Creating an imdb structure

Question

Sivaramakrishnan Rajaraman 2017-3-6

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https://ww2.mathworks.cn/matlabcentral/answers/328403-creating-an-imdb-structure

编辑： Walter Roberson 2018-3-25

Hi,

I have written a new createIMDB function following an online tutorial on Deep Learning by German Ros. The original imdb was written for a binary classification problem. I attempted to change it for a three class problem. Each of my class 'cat', 'dog' and 'rat' contain say, three images, for simplicity. My function goes like this:

function imdb = createIMDB_RAM1(folder)
% imdb is a matlab struct with several fields, such as:
 % - images: contains data, labels, ids dataset mean, etc.
 % - meta: contains meta info useful for statistics and visualization
 % - any other you want to add
imdb = struct();
% let's assume we have a folder with three
% subfolders "cat" "dog" and "rat" containing images
% for a multi-class problem
positives = dir([folder '/cat/*.jpg']);
negatives = dir([folder '/dog/*.jpg']);
neutral = dir([folder '/rat/*.jpg']);
imref = imread([folder '/cat/', positives(1).name]);
[H, W, CH] = size(imref);
% number of images
NPos = numel(positives);
NNeg = numel(negatives);
NNeu = numel(neutral);
N = NPos + NNeg + NNeu;
% we can initialize part of the structures already
meta.sets = {'train', 'val'};
meta.classes = {'cat', 'dog', 'rat'};
% images go here
images.data = zeros(H, W, CH, N, 'single');
% this will contain the mean of the training set
images.data_mean = zeros(H, W, CH, 'single');
% a label per image
images.labels = zeros(1, N);
% vector indicating to which set an image belong, i.e.,
% training, validation, etc.
images.set = zeros(1, N);
numImgsTrain = 0;
% loading positive samples
for i=1:numel(positives)
  
im = single(imread([folder '/cat/', positives(i).name]));
  
images.data(:,:,:, i) = im;
  
images.labels(i) = 2;
% in this case we select the set (train/val) randomly
  
if(randi(10, 1) > 6) % 60% for training and 40% for validation
    
images.set(i) = 1;
  
  
images.data_mean = images.data_mean + im;
  
  
numImgsTrain = numImgsTrain + 1;
  
else
  
  
images.set(i) = 2;
  
end
end
% loading negative samples
for i=1:numel(negatives)
  
im = single(imread([folder '/dog/', negatives(i).name]));
  
images.data(:,:,:, NPos+NNeu+i) = im;
  
images.labels(NPos+NNeu+i) = 1;
% in this case we select the set (train/val) randomly
  
if(randi(10, 1) > 6)
  
  
images.set(NPos+NNeu+i) = 1;
  
  
images.data_mean = images.data_mean + im;
    
numImgsTrain = numImgsTrain + 1;
  
else
    
images.set(NPos+NNeu+i) = 2;
  
end
end
% loading neutral samples
for i=1:numel(neutral)
  
im = single(imread([folder '/rat/', neutral(i).name]));
  
images.data(:,:,:, NPos+NNeg+i) = im;
  
images.labels(NPos+NNeu+i) = 3;
% in this case we select the set (train/val) randomly
  
if(randi(10, 1) > 6)
    
images.set(NPos+NNeg+i) = 1;
    
images.data_mean = images.data_mean + im;
    
numImgsTrain = numImgsTrain + 1;
  
else
    
images.set(NPos+NNeu+i) = 2;
  
end
end
% let's finish to compute the mean
images.data_mean = images.data_mean ./ numImgsTrain;
% now let's add some randomization 
indices = randperm(N);
images.data(:,:,:,:) = images.data(:,:,:,indices);
images.labels(:) = images.labels(indices);
images.set(:) = images.set(indices);
imdb.meta = meta;
imdb.images = images;
end

I'm trying to give labels '1', '2', '3' for 'cat', 'dog' and 'rat' respectively. When i run the function, all the other meta-parameters including the number of sets (train, val), labels ('cat', 'dog', 'rat') are declared properly however the 'labels' and 'sets' are improperly assigned to the images. I'm pretty sure I am committing mistake in assigning the labels and sets inside the 'for' loop.

This is an issue with creating a structure and I'm sure the experts out here can solve my problem in no time. Kindly help rectifying the same with the modified peice of code. Thanks for your assistance.

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Sivaramakrishnan Rajaraman 2017-3-6

编辑：Walter Roberson 2017-3-6

在 MATLAB Online 中打开

Hi Roberson,

Here is the code after the smart indentation. Kindly help with the issue.

    function imdb = createIMDB_RAM1(folder)
    % imdb is a matlab struct with several fields, such as:
    %  - images: contains data, labels, ids dataset mean, etc.
    %  - meta: contains meta info useful for statistics and visualization
    %  - any other you want to add
    imdb = struct();
    % let's assume we have a folder with two
    % subfolders "cat" "nocat" containing images
    % for a binary problem
    positives = dir([folder '/cat/*.jpg']);
    negatives = dir([folder '/dog/*.jpg']);
    neutral = dir([folder '/rat/*.jpg']);
    imref = imread([folder '/cat/', positives(1).name]);
    [H, W, CH] = size(imref);
    % number of images
    NPos = numel(positives);
    NNeg = numel(negatives);
    NNeu = numel(neutral);
    N = NPos + NNeg + NNeu;
    % we can initialize part of the structures already
    meta.sets = {'train', 'val'};
    meta.classes = {'cat', 'dog', 'rat'};
    % images go here
    images.data = zeros(H, W, CH, N, 'single');
    % this will contain the mean of the training set
    images.data_mean = zeros(H, W, CH, 'single');
    % a label per image
    images.labels = zeros(1, N);
    % vector indicating to which set an image belong, i.e.,
    % training, validation, etc.
    images.set = zeros(1, N);
    numImgsTrain = 0;
    % loading positive samples
    for i=1:numel(positives)
        im = single(imread([folder '/cat/', positives(i).name]));
        images.data(:,:,:, i) = im;
        images.labels(i) = 2;
        % in this case we select the set (train/val) randomly
        if(randi(10, 1) > 6)
            images.set(i) = 1;
            images.data_mean = images.data_mean + im;
            numImgsTrain = numImgsTrain + 1;
        else
            images.set(i) = 2;
        end
    end
    % loading negative samples
    for i=1:numel(negatives)
        im = single(imread([folder '/dog/', negatives(i).name]));
        images.data(:,:,:, NPos+i) = im;
        images.labels(NPos+i) = 1;
        % in this case we select the set (train/val) randomly
        if(randi(10, 1) > 6)
            images.set(NPos+i) = 1;
            images.data_mean = images.data_mean + im;
            numImgsTrain = numImgsTrain + 1;
        else
            images.set(NPos+i) = 2;
        end
    end
    % loading neutral samples
    for i=1:numel(neutral)
        im = single(imread([folder '/rat/', neutral(i).name]));
        images.data(:,:,:, NPos+NNeg+i) = im;
        images.labels(NPos+NNeg+i) = 3;
        % in this case we select the set (train/val) randomly
        if(randi(10, 1) > 6)
            images.set(NPos+NNeg+i) = 1;
            images.data_mean = images.data_mean + im;
            numImgsTrain = numImgsTrain + 1;
        else
            images.set(NPos+NNeg+i) = 2;
        end
    end
    % let's finish to compute the mean
    images.data_mean = images.data_mean ./ numImgsTrain;
    % now let's add some randomization
    indices = randperm(N);
    images.data(:,:,:,:) = images.data(:,:,:,indices);
    images.labels(:) = images.labels(indices);
    images.set(:) = images.set(indices);
    imdb.meta = meta;
    imdb.images = images;
    end

Greg Heath 2017-3-7

The classical approach to NN classification of non-overlapping classes is to use {0,1} unit vector targets.

Then the outputs can be interpreted as conditional posterior probabilities.

See any text on pattern recognition.

Hope this helps.

Greg

Sivaramakrishnan Rajaraman 2017-3-7

Hi Roberson, Many thanks for your assistance. As you mentioned, I found that the above code is working fine to create a structure of three classes with their respective labels.

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Creating an imdb structure

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