hough变换-matlab程序

function res=hough(im,RHO_MAX,THETA_MAX) % USE: res=hough(im,RHO_MAX,THETA_MAX) % % Name: hough % % Version: % v2.0 % % Author: Dimitrios Ioannou % dimitris@cyra.com % % % Date: % v.1 08/23/95 % v.1.1 03/13/96 % v.2.0 04/29/99 % % Arguments: % im: is the input,binary, image. If the % image is not binary pixels having % non-zero values are considered as feature points. % RHO_MAX: is an integer number specifying % the rho quantization. % THETA_MAX: is an integer number % specifying the theta quantization % % Purpose: % perform the Hough Transform of a square binary % image. Significantly faster than version 1. % % Dependencies: % None % % Example: v=hough(im,256,256) % input is the image im, and the % quantization is d_rho=X/256 and d_theta=pi/256 % if the size of the image is 256 by 256 % d_rho=1. % % v is the number of votes in the % parameter space. v(i,j) is the number % of votes for the strip having distance from % the center of the image equal to % (i-RHO_MAX/2)*d_rho (d_rho=X/RHO_MAX, the % image is X by X pixels),and its normal has % angle j*d_theta,(d_theta=pi/THETA_MAX) % % for a 256 by 256 image, the center of the % image is the center of the pixel (128,128) % i=1 => rho=(i-1-128)*d_rho=-128*d_rho % i=256 => rho=(i-1-128)*d_rho=127*d_rho % this essentially means that: % 'the image is not symmetric around its center'. % % BUGS FIXES: % does not crash when there is one/zero feature points % if (nargin~=3 | nargout~=1) fprintf(1,'Correct use: res=hough2(im,RHO_MAX,THETA_MAX).\n'); error('0Exiting...\n'); end [X,Y]=size(im); if X~=Y fprintf(1,'Input image is not square.\n'); error('1Exiting...\n'); elseif rem(X,2)==1 fprintf(1,'Input image size has to be even in pixels.\n'); error('Exiting...\n'); end tic d_rho=X/RHO_MAX; d_theta=pi/THETA_MAX; theta=0:d_theta:pi-d_theta; smat=sin(theta); cmat=cos(theta); fprintf('Finding feature points.\n'); [x,y]=find(im); fprintf('Translating so the origin is in the middle of the image.\n'); fprintf('Doing the Hough Transform.\n'); h1=((y-Y/2-1) * smat + (x-X/2-1) * cmat )/d_rho; fprintf('Rounding.\n'); h3=round(h1+(RHO_MAX+1)/2); clear h1 im x y % HACK TO MAKE IT FASTER! % %new stuff from here % efficient counting, instead of using a for loop as in hough1 % % % an improvement in terms of speed % % here are the steps: % % 1) each column of h3 array is sorted % % h3 array contains the calculated rho(s), each column has constant % theta % % 2) expand the image temp and calculate if the differences are % are different than 0 % % if they are it means a new rho, if not same rho % % 3) find nonzero values of difference % % from that we find the rhos (from K) % and the thetas (from j) % and the votes (from i) % % 4) having all these, it's easy to built the Hough matrix % % % step 1 temp=flipud(sort(h3)); % step 2 difference=(diff([max(temp)+1;temp])~=0); fprintf('calc sorting-differences time:\n'); % step 3 K=find(difference); [i,j]=find(difference); rho=temp(K); votes=zeros(size(i)); for counter=1:length(j)-1 if j(counter)==j(counter+1) votes(counter)=i(counter+1)-i(counter); else votes(counter)=size(h3,1)+1-i(counter); end end votes(length(j))=size(h3,1)+1-i(length(j)); % step 4 res=zeros(RHO_MAX,THETA_MAX); for counter=1:length(j) if rho(counter)>-1 & rho(counter)<RHO_MAX res(rho(counter)+1,j(counter))=votes(counter); end end toc