LDA로 headPoseEstimate하기

 앞서 포스팅에서 ANN(Artificial Neural Network)로 headPoseEstimate을 하는 글을 올린적이 있었는데,

 * HeadPoseEstimationUsingANN: http://enginius.tistory.com/307

이번에는 LDA(Linear Discriminant Analysis)를 이용해서 같은 작업을 수행하였다. 

 * LDA: http://enginius.tistory.com/310

 먼저 training data로 LDA를 수행하고, 구해진 W Matrix의 성능을 label과 비교해서 확인하였다. LDA의 특성인 Linear transform 때문에 ANN에 비해 학습 시간이 매우 짧다. 내 노트북에서 대략 5초면 끝난다. ANN은 i7 2600k에서 4시간 정도 학습 시켰었다. 하지만 성능이 떨어진다. 

학습 결과 

매트랩 소스

 1. data를 읽어오는 매트랩 소스

%% Set Directory Path

%dirRootPath = 'C:/Users/CPSLab/Dropbox/연구/CPSLab/구현한 것들/QMULPoseHeads/QMULPoseHeads';  % PC in CPS Lab

dirRootPath = 'C:/Users/Human/Dropbox/연구/CPSLab/구현한 것들/QMULPoseHeads/QMULPoseHeads'; % ma XNote Laptop

%% Get training dataset to 'trainData' and 'trainTargets'

fprintf('1. Get training data from the folder\r\n');

trainFeatureCount   = 50*50;            % the size of the image is 50 by 50

trainDataCount      = 2255;             % total: 2255 - but it will produce out-of-memory error.. I need 64bit MATLAB :(

trainTargetCount    = 5;                % there are total five categories; front, back, left, right face and non-face

% make space for training data (the size is defined above)

trainData           = zeros(trainFeatureCount, trainDataCount*trainTargetCount);

trainTargets        = zeros(trainTargetCount, trainDataCount*trainTargetCount);

iprev = 0;

for i = 1:5

    % for every five categories

    

    % Initialize category

    if iprev ~= i

        iprev = i;

        if i==1

            fprintf('front face \t > ');

            dirPath     = sprintf('%s/train/Data_f', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [1 0 0 0 0]';

        elseif i==2

            fprintf('\r\nback face \t > ');

            dirPath     = sprintf('%s/train/Data_b', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 1 0 0 0]';

        elseif i==3

            fprintf('\r\nleft face \t > ');

            dirPath     = sprintf('%s/train/Data_l', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 1 0 0]';

        elseif i==4

            fprintf('\r\nright face \t > ');

            dirPath     = sprintf('%s/train/Data_r', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 0 1 0]';

        elseif i==5

            fprintf('\r\nnon face \t > ');

            dirPath     = sprintf('%s/train/Data_bg', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 0 0 1]';

        end

    end

    

    k = 1;

    for j = 1 : trainDataCount

        % for every images(five categories) in the category

        image = imread( sprintf('%s/%s', dirPath, fileNames{j} ) );

        Igray = rgb2gray(image);

        Igray = reshape( Igray, trainFeatureCount, 1);

        Igray = Igray - min(Igray);   %%%%%%%%%%

        trainData( : , (i-1)*trainDataCount+j )    = Igray;

        trainTargets( : , (i-1)*trainDataCount+j ) = target;

        

        % make 10 dots total.

        if rem(j, int16(trainDataCount/10) ) == 0

            fprintf('%d ', k);     

            k = k + 1;

        end

    end

end

fprintf('\r\n');

fprintf('Total %d training data loaded to trainData, trainTargets \r\n', trainDataCount*trainTargetCount);

%% Get testing dataset to 'testData' and 'testTargets'

fprintf('1. Get testing data from the folder\r\n');

testFeatureCount   = 50*50;             % the size of the image is 50 by 50

testDataCount      = 1000;              % total: 1000

testTargetCount    = 5;                 % there are total five categories; front, back, left, right face and non-face

% make space for testing data (the size is defined above)

testData           = zeros(testFeatureCount, testDataCount*testTargetCount);

testTargets        = zeros(testTargetCount, testDataCount*testTargetCount);

iprev = 0;

for i = 1:5

    % for every five categories

    

    % Initialize category

    if iprev ~= i

        iprev = i;

        if i==1

            fprintf('front face \t > ');

            dirPath     = sprintf('%s/test/Test_f', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [1 0 0 0 0]';

        elseif i==2

            fprintf('\r\nback face \t > ');

            dirPath     = sprintf('%s/test/Test_b', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 1 0 0 0]';

        elseif i==3

            fprintf('\r\nleft face \t > ');

            dirPath     = sprintf('%s/test/Test_l', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 1 0 0]';

        elseif i==4

            fprintf('\r\nright face \t > ');

            dirPath     = sprintf('%s/test/Test_r', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 0 1 0]';

        elseif i==5

            fprintf('\r\nnon face \t > ');

            dirPath     = sprintf('%s/test/Test_bg', dirRootPath);

            fileList    = dir( sprintf('%s/*.jpg',dirPath) );

            fileNames   = {fileList.name};

            target = [0 0 0 0 1]';

        end

    end

    

    k = 1;

    for j = 1 : testDataCount

        % for every images(five categories) in the category

        image = imread( sprintf('%s/%s', dirPath, fileNames{j} ) );

        Igray = rgb2gray(image);

        Igray = reshape( Igray, testFeatureCount, 1);

        Igray = Igray - min(Igray);   %%%%%%%%%%

        testData( : , (i-1)*testDataCount+j )    = Igray;

        testTargets( : , (i-1)*testDataCount+j ) = target;

        

        % make 10 dots total.

        if rem(j, int16(testDataCount/10) ) == 0

            fprintf('%d ', k);     

            k = k + 1;

        end

    end

end

fprintf('\r\n');

fprintf('Total %d test data loaded to testData, testTargets \r\n', testDataCount*testTargetCount);

%%

clear fileList;

clear fileNames;

clear Image;

clear Igray;

 2. LDA를 수행하고, test하는 매트랩 소스 

%%

clc;

clear all;

%% First, get training and test data from the folder

% data is stored in trainingData, trainingTargets, testData, testTargets

% targetLabel is expressed as follows; class 1 among 5: [1 0 0 0 0];, class 2: [0 1 0 0 0]'

% the n-th column (not row) in data indicates independent vector

% ex)   trainData = [ 1thVector 2ndVector 3rdVector ... ];

%       trainTargets = [1thVectorClass 2ndVectorClass 3rdVectorClass ... ];

getHeadPoseData;

%% Second, do LDA to training data set

% 1. the data matrix must be transposed 

trainData       = trainData';

trainTargets    = trainTargets';

testData        = testData';

testTargets     = testTargets';

% 2. the target matrix must be modified: [1 0 0 0] -> 1 ; [0 1 0 0] -> 2 ; ...

trainTargets    = trainTargets*[1 2 3 4 5]';

testTargets     = testTargets*[1 2 3 4 5]';

%% Now we are ready to perfrom LDA

W = LDA(trainData, trainTargets);

%% Testing the performance of this LDA with trainSet

L = [ones(size(trainData, 1),1) trainData] * W';

P = exp(L) ./ repmat( sum(exp(L),2), [1 5] );

[maxVal maxIndex] = max(P, [], 2);

LDA_Validate = (maxIndex == trainTargets);

LDA_Performance = sum(LDA_Validate)/size(LDA_Validate,1);

fprintf('the performance of this LDA is %.1f with average certainty %.1f \r\n'...

    , LDA_Performance*100, mean(maxVal)*100);

%% Test with the testSet

L = [ones(size(testData, 1),1) testData] * W';

P = exp(L) ./ repmat( sum(exp(L),2), [1 5] );

[maxVal maxIndex] = max(P, [], 2);

LDA_Validate = (maxIndex == testTargets);

LDA_Performance = sum(LDA_Validate)/size(LDA_Validate,1);

fprintf('the test result of this LDA is %.1f with average certainty %.1f \r\n'...

    , LDA_Performance*100, mean(maxVal)*100);

%% 

 3. LDA 함수 매트랩 소스 

% LDA - MATLAB subroutine to perform linear discriminant analysis

% by Will Dwinnell and Deniz Sevis

%

% Use:

% W = LDA(Input,Target,Priors)

%

% W       = discovered linear coefficients (first column is the constants)

% Input   = predictor data (variables in columns, observations in rows)

% Target  = target variable (class labels)

% Priors  = vector of prior probabilities (optional)

%

% Note: discriminant coefficients are stored in W in the order of unique(Target)

function W = LDA(Input, Target, Priors)

% Determine size of input data

[n m] = size(Input);

% Discover and count unique class labels

ClassLabel  = unique(Target);

k           = length(ClassLabel);

% Initialize

nGroup      = NaN(k,1);     % Group counts

GroupMean   = NaN(k,m);     % Group sample means

PooledCov   = zeros(m,m);   % Pooled covariance

W           = NaN(k,m+1);   % model coefficients

if  (nargin >= 3)  PriorProb = Priors;  end

% Loop over classes to perform intermediate calculations

for i = 1:k,

    % Establish location and size of each class

    Group      = (Target == ClassLabel(i));

    nGroup(i)  = sum(double(Group));

    

    % Calculate group mean vectors

    GroupMean(i,:) = mean(Input(Group,:));

    

    % Accumulate pooled covariance information

    PooledCov = PooledCov + ((nGroup(i) - 1) / (n - k) ).* cov(Input(Group,:));

end

% Assign prior probabilities

if  (nargin >= 3)

    % Use the user-supplied priors

    PriorProb = Priors;

else

    % Use the sample probabilities

    PriorProb = nGroup / n;

end

% Loop over classes to calculate linear discriminant coefficients

for i = 1:k,

    % Intermediate calculation for efficiency

    % This replaces:  GroupMean(g,:) * inv(PooledCov)

    Temp = GroupMean(i,:) / PooledCov;

    % Constant

    W(i,1) = -0.5 * Temp * GroupMean(i,:)' + log(PriorProb(i));

    % Linear

    W(i,2:end) = Temp;

end

% Housekeeping

clear Temp

end

% EOF