while running code for meanshift algorithm,shows the error below.how correct it?

Question

ATHIRA P M 2018-10-25

0
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https://ww2.mathworks.cn/matlabcentral/answers/426092-while-running-code-for-meanshift-algorithm-shows-the-error-below-how-correct-it

回答： madhan ravi 2018-10-25

clear all close all clc

     x = double( imread('athira.jpg') );
     y = meanShiftPixCluster(x,20,32);
     sample = zeros(size(x,1),size(x,2));
     sample(1:3:end,1:3:end) = 1;
     R = x(:,:,1); Rx = R(sample==1); Rn = randn( numel(Rx),1 )/3;
     G = x(:,:,2); Gx = G(sample==1); Gn = randn( numel(Rx),1 )/3;
     B = x(:,:,3); Bx = B(sample==1); Bn = randn( numel(Rx),1 )/3;
     figure, 
     subplot(221), imshow(uint8(x)), axis image; title('input image')
     subplot(223), imshow(uint8(y)), axis image; title('output image')
     subplot(222)
     scatter3( Rx(:)-Rn, Gx(:)-Gn, Bx(:)-Bn, 3, [ Rx(:), Gx(:), Bx(:) ]/255 );
     title('Pixel Distribution Before Meanshift')
     xlim([0,255]),ylim([0,255]),zlim([0,255]);axis square
     R = y(:,:,1); Ry = R(sample==1); Rn = randn( numel(Rx),1 )/3;
     G = y(:,:,2); Gy = G(sample==1); Gn = randn( numel(Rx),1 )/3;
     B = y(:,:,3); By = B(sample==1); Bn = randn( numel(Rx),1 )/3;
     subplot(224)
     scatter3( Ry(:)-Rn, Gy(:)-Gn, By(:)-Bn, 3, [ Rx(:), Gx(:), By(:) ]/255 );
     title('Pixel Distribution After Meanshift')
     xlim([0,255]),ylim([0,255]),zlim([0,255]);axis square
% -------------------------------------------------------------------------
% Additional Info:
% This is a toy code of mean shift pixel clustering, but it did cover the
% core idea od mean shift. One may extend the current code for
% multiresolution analysis, or replace new kernel functions instead of
% gaussian kernels used in this implementations. Details of parameters and
% their influence on clustering results and the convergence proof of the
% mean shift algorithm please refer the cited paper. 
%
% Please play with this code with other images and parameters. Contact me
% if you find any bugs.
%
% by Yue Wu
% rex.yue.wu@gmail.com
% 11/30/2013
% 
% -------------------------------------------------------------------------

%% Argument Check if nargin<3 error('please type help for function syntax') elseif nargin == 3 th = 1/100; plotOn = 1; elseif nargin == 4 if th<0 th >255 error('threshold should be in [0,255]') else plotOn = 1; end elseif nargin == 5 if sum(ismember(plotOn,[0,1])) == 0 error('plotOn option has to be 0 or 1') end elseif nargin>5 error('too many input arguments') end %% initialization x = double(x); [height,width,depth] = size(x); y = x; done = 0; iter = 0; if plotOn figure(randi(1000)+1000); end % padding image to deal with pixels on borders xPad = padarray(x,[height,width,0],'symmetric'); % build up look up table to boost computation speed weight_map = exp( -(0:255^2)/hr^2 ); MS = []; %% main loop while ~done weightAccum = 0; yAccum = 0; % only 99.75% area (3sigma) of the entire non-zero Gaussian kernel is considered for i = -hs:hs for j = -hs:hs if ( i~=0 j~=0 ) % spatial kernel weight spatialKernel = 1; % uncomment the following line to active Gausian kernel %spatialKernel = exp(-(i^2+j^2)/(hs/3)^2/2); xThis = xPad(height+i:2*height+i-1, width+j:2*width+j-1, 1:depth); xDiffSq = (y-xThis).^2; % feature kernel weight intensityKernel = repmat( prod( reshape( weight_map( xDiffSq+1 ), height, width, depth) , 3 ), [1,1, depth]); % mixed kernel weight weightThis = spatialKernel.*intensityKernel; % update accumulated weights weightAccum = weightAccum+ weightThis; % update accumulated estimated ys from xs yAccum = yAccum+xThis.*weightThis; end end end % normalized y (see Eq.(20) in the cited paper) yThis = yAccum./(weightAccum+eps); % convergence criterion yMS = mean(abs(round(yThis(:))-round(y(:)))); y = round(yThis); MS(iter+1) = yMS; %xPad = padarray(y,[height,width,0],'symmetric'); if plotOn subplot(121), imshow(uint8(y)),axis image, title(['iteration times = ' num2str(iter) '; averaged mean-shift = ' num2str(yMS)]); subplot(122), plot(0:iter, MS ), xlabel('iteration #'), ylabel('averaged mean shift');axis square drawnow end if yMS <= th % exit if converge done = 1; else % otherwise update estimated y and repeat mean shift iter = iter+1; end end