628 lines
18 KiB
C++
628 lines
18 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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#include <RecorderOpenFace.h>
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#include <RecorderOpenFaceParameters.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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void get_visualization_params(bool& visualize_track, bool& visualize_align, bool& visualize_hog, 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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// 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::GetPose(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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GazeAnalysis::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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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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// Get the input output file parameters
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vector<string> input_files, output_files;
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string output_codec;
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LandmarkDetector::get_video_input_output_params(input_files, output_files, output_codec, arguments);
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// TODO remove
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bool video_input = 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 (TODO should be moved to capture)
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if(input_files.empty())
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{
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vector<string> o_img;
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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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// Deciding what to visualize
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bool visualize_track = false;
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bool visualize_align = false;
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bool visualize_hog = false;
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get_visualization_params(visualize_track, visualize_align, visualize_hog, arguments);
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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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// Load the modules that are being used for tracking and face analysis
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// Load face landmark detector
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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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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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// Load facial feature extractor and AU analyser
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FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
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FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
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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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// TODO this should be moved to a SequenceCapture class
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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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// If image sequence provided, assume the fps is 30
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fps_vid_in = 30;
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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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Recorder::RecorderOpenFaceParameters recording_params(arguments);
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Recorder::RecorderOpenFace open_face_rec(output_files[f_n], input_files[f_n], recording_params);
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int frame_count = 0;
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// Use for timestamping if using a webcam
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int64 t_initial = cv::getTickCount();
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// Timestamp in seconds of current processing
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double time_stamp = 0;
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INFO_STREAM("Starting tracking");
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while (!captured_image.empty())
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{
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// Grab the timestamp first
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if (video_input)
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{
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time_stamp = (double)frame_count * (1.0 / fps_vid_in);
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}
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else
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{
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// if loading images assume 30fps
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time_stamp = (double)frame_count * (1.0 / 30.0);
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}
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// Reading the images
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cv::Mat_<uchar> grayscale_image;
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if (captured_image.channels() == 3)
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{
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cvtColor(captured_image, grayscale_image, CV_BGR2GRAY);
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}
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else
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{
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grayscale_image = captured_image.clone();
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}
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// The actual facial landmark detection / tracking
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bool detection_success;
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if (video_input || images_as_video)
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{
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_model, det_parameters);
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}
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else
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{
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detection_success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_model, det_parameters);
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}
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// Gaze tracking, absolute gaze direction
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cv::Point3f gazeDirection0(0, 0, -1);
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cv::Point3f gazeDirection1(0, 0, -1);
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cv::Vec2d gazeAngle(0, 0);
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if (det_parameters.track_gaze && detection_success && face_model.eye_model)
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{
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GazeAnalysis::EstimateGaze(face_model, gazeDirection0, fx, fy, cx, cy, true);
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GazeAnalysis::EstimateGaze(face_model, gazeDirection1, fx, fy, cx, cy, false);
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gazeAngle = GazeAnalysis::GetGazeAngle(gazeDirection0, gazeDirection1);
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}
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// Do face alignment
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cv::Mat sim_warped_img;
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cv::Mat_<double> hog_descriptor;
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int num_hog_rows = 0, num_hog_cols = 0;
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// As this can be expensive only compute it if needed by output or visualization
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if (recording_params.outputAlignedFaces() || recording_params.outputHOG() || recording_params.outputAUs() || visualize_align || visualize_hog)
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{
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face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, time_stamp, false, !det_parameters.quiet_mode);
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face_analyser.GetLatestAlignedFace(sim_warped_img);
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if (!det_parameters.quiet_mode && visualize_align)
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{
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cv::imshow("sim_warp", sim_warped_img);
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}
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if (recording_params.outputHOG() || (visualize_hog && !det_parameters.quiet_mode))
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{
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face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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if (visualize_hog && !det_parameters.quiet_mode)
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{
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cv::Mat_<double> hog_descriptor_vis;
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FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
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cv::imshow("hog", hog_descriptor_vis);
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}
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}
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}
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// Work out the pose of the head from the tracked model
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cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, fx, fy, cx, cy);
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// Visualising the tracker, TODO this should be in utility
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if (recording_params.outputTrackedVideo() || (visualize_track && !det_parameters.quiet_mode))
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{
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visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
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}
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// Setting up the recorder output
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open_face_rec.SetObservationHOG(detection_success, hog_descriptor, num_hog_rows, num_hog_cols, 31); // The number of channels in HOG is fixed at the moment, as using FHOG
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open_face_rec.SetObservationVisualization(captured_image);
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open_face_rec.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
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open_face_rec.SetObservationGaze(gazeDirection0, gazeDirection1, gazeAngle, LandmarkDetector::CalculateAllEyeLandmarks(face_model));
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open_face_rec.SetObservationLandmarks(face_model.detected_landmarks, face_model.GetShape(fx, fy, cx, cy), face_model.params_global, face_model.params_local, face_model.detection_certainty, detection_success);
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open_face_rec.SetObservationPose(pose_estimate);
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open_face_rec.SetObservationTimestamp(time_stamp);
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open_face_rec.WriteObservation();
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// Grabbing the next frame (todo this should be part of capture)
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if(video_input)
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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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curr_img++;
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if(curr_img < (int)input_image_files[f_n].size())
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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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captured_image = cv::Mat();
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}
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}
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if (!det_parameters.quiet_mode)
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{
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// detect key presses
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char character_press = cv::waitKey(1);
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// restart the tracker
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if(character_press == 'r')
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{
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face_model.Reset();
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}
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// quit the application
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else if(character_press=='q')
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{
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return(0);
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}
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}
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// Update the frame count
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frame_count++;
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if(total_frames != -1)
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{
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if((double)frame_count/(double)total_frames >= reported_completion / 10.0)
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{
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cout << reported_completion * 10 << "% ";
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reported_completion = reported_completion + 1;
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}
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}
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}
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open_face_rec.Close();
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if (output_files.size() > 0 && recording_params.outputAUs())
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{
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cout << "Postprocessing the Action Unit predictions" << endl;
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face_analyser.PostprocessOutputFile(open_face_rec.GetCSVFile()); // TODO this won't work, need the filename
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}
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// Reset the models for the next video
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face_analyser.Reset();
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face_model.Reset();
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frame_count = 0;
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curr_img = -1;
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if (total_frames != -1)
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{
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cout << endl;
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}
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// break out of the loop if done with all the files (or using a webcam)
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if((video_input && f_n == input_files.size() -1) || (!video_input && f_n == input_image_files.size() - 1))
|
|
{
|
|
done = true;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void get_visualization_params(bool& visualize_track, bool& visualize_align, bool& visualize_hog,vector<string> &arguments)
|
|
{
|
|
|
|
bool* valid = new bool[arguments.size()];
|
|
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
valid[i] = true;
|
|
}
|
|
|
|
string output_root = "";
|
|
|
|
visualize_align = false;
|
|
visualize_hog = false;
|
|
visualize_track = false;
|
|
|
|
for (size_t i = 0; i < arguments.size(); ++i)
|
|
{
|
|
if (arguments[i].compare("-verbose") == 0)
|
|
{
|
|
visualize_track = true;
|
|
visualize_align = true;
|
|
visualize_hog = true;
|
|
}
|
|
else if (arguments[i].compare("-vis-align") == 0)
|
|
{
|
|
visualize_align = true;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-vis-hog") == 0)
|
|
{
|
|
visualize_hog = true;
|
|
valid[i] = false;
|
|
}
|
|
else if (arguments[i].compare("-vis-track") == 0)
|
|
{
|
|
visualize_track = true;
|
|
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);
|
|
}
|
|
}
|
|
|
|
} |