497 lines
16 KiB
C++
497 lines
16 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called “open source” software licenses (“Open Source
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// Components”), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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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 <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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// Some initial parameters that can be overriden from command line
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vector<string> files, depth_directories, tracked_videos_output, dummy_out;
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// By default try webcam 0
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int device = 0;
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// 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)
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float fx = 600, fy = 600, cx = 0, cy = 0;
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LandmarkDetector::FaceModelParameters det_params(arguments);
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det_params.use_face_template = true;
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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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// Get the input output file parameters
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bool u;
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LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, u, arguments);
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// Get camera parameters
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LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
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// The modules that are being used for tracking
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vector<LandmarkDetector::CLNF> clnf_models;
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vector<bool> active_models;
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int num_faces_max = 4;
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LandmarkDetector::CLNF clnf_model(det_parameters[0].model_location);
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clnf_model.face_detector_HAAR.load(det_parameters[0].face_detector_location);
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clnf_model.face_detector_location = det_parameters[0].face_detector_location;
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clnf_models.reserve(num_faces_max);
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clnf_models.push_back(clnf_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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clnf_models.push_back(clnf_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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// 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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// 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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if(cx == 0 || cy == 0)
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{
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cx_undefined = true;
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}
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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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// We might specify multiple video files as arguments
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if(files.size() > 0)
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{
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f_n++;
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current_file = files[f_n];
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}
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bool use_depth = !depth_directories.empty();
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// Do some grabbing
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cv::VideoCapture video_capture;
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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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}
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else
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{
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INFO_STREAM( "Attempting to capture from device: " << device );
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video_capture = cv::VideoCapture( device );
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// Read a first frame often empty in camera
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cv::Mat captured_image;
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video_capture >> captured_image;
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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");
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return 1;
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}
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else INFO_STREAM( "Device or file opened");
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cv::Mat captured_image;
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video_capture >> captured_image;
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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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int frame_count = 0;
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// saving the videos
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cv::VideoWriter writerFace;
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if(!tracked_videos_output.empty())
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{
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writerFace = cv::VideoWriter(tracked_videos_output[f_n], CV_FOURCC('D','I','V','X'), 30, captured_image.size(), true);
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}
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// For measuring the timings
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int64 t1,t0 = cv::getTickCount();
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double fps = 10;
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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_<float> depth_image;
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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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// Get depth image
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if(use_depth)
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{
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char* dst = new char[100];
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std::stringstream sstream;
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sstream << depth_directories[f_n] << "\\depth%05d.png";
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sprintf(dst, sstream.str().c_str(), frame_count + 1);
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// Reading in 16-bit png image representing depth
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cv::Mat_<short> depth_image_16_bit = cv::imread(string(dst), -1);
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// Convert to a floating point depth image
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if(!depth_image_16_bit.empty())
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{
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depth_image_16_bit.convertTo(depth_image, CV_32F);
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}
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else
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{
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WARN_STREAM( "Can't find depth image" );
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}
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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 < clnf_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, clnf_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, clnf_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(clnf_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)clnf_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(clnf_models[model].failures_in_a_row > 4)
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{
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active_models[model] = false;
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clnf_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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clnf_models[model].Reset();
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// This ensures that a wider window is used for the initial landmark localisation
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clnf_models[model].detection_success = false;
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detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, face_detections[detection_ind], clnf_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, depth_image, clnf_models[model], det_parameters[model]);
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}
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});
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// Go through every model and visualise the results
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for(size_t model = 0; model < clnf_models.size(); ++model)
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{
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// Visualising the results
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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 = clnf_models[model].detection_certainty;
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double visualisation_boundary = -0.1;
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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(disp_image, clnf_models[model]);
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if(detection_certainty > 1)
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detection_certainty = 1;
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if(detection_certainty < -1)
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detection_certainty = -1;
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detection_certainty = (detection_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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// Work out the pose of the head from the tracked model
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cv::Vec6d pose_estimate = LandmarkDetector::GetCorrectedPoseWorld(clnf_models[model], fx, fy, cx, cy);
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// Draw it in reddish if uncertain, blueish if certain
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LandmarkDetector::DrawBox(disp_image, pose_estimate, cv::Scalar((1-detection_certainty)*255.0,0, detection_certainty*255), thickness, 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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t1 = cv::getTickCount();
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fps = 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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sprintf(fpsC, "%d", (int)fps);
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string fpsSt("FPS:");
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fpsSt += fpsC;
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cv::putText(disp_image, fpsSt, cv::Point(10,20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255,0,0));
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int num_active_models = 0;
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for( size_t active_model = 0; active_model < active_models.size(); active_model++)
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{
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if(active_models[active_model])
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{
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num_active_models++;
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}
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}
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char active_m_C[255];
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sprintf(active_m_C, "%d", num_active_models);
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string active_models_st("Active models:");
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active_models_st += active_m_C;
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cv::putText(disp_image, active_models_st, cv::Point(10,60), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255,0,0));
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if(!det_parameters[0].quiet_mode)
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{
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cv::namedWindow("tracking_result",1);
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cv::imshow("tracking_result", disp_image);
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if(!depth_image.empty())
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{
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// Division needed for visualisation purposes
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imshow("depth", depth_image/2000.0);
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}
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}
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// output the tracked video
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if(!tracked_videos_output.empty())
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{
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writerFace << disp_image;
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}
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video_capture >> captured_image;
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// detect key presses
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char character_press = cv::waitKey(1);
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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 < clnf_models.size(); ++i)
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{
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clnf_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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}
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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 < clnf_models.size(); ++model)
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{
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clnf_models[model].Reset();
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active_models[model] = false;
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}
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// break out of the loop if done with all the files
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if(f_n == files.size() -1)
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{
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done = true;
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}
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}
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return 0;
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}
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