Subscript indices must either be real positive integers or logicals.

Hi there,

I am debugging a code in matlab, but sometime it starts to give me the error "Subscript indices must either be real positive integers or logicals." and it goes on untill I restart my matlab.

here is my code,

close all
clc
image = rgb2gray(imread('pepper.jpg'));
Mean_added = 0;
Variance_added=400;
matrix_uniform = Mean_added+sqrt(Variance_added).*randn(size(image));
noisy_image = image+uint8(matrix_uniform);
denoised_wiener = wiener2(noisy_image,[7 7]);
% K = imrest(noisy_image,'atrimmed',3,3);
diff_wiener = noisy_image - denoised_wiener;
% 
% figure, subplot(1,3,1),imshow(uint8(noisy_image)), subplot(1,3,2), imshow(K), subplot(1,3,3), imhist(uint8(noisy_image))
figure, subplot(1,3,1),imshow(uint8(noisy_image)), subplot(1,3,2), imshow(denoised_wiener), subplot(1,3,3), imhist(uint8(noisy_image))
figure,imshow(diff_wiener)
kurt_Wiener = mean(kurtosis(matrix_uniform))
kurt_Wiener = mean(kurtosis(matrix_uniform(:)))
skew_Wiener = skewness(matrix_uniform(:))
[pixelCounts GLs] = imhist(matrix_uniform);
[skew, kurtosis] = GetSkewAndKurtosis(GLs, pixelCounts)

And the error line which is mentioned is

Error in practice (line 16)
kurt_Wiener = mean(kurtosis(matrix_uniform))

[Wayne King]

Can you please format your code. I was going to do it, but you have a number of comments in there and I'm not sure what you want to leave commented out.

[Iain]

Try ensuring that "kutosis" is cleared at the start of the m-script.

Answer 1

[Image Analyst]

In case you're interested, here's my demo for computing skew and kurtosis:

% Get the mean gray level, standard deviation, skew, and kurtosis from the histogram bin values.
function ComputeImageMomentsDemo()
  try
    clc;    % Clear the command window.
    close all;  % Close all figures (except those of imtool.)
    imtool close all;  % Close all imtool figures.
    workspace;  % Make sure the workspace panel is showing.
    fontSize = 20;

    % Change the current folder to the folder of this m-file.
    if(~isdeployed)
      cd(fileparts(which(mfilename)));
    end

    % Check that user has the Image Processing Toolbox installed.
    hasIPT = license('test', 'image_toolbox');
    if ~hasIPT
      % User does not have the toolbox installed.
      message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
      reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
      if strcmpi(reply, 'No')
        % User said No, so exit.
        return;
      end
    end

    folder = fullfile(matlabroot, '\toolbox\images\imdemos');
    if ~isdir(folder)
      errorMessage = sprintf('Error: The following folder does not exist:\n%s', folder);
      uiwait(warndlg(errorMessage));
      return;
    end
    
    % Check for bmp, tif, jpg, and png images.
    filePattern = fullfile(folder, '*.tif');
    imageFiles1 = dir(filePattern);
    filePattern = fullfile(folder, '*.png');
    imageFiles2 = dir(filePattern);
    filePattern = fullfile(folder, '*.bmp');
    imageFiles3 = dir(filePattern);
    filePattern = fullfile(folder, '*.jpg');
    imageFiles4 = dir(filePattern);
    imageFiles = [imageFiles1; imageFiles2; imageFiles3; imageFiles4];
    numberOfImageFiles = length(imageFiles);
    

    % Bail out if there are no images in the folder.
    if numberOfImageFiles == 0
      errorMessage = sprintf('Error: The following folder does not contain any tif images:\n%s', folder);
      uiwait(warndlg(errorMessage));
      return;
    end
    % Preallocate space for a moments matrix.
    moments = zeros(numberOfImageFiles, 4);
    for k = 1 : numberOfImageFiles
      % Get the baseFileName and extension.
      baseFileName = imageFiles(k).name;
      % Get the extension separately.
      [f b extension] = fileparts(baseFileName);
      % Get rid fo the dot and convert to upper case.
      extension = upper(extension(2:end));
      % Get the full filename, with path prepended.
      fullFileName = fullfile(folder, baseFileName);
      fprintf(1, 'Now reading %s\n', fullFileName);
      if ~exist(fullFileName, 'file')
        % Didn't find it there.  Check the search path for it.
        fullFileName = baseFileName; % No path this time.
        if ~exist(fullFileName, 'file')
          % Still didn't find it.  Alert user.
          errorMessage = sprintf('Error: %s does not exist.', fullFileName);
          uiwait(warndlg(errorMessage));
          return;
        end
      end
      grayImage = imread(fullFileName);
      % Get the dimensions of the image.  numberOfColorBands should be = 1.
      [rows columns numberOfColorBands] = size(grayImage);
      
      % Let's compute moments only for gray scale images.  
      % If we read in a color image, convert to grayscale by taking the green channel.
      if numberOfColorBands >= 2
        grayImage = grayImage(:,:,2);
      end
      % Display the original gray scale image.
      subplot(2, 2, 3);
      imshow(grayImage, []);
      axis on;  % Show pixel coordinates along the axis (edges of the image).
      caption = sprintf('Gray Scale %s-Format Image', extension);
      title(caption, 'FontSize', fontSize);
      
      if k == 1
        % Enlarge figure to full screen.
        set(gcf, 'units','normalized','outerposition',[0 0 1 1]); % Maximize figure.
        % Give a name to the title bar.
        set(gcf,'name','Image Moments Demo','numbertitle','off') 
      end

      % Let's compute and display the histogram.
      [pixelCounts grayLevels] = imhist(grayImage);
      subplot(2, 2, 4); 
      bar(pixelCounts);
      grid on;
      title('Histogram of Gray Scale Image', 'FontSize', fontSize);
      xlim([0 grayLevels(end)]); % Scale x axis manually.

      % Compute the image moments.
      [meanGL stdDev skew kurtosis] = ComputeImageMoments(grayLevels, pixelCounts);
      
      % Plot the image moments for all images up through this one.
      moments(k, 1) = meanGL;
      moments(k, 2) = stdDev;
      moments(k, 3) = skew;
      moments(k, 4) = kurtosis;
      subplot(2,1,1);
      plot(moments, 'LineWidth', 3);
      title('Intensity Moments of Gray Scale Image', 'FontSize', fontSize);
      grid on;
      legend('Mean', 'Standard Deviation', 'Skewness', 'Kurtosis');
      
      message = sprintf('There are %d pixels in %s (#%d of %d).\nThe mean gray level is %.2f.\nThe standard deviation of the gray levels is %.2f.\nThe skewness of the gray levels is %.2f.\nThe kurtosis of the gray levels is %.2f.',...
        sum(pixelCounts), baseFileName, k, numberOfImageFiles, meanGL, stdDev, skew, kurtosis);
      if k < numberOfImageFiles
        promptMessage = sprintf('%s\n\nDo you want to Continue processing,\nor Cancel to abort processing?', message);
        button = questdlg(promptMessage, 'Continue', 'Continue', 'Cancel', 'Continue');
        if strcmp(button, 'Cancel')
          break;
        end
      else
        promptMessage = sprintf('%s\n\nDone with demo!', message);
        msgbox(promptMessage);
      end
    end    % Read in a standard MATLAB gray scale demo image.
  catch ME
    errorMessage = sprintf('Error in ComputeImageMoments().\nThe error reported by MATLAB is:\n\n%s', ME.message);
    fprintf(1, '%s\n', errorMessage);
    uiwait(warndlg(errorMessage));
  end
  return; % from ComputeImageMomentsDemo

%------------------------------------------------------------------------------------------------------
% Computes first, second, third, and fourth central moments of the gray levels.
% Get the mean gray level, standard deviation, skew, and kurtosis from the histogram bin values.
% Note: gray level moments are different than spatial moments which are more like rotational moments of intertia.
% Uses formulas from http://itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
% "Skewness is a measure of symmetry, or more precisely, the lack of symmetry. 
% A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. 
% The skewness for a normal distribution is zero, and any symmetric data should have a skewness near zero. 
% Negative values for the skewness indicate data that are skewed left and positive values for the skewness 
% indicate data that are skewed right. By skewed left, we mean that the left tail is long relative to the right tail.
% 
% Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. 
% That is, data sets with high kurtosis tend to have a distinct peak near the mean, 
% decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend 
% to have a flat top near the mean rather than a sharp peak. A uniform distribution would be the extreme case."
function [meanGL stdDev skew kurtosis] = ComputeImageMoments(GLs, pixelCounts)
  try
    % Get the number of pixels in the histogram.
    numberOfPixels = sum(pixelCounts);
    % Get the mean gray lavel.
    meanGL = sum(GLs .* pixelCounts) / numberOfPixels;
    % Get the variance, which is the second central moment.
    varianceGL = sum((GLs - meanGL) .^ 2 .* pixelCounts) / (numberOfPixels-1);
    % Get the standard deviation.
    stdDev = sqrt(varianceGL);
    % Get the skew.
    skew = sum((GLs - meanGL) .^ 3 .* pixelCounts) / ((numberOfPixels - 1) * stdDev^3);
    % Get the kurtosis.
    kurtosis = sum((GLs - meanGL) .^ 4 .* pixelCounts) / ((numberOfPixels - 1) * stdDev^4);
  catch ME
    errorMessage = sprintf('Error in ComputeImageMoments().\nThe error reported by MATLAB is:\n\n%s', ME.message);
    uiwait(warndlg(errorMessage));
    set(handles.txtInfo, 'String', errorMessage);
  end
  return; % from ComputeImageMoments

Answer 2

[Walter Roberson]

You are using kurtosis both as a function call and a variable. As Ian indicates in his comment, that is going to leave kurtosis as a variable for the next run, giving you problems. It is advised that you do not use function names as variables.