DisplayS3ScoreHistogram

 DisplayS3ScoreHistogram - Compare the distribution of S3 scores on natural images, vs.
       its distribution on positive examples.

0001 % DisplayS3ScoreHistogram - Compare the distribution of S3 scores on natural images, vs.
0002 %       its distribution on positive examples.
0003 %
0004 
0005 clear
0006 close all
0007 addpath('common');
0008 
0009 category = 'neg';
0010 
0011 %% load the S2 (shape motif) dictionary and S3 (shape script) template
0012 load template_params
0013 load result/transformedS2Template.mat
0014 load result/egret/shapescript.mat
0015 
0016 %% load the testing image
0017 %{
0018 This can also be the comment
0019 %}
0020 
0021 Iname = {'neg04.jpg','neg05.jpg','neg06.jpg','neg07.jpg','neg08.jpg',...
0022     'neg21.jpg','neg22.jpg','neg23.jpg','neg24.jpg','neg25.jpg'};
0023 imScale = [.5 .5 .5 .5 .5,...
0024     0.3 .5 .5 .5 .5];
0025 binCenterS3 = [-100:20:400];
0026 
0027 subsampleM1 = 1;
0028 S3Hist = zeros(1,length(binCenterS3));
0029 for iImage = 1:length(Iname)
0030     im = imread( sprintf('image/%s/%s',category,Iname{iImage}) );
0031     im = imresize( im,imScale(iImage),'nearest' );
0032 
0033     if size(im,3) == 3
0034         im = rgb2gray(im);
0035     end
0036 
0037     im = single(im);
0038     S1Map = applyfilterMultiScale({im},GaborFilter, nGaborOri);
0039 
0040     % sigmoid transform
0041     nImage = 1;
0042     heightS1Map = zeros(length(S1Map),1,'single');
0043     widthS1Map = zeros(length(S1Map),1,'single');
0044     for i = 1:length(S1Map)
0045         heightS1Map(i) = size(S1Map{i},1);
0046         widthS1Map(i) = size(S1Map{i},2);
0047     end
0048     tmpNGaborOri = 1;
0049     for i = 1:length(S1Map)
0050         Csigmoid(nImage, heightS1Map(i), widthS1Map(i), tmpNGaborOri, GaborSaturation, S1Map(i));
0051     end
0052 
0053     % compute M1 map
0054     sizeGaborPerScale = zeros(nGaborScale,1,'single');
0055     for iS = 1:nGaborScale
0056         sizeGaborPerScale(iS) = size( GaborFilter{(iS-1)*nGaborOri+1}, 1 );
0057     end
0058     locationShift = .2;
0059     oriShift = 1;
0060     [M1Map M1Trace M1RowShift M1ColShift M1OriShift] = ...
0061         CcomputeMAX1( nGaborOri, S1Map, locationShift, oriShift, sizeGaborPerScale, subsampleM1 );
0062 
0063     % compute S2 map
0064     S2Map = CcomputeSUM2( nGaborOri, M1Map, TransformedTemplate, subsampleS2 );
0065 
0066     % compute M2 map
0067     locationPerturbationFraction = 0.2; subsampleM2 = 1;
0068     M2Map = cell(nTransform, nS2);
0069     M2LocationTrace = cell(nTransform, nS2);
0070     M2TransformTrace = cell(nTransform, nS2);
0071     for iS2 = 1:nS2
0072         sizeTemplate = zeros(nTransform,1);
0073         for iT = 1:nTransform
0074             sizeTemplate(iT) = sqrt( ...
0075                 size(TransformedTemplate{iT,iS2}.sym,1)...
0076                 * size(TransformedTemplate{iT,iS2}.sym,2) ) / subsampleS2;
0077         end
0078         [M2Map(:,iS2) M2LocationTrace(:,iS2) M2TransformTrace(:,iS2) storedM2LocationShift] = ...
0079               CcomputeMAX2( templateAffMat, S2Map((iS2-1)*nTransform + (1:nTransform)), locationPerturbationFraction, ... 
0080                  int32(sizeTemplate), subsampleM2 );
0081         storedSizeS2Template{iS2} = sizeTemplate;
0082     end
0083     disp('done with computing M2');
0084 
0085     %% detection
0086     subsampleS3 = 1;
0087     %
0088     % Compute SUM 3 map :
0089     %
0090     S3Map = CcomputeSUM3_loglinear( M2Map, S3Template, subsampleS3, nTransform );
0091     for ii = 1:numel(S3Map)
0092         tmp = hist(S3Map{ii}(:),binCenterS3);
0093         S3Hist(:) = S3Hist(:) + tmp(:);
0094     end
0095     clear S2Map;
0096     clear M2Map;
0097     clear S3Map;
0098     clear M1Map;
0099     clear S1Map;
0100 end
0101 
0102 
0103 
0104 %% collect statistics on positive images
0105 
0106 count = 1;
0107 
0108 Iname = {'pelican 2_big.jpg','pelican3.jpg',...
0109     'pelican4.jpg','pelican5.jpg','pelican6.jpg',...
0110     'pelican7.jpg','pelican8.jpg','pelican9.jpg',...
0111     'pelican10.jpg'};
0112 Iname_pelican = Iname;
0113 
0114 category = 'pelican';
0115 for i = 1:length(Iname)
0116     load(sprintf('result/%s/detectionResult%d_%s.mat',category,1,Iname{i}),...
0117                 'im','mask','S3Map','M2Map');
0118     tmp = -1e10;
0119     for j = 1:numel(S3Map)
0120         tmp = max(tmp, max(S3Map{j}(:)));
0121     end
0122     S3Pos(count) = tmp;
0123     count = count + 1;
0124 end
0125 
0126 Iname = {'l1.jpg','l2.jpg','l3.jpg','l4.jpg','l5.jpg',...
0127     'l8.jpg','l10.jpg',...
0128     'l13.jpg','l14.jpg','l15.jpg',...
0129     'l17.jpg','l18.jpg','l19.jpg','l20.jpg',...
0130     'l21.jpg', 'l22.jpg',...
0131     'l26.jpg','l30.jpg',...
0132     'l4_dup.jpg'};
0133 Iname_egret = Iname;
0134 category = 'egret';
0135 for i = 1:length(Iname)
0136     load(sprintf('result/%s/detectionResult%d_%s.mat',category,1,Iname{i}),...
0137                 'im','mask','S3Map','M2Map');
0138     tmp = -1e10;
0139     for j = 1:numel(S3Map)
0140         tmp = max(tmp, max(S3Map{j}(:)));
0141     end
0142     S3Pos(count) = tmp;
0143     count = count + 1;
0144 end
0145 
0146 S3PosHist = hist(S3Pos,binCenterS3);
0147 
0148 %% histogram
0149 figure; plot(binCenterS3,S3Hist/sum(S3Hist(:)),'b--','LineWidth',3);
0150 hold on;
0151 plot(binCenterS3,S3PosHist/sum(S3PosHist(:)),'r-','LineWidth',3);
0152 
0153 set(gca,'FontSize',12);
0154 set(get(gca,'XLabel'),'String','S3 score','FontSize',18);
0155 set(get(gca,'YLabel'),'String','Frequency','FontSize',18);
0156 legend( {'natural','positive'},'FontSize',16,0);
0157 
0158 saveas(gcf,'S3histogram.eps','epsc')
0159 saveas(gcf,'S3histogram.png')