How to find the relative position between two objects in the image? - matlab

Question

I would like to find the relative position between the circle (red one in the image) and the door (or the door arrow on the door). I was able to detect the window (red circle on the image) and now would like to know the relative position between the circle and the door(door arrow) and also the minimal distance between the circle and door(door arrow). The minimal distance to to vertical door arrow and the minimal distance to the horizontal door arrow.
I posted the code to find the window(red circle) and two images( original one and the result image of finding the window). Any code or function that gives me that relative position and distances?
Here the code to find the window (MatLab) from Amitay Nachmani from this post
How do I find an object in image/video knowing its real physical dimension?
clear all
% Parameters
minValueWindow = 90;
maxValueWindow = 110;
% Read file
I = imread('image1.jpg');
Igray = rgb2gray(I);
[row,col] = size(Igray);
% Edge detection
Iedge = edge(Igray,'canny',[0 0.3]);
% Hough circle transform
rad = 40:80; % The approximate radius in pixels
detectedCircle = {};
detectedCircleIndex = 1;
for radIndex=1:1:length(rad)
[y0detect,x0detect,Accumulator] = houghcircle(Iedge,rad(1,radIndex),rad(1,radIndex)*pi/2);
if ~isempty(y0detect)
circles = struct;
circles.X = x0detect;
circles.Y = y0detect;
circles.Rad = rad(1,radIndex);
detectedCircle{detectedCircleIndex} = circles;
detectedCircleIndex = detectedCircleIndex + 1;
end
end
% For each detection run a color filter
ang=0:0.01:2*pi;
finalCircles = {};
finalCircleIndex = 1;
for i=1:1:detectedCircleIndex-1
rad = detectedCircle{i}.Rad;
xp = rad*cos(ang);
yp = rad*sin(ang);
for detectedPointIndex=1:1:length(detectedCircle{i}.X)
% Take each detected center and sample the gray image
samplePointsX = round(detectedCircle{i}.X(detectedPointIndex) + xp);
samplePointsY = round(detectedCircle{i}.Y(detectedPointIndex) + yp);
sampleValueInd = sub2ind([row,col],samplePointsY,samplePointsX);
sampleValueMean = mean(Igray(sampleValueInd));
% Check if the circle color is good
if(sampleValueMean > minValueWindow && sampleValueMean < maxValueWindow)
circle = struct();
circle.X = detectedCircle{i}.X(detectedPointIndex);
circle.Y = detectedCircle{i}.Y(detectedPointIndex);
circle.Rad = rad;
finalCircles{finalCircleIndex} = circle;
finalCircleIndex = finalCircleIndex + 1;
end
end
end
% Find Main circle by merging close hyptosis together
for finaCircleInd=1:1:length(finalCircles)
circleCenter(finaCircleInd,1) = finalCircles{finaCircleInd}.X;
circleCenter(finaCircleInd,2) = finalCircles{finaCircleInd}.Y;
circleCenter(finaCircleInd,3) = finalCircles{finaCircleInd}.Rad;
end
[ind,C] = kmeans(circleCenter,2);
c = [length(find(ind==1));length(find(ind==2))];
[~,maxInd] = max(c);
xCircle = median(circleCenter(ind==maxInd,1));
yCircle = median(circleCenter(ind==maxInd,2));
radCircle = median(circleCenter(ind==maxInd,3));
% Plot circle
imshow(Igray);
hold on
ang=0:0.01:2*pi;
xp=radCircle*cos(ang);
yp=radCircle*sin(ang);
plot(xCircle+xp,yCircle+yp,'Color','red', 'LineWidth',5);
And two image