sustaining_gazes/matlab_runners/Head Pose Experiments/calcBUerror.m

function [meanError, all_rot_preds, all_rot_gts, all_errors, rels_all, seq_ids] = calcBUerror(resDir, gtDir)

seqNames = {'jam1','jam2','jam3','jam4','jam5','jam6','jam7','jam8','jam9', ...
    'jim1','jim2','jim3','jim4','jim5','jim6','jim7','jim8','jim9', ...
    'llm1','llm2','llm3','llm4','llm5','llm6','llm7','llm8','llm9', ...
    'ssm1','ssm2','ssm3','ssm4','ssm5','ssm6','ssm7','ssm8','ssm9', ...
    'vam1','vam2','vam3','vam4','vam5','vam6','vam7','vam8','vam9'};

rotMeanErr = zeros(numel(seqNames),3);
rotRMS = zeros(numel(seqNames),3);
rot = cell(1,numel(seqNames));
rotg = cell(1,numel(seqNames));
rels_all = [];

seq_ids = {};

for i = 1:numel(seqNames)
    fname = [resDir seqNames{i} '.csv'];
    if(i == 1)
        % First read in the column names
        tab = readtable(fname);
        column_names = tab.Properties.VariableNames;

        confidence_id = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'confidence'));
        rot_ids = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'pose_R'));
    end

    all_params  = dlmread(fname, ',', 1, 0);
    
    rot{i} = all_params(:, rot_ids);    
    rels = all_params(:, confidence_id);
    
    posesGround =  load ([gtDir seqNames{i} '.dat']);

    % the reliabilities of head pose
    rels_all = cat(1, rels_all, rels);
    
    % Flip because of different conventions
    rot{i}(:,2) = -rot{i}(:,2);
    rot{i}(:,3) = -rot{i}(:,3);
    
    % Convert to radians
    rotg{i} = posesGround(2:end,[7 6 5]) * (pi/180);
        
    % Correct the first frame so it corresponds to (0,0,0), as slightly
    % different pose might be assumed frontal and this corrects for
    % that
    
    % Work out the correction matrix for ground truth
    rot_corr_gt = Euler2Rot(rotg{i}(1,:));
    for r_e = 1:size(rotg{i},1)
        rot_curr_gt = Euler2Rot(rotg{i}(r_e,:));
        rot_new_gt = rot_corr_gt' * rot_curr_gt;
        rotg{i}(r_e,:) = Rot2Euler(rot_new_gt);
    end
    
    % Work out the correction matrix for estimates
    rot_corr_est = Euler2Rot(rot{i}(1,:));
    for r_e = 1:size(rot{i},1)
        rot_curr_est = Euler2Rot(rot{i}(r_e,:));
        rot_new_est = rot_corr_est' * rot_curr_est;
        rot{i}(r_e,:) = Rot2Euler(rot_new_est);
    end
    
    % Convert to degrees
    rotg{i} = rotg{i} * 180 / pi;
    rot{i} = rot{i} * 180 / pi;  
    
    rotMeanErr(i,:) = mean(abs((rot{i}(:,:)-rotg{i}(:,:))));
    rotRMS(i,:) = sqrt(mean(((rot{i}(:,:)-rotg{i}(:,:))).^2)); 
    seq_ids = cat(1, seq_ids, repmat(seqNames(i), size(rot{i},1), 1));
end
allRot = cell2mat(rot');
allRotg = cell2mat(rotg');

meanError = mean(abs((allRot(:,:)-allRotg(:,:))));
all_errors = abs(allRot-allRotg);
rmsError = sqrt(mean(((allRot(:,:)-allRotg(:,:))).^2)); 
errorVariance = var(abs((allRot(:,:)-allRotg(:,:))));      

all_rot_preds = allRot;
all_rot_gts = allRotg;