too many output argument?

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Prerna Surbhi
Prerna Surbhi 2016-4-19
编辑： Walter Roberson 2016-4-20
While implementing k mean cluster image segmentation,getting error at (line 11)
[lb,center] = file(im);
This code is written to implement kmeans clustering for segmenting any Gray or Color image. There is no requirement to mention the number of cluster for clustering. error using code is too many output argument.
please help me to solve it?
Code
clc;clear all;close all;
im=imread('shoe.jpg');
[lb,center] = file(im);
if size(im,3)>1
[lb,center] = ColorClustering(im); % Check Image is Gray or not.
else
[lb,center] = GrayClustering(im);
end
function [lb,center] = GrayClustering(gray)
gray = double(gray);
array = gray(:); % Copy value into an array.
% distth = 25;
i = 0;j=0; % Intialize iteration Counters.
tic
while(true)
seed = mean(array); % Initialize seed Point.
i = i+1; %Increment Counter for each iteration.
while(true)
j = j+1; % Initialize Counter for each iteration.
dist = (sqrt((array-seed).^2)); % Find distance between Seed and Gray Value.
distth = (sqrt(sum((array-seed).^2)/numel(array)));% Find bandwidth for Cluster Center.
% distth = max(dist(:))/5;
qualified = dist<distth;% Check values are in selected Bandwidth or not.
newseed = mean(array(qualified));% Update mean.
if isnan(newseed) % Check mean is not a NaN value.
break;
end
if seed == newseed || j>10 % Condition for convergence and maximum iteration.
j=0;
array(qualified) = [];% Remove values which have assigned to a cluster.
center(i) = newseed; % Store center of cluster.
break;
end
seed = newseed;% Update seed.
end
if isempty(array) || i>10 % Check maximum number of clusters.
i = 0; % Reset Counter.
break;
end
end
toc
center = sort(center); % Sort Centers.
newcenter = diff(center);% Find out Difference between two consecutive Centers.
intercluster = (max(gray(:)/10));% Findout Minimum distance between two cluster Centers.
center(newcenter<=intercluster)=[];% Discard Cluster centers less than distance.
% Make a clustered image using these centers.
vector = repmat(gray(:),[1,numel(center)]); % Replicate vector for parallel operation.
centers = repmat(center,[numel(gray),1]);
distance = ((vector-centers).^2);% Find distance between center and pixel value.
[~,lb] = min(distance,[],2);% Choose cluster index of minimum distance.
lb = reshape(lb,size(gray));% Reshape the labelled index vector.
function [lb,center] = ColorClustering(im)
im = double(im);
red = im(:,:,1); green = im(:,:,2); blue = im(:,:,3);
array = [red(:),green(:),blue(:)];
% distth = 25;
i = 0;j=0;
tic
while(true)
seed(1) = mean(array(:,1));
seed(2) = mean(array(:,2));
seed(3) = mean(array(:,3));
i = i+1;
while(true)
j = j+1;
seedvec = repmat(seed,[size(array,1),1]);
dist = sum((sqrt((array-seedvec).^2)),2);
distth = 0.25*max(dist);
qualified = dist<distth;
newred = array(:,1);
newgreen = array(:,2);
newblue = array(:,3);
newseed(1) = mean(newred(qualified));
newseed(2) = mean(newgreen(qualified));
newseed(3) = mean(newblue(qualified));
if isnan(newseed)
break;
end
if (seed == newseed) || j>10
j=0;
array(qualified,:) = [];
center= newseed(1,10);
% center(2,i) = nnz(qualified);
break;
end
seed = newseed;
end
if isempty(array) || i>10
i = 0;
break;
end
end
toc
centers = sqrt(sum((center.^2),2));
[centers,idx]= sort(centers);
while(true)
newcenter = diff(centers);
intercluster =25; %(max(gray(:)/10));
a = (newcenter<=intercluster);
% center(a,:)=[];
% centers = sqrt(sum((center.^2),2));
centers(a,:) = [];
idx(a,:)=[];
% center(a,:)=0;
if nnz(a)==0
break;
end
end
center1 = center;
center =center1(idx,:);
% [~,idxsort] = sort(centers) ;
vecred = repmat(red(:),[1,size(center,1)]);
vecgreen = repmat(green(:),[1,size(center,1)]);
vecblue = repmat(blue(:),[1,size(center,1)]);
distred = (vecred - repmat(center(:,1)',[numel(red),1])).^2;
distgreen = (vecgreen - repmat(center(:,2)',[numel(red),1])).^2;
distblue = (vecblue - repmat(center(:,3)',[numel(red),1])).^2;
distance = sqrt(distred+distgreen+distblue);
[~,label_vector] = min(distance,[],2);
lb = reshape(label_vector,size(red));
%
rawData1 = importdata(fileToRead1);
% For some simple files (such as a CSV or JPEG files), IMPORTDATA might
% return a simple array. If so, generate a structure so that the output
% matches that from the Import Wizard.
[~,name] = fileparts(fileToRead1);
newData1.(genvarname(name)) = rawData1;
% Create new variables in the base workspace from those fields.
vars = fieldnames(newData1);
for i = 1:length(vars)
assignin('base', vars{i}, newData1.(vars{i}));
end

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回答（1 个）

Walter Roberson
Walter Roberson 2016-4-20
I do not know what file() is. What does
which file
say?
It looks to me as if instead of file() you want ColorClustering()
  2 个评论
Prerna Surbhi
Prerna Surbhi 2016-4-20
编辑：Walter Roberson 2016-4-20
file funtion is just the name of function. it would be any name like ColorClustering also. please help me to solve the problem of getting too many output argument.
Walter Roberson
Walter Roberson 2016-4-20
编辑：Walter Roberson 2016-4-20
Change the line
[lb,center] = file(im);
to
[lb,center] = ColorClustering(im);
Also make sure that ColorClustering is in its own .m file, not part of the same file that you stored GrayClustering in.
(You can have both in one file, but the entire first line that you currently have would have to be changed for that to work.)

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