function [Xfa , fxfa , pfa_current] = generate_fa_features(options)
%
%
% Generate new negative features considered as false alarms for the
% current trained model
%
% Usage
% ------
%
% [Xfa , fxfa , pfa_current] = generate_fa_features(options)
%
% Inputs
% -------
% options Options struture
%
% negatives_path Path from negative images are loaded for generating negative examples
% negext Negatives extension files
% negmax_size Maximum side of each negative image (default = 1000)
% seed Seed value for random generator in order to generate the same positive sets
% resetseed Reset generator with given seed (default resetseed = 1)
% preview Preview current example (default preview = 0)
% standardize Standardize images (default standardize = 1)
% Nnegboost Number of desired negatives examples . Extracted by bilinear
% featype Type of features (featype: 0 <=> Haar, 1 <=> MBLBP, 3 <=> Histogram of MBLBP)
% Outputs
% -------
%
% Xfa features associated with False alarms for current model (d x Nnegboost)
% fxfa Output predicted values of the false alarms (1 x Nnegboost)
% pfa_current Current false alarms probability
%
%
% Author : Sbastien PARIS : sebastien.paris@lsis.org
% ------- Date : 02/25/2011
%
%
if(nargin < 1)
options.negatives_path = fullfile(pwd , 'images' , 'train' , 'negatives');
options.negext = {'jpg'};
options.negmax_size = 1000;
options.Nnegboost = 1000;
options.seed = 5489;
options.resetseed = 1;
options.preview = 0;
options.typefeat = 3;
options.addbias = 1;
options.max_detections = 5000;
options.num_threads = -1;
options.dimsIscan = [24 , 24];
options.scalingbox = [2 , 1.4 , 1.8];
options.mergingbox = [1/2 , 1/2 , 0.8];
options.spyr = [1 , 1 , 1 , 1 ; 1/4 , 1/4 , 1/4 , 1/4];
options.scale = [1];
options.maptable = 0;
options.useFF = 0;
options.cs_opt = 1;
options.improvedLBP = 0;
options.rmextremebins = 0;
options.color = 0;
options.norm = 2;
options.clamp = 0.2;
options.n = 0;
options.L = 1.2;
options.kerneltype = 0;
options.numsubdiv = 8;
options.minexponent = -20;
options.maxexponent = 8;
options.dimsItraining = [24 , 24];
options.rect_param = [1 1 2 2;1 1 2 2;2 2 1 1;2 2 2 2;1 2 1 2;0 0 0 1;0 1 0 0;1 1 1 1;1 1 1 1;1 -1 -1 1];
options.usesingle = 1;
options.transpose = 0;
end
if(~any(strcmp(fieldnames(options) , 'negatives_path')))
options.negatives_path = fullfile(pwd , 'images' , 'train' , 'negatives');
end
if(~any(strcmp(fieldnames(options) , 'negext')))
options.negext = {'jpg'};
end
if(~any(strcmp(fieldnames(options) , 'negmax_size')))
options.negmax_size = 1000;
end
if(~any(strcmp(fieldnames(options) , 'Nnegboost')))
options.Nnegboost = 1000;
end
if(~any(strcmp(fieldnames(options) , 'seed')))
options.seed = 5489;
end
if(~any(strcmp(fieldnames(options) , 'typefeat')))
options.typefeat = 3;
end
if(~any(strcmp(fieldnames(options) , 'addbias')))
options.addbias = 1;
end
if(~any(strcmp(fieldnames(options) , 'boost_ite')))
options.boost_ite = 10;
end
if(~any(strcmp(fieldnames(options) , 'n')))
options.n = 0;
end
if(~any(strcmp(fieldnames(options) , 'L')))
options.L = 0.7;
end
if(~any(strcmp(fieldnames(options) , 'kerneltype')))
options.kerneltype = 0;
end
if(~any(strcmp(fieldnames(options) , 'numsubdiv')))
options.numsubdiv = 8;
end
if(~any(strcmp(fieldnames(options) , 'minexponent')))
options.numsubdiv = -20;
end
if(~any(strcmp(fieldnames(options) , 'maxexponent')))
options.maxexponent = 8;
end
if(~any(strcmp(fieldnames(options) , 'max_detections')))
options.max_detections = 5000;
end
if(~any(strcmp(fieldnames(options) , 'num_threads')))
options.num_threads = -1;
end
if(~any(strcmp(fieldnames(options) , 'dimsIscan')))
options.dimsIscan = [24 , 24];
end
if(~any(strcmp(fieldnames(options) , 'scalingbox')))
options.scalingbox = [2 , 1.4 , 1.8];
end
if(~any(strcmp(fieldnames(options) , 'mergingbox')))
options.mergingbox = [1/2 , 1/2 , 0.8];
end
if(~any(strcmp(fieldnames(options) , 'spyr')))
options.spyr = [1 , 1 , 1 , 1 ; 1/4 , 1/4 , 1/4 , 1/4];
end
if(~any(strcmp(fieldnames(options) , 'scale')))
options.scale = 1;
end
if(~any(strcmp(fieldnames(options) , 'maptable')))
options.maptable = 0;
end
if(~any(strcmp(fieldnames(options) , 'useFF')))
options.useFF = 0;
end
if(~any(strcmp(fieldnames(options) , 'cs_opt')))
options.cs_opt = 1;
end
if(~any(strcmp(fieldnames(options) , 'improvedLBP')))
options.improvedLBP = 0;
end
if(~any(strcmp(fieldnames(options) , 'rmextremebins')))
options.rmextremebins = 0;
end
if(~any(strcmp(fieldnames(options) , 'color')))
options.color = 0;
end
if(~any(strcmp(fieldnames(options) , 'norm')))
options.norm = 2;
end
if(~any(strcmp(fieldnames(options) , 'clamp')))
options.clamp = 0.2;
end
if(~any(strcmp(fieldnames(options) , 'resetseed')))
options.resetseed = 1;
end
if(options.typefeat == 0)
options.d = size(options.F , 2);
elseif(options.typefeat == 1)
options.d = size(options.F , 2);
elseif(options.typefeat == 3)
if(options.cs_opt == 1)
if(options.maptable == 0)
options.Nbins = 16;
elseif(options.maptable == 1)
options.Nbins = 15;
elseif(options.maptable == 2)
options.Nbins = 6;
elseif(options.maptable == 3)
options.Nbins = 6;
end
options.improvedLBP = 0;
else
if(options.maptable == 0)
options.Nbins = 256;
elseif(options.maptable == 1)
options.Nbins = 59;
elseif(options.maptable == 2)
options.Nbins = 36;
elseif(options.maptable == 3)
options.Nbins = 10;
end
end
options.nH = sum(floor(((1 - options.spyr(:,1))./(options.spyr(:,3)) + 1)).*floor((1 - options.spyr(:,2))./(options.spyr(:,4)) + 1));
options.nscale = length(options.scale);
options.d = options.Nbins*(1+options.improvedLBP)*options.nH*options.nscale;
end
%% reset seed eventually %%
if(options.resetseed)
RandStream.setDefaultStream(RandStream.create('mt19937ar','seed',options.seed));
end
Ntotal = options.Nnegboost;
if(options.typefeat == 0)
if(options.transpose)
if(options.usesingle)
Xfa = zeros(Ntotal , options.d , 'single');
else
Xfa = zeros(Ntotal , options.d);
end
else
if(options.usesingle)
Xfa = zeros(options.d , Ntotal , 'single');
else
Xfa = zeros(options.d , Ntotal);
end
end
elseif(options.typefeat == 1)
if(options.transpose)
if(options.usesingle)
Xfa = zeros(Ntotal , options.d , 'single');
else
Xfa = zeros(Ntotal , options.d);
end
else
if(options.usesingle)
Xfa = zeros(options.d , Ntotal , 'single');
else
Xfa = zeros(options.d , Ntotal);
end
end
elseif(options.typefeat == 3)
Xfa = zeros(options.d , Ntotal);
end
fxfa = zeros(1 , Ntotal);
options.postprocessing = 0;
%% Generate Nneg Negatives examples from Negatives images with replacement %%
cophotos = 1;
co = 1;
directory = [];
for i = 1:length(options.negext)
directory = [directory ; dir(fullfile(options.negatives_path , ['*.' options.negext{i}]))] ;
end
nbneg_photos = length(directory);
index_negphotos = randperm(nbneg_photos);
Ineg = imread(fullfile(options.negatives_path , directory(index_negphotos(cophotos)).name));
[Ny , Nx , Nz] = size(Ineg);
if(Nz == 3)
Ineg = rgb2gray(Ineg);
end
maxNyNx = max([Ny Nx]);
if(maxNyNx > options.negmax_size)
ratio = options.negmax_size/maxNyNx;
Ny = ceil(ratio*Ny);
Nx = ceil(ratio*Nx);
Ineg = imresize(Ineg , [Ny Nx]);
end
if(options.preview)
imagesc(Ineg);
end
cte = 1;
cumstat = zeros(1 , 2);
h = waitbar(0,sprintf('Generating negatives'));
set(h , 'name' , sprintf('Boosting negatives stage %d/%d' , options.m , options.boost_ite))
drawnow
if(options.preview)
fig = figure(1);
set(fig,'doublebuffer','on');
colormap(gray)
end
while(co <= Ntotal)
if(options.typefeat == 0)
[D , stat] = detector_haar(Ineg , options);
cumstat = cumstat + stat;
for i = 1:min(Ntotal,size(D,2))
Icrop = imresize(Ineg(D(2,i)-(1-cte)*1:D(2,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1 , D(1,i)-(1-cte)*1:D(1,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1) , options.dimsItraining);
if(options.transpose)
Xfa(co , :) = haar(Icrop , options);
else
Xfa(: , co) = haar(Icrop , options);
end
% [fxfa(co) , yfxfa ] = eval_haar(Icrop , options);
fxfa(co) = D(5 , i);
co = co + 1;
end
elseif(options.typefeat == 1)
[D , stat] = detector_mblbp(Ineg , options);
cumstat = cumstat + stat;
for i = 1:min(Ntotal,size(D,2))
Icrop = imresize(Ineg(D(2,i)-(1-cte)*1:D(2,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1 , D(1,i)-(1-cte)*1:D(1,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1) , options.dimsItraining);
if(options.transpose)
Xfa(co,:) = mblbp(Icrop , options);
else
Xfa(: , co) = mblbp(Icrop , options);
end
% [fxfa(co) , yfxfa ] = eval_mblbp(Icrop , options);
fxfa(co) = D(5 , i);
co = co + 1;
end
elseif(options.typefeat == 3)
[D , stat] = detector_mlhmslbp_spyr(Ineg , options);
cumstat = cumstat + stat;
for i = 1:min(Ntotal,size(D,2))
Icrop = Ineg(D(2,i)-(1-cte)*1:D(2,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1 , D(1,i)-(1-cte)*1:D(1,i)-(1-cte)*1+D(3,i)+(1-cte)*2-1);
[fxfa(co) , yfxfa , Xfa(: , co) ] = eval_hmblbp_spyr_subwindow(Icrop , options);
co = co + 1;
end
end
if(cophotos < nbneg_photos)
cophotos = cophotos + 1;
else
cophotos = 1;
index_negphotos = randperm(nbneg_photos);
end
Ineg = imread(fullfile(options.negatives_path , directory(index_negphotos(cophotos)).name));
[Ny , Nx , Nz] = size(Ineg);
if(Nz == 3)
Ineg = rgb2gray(Ineg);
end
if(options.preview)
imagesc(Ineg);
plot_rectangle(D)
end
waitbar((co - 1)/Ntotal , h , sprintf('#Neg = %d, Pfa = %10.9f' , co , cumstat(1)/cumstat(2)));
drawnow
end
close(h)
pfa_current = cumstat(1)/cumstat(2);
if((options.transpose) && (options.typefeat < 2))
Xfa = Xfa(1:Ntotal,:);
else
Xfa = Xfa(:,1:Ntotal);
end
fxfa = fxfa(1:Ntotal);
