Fixed comments formatting and includes

CMakeLists.txt

@@ -57,8 +57,8 @@ set(HEADERS
 	include/eos/morphablemodel/PcaModel.hpp
 	include/eos/morphablemodel/MorphableModel.hpp
 	include/eos/morphablemodel/io/cvssp.hpp
-	include/eos/fitting/AffineCameraEstimation.hpp
-	include/eos/fitting/LinearShapeFitting.hpp
+	include/eos/fitting/affine_camera_estimation.hpp
+	include/eos/fitting/linear_shape_fitting.hpp
 	include/eos/render/Mesh.hpp
 	include/eos/render/utils.hpp
 )

examples/fit_model.cpp

@@ -18,8 +18,8 @@
  * limitations under the License.
  */
 #include "eos/core/LandmarkMapper.hpp"
-#include "eos/fitting/AffineCameraEstimation.hpp"
-#include "eos/fitting/LinearShapeFitting.hpp"
+#include "eos/fitting/affine_camera_estimation.hpp"
+#include "eos/fitting/linear_shape_fitting.hpp"
 #include "eos/morphablemodel/io/cvssp.hpp"
 #include "eos/render/utils.hpp"
 

include/eos/fitting/affine_camera_estimation.hpp

 /*
  * Eos - A 3D Morphable Model fitting library written in modern C++11/14.
  *
- * File: include/eos/fitting/AffineCameraEstimation.hpp
+ * File: include/eos/fitting/affine_camera_estimation.hpp
  *
  * Copyright 2014, 2015 Patrik Huber
  *
@@ -84,8 +84,8 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 	// 2) multiply every vectors coordinate by sqrt(2)/avgnorm
 	float scaleFactor = std::sqrt(2) / averageNorm;
 	matImagePoints *= scaleFactor; // add unit homogeneous component here
-								   // The points in matImagePoints now have a RMS distance from the origin of sqrt(2).
-								   // The normalisation matrix so that the 2D points are mean-free and their norm is as described above.
+	// The points in matImagePoints now have a RMS distance from the origin of sqrt(2).
+	// The normalisation matrix so that the 2D points are mean-free and their norm is as described above.
 	Mat T = Mat::zeros(3, 3, CV_32FC1);
 	T.at<float>(0, 0) = scaleFactor; // s_x
 	T.at<float>(1, 1) = scaleFactor; // s_y
@@ -95,7 +95,7 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 
 	// center the model points to the origin:
 	Mat tmpOrigMdlPoints = matModelPoints.clone(); // Temp for testing: Save the original coordinates.
-												   // translate the centroid of the model points to the origin:
+	// translate the centroid of the model points to the origin:
 	Mat modelPointsMean; // use non-homogeneous coords for the next few steps? (less submatrices etc overhead)
 	cv::reduce(matModelPoints, modelPointsMean, 0, CV_REDUCE_AVG);
 	modelPointsMean = cv::repeat(modelPointsMean, matModelPoints.rows, 1);
@@ -110,8 +110,8 @@ cv::Mat estimate_affine_camera(std::vector<cv::Vec2f> image_points, std::vector<
 	// 2) multiply every vectors coordinate by sqrt(3)/avgnorm
 	scaleFactor = std::sqrt(3) / averageNorm;
 	matModelPoints *= scaleFactor; // add unit homogeneous component here
-								   // The points in matModelPoints now have a RMS distance from the origin of sqrt(3).
-								   // The normalisation matrix so that the 3D points are mean-free and their norm is as described above.
+	// The points in matModelPoints now have a RMS distance from the origin of sqrt(3).
+	// The normalisation matrix so that the 3D points are mean-free and their norm is as described above.
 	Mat U = Mat::zeros(4, 4, CV_32FC1);
 	U.at<float>(0, 0) = scaleFactor; // s_x
 	U.at<float>(1, 1) = scaleFactor; // s_y

include/eos/fitting/linear_shape_fitting.hpp

 /*
  * Eos - A 3D Morphable Model fitting library written in modern C++11/14.
  *
- * File: include/eos/fitting/LinearShapeFitting.hpp
+ * File: include/eos/fitting/linear_shape_fitting.hpp
  *
  * Copyright 2014, 2015 Patrik Huber
  *
@@ -91,7 +91,7 @@ inline std::vector<float> fit_shape_to_landmarks_linear(morphablemodel::Morphabl
 		Sigma.at<float>(i, i) = 1.0f / sigma_2D_3D; // the higher the sigma_2D_3D, the smaller the diagonal entries of Sigma will be
 	}
 	Mat Omega = Sigma.t() * Sigma; // just squares the diagonal
-								   // The landmarks in matrix notation (in homogeneous coordinates), $3N\times 1$
+	// The landmarks in matrix notation (in homogeneous coordinates), $3N\times 1$
 	Mat y = Mat::ones(3 * landmarks.size(), 1, CV_32FC1);
 	for (int i = 0; i < landmarks.size(); ++i) {
 		y.at<float>(3 * i, 0) = landmarks[i][0];
@@ -112,8 +112,8 @@ inline std::vector<float> fit_shape_to_landmarks_linear(morphablemodel::Morphabl
 	// Bring into standard regularised quadratic form with diagonal distance matrix Omega
 	Mat A = P * V_hat_h; // camera matrix times the basis
 	Mat b = P * v_bar - y; // camera matrix times the mean, minus the landmarks.
-						   //Mat c_s; // The x, we solve for this! (the variance-normalised shape parameter vector, $c_s = [a_1/sigma_{s,1} , ..., a_m-1/sigma_{s,m-1}]^t$
-						   //int numShapePc = morphableModel.getShapeModel().getNumberOfPrincipalComponents();
+	//Mat c_s; // The x, we solve for this! (the variance-normalised shape parameter vector, $c_s = [a_1/sigma_{s,1} , ..., a_m-1/sigma_{s,m-1}]^t$
+	//int numShapePc = morphableModel.getShapeModel().getNumberOfPrincipalComponents();
 	int num_shape_pc = num_coeffs_to_fit;
 	Mat AtOmegaA = A.t() * Omega * A;
 	Mat AtOmegaAReg = AtOmegaA + lambda * Mat::eye(num_shape_pc, num_shape_pc, CV_32FC1);