How to draw vertical and horizontal histogram of an image ?

I need to draw vertical and horizontal histogram of a gray scale image to find out the rectangular ROI of my image. Is it possible ?

Answer 1

[Image Analyst]

I wouldn't call them histograms but I think you are referring to the mean vertical or horizontal profile that you get by summing or averaging gray levels horizontally or vertically. To do that you'd do:

verticalProfile = sum(grayImage, 2);
horizontalProfile = sum(grayImage, 1);

or you can use mean() instead of sum().

[Sudipto Chaki]

Thanks for your answer. But, is it possible to extract Rectangular ROI based on vertical or horizontal profile?

[Sudipto Chaki]

Basically I need to extract the following ROI automatically.

Answer 2

[Image Analyst]

Try this:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;

%===============================================================================
% Get the name of the image the user wants to use.
baseFileName = '6.jpg';
folder = pwd;
fullFileName = fullfile(folder, baseFileName);

% Check if file exists.
if ~exist(fullFileName, 'file')
  % The file doesn't exist -- didn't find it there in that folder.
  % Check the entire search path (other folders) for the file by stripping off the folder.
  fullFileNameOnSearchPath = baseFileName; % No path this time.
  if ~exist(fullFileNameOnSearchPath, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end

%=======================================================================================
% Read in demo image.
rgbImage = imread(fullFileName);
% Get the dimensions of the image.
[rows, columns, numberOfColorChannels] = size(rgbImage)

% Display image.
subplot(2, 2, 1);
imshow(rgbImage, []);
impixelinfo;
axis on;
caption = sprintf('Original Color Image\n%s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0.05 1 0.95]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
drawnow;

[mask, maskedRGBImage] = createMask(rgbImage);
% Extract the largest blob only.  That will be the hand.
mask = bwareafilt(mask, 1);
% Get the convex hull to get rid of boundary noise (wiggles).
mask = bwconvhull(mask);

% Display the mask image.
subplot(2, 2, 2);
imshow(mask);
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;

% Mask the image using bsxfun() function to multiply the mask by each channel individually.
maskedRgbImage = bsxfun(@times, rgbImage, cast(mask, 'like', rgbImage));
% Crop the image
[maskRows, maskColumns] = find(mask);
row1 = min(maskRows);
row2 = max(maskRows);
col1 = min(maskColumns);
col2 = max(maskColumns);
% Plot the box
xBox = [col1, col2, col2, col1, col1];
yBox = [row1, row1, row2, row2, row1];
hold on;
plot(xBox, yBox, 'r-', 'LineWidth', 2);

% Crop the masked image.
maskedRgbImage = maskedRgbImage(row1:row2, col1:col2, :);
% Get the dimensions of the image.
[maskRows, maskColumns, numberOfColorChannels] = size(maskedRgbImage)

% Display the image.
subplot(2, 2, 3);
imshow(maskedRgbImage, []);
impixelinfo;
title('Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;

% Crop the numbers out.  From the circular mask, the numbers are between
% 17.7% and 25.9% of the height of the image, and between
% 26.6 and 40.2% of the width of the image
row1 = round(.244 * maskRows)
row2 = round(.357 * maskRows)
col1 = round(.212 * maskColumns)
col2 = round(.526 * maskColumns)
% Plot the box
xBox = [col1, col2, col2, col1, col1];
yBox = [row1, row1, row2, row2, row1];
hold on;
plot(xBox, yBox, 'r-', 'LineWidth', 2);

maskedRgbImage = maskedRgbImage(row1:row2, col1:col2, :);

% Display the image.
subplot(2, 2, 4);
imshow(maskedRgbImage, []);
impixelinfo;
title('Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis on;
drawnow;

fprintf('DONE!\n');
uiwait(helpdlg('Done!'));

function [BW,maskedRGBImage] = createMask(RGB)
%createMask  Threshold RGB image using auto-generated code from colorThresholder app.
%  [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
%  auto-generated code from the colorThresholder app. The colorspace and
%  range for each channel of the colorspace were set within the app. The
%  segmentation mask is returned in BW, and a composite of the mask and
%  original RGB images is returned in maskedRGBImage.

% Auto-generated by colorThresholder app on 06-Oct-2018
%------------------------------------------------------

% Convert RGB image to chosen color space
I = rgb2hsv(RGB);

% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.201;
channel1Max = 0.000;

% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.000;
channel2Max = 0.236;

% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.303;
channel3Max = 1.000;

% Create mask based on chosen histogram thresholds
sliderBW = ( (I(:,:,1) >= channel1Min) | (I(:,:,1) <= channel1Max) ) & ...
  (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
  (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;

% Initialize output masked image based on input image.
maskedRGBImage = RGB;

% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;

end

[Sudipto Chaki]

Thank you very much. But, the problem is that, this technique cannot be able to find out ROI of other images. For clarification, output image is attached. Would you please look into it?

[Sudipto Chaki]

Sample image

[Image Analyst]

If I get it working for this model of meter, then you'll just come up with another model that doesn't work. I can't keep tweaking the algorithm for you to handle more and more different models. I think you have the idea and you can take it over from here. Good luck.

[Sudipto Chaki]

Oh!, I am really sorry. one more question, is there any way to calculate the following ratios automatically? % Crop the numbers out. From the circular mask, the numbers are between % 17.7% and 25.9% of the height of the image, and between % 26.6 and 40.2% of the width of the image

[Image Analyst]

I did it by mousing over the image. If the location varies by meter model, then you might have to give a larger region, then threshold that to try to get the numbers inside. Also, you did not find the mask so if the intensity varies all over the place, you might try imfindcircles(). Or see the attached paper.

[Sudipto Chaki]

Thanks a lot :)

[Sudipto Chaki]

I used colorThresholder app, but it didn't show the channel value that you used in your createmask() function. How did you get the value? It would be nice if you tell me the process.

[Image Analyst]

It thresholds them all. If you don't want one color channel to be considered, then don't adjust the thresholds at all for that/those channels.

[Sudipto Chaki]

Ok, thanks. I need to convert the following digit image into (3*2) blocks. Then, I want to find out the horizontal and vertical edges in each block separately if exists. Is it possible and how?

[Image Analyst]

You can use imgradientxy().

[Sudipto Chaki]

How to convert the digit image into (3*2) blocks where I can check vertical and horizontal edges using imgradientxy()? Actually, I need to check if vertical or horizontal edges exists in each block? If exists I need to return binary value 1.