float CalculateError(const Mat_<float>& ground_truth_shape, const Mat_<float>& predicted_shape) {
Mat_<float> temp;
//temp = ground_truth_shape.rowRange(36, 37)-ground_truth_shape.rowRange(45, 46);
temp = ground_truth_shape.rowRange(1, 2) - ground_truth_shape.rowRange(6, 7);
float x = mean(temp.col(0))[0];
float y = mean(temp.col(1))[1];
float interocular_distance = sqrt(x*x + y*y);
float sum = 0;
for (int i = 0; i < ground_truth_shape.rows; i++) {
sum += norm(ground_truth_shape.row(i) - predicted_shape.row(i));
}
return sum / (ground_truth_shape.rows*interocular_distance);
}
Point3f GetPupilPosition(Mat_<double> eyeLdmks3d){
eyeLdmks3d = eyeLdmks3d.t();
Mat_<double> irisLdmks3d = eyeLdmks3d.rowRange(0,8);
Point3f p (mean(irisLdmks3d.col(0))[0], mean(irisLdmks3d.col(1))[0], mean(irisLdmks3d.col(2))[0]);
return p;
}
void adjustImage(Mat_<uchar>& img,
Mat_<float>& ground_truth_shape,
BoundingBox& bounding_box){
/*imshow("test",img);
waitKey(0);*/
float left_x = max(1.0, (double)bounding_box.centroid_x - bounding_box.width*0.8);
float top_y = max(1.0, (double)bounding_box.centroid_y - bounding_box.height*0.8);
float right_x = min(img.cols-1.0,(double)bounding_box.centroid_x+bounding_box.width*0.8);
float bottom_y= min(img.rows-1.0,(double)bounding_box.centroid_y+bounding_box.height*0.8);
img = img.rowRange((int)top_y,(int)bottom_y).colRange((int)left_x,(int)right_x).clone();
bounding_box.start_x = ((int)right_x - (int)left_x)*CROP;
bounding_box.start_y = ((int)bottom_y - (int)top_y)*CROP;
bounding_box.width = ((int)right_x - (int)left_x)*(1-2*CROP)-1;
bounding_box.height = ((int)bottom_y - (int)top_y)*(1-2*CROP)-1;
bounding_box.centroid_x = bounding_box.start_x + bounding_box.width / 2.0;
bounding_box.centroid_y = bounding_box.start_y + bounding_box.height / 2.0;
for(int i=0;i<ground_truth_shape.rows;i++){
ground_truth_shape(i,0) = ground_truth_shape(i,0)-left_x;
ground_truth_shape(i,1) = ground_truth_shape(i,1)-top_y;
}
//imshow("test1",img);
//waitKey(0);
float ori_height=img.rows;
float ori_weight=img.cols;
if (ori_height>MAXHEIGHT_POS)
{
float scale=MAXHEIGHT_POS/ori_height;
resize(img,img,Size(ori_weight*scale,ori_height*scale));
bounding_box.start_x*=scale;
bounding_box.start_y*=scale;
bounding_box.centroid_x*=scale;
bounding_box.centroid_y*=scale;
bounding_box.width*=scale;
bounding_box.height*=scale;
for(int i=0;i<ground_truth_shape.rows;i++){
ground_truth_shape(i,0) *= scale;
ground_truth_shape(i,1) *= scale;
}
}
/*rectangle(img,Point(bounding_box.start_x,bounding_box.start_y),Point(bounding_box.start_x+bounding_box.width,bounding_box.start_y+bounding_box.height),Scalar(255));
for(int i=0;i<ground_truth_shape.rows;i++){
circle(img,Point(ground_truth_shape(i,0),ground_truth_shape(i,1)),3,Scalar(255));
}
imshow("test2",img);
waitKey(0);*/
}
int MultipleGraphsClassifier::predict(DisjointSetForest &segmentation, const Mat_<Vec3b> &image, const Mat_<float> &mask) {
int maxGraphSize = max(segmentation.getNumberOfComponents(), this->maxTrainingGraphSize);
int minGraphSize = min(segmentation.getNumberOfComponents(), this->minTrainingGraphSize);
if (minGraphSize <= this->k) {
cout<<"the smallest graph has size "<<minGraphSize<<", cannot compute "<<this->k<<" eigenvectors"<<endl;
exit(EXIT_FAILURE);
}
// compute each feature graph for the test sample
vector<WeightedGraph> featureGraphs;
featureGraphs.reserve(this->features.size());
for (int i = 0; i < (int)this->features.size(); i++) {
featureGraphs.push_back(this->computeFeatureGraph(i, segmentation, image, mask));
}
// order graphs by feature, to compute pattern vectors feature by
// feature.
vector<vector<WeightedGraph> > graphsByFeatures(this->features.size());
// add feature graphs for the test sample at index 0
for (int i = 0; i < (int)this->features.size(); i++) {
graphsByFeatures[i].reserve(this->trainingFeatureGraphs.size() + 1);
graphsByFeatures[i].push_back(featureGraphs[i]);
}
// add feature graphs for each training sample
for (int i = 0; i < (int)this->trainingFeatureGraphs.size(); i++) {
for (int j = 0; j < (int)this->features.size(); j++) {
graphsByFeatures[j].push_back(get<0>(this->trainingFeatureGraphs[i])[j]);
}
}
// compute the corresponding pattern vectors
vector<vector<VectorXd> > patternsByFeatures(this->features.size());
for (int i = 0; i < (int)this->features.size(); i++) {
patternsByFeatures[i] = patternVectors(graphsByFeatures[i], this->k, maxGraphSize);
}
// concatenate pattern vectors by image, converting to opencv format
// to work with cv's ML module.
Mat_<float> longPatterns = Mat_<float>::zeros(this->trainingFeatureGraphs.size() + 1, maxGraphSize * k * this->features.size());
for (int i = 0; i < (int)this->trainingFeatureGraphs.size() + 1; i++) {
VectorXd longPattern(maxGraphSize * k * this->features.size());
for (int j = 0; j < this->features.size(); j++) {
longPattern.block(j * maxGraphSize * k, 0, maxGraphSize * k, 1) = patternsByFeatures[j][i];
}
Mat_<double> cvLongPattern;
eigenToCv(longPattern, cvLongPattern);
Mat_<float> castLongPattern = Mat_<float>(cvLongPattern);
castLongPattern.copyTo(longPatterns.row(i));
}
// classification of long patterns using SVM
CvKNearest svmClassifier;
Mat_<int> classes(this->trainingFeatureGraphs.size(), 1);
for (int i = 0; i < (int)this->trainingFeatureGraphs.size(); i++) {
classes(i,0) = get<1>(this->trainingFeatureGraphs[i]);
}
svmClassifier.train(longPatterns.rowRange(1, longPatterns.rows), classes);
return (int)svmClassifier.find_nearest(longPatterns.row(0), 1);
}
