How can read multiple images from different subfolders

I have folder name data and under this folder 30 folders (named A1 to A30) and each one have many colored images RGB (each image sized to 150*150 pixels) around 8000 images. I would like to read those images and save them to one matrix which has size 150*150*3*8000 (4d matrix). I tried many code but could not come up with an efficient code. So, could someone please suggest me an idea or code at least how to read those images from different folders and save them.

Answer 1

[Image Analyst]

See attached code.

[Salem]

WOW, thank you for the quick answer and awesome code. Works 100%.

[ankit sharma]

The code provided by you was helpful. I was able to load the images from different subfolders. Now I want to apply the face recognition algorithm on these images. But it is showing error in my next line i.e. "montage(faceDatabase(1).ImageLocation);" as "Improper index matrix reference". I am attaching the code in this comment. Please help me with my error in 67th line.

if true
  % clc;
clear all;
close all;
% Load Image Information from ATT Face Database Directory
% Start with a folder and get a list of all subfolders.
% Finds and prints names of all PNG, JPG, and TIF images in 
% that folder and all of its subfolders.
% Similar to imageSet() function in the Computer Vision System Toolbox
% Define a starting folder.
start_path = fullfile(matlabroot, '\toolbox\images\imdemos');

% Ask user to confirm or change. topLevelFolder = uigetdir(start_path); if topLevelFolder == 0 return; end

% Get list of all subfolders. allSubFolders = genpath(topLevelFolder);

% Parse into a cell array. remain = allSubFolders; listOfFolderNames = {}; while true [singleSubFolder, remain] = strtok(remain, ';'); if isempty(singleSubFolder) break; end listOfFolderNames = [listOfFolderNames singleSubFolder]; end

numberOfFolders = length(listOfFolderNames)

% Process all image files in those folders. for k = 1 : numberOfFolders % Get this folder and print it out. thisFolder = listOfFolderNames{k}; fprintf('Processing folder %s\n', thisFolder); % Get Pgm files. filePattern = sprintf('%s/*.pgm', thisFolder); baseFileNames = dir(filePattern); numberOfImageFiles = length(baseFileNames); % Now we have a list of all files in this folder. if numberOfImageFiles >= 1 % Go through all those image files. for f = 1 : numberOfImageFiles fullFileName = fullfile(thisFolder, baseFileNames(f).name); faceDatabase =imread(fullFileName);

           imshow(faceDatabase);

fprintf(' Processing image file %s\n', fullFileName); end else fprintf(' Folder %s has no image files in it.\n', thisFolder); end end

%% Display Montage of First Face figure; montage(faceDatabase(1).ImageLocation); title('Images of Single Face');

%% Display Query Image and Database Side-Side personToQuery = 1; galleryImage = read(faceDatabase(personToQuery),1); figure; for i=1:size(faceDatabase,2) imageList(i) = faceDatabase(i).ImageLocation(5); end subplot(1,2,1); imshow(galleryImage); subplot(1,2,2); montage(imageList); diff = zeros(1,9);

%% Split Database into Training & Test Sets [training,test] = partition(faceDatabase,[0.8 0.2]);

%% Extract and display Histogram of Oriented Gradient Features for single face person = 5; [hogFeature, visualization]= ... extractHOGFeatures(read(training(person),1)); figure; subplot(2,1,1); imshow(read(training(person),1)); title('Input Face'); subplot(2,1,2); plot(visualization); title('HoG Feature');

%% Extract HOG Features for training set trainingFeatures = zeros(size(training,2)*training(1).Count,4680); featureCount = 1; for i=1:size(training,2) for j = 1:training(i).Count trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j)); trainingLabel{featureCount} = training(i).Description; featureCount = featureCount + 1; end personIndex{i} = training(i).Description; end

%% Create 40 class classifier using fitcecoc faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

%% Test Images from Test Set person = 1; queryImage = read(test(person),1); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures);

% Map back to training set to find identity booleanIndex = strcmp(personLabel, personIndex); integerIndex = find(booleanIndex); subplot(1,2,1); imshow(queryImage); title('Query Face'); subplot(1,2,2); imshow(read(training(integerIndex),1)); title('Matched Class');

%% Test First 5 People from Test Set figure; figureNum = 1; for person=1:5 for j = 1:test(person).Count queryImage = read(test(person),j); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures);

        % Map back to training set to find identity
        booleanIndex = strcmp(personLabel, personIndex);
        integerIndex = find(booleanIndex);
        subplot(2,2,figureNum);
        imshow(imresize(queryImage,3));
        title('Query Face');
        subplot(2,2,figureNum+1);
        imshow(imresize(read(training(integerIndex),1),3));
        title('Matched Class');
        figureNum = figureNum+2;

    end
    figure;
    figureNum = 1;

end

end

Answer 2

[ankit sharma]

The code provided by you was helpful. I was able to load the images from different subfolders. Now I want to apply the face recognition algorithm on these images. But it is showing error in my next line i.e. "montage(faceDatabase(1).ImageLocation);" as "Improper index matrix reference". I am attaching the code in this comment. Please help me with my error in 67th line.

if true
  % clc;
clear all;
close all;
% Load Image Information from ATT Face Database Directory
% Start with a folder and get a list of all subfolders.
% Finds and prints names of all PNG, JPG, and TIF images in 
% that folder and all of its subfolders.
% Similar to imageSet() function in the Computer Vision System Toolbox
% Define a starting folder.
start_path = fullfile(matlabroot, '\toolbox\images\imdemos');

% Ask user to confirm or change. topLevelFolder = uigetdir(start_path); if topLevelFolder == 0 return; end

% Get list of all subfolders. allSubFolders = genpath(topLevelFolder);

% Parse into a cell array. remain = allSubFolders; listOfFolderNames = {}; while true [singleSubFolder, remain] = strtok(remain, ';'); if isempty(singleSubFolder) break; end listOfFolderNames = [listOfFolderNames singleSubFolder]; end

numberOfFolders = length(listOfFolderNames)

% Process all image files in those folders. for k = 1 : numberOfFolders % Get this folder and print it out. thisFolder = listOfFolderNames{k}; fprintf('Processing folder %s\n', thisFolder); % Get Pgm files. filePattern = sprintf('%s/*.pgm', thisFolder); baseFileNames = dir(filePattern); numberOfImageFiles = length(baseFileNames); % Now we have a list of all files in this folder. if numberOfImageFiles >= 1 % Go through all those image files. for f = 1 : numberOfImageFiles fullFileName = fullfile(thisFolder, baseFileNames(f).name); faceDatabase =imread(fullFileName);

           imshow(faceDatabase);

fprintf(' Processing image file %s\n', fullFileName); end else fprintf(' Folder %s has no image files in it.\n', thisFolder); end end

%% Display Montage of First Face figure; montage(faceDatabase(1).ImageLocation); title('Images of Single Face');

%% Display Query Image and Database Side-Side personToQuery = 1; galleryImage = read(faceDatabase(personToQuery),1); figure; for i=1:size(faceDatabase,2) imageList(i) = faceDatabase(i).ImageLocation(5); end subplot(1,2,1); imshow(galleryImage); subplot(1,2,2); montage(imageList); diff = zeros(1,9);

%% Split Database into Training & Test Sets [training,test] = partition(faceDatabase,[0.8 0.2]);

%% Extract and display Histogram of Oriented Gradient Features for single face person = 5; [hogFeature, visualization]= ... extractHOGFeatures(read(training(person),1)); figure; subplot(2,1,1); imshow(read(training(person),1)); title('Input Face'); subplot(2,1,2); plot(visualization); title('HoG Feature');

%% Extract HOG Features for training set trainingFeatures = zeros(size(training,2)*training(1).Count,4680); featureCount = 1; for i=1:size(training,2) for j = 1:training(i).Count trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j)); trainingLabel{featureCount} = training(i).Description; featureCount = featureCount + 1; end personIndex{i} = training(i).Description; end

%% Create 40 class classifier using fitcecoc faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

%% Test Images from Test Set person = 1; queryImage = read(test(person),1); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures);

% Map back to training set to find identity booleanIndex = strcmp(personLabel, personIndex); integerIndex = find(booleanIndex); subplot(1,2,1); imshow(queryImage); title('Query Face'); subplot(1,2,2); imshow(read(training(integerIndex),1)); title('Matched Class');

%% Test First 5 People from Test Set figure; figureNum = 1; for person=1:5 for j = 1:test(person).Count queryImage = read(test(person),j); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures);

        % Map back to training set to find identity
        booleanIndex = strcmp(personLabel, personIndex);
        integerIndex = find(booleanIndex);
        subplot(2,2,figureNum);
        imshow(imresize(queryImage,3));
        title('Query Face');
        subplot(2,2,figureNum+1);
        imshow(imresize(read(training(integerIndex),1),3));
        title('Matched Class');
        figureNum = figureNum+2;

    end
    figure;
    figureNum = 1;

end

end

Answer 3

[Sajja Tulasi Krishna]

I have folder name data and under this folder 40 folders (named A1 to A40) and each one have 10 gray images so total 400 images. I would like to compare each image with other 399 images .I have comparision code.I tried but could not come up with an efficient code. So, could someone please suggest me an idea or code at least how to read those images.