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evaluateModel.m
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59 lines (51 loc) · 1.8 KB
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function [matches, negs] = evaluateModel(...
testImages, testBoxes, testBoxImages, w, hogCellSize, scales)
clear matches ;
negs = {} ;
for i=1:numel(testImages)
% Detect on test image
im = imread(testImages{i}) ;
im = im2single(im) ;
[detections, scores, hog] = detect(im, w, hogCellSize, scales) ;
% Non-maxima suppression
keep = boxsuppress(detections, scores, 0.5) ;
keep = find(keep) ;
keep = vl_colsubset(keep, 15, 'beginning') ;
detections = detections(:, keep) ;
scores = scores(keep) ;
% Find all the objects in the target image
ok = find(strcmp(testImages{i}, testBoxImages)) ;
gtBoxes = testBoxes(:, ok) ;
gtDifficult = false(1, numel(ok)) ;
matches(i) = evalDetections(...
gtBoxes, gtDifficult, ...
detections, scores) ;
% Visualize progres
clf;
subplot(1,3,[1 2]) ;
imagesc(im) ; axis equal ; hold on ;
labels = arrayfun(@(x)sprintf('%d',x),1:size(detections,2),'uniformoutput',0) ;
sp = fliplr(find(matches(i).detBoxFlags == -1)) ;
sn = fliplr(find(matches(i).detBoxFlags == +1)) ;
vl_plotbox(detections(:, sp), 'r', 'linewidth', 1, 'label', labels(sp)) ;
vl_plotbox(detections(:, sn), 'g', 'linewidth', 2, 'label', labels(sn)) ;
vl_plotbox(gtBoxes, 'b', 'linewidth', 1) ;
title(sprintf('Image %d of %d', i, numel(testImages))) ;
axis off ;
subplot(1,3,3) ;
vl_pr([matches.labels], [matches.scores]) ;
% If required, collect top negative features
if nargout > 1
overlaps = boxoverlap(gtBoxes, detections) ;
overlaps(end+1,:) = 0 ;
overlaps = max(overlaps,[],1) ;
detections(:, overlaps >= 0.25) = [] ;
detections = vl_colsubset(detections, 10, 'beginning') ;
negs{end+1} = extract(hog, hogCellSize, scales, w, detections) ;
end
% Break here with the debugger
drawnow ;
end
if nargout > 1
negs = cat(4, negs{:}) ;
end