function options = perf_dr_fa(options , model)
%
% Compute detection rate versus number of false alarms
% for face detectors
%
%
%
% clear, close all
% options.pathfacesimg = 'C:\utilisateurs\SeBy\Matlab\MIT-CMU-frontal-face-set-4-Timo\MIT+CMU frontal face set 4 Timo';
% options.gt_file = 'C:\utilisateurs\SeBy\Matlab\MIT-CMU-frontal-face-set-4-Timo\FacePara5points.txt';
% options.dataset = 1;
% options.refbox = 0;
% options.overlapping = 0.5;
% options.minwratio = 0.4;
% options.maxwratio = 2;
% options.threshmin = -1;
% options.threshmax = 1;
% options.nbins = 50;
% options.detector = 0;
% options.preview = 1;
%
% load ('C:\utilisateurs\SeBy\Matlab\fdtool\model_detector_haar_24x24.mat')
% model.cascade = [1 , 2 , 3 , 4 , 10 , 20 , 30 , 30 ; -0.75 , -0.6 , -0.6, -0.5, -0.4 , -0.3 , -0.2 , 0];
%% model.cascade = [1 , 2 , 3 , 4 , 10 , 20 , 30 , 30 ; -0.75 ,-0.6 , -0.5, -0.25, -0.2 , 0 , 0 , 0];
% model.scalingbox = [1.15 , 1.30 , 1.6];
% model.mergingbox = [1/2 , 1/2 , 1/3];
% model.min_detect = 1;
% model.postprocessing = 1;
%
% options = perf_dr_fa(options , model);
%
% figure(1)
% h = semilogx(options.fppw_mean , 1-options.tpr_mean);
% h = semilogx(sort(options.fppw_mean) , sort(1-options.tpr_mean , 'descend'));
% set(h , 'linewidth' , 2)
% axis([min(options.fppw_mean)*0.95 , max(options.fppw_mean)*1.05 , -0.05 , 1.05])
% xlabel('False Positive Per Window (FFFW)')
% ylabel('Miss rate')
% h = title('MIT-CMU-frontal');
% set(h , 'fontsize' , 13);
%
% thresh = options.threshmin:(options.threshmax-options.threshmin)/(options.nbins-1):options.threshmax;
% figure(2)
% plot(options.fpp(end:-1:1) , options.tpp(end:-1:1) , 'linewidth' , 2)
% xlabel('False Positives')
% % addTopXAxis('expression', 'thresh''', 'xLabStr', '\lambda')
% ylabel('True detection rate')
% axis([min(options.fpp)*0.9 , max(options.fpp)*1.005 , -0.05 , 1.05])
% grid on
% h = title('MIT-CMU-frontal');
% set(h , 'fontsize' , 13);
%
% [options.nbtruedetperim ; options.tpperim(1 , :)]
%
[pathgt, gt_file, gt_ext] = fileparts(options.gt_file);
fid = fopen(fullfile(pathgt , [gt_file , gt_ext]));
if(options.preview)
fig = figure(1);
set(fig,'doublebuffer','on');
colormap(gray)
end
if(options.dataset == 1)
C = textscan(fid,'%s%d%d%d%d%d%d%d%d%d%d%d%d');
options.detperfile = C{1};
yle = double(C{2})';
xle = double(C{3})';
yre = double(C{4})';
xre = double(C{5})';
xlm = double(C{8})';
ylm = double(C{9})';
xrm = double(C{12})';
yrm = double(C{13})';
% w = sqrt((xle-xre).^2 + (yle-yre).^2)/0.4;
w = sqrt((xle-xre).^2 + (yle-yre).^2)/0.47;
h = 1.42*w;
% x = xle - 0.3*w;
x = xle - 0.52*w;
y = (yle + yre)./2 - 0.5*h;
options.facebox = [y ; x ; h ; w];
wmax = 1.15*w;
hmax = 1.15*h;
xmax = x -(wmax - w)./2;
ymax = y -(hmax - h)./2;
options.facemax = [ymax ; xmax ; hmax ; wmax];
xmin = xle - 0.11*w;
ymin = (yle + yre)/2 - 0.06*h;
wmin = (xre + 0.11*w) - xmin;
hmin = (ylm + yrm)/2+0.07*h - ymin;
options.facemin = [ ymin; xmin ; hmin ; wmin];
if(options.refbox == 0)
refbox = options.facebox;
elseif(options.refbox == 1)
refbox = options.facemax;
elseif(options.refbox == 2)
refbox = options.facemin;
end
options.image_names = unique(options.detperfile);
options.nb_images = length(options.image_names);
options.nbtruedetperim = zeros(1 , options.nb_images);
options.nbestdetperim = zeros(options.nbins , options.nb_images);
options.tpperim = zeros(options.nbins , options.nb_images);
options.fpperim = zeros(options.nbins , options.nb_images);
options.totalscan = zeros(options.nbins , options.nb_images);
options.deltathresh = (options.threshmax-options.threshmin)/(options.nbins - 1);
if(options.detector == 3)
bias = model.w(end);
end
for i = 1 : options.nb_images
pathi = fullfile(options.pathfacesimg , options.image_names{i});
I = imread(pathi);
[Ny , Nx , Nz] = size(I);
if(Nz == 3)
I = rgb2gray(I);
end
indexi = find(strcmp(options.image_names{i} , options.detperfile));
options.nbtruedetperim(i) = length(indexi);
for b = 1:options.nbins
if(options.detector == 0)
thresh = options.threshmin + (b-1)*options.deltathresh;
model.cascade(2, end) = thresh;
[D , stat] = detector_haar(I , model);
D = D(: , D(4 , :)>=model.min_detect);
elseif(options.detector == 1)
thresh = options.threshmin + (b-1)*options.deltathresh;
model.cascade(2, end) = thresh;
[D , stat] = detector_mblbp(I , model);
D = D(: , D(4 , :)>=model.min_detect);
elseif(options.detector == 2)
thresh = options.threshmin + (b-1)*options.deltathresh;
model.w(end) = bias + thresh;
[D , stat] = detector_mlhmslbp_spyr(I , model);
D = D(: , D(4 , :)>=model.min_detect);
end
options.totalscan(b , i) = sum(stat);
options.nbestdetperim(b,i) = size(D , 2);
indexdet = (1:options.nbestdetperim(b,i));
indextrue = [];
for j = 1 : options.nbtruedetperim(i)
refboxj = refbox(: , indexi(j));
x1t = refboxj(1);
y1t = refboxj(2);
wt = refboxj(3);
ht = refboxj(4);
x2t = x1t + wt;
y2t = y1t + ht;
wtmin = options.minwratio*wt;
wtmax = options.maxwratio*wt;
htmin = options.minwratio*ht;
htmax = options.maxwratio*ht;
areat = (x2t-x1t+1).*(y2t-y1t+1);
last = length(indexdet);
suppress = [];
overmax = -inf;
for pos = 1:last
if(indexdet(pos)~= 0)
posi = pos;
x1e = D(1,posi);
y1e = D(2,posi);
we = D(3,posi);
he = D(3,posi);
x2e = x1e + we;
y2e = y1e + he;
xx1 = max(x1t , x1e);
yy1 = max(y1t , y1e);
xx2 = min(x2t , x2e);
yy2 = min(y2t , y2e);
ww = xx2-xx1+1;
hh = yy2-yy1+1;
if ((ww > 0) && (hh > 0) && (we > wtmin) && (he > htmin) && (we < wtmax) && (he < htmax))
% compute overlap
o = ww * hh / areat;
if ((o > options.overlapping) && (o > overmax))
suppress = pos;
overmax = o;
end
end
end
end
if(~isempty(suppress))
options.tpperim(b,i) = options.tpperim(b,i)+1;
indexdet(suppress) = 0;
indextrue = [indextrue , suppress];
end
end
options.fpperim(b,i) = options.nbestdetperim(b,i) - options.tpperim(b,i);
if(options.preview)
indexfalsea = 1:size(D , 2);
indexfalsea(indextrue) = [];
imagesc(I)
hold on
plot_rectangle(D(: , indexfalsea) , 'r');
plot_rectangle(D(: , indextrue) , 'g');
plot_rectangle(refbox(: , indexi) , 'b');
colormap(gray)
h = text(D(1 , :) , D(2 , :) , int2str((1:size(D,2))'));
set(h , 'fontsize' , 12 ,'color' , [1 1 0]);
hold off
h=title(sprintf('image (%d/%d), true detec (%d/%d), false alarms = %d, detected windows = %d, scanned windows = %d, \\lambda = %4.2f' , i , options.nb_images , options.tpperim(b,i) , options.nbtruedetperim(i) , options.fpperim(b,i) , options.nbestdetperim(b,i) , options.totalscan(b , i) , thresh));
set(h , 'fontsize' , 13 );
drawnow
% pause(0.01)
end
end
end
options.tpr_mean = mean(options.tpperim./options.nbtruedetperim(ones(options.nbins , 1) , :) , 2);
options.fppw_mean = mean(options.tpperim./options.totalscan , 2);
options.tpp = sum(options.tpperim , 2)/sum(options.nbtruedetperim);
options.fpp = sum(options.fpperim , 2);
end
fclose(fid);
