How can I have Matlab use deep learning to identify key items in an image?

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I am trying to have MATLAB use deep learning to automatically identify specific items within an image (namely, all the triangles located at the nodes). Can anyone give any input on how I would go about having MATLAB do this? Attached is:
  1. The raw input image
  2. A modified version of the input image that shows the objects of interest in the image (i.e., the triangles)
The x,y coordinates of the triangle vertices is what I ultimately need. Thanks in advance for your help!
1)
2)

Respuesta aceptada

Image Analyst
Image Analyst el 21 de Mayo de 2022
I'm sure if you had tried, you would have figured this out.
% Demo by Image Analyst
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;
markerSize = 40;
%--------------------------------------------------------------------------------------------------------
% READ IN IMAGE
folder = pwd;
baseFileName = 'triangles.png';
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
rgbImage = 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(rgbImage)
if numberOfColorChannels > 1
% It's not really gray scale like we expected - it's color.
fprintf('It is not really gray scale like we expected - it is color\n');
% Extract the blue channel.
grayImage = rgbImage(:, :, 3);
else
grayImage = rgbImage;
end
% Update 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)
%--------------------------------------------------------------------------------------------------------
% Display the image.
subplot(2, 2, 1);
imshow(grayImage);
impixelinfo;
axis('on', 'image');
title('Original Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Maximize window.
g = gcf;
g.WindowState = 'maximized';
drawnow;
% Display histogram.
subplot(2, 2, 2);
histogram(grayImage);
grid on;
drawnow;
title('Histogram of Image', 'FontSize', fontSize, 'Interpreter', 'None');
%--------------------------------------------------------------------------------------------------------
% Binarize the image to get a mask.
lowThreshold = 0;
highThreshold = 182;
% https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle
% threshold(lowThreshold, highThreshold, grayImage);
mask = grayImage >= lowThreshold & grayImage <= highThreshold;
% Put red threshold line on histogram so they know where it was thresholded at.
xline(highThreshold, 'Color', 'r', 'LineWidth', 2)
% Get rid of thin tendrils.
mask = imopen(mask, true(7));
% Take only the largest blobs.
mask = bwareafilt(mask, 2500);
% Display mask image.
subplot(2, 2, 3);
imshow(mask);
hold on;
impixelinfo;
axis('on', 'image');
drawnow;
title('Mask, a Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Label the two blobs so we can fit a line through wach one, one at a time.
[labeledImage, numBlobs] = bwlabel(mask);
% Get areas
props = regionprops(mask, 'Area', 'Centroid');
allAreas = [props.Area]
xy = vertcat(props.Centroid)
subplot(2, 2, 4);
histogram(allAreas, 10);
caption = sprintf('Size distribution of %d triangles', length(allAreas))
title(caption, 'FontSize',fontSize);
grid on;
xlabel('Area in Pixels', 'FontSize',fontSize)
ylabel('Count', 'FontSize',fontSize)
  9 comentarios
Image Analyst
Image Analyst el 23 de Mayo de 2022
You can use segnet but you'll have to have lots of example images. I am working on a general purpose segnet training app but it's not ready to be released yet. You might just use what I gave you and for each centroid crop out a little box and then try to better threshold the dark triangles. Then find the vertices using bwboundaries() and findpeaks() on the centroid to border distances.
Steven D
Steven D el 23 de Mayo de 2022
I'll definitely give it a shot. Thanks again for all your help!

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Más respuestas (1)

Image Analyst
Image Analyst el 19 de Mayo de 2022
You can probably get those with traditional methods like thresholding and blob analysis.
For a deep learning solution you can use SegNet.
  1 comentario
Steven D
Steven D el 21 de Mayo de 2022
Thanks for your comment. Would you happen to have any specific code examples that I could try?

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