358 lines
11 KiB
C++
358 lines
11 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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// FaceTrackingVid.cpp : Defines the entry point for the console application for tracking faces in videos.
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// Libraries for landmark detection (includes CLNF and CLM modules)
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#include "LandmarkCoreIncludes.h"
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#include "GazeEstimation.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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// Boost includes
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#include <filesystem.hpp>
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#include <filesystem/fstream.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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// Some globals for tracking timing information for visualisation
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double fps_tracker = -1.0;
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int64 t0 = 0;
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// Visualising the results
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void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy)
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{
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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 = face_model.detection_certainty;
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bool detection_success = face_model.detection_success;
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double visualisation_boundary = 0.2;
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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(captured_image, face_model);
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double vis_certainty = detection_certainty;
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if (vis_certainty > 1)
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vis_certainty = 1;
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if (vis_certainty < -1)
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vis_certainty = -1;
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vis_certainty = (vis_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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cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(face_model, fx, fy, cx, cy);
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// Draw it in reddish if uncertain, blueish if certain
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LandmarkDetector::DrawBox(captured_image, pose_estimate_to_draw, cv::Scalar((1 - vis_certainty)*255.0, 0, vis_certainty * 255), thickness, fx, fy, cx, cy);
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if (det_parameters.track_gaze && detection_success && face_model.eye_model)
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{
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FaceAnalysis::DrawGaze(captured_image, face_model, gazeDirection0, gazeDirection1, 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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double t1 = cv::getTickCount();
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fps_tracker = 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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std::sprintf(fpsC, "%d", (int)fps_tracker);
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string fpsSt("FPS:");
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fpsSt += fpsC;
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cv::putText(captured_image, fpsSt, cv::Point(10, 20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255, 0, 0));
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if (!det_parameters.quiet_mode)
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{
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cv::namedWindow("tracking_result", 1);
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cv::imshow("tracking_result", captured_image);
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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, output_video_files, out_dummy;
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// By default try webcam 0
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int device = 0;
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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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// Get the input output file parameters
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// Indicates that rotation should be with respect to world or camera coordinates
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bool u;
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string output_codec;
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LandmarkDetector::get_video_input_output_params(files, out_dummy, output_video_files, u, output_codec, arguments);
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// The modules that are being used for tracking
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LandmarkDetector::CLNF clnf_model(det_parameters.model_location);
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// Grab camera parameters, if they are not defined (approximate values will be used)
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float fx = 0, fy = 0, cx = 0, cy = 0;
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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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// 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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bool fx_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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if (fx == 0 || fy == 0)
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{
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fx_undefined = true;
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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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det_parameters.track_gaze = true;
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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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else
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{
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// If we want to write out from webcam
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f_n = 0;
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}
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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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if (!boost::filesystem::exists(current_file))
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{
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FATAL_STREAM("File does not exist");
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return 1;
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}
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current_file = boost::filesystem::path(current_file).generic_string();
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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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// Use a rough guess-timate of focal length
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if (fx_undefined)
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{
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fx = 500 * (captured_image.cols / 640.0);
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fy = 500 * (captured_image.rows / 480.0);
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fx = (fx + fy) / 2.0;
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fy = fx;
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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 (!output_video_files.empty())
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{
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try
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{
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writerFace = cv::VideoWriter(output_video_files[f_n], CV_FOURCC(output_codec[0], output_codec[1], output_codec[2], output_codec[3]), 30, captured_image.size(), true);
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}
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catch(cv::Exception e)
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{
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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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}
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}
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// Use for timestamping if using a webcam
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int64 t_initial = cv::getTickCount();
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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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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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// The actual facial landmark detection / tracking
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bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_model, det_parameters);
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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_model.detection_certainty;
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// Gaze tracking, absolute gaze direction
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cv::Point3f gazeDirection0(0, 0, -1);
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cv::Point3f gazeDirection1(0, 0, -1);
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if (det_parameters.track_gaze && detection_success && clnf_model.eye_model)
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{
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FaceAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true);
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FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
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}
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visualise_tracking(captured_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
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// output the tracked video
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if (!output_video_files.empty())
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{
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writerFace << captured_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 tracker
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if(character_press == 'r')
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{
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clnf_model.Reset();
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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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clnf_model.Reset();
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// break out of the loop if done with all the files (or using a webcam)
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if(f_n == files.size() -1 || files.empty())
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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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