, 0, vis_certainty * 255), thickness, fx, fy, cx, cy);
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if (det_parameters.track_gaze && detection_success && face_model.eye_model)
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{
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FaceAnalysis::DrawGaze(captured_image, face_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy);
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}
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}
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// Work out the framerate
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if (frame_count % 10 == 0)
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{
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double t1 = cv::getTickCount();
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fps_tracker = 10.0 / (double(t1 - t0) / cv::getTickFrequency());
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t0 = t1;
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}
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// Write out the framerate on the image before displaying it
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char fpsC[255];
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std::sprintf(fpsC, "%d", (int)fps_tracker);
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string fpsSt("FPS:");
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fpsSt += fpsC;
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cv::putText(captured_image, fpsSt, cv::Point(10, 20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255, 0, 0), 1, CV_AA);
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if (!det_parameters.quiet_mode)
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{
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cv::namedWindow("tracking_result", 1);
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cv::imshow("tracking_result", captured_image);
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}
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}
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void prepareOutputFile(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
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bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
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int num_landmarks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg);
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// Output all of the information into one file in one go (quite a few parameters, but simplifies the flow)
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void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
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bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
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const LandmarkDetector::CLNF& face_model, int frame_count, double time_stamp, bool detection_success,
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cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
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const FaceAnalysis::FaceAnalyser& face_analyser);
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void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file, bool dynamic);
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int main (int argc, char **argv)
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{
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vector<string> arguments = get_arguments(argc, argv);
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// Search paths
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boost::filesystem::path config_path = boost::filesystem::path(CONFIG_DIR);
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boost::filesystem::path parent_path = boost::filesystem::path(arguments[0]).parent_path();
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// Some initial parameters that can be overriden from command line
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vector<string> input_files, depth_directories, output_files, tracked_videos_output;
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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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// Always track gaze in feature extraction
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det_parameters.track_gaze = true;
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// Get the input output file parameters
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// Indicates that rotation should be with respect to camera or world coordinates
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bool use_world_coordinates;
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string output_codec; //not used but should
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LandmarkDetector::get_video_input_output_params(input_files, depth_directories, output_files, tracked_videos_output, use_world_coordinates, output_codec, arguments);
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bool video_input = true;
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bool verbose = true;
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bool images_as_video = false;
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vector<vector<string> > input_image_files;
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// Adding image support for reading in the files
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if(input_files.empty())
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{
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vector<string> d_files;
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vector<string> o_img;
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vector<cv::Rect_<double>> bboxes;
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get_image_input_output_params_feats(input_image_files, images_as_video, arguments);
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if(!input_image_files.empty())
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{
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video_input = false;
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}
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}
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// Grab camera parameters, if they are not defined (approximate values will be used)
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float fx = 0, fy = 0, cx = 0, cy = 0;
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int d = 0;
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// Get camera parameters
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LandmarkDetector::get_camera_params(d, fx, fy, cx, cy, arguments);
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// If cx (optical axis centre) is undefined will use the image size/2 as an estimate
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bool cx_undefined = false;
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bool fx_undefined = false;
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if (cx == 0 || cy == 0)
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{
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cx_undefined = true;
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}
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if (fx == 0 || fy == 0)
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{
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fx_undefined = true;
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}
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// The modules that are being used for tracking
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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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vector<string> output_similarity_align;
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vector<string> output_hog_align_files;
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double sim_scale = -1;
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int sim_size = 112;
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bool grayscale = false;
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bool video_output = false;
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bool dynamic = true; // Indicates if a dynamic AU model should be used (dynamic is useful if the video is long enough to include neutral expressions)
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int num_hog_rows;
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int num_hog_cols;
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// By default output all parameters, but these can be turned off to get smaller files or slightly faster processing times
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// use -no2Dfp, -no3Dfp, -noMparams, -noPose, -noAUs, -noGaze to turn them off
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bool output_2D_landmarks = true;
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bool output_3D_landmarks = true;
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bool output_model_params = true;
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bool output_pose = true;
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bool output_AUs = true;
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bool output_gaze = true;
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get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic,
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output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
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// Used for image masking
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string tri_loc;
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boost::filesystem::path tri_loc_path = boost::filesystem::path("model/tris_68_full.txt");
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if (boost::filesystem::exists(tri_loc_path))
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{
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tri_loc = tri_loc_path.string();
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}
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else if (boost::filesystem::exists(parent_path/tri_loc_path))
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{
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tri_loc = (parent_path/tri_loc_path).string();
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}
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else if (boost::filesystem::exists(config_path/tri_loc_path))
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{
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tri_loc = (config_path/tri_loc_path).string();
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}
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else
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{
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cout << "Can't find triangulation files, exiting" << endl;
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return 1;
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}
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// Will warp to scaled mean shape
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cv::Mat_<double> similarity_normalised_shape = face_model.pdm.mean_shape * sim_scale;
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// Discard the z component
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similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
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// If multiple video files are tracked, use this to indicate if we are done
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bool done = false;
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int f_n = -1;
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int curr_img = -1;
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string au_loc;
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string au_loc_local;
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if (dynamic)
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{
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au_loc_local = "AU_predictors/AU_all_best.txt";
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}
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else
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{
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au_loc_local = "AU_predictors/AU_all_static.txt";
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}
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boost::filesystem::path au_loc_path = boost::filesystem::path(au_loc_local);
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if (boost::filesystem::exists(au_loc_path))
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{
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au_loc = au_loc_path.string();
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}
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else if (boost::filesystem::exists(parent_path/au_loc_path))
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{
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au_loc = (parent_path/au_loc_path).string();
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}
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else if (boost::filesystem::exists(config_path/au_loc_path))
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{
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au_loc = (config_path/au_loc_path).string();
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}
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else
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{
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cout << "Can't find AU prediction files, exiting" << endl;
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return 1;
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}
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// Creating a face analyser that will be used for AU extraction
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// Make sure sim_scale is proportional to sim_size if not set
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if (sim_scale == -1) sim_scale = sim_size * (0.7 / 112.0);
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), sim_scale, sim_size, sim_size, au_loc, tri_loc);
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while(!done) // this is not a for loop as we might also be reading from a webcam
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{
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string current_file;
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cv::VideoCapture video_capture;
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cv::Mat captured_image;
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int total_frames = -1;
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int reported_completion = 0;
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double fps_vid_in = -1.0;
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if(video_input)
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{
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// We might specify multiple video files as arguments
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if(input_files.size() > 0)
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{
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f_n++;
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current_file = input_files[f_n];
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}
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else
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{
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// If we want to write out from webcam
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f_n = 0;
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}
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// Do some grabbing
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if( current_file.size() > 0 )
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{
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INFO_STREAM( "Attempting to read from file: " << current_file );
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video_capture = cv::VideoCapture( current_file );
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total_frames = (int)video_capture.get(CV_CAP_PROP_FRAME_COUNT);
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fps_vid_in = video_capture.get(CV_CAP_PROP_FPS);
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// Check if fps is nan or less than 0
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if (fps_vid_in != fps_vid_in || fps_vid_in <= 0)
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{
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INFO_STREAM("FPS of the video file cannot be determined, assuming 30");
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fps_vid_in = 30;
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}
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}
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if (!video_capture.isOpened())
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{
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FATAL_STREAM("Failed to open video source, exiting");
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return 1;
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}
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else
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{
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INFO_STREAM("Device or file opened");
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}
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video_capture >> captured_image;
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}
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else
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{
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f_n++;
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curr_img++;
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if(!input_image_files[f_n].empty())
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{
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string curr_img_file = input_image_files[f_n][curr_img];
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captured_image = cv::imread(curr_img_file, -1);
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}
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else
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{
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FATAL_STREAM( "No .jpg or .png images in a specified drectory, exiting" );
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return 1;
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}
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}
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// If optical centers are not defined just use center of image
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if(cx_undefined)
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{
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cx = captured_image.cols / 2.0f;
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cy = captured_image.rows / 2.0f;
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}
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// Use a rough guess-timate of focal length
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if (fx_undefined)
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{
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fx = 500 * (captured_image.cols / 640.0);
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fy = 500 * (captured_image.rows / 480.0);
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fx = (fx + fy) / 2.0;
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fy = fx;
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}
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// Creating output files
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std::ofstream output_file;
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if (!output_files.empty())
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{
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output_file.open(output_files[f_n], ios_base::out);
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prepareOutputFile(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, face_model.pdm.NumberOfPoints(), face_model.pdm.NumberOfModes(), face_analyser.GetAUClassNames(), face_analyser.GetAURegNames());
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}
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// Saving the HOG features
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std::ofstream hog_output_file;
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if(!output_hog_align_files.empty())
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{
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hog_output_file.open(output_hog_align_files[f_n], ios_base::out | ios_base::binary);
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}
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// saving the videos
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cv::VideoWriter writerFace;
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if(!tracked_videos_output.empty())
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{
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try
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{
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writerFace = cv::VideoWriter(tracked_videos_output[f_n], CV_FOURCC(output_codec[0],output_codec[1],output_codec[2],output_codec[3]), fps_vid_in, captured_image.size(), true);
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}
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catch(cv::Exception e)
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{
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|
WARN_STREAM( "Could not open VideoWriter, OUTPUT FILE WILL NOT BE WRITTEN. Currently using codec " << output_codec << ", try using an other one (-oc option)");
|
|
}
|
|
|
|
|
|
}
|
|
|
|
int frame_count = 0;
|
|
|
|
// This is useful for a second pass run (if want AU predictions)
|
|
vector<cv::Vec6d> params_global_video;
|
|
vector<bool> successes_video;
|
|
vector<cv::Mat_<double>> params_local_video;
|
|
vector<cv::Mat_<double>> detected_landmarks_video;
|
|
|
|
// Use for timestamping if using a webcam
|
|
int64 t_initial = cv::getTickCount();
|
|
|
|
bool visualise_hog = verbose;
|
|
|
|
// Timestamp in seconds of current processing
|
|
double time_stamp = 0;
|
|
|
|
INFO_STREAM( "Starting tracking");
|
|
while(!captured_image.empty())
|
|
{
|
|
|
|
// Grab the timestamp first
|
|
if (video_input)
|
|
{
|
|
time_stamp = (double)frame_count * (1.0 / fps_vid_in);
|
|
}
|
|
else
|
|
{
|
|
// if loading images assume 30fps
|
|
time_stamp = (double)frame_count * (1.0 / 30.0);
|
|
}
|
|
|
|
// Reading the images
|
|
cv::Mat_<uchar> grayscale_image;
|
|
|
|
if(captured_image.channels() == 3)
|
|
{
|
|
cvtColor(captured_image, grayscale_image, CV_BGR2GRAY);
|
|
}
|
|
else
|
|
{
|
|
grayscale_image = captured_image.clone();
|
|
}
|
|
|
|
// The actual facial landmark detection / tracking
|
|
bool detection_success;
|
|
|
|
if(video_input || images_as_video)
|
|
{
|
|
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_model, det_parameters);
|
|
}
|
|
else
|
|
{
|
|
detection_success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_model, det_parameters);
|
|
}
|
|
|
|
// Gaze tracking, absolute gaze direction
|
|
cv::Point3f gazeDirection0(0, 0, -1);
|
|
cv::Point3f gazeDirection1(0, 0, -1);
|
|
|
|
if (det_parameters.track_gaze && detection_success && face_model.eye_model)
|
|
{
|
|
FaceAnalysis::EstimateGaze(face_model, gazeDirection0, fx, fy, cx, cy, true);
|
|
FaceAnalysis::EstimateGaze(face_model, gazeDirection1, fx, fy, cx, cy, false);
|
|
}
|
|
|
|
// Do face alignment
|
|
cv::Mat sim_warped_img;
|
|
cv::Mat_<double> hog_descriptor;
|
|
|
|
// But only if needed in output
|
|
if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs)
|
|
{
|
|
face_analyser.AddNextFrame(captured_image, face_model, time_stamp, false, !det_parameters.quiet_mode);
|
|
face_analyser.GetLatestAlignedFace(sim_warped_img);
|
|
|
|
if(!det_parameters.quiet_mode)
|
|
{
|
|
cv::imshow("sim_warp", sim_warped_img);
|
|
}
|
|
if(hog_output_file.is_open())
|
|
{
|
|
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
|
|
|
|
if(visualise_hog && !det_parameters.quiet_mode)
|
|
{
|
|
cv::Mat_<double> hog_descriptor_vis;
|
|
FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
|
|
cv::imshow("hog", hog_descriptor_vis);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Work out the pose of the head from the tracked model
|
|
cv::Vec6d pose_estimate;
|
|
if(use_world_coordinates)
|
|
{
|
|
pose_estimate = LandmarkDetector::GetCorrectedPoseWorld(face_model, fx, fy, cx, cy);
|
|
}
|
|
else
|
|
{
|
|
pose_estimate = LandmarkDetector::GetCorrectedPoseCamera(face_model, fx, fy, cx, cy);
|
|
}
|
|
|
|
if (hog_output_file.is_open())
|
|
{
|
|
output_HOG_frame(&hog_output_file, detection_success, hog_descriptor, num_hog_rows, num_hog_cols);
|
|
}
|
|
|
|
// Write the similarity normalised output
|
|
if (!output_similarity_align.empty())
|
|
{
|
|
|
|
if (sim_warped_img.channels() == 3 && grayscale)
|
|
{
|
|
cvtColor(sim_warped_img, sim_warped_img, CV_BGR2GRAY);
|
|
}
|
|
|
|
char name[100];
|
|
|
|
// Filename is based on frame number
|
|
std::sprintf(name, "frame_det_%06d.bmp", frame_count + 1);
|
|
|
|
// Construct the output filename
|
|
boost::filesystem::path slash("/");
|
|
|
|
std::string preferredSlash = slash.make_preferred().string();
|
|
|
|
string out_file = output_similarity_align[f_n] + preferredSlash + string(name);
|
|
bool write_success = imwrite(out_file, sim_warped_img);
|
|
|
|
if (!write_success)
|
|
{
|
|
cout << "Could not output similarity aligned image image" << endl;
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
// Visualising the tracker
|
|
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
|
|
|
|
// Output the landmarks, pose, gaze, parameters and AUs
|
|
outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze,
|
|
face_model, frame_count, time_stamp, detection_success, gazeDirection0, gazeDirection1,
|
|
pose_estimate, fx, fy, cx, cy, face_analyser);
|
|
|
|
// output the tracked video
|
|
if(!tracked_videos_output.empty())
|
|
{
|
|
writerFace << captured_image;
|
|
}
|
|
|
|
if(video_input)
|
|
{
|
|
video_capture >> captured_image;
|
|
}
|
|
else
|
|
{
|
|
curr_img++;
|
|
if(curr_img < (int)input_image_files[f_n].size())
|
|
{
|
|
string curr_img_file = input_image_files[f_n][curr_img];
|
|
captured_image = cv::imread(curr_img_file, -1);
|
|
}
|
|
else
|
|
{
|
|
captured_image = cv::Mat();
|
|
}
|
|
}
|
|
|
|
if (!det_parameters.quiet_mode)
|
|
{
|
|
// detect key presses
|
|
char character_press = cv::waitKey(1);
|
|
|
|
// restart the tracker
|
|
if(character_press == 'r')
|
|
{
|
|
face_model.Reset();
|
|
}
|
|
// quit the application
|
|
else if(character_press=='q')
|
|
{
|
|
return(0);
|
|
}
|
|
}
|
|
|
|
// Update the frame count
|
|
frame_count++;
|
|
|
|
if(total_frames != -1)
|
|
{
|
|
if((double)frame_count/(double)total_frames >= reported_completion / 10.0)
|
|
{
|
|
cout << reported_completion * 10 << "% ";
|
|
reported_completion = reported_completion + 1;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
output_file.close();
|
|
|
|
if (output_files.size() > 0 && output_AUs)
|
|
{
|
|
cout << "Postprocessing the Action Unit predictions" << endl;
|
|
face_analyser.PostprocessOutputFile(output_files[f_n], dynamic);
|
|
}
|
|
// Reset the models for the next video
|
|
face_analyser.Reset();
|
|
face_model.Reset();
|
|
|
|
frame_count = 0;
|
|
curr_img = -1;
|
|
|
|
if (total_frames != -1)
|
|
{
|
|
cout << endl;
|
|
}
|
|
|
|
// break out of the loop if done with all the files (or using a webcam)
|
|
if((video_input && f_n == input_files.size() -1) || (!video_input && f_n == input_image_files.size() - 1))
|
|
{
|
|
done = true;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void prepareOutputFile(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
|
|
bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
|
|
int num_landmarks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)
|
|
{
|
|
|
|
*output_file << "frame, timestamp, confidence, success";
|
|
|
|
if (output_gaze)
|
|
{
|
|
*output_file << ", gaze_0_x, gaze_0_y, gaze_0_z, gaze_1_x, gaze_1_y, gaze_1_z";
|
|
}
|
|
|
|
if (output_pose)
|
|
{
|
|
*output_file << ", pose_Tx, pose_Ty, pose_Tz, pose_Rx, pose_Ry, pose_Rz";
|
|
}
|
|
|
|
if (output_2D_landmarks)
|
|
{
|
|
for (int i = 0; i < num_landmarks; ++i)
|
|
{
|
|
*output_file << ", x_" << i;
|
|
}
|
|
for (int i = 0; i < num_landmarks; ++i)
|
|
{
|
|
*output_file << ", y_" << i;
|
|
}
|
|
}
|
|
|
|
if (output_3D_landmarks)
|
|
{
|
|
for (int i = 0; i < num_landmarks; ++i)
|
|
{
|
|
*output_file << ", X_" << i;
|
|
}
|
|
for (int i = 0; i < num_landmarks; ++i)
|
|
{
|
|
*output_file << ", Y_" << i;
|
|
}
|
|
for (int i = 0; i < num_landmarks; ++i)
|
|
{
|
|
*output_file << ", Z_" << i;
|
|
}
|
|
}
|
|
|
|
// Outputting model parameters (rigid and non-rigid), the first parameters are the 6 rigid shape parameters, they are followed by the non rigid shape parameters
|
|
if (output_model_params)
|
|
{
|
|
*output_file << ", p_scale, p_rx, p_ry, p_rz, p_tx, p_ty";
|
|
for (int i = 0; i < num_model_modes; ++i)
|
|
{
|
|
*output_file << ", p_" << i;
|
|
}
|
|
}
|
|
|
|
if (output_AUs)
|
|
{
|
|
std::sort(au_names_reg.begin(), au_names_reg.end());
|
|
for (string reg_name : au_names_reg)
|
|
{
|
|
*output_file << ", " << reg_name << "_r";
|
|
}
|
|
|
|
std::sort(au_names_class.begin(), au_names_class.end());
|
|
for (string class_name : au_names_class)
|
|
{
|
|
*output_file << ", " << class_name << "_c";
|
|
}
|
|
}
|
|
|
|
*output_file << endl;
|
|
|
|
}
|
|
|
|
// Output all of the information into one file in one go (quite a few parameters, but simplifies the flow)
|
|
void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
|
|
bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
|
|
const LandmarkDetector::CLNF& face_model, int frame_count, double time_stamp, bool detection_success,
|
|
cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
|
|
const FaceAnalysis::FaceAnalyser& face_analyser)
|
|
{
|
|
|
|
double confidence = 0.5 * (1 - face_model.detection_certainty);
|
|
|
|
*output_file << frame_count + 1 << ", " << time_stamp << ", " << confidence << ", " << detection_success;
|
|
|
|
// Output the estimated gaze
|
|
if (output_gaze)
|
|
{
|
|
*output_file << ", " << gazeDirection0.x << ", " << gazeDirection0.y << ", " << gazeDirection0.z
|
|
<< ", " << gazeDirection1.x << ", " << gazeDirection1.y << ", " << gazeDirection1.z;
|
|
}
|
|
|
|
// Output the estimated head pose
|
|
if (output_pose)
|
|
{
|
|
if(face_model.tracking_initialised)
|
|
{
|
|
*output_file << ", " << pose_estimate[0] << ", " << pose_estimate[1] << ", " << pose_estimate[2]
|
|
<< ", " << pose_estimate[3] << ", " << pose_estimate[4] << ", " << pose_estimate[5];
|
|
}
|
|
else
|
|
{
|
|
*output_file << ", 0, 0, 0, 0, 0, 0";
|
|
}
|
|
}
|
|
|
|
// Output the detected 2D facial landmarks
|
|
if (output_2D_landmarks)
|
|
{
|
|
for (int i = 0; i < face_model.pdm.NumberOfPoints() * 2; ++i)
|
|
{
|
|
if(face_model.tracking_initialised)
|
|
{
|
|
*output_file << ", " << face_model.detected_landmarks.at<double>(i);
|
|
}
|
|
else
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
}
|
|
|
|
// Output the detected 3D facial landmarks
|
|
if (output_3D_landmarks)
|
|
{
|
|
cv::Mat_<double> shape_3D = face_model.GetShape(fx, fy, cx, cy);
|
|
for (int i = 0; i < face_model.pdm.NumberOfPoints() * 3; ++i)
|
|
{
|
|
if (face_model.tracking_initialised)
|
|
{
|
|
*output_file << ", " << shape_3D.at<double>(i);
|
|
}
|
|
else
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
}
|
|
|
|
if (output_model_params)
|
|
{
|
|
for (int i = 0; i < 6; ++i)
|
|
{
|
|
if (face_model.tracking_initialised)
|
|
{
|
|
*output_file << ", " << face_model.params_global[i];
|
|
}
|
|
else
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
for (int i = 0; i < face_model.pdm.NumberOfModes(); ++i)
|
|
{
|
|
if(face_model.tracking_initialised)
|
|
{
|
|
*output_file << ", " << face_model.params_local.at<double>(i, 0);
|
|
}
|
|
else
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
if (output_AUs)
|
|
{
|
|
auto aus_reg = face_analyser.GetCurrentAUsReg();
|
|
|
|
vector<string> au_reg_names = face_analyser.GetAURegNames();
|
|
std::sort(au_reg_names.begin(), au_reg_names.end());
|
|
|
|
// write out ar the correct index
|
|
for (string au_name : au_reg_names)
|
|
{
|
|
for (auto au_reg : aus_reg)
|
|
{
|
|
if (au_name.compare(au_reg.first) == 0)
|
|
{
|
|
*output_file << ", " << au_reg.second;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (aus_reg.size() == 0)
|
|
{
|
|
for (size_t p = 0; p < face_analyser.GetAURegNames().size(); ++p)
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
|
|
auto aus_class = face_analyser.GetCurrentAUsClass();
|
|
|
|
vector<string> au_class_names = face_analyser.GetAUClassNames();
|
|
std::sort(au_class_names.begin(), au_class_names.end());
|
|
|
|
// write out ar the correct index
|
|
for (string au_name : au_class_names)
|
|
{
|
|
for (auto au_class : aus_class)
|
|
{
|
|
if (au_name.compare(au_class.first) == 0)
|
|
{
|
|
*output_file << ", " << au_class.second;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (aus_class.size() == 0)
|
|
{
|
|
for (size_t p = 0; p < face_analyser.GetAUClassNames().size(); ++p)
|
|
{
|
|
*output_file << ", 0";
|
|
}
|
|
}
|
|
}
|
|
*output_file << endl;
|
|
}
|
|
|
|
|
|
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
|
|
int &similarity_size, bool &grayscale, bool& verbose, bool& dynamic,
|
|
bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze,
|
|
vector<string> &arguments)
|
|
{
|
|
output_similarity_aligned.clear();
|
|
output_hog_aligned_files.clear();
|
|
|
|
bool* valid = new bool[arguments.size()];
|
|
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
valid[i] = true;
|
|
}
|
|
|
|
string output_root = "";
|
|
|
|
// By default the model is dynamic
|
|
dynamic = true;
|
|
|
|
string separator = string(1, boost::filesystem::path::preferred_separator);
|
|
|
|
// First check if there is a root argument (so that videos and outputs could be defined more easilly)
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
if (arguments[i].compare("-root") == 0)
|
|
{
|
|
output_root = arguments[i + 1] + separator;
|
|
i++;
|
|
}
|
|
if (arguments[i].compare("-outroot") == 0)
|
|
{
|
|
output_root = arguments[i + 1] + separator;
|
|
i++;
|
|
}
|
|
}
|
|
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
if (arguments[i].compare("-simalign") == 0)
|
|
{
|
|
output_similarity_aligned.push_back(output_root + arguments[i + 1]);
|
|
create_directory(output_root + arguments[i + 1]);
|
|
valid[i] = false;
|
|
valid[i + 1] = false;
|
|
i++;
|
|
}
|
|
else if (arguments[i].compare("-hogalign") == 0)
|
|
{
|
|
output_hog_aligned_files.push_back(output_root + arguments[i + 1]);
|
|
create_directory_from_file(output_root + arguments[i + 1]);
|
|
valid[i] = false;
|
|
valid[i + 1] = false;
|
|
i++;
|
|
}
|
|
else if (arguments[i].compare("-verbose") == 0)
|
|
{
|
|
verbose = true;
|
|
}
|
|
else if (arguments[i].compare("-au_static") == 0)
|
|
{
|
|
dynamic = false;
|
|
}
|
|
else if (arguments[i].compare("-g") == 0)
|
|
{
|
|
grayscale = true;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-simscale") == 0)
|
|
{
|
|
similarity_scale = stod(arguments[i + 1]);
|
|
valid[i] = false;
|
|
valid[i + 1] = false;
|
|
i++;
|
|
}
|
|
else if (arguments[i].compare("-simsize") == 0)
|
|
{
|
|
similarity_size = stoi(arguments[i + 1]);
|
|
valid[i] = false;
|
|
valid[i + 1] = false;
|
|
i++;
|
|
}
|
|
else if (arguments[i].compare("-no2Dfp") == 0)
|
|
{
|
|
output_2D_landmarks = false;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-no3Dfp") == 0)
|
|
{
|
|
output_3D_landmarks = false;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-noMparams") == 0)
|
|
{
|
|
output_model_params = false;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-noPose") == 0)
|
|
{
|
|
output_pose = false;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-noAUs") == 0)
|
|
{
|
|
output_AUs = false;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-noGaze") == 0)
|
|
{
|
|
output_gaze = false;
|
|
valid[i] = false;
|
|
}
|
|
}
|
|
|
|
for (int i = arguments.size() - 1; i >= 0; --i)
|
|
{
|
|
if (!valid[i])
|
|
{
|
|
arguments.erase(arguments.begin() + i);
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
|
|
// Can process images via directories creating a separate output file per directory
|
|
void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments)
|
|
{
|
|
bool* valid = new bool[arguments.size()];
|
|
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
valid[i] = true;
|
|
if (arguments[i].compare("-fdir") == 0)
|
|
{
|
|
|
|
// parse the -fdir directory by reading in all of the .png and .jpg files in it
|
|
path image_directory(arguments[i + 1]);
|
|
|
|
try
|
|
{
|
|
// does the file exist and is it a directory
|
|
if (exists(image_directory) && is_directory(image_directory))
|
|
{
|
|
|
|
vector<path> file_in_directory;
|
|
copy(directory_iterator(image_directory), directory_iterator(), back_inserter(file_in_directory));
|
|
|
|
// Sort the images in the directory first
|
|
sort(file_in_directory.begin(), file_in_directory.end());
|
|
|
|
vector<string> curr_dir_files;
|
|
|
|
for (vector<path>::const_iterator file_iterator(file_in_directory.begin()); file_iterator != file_in_directory.end(); ++file_iterator)
|
|
{
|
|
// Possible image extension .jpg and .png
|
|
if (file_iterator->extension().string().compare(".jpg") == 0 || file_iterator->extension().string().compare(".png") == 0)
|
|
{
|
|
curr_dir_files.push_back(file_iterator->string());
|
|
}
|
|
}
|
|
|
|
input_image_files.push_back(curr_dir_files);
|
|
}
|
|
}
|
|
catch (const filesystem_error& ex)
|
|
{
|
|
cout << ex.what() << '\n';
|
|
}
|
|
|
|
valid[i] = false;
|
|
valid[i + 1] = false;
|
|
i++;
|
|
}
|
|
else if (arguments[i].compare("-asvid") == 0)
|
|
{
|
|
as_video = true;
|
|
}
|
|
}
|
|
|
|
// Clear up the argument list
|
|
for (int i = arguments.size() - 1; i >= 0; --i)
|
|
{
|
|
if (!valid[i])
|
|
{
|
|
arguments.erase(arguments.begin() + i);
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
void output_HOG_frame(std::ofstream* hog_file, bool good_frame, const cv::Mat_<double>& hog_descriptor, int num_rows, int num_cols)
|
|
{
|
|
|
|
// Using FHOGs, hence 31 channels
|
|
int num_channels = 31;
|
|
|
|
hog_file->write((char*)(&num_cols), 4);
|
|
hog_file->write((char*)(&num_rows), 4);
|
|
hog_file->write((char*)(&num_channels), 4);
|
|
|
|
// Not the best way to store a bool, but will be much easier to read it
|
|
float good_frame_float;
|
|
if (good_frame)
|
|
good_frame_float = 1;
|
|
else
|
|
good_frame_float = -1;
|
|
|
|
hog_file->write((char*)(&good_frame_float), 4);
|
|
|
|
cv::MatConstIterator_<double> descriptor_it = hog_descriptor.begin();
|
|
|
|
for (int y = 0; y < num_cols; ++y)
|
|
{
|
|
for (int x = 0; x < num_rows; ++x)
|
|
{
|
|
for (unsigned int o = 0; o < 31; ++o)
|
|
{
|
|
|
|
float hog_data = (float)(*descriptor_it++);
|
|
hog_file->write((char*)&hog_data, 4);
|
|
}
|
|
}
|
|
}
|
|
}
|