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% Specify the folders containing the original and pre-processed images original_folder = 'C:\Users\lalai\OneDrive\Documents\MATLAB\Metrics_Preprocess\Resized_Datasets_with_hair'; preprocessed_folder = 'C:\Users\lalai\OneDrive\Documents\MATLAB\Metrics_Preprocess\Preprocessed'; % Get the list of image files in the folders original_files = dir(fullfile(original_folder, '*.jpg')); preprocessed_files = dir(fullfile(preprocessed_folder, '*.jpg')); % Initialize variables to store the metrics psnr_values = zeros(numel(original_files), 1); ssim_values = zeros(numel(original_files), 1); iou_values = zeros(numel(original_files), 1); mse_values = zeros(numel(original_files), 1); % Loop over the images in the folders and compute the metrics for i = 1:numel(original_files) % Load the original and preprocessed images original = imread(fullfile(original_folder, original_files(i).name)); preprocessed = imread(fullfile(preprocessed_folder, preprocessed_files(i).name)); % Compute the PSNR psnr_values(i) = psnr(preprocessed, original); % Compute the SSIM ssim_values(i) = ssim(preprocessed, original); % Calculate the mean squared error mse_values(i) = immse(preprocessed, original); end % Compute the average metrics avg_psnr = mean(psnr_values); avg_ssim = mean(ssim_values); avg_mse = mean(mse_values); % Display the results fprintf('Average PSNR: %.2f dB\n', avg_psnr); fprintf('Average SSIM: %.4f\n', avg_ssim); fprintf('Average MSE: %.4f\n', avg_mse);