61 lines
1.7 KiB
Matlab
61 lines
1.7 KiB
Matlab
% compile.m should work for Linux and Mac.
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% To Windows users:
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% If you are using a Windows machine, please use the basic convolution (fconv.cc).
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% This can be done by commenting out line 13 and uncommenting line 15 in
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% compile.m
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compile;
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% load and visualize model
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% Pre-trained model with 146 parts. Works best for faces larger than 80*80
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% load face_p146_small.mat
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% % Pre-trained model with 99 parts. Works best for faces larger than 150*150
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load face_p99.mat
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% % Pre-trained model with 1050 parts. Give best performance on localization, but very slow
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% load multipie_independent.mat
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% disp('Model visualization');
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% visualizemodel(model,1:13);
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% disp('press any key to continue');
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% pause;
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% 5 levels for each octave
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model.interval = 5;
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% set up the threshold
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model.thresh = min(-0.65, model.thresh);
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% define the mapping from view-specific mixture id to viewpoint
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if length(model.components)==13
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posemap = 90:-15:-90;
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elseif length(model.components)==18
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posemap = [90:-15:15 0 0 0 0 0 0 -15:-15:-90];
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else
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error('Can not recognize this model');
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end
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ims = dir('images/*.jpg');
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for i = 1:length(ims),
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fprintf('testing: %d/%d\n', i, length(ims));
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im = imread(['images/' ims(i).name]);
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clf; imagesc(im); axis image; axis off; drawnow;
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tic;
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bs = detect(im, model, model.thresh);
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bs = clipboxes(im, bs);
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bs = nms_face(bs,0.3);
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dettime = toc;
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% show highest scoring one
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figure,showboxes(im, bs(1),posemap),title('Highest scoring detection');
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% show all
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figure,showboxes(im, bs,posemap),title('All detections above the threshold');
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fprintf('Detection took %.1f seconds\n',dettime);
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disp('press any key to continue');
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pause;
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close all;
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end
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disp('done!');
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