void Horus::getPCA(const vector<cv::Point> &contour, float ¢er, float &angle)
{
//Construct a buffer used by the pca analysis
int sz = static_cast<int>(contour.size());
Mat data_pts = Mat(sz, 2, CV_32FC1);
for (int i = 0; i < data_pts.rows; ++i) {
data_pts.at<float>(i, 0) = contour[i].x;
data_pts.at<float>(i, 1) = contour[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);
//Store the center of the object
cv::Point cntr = cv::Point(static_cast<int>(pca_analysis.mean.at<float>(0, 0)),
static_cast<int>(pca_analysis.mean.at<float>(0, 1)));
//Store the eigenvalues and eigenvectors
vector<cv::Point2d> eigen_vecs(2);
vector<float> eigen_val(2);
for (int i = 0; i < 2; ++i) {
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<float>(i, 0),
pca_analysis.eigenvectors.at<float>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<float>(0, i);
}
angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
}
double HandDetector::GetOrientation(const vector<Point> &pts, Mat &img)
{
if (pts.size() == 0) return false;
//Construct a buffer used by the pca analysis
Mat data_pts = Mat(pts.size(), 2, CV_64FC1);
for (int i = 0; i < data_pts.rows; ++i)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);
//Store the position of the object
Point pos = Point(pca_analysis.mean.at<double>(0, 0),
pca_analysis.mean.at<double>(0, 1));
//Store the eigenvalues and eigenvectors
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; ++i)
{
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
}
// Draw the principal components
circle(img, pos, 3, CV_RGB(255, 0, 255), 2);
//line(img, pos, pos + 0.02 * Point(eigen_vecs[0].x * eigen_val[0], eigen_vecs[0].y * eigen_val[0]) , CV_RGB(255, 255, 0));
//line(img, pos, pos + 0.02 * Point(eigen_vecs[1].x * eigen_val[1], eigen_vecs[1].y * eigen_val[1]) , CV_RGB(0, 255, 255));
Point p1 = pos + 0.02 * Point(eigen_vecs[0].x * eigen_val[0], eigen_vecs[0].y * eigen_val[0]);
Point p2 = pos + 0.02 * Point(eigen_vecs[1].x * eigen_val[1], eigen_vecs[1].y * eigen_val[1]);
DrawAxis(img, pos, p1, Scalar(0, 255, 0), 1);
DrawAxis(img, pos, p2, Scalar(255, 255, 0), 5);
return atan2(eigen_vecs[0].y, eigen_vecs[0].x)*(180/CV_PI);
}
// Calculate the angle in degrees of a certain line.
double NaoVision::getAngleDegrees(const vector<Point> &pts, Mat &img) {
// Construct a buffer used by the pca analysis.
int sz = static_cast<int>(pts.size());
Mat data_pts = Mat(sz, 2, CV_64FC1);
for(int i = 0; i < data_pts.rows; ++i) {
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
// Perform PCA analysis.
PCA pca_analysis(data_pts, Mat(), CV_PCA_DATA_AS_ROW);
// Store the center of the object.
Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
// Store the eigenvalues and eigenvectors.
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for(int i = 0; i < 2; ++i) {
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(0, i);
}
// Draw the principal components.
circle(img, cntr, 3, Scalar(255, 0, 255), 2);
Point p1 = cntr + 0.02 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
Point p2 = cntr - 0.02 * Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // Angle in radians.
double degrees = angle * 180 / CV_PI; // Convert radians to degrees (0-180 range).
degrees = degrees < 0 ? degrees + 180 : degrees;
return degrees;
}
double Segmentation::getOrientation(std::vector<cv::Point> &pts, cv::Mat &img)
{
if (pts.size() == 0) return false;
//Construct a buffer used by the pca analysis
cv::Mat data_pts = cv::Mat(pts.size(), 2, CV_64FC1);
for (int i = 0; i < data_pts.rows; ++i)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
cv::PCA pca_analysis(data_pts, cv::Mat(), CV_PCA_DATA_AS_ROW);
//Store the position of the object
cv::Point2i pos = cv::Point2i(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
//Store the eigenvalues and eigenvectors
std::vector<cv::Point2d> eigen_vecs(2);
std::vector<double> eigen_val(2);
for (int i = 0; i < 2; ++i)
{
eigen_vecs[i] = cv::Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(0, i);
}
// Draw the principal components
cv::circle(img, pos, 3, CV_RGB(255, 0, 255), 2);
cv::line(img, pos, pos + 0.02 * cv::Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]),static_cast<int> (eigen_vecs[0].y * eigen_val[0])) , CV_RGB(255, 255, 0));
cv::line(img, pos, pos + 0.02 * cv::Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1])) , CV_RGB(0, 255, 255));
return atan2(eigen_vecs[0].y, eigen_vecs[0].x);
}
void Projector::objProjectionOffline(std::string objPath, std::string objName, bool gpuView)
{
std::cout << "Camera init: ";
objObject obj(objPath, objName);
obj.loadData();
cout << "DONE\n";
cv::namedWindow("objTest", CV_WINDOW_NORMAL);
cv::moveWindow("objTest", 0, 0);
indices.resize(480);
for (int i = 0; i < 480; i++) indices[i].resize(640);
libfreenect2::Registration* registration = new libfreenect2::Registration(_dev->getIrCameraParams(), _dev->getColorCameraParams());
libfreenect2::Frame undistorted(512, 424, 4), registered(512, 424, 4);
libfreenect2::FrameMap frames;
SimpleViewer viewer;
bool shutdown = false;
cv::Mat board(480, 640, CV_8UC4, cv::Scalar::all(255));
cv::Vec3f prevNormal(-1, -1, -1);
if (!gpuView) {
cv::namedWindow("reprojection", CV_WINDOW_NORMAL);
cv::moveWindow("reprojection", 200, 200);
//setWindowProperty("reprojection", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
}
else {
viewer.setSize(480, 640); // TO-DO change resolution
viewer.initialize();
libfreenect2::Frame b(640, 480, 4);
b.data = board.data;
viewer.addFrame("RGB", &b);
shutdown = shutdown || viewer.render();
}
while (!shutdown)
{
board = cv::Mat(480, 640, CV_8UC4, cv::Scalar::all(255));
std::vector<cv::Point3f> plnSrc;
if (!gpuView) cv::imshow("reprojection", board);
(_listener)->waitForNewFrame(frames);
libfreenect2::Frame *rgb = frames[libfreenect2::Frame::Color];
libfreenect2::Frame *depth = frames[libfreenect2::Frame::Depth];
registration->apply(rgb, depth, &undistorted, ®istered, true, NULL, NULL);
PlaneData pln = findRectangle(registration, &undistorted, ®istered);
if (pln.points.size() > 0) {
std::vector<cv::Point2f> projected = projectPoints(pln.points);
cv::Mat cont = cv::Mat(480, 640, CV_8UC1, cv::Scalar::all(0));
for (int i = 0; i < projected.size(); i++)
{
if (480 - projected[i].x >0 && projected[i].y > 0 && 480 - projected[i].x < 475 && projected[i].y < 630) {
cv::Mat ROI = board(cv::Rect(static_cast<int>(projected[i].y), static_cast<int>(480 - projected[i].x), 2, 2));
ROI.setTo(cv::Scalar(250, 100, 100, 100));
cont.at<uchar>(static_cast<int>(480 - projected[i].x), static_cast<int>(projected[i].y), 0) = 255;
plnSrc.push_back(pln.points[i]);
}
}
vector<vector<cv::Point> > contours;
vector<cv::Vec4i> hierarchy;
cv::GaussianBlur(cont, cont, cv::Size(7, 7), 5, 11);
findContours(cont, contours, hierarchy, cv::RETR_CCOMP, cv::CHAIN_APPROX_NONE, cv::Point(0, 0));
vector<vector<cv::Point> > contours_poly(contours.size());
vector<cv::Rect> boundRect(contours.size());
vector<cv::Point2f>center(contours.size());
vector<float>radius(contours.size());
for (int i = 0; i < contours.size(); i++)
{
cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 10, true);
}
for (int i = 0; i < contours.size(); i++)
{
drawContours(board, contours_poly, 0, cv::Scalar(0, 255, 0), 5);
}
cv::Mat data_pts = cv::Mat(300, 3, CV_64FC1);
cv::Vec3f normal(0, 0, 0);
int jump = plnSrc.size() / 300;
for (int i = 0; i < 100; i++) {
data_pts.at<double>(i, 0) = plnSrc[i*jump].x;
data_pts.at<double>(i, 1) = plnSrc[i*jump].y;
data_pts.at<double>(i, 2) = plnSrc[i*jump].z;
data_pts.at<double>(i + 100, 0) = plnSrc[(i + 100)*jump].x;
data_pts.at<double>(i + 100, 1) = plnSrc[(i + 100)*jump].y;
data_pts.at<double>(i + 100, 2) = plnSrc[(i + 100)*jump].z;
data_pts.at<double>(i + 200, 0) = plnSrc[(i + 200) *jump].x;
data_pts.at<double>(i + 200, 1) = plnSrc[(i + 200)*jump].y;
data_pts.at<double>(i + 200, 2) = plnSrc[(i + 200)*jump].z;
}
cv::PCA pca_analysis(data_pts, cv::Mat(), CV_PCA_DATA_AS_ROW);
cv::Vec3f cntr = cv::Vec3f((pca_analysis.mean.at<double>(0, 0)),
(pca_analysis.mean.at<double>(0, 1)),
(pca_analysis.mean.at<double>(0, 2)));
vector<cv::Point3f> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; ++i)
{
eigen_vecs[i] = cv::Point3f(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1),
pca_analysis.eigenvectors.at<double>(i, 2));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(0, i);
}
cv::Vec3f p1 = cv::Vec3f((eigen_vecs[0].x * eigen_val[0]), (eigen_vecs[0].y * eigen_val[0]), (eigen_vecs[0].z * eigen_val[0]));
cv::Vec3f p2 = cv::Vec3f((eigen_vecs[1].x * eigen_val[1]), (eigen_vecs[1].y * eigen_val[1]), (eigen_vecs[1].z * eigen_val[1]));
normal = p1.cross(p2);
normal = cv::normalize(normal);
//pln.center = cntr;
pln.normal = normal;
obj.setCamera(cv::Point3f(pln.center.x, -pln.center.y, -pln.center.z + 150),
cv::Vec3f(pln.normal[0], pln.normal[1], pln.normal[2]));
if (!gpuView) imshow("reprojection", board);
else {
libfreenect2::Frame b(640, 480, 4);
b.data = board.data;
viewer.addFrame("RGB", &b);
shutdown = shutdown || viewer.render();
}
}
cv::Mat im = obj.render();
cv::imshow("objTest", im);
//}
(_listener)->release(frames);
if (!gpuView) {
int op = cv::waitKey(50);
if (op == 100 || (char)(op) == 'd') right -= 1;
if (op == 115 || (char)(op) == 's') up += 1;
if (op == 97 || (char)(op) == 'a') right += 1;
if (op == 119 || (char)(op) == 'w') up -= 1;
if (op == 114 || (char)(op) == 'r') rotX -= 0.5;
if (op == 102 || (char)(op) == 'f') rotX += 0.5;
if (op == 1113997 || op == 1048586 || op == 1048608 || op == 10 || op == 32)
{
std::cout << "right = " << right << ";\nup = " << up << ";\nrotX = " << rotX << ";\n";
break;
}
}
else {
//right = 0;
//up = 0;
//rotX = 0;
right = viewer.offsetX;
up = viewer.offsetY;
rotX = viewer.rot;
}
}
if (!gpuView) cv::destroyWindow("reprojection");
else {
viewer.stopWindow();
}
cv::destroyWindow("objTest");
}
mitk::GIFVolumetricStatistics::FeatureListType mitk::GIFVolumetricStatistics::CalculateFeatures(const Image::Pointer & image, const Image::Pointer &mask)
{
FeatureListType featureList;
if (image->GetDimension() < 3)
{
return featureList;
}
AccessByItk_3(image, CalculateVolumeStatistic, mask, featureList, FeatureDescriptionPrefix());
AccessByItk_3(mask, CalculateLargestDiameter, image, featureList, FeatureDescriptionPrefix());
vtkSmartPointer<vtkImageMarchingCubes> mesher = vtkSmartPointer<vtkImageMarchingCubes>::New();
vtkSmartPointer<vtkMassProperties> stats = vtkSmartPointer<vtkMassProperties>::New();
mesher->SetInputData(mask->GetVtkImageData());
mesher->SetValue(0, 0.5);
stats->SetInputConnection(mesher->GetOutputPort());
stats->Update();
double pi = vnl_math::pi;
double meshVolume = stats->GetVolume();
double meshSurf = stats->GetSurfaceArea();
double pixelVolume = featureList[1].second;
double pixelSurface = featureList[3].second;
MITK_INFO << "Surface: " << pixelSurface << " Volume: " << pixelVolume;
double compactness1 = pixelVolume / (std::sqrt(pi) * std::pow(meshSurf, 2.0 / 3.0));
double compactness1Pixel = pixelVolume / (std::sqrt(pi) * std::pow(pixelSurface, 2.0 / 3.0));
//This is the definition used by Aertz. However, due to 2/3 this feature is not demensionless. Use compactness3 instead.
double compactness2 = 36 * pi*pixelVolume*pixelVolume / meshSurf / meshSurf / meshSurf;
double compactness2MeshMesh = 36 * pi*meshVolume*meshVolume / meshSurf / meshSurf / meshSurf;
double compactness2Pixel = 36 * pi*pixelVolume*pixelVolume / pixelSurface / pixelSurface / pixelSurface;
double compactness3 = pixelVolume / (std::sqrt(pi) * std::pow(meshSurf, 3.0 / 2.0));
double compactness3MeshMesh = meshVolume / (std::sqrt(pi) * std::pow(meshSurf, 3.0 / 2.0));
double compactness3Pixel = pixelVolume / (std::sqrt(pi) * std::pow(pixelSurface, 3.0 / 2.0));
double sphericity = std::pow(pi, 1 / 3.0) *std::pow(6 * pixelVolume, 2.0 / 3.0) / meshSurf;
double sphericityMesh = std::pow(pi, 1 / 3.0) *std::pow(6 * meshVolume, 2.0 / 3.0) / meshSurf;
double sphericityPixel = std::pow(pi, 1 / 3.0) *std::pow(6 * pixelVolume, 2.0 / 3.0) / pixelSurface;
double surfaceToVolume = meshSurf / meshVolume;
double surfaceToVolumePixel = pixelSurface / pixelVolume;
double sphericalDisproportion = meshSurf / 4 / pi / std::pow(3.0 / 4.0 / pi * pixelVolume, 2.0 / 3.0);
double sphericalDisproportionMesh = meshSurf / 4 / pi / std::pow(3.0 / 4.0 / pi * meshVolume, 2.0 / 3.0);
double sphericalDisproportionPixel = pixelSurface / 4 / pi / std::pow(3.0 / 4.0 / pi * pixelVolume, 2.0 / 3.0);
double asphericity = std::pow(1.0/compactness2, (1.0 / 3.0)) - 1;
double asphericityMesh = std::pow(1.0 / compactness2MeshMesh, (1.0 / 3.0)) - 1;
double asphericityPixel = std::pow(1.0/compactness2Pixel, (1.0 / 3.0)) - 1;
//Calculate center of mass shift
int xx = mask->GetDimensions()[0];
int yy = mask->GetDimensions()[1];
int zz = mask->GetDimensions()[2];
double xd = mask->GetGeometry()->GetSpacing()[0];
double yd = mask->GetGeometry()->GetSpacing()[1];
double zd = mask->GetGeometry()->GetSpacing()[2];
vtkSmartPointer<vtkDoubleArray> dataset1Arr = vtkSmartPointer<vtkDoubleArray>::New();
vtkSmartPointer<vtkDoubleArray> dataset2Arr = vtkSmartPointer<vtkDoubleArray>::New();
vtkSmartPointer<vtkDoubleArray> dataset3Arr = vtkSmartPointer<vtkDoubleArray>::New();
dataset1Arr->SetNumberOfComponents(1);
dataset2Arr->SetNumberOfComponents(1);
dataset3Arr->SetNumberOfComponents(1);
dataset1Arr->SetName("M1");
dataset2Arr->SetName("M2");
dataset3Arr->SetName("M3");
vtkSmartPointer<vtkDoubleArray> dataset1ArrU = vtkSmartPointer<vtkDoubleArray>::New();
vtkSmartPointer<vtkDoubleArray> dataset2ArrU = vtkSmartPointer<vtkDoubleArray>::New();
vtkSmartPointer<vtkDoubleArray> dataset3ArrU = vtkSmartPointer<vtkDoubleArray>::New();
dataset1ArrU->SetNumberOfComponents(1);
dataset2ArrU->SetNumberOfComponents(1);
dataset3ArrU->SetNumberOfComponents(1);
dataset1ArrU->SetName("M1");
dataset2ArrU->SetName("M2");
dataset3ArrU->SetName("M3");
for (int x = 0; x < xx; x++)
{
for (int y = 0; y < yy; y++)
{
for (int z = 0; z < zz; z++)
{
itk::Image<int,3>::IndexType index;
index[0] = x;
index[1] = y;
index[2] = z;
mitk::ScalarType pxImage;
mitk::ScalarType pxMask;
mitkPixelTypeMultiplex5(
mitk::FastSinglePixelAccess,
image->GetChannelDescriptor().GetPixelType(),
image,
image->GetVolumeData(),
index,
pxImage,
0);
mitkPixelTypeMultiplex5(
mitk::FastSinglePixelAccess,
mask->GetChannelDescriptor().GetPixelType(),
mask,
mask->GetVolumeData(),
index,
pxMask,
0);
//Check if voxel is contained in segmentation
if (pxMask > 0)
{
dataset1ArrU->InsertNextValue(x*xd);
dataset2ArrU->InsertNextValue(y*yd);
dataset3ArrU->InsertNextValue(z*zd);
if (pxImage == pxImage)
{
dataset1Arr->InsertNextValue(x*xd);
dataset2Arr->InsertNextValue(y*yd);
dataset3Arr->InsertNextValue(z*zd);
}
}
}
}
}
vtkSmartPointer<vtkTable> datasetTable = vtkSmartPointer<vtkTable>::New();
datasetTable->AddColumn(dataset1Arr);
datasetTable->AddColumn(dataset2Arr);
datasetTable->AddColumn(dataset3Arr);
vtkSmartPointer<vtkTable> datasetTableU = vtkSmartPointer<vtkTable>::New();
datasetTableU->AddColumn(dataset1ArrU);
datasetTableU->AddColumn(dataset2ArrU);
datasetTableU->AddColumn(dataset3ArrU);
vtkSmartPointer<vtkPCAStatistics> pcaStatistics = vtkSmartPointer<vtkPCAStatistics>::New();
pcaStatistics->SetInputData(vtkStatisticsAlgorithm::INPUT_DATA, datasetTable);
pcaStatistics->SetColumnStatus("M1", 1);
pcaStatistics->SetColumnStatus("M2", 1);
pcaStatistics->SetColumnStatus("M3", 1);
pcaStatistics->RequestSelectedColumns();
pcaStatistics->SetDeriveOption(true);
pcaStatistics->Update();
vtkSmartPointer<vtkDoubleArray> eigenvalues = vtkSmartPointer<vtkDoubleArray>::New();
pcaStatistics->GetEigenvalues(eigenvalues);
pcaStatistics->SetInputData(vtkStatisticsAlgorithm::INPUT_DATA, datasetTableU);
pcaStatistics->Update();
vtkSmartPointer<vtkDoubleArray> eigenvaluesU = vtkSmartPointer<vtkDoubleArray>::New();
pcaStatistics->GetEigenvalues(eigenvaluesU);
std::vector<double> eigen_val(3);
std::vector<double> eigen_valUC(3);
eigen_val[2] = eigenvalues->GetValue(0);
eigen_val[1] = eigenvalues->GetValue(1);
eigen_val[0] = eigenvalues->GetValue(2);
eigen_valUC[2] = eigenvaluesU->GetValue(0);
eigen_valUC[1] = eigenvaluesU->GetValue(1);
eigen_valUC[0] = eigenvaluesU->GetValue(2);
double major = 4*sqrt(eigen_val[2]);
double minor = 4*sqrt(eigen_val[1]);
double least = 4*sqrt(eigen_val[0]);
double elongation = (major == 0) ? 0 : sqrt(eigen_val[1] / eigen_val[2]);
double flatness = (major == 0) ? 0 : sqrt(eigen_val[0] / eigen_val[2]);
double majorUC = 4*sqrt(eigen_valUC[2]);
double minorUC = 4*sqrt(eigen_valUC[1]);
double leastUC = 4*sqrt(eigen_valUC[0]);
double elongationUC = majorUC == 0 ? 0 : sqrt(eigen_valUC[1] / eigen_valUC[2]);
double flatnessUC = majorUC == 0 ? 0 : sqrt(eigen_valUC[0] / eigen_valUC[2]);
std::string prefix = FeatureDescriptionPrefix();
featureList.push_back(std::make_pair(prefix + "Volume (mesh based)",meshVolume));
featureList.push_back(std::make_pair(prefix + "Surface (mesh based)",meshSurf));
featureList.push_back(std::make_pair(prefix + "Surface to volume ratio (mesh based)",surfaceToVolume));
featureList.push_back(std::make_pair(prefix + "Sphericity (mesh based)",sphericity));
featureList.push_back(std::make_pair(prefix + "Sphericity (mesh, mesh based)", sphericityMesh));
featureList.push_back(std::make_pair(prefix + "Asphericity (mesh based)", asphericity));
featureList.push_back(std::make_pair(prefix + "Asphericity (mesh, mesh based)", asphericityMesh));
featureList.push_back(std::make_pair(prefix + "Compactness 1 (mesh based)", compactness3));
featureList.push_back(std::make_pair(prefix + "Compactness 1 old (mesh based)" ,compactness1));
featureList.push_back(std::make_pair(prefix + "Compactness 2 (mesh based)",compactness2));
featureList.push_back(std::make_pair(prefix + "Compactness 1 (mesh, mesh based)", compactness3MeshMesh));
featureList.push_back(std::make_pair(prefix + "Compactness 2 (mesh, mesh based)", compactness2MeshMesh));
featureList.push_back(std::make_pair(prefix + "Spherical disproportion (mesh based)", sphericalDisproportion));
featureList.push_back(std::make_pair(prefix + "Spherical disproportion (mesh, mesh based)", sphericalDisproportionMesh));
featureList.push_back(std::make_pair(prefix + "Surface to volume ratio (voxel based)", surfaceToVolumePixel));
featureList.push_back(std::make_pair(prefix + "Sphericity (voxel based)", sphericityPixel));
featureList.push_back(std::make_pair(prefix + "Asphericity (voxel based)", asphericityPixel));
featureList.push_back(std::make_pair(prefix + "Compactness 1 (voxel based)", compactness3Pixel));
featureList.push_back(std::make_pair(prefix + "Compactness 1 old (voxel based)", compactness1Pixel));
featureList.push_back(std::make_pair(prefix + "Compactness 2 (voxel based)", compactness2Pixel));
featureList.push_back(std::make_pair(prefix + "Spherical disproportion (voxel based)", sphericalDisproportionPixel));
featureList.push_back(std::make_pair(prefix + "PCA Major axis length",major));
featureList.push_back(std::make_pair(prefix + "PCA Minor axis length",minor));
featureList.push_back(std::make_pair(prefix + "PCA Least axis length",least));
featureList.push_back(std::make_pair(prefix + "PCA Elongation",elongation));
featureList.push_back(std::make_pair(prefix + "PCA Flatness",flatness));
featureList.push_back(std::make_pair(prefix + "PCA Major axis length (uncorrected)", majorUC));
featureList.push_back(std::make_pair(prefix + "PCA Minor axis length (uncorrected)", minorUC));
featureList.push_back(std::make_pair(prefix + "PCA Least axis length (uncorrected)", leastUC));
featureList.push_back(std::make_pair(prefix + "PCA Elongation (uncorrected)", elongationUC));
featureList.push_back(std::make_pair(prefix + "PCA Flatness (uncorrected)", flatnessUC));
return featureList;
}
