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sergeyk
selective_search_ijcv_with_python
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selective_search_ijcv_with_python
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BoxAverageBestOverlap.m
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master
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selective_search_ijcv_with_python
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BoxAverageBestOverlap.m
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function [abo mabo boScores avgNumBoxes] = BoxAverageBestOverlap(gtBoxes, gtNrs, testBoxes)
% [abo mabo boScores avgNumBoxes] = BoxAverageBestOverlap(gtBoxes, gtNrs, testBoxes)
%
% Calculate Average Best Overlap scores
%
% gtBoxes: Cell array of ground truth boxes per class (see
% GetAllObjectBoxes)
% gtNrs: Cell array with image nrs corresponding to ground truth.
% testBoxes: Cell array of testboxes per image.
%
% abo: Average Best Overlap per class (Pascal Overlap criterion)
% mabo: Mean Average Best Overlap (mean(abo))
% boScores: Best Overlap Score per GT box.
% avgNumBoxes: Average number of boxes per image
%
% Jasper Uijlings - 2013
% Check nr of gt elements
nClasses = length(gtBoxes);
boScores = cell(1, nClasses);
for cI = 1:nClasses
boScores{cI} = zeros(size(gtBoxes{cI}, 1),1);
end
% indices per class
classIdx = ones(1, nClasses);
for cI = 1:length(gtBoxes)
for i = 1:size(gtBoxes{cI}, 1)
boScores{cI}(classIdx(cI)) = ...
BoxBestOverlap(gtBoxes{cI}(i,:), testBoxes{gtNrs{cI}(i)});
classIdx(cI) = classIdx(cI) + 1;
end
end
% Calculation abo and mabo measures
abo = zeros(nClasses, 1);
for cI = 1:nClasses
abo(cI) = mean(boScores{cI});
end
mabo = mean(abo);
% Calculation avgNumBoxes
numBoxes = zeros(length(testBoxes), 1);
for i=1:length(testBoxes)
numBoxes(i) = size(testBoxes{i}, 1);
end
avgNumBoxes = mean(numBoxes);
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