Updated bfm-binary-to-cereal to use Eigen

Also removed division by 1000 of the mean. I don't think it makes sense to make this modification. Leave it as it is in the original.

utils/bfm-binary-to-cereal.cpp

@@ -19,7 +19,7 @@
  */
 #include "eos/morphablemodel/MorphableModel.hpp"
 
-#include "opencv2/core/core.hpp"
+#include "Eigen/Core"
 
 #include "boost/program_options.hpp"
 #include "boost/filesystem.hpp"
@@ -35,11 +35,10 @@ namespace fs = boost::filesystem;
 using std::cout;
 using std::endl;
 using std::vector;
-using cv::Mat;
 
-std::vector<cv::Vec2f> read_texcoords_from_obj(std::string obj_file)
+std::vector<std::array<double, 2>> read_texcoords_from_obj(std::string obj_file)
 {
-	std::vector<cv::Vec2f> texcoords;
+	std::vector<std::array<double, 2>> texcoords;
 
 	std::ifstream file(obj_file);
 	if (!file.is_open()) {
@@ -55,7 +54,7 @@ std::vector<cv::Vec2f> read_texcoords_from_obj(std::string obj_file)
 		}
 		std::stringstream lineStream(line);
 
-		cv::Vec2f tc;
+		std::array<double, 2> tc;
 		std::string throw_away;
 		if (!(lineStream >> throw_away >> tc[0] >> tc[1])) {
 			throw std::runtime_error(std::string("Texture coordinates format error while parsing the line: " + line));
@@ -99,7 +98,7 @@ int main(int argc, char *argv[])
 	catch (const po::error& e) {
 		cout << "Error while parsing command-line arguments: " << e.what() << endl;
 		cout << "Use --help to display a list of options." << endl;
-		return EXIT_SUCCESS;
+		return EXIT_FAILURE;
 	}
 
 	std::ifstream file(bfm_file.string(), std::ios::binary);
@@ -109,7 +108,7 @@ int main(int argc, char *argv[])
 	}
 
 	// We process the texcoords first, as reading the model takes longer
-	std::vector<cv::Vec2f> texture_coordinates;
+	std::vector<std::array<double, 2>> texture_coordinates;
 	if (!obj_file.empty())
 	{
 		texture_coordinates = read_texcoords_from_obj(obj_file.string());
@@ -130,33 +129,34 @@ int main(int argc, char *argv[])
 	int num_shape_basis_vectors = 0;
 	file.read(reinterpret_cast<char*>(&num_shape_basis_vectors), 4);
 
+	using Eigen::VectorXf;
+	using Eigen::MatrixXf;
+
 	// Read the mean:
-	// We additionally divide each coordinate by 1000 to get from the domain
-	// of values in the BFM (e.g. -57000) to the BFM (values around e.g. -57).
-	Mat mean_shape(num_vertices * 3, 1, CV_32FC1);
+	VectorXf mean_shape(num_vertices * 3);
 	for (int i = 0; i < num_vertices * 3; ++i) {
 		float value = 0.0f;
 		file.read(reinterpret_cast<char*>(&value), 4);
-		mean_shape.at<float>(i) = value / 1000.0f;
+		mean_shape(i) = value;
 	}
 
 	// Read the unnormalised shape basis matrix:
-	Mat unnormalised_pca_basis_shape(num_vertices * 3, num_shape_basis_vectors, CV_32FC1); // m x n (rows x cols) = numShapeDims x numShapePcaCoeffs
-	std::cout << "Loading shape PCA basis matrix with " << unnormalised_pca_basis_shape.rows << " rows and " << unnormalised_pca_basis_shape.cols << " cols." << std::endl;
+	MatrixXf unnormalised_pca_basis_shape(num_vertices * 3, num_shape_basis_vectors); // m x n (rows x cols) = numShapeDims x numShapePcaCoeffs
+	std::cout << "Loading shape PCA basis matrix with " << unnormalised_pca_basis_shape.rows() << " rows and " << unnormalised_pca_basis_shape.cols() << " cols." << std::endl;
 	for (int col = 0; col < num_shape_basis_vectors; ++col) {
 		for (int row = 0; row < num_vertices * 3; ++row) {
 			float value = 0.0f;
 			file.read(reinterpret_cast<char*>(&value), 4);
-			unnormalised_pca_basis_shape.at<float>(row, col) = value;
+			unnormalised_pca_basis_shape(row, col) = value;
 		}
 	}
 
 	// Read the shape eigenvalues:
-	Mat eigenvalues_shape(num_shape_basis_vectors, 1, CV_32FC1);
+	VectorXf eigenvalues_shape(num_shape_basis_vectors);
 	for (int i = 0; i < num_shape_basis_vectors; ++i) {
 		float value = 0.0f;
 		file.read(reinterpret_cast<char*>(&value), 4);
-		eigenvalues_shape.at<float>(i, 0) = value;
+		eigenvalues_shape(i) = value;
 	}
 
 	// Read number of triangles and then the triangle list:
@@ -178,7 +178,7 @@ int main(int argc, char *argv[])
 	}
 
 	// We read the unnormalised basis from the file. Now let's normalise it and store the normalised basis separately.
-	Mat normalised_pca_basis_shape = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_shape, eigenvalues_shape);
+	auto normalised_pca_basis_shape = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_shape, eigenvalues_shape);
 	morphablemodel::PcaModel shape_model(mean_shape, normalised_pca_basis_shape, eigenvalues_shape, triangle_list);
 
 	// Reading the colour (albedo) model:
@@ -198,42 +198,42 @@ int main(int argc, char *argv[])
 
 	// Read the mean:
 	// We additionally divide each value by 255 to get from the domain
-	// of values in the BFM ([0, 255]) to the BFM ([0, 1]).
-	Mat mean_color(num_vertices_color * 3, 1, CV_32FC1);
+	// of values in the BFM ([0, 255]) to the SFM ([0, 1]).
+	VectorXf mean_color(num_vertices_color * 3);
 	for (int i = 0; i < num_vertices_color * 3; ++i) {
 		float value = 0.0f;
 		file.read(reinterpret_cast<char*>(&value), 4);
-		mean_color.at<float>(i) = value / 255.0f;
+		mean_color(i) = value / 255.0f;
 	}
 
 	// Read the unnormalised colour basis matrix:
-	Mat unnormalised_pca_basis_color(num_vertices_color * 3, num_color_basis_vectors, CV_32FC1); // m x n (rows x cols) = num_colour_dims x num_colour_bases
-	std::cout << "Loading colour PCA basis matrix with " << unnormalised_pca_basis_color.rows << " rows and " << unnormalised_pca_basis_color.cols << " cols." << std::endl;
+	MatrixXf unnormalised_pca_basis_color(num_vertices_color * 3, num_color_basis_vectors); // m x n (rows x cols) = num_colour_dims x num_colour_bases
+	std::cout << "Loading colour PCA basis matrix with " << unnormalised_pca_basis_color.rows() << " rows and " << unnormalised_pca_basis_color.cols() << " cols." << std::endl;
 	for (int col = 0; col < num_color_basis_vectors; ++col) {
 		for (int row = 0; row < num_vertices_color * 3; ++row) {
 			float value = 0.0f;
 			file.read(reinterpret_cast<char*>(&value), 4);
-			unnormalised_pca_basis_color.at<float>(row, col) = value;
+			unnormalised_pca_basis_color(row, col) = value;
 		}
 	}
 
 	// Read the colour eigenvalues:
-	Mat eigenvalues_color(num_color_basis_vectors, 1, CV_32FC1);
+	VectorXf eigenvalues_color(num_color_basis_vectors);
 	for (int i = 0; i < num_color_basis_vectors; ++i) {
 		float value = 0.0f;
 		file.read(reinterpret_cast<char*>(&value), 4);
-		eigenvalues_color.at<float>(i, 0) = value;
+		eigenvalues_color(i) = value;
 	}
 
 	// We read the unnormalised basis from the file. Now let's normalise it and store the normalised basis separately.
-	Mat normalised_pca_basis_color = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_color, eigenvalues_color);
+	auto normalised_pca_basis_color = morphablemodel::normalise_pca_basis(unnormalised_pca_basis_color, eigenvalues_color);
 	morphablemodel::PcaModel color_model(mean_color, normalised_pca_basis_color, eigenvalues_color, triangle_list);
 
 	file.close();
 
 	if (shape_model.get_data_dimension() / 3 != texture_coordinates.size())
 	{
-		std::cout << "Warning: PCA shape model's data dimension is different from the number of texture coordinates given. The converted model is still saved, but most likely not work correctly for texturing." << std::endl;
+		std::cout << "Warning: PCA shape model's data dimension is different from the number of texture coordinates given. The converted model is still saved, but does most likely not work correctly for texturing." << std::endl;
 	}
 
 	morphablemodel::MorphableModel morphable_model(shape_model, color_model, texture_coordinates);