230 lines
7.9 KiB
C++
230 lines
7.9 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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#include <SequenceCapture.h>
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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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int frame_count = 0;
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// Visualising the results, TODO move to separate
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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, 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 = 0.4;
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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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// Scale from 0 to 1, to allow to indicated by colour how confident we are in the tracking
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vis_certainty = (vis_certainty - visualisation_boundary) / (1 - visualisation_boundary);
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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::GetPose(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(vis_certainty*255.0, 0, (1 - 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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GazeAnalysis::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 TODO
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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), 1, CV_AA);
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frame_count++;
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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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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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det_parameters.track_gaze = true;
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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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// Open a sequence
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Utilities::SequenceCapture sequence_reader;
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while (true) // this is not a for loop as we might also be reading from a webcam
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{
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// The sequence reader chooses what to open based on command line arguments provided
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if(!sequence_reader.Open(arguments) && sequence_reader.no_input_specified)
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{
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// If that fails, revert to webcam
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INFO_STREAM("No input specified, attempting to open a webcam 0");
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if (!sequence_reader.OpenWebcam(0))
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ERROR_STREAM("Failed to open the webcam");
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}
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else
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{
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ERROR_STREAM("Failed to open a sequence");
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break;
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}
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INFO_STREAM("Device or file opened");
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cv::Mat captured_image = sequence_reader.GetNextFrame();
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INFO_STREAM("Starting tracking");
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while (!captured_image.empty()) // this is not a for loop as we might also be reading from a webcam
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{
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// Reading the images
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cv::Mat_<uchar> grayscale_image = sequence_reader.GetGrayFrame();
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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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GazeAnalysis::EstimateGaze(clnf_model, gazeDirection0, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, true);
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GazeAnalysis::EstimateGaze(clnf_model, gazeDirection1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy, false);
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}
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visualise_tracking(captured_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
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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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}
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// Reset the model, for the next video
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clnf_model.Reset();
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}
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return 0;
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}
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