How to perform normalization of images, splitting of datasets and data processing correctly?

I’m fairly new to MATLAB. I have created a file with 500 images of apples, all of them have a size of 32*32*3. I want to normalize the images to obtain a dimension of 1024.

I've split the first 450 images into a training dataset and the remaining 50 images into a testing dataset. The next step would be to apply global mean and variance to the images, but I don't know how to write a function in MATLAB to find out the mean and variance. Can anyone help me?

images ='D:\group_2\apples';

for i=1:500;
    jpgfiles=dir(fullfile(images,'\*.jpg*'));
    n=numel(jpgfiles(i));
    im=jpgfiles(i).name
    im1=imread(fullfile(images,im));
    gray_images= rgb2gray(im1);
    new_images = reshape(gray_images, [1024,1])
    imshow(gray_images);    
end

% MATLAB returns an error indicating that the index exceeds the number of array elements(0)

[setTrain, setTest] = partition(images, [0.9, 0.1], 'randomized');
mean1 = mean2(new_images)
sd1 = std2(new_images)

grayImage = (double(new_images) - mean1) * (0.005/sd1);
subplot(2, 1, 2);
imshow(grayImage, []);
mean2 = mean2(grayImage)
sd2 = std2(grayImage)

Answer 1

[Image Analyst]

Pull dir() out of the loop so you only look as many time as there are images. There might not be 500 images like you thought.

imageFolder ='D:\group_2\apples';
jpgFiles = dir(fullfile(imageFolder, '*.jpg*'));
numImages = numel(jpgFiles)
for k = 1 : numImages;
    % Create full file name.
    fullFileName = fullfile(jpgfiles(k).folder, jpgfiles(k).name);
    % Read in image.
    thisImage = imread(fullFileName);
    % Cast to gray scale if necessary
    if ndims(thisImage) == 3
        % It's RGB.  Convert to gray scale.
        gray_image = rgb2gray(thisImage);
    else
        % It's already gray scale.
        gray_image = thisImage;
    end
    % 32*32 is 1024.  Reshape gray_image into a column vector with 1024 rows but only one column.
    % Not sure why this is wanted.
    new_image = reshape(gray_image, [], 1]); % or (simpler) new_image = gray_image(:)
    % Display the gray scale column vector.
    imshow(gray_images);    % Will appear as a vertical line.
end

Answer 2

[yanqi liu]

yes，sir，may be use

mat2gray 、rescale、mapminmax to process data，such as

img = double(imread('rice.png'));
[~,PS] = mapminmax(img,0,1);
Y = mapminmax('apply',img,PS);
Y2 = mat2gray(img);
Y3 = rescale(img, 0, 1);