Nearest-Neighbour-Search-Neural-Networks-Product-Quantization/TEXMEX/Euclidean NNS/EuclideanNNS.m

clear all
close all
clc

addpath('../Functions');

imgSz=128;           % Lateral Image pixel size
%imgSzSq=imgSz^2;    % Square Image pixel size
Ntr=1e6;             % Number of pictures train set
Ntests=1e4;          % Number of pictures test set
%fileOffsetPic=16;   % Bytes of pictures file offset
%fileOffsetLab=8;    % Bytes of label file offset
ENNSon=1;            % 

fprintf('Loading data... \n');
XSet=fvecs_read('../Data/sift/sift_base.fvecs');
YSet=fvecs_read('../Data/sift/sift_query.fvecs');
%YLab=ivecs_read('../Data/sift/sift_groundtruth.ivecs');

break;

%% Reading Binary Training Images and Labels
% 
% [XSet, XLab]=ReadingPicSetLabSet(imgSz, Ntr, fileOffsetPic, '../Data/train-images.idx3-ubyte',...
%                                   fileOffsetLab, '../Data/train-labels.idx1-ubyte', 0);
% 
%% Reading Binary Testing Images and Labels
% [YSet, YLab]=ReadingPicSetLabSet(imgSz, Ntests, fileOffsetPic, '../Data/t10k-images.idx3-ubyte',...
%                                   fileOffsetLab, '../Data/t10k-labels.idx1-ubyte', 0);

%% Computing NNS with Euclidean Distance

if(ENNSon)
    fprintf('Computing Euclidean NNS: ...    ');

    dEuc=zeros(1,Ntr);
    indmin=zeros(1,Ntests);

    for ii=1:Ntests
        for jj=1:Ntr
            Xjj=XSet(imgSz*(jj-1)+1:imgSz*jj);
            Yii=YSet(imgSz*(ii-1)+1:imgSz*ii);
            dEuc(jj)=sum((Xjj-Yii).^2);
        end
        [~,indmin(ii)]=min(dEuc);
        fprintf('\b\b\b\b\b\b%3.2f %%',ii/Ntests*100);
    end
    save('indminEuNNS.mat','indmin');
else
    fprintf('Loading Euclidean NNS results ...');
    load('indminEuNNS.mat')
end

%% Plot some of the matches
%Nstart=9983;
%for kk=Nstart+1:Nstart+10%Ntests
    %subplot(2,10,kk-Nstart)
    %imshow255(0,imgSz,YSet')
    %xlabel(int2str(YLab(kk)));
    %title('Test Image')
    %subplot(2,10,kk-Nstart+10)
    %figure()
    %imshow255(0,imgSz,XSet(indmin(1))')
    %xlabel(int2str(XLab(indmin(kk))));
    %title('Match Image')
%end

%NErrors=Ntests-sum(XLab(indmin)==YLab(1:length(indmin)));
%Performances=100-NErrors/Ntests*100