How is the pre-process used in my Lecturer's code?

Im learning about image classification and there's a section that "pre-processes the image but i can't work out if its being used

clear  
close all 
clc

% Load training and test data using |imageDatastore|.
Training_Dir   = fullfile(toolboxdir('vision'),'visiondata','digits','synthetic');
Testing_Dir = fullfile(toolboxdir('vision'),'visiondata','digits','handwritten');

% imageDatastore recursively scans the directory tree containing the
% 
trainingSet = imageDatastore(Training_Dir,'IncludeSubfolders',true,'LabelSource','foldernames');
testingSet     = imageDatastore(Testing_Dir,'IncludeSubfolders',true,'LabelSource','foldernames');
%% Show a few of the training and test images
close all 
figure('Name','Training Image Examples','NumberTitle','off');
movegui("northwest")
subplot(3,3,1);
imshow(trainingSet.Files{12});

subplot(3,3,2);
imshow(trainingSet.Files{315});

subplot(3,3,3);
imshow(trainingSet.Files{810});
subplot(3,3,4);
imshow(trainingSet.Files{1000});
subplot(3,3,5);
imshow(trainingSet.Files{500});

subplot(3,3,6);
imshow(testingSet.Files{10});

subplot(3,3,7);
imshow(testingSet.Files{35});

subplot(3,3,8);
imshow(testingSet.Files{80});
subplot(3,3,9);
imshow(testingSet.Files{105});

%% Show pre-processing  and Feature Extraction results
exTestImage = readimage(testingSet,12);
Gray_IM=im2gray(exTestImage);
Binary_IM = imbinarize(Gray_IM);

figure('Name','Preprocessed Image','NumberTitle','off');
movegui("north")

subplot(1,3,1)
imshow(exTestImage)
subplot(1,3,2)
imshow(Gray_IM)
subplot(1,3,3)
imshow(Binary_IM)

%%  Extract HOG features from image

[img, imgInfo] = readimage(trainingSet, 508);
imgInfo
cellSize = [1 1];
[hog_4x4, vis4x4] = extractHOGFeatures(img,'CellSize',cellSize);
figure('Name','Histogram of Gradients','NumberTitle','off')
movegui("northeast")

plot(vis4x4); 
figure('Name','Training Data with HOG','NumberTitle','off');

movegui("southwest")

imshow(img, 'InitialMagnification',2000)
hold on
plot(vis4x4,'color', 'b')
%set(gcf,'position',[0,0,800,800])

hold off

%% Extracting HOG Features

% HOG features from each image in training set.


hogFeatureSize = length(hog_4x4);
numImages = numel(trainingSet.Files);
trainingFeatures = zeros(numImages,hogFeatureSize,'single');
for i = 1:numImages
    img = readimage(trainingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    trainingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end

% similar procedure will be used  with testing set.

numImages_test = numel(testingSet.Files);

testingFeatures = zeros(numImages_test,hogFeatureSize,'single');
for i = 1:numImages_test
    img = readimage(testingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    testingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end
%% Train and Testing a Decision Tree Classifier


% Get labels for each image.
trainingLabels = trainingSet.Labels;
tree = fitctree(trainingFeatures,trainingLabels);

% Evaluating the the Classifier
% Get labels for each image.
testLabels = testingSet.Labels;

predictedLabels = predict(tree, testingFeatures);

% Tabulate the results using a confusion matrix.
 figure("Name","Confusion Matrix",'NumberTitle','off')
 movegui("south")
cm = confusionchart(testLabels,predictedLabels,'RowSummary','row-normalized','ColumnSummary','column-normalized');

%confMat = confusionmat(testLabels, predictedLabels);

%% Testing the performance of Trained model on some testing images 

M=1;
predicted_Image = predict(tree, testingFeatures(M,:));
figure("Name","Predicted Digit", "NumberTitle","off")
imshow(testingSet.Files{M}, 'InitialMagnification',800);
caption = sprintf('Predicted Digit is: %c', predicted_Image );
title(caption, 'FontSize', 18);

Answer 1

[Walter Roberson]

The section

%% Show pre-processing  and Feature Extraction results

is used to display example results for very specific images.

The section

for i = 1:numImages
    img = readimage(trainingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    trainingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end

is doing the preprocessing for all of the images in the training set.

for i = 1:numImages_test
    img = readimage(testingSet,i);
    
    img = im2gray(img); %convertign the specified truecolor The RGB Images to a grayscale intensity image 
    
   
    img = imbinarize(img);   %  pre-processing step to conver the Imges into black & white
    
    testingFeatures(i, :) = extractHOGFeatures(img,'CellSize',cellSize);  
end

is doing the preprocessing for all of the files in the testing set.

The preprocessed training image data is used in

tree = fitctree(trainingFeatures,trainingLabels);

The preprocessed test image data is used in

predictedLabels = predict(tree, testingFeatures);