Hi, I have written the following matlab code to do the following:- load rgb image of surface contrast stretch convert rgb to gray scale image segmentation morphological operations (thin, clean , fill, etc...) imtool for pixel length determination Calculation of crack length based on calibration of image and above determined pixel lenght. My aim is to develop the SIMPLEST matlab code for automatic detection of cracks and estimate the length of the crack (if possible other geometrical properties) from a sample image. The code is shown below: %%load image I=imread('two.jpg'); figure,imshow(I) title('Original image') %%Image adjust Istrech = imadjust(I,stretchlim(I)); figure,imshow(Istrech) title('Contrast stretched image') %%Convert RGB image to gray Igray_s = rgb2gray(Istrech); figure,imshow(Igray_s,[]) title('RGB to gray (contrast stretched) ') %%Image segmentation by thresholding %use incremental value to run this selection till required threshold 'level' is %achieved level = 0.08; Ithres = im2bw(Igray_h,level); figure,imshow(Ithres) title('Segmented cracks') %%Image morphological operation BW = bwmorph(gradmag,'clean',10); figure,imshow(BW) title('Cleaned image') BW = bwmorph(gradmag,'thin', inf); figure,imshow(BW) title('Thinned image') BW = imfill(gradmag, 'holes') figure,imshow(BW) title('Filled image') %%Image tool figure,imtool(BW1) figure,imtool(I) %%Calaculate crack length calibration_length=0.001; calibration_pixels=1000; crack_pixel=35; crack_length=(crack_pixel *calibration_length)/calibration_pixels; Please, I need help from image specialist to improve the code from above to meet my aim. I have also attached a sample picture that I am using for this code.
Prashant Kumar answered .
2025-11-20
You're just arbitrarily setting
crack_pixel=35;
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
imtool close all; % Close all imtool figures if you have the Image Processing Toolbox.
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 18;
% 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
% Read in a standard MATLAB gray scale demo image.
button = menu('Use which demo image?', 'MRI', 'Moon', 'Tire', 'Spine', 'Saturn');
if button == 1
baseFileName = 'mri.tif';
elseif button == 2
baseFileName = 'moon.tif';
elseif button == 3
baseFileName = 'tire.tif';
elseif button == 4
baseFileName = 'spine.tif';
else
baseFileName = 'saturn.webp';
end
% Read in a standard MATLAB gray scale demo image.
folder = fileparts(which('cameraman.tif')); % Get demos folder.
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
% File doesn't exist -- didn't find it there. Check the search path for it.
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
originalImage = imread(fullFileName);
% Display the original gray scale image.
hFig = figure;
subplot(2, 2, 1);
imshow(originalImage, []);
axis on;
title('Original Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
% Get the dimensions of the image.
% numberOfColorBands should be = 1.
[rows, columns, numberOfColorBands] = size(originalImage);
if numberOfColorBands > 1
% It's not really gray scale like we expected - it's color.
% Convert it to gray scale by taking only the green channel.
grayImage = originalImage(:, :, 2); % Take green channel.
else
% It's already grayscale.
grayImage = originalImage;
end
% Binarize the image
level = graythresh(grayImage);
binaryImage = im2bw(grayImage, level);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
axis on;
title('Initial Binary Image', 'FontSize', fontSize);
% Fill holes
binaryImage = imfill(binaryImage, 'holes');
% Get rid of anything less than 10% of the image
binaryImage = bwareaopen(binaryImage, round(0.1*numel(binaryImage)));
% Display the image.
subplot(2, 2, 4);
imshow(binaryImage, []);
axis on;
hold on;
caption = sprintf('Filled, Cleaned Binary Image with\nBoundaries and Feret Diameters');
title(caption, 'FontSize', fontSize);
% Copy the gray scale image to the lower left.
subplot(2, 2, 3);
imshow(originalImage, []);
caption = sprintf('Original Image with\nBoundaries and Feret Diameters');
title(caption, 'FontSize', fontSize);
axis on;
hold on;
% Label the image so we can get the average perpendicular width.
labeledImage = bwlabel(binaryImage);
% Measure the area
measurements = regionprops(labeledImage, 'Area');
% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
% Plot the borders of all the coins on the original grayscale image using the coordinates returned by bwboundaries.
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for blobIndex = 1 : numberOfBoundaries
thisBoundary = boundaries{blobIndex};
x = thisBoundary(:, 2); % x = columns.
y = thisBoundary(:, 1); % y = rows.
% Find which two bounary points are farthest from each other.
maxDistance = -inf;
for k = 1 : length(x)
distances = sqrt( (x(k) - x) .^ 2 + (y(k) - y) .^ 2 );
[thisMaxDistance, indexOfMaxDistance] = max(distances);
if thisMaxDistance > maxDistance
maxDistance = thisMaxDistance;
index1 = k;
index2 = indexOfMaxDistance;
end
end
% Find the midpoint of the line.
xMidPoint = mean([x(index1), x(index2)]);
yMidPoint = mean([y(index1), y(index2)]);
longSlope = (y(index1) - y(index2)) / (x(index1) - x(index2))
perpendicularSlope = -1/longSlope
% Use point slope formula (y-ym) = slope * (x - xm) to get points
y1 = perpendicularSlope * (1 - xMidPoint) + yMidPoint;
y2 = perpendicularSlope * (columns - xMidPoint) + yMidPoint;
% Get the profile perpendicular to the midpoint so we can find out when if first enters and last leaves the object.
[cx,cy,c] = improfile(binaryImage,[1, columns], [y1, y2], 1000);
% Get rid of NAN's that occur when the line's endpoints go above or below the image.
c(isnan(c)) = 0;
firstIndex = find(c, 1, 'first');
lastIndex = find(c, 1, 'last');
% Compute the distance of that perpendicular width.
perpendicularWidth = sqrt( (cx(firstIndex) - cx(lastIndex)) .^ 2 + (cy(firstIndex) - cy(lastIndex)) .^ 2 );
% Get the average perpendicular width. This will approximately be the area divided by the longest length.
averageWidth = measurements(blobIndex).Area / maxDistance;
% Plot the boundaries, line, and midpoints over the two images.
% Plot the boundary over the gray scale image
subplot(2, 2, 3);
plot(x, y, 'y-', 'LineWidth', 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], 'Color', 'r', 'LineWidth', 3);
plot(xMidPoint, yMidPoint, 'r*', 'MarkerSize', 15, 'LineWidth', 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], 'Color', 'g', 'LineWidth', 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], 'Color', 'm', 'LineWidth', 3);
% Plot the boundary over the binary image
subplot(2, 2, 4);
plot(x, y, 'y-', 'LineWidth', 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], 'Color', 'r', 'LineWidth', 3);
plot(xMidPoint, yMidPoint, 'r*', 'MarkerSize', 15, 'LineWidth', 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], 'Color', 'g', 'LineWidth', 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], 'Color', 'm', 'LineWidth', 3);
message = sprintf('The longest line is red.\nPerpendicular to that, at the midpoint, is green.\nMax distance for blob #%d = %.2f\nPerpendicular distance at midpoint = %.2f\nAverage perpendicular width = %.2f (approximately\nArea = %d', ...
blobIndex, maxDistance, perpendicularWidth, averageWidth, measurements(blobIndex).Area);
fprintf('%s\n', message);
uiwait(helpdlg(message));
end
hold off;
close(hFig);