edgeBoxesDemoCanny.m

% Demo for Edge Boxes (please see readme.txt first).

%% load pre-trained edge detection model and set opts (see edgesDemo.m)
model=load('models/forest/modelBsds'); model=model.model;
model.opts.multiscale=0; model.opts.sharpen=2; model.opts.nThreads=4;

%% set up opts for edgeBoxes (see edgeBoxes.m)
opts = edgeBoxes;
opts.alpha = .65;     % step size of sliding window search
opts.beta  = .75;     % nms threshold for object proposals
opts.minScore = .01;  % min score of boxes to detect
opts.maxBoxes = 1e4;  % max number of boxes to detect

%% detect Edge Box bounding box proposals (see edgeBoxes.m)
I = imread('test1.jpg');
tic, bbs=edgeBoxesCanny(I,model,opts); toc

n = 15;
bbtmp = bbs(1:n, :);
bbtmp = [bbtmp, ones(n, 1)];
figure(); bbGt('showRes',I,[],bbtmp);

% %% show evaluation results (using pre-defined or interactive boxes)
% gt=[122 248 92 65; 193 82 71 53; 410 237 101 81; 204 160 114 95; ...
%   9 185 86 90; 389 93 120 117; 253 103 107 57; 81 140 91 63];
% if(1), gt='Please select an object box.'; disp(gt); figure(1); imshow(I);
%   title(gt); [~,gt]=imRectRot('rotate',0); gt=gt.getPos(); end
% gt(:,5)=0; [gtRes,dtRes]=bbGt('evalRes',gt,double(bbs),.5);
% figure(); bbGt('showRes',I,gtRes,dtRes(dtRes(:,6)==1,:));
% r = find(dtRes(:, end));
% disp('Rank:');
% disp(r);
% fprintf('Out of %d\n', size(dtRes, 1));
% title('green=matched gt  red=missed gt  dashed-green=matched detect');