Removed old BFM to json converter script

This didn't really work too well, the file just gets too big. Maybe if we could improve the generated json in cereal, but that's too much effort for now.

share/convert_bfm2009_to_json.m

-% Converts the 2009 Basel Face Model (BFM, [1]) to a json file that can be
-% read by the eos cereal importer. The json-to-cereal-binary app can
-% subsequently be used to generate a small eos .bin file.
-%
-% [1]: A 3D Face Model for Pose and Illumination Invariant Face
-% Recognition, P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter,
-% AVSS 2009.
-% http://faces.cs.unibas.ch/bfm/main.php?nav=1-0&id=basel_face_model
-%
-% Notes:
-%  - The script takes quite a while to run (>= 10 minutes)
-%  - Produces quite unoptimised json (and a large file). Check with cereal
-%    documentation if that can be improved.
-%
-% Developer notes:
-%  - The BFM data type is single, SFM is double? Does json make a difference?
-%  - Sort out (un)normalised basis, which one is stored in the BFM?
-%  - Domains:
-%    Colour: BFM:  [0, 255], SFM: [0, 1].
-%    Shape: BFM: in mm (e.g. 50000), SFM: in cm, e.g. 50.
-%  - Texture coordinates (model.texture_coordinates) would be saved in the
-%    same way as triangle_list, but the BFM doesn't have any.
-%  - BFM Matlab file contains the "unnormalised", orthonormal bases (as do
-%    the Surrey .scm files).
-%
-function [] = convert_bfm2009_to_json(bfm_file, json_out_file)
-
-if (~exist('bfm_file', 'var'))
-    bfm_file = 'D:/Github/data/bfm/PublicMM1/01_MorphableModel.mat';
-end
-if (~exist('json_out_file', 'var'))
-    json_out_file = 'bfm.json';
-end
-
-bfm = load(bfm_file);
-
-% Leave 'nt' on the default. This is only to produce a small output model
-% for testing purposes. It'll result in only part of the mesh.
-nt = size(bfm.shapeMU, 1); % num triangles times 3
-nb = 99; %size(bfm.shapePC, 2);
-
-model.cereal_class_version = 0;
-model.shape_model.mean.data = bfm.shapeMU(1:nt);
-model.shape_model.normalised_pca_basis.data = normalise_pca_basis(bfm.shapePC(1:nt, 1:nb), bfm.shapeEV(1:nb));
-model.shape_model.unnormalised_pca_basis.data = bfm.shapePC(1:nt, 1:nb);
-model.shape_model.eigenvalues.data = bfm.shapeEV(1:nb);
-model.shape_model.triangle_list = {}; % will be populated below
-model.color_model.mean.data = bfm.texMU(1:nt);
-model.color_model.normalised_pca_basis.data = normalise_pca_basis(bfm.texPC(1:nt, 1:nb), bfm.texEV(1:nb));
-model.color_model.unnormalised_pca_basis.data = bfm.texPC(1:nt, 1:nb);
-model.color_model.eigenvalues.data = bfm.texEV(1:nb);
-model.color_model.triangle_list = {}; % will be populated below
-model.texture_coordinates = {}; % the BFM doesn't have any texcoords
-
-model.shape_model.mean.data = model.shape_model.mean.data / 1000;
-model.color_model.mean.data = model.color_model.mean.data / 255;
-% Divide the basis? The Eigenvectors?
-% For the normalised basis, divide before or after the normalisation?
-
-for i = 1:length(bfm.tl)
-    v0 = bfm.tl(i, 1) - 1;
-    v1 = bfm.tl(i, 2) - 1;
-    v2 = bfm.tl(i, 3) - 1;
-    if (v0 >= nt/3 || v1 >= nt/3 || v2 >= nt/3)
-        continue;
-    end
-    t.value0 = v0;
-    t.value1 = v1;
-    t.value2 = v2;
-    model.shape_model.triangle_list{i} = t;
-    model.color_model.triangle_list{i} = t;
-end
-
-bfm_json = savejson('morphable_model', model, json_out_file);
-
-end
-
-% Taken 1:1 from include/eos/morphablemodel/PcaModel.hpp:
-%
-% * Takes an unnormalised PCA basis matrix (a matrix consisting
-% * of the eigenvectors and normalises it, i.e. multiplies each
-% * eigenvector by the square root of its corresponding
-% * eigenvalue.
-% *
-% * @param[in] unnormalised_basis An unnormalised PCA basis matrix.
-% * @param[in] eigenvalues A row or column vector of eigenvalues.
-% * @return The normalised PCA basis matrix.
-function [normalised_basis] = normalise_pca_basis(unnormalised_basis, eigenvalues)
-	normalised_basis = zeros(size(unnormalised_basis));
-	for i = 1:length(eigenvalues)
-		sqrt_of_eigenvalues(i) = sqrt(eigenvalues(i));
-    end
-	% Normalise the basis: We multiply each eigenvector (i.e. each column) with the square root of its corresponding eigenvalue
-	for basis = 1:size(unnormalised_basis, 2)
-		normalised_eigenvector = unnormalised_basis(:, basis).*sqrt_of_eigenvalues(basis);
-		normalised_basis(:, basis) = normalised_eigenvector;
-    end
-end