How can I solve the error 'Too many output arguments' when creating a histogram in Matllab Appdesigner?

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Kaitlynn Vriend
Kaitlynn Vriend 2021-5-10
回答： Vidhi Agarwal 2024-4-24
I'm trying to create a histogram of the theta and phi orientation of collagen fibers. When I try to plot the histogram I get the following error message:
Error using histogram/meanc
Too many output arguments.
Error in histogram/OrientationhistogramofthetaButtonPushed (line 283)
aSDE = meanc(app)*180/pi;
Error using matlab.ui.control.internal.controller.ComponentController/executeUserCallback (line 410)
Error while evaluating Button PrivateButtonPushedFcn.
Below you can see the code I'm using. I would appreciate your help with finding a solution to this.
classdef histogram < matlab.apps.AppBase
% Properties that correspond to app components
properties (Access = public)
UIFigure matlab.ui.Figure
OrientationhistogramofphiButton matlab.ui.control.Button
OrientationhistogramofthetaButton matlab.ui.control.Button
DcollagenorientationofphiButton matlab.ui.control.Button
DcollagenorientationofthetaButton matlab.ui.control.Button
LoaddataButton matlab.ui.control.Button
UIAxes matlab.ui.control.UIAxes
end
methods (Access = private)
function meanc(app)
shgim = mean(double(shgim));3;
calcfibangspeed(app,shgim,armd,filton)= [aS];
% find size of image
sz=size(shgim,1);
sz2=size(shgim,2);
% filters the data using a Gaussian kernel
cdata=(shgim)/max(max(shgim));
if filton
hf = fspecial('gaussian',[9 9],1.2);
im=imfilter((double(cdata)+.0000001),hf);
else
im=cdata;
end
% create coordinates of window
[xk yk]=meshgrid(1:armd*2+1,1:armd*2+1);
% determine angle of each coordinate relative to center pixels xk and yk
xk=xk-armd-1;
yk=yk-armd-1;
[ang,radd] = cart2pol(xk,-1*yk);
% put all angular data between 0 and pi
ang=ang+pi*(ang<0);
% weighting factor 1/radius
wei=1./((radd));
wei=wei.*(wei>0);
% initialize variables
C=zeros(sz,sz2);
S=zeros(sz,sz2);
% This set of loops is where vector summation occurs. For each iteration a
% single directional vector is added to a running total.
for i=-armd:-1
for j=-armd:0
% creates 3 copies of the image: the original and two copies shifted
% in opposite directions by i and j
fim = circshift(im,[i j]);
bim = circshift(im,[-i -j]);
tim(:,:,1)=fim;
tim(:,:,2)=im;
tim(:,:,3)=bim;
% the standard deviation of tim is computed in the 3rd dimension,
% which means we're measuring how intensity varies between each
% pixel and its neighbors at relative shifts of i,j and -i,-j.
% We weight the orientation vector by the max possible standard
% deviation and the measured standard deviation
dc=sqrt(1/3)-(std(tim,0,3));
if ((i~=0)|(j~=0))
% we sum the x and y components of each vector and mulitply it
% by our weighting factors dc and wei
% note that because these are axial data (0deg=180deg), we
% multiply the angle by 2 (later we divide by 2)
C=C+(cos(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
S=S+(sin(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
end
end
end
for i=-armd:0
for j=1:armd
% creates 3 copies of the image: the original and two copies shifted
% in opposite directions by i and j
fim = circshift(im,[i j]);
bim = circshift(im,[-i -j]);
tim(:,:,1)=fim;
tim(:,:,2)=im;
tim(:,:,3)=bim;
% the standard deviation of tim is computed in the 3rd dimension,
% which means we're measuring how intensity varies between each
% pixel and its neighbors at relative shifts of i,j and -i,-j.
% We weight the orientation vector by the max possible standard
% deviation and the measured standard deviation
dc=sqrt(1/3)-(std(tim,0,3));
if ((i~=0)|(j~=0))
% we sum the x and y components of each vector and mulitply it
% by our weighting factors dc and wei
% note that because these are axial data (0deg=180deg), we
% multiply the angle by 2 (later we divide by 2)
C=C+(cos(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
S=S+(sin(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
end
end
end
meanc = atan2(S,C)/2;
meanc=(meanc).*(meanc>=0)+(meanc+pi).*(meanc<0);
aS=meanc;
end
function calcfibang3D(app)
function [aSDE,pSDER]=calcfibang3D(app, shgim,armdx,armdz,filton,para)
% This function calculates the voxel-wise orientation. The output and input
% indices are explained in the main program
im = shgim;
im = double(im);
sz = size(im,1);
sz2 = size(im,2);
sz3 = size(im,3);
% Here starts the determination of theta
terimc = zeros(sz,sz2,3*sz3);
terimc(:,:,1:sz3) = im;
terimc(:,:,(sz3+1):(2*sz3)) = im;
terimc(:,:,(2*sz3+1):(3*sz3)) = im;
imc = zeros(sz,sz2,sz3);
for i = 1:sz3
imc(:,:,i) = mean(terimc(:,:,(sz3+i-armdz):(sz3+i+armdz)),3);
end
clear terimc
meanc = zeros(sz,sz2,sz3);
h = waitbar(0,'Please Wait');
ij = 0;
nij = sz3;
for i = 1:sz3
ij = ij+1;
waitbar(ij/nij)
meanc(:,:,i) = calcfibangspeed(app,imc(:,:,i),armdx,filton);
end
close(h)
clear imc
% Here starts the determination of beta
terimj = zeros(3*sz,sz2,sz3);
terimj(1:sz,:,:) = im;
terimj((sz+1):(2*sz),:,:) = im;
terimj((2*sz+1):(3*sz),:,:) = im;
imj = zeros(sz,sz2,sz3);
for i = 1:sz
imj(i,:,:) = mean(terimj((sz+i-armdz):(sz+i+armdz),:,:),1);
end
clear terimj
% To calculate the orientation in 2D manner
meanj = zeros(sz,sz2,sz3);
h = waitbar(0,'Please Wait')
ij = 0;
nij = sz;
for i = 1:sz
ij = ij+1;
waitbar(ij/nij)
meanj(i,:,:) = calcfibangspeed(squeeze(imj(i,:,:)),armdx,filton);
end
close(h)
clear imj
meanj = (pi/2-meanj).*(meanj<=pi/2)+(3*pi/2-meanj).*(meanj>pi/2);
% Here starts the determination of gamma
terimg = zeros(sz,3*sz2,sz3);
terimg(:,1:sz2,:) = im;
terimg(:,(sz2+1):(2*sz2),:) = im;
terimg(:,(2*sz2+1):(3*sz2),:) = im;
img = zeros(sz,sz2,sz3);
for i = 1:sz
img(:,i,:) = mean(terimg(:,(sz2+i-armdz):(sz2+i+armdz),:),2);
end
clear terimg
% To calculate the orientation in 2D manner
meang = zeros(sz,sz2,sz3);
h = waitbar(0,'Please Wait')
ij = 0;
nij = sz2;
for i = 1:sz2
ij = ij+1;
waitbar(ij/nij)
meang(:,i,:) = calcfibangspeed(app,squeeze(img(:,i,:)),armdx,filton);
end
close(h)
clear img
meang = (pi/2+meang).*(meang<=pi/2)+(meang-(pi/2)).*(meang>pi/2);
% Here to acquire the orientation of phi
meanp = atan(sqrt(1./(tan(meanj).^2)+1./(tan(meang).^2)));
meanplim = meanp;
for i = 1:sz
for j = 1:sz2
for k = 1:sz3
if meanj(i,j,k) <= pi/2
meanp(i,j,k) = meanp(i,j,k);
else
meanp(i,j,k) = pi-meanp(i,j,k);
end
end
end
end
% Here to acquire the real phi orientation regardless of Z resolution
meanpreal = (pi/2)-(atan(para*tan((pi/2)-meanplim)));
for i = 1:sz
for j = 1:sz2
for k = 1:sz3
if meanj(i,j,k) <= pi/2
meanpreal(i,j,k) = meanpreal(i,j,k);
else
meanpreal(i,j,k) = pi-meanpreal(i,j,k);
end
end
end
end
% Here prepares the final output
aSDE = meanc*180/pi;
pSDER = meanpreal*180/pi;
end
end
end
% Callbacks that handle component events
methods (Access = private)
% Button pushed function: LoaddataButton
function LoaddataButtonPushed(app, event)
sz = 512 ;
sz2 = 512 ;
sz3 = 30 ; % 46; % modify 1, define sz, sz2, and sz3 according to the size of the 3D image
shgstack = zeros(sz,sz2,sz3);
for i = 1:sz3
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%7 modify
% shgstack(:,:,i) = imread(['C:\Users\myy15\Desktop\human500um1um\humanskin250umz1um\noshift2\',num2str(i),'.tif']); % modify 2
shgstack(:,:,i) = imread(['/Users/kaitlynn/Desktop/Matlab/imaging/',num2str(i),'.tif']); % modify 2
% shgstack(:,:,i) = imread(['C:\Users\myy15\Desktop\human500um1um\stack2\',num2str(i),'.tif']); % modify 2
% 'shgstack' is the 3D image used for directional variance analysis.
% Define the path of the folder where the images are saved
end
end
% Button pushed function: DcollagenorientationofthetaButton
function DcollagenorientationofthetaButtonPushed(app, event)
end
% Button pushed function: OrientationhistogramofthetaButton
function OrientationhistogramofthetaButtonPushed(app, event)
aSDE = meanc(app)*180/pi;
histogram(app.UIAxes,aSDE, sz,sz2, sz3) ;
end
end
% Component initialization
methods (Access = private)
% Create UIFigure and components
function createComponents(app)
% Create UIFigure and hide until all components are created
app.UIFigure = uifigure('Visible', 'off');
app.UIFigure.Position = [100 100 640 480];
app.UIFigure.Name = 'MATLAB App';
% Create UIAxes
app.UIAxes = uiaxes(app.UIFigure);
title(app.UIAxes, 'Title')
xlabel(app.UIAxes, 'X')
ylabel(app.UIAxes, 'Y')
zlabel(app.UIAxes, 'Z')
app.UIAxes.Position = [297 240 300 185];
% Create LoaddataButton
app.LoaddataButton = uibutton(app.UIFigure, 'push');
app.LoaddataButton.ButtonPushedFcn = createCallbackFcn(app, @LoaddataButtonPushed, true);
app.LoaddataButton.Position = [52 424 100 22];
app.LoaddataButton.Text = 'Load data';
% Create DcollagenorientationofthetaButton
app.DcollagenorientationofthetaButton = uibutton(app.UIFigure, 'push');
app.DcollagenorientationofthetaButton.ButtonPushedFcn = createCallbackFcn(app, @DcollagenorientationofthetaButtonPushed, true);
app.DcollagenorientationofthetaButton.Position = [10 364 184 22];
app.DcollagenorientationofthetaButton.Text = '2D collagen orientation of theta';
% Create DcollagenorientationofphiButton
app.DcollagenorientationofphiButton = uibutton(app.UIFigure, 'push');
app.DcollagenorientationofphiButton.Position = [16 314 173 22];
app.DcollagenorientationofphiButton.Text = '2D collagen orientation of phi';
% Create OrientationhistogramofthetaButton
app.OrientationhistogramofthetaButton = uibutton(app.UIFigure, 'push');
app.OrientationhistogramofthetaButton.ButtonPushedFcn = createCallbackFcn(app, @OrientationhistogramofthetaButtonPushed, true);
app.OrientationhistogramofthetaButton.Position = [13 190 176 22];
app.OrientationhistogramofthetaButton.Text = 'Orientation histogram of theta';
% Create OrientationhistogramofphiButton
app.OrientationhistogramofphiButton = uibutton(app.UIFigure, 'push');
app.OrientationhistogramofphiButton.Position = [19 130 165 22];
app.OrientationhistogramofphiButton.Text = 'Orientation histogram of phi';
% Show the figure after all components are created
app.UIFigure.Visible = 'on';
end
end
% App creation and deletion
methods (Access = public)
% Construct app
function app = histogram
% Create UIFigure and components
createComponents(app)
% Register the app with App Designer
registerApp(app, app.UIFigure)
if nargout == 0
clear app
end
end
% Code that executes before app deletion
function delete(app)
% Delete UIFigure when app is deleted
delete(app.UIFigure)
end
end
end

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回答（1 个）

Vidhi Agarwal
Vidhi Agarwal 2024-4-24
Hi Kaitlynn,
I understand you've encountered a challenge with an error stating "Too many output arguments" within your MATLAB application. It seems this difficulty may arise from the way the “meanc” function has been defined in the code.
To address this issue, it is suggested to revise the function's signature to enable it to return an output, as shown below:
function aS = meanc(app)
%%function as it is.
end
By implementing this adjustment, the “meanc” function will be configured to return a value, aligning with the original intention. This modification might help in resolving the encountered error.
For a better understanding of functions in MATLAB, you can refer to the following documentation:

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