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Xucong Zhang 2019-01-10 13:26:03 +01:00
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#include <iostream>
#include "gaze_estimator.hpp"
using namespace std;
using namespace cv;
namespace opengaze{
GazeEstimator::GazeEstimator() {
}
GazeEstimator::~GazeEstimator() {}
void GazeEstimator::setRootPath(std::string root_path) {
normalizer_.loadFaceModel(root_path);
}
void GazeEstimator::estimateGaze(cv::Mat input_image, std::vector<opengaze::Sample> &outputs) {
face_detector_.track_faces(input_image, outputs); // detect faces and facial landmarks
for (int i=0; i< outputs.size(); ++i) {
// estimate head pose first, no matter what gaze estimation method, head pose is estimated here
normalizer_.estimateHeadPose(outputs[i].face_data.landmarks, outputs[i]);
if (method_type_ == Method::MPIIGaze){
// if we use face model
if (input_type_ == InputType::face){
Mat face_patch = normalizer_.normalizeFace(input_image, outputs[i]);
//outputs[i].face_patch_data.debug_img = face_patch;
Point3f gaze_norm = gaze_predictor_.predictGazeMPIIGaze(face_patch); // gaze estimates in normalization space
Mat gaze_3d = normalizer_.cvtToCamera(gaze_norm, outputs[i].face_patch_data.face_rot); // convert gaze to camera coordinate system
gaze_3d.copyTo(outputs[i].gaze_data.gaze3d);
}
else if (input_type_ == InputType::eye) {
vector<cv::Mat> eye_iamges = normalizer_.normalizeEyes(input_image, outputs[i]); // generate eye images
// for left eye
Point3f gaze_norm = gaze_predictor_.predictGazeMPIIGaze(eye_iamges[0]);
Mat gaze_3d = normalizer_.cvtToCamera(gaze_norm, outputs[i].eye_data.leye_rot);
gaze_3d.copyTo(outputs[i].gaze_data.lgaze3d);
// for right eye
Mat flip_right;
flip(eye_iamges[0], flip_right, 1);
gaze_norm = gaze_predictor_.predictGazeMPIIGaze(flip_right); // for left right image input
gaze_norm.x *= -1.0;
gaze_3d = normalizer_.cvtToCamera(gaze_norm, outputs[i].face_patch_data.face_rot); // convert gaze to camera coordinate system
gaze_3d.copyTo(outputs[i].gaze_data.rgaze3d);
}
}
else if (method_type_ == Method::OpenFace) {
cout << "Please use gaze estimation method MPIIGaze." << endl;
exit(EXIT_FAILURE);
}
}
}
void GazeEstimator::getImagePatch(cv::Mat input_image, std::vector<opengaze::Sample> &outputs) {
face_detector_.track_faces(input_image, outputs); // detect faces and facial landmarks
for (int i=0; i< outputs.size(); ++i) {
// estimate head pose first, no matter what gaze estimation method, head pose is estimated here
normalizer_.estimateHeadPose(outputs[i].face_data.landmarks, outputs[i]);
if (method_type_ == Method::MPIIGaze){
// if we use face model
if (input_type_ == InputType::face){
outputs[i].face_patch_data.face_patch = normalizer_.normalizeFace(input_image, outputs[i]);
}
else if (input_type_ == InputType::eye) {
vector<cv::Mat> eye_iamges = normalizer_.normalizeEyes(input_image, outputs[i]); // generate eye images
outputs[i].eye_data.leye_img = eye_iamges[0];
outputs[i].eye_data.reye_img = eye_iamges[1];
}
}
else if (method_type_ == Method::OpenFace) {
cout << "Please use method MPIIGaze for image patch extraction." << endl;
exit(EXIT_FAILURE);
}
}
}
void GazeEstimator::setMethod(Method input_method_type, const std::vector<std::string> arguments={}) {
method_type_ = input_method_type;
if (method_type_ == Method::MPIIGaze) {
gaze_predictor_.initiaMPIIGaze(arguments);
if (arguments.size() < 2)
input_type_ = InputType::face;
else {
if (arguments[2] == "face"){
input_type_ = InputType::face;
normalizer_.setParameters(1600, 1000, 224, 224);
}
else if (arguments[2] == "eye") {
input_type_ = InputType::eye;
normalizer_.setParameters(960, 600, 60, 36);
}
}
}
}
void GazeEstimator::setCameraParameters(cv::Mat camera_matrix, cv::Mat camera_dist) {
camera_matrix_ = move(camera_matrix);
camera_dist_ = move(camera_dist_);
normalizer_.setCameraMatrix(camera_matrix_);
}
void GazeEstimator::initialFaceDetector(int number_users){
face_detector_.initialize(number_users);
face_detector_.setMethodType(FaceDetector::Method::OpenFace);
}
};