function [fx , y] = eval_model_dataset(options , model)
%
% Evaluate trained model on a set of extracted Positives and Negatives pictures
% from positves and negatives folder respectively
%
% Usage
% ------
%
% [fx , y] = eval_model_dataset(options , model);
%
% Inputs
% -------
% options Options struture
%
% positives_path Path from positives images are loaded for generating positives examples
% negatives_path Path from negative images are loaded for generating negative examples
% posext Positives extension files
% negext Negatives extension files
% negmax_size Maximum side of each negative image (default = 1000)
% standardize Standardize images (default standardize = 1)
% 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)
% Npos Number of desired positives examples
% Nneg Number of desired negatives examples. Extracted by bilinear
% interpolation from the negatives database
% 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)
% probaswitchIneg Probability to swith from another picture in the negatives database (default probaswitchIneg = 0.005)
% 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)
%
% model Model structure of the detector
% Haar/MBLBP/HMBLBP model accepted
%
%
% Outputs
% -------
%
% fx Predicted value for each extracted picts (1 x (options.Npos+options.Nneg))
% y True label (1 x (options.Npos+options.Nneg)) with yi=+1 for faces, yi=-1 else
%
%
% Author : Sbastien PARIS : sebastien.paris@lsis.org
% ------- Date : 02/25/2011
%
% Example 1 With HMSLBP_spyr
% ---------
%
%
% close all
% options.positives_path = fullfile(pwd , 'images' , 'test' , 'positives');
% options.negatives_path = fullfile(pwd , 'images' , 'test' , 'negatives');
% options.posext = {'pgm'};
% options.negext = {'jpg'};
% options.seed = 5489;
% options.resetseed = 1;
% options.standardize = 1;
% options.preview = 0;
% options.Npos = 3000;
% options.Nneg = 5000;
% options.probaflipIpos = 0.5;
% options.probarotIpos = 0.00;
% options.m_angle = 0;
% options.sigma_angle = 5^2;
% options.probaswitchIneg = 0.5;
% options.posscalemin = 1;
% options.posscalemax = 1;
% options.negscalemin = 0.7;
% options.negscalemax = 3;
% options.typefeat = 3;
%
% load model_hmblbp_R4.mat
%
% [fx , y] = eval_model_dataset(options , model);
% yest = sign(fx);
% accuracy = sum(yest==y)/length(y);
% [tpp , fpp] = basicroc(y , fx);
% 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])
%
%
% Example 2 With Haar features + Adaboosting
% ---------
%
%
% close all
% options.positives_path = fullfile(pwd , 'images' , 'test' , 'positives');
% options.negatives_path = fullfile(pwd , 'images' , 'test' , 'negatives');
% options.posext = {'pgm'};
% options.negext = {'jpg'};
% options.preview = 0;
% options.standardize = 1;
% options.Npos = 3000;
% options.Nneg = 5000;
% options.negmax_size = 1000;
% options.probaflipIpos = 0.5;
% options.probarotIpos = 0.00;
% options.m_angle = 0;
% options.sigma_angle = 5^2;
% options.seed = 5489;
% options.resetseed = 1;
% options.probaswitchIneg = 0.5;
% options.posscalemin = 1;
% options.posscalemax = 1;
% options.negscalemin = 0.7;
% options.negscalemax = 3;
% options.typefeat = 0;
%
% load model_detector_haar_24x24.mat
% model.cascade = [size(model.param , 2) ; 0];
%
% [fx , y] = eval_model_dataset(options , model);
% yest = sign(fx);
% accuracy = sum(yest==y)/length(y);
% [tpp , fpp] = basicroc(y , fx);
% 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])
%
%
% Example 3 With MBLBP features + Adaboosting
% ---------
%
%
% close all
% options.positives_path = fullfile(pwd , 'images' , 'test' , 'positives');
% options.negatives_path = fullfile(pwd , 'images' , 'test' , 'negatives');
% options.posext = {'pgm'};
% options.negext = {'jpg'};
% options.negmax_size = 1000;
% options.preview = 0;
% options.standardize = 1;
% options.Npos = 3000;
% options.Nneg = 5000;
% options.probaflipIpos = 0.5;
% options.probarotIpos = 0.00;
% options.m_angle = 0;
% options.sigma_angle = 5^2;
% options.seed = 5489;
% options.resetseed = 1;
% options.probaswitchIneg = 0.5;
% options.posscalemin = 1;
% options.posscalemax = 1;
% options.negscalemin = 0.7;
% options.negscalemax = 3;
% options.typefeat = 1;
%
% load model_detector_mblbp_24x24_4.mat
% model.cascade = [size(model.param , 2) ; 0];
%
% [fx , y] = eval_model_dataset(options , model);
% yest = sign(fx);
% accuracy = sum(yest==y)/length(y);
% [tpp , fpp] = basicroc(y , fx);
% 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])
if(nargin < 1)
options.positives_path = fullfile(pwd , 'images' , 'train' , 'positives');
options.negatives_path = fullfile(pwd , 'images' , 'train' , 'negatives');
options.posext = {'png'};
options.negext = {'jpg'};
options.negmax_size = 1000;
options.Npostrain = 10000;
options.Nnegtrain = 10000;
options.Npostest = 10000;
options.Nnegtest = 10000;
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.probaswitchIneg = 0.9;
options.posscalemin = 0.25;
options.posscalemax = 1.75;
options.negscalemin = 0.7;
options.negscalemax = 3;
options.typefeat = 3;
end
if(~any(strcmp(fieldnames(options) , 'positives_path')))
options.positives_path = fullfile(pwd , 'images' , 'train' , 'positives');
end
if(~any(strcmp(fieldnames(options) , 'negatives_path')))
options.negatives_path = fullfile(pwd , 'images' , 'train' , 'negatives');
end
if(~any(strcmp(fieldnames(options) , 'posext')))
options.posext = {'png'};
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) , 'Npos')))
options.Npos = 10000;
end
if(~any(strcmp(fieldnames(options) , 'Nneg')))
options.Nneg = 10000;
end
if(~any(strcmp(fieldnames(options) , 'typefeat')))
options.typefeat = 3;
end
if(~any(strcmp(fieldnames(options) , 'seed')))
options.seed = 5489;
end
if(~any(strcmp(fieldnames(options) , 'probaswitchIneg')))
options.probaswitchIneg = 0.005;
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) , 'posscalemin')))
options.posscalemin = 0.25;
end
if(~any(strcmp(fieldnames(options) , 'posscalemax')))
options.posscalemax = 1.75;
end
if(~any(strcmp(fieldnames(options) , 'negscalemin')))
options.posscalemin = 0.7;
end
if(~any(strcmp(fieldnames(options) , 'negscalemax')))
options.posscalemax = 3;
end
if(~any(strcmp(fieldnames(options) , 'resetseed')))
options.resetseed = 1;
end
if(options.typefeat == 0)
end
if(options.typefeat == 3)
if(model.cs_opt == 1)
if(model.maptable == 0)
model.Nbins = 16;
elseif(model.maptable == 1)
model.Nbins = 15;
elseif(model.maptable == 2)
model.Nbins = 6;
elseif(model.maptable == 3)
model.Nbins = 6;
end
model.improvedLBP = 0;
else
if(model.maptable == 0)
model.Nbins = 256;
elseif(model.maptable == 1)
model.Nbins = 59;
elseif(model.maptable == 2)
model.Nbins = 36;
elseif(model.maptable == 3)
model.Nbins = 10;
end
end
model.nH = sum(floor(((1 - model.spyr(:,1))./(model.spyr(:,3)) + 1)).*floor((1 - model.spyr(:,2))./(model.spyr(:,4)) + 1));
model.nscale = length(model.scale);
options.d = model.Nbins*(1+model.improvedLBP)*model.nH*model.nscale;
end
%% reset seed eventually %%
if(options.resetseed)
RandStream.setDefaultStream(RandStream.create('mt19937ar','seed',options.seed));
end
Ntotal = options.Npos + options.Nneg;
fx = zeros(1 , Ntotal);
y = zeros(1 , Ntotal);
%% Generate Npos Positives examples from Xpos without replacement %%
options.std_angle = sqrt(options.sigma_angle);
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('Generating positives'));
co = 1;
cophotos = 1;
if(options.preview)
fig = figure(1);
set(fig,'doublebuffer','on');
colormap(gray)
end
while(co <= options.Npos)
Ipos = imread(fullfile(options.positives_path , directory(index_posphotos(cophotos)).name));
[Ny , Nx , Nz] = size(Ipos);
if(Nz == 3)
Ipos = rgb2gray(Ipos);
end
%% Flip Positives %%
if(rand < options.probaflipIpos)
Ipos = Ipos(: , end:-1:1) ;
end
%% Rotation positives
if(rand < options.probarotIpos)
Ipos = fast_rotate(Ipos , options.m_angle + options.std_angle*randn(1,1));
end
%% Scaling positives
scale = options.posscalemin + (options.posscalemax-options.posscalemin)*rand;
Nys = round(Ny*scale);
Nxs = round(Nx*scale);
Ipos = imresize(Ipos , [Nys , Nxs]);
%% 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)
fx(co) = eval_haar_subwindow(Ipos , model);
end
if(options.typefeat == 1)
fx(co) = eval_mblbp_subwindow(Ipos , model);
end
if(options.typefeat == 3)
fx(co) = eval_hmblbp_spyr_subwindow(Ipos , model);
end
y(co) = 1;
if(options.preview)
imagesc(Ipos);
end
waitbar(co/options.Npos , h , sprintf('#pos = %d' , co));
drawnow
co = co + 1;
if(cophotos < nbpos_photos)
cophotos = cophotos + 1;
else
cophotos = 1;
index_posphotos = randperm(nbpos_photos);
end
options.ny = Ny;
options.nx = Nx;
end
close(h)
%% Generate Nneg Negatives examples from Negatives images with replacement %%
cophotos = 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(min(Ny,Nx) < options.negscalemax*max(options.ny,options.nx))
scalemax = min(Ny,Nx)/max(options.ny,options.nx);
else
scalemax = options.negscalemax;
end
h = waitbar(0,sprintf('Generating negatives'));
set(h , 'name' , sprintf('Generating negatives'))
while(co <= Ntotal)
if(rand < options.probaswitchIneg)
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(min(Ny,Nx) < options.negscalemax*max(options.ny,options.nx))
scalemax = min(Ny,Nx)/max(options.ny,options.nx);
end
end
%% Scaling Negatives
scale = options.negscalemin + (scalemax-options.negscalemin)*rand;
nys = min(Ny , round(options.ny*scale));
nxs = min(Nx , round(options.nx*scale));
yy = ceil(1 + (Ny - nys - 1)*rand);
xx = ceil(1 + (Nx - nxs - 1)*rand);
Itemp = Ineg(yy:yy+nys-1 , xx:xx+nxs-1);
Itemp = imresize(Itemp , [options.ny , options.nx]);
%% standardize Negatives %%
if(options.standardize)
dIneg = double(Itemp);
stdIneg = std(dIneg(:));
if(stdIneg~=0)
dIneg = dIneg/stdIneg;
end
minIneg = min(dIneg(:));
maxIneg = max(dIneg(:));
Itemp = uint8(floor(255*(dIneg-minIneg)/(maxIneg-minIneg)));
end
if(std(double(Itemp(:)),1)~= 0)
if(options.typefeat == 0)
fx(co) = eval_haar_subwindow(Itemp , model);
end
if(options.typefeat == 1)
fx(co) = eval_mblbp_subwindow(Itemp , model);
end
if(options.typefeat == 3)
fx(co) = eval_hmblbp_spyr_subwindow(Itemp , model);
end
y(co) = -1;
if(options.preview)
imagesc(Ineg);
hold on
hh = rectangle('position' , [x , y , nxs , nys]);
set(hh , 'linewidth' , 3 , 'edgecolor' , [1 0 0])
hold off
end
waitbar((co-options.Npos)/options.Nneg , h , sprintf('#Neg = %d' , co-options.Npos));
drawnow
co = co + 1;
end
end
close(h)
