sustaining_gazes/exe/FaceLandmarkImg/FaceLandmarkImg.cpp

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
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// * 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// FaceLandmarkImg.cpp : Defines the entry point for the console application for detecting landmarks in images.
#include "LandmarkCoreIncludes.h"
// System includes
#include <fstream>
// OpenCV includes
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc.hpp>
// Boost includes
#include <filesystem.hpp>
#include <filesystem/fstream.hpp>
#include <dlib/image_processing/frontal_face_detector.h>
#include <tbb/tbb.h>
#include <FaceAnalyser.h>
#include <GazeEstimation.h>
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#include <ImageCapture.h>
#include <Visualizer.h>
#include <VisualizationUtils.h>
#include <RecorderOpenFace.h>
#include <RecorderOpenFaceParameters.h>
#ifndef CONFIG_DIR
#define CONFIG_DIR "~"
#endif
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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;
}
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// TODO rem
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void create_display_image(const cv::Mat& orig, cv::Mat& display_image, LandmarkDetector::CLNF& clnf_model)
{
// Draw head pose if present and draw eye gaze as well
// preparing the visualisation image
display_image = orig.clone();
// Creating a display image
cv::Mat xs = clnf_model.detected_landmarks(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2));
cv::Mat ys = clnf_model.detected_landmarks(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2));
double min_x, max_x, min_y, max_y;
cv::minMaxLoc(xs, &min_x, &max_x);
cv::minMaxLoc(ys, &min_y, &max_y);
double width = max_x - min_x;
double height = max_y - min_y;
int minCropX = max((int)(min_x-width/3.0),0);
int minCropY = max((int)(min_y-height/3.0),0);
int widthCrop = min((int)(width*5.0/3.0), display_image.cols - minCropX - 1);
int heightCrop = min((int)(height*5.0/3.0), display_image.rows - minCropY - 1);
double scaling = 350.0/widthCrop;
// first crop the image
display_image = display_image(cv::Rect((int)(minCropX), (int)(minCropY), (int)(widthCrop), (int)(heightCrop)));
// now scale it
cv::resize(display_image.clone(), display_image, cv::Size(), scaling, scaling);
// Make the adjustments to points
xs = (xs - minCropX)*scaling;
ys = (ys - minCropY)*scaling;
cv::Mat shape = clnf_model.detected_landmarks.clone();
xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2)));
ys.copyTo(shape(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2)));
// Do the shifting for the hierarchical models as well
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
{
cv::Mat xs = clnf_model.hierarchical_models[part].detected_landmarks(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2));
cv::Mat ys = clnf_model.hierarchical_models[part].detected_landmarks(cv::Rect(0, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2));
xs = (xs - minCropX)*scaling;
ys = (ys - minCropY)*scaling;
cv::Mat shape = clnf_model.hierarchical_models[part].detected_landmarks.clone();
xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2)));
ys.copyTo(shape(cv::Rect(0, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2)));
}
//LandmarkDetector::Draw(display_image, clnf_model);
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}
int main (int argc, char **argv)
{
//Convert arguments to more convenient vector form
vector<string> arguments = get_arguments(argc, argv);
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// Prepare for image reading
Utilities::ImageCapture image_reader;
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// The sequence reader chooses what to open based on command line arguments provided
if (!image_reader.Open(arguments))
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{
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cout << "Could not open any images" << endl;
return 1;
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}
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// Load the models if images found
LandmarkDetector::FaceModelParameters det_parameters(arguments);
// No need to validate detections, as we're not doing tracking
det_parameters.validate_detections = false;
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// The modules that are being used for tracking
cout << "Loading the model" << endl;
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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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cout << "Model loaded" << endl;
// Load facial feature extractor and AU analyser (make sure it is static)
FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
face_analysis_params.OptimizeForImages();
FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
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// If bounding boxes not provided, use a face detector
cv::CascadeClassifier classifier(det_parameters.face_detector_location);
dlib::frontal_face_detector face_detector_hog = dlib::get_frontal_face_detector();
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// A utility for visualizing the results
Utilities::Visualizer visualizer(arguments);
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cv::Mat captured_image;
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captured_image = image_reader.GetNextImage();
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cout << "Starting tracking" << endl;
while (!captured_image.empty())
{
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Utilities::RecorderOpenFaceParameters recording_params(arguments, false);
Utilities::RecorderOpenFace open_face_rec(image_reader.name, recording_params, arguments);
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visualizer.SetImage(captured_image, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy);
if (recording_params.outputGaze() && !face_model.eye_model)
cout << "WARNING: no eye model defined, but outputting gaze" << endl;
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// Making sure the image is in uchar grayscale
cv::Mat_<uchar> grayscale_image = image_reader.GetGrayFrame();
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// Detect faces in an image
vector<cv::Rect_<double> > face_detections;
if (image_reader.has_bounding_boxes)
{
face_detections = image_reader.GetBoundingBoxes();
}
else
{
if (det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
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{
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vector<double> confidences;
LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_detector_hog, confidences);
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}
else
{
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LandmarkDetector::DetectFaces(face_detections, grayscale_image, classifier);
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}
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}
// Detect landmarks around detected faces
int face_det = 0;
// perform landmark detection for every face detected
for (size_t face = 0; face < face_detections.size(); ++face)
{
// if there are multiple detections go through them
bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_detections[face], face_model, det_parameters);
// Estimate head pose and eye gaze
cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy);
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// Gaze tracking, absolute gaze direction
cv::Point3f gaze_direction0(0, 0, -1);
cv::Point3f gaze_direction1(0, 0, -1);
cv::Vec2d gaze_angle(0, 0);
if (success && face_model.eye_model)
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{
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GazeAnalysis::EstimateGaze(face_model, gaze_direction0, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy, true);
GazeAnalysis::EstimateGaze(face_model, gaze_direction1, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy, false);
gaze_angle = GazeAnalysis::GetGazeAngle(gaze_direction0, gaze_direction1);
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}
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cv::Mat sim_warped_img;
cv::Mat_<double> hog_descriptor; int num_hog_rows = 0, num_hog_cols = 0;
// Perform AU detection and HOG feature extraction, as this can be expensive only compute it if needed by output or visualization
if (recording_params.outputAlignedFaces() || recording_params.outputHOG() || recording_params.outputAUs() || visualizer.vis_align || visualizer.vis_hog)
{
face_analyser.PredictStaticAUsAndComputeFeatures(captured_image, face_model.detected_landmarks);
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face_analyser.GetLatestAlignedFace(sim_warped_img);
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
}
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// Displaying the tracking visualizations
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visualizer.SetObservationFaceAlign(sim_warped_img);
visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, face_model.detection_success);
visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
visualizer.SetObservationGaze(gaze_direction0, gaze_direction1, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy), face_model.detection_certainty);
// Setting up the recorder output
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open_face_rec.SetObservationHOG(face_model.detection_success, hog_descriptor, num_hog_rows, num_hog_cols, 31); // The number of channels in HOG is fixed at the moment, as using FHOG
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open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
open_face_rec.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
open_face_rec.SetObservationLandmarks(face_model.detected_landmarks, face_model.GetShape(image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy),
face_model.params_global, face_model.params_local, face_model.detection_certainty, face_model.detection_success);
open_face_rec.SetObservationPose(pose_estimate);
open_face_rec.SetObservationGaze(gaze_direction0, gaze_direction1, gaze_angle, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy));
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open_face_rec.SetObservationFaceAlign(sim_warped_img);
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open_face_rec.WriteObservation();
}
visualizer.ShowObservation();
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// Grabbing the next frame in the sequence
captured_image = image_reader.GetNextImage();
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}
return 0;
}