Hi @Justin
I understand you're preparing a large dataset of images for training a Self-Organizing Map (SOM) and need to convert your array of images into a 2D matrix.
Neural network clustering algorithms typically require input in a 2D array format (M x N), where M is the number of observations and N is the number of features. In your case, M would be 7112 (the number of images) and N would be 16384 (the result of reshaping each 128x128 image into a 1D array).
Here's how you can modify your code:
files = dir('*.png');
num = length(files);
% Initialize the 2D matrix
% Each row will represent one image, each column a pixel
arrayImage2D = zeros(num, 128*128);
for i = 1:num
% Read the image
img = imread(files(i).name);
% Ensure the image is in double format
img = im2double(img);
% Reshape the 2D image into a 1D vector and store it as a row in arrayImage2D
arrayImage2D(i, :) = reshape(img, 1, []);
end
% Now arrayImage2D is a 2D matrix where each row represents an image
% The size should be [7112, 16384] for your dataset
size(arrayImage2D)
This 2D format should be compatible with MATLAB's Neural Network Toolbox for training a Self-Organizing Map.
For more information on the reshape function, you can refer to the MATLAB documentation:
