function [Xnd , fxnd , pd_current] = generate_nd_features(options)
%
%
% Generate new positive features considered as non detection for the
% current trained model
%
% Usage
% ------
%
% [Xnd , fxnd , pd_current] = generate_nd_features(options)
%
% Inputs
% -------
% options Options struture
%
% positives_path Path from positives images are loaded for generating positives examples
% posext Positives extension files
% Nposboost Number of desired positives examples for training set (Npostrain+Npostest <= Npostotal)
% seed Seed value for random generator in order to generate the same positive/negative sets
% resetseed Reset generator with given seed (default resetseed = 1)
% standardize Standardize images (default standardize = 1)
% preview Preview current example (default preview = 0)
% probaflipIpos Probability to flip Positive examples (default probaflipIpos = 0.5)
% probarotIpos Probability to rotate Positives examples with an angle~N(m_angle,sigma_angle) (default probarotIpos = 0.01)
% m_angle Mean rotation angle value in degree (default mangle = 0)
% sigma_angle variance of the rotation angle value (default sigma_angle = 5^2)
% posscalemin Minimum scaling factor to apply on positives patch subwindows (default scalemin = 0.25)
% posscalemax Maximum scaling factor to apply on positives patch subwindows (default scalemax = 2)
% negscalemin Minimum scaling factor to apply on negatives patch subwindows (default scalemin = 1)
% negscalemax Maximum scaling factor to apply on negatives patch subwindows (default scalemax = 5)
% featype Type of features (featype: 0 <=> Haar, 1 <=> MBLBP, 3 <=> Histogram of MBLBP)
% Outputs
% -------
%
% Xnd features associated with False alarms for current model (d x Nnegboost)
% fxnd Output predicted values of the false alarms (1 x Nnegboost)
% pd_current Probability of non-detections
%
%
% Author : Sbastien PARIS : sebastien.paris@lsis.org
% ------- Date : 02/25/2011
%
%
% close all
% options.positives_path = fullfile(pwd , 'images' , 'train' , 'positives');
% options.posext = {'png'};
% options.preview = 0;
% options.Nposboost = 50;
% options.seed = 5489;
% options.probaflipIpos = 0.5;
% options.probarotIpos = 0.05;
% options.m_angle = 0;
% options.sigma_angle = 5^2;
% options.posscalemin = 0.25;
% options.posscalemax = 1.75;
% options.negscalemin = 0.7;
% options.negscalemax = 3;
% options.typefeat = 3;
% 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 = 1;
% options.color = 0;
% options.norm = 4;
% options.clamp = 0.2;
% options.s = 2;
% options.B = 1;
% options.c = 5;
%
% [Xtrain , ytrain , Xtest , ytest] = generate_face_features(options);
% indp = find(ytest == 1);
% indn = find(ytest ==-1);
% wpos = options.Nnegtrain/options.Npostrain;
% options.model = train_dense(ytrain' , Xtrain , sprintf('-s %d -B %d -c %d' , options.s , options.B , options.c) , 'col');
%% options.model = train_dense(ytrain' , Xtrain , sprintf('-s %d -B %d -c %d -w1 %f' , options.s , options.B , options.c , wpos) , 'col');
% fxtest = options.model.w(1:end-1)*Xtest + options.model.w(end);
% if(options.model.Label(1)==-1)
% fxtest = -fxtest;
% end
% ytest_est = sign(fxtest);
% accuracy = sum(ytest_est == ytest)/length(ytest);
% tp = sum(ytest_est(indp) == ytest(indp))/length(indp);
% fp = 1 - sum(ytest_est(indn) == ytest(indn))/length(indn);
% perf = sum(ytest_est == ytest)/length(ytest);
%
% if(options.model.Label(1) == 1)
% [tpp , fpp] = basicroc(ytest , fxtest);
% else
% [tpp , fpp] = basicroc(ytest , fxtest);
% end
%
% auc_est = auroc(tpp', fpp');
%
% figure(1)
% plot(fpp , tpp , 'b', 'linewidth' , 2)
% grid on
% title(sprintf('Accuracy = %4.3f, AUC = %4.3f' , accuracy , auc_est))
% axis([-0.02 , 1.02 , -0.02 , 1.02])
%
% figure(2)
% plot(options.model.w)
%
% w = options.model.w;
% save modelw8 w
%
if(nargin < 1)
options.positives_path = fullfile(pwd , 'images' , 'train' , 'positives');
options.posext = {'png'};
options.Nposboost = 50;
options.seed = 5489;
options.resetseed = 1;
options.preview = 0;
options.standardize = 1;
options.probaflipIpos = 0.5;
options.probarotIpos = 0.05;
options.m_angle = 0;
options.sigma_angle = 7^2;
options.posscalemin = 0.25;
options.posscalemax = 1.75;
options.negscalemin = 0.7;
options.negscalemax = 3;
options.typefeat = 3;
options.addbias = 1;
options.num_threads = -1;
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) , 'positives_path')))
options.positives_path = fullfile(pwd , 'images' , 'train' , 'positives');
end
if(~any(strcmp(fieldnames(options) , 'posext')))
options.posext = {'png'};
end
if(~any(strcmp(fieldnames(options) , 'Nposboost')))
options.Nposboost = 50;
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) , 'seed')))
options.seed = 5489;
end
if(~any(strcmp(fieldnames(options) , 'resetseed')))
options.resetseed = 1;
end
if(~any(strcmp(fieldnames(options) , 'preview')))
options.preview = 0;
end
if(~any(strcmp(fieldnames(options) , 'standardize')))
options.standardize = 1;
end
if(~any(strcmp(fieldnames(options) , 'probaflipIpos')))
options.probaflipIpos = 0.5;
end
if(~any(strcmp(fieldnames(options) , 'probarotIpos')))
options.probarotIpos = 0.05;
end
if(~any(strcmp(fieldnames(options) , 'm_angle')))
options.m_angle = 0.0;
end
if(~any(strcmp(fieldnames(options) , 'sigma_angle')))
options.sigma_angle = 5^2;
end
if(~any(strcmp(fieldnames(options) , 'typefeat')))
options.typefeat = 3;
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) , 'num_threads')))
options.num_threads = -1;
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) , 'posscalemin')))
options.posscalemin = 0.25;
end
if(~any(strcmp(fieldnames(options) , 'posscalemax')))
options.posscalemax = 1.75;
end
if(~any(strcmp(fieldnames(options) , 'dimsItraining')))
options.dimsItraining = [24 , 24];
end
if(~any(strcmp(fieldnames(options) , 'transpose')))
options.transpose = 0;
end
if(~any(strcmp(fieldnames(options) , 'usesingle')))
options.single = 1;
end
if( (options.typefeat == 0) && ~any(strcmp(fieldnames(options) , 'rect_param')))
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];
end
if( (options.typefeat == 1) && ~any(strcmp(fieldnames(options) , 'map')))
options.map = uint8(0:255);
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
options.std_angle = sqrt(options.sigma_angle);
%% reset seed eventually %%
if(options.resetseed)
RandStream.setDefaultStream(RandStream.create('mt19937ar','seed',options.seed));
end
%RandStream.setDefaultStream(RandStream.create('mt19937ar','seed',options.seed));
Ntotal = options.Nposboost;
if(options.typefeat == 0)
if(options.transpose)
if(options.usesingle)
Xnd = zeros(Ntotal , options.d , 'single');
else
Xnd = zeros(Ntotal , options.d);
end
else
if(options.usesingle)
Xnd = zeros(options.d , Ntotal , 'single');
else
Xnd = zeros(options.d , Ntotal);
end
end
elseif(options.typefeat == 1)
if(options.transpose)
if(options.usesingle)
Xnd = zeros(Ntotal , options.d , 'single');
else
Xnd = zeros(Ntotal , options.d);
end
else
if(options.usesingle)
Xnd = zeros(options.d , Ntotal , 'single');
else
Xnd = zeros(options.d , Ntotal);
end
end
elseif(options.typefeat == 3)
Xnd = zeros(options.d , Ntotal);
end
%Xnd = zeros(options.d , Ntotal);
fxnd = zeros(1 , Ntotal);
%% Generate Npos Positives examples from Xpos without replacement %%
directory = [];
for i = 1:length(options.posext)
directory = [directory ; dir(fullfile(options.positives_path , ['*.' options.posext{i}]))] ;
end
nbpos_photos = length(directory);
index_posphotos = randperm(nbpos_photos);
h = waitbar(0,sprintf('Generating positives' ));
set(h , 'name' , sprintf('Boosting positives stage %d/%d' , options.m , options.boost_ite'));
drawnow
co = 1;
copos = 1;
cophotos = 1;
if(options.preview)
fig = figure(1);
set(fig,'doublebuffer','on');
colormap(gray)
end
while(co <= Ntotal)
Ipos = imread(fullfile(options.positives_path , directory(index_posphotos(cophotos)).name));
[Ny , Nx , Nz] = size(Ipos);
if(Nz == 3)
Ipos = rgb2gray(Ipos);
end
if(rand < options.probaflipIpos)
Ipos = Ipos(: , end:-1:1) ;
end
if(rand < options.probarotIpos)
Ipos = fast_rotate(Ipos , options.m_angle + options.std_angle*randn(1,1));
end
scale = options.posscalemin + (options.posscalemax-options.posscalemin)*rand;
Nys = round(Ny*scale);
Nxs = round(Nx*scale);
%% Resize Ipos %%
Ipos = imresize(Ipos , [Nys , Nxs]);
if((options.typefeat == 0) || (options.typefeat == 1))
Ipos = imresize(Ipos , options.dimsItraining);
end
%% Standardize image %%
if(options.standardize)
dIpos = double(Ipos);
stdIpos = std(dIpos(:));
if(stdIpos~=0)
dIpos = dIpos/stdIpos;
end
minIpos = min(dIpos(:));
maxIpos = max(dIpos(:));
Ipos = uint8(floor(255*(dIpos-minIpos)/(maxIpos-minIpos)));
end
if(options.typefeat == 0)
Xtemp = haar(Ipos , options);
[fx , ytemp] = eval_haar(Ipos , options);
elseif(options.typefeat == 1)
Xtemp = mblbp(Ipos , options);
[fx , ytemp] = eval_mblbp(Ipos , options);
elseif(options.typefeat == 3)
[fx , ytemp , Xtemp] = eval_hmblbp_spyr_subwindow(Ipos , options);
end
if(fxtemp < 0)
if(options.transpose)
Xnd(co , :) = Xtemp;
else
Xnd(: , co) = Xtemp;
end
fxnd(co) = fxtemp;
co = co + 1;
if(options.preview)
imagesc(Ipos);
end
end
copos = copos + 1;
waitbar((co - 1)/Ntotal , h , sprintf('#pos = %d, Pd = %10.9f' , co - 1 , 1 - ((co - 1)/(copos - 1))));
drawnow
if(cophotos < nbpos_photos)
cophotos = cophotos + 1;
else
cophotos = 1;
index_posphotos = randperm(nbpos_photos);
end
end
close(h)
pd_current = 1 - ((co - 1)/(copos - 1));
if((options.transpose) && (options.typefeat < 2))
Xnd = Xnd(: , 1:Ntotal);
else
Xnd = Xnd(: , 1:Ntotal);
end
fxnd = fxnd(1:Ntotal);
