%% Demo illustrating HAAR features
%
%
% File : haar_featlist
%
% ---------------------------------------------
%
% Haar features list parameters. Give all Haar features parameters
% for a Image I (ny x nx) and dictionary of Haar pattern given
% by rect_param
%
% Usage
% ------
%
% F = haar_featlist([ny] , [nx] , [rect_param]);
%
%
% Inputs
% -------
%
% ny Number of rows of the pattern (default ny = 24)
%
% nx Number of columns of the pattern (default nx = ny)
%
% rect_param Features rectangles parameters (10 x nR), where nR is the total number of rectangles for the patterns.
% (default Vertical(2 x 1) [1 ; -1] and Horizontal(1 x 2) [-1 , 1] patterns)
% rect_param(: , i) = [ip ; wp ; hp ; nrip ; nr ; xr ; yr ; wr ; hr ; sr], where
% ip index of the current pattern. ip = [1,...,nP], where nP is the total number of patterns
% wp width of the current pattern
% hp height of the current pattern
% nrip total number of rectangles for the current pattern ip
% nr index of the current rectangle of the current pattern, nr=[1,...,nrip]
% xr,yr top-left coordinates of the current rectangle of the current pattern
% wr,hr width and height of the current rectangle of the current pattern
% sr weights of the current rectangle of the current pattern
%
% Please run gui_features_dictionary in the \gui subdir to build such parameters
%
%
% Outputs
% -------
%
% F Features's list (6 x nF) in UINT32 where nF designs the total number of Haar features
% F(: , i) = [if ; xf ; yf ; wf ; hf ; ir]
% if index of the current feature, if = [1,....,nF] where nF is the total number of Haar features (see nbfeat_haar function)
% xf,yf top-left coordinates of the current feature of the current pattern
% wf,hf width and height of the current feature of the current pattern
% ir index of rectangle definition of the current feature in rect_param
%
%
%
%
% File : haar
%
% ---------------------------------------------
%
% Compute HAAR features of image I
%
% Usage
%
% ------
%
% z = haar(I , [options] );
%
%
% Inputs
% -------
%
% I Images (Ny x Nx x N) in UINT8 format
%
% options
%
% rect_param Features rectangles parameters (10 x nR), where nR is the total number of rectangles for the patterns.
% (default Vertical(2 x 1) [1 ; -1] and Horizontal(1 x 2) [-1 , 1] patterns)
% rect_param(: , i) = [ip ; wp ; hp ; nrip ; nr ; xr ; yr ; wr ; hr ; sr], where
% ip index of the current pattern. ip = [1,...,nP], where nP is the total number of patterns
% wp width of the current pattern
% hp height of the current pattern
% nrip total number of rectangles for the current pattern ip
% nr index of the current rectangle of the current pattern, nr=[1,...,nrip]
% xr,yr top-left coordinates of the current rectangle of the current pattern
% wr,hr width and height of the current rectangle of the current pattern
% sr weights of the current rectangle of the current pattern
%
% F Features's list (6 x nF) in UINT32 where nF designs the total number of Haar features
% F(: , i) = [if ; xf ; yf ; wf ; hf ; ir]
% if index of the current feature, if = [1,....,nF] where nF is the total number of Haar features (see nbfeat_haar function)
% xf,yf top-left coordinates of the current feature of the current pattern
% wf,hf width and height of the current feature of the current pattern
% ir index of rectangle definition of the current feature in rect_param
%
% standardize Standardize Input Images 1 = yes, 0 = no (default = 1)
%
% usesingle Output in single format if usesingle = 1 (default usesingle = 0)
%
% transpose Transpose Output if tranpose = 1 (in order to speed up Boosting algorithm, default tranpose = 0)
%
% If compiled with the "OMP" compilation flag
%
% num_threads Number of threads. If num_threads = -1, num_threads = number of core (default num_threads = -1)
%
%
%
% Outputs
%
% -------
%
% z Haar features matrix (nF x N) or (N x nF) (acoording to transpose option) in single/double format (according to usesingle options)
% for each positions (y,x) in [1+h,...,ny-h]x[1+w,...,nx-w] and (w,h) integral block size.
%% First example : compute Haar features for nP type of patterns
clear, close all,clc,drawnow
load viola_24x24
options = load('haar_dico_2.mat');
nP = length(unique(options.rect_param(1 , :)));
Ny = 24;
Nx = 24;
N = 200;
options.F = haar_featlist(Ny , Nx , options.rect_param);
pos = find(y == 1);
neg = find(y == -1);
I = (X(: , : , [pos(1:N) , neg(1:N)]));
tic,z = haar(I , options);,toc
imagesc(z)
title(sprintf('Haar''s features with %d different patterns' , nP));
colormap(gray)
% disp('Press key to continue')
% pause
%% Second example : Display Haar features for a particular pattern scale
clear, close all
load viola_24x24
options = load('haar_dico_2.mat');
nF = length(unique(options.rect_param(1 , :)));
[Ny,Nx,P] = size(X);
Nimage = 100;
pattern = [2 ; 1];
FF = haar_featlist(Ny , Nx , options.rect_param);
ind = find(sum(FF([4;5] , :) == repmat(pattern , 1 , size(FF , 2))) == 2);
options.F = FF(: , ind);
y = options.F(3 , :);
x = options.F(2 , :);
ind = (y + 1 + (x)*Ny)';
% index = repmat(ind , 1 , P) + repmat((0:Ny*Nx:Ny*Nx*(P-1))' , 1 , P);
z = haar(X , options);
Ihaar = zeros(Ny , Nx , P , class(z));
for i = 1:P
Ihaar(ind + (i-1)*Ny*Nx) = z(: , i);
Ihaar(: , : , i) = Ihaar(: , : , i)';
end
figure
display_database(X);
title(sprintf('Original database'));
figure
display_database(Ihaar);
title(sprintf('Haar feature''s with pattern [%d %d]' , pattern(1) , pattern(2)));
% disp('Press key to continue')
% pause
%% Third example : Display a particular Haar feature computed on all the database
clear, close all
load viola_24x24
options = load('haar_dico_2.mat');
nP = length(unique(options.rect_param(1 , :)));
Ny = 24;
Nx = 24;
nF = 1;
FF = haar_featlist(Ny , Nx , options.rect_param);
indpos = find(y == 1);
indneg = find(y == -1);
options.F = FF(: , nF);
tic,z = haar(X , options);,toc
plot(indpos , z(indpos) , indneg , z(indneg) , 'r')
title(sprintf('Haar feature n=%d for %d different patterns' , nF , nP));
legend('Faces' , 'Non-faces')
% disp('Press key to continue')
% pause
%% Fourth example : Haar feature's computed on Face/Non-Face image
clear, close all
load viola_24x24
options = load('haar_dico_2.mat');
Ny = 24;
Nx = 24;
options.F = haar_featlist(Ny , Nx , options.rect_param);
nF = size(options.F , 2);
indpos = find(y==1);
indneg = find(y==-1);
tic,zpos = haar(X(: , : , indpos(1)) , options );,toc
tic,zneg = haar(X(: , : , indneg(1)) , options );,toc
figure
plot((1:nF)' , zpos , (1:nF)' , zneg , 'r')
title(sprintf('Haar feature''s computed on Face/Non-Face image'));
legend('Positive' , 'Negative')
% disp('Press key to continue')
% pause
%% Fifth example : Display Best Feature values computed on Viola-Jones database separating the 2 classes
load viola_24x24
load model_detector_haar_24x24
options = load('haar_dico_2.mat');
bestFeat = model.param(1 , 1); %40478+1;
thresh = model.param(2 , 1);
[Ny,Nx,P] = size(X);
FF = haar_featlist(Ny , Nx , options.rect_param);
options.F = FF(: , bestFeat);
z = haar(X , options);
indpos = find(y==1);
indneg = find(y==-1);
figure
plot(indpos , z(indpos) , indneg , z(indneg) , 'r' , (1:length(z)) , thresh*ones(1,length(z)) , 'g')
legend('Faces' , 'Non-faces' , '\theta')
title(sprintf('Best Haar Feature = %d' , bestFeat))
figure
[Nneg , Xneg] = hist(double(z(indneg)) , 100 , 'r' );
bar(Xneg , Nneg)
set(get(gca , 'children') , 'facecolor' , [1 0 1])
hold on
[Npos , Xpos] = hist(double(z(indpos)) , 100 );
bar(Xpos , Npos);
plot(thresh*ones(1,2) , [0 , max([Nneg , Npos])] , 'g' , 'linewidth' , 2)
hold off
legend(get(gca , 'children') , '\theta', 'Faces' , 'Non-faces' )
title(sprintf('Best Haar Feature = %d' , bestFeat))
% disp('Press key to continue')
% pause
%% Sixth example : Display best Haar's Features from Adaboosting & FastAdaboosting
clear
load viola_24x24
options = load('haar_dico_2.mat');
y = int8(y);
II = image_integral_standard(X);
[Ny , Nx , P] = size(II);
Nimage = 110;
nb_feats = 3;
options.T = nb_feats;
options.F = haar_featlist(Ny , Nx , options.rect_param);
I = X(: , : , Nimage);
index = randperm(length(y));
tic,options.param0 = haar_adaboost_binary_train_cascade(II(: , : , index) , y(index) , options);,toc %a bit long ....%
options.G = Haar_matG(Ny , Nx , options.rect_param);
tic,options.param1 = fast_haar_adaboost_binary_train_cascade(II(: , : , index) , y(index) , options);,toc
figure
imagesc(I)
hold on
best_feats = (options.F(: , options.param0(1 , 1:nb_feats)));
x = double(best_feats(2 , :)) + 0.5 ;
y = double(best_feats(3 , :)) + 0.5;
w = best_feats(4 , :);
h = best_feats(5 , :);
indR = fix(best_feats(6 , :) + 1)/10 + 1;
R = options.rect_param(4 , indR);
for f = 1 : nb_feats
for r = 0:R(f)-1
coeffw = w(f)/options.rect_param(2 , indR(f) + r);
coeffh = h(f)/options.rect_param(3 , indR(f) + r);
xr = (x(f) + double(coeffw*options.rect_param(6 , indR(f) + r)));
yr = (y(f) + double(coeffh*options.rect_param(7 , indR(f) + r))) ;
wr = double(coeffw*(options.rect_param(8 , indR(f) + r) - 0));
hr = double(coeffh*(options.rect_param(9 , indR(f) + r) - 0));
s = options.rect_param(10 , indR(f) + r);
if (s == 1)
color = [0.9 0.9 0.9];
else
color = [0.1 0.1 0.1];
end
hh = rectangle('Position', [xr, yr , wr , hr] );
p = patch([xr , xr+wr , xr + wr , xr] , [yr , yr , yr + hr , yr + hr] , color);
alpha(p , 0.8);
set(hh , 'linewidth' , 2 , 'EdgeColor' , [1 0 0])
end
end
hold off
title(sprintf('Best %d Haar features with Adaboost' , nb_feats) , 'fontsize' , 13)
colormap(gray)
figure
imagesc(I)
hold on
best_feats = (options.F(: , options.param1(1 , 1:nb_feats)));
x = double(best_feats(2 , :)) + 0.5 ;
y = double(best_feats(3 , :)) + 0.5;
w = best_feats(4 , :);
h = best_feats(5 , :);
indR = fix(best_feats(6 , :) + 1)/10 + 1;
R = options.rect_param(4 , indR);
for f = 1 : nb_feats
for r = 0:R(f)-1
coeffw = w(f)/options.rect_param(2 , indR(f) + r);
coeffh = h(f)/options.rect_param(3 , indR(f) + r);
xr = (x(f) + double(coeffw*options.rect_param(6 , indR(f) + r)));
yr = (y(f) + double(coeffh*options.rect_param(7 , indR(f) + r))) ;
wr = double(coeffw*(options.rect_param(8 , indR(f) + r) - 0));
hr = double(coeffh*(options.rect_param(9 , indR(f) + r) - 0));
s = options.rect_param(10 , indR(f) + r);
if (s == 1)
color = [0.9 0.9 0.9];
else
color = [0.1 0.1 0.1];
end
hh = rectangle('Position', [xr, yr , wr , hr] );
p = patch([xr , xr+wr , xr + wr , xr] , [yr , yr , yr + hr , yr + hr] , color);
alpha(p , 0.8);
set(hh , 'linewidth' , 2 , 'EdgeColor' , [1 0 0])
end
end
hold off
title(sprintf('Best %d Haar features with FastAdaboost' , nb_feats) , 'fontsize' , 13)
colormap(gray)
% disp('Press key to continue')
% pause
%% Seventh example : FastAdaboosting on Haar's features with 2 different types of pattern
clear
load viola_24x24
options = load('haar_dico_2.mat');
[Ny , Nx , P] = size(X);
options.T = 100;
options.fine_threshold = 1;
II = image_integral_standard(X);
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
options.F = haar_featlist(Ny , Nx , options.rect_param);
options.G = Haar_matG(Ny , Nx , options.rect_param);
tic,options.param = fast_haar_adaboost_binary_train_cascade(II , y , options);,toc
[yest , fx] = haar_adaboost_binary_predict_cascade(II , options);
tp = sum(yest(indp) == y(indp))/length(indp)
fp = 1 - sum(yest(indn) == y(indn))/length(indn)
perf = sum(yest == y)/length(y)
[tpp , fpp] = basicroc(y , fx);
figure
plot(fx)
figure
plot(fpp , tpp , 'linewidth' , 2)
axis([-0.02 , 1.02 , -0.02 , 1.02])
grid on
title(sprintf('ROC for Fastadaboosting with T = %d' , options.T))
figure
plot(abs(options.param(3 , :)) , 'linewidth' , 2)
grid on
xlabel('Weaklearner m')
ylabel('|a_m|')
title('Weights of the Weaklearner')
% disp('Press key to continue')
% pause
