Try this:
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 22;
%--------------------------------------------------------------------------------------------------------
% READ IN IMAGE
folder = pwd;
baseFileName = 'black dot.jpg';
grayImage = imread(baseFileName);
% 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(grayImage);
% 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.
end
%--------------------------------------------------------------------------------------------------------
% Display the image with the original, linear colormap.
subplot(2, 2, 1);
imshow(grayImage);
axis('on', 'image');
title('Original Color Map', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
colorbar;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
set(gca,'ColorScale','log')
%--------------------------------------------------------------------------------------------------------
% Show the histogram:
subplot(2, 2, 2);
imhist(grayImage);
grid on;
title('Histogram', 'FontSize', fontSize, 'Interpreter', 'None');
%--------------------------------------------------------------------------------------------------------
% BINARIZE THE IMAGE
threshold = 180;
xline(threshold, 'Color', 'r', 'LineWidth', 2);
mask = grayImage <= 180;
%--------------------------------------------------------------------------------------------------------
% Display the mask.
subplot(2, 2, 3);
imshow(mask);
axis('on', 'image');
title('Mask', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
drawnow;
% Get the surrounding background area.
background = bwareafilt(mask, 1);
% Erase it from the mask.
mask(background) = false;
%--------------------------------------------------------------------------------------------------------
% Display the mask.
% Label each blob with 8-connectivity, so we can make measurements of it
[labeledImage, numberOfBlobs] = bwlabel(mask, 8);
% Apply a variety of pseudo-colors to the regions.
coloredLabelsImage = label2rgb (labeledImage, 'hsv', 'k', 'shuffle');
% Display the pseudo-colored image.
subplot(2, 2, 4);
imshow(coloredLabelsImage);
axis('on', 'image');
caption = sprintf('%d distinct blobs', numberOfBlobs);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
drawnow;
%--------------------------------------------------------------------------------------------------------
% Measure area and locations
props = regionprops(mask, 'Area', 'Centroid');
allAreas = [props.Area]
xy = vertcat(props.Centroid) % get (x,y) of each centroid.
You can see it found 13 dark defects with that threshold. You can filter out some blobs you don't want based on their properties, like location, intensity, shape, or whatever.
