340 lines
10 KiB
C++
340 lines
10 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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// FaceTrackingVidMulti.cpp : Defines the entry point for the multiple face tracking console application.
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#include "LandmarkCoreIncludes.h"
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#include "VisualizationUtils.h"
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#include "Visualizer.h"
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#include "SequenceCapture.h"
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#include <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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#define INFO_STREAM( stream ) \
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std::cout << stream << std::endl
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#define WARN_STREAM( stream ) \
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std::cout << "Warning: " << stream << std::endl
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#define ERROR_STREAM( stream ) \
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std::cout << "Error: " << stream << std::endl
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static void printErrorAndAbort( const std::string & error )
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{
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std::cout << error << std::endl;
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abort();
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}
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#define FATAL_STREAM( stream ) \
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printErrorAndAbort( std::string( "Fatal error: " ) + stream )
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using namespace std;
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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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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 NonOverlapingDetections(const vector<LandmarkDetector::CLNF>& clnf_models, vector<cv::Rect_<double> >& face_detections)
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{
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// Go over the model and eliminate detections that are not informative (there already is a tracker there)
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for(size_t model = 0; model < clnf_models.size(); ++model)
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{
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// See if the detections intersect
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cv::Rect_<double> model_rect = clnf_models[model].GetBoundingBox();
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for(int detection = face_detections.size()-1; detection >=0; --detection)
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{
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double intersection_area = (model_rect & face_detections[detection]).area();
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double union_area = model_rect.area() + face_detections[detection].area() - 2 * intersection_area;
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// If the model is already tracking what we're detecting ignore the detection, this is determined by amount of overlap
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if( intersection_area/union_area > 0.5)
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{
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face_detections.erase(face_detections.begin() + detection);
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}
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}
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}
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}
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int main (int argc, char **argv)
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{
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vector<string> arguments = get_arguments(argc, argv);
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LandmarkDetector::FaceModelParameters det_params(arguments);
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// This is so that the model would not try re-initialising itself
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det_params.reinit_video_every = -1;
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det_params.curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
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vector<LandmarkDetector::FaceModelParameters> det_parameters;
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det_parameters.push_back(det_params);
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// The modules that are being used for tracking
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vector<LandmarkDetector::CLNF> face_models;
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vector<bool> active_models;
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int num_faces_max = 4;
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LandmarkDetector::CLNF face_model(det_parameters[0].model_location);
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face_model.face_detector_HAAR.load(det_parameters[0].face_detector_location);
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face_model.face_detector_location = det_parameters[0].face_detector_location;
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face_models.reserve(num_faces_max);
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face_models.push_back(face_model);
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active_models.push_back(false);
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for (int i = 1; i < num_faces_max; ++i)
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{
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face_models.push_back(face_model);
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active_models.push_back(false);
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det_parameters.push_back(det_params);
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}
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// Open a sequence
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Utilities::SequenceCapture sequence_reader;
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// A utility for visualizing the results (show just the tracks)
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Utilities::Visualizer visualizer(true, false, false);
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// Tracking FPS for visualization
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Utilities::FpsTracker fps_tracker;
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fps_tracker.AddFrame();
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int sequence_number = 0;
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while(true) // this is not a for loop as we might also be reading from a webcam
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{
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// The sequence reader chooses what to open based on command line arguments provided
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if (!sequence_reader.Open(arguments))
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{
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// If failed to open because no input files specified, attempt to open a webcam
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if (sequence_reader.no_input_specified && sequence_number == 0)
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{
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// If that fails, revert to webcam
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INFO_STREAM("No input specified, attempting to open a webcam 0");
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if (!sequence_reader.OpenWebcam(0))
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{
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ERROR_STREAM("Failed to open the webcam");
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break;
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}
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}
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else
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{
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break;
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}
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}
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INFO_STREAM("Device or file opened");
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cv::Mat captured_image = sequence_reader.GetNextFrame();
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int frame_count = 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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// Reading the images
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cv::Mat_<uchar> grayscale_image;
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cv::Mat disp_image = captured_image.clone();
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if(captured_image.channels() == 3)
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{
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cv::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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vector<cv::Rect_<double> > face_detections;
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bool all_models_active = true;
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for(unsigned int model = 0; model < face_models.size(); ++model)
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{
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if(!active_models[model])
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{
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all_models_active = false;
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}
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}
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// Get the detections (every 8th frame and when there are free models available for tracking)
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if(frame_count % 8 == 0 && !all_models_active)
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{
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if(det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
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{
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vector<double> confidences;
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LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_models[0].face_detector_HOG, confidences);
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}
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else
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{
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LandmarkDetector::DetectFaces(face_detections, grayscale_image, face_models[0].face_detector_HAAR);
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}
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}
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// Keep only non overlapping detections (also convert to a concurrent vector
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NonOverlapingDetections(face_models, face_detections);
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vector<tbb::atomic<bool> > face_detections_used(face_detections.size());
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// Go through every model and update the tracking
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tbb::parallel_for(0, (int)face_models.size(), [&](int model){
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//for(unsigned int model = 0; model < clnf_models.size(); ++model)
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//{
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bool detection_success = false;
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// If the current model has failed more than 4 times in a row, remove it
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if(face_models[model].failures_in_a_row > 4)
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{
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active_models[model] = false;
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face_models[model].Reset();
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}
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// If the model is inactive reactivate it with new detections
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if(!active_models[model])
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{
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for(size_t detection_ind = 0; detection_ind < face_detections.size(); ++detection_ind)
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{
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// if it was not taken by another tracker take it (if it is false swap it to true and enter detection, this makes it parallel safe)
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if(face_detections_used[detection_ind].compare_and_swap(true, false) == false)
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{
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// Reinitialise the model
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face_models[model].Reset();
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// This ensures that a wider window is used for the initial landmark localisation
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face_models[model].detection_success = false;
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], face_models[model], det_parameters[model]);
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// This activates the model
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active_models[model] = true;
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// break out of the loop as the tracker has been reinitialised
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break;
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}
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}
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}
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else
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{
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// The actual facial landmark detection / tracking
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_models[model], det_parameters[model]);
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}
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});
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// Keeping track of FPS
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fps_tracker.AddFrame();
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visualizer.SetImage(captured_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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// Go through every model and visualise the results
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for(size_t model = 0; model < face_models.size(); ++model)
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{
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// Visualising the results
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if(active_models[model])
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{
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visualizer.SetObservationLandmarks(face_models[model].detected_landmarks, face_models[model].detection_certainty);
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visualizer.SetObservationPose(LandmarkDetector::GetPose(face_models[model], sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_models[model].detection_certainty);
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}
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}
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visualizer.SetFps(fps_tracker.GetFPS());
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// show visualization and detect key presses
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char character_press = visualizer.ShowObservation();
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// restart the trackers
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if(character_press == 'r')
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{
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for(size_t i=0; i < face_models.size(); ++i)
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{
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face_models[i].Reset();
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active_models[i] = false;
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}
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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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// Update the frame count
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frame_count++;
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// Grabbing the next frame in the sequence
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captured_image = sequence_reader.GetNextFrame();
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}
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frame_count = 0;
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// Reset the model, for the next video
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for(size_t model=0; model < face_models.size(); ++model)
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{
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face_models[model].Reset();
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active_models[model] = false;
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}
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sequence_number++;
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}
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return 0;
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}
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