482 lines
14 KiB
C++
482 lines
14 KiB
C++
///////////////////////////////////////////////////////////////////////////////
|
|
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
|
|
// all rights reserved.
|
|
//
|
|
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
|
|
//
|
|
// 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
|
|
|
|
// * 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 Baltrušaitis, 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 Baltrušaitis, 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 Baltrušaitis, 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 Baltrušaitis, 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, depth_directories, tracked_videos_output, dummy_out;
|
|
|
|
// 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
|
|
bool u;
|
|
string output_codec;
|
|
LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, u, output_codec, arguments);
|
|
// 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];
|
|
}
|
|
|
|
bool use_depth = !depth_directories.empty();
|
|
|
|
// 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;
|
|
}
|
|
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)");
|
|
}
|
|
}
|
|
|
|
// 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();
|
|
}
|
|
|
|
// Get depth image
|
|
if(use_depth)
|
|
{
|
|
char* dst = new char[100];
|
|
std::stringstream sstream;
|
|
|
|
sstream << depth_directories[f_n] << "\\depth%05d.png";
|
|
sprintf(dst, sstream.str().c_str(), frame_count + 1);
|
|
// Reading in 16-bit png image representing depth
|
|
cv::Mat_<short> depth_image_16_bit = cv::imread(string(dst), -1);
|
|
|
|
// Convert to a floating point depth image
|
|
if(!depth_image_16_bit.empty())
|
|
{
|
|
depth_image_16_bit.convertTo(depth_image, CV_32F);
|
|
}
|
|
else
|
|
{
|
|
WARN_STREAM( "Can't find depth image" );
|
|
}
|
|
}
|
|
|
|
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, depth_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]);
|
|
|
|
// 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, depth_image, clnf_models[model], det_parameters[model]);
|
|
}
|
|
});
|
|
|
|
// 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
|
|
cv::Vec6d pose_estimate = LandmarkDetector::GetCorrectedPoseWorld(clnf_models[model], fx, fy, cx, cy);
|
|
|
|
// 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);
|
|
|
|
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);
|
|
|
|
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;
|
|
}
|
|
|