493 lines
16 KiB
C++
493 lines
16 KiB
C++
///////////////////////////////////////////////////////////////////////////////
|
||
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
|
||
// all rights reserved.
|
||
//
|
||
// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
|
||
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
|
||
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
|
||
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
|
||
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
|
||
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||
// POSSIBILITY OF SUCH DAMAGE.
|
||
//
|
||
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
|
||
// of the Software may be covered by so-called “open source” software licenses (“Open Source
|
||
// Components”), which means any software licenses approved as open source licenses by the
|
||
// Open Source Initiative or any substantially similar licenses, including without limitation any
|
||
// license that, as a condition of distribution of the software licensed under such license,
|
||
// requires that the distributor make the software available in source code format. Licensor shall
|
||
// provide a list of Open Source Components for a particular version of the Software upon
|
||
// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
|
||
// the extent required by the licenses covering Open Source Components, the terms of such
|
||
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
|
||
// licenses applicable to Open Source Components prohibit any of the restrictions in this
|
||
// License Agreement with respect to such Open Source Component, such restrictions will not
|
||
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
|
||
// Open Source Components require Licensor to make an offer to provide source code or
|
||
// related information in connection with the Software, such offer is hereby made. Any request
|
||
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
|
||
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
|
||
// delivery of the Software.
|
||
|
||
// * 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;
|
||
LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, u, 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" );
|
||
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())
|
||
{
|
||
writerFace = cv::VideoWriter(tracked_videos_output[f_n], CV_FOURCC('D','I','V','X'), 30, captured_image.size(), true);
|
||
}
|
||
|
||
// 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));
|
||
|
||
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));
|
||
|
||
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;
|
||
}
|
||
|