eval_mh.m

function eval_mh()

import datasets.*;
import benchmarks.*;
import localFeatures.*;

set(0,'DefaultFigureVisible','off');


%dataset_name = 'oxford';
dataset_name = 'dtu';

if strcmp(dataset_name, 'oxford')
    categories = datasets.VggAffineDataset.AllCategories;
elseif strcmp(dataset_name, 'dtu')
    categories = datasets.DTURobotDataset.AllCategories;
else
    error('invalid dataset name')
end

for category_idx = 1:numel(categories)
    category_name = categories{category_idx};
    if strcmp(dataset_name, 'oxford')
        dataset = datasets.VggAffineDataset('Category', category_name);
    else strcmp(dataset_name, 'dtu')
        dataset = datasets.DTURobotDataset('Category','arc2');
    end
    % --------------------------------------------------------------------
    % PART 1: Detector repeatability
    % --------------------------------------------------------------------

    vlcovdetDetector = VlFeatCovdet()
    siftDetector = VlFeatSift();
    mhDetector = MultiscaleHarris();
    mhDetector.Opts.localization = 1;
    mh_woDetector = MultiscaleHarris();
    mh_woDetector.Opts.localization = 0;
    lcDetector = LindebergCorners();
    lcDetector.Opts.localization = 1;
    lc_woDetector = LindebergCorners();
    lc_woDetector.Opts.localization = 0;
    mser = VlFeatMser();


    repBenchmark = RepeatabilityBenchmark('Mode','Repeatability');

    featExtractors = {vlcovdetDetector, siftDetector, mser, lcDetector, lc_woDetector, mhDetector, mh_woDetector};
    detectorNames = {'VLCovDet', 'VLSIFT', 'MSER', 'LC w. localization', 'LC w.o. localization', 'MH w. localization', 'MH w.o. localization'};

%    featExtractors = {mh_woDetector};
%    detectorNames = {'MH w.o. localization'};

    repeatability = [];
    numCorresp = [];


    for d = 1:numel(featExtractors)
      % use a maximum of three scenes for this demo.
      scenes = dataset.NumScenes;
      for sceneNo = 1:scenes
        for labelNo = 1:dataset.NumLabels
          img_ref_id = dataset.getReferenceImageId(labelNo, sceneNo);
          img_id = dataset.getImageId(labelNo, sceneNo);
          [repeatability(d, labelNo, sceneNo) numCorresp(d, labelNo, sceneNo)] = ...
              repBenchmark.testFeatureExtractor(featExtractors{d}, dataset, ... 
                                                img_ref_id, img_id);
        end
      end
    end


    printScores(detectorNames, 100 * repeatability, 'Repeatability');
    printScores(detectorNames, numCorresp, 'Number of correspondences');

    figure(2); clf; 
    plotScores(detectorNames, dataset, 100 * repeatability, 'Repeatability');
    printFigure(['results_' dataset_name], [category_name '_repeatability']);

    figure(3); clf; 
    plotScores(detectorNames, dataset, numCorresp, 'Number of correspondences');
    printFigure(['results_' dataset_name], [category_name '_num-correspondences']);


    % --------------------------------------------------------------------
    % PART 2: Detector matching score
    % --------------------------------------------------------------------

    vlcovdetWithSift = DescriptorAdapter(vlcovdetDetector, siftDetector);
    mserWithSift = DescriptorAdapter(mser, siftDetector);
    mhWithSift = DescriptorAdapter(mhDetector, siftDetector);
    mh_woWithSift = DescriptorAdapter(mh_woDetector, siftDetector);
    lcWithSift = DescriptorAdapter(lcDetector, siftDetector);
    lc_woWithSift = DescriptorAdapter(lc_woDetector, siftDetector);

    featExtractors = {vlcovdetWithSift, siftDetector, mserWithSift, mhWithSift, mh_woWithSift, lcWithSift, lc_woWithSift};
    detectorNames = {'VLCovDet', 'SIFT', 'MSER', 'LC w. localization', 'LC w.o. localization', 'MH w. localization', 'MH w.o. localization'};

    matchingBenchmark = RepeatabilityBenchmark('Mode','MatchingScore');

    matchScore = [];
    numMatches = [];

    for d = 1:numel(featExtractors)
      % use a maximum of three scenes for this demo.
      scenes = dataset.NumScenes;
      for sceneNo = 1:scenes
        for labelNo = 1:dataset.NumLabels
          img_ref_id = dataset.getReferenceImageId(labelNo, sceneNo);
          img_id = dataset.getImageId(labelNo, sceneNo);
          [matchScore(d, labelNo, sceneNo) numMatches(d, labelNo, sceneNo)] = ...
              matchingBenchmark.testFeatureExtractor(featExtractors{d}, ...
                                    dataset, img_ref_id, img_id);
        end
      end
    end

    printScores(detectorNames, matchScore*100, 'Match Score');
    printScores(detectorNames, numMatches, 'Number of matches') ;

    figure(5); clf; 
    plotScores(detectorNames, dataset, matchScore*100,'Matching Score (with SIFT description)');
    printFigure(['results_' dataset_name], [category_name '_matching_score']);

    figure(6); clf; 
    plotScores(detectorNames, dataset, numMatches,'Number of matches (with SIFT description)');
    printFigure(['results_' dataset_name], [category_name '_num-matches']);

end % dataset category



% --------------------------------------------------------------------
% Helper functions
% --------------------------------------------------------------------

function printScores(detectorNames, scores, name)
  numDetectors = numel(detectorNames);
  maxNameLen = length('Method name');
  for k = 1:numDetectors
    maxNameLen = max(maxNameLen,length(detectorNames{k}));
  end
  fprintf(['\n', name,':\n']);
  formatString = ['%' sprintf('%d',maxNameLen) 's:'];
  fprintf(formatString,'Method name');
  for k = 2:size(scores,2)
    fprintf('\tImg#%02d',k);
  end
  fprintf('\n');
  for k = 1:numDetectors
    fprintf(formatString,detectorNames{k});
    for l = 2:size(scores,2)
      fprintf('\t%6s',sprintf('%.2f',scores(k,l)));
    end
    fprintf('\n');
  end
end

function plotScores(detectorNames, dataset, score, titleText)
  xstart = 1;
  xend = size(score,2);
  if ndims(score) == 2
    plot(xstart:xend,score(:,xstart:xend)','+-','linewidth', 2); hold on ;
  else
    score_std = std(score,0,3);
    score = mean(score,3);
    X = repmat(xstart:xend,[size(score, 1) 1])';
    Y = score(:,xstart:xend)';
    E = score_std(:,xstart:xend)';
    errorbar(X,Y,E,'+-','linewidth', 2); hold on ;
  end
  xLabel = dataset.ImageNamesLabel;
  xTicks = dataset.ImageNames;
  plot(xstart:xend,score(:,xstart:xend)','+-','linewidth', 2); hold on ;
  ylabel(titleText) ;
  xlabel(xLabel);
  set(gca,'XTick',xstart:1:xend);
  set(gca,'XTickLabel',xTicks);
  title(titleText);
  set(gca,'xtick',1:size(score,2));
  maxScore = max([max(max(score)) 1]);
  meanEndValue = mean(score(:,xend));
  legendLocation = 'SouthEast';
  if meanEndValue < maxScore/2
    legendLocation = 'NorthEast';
  end
  legend(detectorNames,'Location',legendLocation);
  grid on ;
  axis([xstart xend 0 maxScore]);
end
end


function printFigure(path, fileName, R, ext)
if isempty(path), return; end;
if ~exist(path, 'dir')
  mkdir(path) ;
end
if ~exist('R','var')
  R = 0.75;
end
vl_printsize(gcf, R) ;

if ~exist('ext','var')
  ext = 'pdf';
end
filePath = fullfile(path, [fileName '.' ext]) ;
saveas(gcf, filePath, ext)
end