function imdb = alot_get_database(alotDir, varargin)
opts.seed = 0 ;
opts.version = 'grey2';
opts = vl_argparse(opts, varargin) ;

rng(opts.seed, 'twister');

imdb.imageDir = fullfile(alotDir, opts.version) ;
imdb.maskDir = fullfile(alotDir, 'mask'); % DO NOT USE

cats = dir(imdb.imageDir);
cats = cats([cats.isdir] & ~ismember({cats.name}, {'.','..'})) ;
imdb.classes.name = {cats.name} ;
imdb.images.id = [] ;

for c=1:numel(cats)
  ims = dir(fullfile(imdb.imageDir, imdb.classes.name{c}, '*.png'));
  %imdb.images.name{c} = fullfile(imdb.classes.name{c}, {ims.name}) ;
  imdb.images.name{c} = cellfun(@(S) fullfile(imdb.classes.name{c}, S), ...
    {ims.name}, 'Uniform', 0);
  imdb.images.label{c} = c * ones(1,numel(ims)) ;
  if numel(ims) ~= 100, error('ops') ; end
  % http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-12.pdf
  % uses 20 for training, 80 for testing, per class
  sets = [1 * ones(1,20), 3 * ones(1,80)];
  imdb.images.set{c} = sets(randperm(100)) ;
end
imdb.images.name = horzcat(imdb.images.name{:}) ;
imdb.images.label = horzcat(imdb.images.label{:}) ;
imdb.images.set = horzcat(imdb.images.set{:}) ;
imdb.images.id = 1:numel(imdb.images.name) ;

imdb.segments = imdb.images ;
imdb.segments.imageId = imdb.images.id ;
imdb.segments.mask = strrep(imdb.images.name, 'image', 'mask') ;

% make this compatible with the OS imdb
imdb.meta.classes = imdb.classes.name ;
imdb.meta.inUse = true(1,numel(imdb.meta.classes)) ;
imdb.segments.difficult = false(1, numel(imdb.segments.id)) ;