Determine the orientation of a triangle in Matlab - matlab

Question

I am working on a project to perform automatically the landing of a quadrotor by visual recognition on a target. I have the code to detect the target through HOG features. Now the idea is to find the triangle, which is isosceles, and measure the lines so that I can determine the orientation that way. I have tried Hough, but I cannot manage to succeed.
The target is a proposed one
, and it consists of an isosceles triangle inside a circle. But if you can think of a better one, please let me know.
Please, ask any questions if anything is unclear. Thank you very much
Update 1:
#McMa 's idea works well when I deal only with the target as an image. This is the code:
clc; close all;
im=imread('target.bmp');
im=rgb2gray(im);
im2=imcrop(im,[467.51 385.51 148.98 61.98]);
im2=imcomplement(im2);
im2=imrotate(im2,0);
s=regionprops(im2,'Area','Centroid','Extrema','Orientation');
[imH,imW]=size(im2);
if imH-s(end).Centroid(2) < imH/2
state=1; % Upright
else
state=2; % Upside down
end
imshow(im2);hold on
plot(s(end).Centroid(1), s(end).Centroid(2), 'b*')
if s(end).Orientation>0
degrees=s(end).Orientation;
else
degrees=s(end).Orientation+180;
end
if (0<degrees)&&(degrees<89.99) && state==2
degrees=degrees+180;
elseif (90<degrees) && (degrees<179) && state==1
degrees=degrees+180;
end
fprintf('The orientation is %g degrees\n',degrees)
Update 2:
Now I have another problem: I need to know somehow whether the camera is seeing the whole target or only the small circle+triangle. I need this before computing the orientation.
I have tried many options. For example, I wanted to count the number of circles, so if there are 2, it is seeing the big target, and if there is 1, just the small one. But they are not well detected. Even if I play with the sensitivity, it's not going to be a robust method.
Image: https://www.dropbox.com/s/7mbpna3xfquq5n7/P0016.bmp?dl=0
Classifer: https://www.dropbox.com/s/236vm3romw56983/Cascade1Matlab.xml?dl=0
im=imread('P0016.bmp');
detector = vision.CascadeObjectDetector('Cascade1Matlab.xml');
bbox = step(detector, im); % Detect the target.
detectedImg = insertObjectAnnotation(im, 'rectangle', bbox, 'target'); % Insert bounding boxes and return marked image.
imshow(detectedImg)
BW=rgb2gray(im);
BW=imcrop(BW,bbox(1,:) +[0 0 10 10]);
[imH,imW]=size(im);
centers = imfindcircles(im,[1 round(imH)]);
figure;hold on;
imshow(im);
plot(centers(:,1),centers(:,2),'r*','LineWidth',4)
I also tried with other approaches such as the Euler number, but with no success, I can't find anything that works properly.

Answer 1

I think the easiest and fastest way would be finding your target and binarize the image. Afterwards use regionprops() and read the "Orientation" property to read the orientation.
If you can't use that toolbox the function is very easily implement by calculating the covariance matrix of your region. Let me know if you need some tips on this.
Edit:
I just so happend to have some nicely vectorized functions around here ;) so if speed is a top priority, you can easily write your own regionprops() trimmed to the bare minimum like this:
function M=ImMoment(Image,ii,jj)
ImSize=size(Image);
K=repmat((1:ImSize(1))',1,ImSize(2)).^ii;
J=repmat(1:ImSize(2),ImSize(1),1).^jj;
M=K.*J.*Image;
M=sum(M(:));
end
for the image moments and
function [Matrix,Centroid,Angle]=CovMat(Image)
Centroid=[ImMoment(Image,0,1)/ImMoment(Image,0,0),...
ImMoment(Image,1,0)/ImMoment(Image,0,0)];
Miu20=ImMoment(Image,0,2)/ImMoment(Image,0,0)-Centroid(1)^2;
Miu02=ImMoment(Image,2,0)/ImMoment(Image,0,0)-Centroid(2)^2;
Miu11=ImMoment(Image,1,1)/ImMoment(Image,0,0)-Centroid(1)*Centroid(2);
Matrix=[Miu20,Miu11 %Covariance Matrix in case you need it for anything...
Miu11,Miu02];
Angle=1/2*atand(2*Miu11/(Miu20-Miu02)); %Your orientation
end
for your orientation and covariance matrix. more about it here.
Image moments are very might, have fun!

Answer 2

I have a very blunt solution in mind. It may work. I have not actually tried it, since there is no image to work on. So if it fails, post the error.
Assumptions:- You have filtered the image and obtained the binary image that contains only triangle "OR" with uniform noise.
Now you can take a 0 degree image (image1). Filter it and obtain binary image (bw1).
So when you are trying to land your quadrotor, take image (image2), convert it binary (bw2).
Now find the correlation between these two images {corr2(bw1, bw2)}. Store this in a variable.
Rotate image with a step angle. Let angle be 5 degrees. {imrotate(bw2, 5)}
Now again find correlation between these two images.
Do this for all angles.
The orientation would the angle (no. of rotation * 5) where the correlation is maximum.
The term maximum signifies that, you may not find the correlation to be 1 as this highly depends on your filtering techniques to obtain perfect binary image.
I also accept that computing the correlation for all the angles requires high computation speed as well as long time. This would be really difficult to achieve in real time if you do not have high computation speed. (In this case you can look into Parallel Computing Toolbox) specially parfor.
Hope this was useful to you. Post a comment if you face any error.
Finally good luck. Nice project.
P.S. Pad white or black pixels depending on your binary image while rotating image.