Added Python bindings for model constructors and more accessors

This allows to completely create a model in Python from numpy matrices, and store with save_model.

python/generate-python-bindings.cpp

@@ -87,17 +87,23 @@ PYBIND11_PLUGIN(eos) {
 	py::module morphablemodel_module = eos_module.def_submodule("morphablemodel", "Functionality to represent a Morphable Model, its PCA models, and functions to load models and blendshapes.");
 
 	py::class_<morphablemodel::PcaModel>(morphablemodel_module, "PcaModel", "Class representing a PcaModel with a mean, eigenvectors and eigenvalues, as well as a list of triangles to build a mesh.")
+		.def(py::init<>(), "Creates an empty model.")
+		.def(py::init<Eigen::VectorXf, Eigen::MatrixXf, Eigen::VectorXf, std::vector<std::array<int, 3>>>(), "Construct a PCA model from given mean, normalised PCA basis, eigenvalues and triangle list.", py::arg("mean"), py::arg("pca_basis"), py::arg("eigenvalues"), py::arg("triangle_list"))
 		.def("get_num_principal_components", &morphablemodel::PcaModel::get_num_principal_components, "Returns the number of principal components in the model.")
 		.def("get_data_dimension", &morphablemodel::PcaModel::get_data_dimension, "Returns the dimension of the data, i.e. the number of shape dimensions.")
 		.def("get_triangle_list", &morphablemodel::PcaModel::get_triangle_list, "Returns a list of triangles on how to assemble the vertices into a mesh.")
 		.def("get_mean", &morphablemodel::PcaModel::get_mean, "Returns the mean of the model.")
 		.def("get_mean_at_point", &morphablemodel::PcaModel::get_mean_at_point, "Return the value of the mean at a given vertex index.", py::arg("vertex_index"))
+		.def("get_normalised_pca_basis", [](const morphablemodel::PcaModel& m) { return m.get_normalised_pca_basis(); }, "Returns the PCA basis matrix, i.e. the eigenvectors. Each column of the matrix is an eigenvector.") // use py::overload in VS2017
+		.def("get_eigenvalues", &morphablemodel::PcaModel::get_eigenvalues, "Returns the models eigenvalues.")
 		.def("draw_sample", (Eigen::VectorXf(morphablemodel::PcaModel::*)(std::vector<float>) const)&morphablemodel::PcaModel::draw_sample, "Returns a sample from the model with the given PCA coefficients. The given coefficients should follow a standard normal distribution, i.e. not be scaled with their eigenvalues/variances.", py::arg("coefficients"))
 		;
 
 	py::class_<morphablemodel::MorphableModel>(morphablemodel_module, "MorphableModel", "A class representing a 3D Morphable Model, consisting of a shape- and colour (albedo) PCA model, as well as texture (uv) coordinates.")
+		.def(py::init<morphablemodel::PcaModel, morphablemodel::PcaModel, std::vector<std::array<double, 2>>>(), "Create a Morphable Model from a shape and a colour PCA model, and optional texture coordinates.", py::arg("shape_model"), py::arg("color_model"), py::arg("texture_coordinates") = std::vector<std::array<double, 2>>())
 		.def("get_shape_model", [](const morphablemodel::MorphableModel& m) { return m.get_shape_model(); }, "Returns the PCA shape model of this Morphable Model.") // Not sure if that'll really be const in Python? I think Python does a copy each time this gets called?
-		.def("get_color_model", [](const morphablemodel::MorphableModel& m) { return m.get_color_model(); }, "Returns the PCA colour (albedo) model of this Morphable Model.")
+		.def("get_color_model", [](const morphablemodel::MorphableModel& m) { return m.get_color_model(); }, "Returns the PCA colour (albedo) model of this Morphable Model.") // (continued from above:) We may want to use py::overload, but in any case, we need to tell pybind11 if it should use the const or non-const overload.
+		.def("get_mean", &morphablemodel::MorphableModel::get_mean, "Returns the mean of the shape- and colour model as a Mesh.")
 		;
 
 	morphablemodel_module.def("load_model", &morphablemodel::load_model, "Load a Morphable Model from a cereal::BinaryInputArchive (.bin) from the harddisk.", py::arg("filename"));