sustaining_gazes/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
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// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
//
// OpenFace: an open source facial behavior analysis toolkit
// Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency
// in IEEE Winter Conference on Applications of Computer Vision, 2016
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltru<72>aitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
// Tadas Baltru<72>aitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
// Tadas Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// FaceTrackingVidMulti.cpp : Defines the entry point for the multiple face tracking console application.
#include "LandmarkCoreIncludes.h"
#include <fstream>
#include <sstream>
// OpenCV includes
#include <opencv2/videoio/videoio.hpp> // Video write
#include <opencv2/videoio/videoio_c.h> // Video write
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#define INFO_STREAM( stream ) \
std::cout << stream << std::endl
#define WARN_STREAM( stream ) \
std::cout << "Warning: " << stream << std::endl
#define ERROR_STREAM( stream ) \
std::cout << "Error: " << stream << std::endl
static void printErrorAndAbort( const std::string & error )
{
std::cout << error << std::endl;
abort();
}
#define FATAL_STREAM( stream ) \
printErrorAndAbort( std::string( "Fatal error: " ) + stream )
using namespace std;
vector<string> get_arguments(int argc, char **argv)
{
vector<string> arguments;
for(int i = 0; i < argc; ++i)
{
arguments.push_back(string(argv[i]));
}
return arguments;
}
void NonOverlapingDetections(const vector<LandmarkDetector::CLNF>& clnf_models, vector<cv::Rect_<double> >& face_detections)
{
// Go over the model and eliminate detections that are not informative (there already is a tracker there)
for(size_t model = 0; model < clnf_models.size(); ++model)
{
// See if the detections intersect
cv::Rect_<double> model_rect = clnf_models[model].GetBoundingBox();
for(int detection = face_detections.size()-1; detection >=0; --detection)
{
double intersection_area = (model_rect & face_detections[detection]).area();
double union_area = model_rect.area() + face_detections[detection].area() - 2 * intersection_area;
// If the model is already tracking what we're detecting ignore the detection, this is determined by amount of overlap
if( intersection_area/union_area > 0.5)
{
face_detections.erase(face_detections.begin() + detection);
}
}
}
}
int main (int argc, char **argv)
{
vector<string> arguments = get_arguments(argc, argv);
// Some initial parameters that can be overriden from command line
vector<string> files, tracked_videos_output, dummy_out;
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// By default try webcam 0
int device = 0;
// cx and cy aren't necessarilly in the image center, so need to be able to override it (start with unit vals and init them if none specified)
float fx = 600, fy = 600, cx = 0, cy = 0;
LandmarkDetector::FaceModelParameters det_params(arguments);
det_params.use_face_template = true;
// This is so that the model would not try re-initialising itself
det_params.reinit_video_every = -1;
det_params.curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
vector<LandmarkDetector::FaceModelParameters> det_parameters;
det_parameters.push_back(det_params);
// Get the input output file parameters
string output_codec;
LandmarkDetector::get_video_input_output_params(files, dummy_out, tracked_videos_output, output_codec, arguments);
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// Get camera parameters
LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
// The modules that are being used for tracking
vector<LandmarkDetector::CLNF> clnf_models;
vector<bool> active_models;
int num_faces_max = 4;
LandmarkDetector::CLNF clnf_model(det_parameters[0].model_location);
clnf_model.face_detector_HAAR.load(det_parameters[0].face_detector_location);
clnf_model.face_detector_location = det_parameters[0].face_detector_location;
clnf_models.reserve(num_faces_max);
clnf_models.push_back(clnf_model);
active_models.push_back(false);
for (int i = 1; i < num_faces_max; ++i)
{
clnf_models.push_back(clnf_model);
active_models.push_back(false);
det_parameters.push_back(det_params);
}
// If multiple video files are tracked, use this to indicate if we are done
bool done = false;
int f_n = -1;
// If cx (optical axis centre) is undefined will use the image size/2 as an estimate
bool cx_undefined = false;
if(cx == 0 || cy == 0)
{
cx_undefined = true;
}
while(!done) // this is not a for loop as we might also be reading from a webcam
{
string current_file;
// We might specify multiple video files as arguments
if(files.size() > 0)
{
f_n++;
current_file = files[f_n];
}
// Do some grabbing
cv::VideoCapture video_capture;
if( current_file.size() > 0 )
{
INFO_STREAM( "Attempting to read from file: " << current_file );
video_capture = cv::VideoCapture( current_file );
}
else
{
INFO_STREAM( "Attempting to capture from device: " << device );
video_capture = cv::VideoCapture( device );
// Read a first frame often empty in camera
cv::Mat captured_image;
video_capture >> captured_image;
}
if (!video_capture.isOpened())
{
FATAL_STREAM("Failed to open video source");
return 1;
}
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else INFO_STREAM( "Device or file opened");
cv::Mat captured_image;
video_capture >> captured_image;
// If optical centers are not defined just use center of image
if(cx_undefined)
{
cx = captured_image.cols / 2.0f;
cy = captured_image.rows / 2.0f;
}
int frame_count = 0;
// saving the videos
cv::VideoWriter writerFace;
if(!tracked_videos_output.empty())
{
try
{
writerFace = cv::VideoWriter(tracked_videos_output[f_n], CV_FOURCC(output_codec[0],output_codec[1],output_codec[2],output_codec[3]), 30, captured_image.size(), true);
}
catch(cv::Exception e)
{
WARN_STREAM( "Could not open VideoWriter, OUTPUT FILE WILL NOT BE WRITTEN. Currently using codec " << output_codec << ", try using an other one (-oc option)");
}
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}
// For measuring the timings
int64 t1,t0 = cv::getTickCount();
double fps = 10;
INFO_STREAM( "Starting tracking");
while(!captured_image.empty())
{
// Reading the images
cv::Mat_<float> depth_image;
cv::Mat_<uchar> grayscale_image;
cv::Mat disp_image = captured_image.clone();
if(captured_image.channels() == 3)
{
cv::cvtColor(captured_image, grayscale_image, CV_BGR2GRAY);
}
else
{
grayscale_image = captured_image.clone();
}
vector<cv::Rect_<double> > face_detections;
bool all_models_active = true;
for(unsigned int model = 0; model < clnf_models.size(); ++model)
{
if(!active_models[model])
{
all_models_active = false;
}
}
// Get the detections (every 8th frame and when there are free models available for tracking)
if(frame_count % 8 == 0 && !all_models_active)
{
if(det_parameters[0].curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
{
vector<double> confidences;
LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, clnf_models[0].face_detector_HOG, confidences);
}
else
{
LandmarkDetector::DetectFaces(face_detections, grayscale_image, clnf_models[0].face_detector_HAAR);
}
}
// Keep only non overlapping detections (also convert to a concurrent vector
NonOverlapingDetections(clnf_models, face_detections);
vector<tbb::atomic<bool> > face_detections_used(face_detections.size());
// Go through every model and update the tracking
tbb::parallel_for(0, (int)clnf_models.size(), [&](int model){
//for(unsigned int model = 0; model < clnf_models.size(); ++model)
//{
bool detection_success = false;
// If the current model has failed more than 4 times in a row, remove it
if(clnf_models[model].failures_in_a_row > 4)
{
active_models[model] = false;
clnf_models[model].Reset();
}
// If the model is inactive reactivate it with new detections
if(!active_models[model])
{
for(size_t detection_ind = 0; detection_ind < face_detections.size(); ++detection_ind)
{
// 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)
if(face_detections_used[detection_ind].compare_and_swap(true, false) == false)
{
// Reinitialise the model
clnf_models[model].Reset();
// This ensures that a wider window is used for the initial landmark localisation
clnf_models[model].detection_success = false;
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]);
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// This activates the model
active_models[model] = true;
// break out of the loop as the tracker has been reinitialised
break;
}
}
}
else
{
// The actual facial landmark detection / tracking
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_models[model], det_parameters[model]);
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}
});
// Go through every model and visualise the results
for(size_t model = 0; model < clnf_models.size(); ++model)
{
// Visualising the results
// Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised
double detection_certainty = clnf_models[model].detection_certainty;
double visualisation_boundary = -0.1;
// Only draw if the reliability is reasonable, the value is slightly ad-hoc
if(detection_certainty < visualisation_boundary)
{
LandmarkDetector::Draw(disp_image, clnf_models[model]);
if(detection_certainty > 1)
detection_certainty = 1;
if(detection_certainty < -1)
detection_certainty = -1;
detection_certainty = (detection_certainty + 1)/(visualisation_boundary +1);
// A rough heuristic for box around the face width
int thickness = (int)std::ceil(2.0* ((double)captured_image.cols) / 640.0);
// Work out the pose of the head from the tracked model
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cv::Vec6d pose_estimate = LandmarkDetector::GetPose(clnf_models[model], fx, fy, cx, cy);
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// Draw it in reddish if uncertain, blueish if certain
LandmarkDetector::DrawBox(disp_image, pose_estimate, cv::Scalar((1-detection_certainty)*255.0,0, detection_certainty*255), thickness, fx, fy, cx, cy);
}
}
// Work out the framerate
if(frame_count % 10 == 0)
{
t1 = cv::getTickCount();
fps = 10.0 / (double(t1-t0)/cv::getTickFrequency());
t0 = t1;
}
// Write out the framerate on the image before displaying it
char fpsC[255];
sprintf(fpsC, "%d", (int)fps);
string fpsSt("FPS:");
fpsSt += fpsC;
cv::putText(disp_image, fpsSt, cv::Point(10,20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255,0,0), 1, CV_AA);
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int num_active_models = 0;
for( size_t active_model = 0; active_model < active_models.size(); active_model++)
{
if(active_models[active_model])
{
num_active_models++;
}
}
char active_m_C[255];
sprintf(active_m_C, "%d", num_active_models);
string active_models_st("Active models:");
active_models_st += active_m_C;
cv::putText(disp_image, active_models_st, cv::Point(10,60), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255,0,0), 1, CV_AA);
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if(!det_parameters[0].quiet_mode)
{
cv::namedWindow("tracking_result",1);
cv::imshow("tracking_result", disp_image);
if(!depth_image.empty())
{
// Division needed for visualisation purposes
imshow("depth", depth_image/2000.0);
}
}
// output the tracked video
if(!tracked_videos_output.empty())
{
writerFace << disp_image;
}
video_capture >> captured_image;
// detect key presses
char character_press = cv::waitKey(1);
// restart the trackers
if(character_press == 'r')
{
for(size_t i=0; i < clnf_models.size(); ++i)
{
clnf_models[i].Reset();
active_models[i] = false;
}
}
// quit the application
else if(character_press=='q')
{
return(0);
}
// Update the frame count
frame_count++;
}
frame_count = 0;
// Reset the model, for the next video
for(size_t model=0; model < clnf_models.size(); ++model)
{
clnf_models[model].Reset();
active_models[model] = false;
}
// break out of the loop if done with all the files
if(f_n == files.size() -1)
{
done = true;
}
}
return 0;
}