Wall crack detection using image processing

I am working on a project trying to identify cracks in concrete walls using a preloaded pic. I’m looking for some help on how to identify the cracks and evaluate their size and depth. If anyone has some suggestions or a similar code, it would be appreciated.

thanks

Answer 1

[Sushmita Chinnu]

clearvars;

close all;

clc;

fontSize = 15;

% Read in original image, with white lightning on black background.

baseFileName = 'mean_width_of_blob.png';

fullFileName = fullfile(pwd, baseFileName);

grayImage = imread(fullFileName);

% Get the dimensions of the image.

% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.

[rows, columns, numberOfColorChannels] = size(grayImage)

if numberOfColorChannels > 1

% It's not really gray scale like we expected - it's color.

% Use weighted sum of ALL channels to create a gray scale image.

% grayImage = rgb2gray(rgbImage);

% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,

% which in a typical snapshot will be the least noisy channel.

grayImage = grayImage(:, :, 2); % Take green channel.

else

grayImage = grayImage; % It's already gray scale.

end

% Now it's gray scale with range of 0 to 255.

subplot(2, 3, 1);

imshow(grayImage, [])

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Original Image', 'FontSize', fontSize);

% Binarize the image.

mask = imbinarize(grayImage);

% Fill holes.

mask = imfill(mask, 'holes');

% Take largest blob only.

mask = bwareafilt(mask, 1);

subplot(2, 3, 2);

imshow(mask)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Mask', 'FontSize', fontSize)

% Compute the skeleton

skelImage = bwskel(mask);

subplot(2, 3, 3);

imshow(skelImage)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Thinned', 'FontSize', fontSize)

% Enlarge figure to full screen.

g = gcf;

g.WindowState = 'maximized';

% Compute the Euclidean distance image.

edtImage = bwdist(~mask);

subplot(2, 3, 4);

imshow(edtImage, [])

title('Distance Transform Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Multiply them to get an image where the only pixels in the image

% are along the skeleton and their value is the radius.

% Multiply radius image by 2 to get diameter image.

diameterImage = 2 * edtImage .* single(skelImage);

subplot(2, 3, 5);

imshow(diameterImage, [])

title('Diameter Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Get the widths. These will be where the image is not zero.

widths = diameterImage(diameterImage > 0);

% Show histogram of widths.

subplot(2, 3, 6);

histogram(widths);

grid on;

xlabel('Width in Pixels', 'FontSize', fontSize);

ylabel('Count', 'FontSize', fontSize);

% Compute the mean width

meanWidth = mean(widths)

% Put a line on the histogram at the mean width

xline(meanWidth, 'LineWidth', 2, 'Color', 'r');

caption = sprintf('Histogram of Widths. Mean Width = %.1f Pixels', meanWidth);

title(caption, 'FontSize', fontSize);

message = sprintf('Mean Width = %.1f Pixels', meanWidth);

msgbox(message);