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How to provide input to BagOfFeatures() function in MATLAB in the form of imageSet or imageDataStore?


I want to use bagOfFeatures() function of MATLAB. But it requires input in the form of imageSet or imageDataStore. The code I want to run is given below:

Dataset = 'D:\dsktop\kinect_leap_dataset\acquisitions';
thresh1 = 0;
thresh2 = 20;

k = dir(fullfile(Dataset,'\P*\G*\*_depth.png')); 
kf = {k(~[k.isdir]).folder};
kn = {k(~[k.isdir]).name};

for j=1:length(k)
% Applying thresholding to the original image
    full_name = horzcat(kf{j},filesep,kn{j});
    image = imread(full_name);
    image_bin1 = (image < thresh2);
    image_bin2 = (thresh1 < image);
    image_bin = abs(image_bin2- image_bin1);
    sequence{i} = image_bin;
end
% Bag of Features
bag = bagOfFeatures(sequence);

But the "sequence" is a cell class and so bagOfFeatures() is giving me errors. So I tried this:

Dataset = 'D:\dsktop\kinect_leap_dataset\acquisitions';
imgFolder = fullfile(Dataset);
imgSets = imageSet(imgFolder, 'recursive');
imgSets.Description

But now the problem is how to do the processing (thresholding) on images saved in imgSets. Also, after processing how to save all the "image_bin" images in imageSet class so that I can give them as input to the BagOfFeatures() function.


Solution

  • I solved this problem myself. To give input as an imageSet or imageStrore to BagOfFeatures(), we have to store all the "image_bin" images in a folder in PC. For that, we have to create that folder in the desired location as

    mkdir D:\.............\cropped
    

    Then, we have to save the "image_bin" in that folder in a loop as

    file_name = sprintf('D:/............/cropped/image_bin_%d.png',j);
    imwrite(image_bin, file_name);
    

    Then, the data from above folder is read in MATLAB memory as

    imds = imageDatastore('D:/.............../cropped', 'Labels', label);
    % label is an array of labels made by the user 
    [trainingSet, validationSet] = splitEachLabel(imds, 0.3, 'randomize');
    
    % Bag of Features
    bag = bagOfFeatures(trainingSet);
    

    And It's done.