Rosetta Code

Hough transform: Difference between revisions

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→‎{{header|MATLAB}}: Changed the code, made it more efficient.
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<lang MATLAB>function [rho,theta,houghSpace] = houghTransform(theImage,thetaSampleFrequency)
 
%Define the hough space
theImage = flipud(theImage); %Because the "origin" of a picture is the top-left corner
if indextheImage <= numelflipud(theImage);▼
[width,height] = size(theImage);
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numThetas = numel(theta);
▼
houghSpace = zeros(numel(rho),numThetas);
▲
accumulator = zeros(numel(theImage),numThetas);▼
%Find the "edge" pixels
[xIndicies,yIndicies] = find(theImage);
▼
%Preallocate space for the accumulator array
numEdgePixels = numel(xIndicies);
▲ accumulator = zeros(numel(theImage)numEdgePixels,numThetas);
%Preallocate cosine and sine calculations to increase speed. In
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sine = (0:height-1)'*sin(theta); %Matrix Outerproduct
accumulator(index(1:numEdgePixels),:) = cosine(xxIndicies,:) + sine(yyIndicies,:);▼
%Calculate the hough transform of each "edge" pixel and store that
 
%curve in the accumulator array. The columns of which are indexed by
%thetaScan andover the rowsthetas areand anbin the arbitraryrhos 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);
Retrieved from "https://rosettacode.org/wiki/Hough_transform"