You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: OneScaleObjectDetection.m
    path of file: ./repos/ufpa-face-detection/ViolaJones_version0b/SubFunctions
    
    the code of the file until where you have to start completion: function [x,y]=OneScaleObjectDetection( x, y, Scale, IntegralImages, w,h,HaarCasade)
% [x,y]=OneScaleObjectDetection( x, y, Scale, IntegralImages, w,h,HaarCasade)
%

% Calculate the mean 
InverseArea = 1 / (w*h);
mean =  GetSumRect(IntegralImages.ii,x,y,w,h)*InverseArea;
% Use the mean and squared integral image to calculate the grey-level
% Varianceiance, of every search window
Variance = GetSumRect(IntegralImages.ii2,x,y,w,h)*InverseArea - (mean.^2);
% Convert the Varianceiation to Standard Deviation
Variance(Variance<1)=1; StandardDeviation =sqrt(Variance);

% The haarcasade contains a row of classifier-trees. The classifiers
% are executed one at the time. If a coordinate doesn't pass the classifier
% threshold it is removed, otherwise it goes into the next classifier

% Loop through all classifier stages
for i_stage = 1:length(HaarCasade.stages),
    stage = HaarCasade.stages(i_stage);
    Trees=stage.trees;
    StageSum = zeros(size(x));
    
    % Loop through a classifier tree
    for i_tree=1:length(Trees)

    
function [x,y]=OneScaleObjectDetection( x, y, Scale, IntegralImages, w,h,HaarCasade)
% [x,y]=OneScaleObjectDetection( x, y, Scale, IntegralImages, w,h,HaarCasade)
%

% Calculate the mean 
InverseArea = 1 / (w*h);
mean =  GetSumRect(IntegralImages.ii,x,y,w,h)*InverseArea;
% Use the mean and squared integral image to calculate the grey-level
% Varianceiance, of every search window
Variance = GetSumRect(IntegralImages.ii2,x,y,w,h)*InverseArea - (mean.^2);
% Convert the Varianceiation to Standard Deviation
Variance(Variance<1)=1; StandardDeviation =sqrt(Variance);

% The haarcasade contains a row of classifier-trees. The classifiers
% are executed one at the time. If a coordinate doesn't pass the classifier
% threshold it is removed, otherwise it goes into the next classifier

% Loop through all classifier stages
for i_stage = 1:length(HaarCasade.stages),
    stage = HaarCasade.stages(i_stage);
    Trees=stage.trees;
    StageSum = zeros(size(x));
    
    % Loop through a classifier tree
    for i_tree=1:length(Trees)
        Tree = Trees(i_tree).value;
        % Executed the classifier
        TreeSum=TreeObjectDetection(zeros(size(x)),Tree,Scale,x,y,IntegralImages.ii,StandardDeviation,InverseArea);
        StageSum = StageSum + TreeSum;
    end