Demo_main

% ==== PCANet Demo =======
% T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, review zhongfei
% "PCANet: A simple deep learning baseline for image classification?" submitted to IEEE TIP. 
% ArXiv eprint: http://arxiv.org/abs/1404.3606 
% Tsung-Han Chan [thchan@ieee.org]
% Please email me if you find bugs, or have suggestions or questions!
% ========================
clear all;
close all; 
clc; 
addpath('./Utils');
addpath('./Liblinear');
make;

TrnSize = 10000; 
ImgSize = 40; 

%% Loading data 
file_path_cells =  'data\Cell\';
cells_path_list = dir(strcat(file_path_cells,'*.jpg'));
cells_num = length(cells_path_list); 
cells_data = [];
if cells_num > 0 
        for j = 1:cells_num   
            cells_name = cells_path_list(j).name;
            cells =  imread(strcat(file_path_cells,cells_name));
            cells = rgb2gray(cells);
            c = cells(:);
            cells_data = [cells_data,c];
        end  
end 
cells_Labels = [ones(cells_num,1)];

file_path_NoneCells =  'data\NoneCell\';
NoneCells_path_list = dir(strcat(file_path_NoneCells,'*.jpg'));
NoneCells_num = length(NoneCells_path_list); 
NoneCells_data = [];
if NoneCells_num > 0 
        for j = 1:NoneCells_num   
            NoneCells_name = NoneCells_path_list(j).name;
            NoneCells =  imread(strcat(file_path_NoneCells,NoneCells_name)); 
            NoneCells = rgb2gray(NoneCells);
            m = NoneCells(:);
            NoneCells_data = [NoneCells_data,m];
        end  
end 
NoneCells_Labels = [zeros(NoneCells_num,1)-1];

sum_data = [cells_data,NoneCells_data];
sum_labels = [cells_Labels;NoneCells_Labels];
TrnData = sum_data(:,1:2:end);
TestData = sum_data(:,2:2:end);
TrnLabels = sum_labels(1:2:end);
TestLabels =sum_labels(2:2:end);
train_sample = size(TrnData,2);
test_sample = size(TestData,2);
ImgFormat = 'gray'; 
TrnLabels = double(TrnLabels);
TestLabels = double(TestLabels);
%% For this demo, we subsample the Training and Testing sets 
nTestImg = length(TestLabels);
%% PCANet parameters (they should be funed based on validation set; i.e., ValData & ValLabel)
PCANet.NumStages = 2;
PCANet.PatchSize = [13 13];
PCANet.NumFilters = [40 8];
PCANet.HistBlockSize = [8 8];
PCANet.BlkOverLapRatio = 0.5;
PCANet.Pyramid = [4 2 1];
fprintf('\n ====== PCANet Parameters ======= \n')
PCANet
%% PCANet Training with 10000 samples
fprintf('\n ====== PCANet Training ======= \n')
TrnData_ImgCell = mat2imgcell(double(TrnData),ImgSize,ImgSize,ImgFormat); % convert columns in TrnData to cells 
tic; 
[ftrain, V, BlkIdx] = PCANet_train(TrnData_ImgCell,PCANet,1); % BlkIdx serves the purpose of learning block-wise DR projection matrix; e.g., WPCA
PCANet_TrnTime = toc;
%% PCA hashing over histograms
c = 10; 
fprintf('\n ====== Training Linear SVM Classifier ======= \n')
display(['now testing c = ' num2str(c) '...'])
tic;
models = train(TrnLabels, ftrain', ['-s 1 -c ' num2str(c) ' -q']); % we use linear SVM classifier (C = 10), calling liblinear library
LinearSVM_TrnTime = toc;
save('model\PCANet_model.mat','V','models','PCANet');
%% PCANet Feature Extraction and Testing 
TestData_ImgCell = mat2imgcell(TestData,ImgSize,ImgSize,ImgFormat); % convert columns in TestData to cells 
% clear TestData; 
fprintf('\n ====== PCANet Testing ======= \n')
nCorrRecog = 0;
RecHistory = zeros(nTestImg,1);
x = 0;
tic; 
for idx = 1:1:nTestImg
    ftest = PCANet_FeaExt(TestData_ImgCell(idx),V,PCANet); % extract a test feature using trained PCANet model 

    [xLabel_est, accuracy, decision_values] = predict(TestLabels(idx),...
        sparse(ftest'), models, '-q');   
    if xLabel_est == TestLabels(idx)
        RecHistory(idx) = 1;
        nCorrRecog = nCorrRecog + 1; 
    end    
    if 0==mod(idx,nTestImg/100); 
        fprintf('Accuracy up to %d tests is %.2f%%; taking %.2f secs per testing sample on average. \n',...
            [idx 100*nCorrRecog/idx toc/idx]); 
    end   
    TestData_ImgCell{idx} = [];    
end
Averaged_TimeperTest = toc/nTestImg;
Accuracy = nCorrRecog/nTestImg; 
ErRate = 1 - Accuracy;
%% Results display
fprintf('\n ===== Results of PCANet, followed by a linear SVM classifier =====');
fprintf('\n     trainning_samples_num: %d test_samples_num: %d .',train_sample,test_sample );
fprintf('\n     PCANet training time: %.2f secs.', PCANet_TrnTime);
fprintf('\n     Linear SVM training time: %.2f secs.', LinearSVM_TrnTime);
fprintf('\n     Testing error rate: %.2f%', 100*ErRate);
fprintf('\n     Testing accuracy rate: %.2f%', 100*Accuracy);
fprintf('\n     Average testing time %.2f secs per test sample. \n\n',Averaged_TimeperTest);