Hough transform: Difference between revisions

=={{header|MATLAB}}==

<lang MATLAB>function [rho,theta,houghSpace] = houghTransform(theImage,thetaSampleFrequency)

theImage = flipud(theImage);

[width,height] = size(theImage);

rhoLimit = norm([width height]);

rho = (-rhoLimit:1:rhoLimit);

theta = (0:thetaSampleFrequency:pi);

numThetas = numel(theta);

houghSpace = zeros(numel(rho),numThetas);

accumulator = zeros(numel(theImage),numThetas);

%Preallocate cosine and sine calculations to increase speed. In

%addition to precallculating sine and cosine we are also multiplying

%them by the proper pixel weights such that the rows will be indexed by

%the pixel number and the columns will be indexed by the thetas.

%Example: cosine(3,:) is 2*cosine(0 to pi)

% cosine(:,1) is (0 to width of image)*cosine(0)

cosine = (0:width-1)'*cos(theta); %Matrix Outerproduct

sine = (0:height-1)'*sin(theta); %Matrix Outerproduct

%Calculate the hough transform of each "edge" pixel and store that

%curve in the accumulator array. The columns of which are indexed by

%theta and the rows are an arbitrary index.

index = 1;

for x = (1:width)

for y = (1:height)

if(theImage(x,y))

accumulator(index,:) = cosine(x,:) + sine(y,:);

index = index + 1;

end

end

end

%%During the accumulator preallocation there is enough memory allocated

%%should every single pixel happen to be an "edge" pixel. This little

%%line deallocates the unused memory.

if index < numel(theImage)

accumulator(index:end,:) = [];

end

for i = (1:numThetas)

houghSpace(:,i) = hist(accumulator(:,i),rho);

end

pcolor(theta,rho,houghSpace);

shading flat;

title('Hough Transform');

xlabel('Theta (radians)');

ylabel('Rho (pixels)');

colormap('gray');

end</lang>

Sample Usage:

<lang MATLAB>>> uiopen('C:\Documents and Settings\owner\Desktop\Chris\MATLAB\RosettaCode\180px-Pentagon.png',1)

>> houghTransform(cdata(:,:,1)<255,1/200);</lang>