Finding the minimum distance between two objects, for multiple images.

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I am attaching an input as well as an output image(distance between two bones) for your review, I am also attaching the code that helped me find the distance between the bones.
I have a folder for 200 individuals, each individual having approximately 150 such bones samples.
I want to apply this algorithm for every individual having 150 bone samples(each varying in sizes and distances).
For every individual, I want to find the average minimum distance by taking an average of these 150 bone samples and store the result in a vector. I want to do the same for all 200 individuals.
  • How can I iterate through these images (200*150 images) and load them to apply this algorithm on each one of them.
  • How can I find the average distance that I obtain after finding the distance from 150 bone samples and store the result in a vector and do the same for 200 individuals?
Please help me understand this.
Thank you.
% Read in a standard MATLAB color demo image.
folder = 'D:\MathWorks_MATLAB_R2020a_v9.8.0.1323502\MathWorks_MATLAB_R2020a_v9.8.0.1323502'; %'C:\Users\Lakshya\Documents\Temporary';
baseFileName = '9002116_060_pred.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
% Didn't find it there. Check the search path for it.
fullFileName = baseFileName; % No path this time.
if ~exist(fullFileName, 'file')
% Still didn't find it. Alert user.
errorMessage = sprintf('Error: %s does not exist.', fullFileName);
grayImage = imread(fullFileName);
% Get the dimensions of the image. numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(grayImage);
% Display the original image.
subplot(2, 2, 1);
axis on;
title('Original Gray Scale Image');
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Binarize the image
binaryImage = imbinarize(grayImage);
% Display the image.
subplot(2, 2, 2);
title('Binary Image');
% Fill the outline to make it solid so we don't get boundaries
% on both the inside of the shape and the outside of the shape.
binaryImage = imfill(binaryImage, 'holes');
% Display the image.
subplot(2, 2, 3);
% 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.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary(:,1), 'r', 'LineWidth', 3);
title('Filled Binary Image with Boundaries');
hold off;
% Define object boundaries
numberOfBoundaries = size(boundaries, 1)
boundary1 = boundaries{1};
boundary2 = boundaries{2};
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
overallMinDistance = inf; % Initialize.
index1 = 1;
index2 = 1;
for k = 1 : length(boundary2)
boundary2x = boundary2(k, 2);
boundary2y = boundary2(k, 1);
% For this blob, compute distances from boundaries to edge.
allDistances = sqrt((boundary1x - boundary2x).^2 + (boundary1y - boundary2y).^2);
% Find closest point, min distance.
[minDistance(k), indexOfMin] = min(allDistances);
if minDistance(k) < overallMinDistance
overallMinDistance = minDistance(k);
x1 = boundary1x(indexOfMin);
y1 = boundary1y(indexOfMin);
x2 = boundary2x;
y2 = boundary2y;
index2 = k;
index1 = indexOfMin;
% Report to command window.
fprintf('Min Distance from sqrt() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
overallMinDistance, index1, index2);
hFig = figure;
h1 = subplot(1, 2, 1);
axis on;
title('Closest Distance from sqrt()');
h2 = subplot(1, 2, 2);
axis on;
title('Closest Distances from pdist2()');
hFig.WindowState = 'maximized';
hold on;
% Draw a line between point 1 and 2
line(h1, [x1, x2], [y1, y2], 'Color', 'y', 'LineWidth', 3);
% For comparison, use pdist2()
allDistances2 = pdist2(boundary1, boundary2);
minDistance2 = min(allDistances2(:));
% Find all points that have that min distance - there may be several that have it.
[r, c] = find(allDistances2 == minDistance2)
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
boundary2x = boundary2(:, 2);
boundary2y = boundary2(:, 1);
for k = 1 : length(r)
% Report to command window.
index1 = r(k);
index2 = c(k);
fprintf('Min Distance from pdist2() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
minDistance2, index1, index2);
xLine = [boundary1x(index1), boundary2x(index2)];
yLine = [boundary1y(index1), boundary2y(index2)];
line(h2, xLine, yLine, 'Color', 'm', 'LineWidth', 1.5);
Vihan P
Vihan P on 29 Apr 2021
Dataset: There are a total of 420 images, where 84 images belong to 1 case, and I have created a dataset for 5 such cases.
What I mean is, the way the distance between the two bones in one image was found, I need to find the distance between the two bones for all these images(420 images). Lastly, I need to compute the mean of distances per case for the 84 images in that case which determines an average distance for that 1 case, and put the average value of those 84 images in a vector. I need to do the same for 5 such cases and have 5 average values put in 5 vector.

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