How do I find an object in image/video knowing its real physical dimension? - c++

Question

I have a sample of images and would like to detect the object among others in the image/video already knowing in advance the real physical dimensions of that object. I have one of the image sample (its airplane door) and would like to find the window in the airplane door knowing its physical dimensions(let we say it has inner radius of 20cm and out radius of 23cm) and its real world position in the door (for example its minimal distance to the door frame is 15cm) .Also I can know prior my camera resolution. Any matlab code or OpenCV C++ that can do that automatically with image processing?
Here is my image sample
And more complex image with round logos.
I run the code for second complex image and do not get the same results. Here is the image result.

Answer 1

You are looking for a circle in the image so i suggest you use Hough circle transform.
Convert image to gray
Find edges in the image
Use Hugh circle transform to find circles in the image.
For each candidate circle sample the values of the circle and if the values corresponds to a predefined values accept.
The code:
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);
The resulted image:
Remarks:
For other images will still have to fine tune several parameters like the radius that you search for the color and Hough circle threshold and canny edge thresholds.
In the function i searched for circle with radius from 40 pixels to 80. In here you can use your prior information about the real world radius of the window and the resolution of the camera. If you know approximately the distance the camera was from the airplane and the resolution of the camera and also the window radius in cm you can use this to get the radius in pixels and use this for the hough circle transform.

Answer 2

I wouldn't worry too much about the exact geometry and calibration and rather find the window by its own characteristics.
Binarization works relatively well, be it on the whole image or in a large region of interest.
Then you can select the most likely blob based on it approximate area and/or circularity.