Hough-transform for Circle Detection
For detection circle with unknown radius, another parameter need to be make as variable, which is, the radius R.
1. Reading image and the convert to binary image. After that, the location of the value '1' is found using 'find' function. (assume the image is already preprocess, if not, the 'edge' function might help)
clear all;clc;
I = imread('pic22.bmp');
I =im2bw(I);
[y,x]=find(I);
[sy,sx]=size(I);
imshow(I);
2. Find all the require information for the transformatin. the 'totalpix' is the numbers of '1' in the image.
totalpix = length(x);
3. Preallocate memory for the Hough Matrix. The R is known in the range from 1 to 50, so we reserve 50 "layers" for the HM matrix.
HM = zeros(sy,sx,50);
R = 1:50;
R2 = R.^2;
sz = sy*sx;
4. Performing Hough Transform. Notice the accumulator is located in the inner for loop.
for cnt = 1:totalpix
for cntR = 1:50
b = 1:sy;
a = (round(x(cnt) - sqrt(R2(cntR) - (y(cnt) - [1:sy]).^2)));
b = b(imag(a)==0 & a>0);
a = a(imag(a)==0 & a>0);
ind = sub2ind([sy,sx],b,a);
HM(sz*(cntR-1)+ind) = HM(sz*(cntR-1)+ind) + 1;
end
end
5. Find for the maximum value for each layer, or in other words, the layer with maximum value will indicate the correspond R for the circle.
for cnt = 1:50
H(cnt) = max(max(HM(:,:,cnt)));
end
plot(H,'*-');
6. Extract the information from the layer with maximum value, and overlap with the original image.
[maxval, maxind] = max(H);
[B,A] = find(HM(:,:,maxind)==maxval);
imshow(I); hold on;
plot(mean(A),mean(B),'xr')
text(mean(A),mean(B),num2str(maxind),'color','green')
7. This example use the "for loop" extensively, and the speed of the program is rather slow. Vectorized the code might speed up the execution time.
Measuring Object Length (with a Reference Object)
Let's see this simple example:
1. Reading image and show the true color image.
clear all;clc;
I = imread('pic29.tif');
imshow (I);
2. Assuming that we know the length of the red object (as reference) which is 5 cm, locate the red object with simple command and find the length in pixels:
Ired_labeled = bwlabel(Ired);
Ired_props = regionprops(Ired_labeled);
Ired_length_in_pixel = Ired_props.BoundingBox(3);
disp(Ired_length_in_pixel);
212
3. To measure the length of the green object, extract the object:
Igreen_labeled = bwlabel(Igreen);
Igreen_props = regionprops(Igreen_labeled);
Igreen_length_in_pixel = Igreen_props.BoundingBox(3);
disp(Igreen_length_in_pixel);
146
4. Calculate the length of green object in cm
Igreen_length_in_cm = Igreen_length_in_pixel/Ired_length_in_pixel*5;
disp(Igreen_length_in_cm);
3.4434
For detection circle with unknown radius, another parameter need to be make as variable, which is, the radius R.
1. Reading image and the convert to binary image. After that, the location of the value '1' is found using 'find' function. (assume the image is already preprocess, if not, the 'edge' function might help)
clear all;clc;
I = imread('pic22.bmp');
I =im2bw(I);
[y,x]=find(I);
[sy,sx]=size(I);
imshow(I);
2. Find all the require information for the transformatin. the 'totalpix' is the numbers of '1' in the image.
totalpix = length(x);
3. Preallocate memory for the Hough Matrix. The R is known in the range from 1 to 50, so we reserve 50 "layers" for the HM matrix.
HM = zeros(sy,sx,50);
R = 1:50;
R2 = R.^2;
sz = sy*sx;
4. Performing Hough Transform. Notice the accumulator is located in the inner for loop.
for cnt = 1:totalpix
for cntR = 1:50
b = 1:sy;
a = (round(x(cnt) - sqrt(R2(cntR) - (y(cnt) - [1:sy]).^2)));
b = b(imag(a)==0 & a>0);
a = a(imag(a)==0 & a>0);
ind = sub2ind([sy,sx],b,a);
HM(sz*(cntR-1)+ind) = HM(sz*(cntR-1)+ind) + 1;
end
end
5. Find for the maximum value for each layer, or in other words, the layer with maximum value will indicate the correspond R for the circle.
for cnt = 1:50
H(cnt) = max(max(HM(:,:,cnt)));
end
plot(H,'*-');
6. Extract the information from the layer with maximum value, and overlap with the original image.
[maxval, maxind] = max(H);
[B,A] = find(HM(:,:,maxind)==maxval);
imshow(I); hold on;
plot(mean(A),mean(B),'xr')
text(mean(A),mean(B),num2str(maxind),'color','green')
7. This example use the "for loop" extensively, and the speed of the program is rather slow. Vectorized the code might speed up the execution time.
Measuring Object Length (with a Reference Object)
Let's see this simple example:
1. Reading image and show the true color image.
clear all;clc;
I = imread('pic29.tif');
imshow (I);
2. Assuming that we know the length of the red object (as reference) which is 5 cm, locate the red object with simple command and find the length in pixels:
Ired_labeled = bwlabel(Ired);
Ired_props = regionprops(Ired_labeled);
Ired_length_in_pixel = Ired_props.BoundingBox(3);
disp(Ired_length_in_pixel);
212
3. To measure the length of the green object, extract the object:
Igreen_labeled = bwlabel(Igreen);
Igreen_props = regionprops(Igreen_labeled);
Igreen_length_in_pixel = Igreen_props.BoundingBox(3);
disp(Igreen_length_in_pixel);
146
4. Calculate the length of green object in cm
Igreen_length_in_cm = Igreen_length_in_pixel/Ired_length_in_pixel*5;
disp(Igreen_length_in_cm);
3.4434
