%% Demo illustrating Circular Histogram Local Binary Pattern (chlbp)
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% File : chlbp
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% -------------
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% Circular Histogram Local Binary Pattern.
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% Return the Circular Histogram of Local Binary Patterns for image I.
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% Usage
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% ------
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% H = chlbp(I , [options] );
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% Inputs
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% -------
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% I Input image (ny x nx x P) in UINT8 format.
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% options
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% N Number of sampling points (1 x nR) (default [N=8])
% R Vector of Radius (1 x nR) (default [R=1]) ny>2max(R)+1 & nx>2max(R)+1
% map Mapping of the chlbp (2^Nmax x nR) in double format (default map = (0 : 255))
% shiftbox Shifting box parameters shiftbox (2 x 2 x nR) where [by , bx ; deltay , deltax] x nR (default shiftbox = [ny , nx ; 0 , 0])
% by, bx denote the size of subwindows analysis, deltay, deltax represent the shift the subwindows analysis.
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% Outputs
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% -------
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% H chlbp features (bin*nH x P) int UINT32 format where nH = nR*max(floor((ny - shiftbox(1,1,:))./shiftbox(2,1,:)) + 1 , 1)*max(floor((nx - shiftbox(1,2,:))./shiftbox(2,2,:)) + 1 , 1)
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% File : chlbp_gentleboost_binary_train_cascade
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% Train Circular Histogram Local Binary Pattern with gentleboosting classifier for binary problem
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% Usage
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% ------
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% param = chlbp_gentleboost_binary_train_cascade(X , y , [options] );
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%
% Inputs
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% -------
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% X Features matrix (d x N) in UINT32 format
% y Binary labels (1 x N), y[i] = {-1 , 1} in INT8 format
% options
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% T Number of weak learners (default T = 100)
% weaklearner Choice of the weak learner used in the training phase (default weaklearner = 0)
% weaklearner = 0 <=> minimizing the weighted error : sum(w * |z - h(x;(th,a,b))|^2) / sum(w), where h(x;(th,a,b)) = (a*(x>th) + b) in R
% weaklearner = 1 <=> minimizing the weighted error : sum(w * |z - h(x;(a,b))|^2), where h(x;(a,b)) = sigmoid(x ; a,b) in R
% lambda Regularization parameter for the perceptron's weights'update (weaklearner = 1, default lambda = 1e-3)
% max_ite Maximum number of iteration (default max_ite = 10)
% epsi Sigmoid parameter (default epsi = 1)
% premodel Classifier's premodels parameter up to n-1 stage (4 x Npremodels)(default premodel = [] for stage n=1)
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%
% If compiled with the "OMP" compilation flag
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% num_threads Number of threads. If num_threads = -1, num_threads = number of core (default num_threads = -1)
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% Outputs
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% -------
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% param param output (4 x T) for current stage n of the classifier's premodel
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% featureIdx Feature indexes of the T best weaklearners (1 x T)
% th Optimal Threshold parameters (1 x T)
% a Affine parameter(1 x T)
% b Bias parameter (1 x T)
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%
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% File : chlbp_gentleboost_binary_train_cascade
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% ---------------------------------------------
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% Predict data label with a Strong Classifier trained with chlbp_gentleboost_binary_predict_cascade
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% Usage
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% ------
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% [yest , fx] = chlbp_gentleboost_binary_predict_cascade(X , [options]);
%
%
% Inputs
%
% -------
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% X Features matrix (d x N) in INT32 format
% options
%
% param Trained param structure (4 x T)
% weaklearner Choice of the weak learner used in the training phase (default weaklearner = 0)
% weaklearner = 0 <=> minimizing the weighted error : sum(w * |z - h(x;(th,a,b))|^2) / sum(w), where h(x;(th,a,b)) = (a*(x>th) + b) in R
% weaklearner = 1 <=> minimizing the weighted error : sum(w * |z - h(x;(a,b))|^2), where h(x;(th,a)) = sigmoid(x ; a,b) in R
% epsi Epsilon constant in the sigmoid function used in the perceptron (default epsi = 1)
% cascade_type Type of cascade structure : 0 for coventional cascade, 1 for multi-exit cascade (default cascade_type = 0)
% cascade Cascade parameters (2 x Ncascade) where cascade(1 , :) represents Entrance/Exit nodes.
%
% If cascade_type = 0, i.e. coventional cascade, Entrance nodes are [1 , cumsum(cascade(1 , 1:end-1))+1] and exit nodes are cumsum(cascade(1 , :))
% If cascade_type = 1, i.e. multi-exit cascade, Entrance node is 1, exit nodes are cumsum(cascade(1 , :))
% cascade(2 , :) reprensent thresholds for each segment
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% Outputs
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% -------
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% yest Estimated labels (1 x N) in INT8 format
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% fx Additive models (1 x N)
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%
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% File : chlbp_adaboost_binary_train_cascade
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% ---------------------------------------------
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%
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% Train Circular Histogram Local Binary Pattern with adaboost classifier for binary problem
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% Usage
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% ------
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% param = chlbp_adaboost_binary_train_cascade(X , y , [options]);
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%
%
% Inputs
%
% -------
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% X Features matrix (d x N) in UINT32 format
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% y Binary labels (1 x N), y[i] = {-1 , 1} in INT8 format
% options
% T Number of weak learners (default T = 100)
%
% weaklearner Choice of the weak learner used in the training phase (default weaklearner = 2)
% weaklearner = 2 <=> minimizing the weighted error : sum(w * |z - h(x;(th,a))|), where h(x;(th,a)) = a*sign(z - th) in [-1,1] for discrete adaboost
%
% premodel Classifier's premodels parameter up to n-1 stage (4 x Npremodels)(default premodel = [] for stage n=1)
%
% 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
%
% -------
%
%
% param param output (4 x T) for current stage n of the classifier's premodel
% featureIdx Feature indexes of the T best weaklearners (1 x T)
% th Optimal Threshold parameters (1 x T)
% a WeakLearner's weights (1 x T) in R (at = ct*pt, where pt = polarity)
% b Zeros (1 x T), i.e. b = zeros(1 , T)
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%
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% File : chlbp_adaboost_binary_predict_cascade
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% ---------------------------------------------
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%
% Predict data label with a Strong Classifier trained with chlbp_adaboost_binary_train_cascade
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% Usage
%
% ------
%
% [yest , fx] = chlbp_adaboost_binary_predict_cascade(X , [options]);
%
%
% Inputs
%
% -------
%
% X Features matrix (d x N) in INT32 format
% options
%
% param Trained param structure (4 x T)
% weaklearner Choice of the weak learner used in the training phase
% weaklearner = 2 <=> minimizing the weighted error : sum(w * |z - h(x;(th,a))|), where h(x;(th,a)) = a*sign(z - th) in [-1,1] for discrete adaboost
% cascade_type Type of cascade structure : 0 for coventional cascade, 1 for multi-exit cascade
% cascade Cascade parameters (2 x Ncascade) where cascade(1 , :) represents Entrance/Exit nodes.
%
% If cascade_type = 0, i.e. coventional cascade, Entrance nodes are [1 , cumsum(cascade(1 , 1:end-1))+1] and exit nodes are cumsum(cascade(1 , :))
% If cascade_type = 1, i.e. multi-exit cascade, Entrance node is 1, exit nodes are cumsum(cascade(1 , :))
% cascade(2 , :) reprensent thresholds for each segment
%
% Outputs
% -------
%
% yest Estimated labels (1 x N) in INT8 format
% fx Additive models (1 x N)
%% First example : compute Uniform CHLBP Features with N =8 neighbours and Radius R = 1 with a subwindows analysis of 64x64 shifted by 16x16
clear,close all,clc,drawnow
P = 256;
I = (imresize(imread('rice.png') , [P , P]));
options.N = [8];
options.R = [1];
options.map = zeros(2^max(options.N) , length(options.N));
for i =1:length(options.N)
mapping = getmapping(options.N(i),'u2');
options.map(: , i) = mapping.table';
end
options.shiftbox = cat(3 , [P/4 , P/4 ; P/8 , P/8]);
H = chlbp(I , options);
plot(H)
title(sprintf('Uniform CHLBP Histogram with N = %d, R = %d' , options.N , options.R))
% disp('Press key to continue')
% pause
%% Second example : compute CHLBP_{8;1}^u Features on Viola-Jones database
clear
load viola_24x24
Ny = 24;
Nx = 24;
options.N = 8;
options.R = 1;
mapping = getmapping(options.N,'u2');
options.map = mapping.table';
H = chlbp(X , options);
imagesc(H)
title(sprintf('Uniform CHLBP Histogram with N = %d, R = %d' , options.N , options.R))
% disp('Press key to continue')
% pause
%% Third example : compute CHLBP_{8;1}^u + CHLBP_{4;1} Features
Ny = 19;
Nx = 19;
X = uint8(ceil(256*rand(Ny , Nx)));
options.N = [8 , 4];
options.R = [1 , 1];
options.map = zeros(2^max(options.N) , length(options.N));
mapping = getmapping(options.N(1),'u2');
options.map(: , 1) = mapping.table';
options.map(1:2^options.N(2) , 2) = (0:2^options.N(2)-1)';
options.shiftbox = cat(3 , [Ny , Nx ; 0 , 0] , [10 , 10 ; 4 , 4]);
H = chlbp(X , options);
plot(H)
title('CHLBP_{8;1}^u + CHLBP_{4;1} Histogram')
% disp('Press key to continue')
% pause
%% Fourth example : compute CHLBP_{8;1}^u + CHLBP_{4;1} Features + Adaboosting with T weaklearners (Decision Stump)
clear
load viola_24x24
Ny = 24;
Nx = 24;
options.N = [8 , 4];
options.R = [1 , 1];
options.map = zeros(2^max(options.N) , length(options.N));
mapping = getmapping(options.N(1),'u2');
options.map(1:2^options.N(1) , 1) = mapping.table';
options.map(1:2^options.N(2) , 2) = (0:2^options.N(2)-1)';
shiftbox = cat(3 , [Ny , Nx ; 1 , 1] , [16 , 16 ; 4 , 4]);
options.T = 50;
H = chlbp(X , options);
figure
imagesc(H)
title('CHLBP Features')
drawnow
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
index = randperm(length(y)); %shuffle data to avoid numerical discrepancies with long sequence of same label
options.param = chlbp_adaboost_binary_train_cascade(H(: , index) , y(index) , options);
[yest_train , fx_train] = chlbp_adaboost_binary_predict_cascade(H , options);
tp_train = sum(yest_train(indp) == y(indp))/length(indp)
fp_train = 1 - sum(yest_train(indn) == y(indn))/length(indn)
Perf_train = sum(yest_train == y)/length(y)
[tpp_train , fpp_train] = basicroc(y , fx_train);
[dum , ind] = sort(y , 'descend');
figure
plot(fx_train(ind))
title(sprintf('Output of the strong classifier for train data with T = %d' , options.T))
load jensen_24x24
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
H = chlbp(X , options);
[yest_test , fx_test] = chlbp_adaboost_binary_predict_cascade(H , options);
tp_test = sum(yest_test(indp) == y(indp))/length(indp)
fp_test = 1 - sum(yest_test(indn) == y(indn))/length(indn)
Perf_test = sum(yest_test == y)/length(y)
[dum , ind] = sort(y , 'descend');
figure
plot(fx_test(ind))
title(sprintf('Output of the strong classifier for test data with T = %d' , options.T))
[tpp_test , fpp_test] = basicroc(y , fx_test);
figure
plot(fpp_train , tpp_train , fpp_test , tpp_test , 'r' , 'linewidth' , 2)
axis([-0.02 , 1.02 , -0.02 , 1.02])
title(sprintf('ROC for CHLBP features with T = %d' , options.T))
legend('Train' , 'Test' , 'Location' , 'SouthEast')
% disp('Press key to continue')
% pause
%% Fifth example : compute CHLBP_{8;1}^u + CHLBP_{4;1} + CHLBP_{12;2}^u Features + Gentleboosting with T weaklearners (Decision Stump)
clear
load viola_24x24
Ny = 24;
Nx = 24;
options.N = [8 , 4 , 12];
options.R = [1 , 1 , 2];
options.map = zeros(2^max(options.N) , length(options.N));
mapping = getmapping(options.N(1),'u2');
options.map(1:2^options.N(1) , 1) = mapping.table';
options.map(1:2^options.N(2) , 2) = (0:2^options.N(2)-1)';
mapping = getmapping(options.N(3),'u2');
options.map(1:2^options.N(3) , 3) = mapping.table';
options.shiftbox = cat(3 , [Ny , Nx ; 1 , 1] , [16 , 16 ; 4 , 4] , [Ny , Nx ; 1 , 1]);
options.T = 50;
H = chlbp(X , options);
figure
imagesc(H)
title('CHLBP Features')
drawnow
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
index = randperm(length(y)); %shuffle data to avoid numerical discrepancies with long sequence of same label
options.param = chlbp_gentleboost_binary_train_cascade(H(: , index) , y(index) , options);
[yest_train , fx_train] = chlbp_gentleboost_binary_predict_cascade(H , options);
tp_train = sum(yest_train(indp) == y(indp))/length(indp)
fp_train = 1 - sum(yest_train(indn) == y(indn))/length(indn)
Perf_train = sum(yest_train == y)/length(y)
[dum , ind] = sort(y , 'descend');
figure
plot(fx_train(ind))
title(sprintf('Output of the strong classifier for train data with T = %d' , options.T))
[tpp_train , fpp_train] = basicroc(y , fx_train);
load jensen_24x24
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
H = chlbp(X , options);
[yest_test , fx_test] = chlbp_gentleboost_binary_predict_cascade(H , options);
tp_test = sum(yest_test(indp) == y(indp))/length(indp)
fp_test = 1 - sum(yest_test(indn) == y(indn))/length(indn)
Perf_test = sum(yest_test == y)/length(y)
[dum , ind] = sort(y , 'descend');
figure
plot(fx_test(ind))
title(sprintf('Output of the strong classifier for test data with T = %d' , options.T))
[tpp_test , fpp_test] = basicroc(y , fx_test);
figure
plot(fpp_train , tpp_train , fpp_test , tpp_test , 'r' , 'linewidth' , 2)
axis([-0.02 , 1.02 , -0.02 , 1.02])
title(sprintf('ROC for CHLBP features with T = %d' , options.T))
legend('Train' , 'Test', 'Location' , 'SouthEast')
% disp('Press key to continue')
% pause
%% Sith example : CHLBP_{8;1}^u versus CHLBP_{8;1} Features + Gentleboosting with T weaklearners (Decision Stump)
clear
load viola_24x24
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
Ny = 24;
Nx = 24;
options.N = 8;
options.R = 1;
options.map = zeros(2^max(options.N) , length(options.N));
options.T = 50;
mapping = getmapping(options.N(1),'u2');
options.map = mapping.table';
H0 = chlbp(X , options);
options.map = (0:2^options.N-1)';
H1 = chlbp(X , options);
figure
imagesc(H0)
title(sprintf('Uniform CHLBP Features with N = %d, R = %2.1f' , options.N , options.R))
drawnow
figure
imagesc(H1)
title(sprintf('CHLBP Features with N = %d, R = %2.1f' , options.N , options.R))
drawnow
index = randperm(length(y)); %shuffle data to avoid numerical discrepancies with long sequence of same label
options0.param = chlbp_gentleboost_binary_train_cascade(H0(: , index) , y(index) , options);
[yest0_train , fx0_train] = chlbp_gentleboost_binary_predict_cascade(H0 , options0);
options1.param = chlbp_gentleboost_binary_train_cascade(H1(: , index) , y(index) , options);
[yest1_train , fx1_train] = chlbp_gentleboost_binary_predict_cascade(H1 , options1);
tp0_train = sum(yest0_train(indp) == y(indp))/length(indp)
fp0_train = 1 - sum(yest0_train(indn) == y(indn))/length(indn)
Perf0_train = sum(yest0_train == y)/length(y)
tp1_train = sum(yest1_train(indp) == y(indp))/length(indp)
fp1_train = 1 - sum(yest1_train(indn) == y(indn))/length(indn)
Perf1_train = sum(yest1_train == y)/length(y)
[dum , ind] = sort(y , 'descend');
figure
plot((1:length(y)) , fx0_train(ind) , (1:length(y)) , fx1_train(ind) , 'r')
title(sprintf('Output of the strong classifier for train data with T = %d' , options.T))
legend('Uniform CHLBP' , 'CHLBP')
[tpp0_train , fpp0_train] = basicroc(y , fx0_train);
[tpp1_train , fpp1_train] = basicroc(y , fx1_train);
load jensen_24x24
y = int8(y);
indp = find(y == 1);
indn = find(y ==-1);
mapping = getmapping(options.N(1),'u2');
options.map = mapping.table';
H0 = chlbp(X , options);
options.map = (0:2^options.N-1)';
H1 = chlbp(X , options);
[yest0_test , fx0_test] = chlbp_gentleboost_binary_predict_cascade(H0 , options0);
[yest1_test , fx1_test] = chlbp_gentleboost_binary_predict_cascade(H1 , options1);
tp0_test = sum(yest0_test(indp) == y(indp))/length(indp)
fp0_test = 1 - sum(yest0_test(indn) == y(indn))/length(indn)
Perf0_test = sum(yest0_test == y)/length(y)
tp1_test = sum(yest1_test(indp) == y(indp))/length(indp)
fp1_test = 1 - sum(yest1_test(indn) == y(indn))/length(indn)
Perf1_test = sum(yest1_test == y)/length(y)
[dum , ind] = sort(y , 'descend');
figure
plot((1:length(y)) , fx0_test(ind) , (1:length(y)) , fx1_test(ind) , 'r')
title(sprintf('Output of the strong classifier for test data with T = %d' , options.T))
legend('Uniform CHLBP' , 'CHLBP')
[tpp0_test , fpp0_test] = basicroc(y , fx0_test);
[tpp1_test , fpp1_test] = basicroc(y , fx1_test);
figure
plot(fpp0_train , tpp0_train , 'b' , fpp0_test , tpp0_test , 'r' , fpp1_train , tpp1_train , 'b--' , fpp1_test , tpp1_test , 'r--' , 'linewidth' , 2)
axis([-0.02 , 1.02 , -0.02 , 1.02])
title(sprintf('ROC for U-CHLBP and CHLBP features with T = %d' , options.T))
legend('Train U-CHLBP' , 'Test U-CHLBP' , 'Train CHLBP' , 'Test CHLBP' , 'Location' , 'SouthEast')
