close all
% add vlfeat toolbox
run('vlfeat-0.9.21\toolbox\vl_setup');

%% Train a codebook

% trainning data generation
numDatabase    = 10000;
dimLocalDesc   = 128;
X = uint8(floor(255*rand(dimLocalDesc,numDatabase))); % can be computed by vl_sift

% setting params to KMeans
K              = 10; % number of clusters
[centers,assn] = vl_ikmeans(X,K,'verbose');

% build a kd-tree to softassignment
kdtree         = vl_kdtreebuild(single(centers),'verbose');

%% Compute IDF for database
nn_kdtree      = vl_kdtreequery(kdtree, single(centers), single(X));
assignments    = zeros(K,size(X,2)); 
assignments(sub2ind(size(assignments), nn_kdtree, 1:length(nn))) = 1;
% or: assignments(sub2ind(size(assignments), assn, 1:length(nn))) = 1;

IDF            = log(numDatabase*ones(K,1)./(sum(assignments,2) + 1) );


%% Encode with BOW and VLAD
numofLocalKPs  = 100; % number of keypoints 

query          = uint8(floor(255*rand(dimLocalDesc,numofLocalKPs)));  % can be computed by vl_sift
% todo: can be retrieval with vl_ikmeanspush(query,centers) ;
nn             = vl_kdtreequery(kdtree, single(centers), single(query));

% soft assignment
query_assign   = zeros(K,numofLocalKPs);  %only 1 each col 
query_assign(sub2ind(size(query_assign), nn, 1:length(nn))) = 1;

% encode with vlad and bow
enc_vlad       = vl_vlad(single(query),single(centers),single(query_assign),'verbose');

enc_bow        = sum(query_assign,2);
numTotalWords  = sum(enc_bow);
TF             = enc_bow / numTotalWords;
enc_bow        = TF .* IDF;

% show
bar(enc_bow);
set(gcf,'color',[1 1 1]);