Hough Transform Lines (Octave)

Hough transform was developed to detect lines in 2D pictures.
This post documents simple examples and results I got while working on UD810.

Functions implemented:
  • hough_lines_acc(img_edgs, rho_resolution, theta_resolution)
  • hough_peaks(Hough_acc, NHoodSize)
  • hough_lines_draw(img, output, peaks, rho, theta)

 % create an example array
img = zeros(200, 200);

% Add lines
img(50*200+50 : 201 : 150*200 +150) = 255;
img(50*200+150 : 199 : 150*200 + 50) = 255;

% converting to gray image
I = mat2gray(img);










% extract edges using Canny operator
edges = edge(I, 'canny');










% Hough Transform
[H, T, R] = hough_lines_acc(edges);
% The tricky part of implementing this function is
% to handle negative values:
% rhos and thetas can be positive and negative
% but array index must be positive

% plot voting lines in Hough domain
fig1 = figure 1;
imagesc(H);
saveas(fig1,'lines_in_hough_domain.png');















% Plot hough lines 
% Don't forget use cosd and sind because Theta is in degree
hough_lines_draw(I, 'output.png', P, R, T);

% calculate peaks
% This function not only sort and pick the top peaks
% but also needs to eliminate neighbours (nHoodSize, an odd parameter)
% for each peaks found
% The tricky part is to handle boundary condition, rest is straightforward

P = hough_peaks(H,2) % since we know there are two lines in this example


% Plot outputs from each step
















% Try the flow on football field photo
img = imread('football-field.jpeg');






% Converting to gray image, then extract edges
I2 = rgb2gray(img);

edges = edge(I2, 'canny')
[H, R, T] = hough_lines_acc(edges);















%Plot Hough Domain
imagesc(H);











P = hough_peaks(H,20);
% draw original image, lines and Hough lines
hough_lines_draw(I2, 'football_output.png', P, R, T);

















Published: October 20 2016

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