Distance between the dots

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Viswajit Talluru 2024-8-27

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编辑： DGM 2024-9-17

I want to calculate the distance between the top and bottom dot.

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Viswajit Talluru 2024-8-27

This is not for a homework. We heat the samples and just want to know if the samples expand or not. Just some number from picture to picture. Samples about 40 mm.

DGM 2024-8-27

Without calibration information, the size of anything is ambiguous in absolute terms. I'm assuming that the camera is not fixed inside the oven, so if the object position, camera position or lens parameters change, then relative measurements are also ambiguous. What is the distance between the target and the back of the oven, relative to the distance between the target and the camera?

It's also not very sensible to use a single image to design a segmentation routine that needs to handle many other possibly different images. I say that because I see four irregular dots. I can't trust that the dots will be clearly defined in other images, given that one of the four is barely present in this image.

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Answer 1

Aastha 2024-9-16

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Hi @Viswajit Talluru,

I understand that you are trying to find the distance between dots in the image that you have provided.

To accomplish this, I have assumed that the location of the cylinder is known and can be cropped out as part of the processing.

You may refer to the steps mentioned below to find the distance between the dots:

1. Import the cropped image into MATLAB using the “imread” function.

image = imread("image_cropped.jpeg"); 
figure(1);  
imshow(image);  

2. To isolate the dots, you can binarize the image using the “imbinarize” function in MATLAB. To binarize the image, first it should be converted to Gary scale using the “rgb2gray“ function.

if size(image, 3) == 3 
    grayImg = rgb2gray(image); 
else
    grayImg = image; 
end 
binaryImg = imbinarize(grayImg); 

3. The binarization process yields artifacts in form of small specks which can be removed using an Erosion Filter. This can be done using the code snippet below:

se = strel('disk', 1); 
erodedImg = imerode(binaryImg, se); 

4. Once the image has been binarized and processed, you can find the location of the dots using the “bwconncomp” function that finds the connected components. This provides clusters which indicate all the pixels that are part of the dots. You may refer to the code snippet below to achieve this:

cc = bwconncomp(erodedImg);
minSize = 50; 
largeComponents = find(cellfun(@numel, cc.PixelIdxList) >= minSize); 

5. The distances between the dots can be seen as the distance between the centroids of the dots.Thus, you can first find the centroids of the dots using the connected components. Subsequently, the distance between the centroids can be computed using the code snippet below:

props = regionprops(filteredImg, 'Centroid'); 
centroids = cat(1, props.Centroid); 
for i = 1:size(centroids, 1) 
    for j = i+1:size(centroids, 1) 
        dist = norm(centroids(i, :) - centroids(j, :)); 
        line([centroids(i, 1), centroids(j, 1)], [centroids(i, 2), centroids(j, 2)],    'Color', 'cyan'); 
        text((centroids(i, 1) + centroids(j, 1)) / 2, (centroids(i, 2) + centroids(j, 2)) / 2, sprintf('%.2f', dist), ...);
        fprintf('Distance between centroid %d and %d: %.2f\n', i, j, dist); 
    end 
end 
centroids = cat(1, props.Centroid); 
for i = 1:size(centroids, 1) 
    for j = i+1:size(centroids, 1) 
        dist = norm(centroids(i, :) - centroids(j, :)); 
        line([centroids(i, 1), centroids(j, 1)], [centroids(i, 2), centroids(j, 2)],    'Color', 'cyan'); 
        text((centroids(i, 1) + centroids(j, 1)) / 2, (centroids(i, 2) + centroids(j, 2)) / 2, sprintf('%.2f', dist), ...); 
        fprintf('Distance between centroid %d and %d: %.2f\n', i, j, dist); 
    end 
end 