Error while evaluating uicontrol Callback

Question

Nama Mustafa 2020-12-1

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/671353-error-while-evaluating-uicontrol-callback

评论： Rik 2020-12-10

在 MATLAB Online 中打开

Im getting this error

Error in ==> BrainMRI_GUI at 44

gui_mainfcn(gui_State, varargin{:});

Error in ==> @(hObject,eventdata)BrainMRI_GUI('pushbutton2_Callback',hObject,eventdata,guidata(hObject))

??? Error while evaluating uicontrol Callback

m=the code is:

function varargout = BrainMRI_GUI(varargin)
% BRAINMRI_GUI MATLAB code for BrainMRI_GUI.fig
%      BRAINMRI_GUI, by itself, creates a new BRAINMRI_GUI or raises the existing
%      singleton*.
%
%      H = BRAINMRI_GUI returns the handle to a new BRAINMRI_GUI or the handle to
%      the existing singleton*.
%
%      BRAINMRI_GUI('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in BRAINMRI_GUI.M with the given input arguments.
%
%      BRAINMRI_GUI('Property','Value',...) creates a new BRAINMRI_GUI or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before BrainMRI_GUI_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to BrainMRI_GUI_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help BrainMRI_GUI
% Last Modified by GUIDE v2.5 20-May-2015 08:01:12
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @BrainMRI_GUI_OpeningFcn, ...
                   'gui_OutputFcn',  @BrainMRI_GUI_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before BrainMRI_GUI is made visible.
function BrainMRI_GUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to BrainMRI_GUI (see VARARGIN)
% Choose default command line output for BrainMRI_GUI
handles.output = hObject;
ss = ones(200,200);
axes(handles.axes1);
imshow(ss);
axes(handles.axes2);
imshow(ss);
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes BrainMRI_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = BrainMRI_GUI_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
[FileName,PathName] = uigetfile('*.jpg;*.png;*.bmp','Pick an MRI Image');
if isequal(FileName,0)||isequal(PathName,0)
    warndlg('User Press Cancel');
else
    P = imread([PathName,FileName]);
    P = imresize(P,[200,200]);
   % input =imresize(a,[512 512]); 
  
  axes(handles.axes1)
  imshow(P);title('Brain MRI Image');
 % helpdlg(' Multispectral Image is Selected ');
 % set(handles.edit1,'string',Filename);
 % set(handles.edit2,'string',Pathname);
  handles.ImgData = P;
%  handles.FileName = FileName;
  guidata(hObject,handles);
end
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
if isfield(handles,'ImgData')
    %if isfield(handles,'imgData')
    I = handles.ImgData;
gray = rgb2gray(I);
% Otsu Binarization for segmentation
level = graythresh(I);
%gray = gray>80;
img = im2bw(I,.6);
img = bwareaopen(img,80); 
img2 = im2bw(I);
% Try morphological operations
%gray = rgb2gray(I);
%tumor = imopen(gray,strel('line',15,0));
axes(handles.axes2)
imshow(img);title('Segmented Image');
%imshow(tumor);title('Segmented Image');
handles.ImgData2 = img2;
guidata(hObject,handles);
signal1 = img2(:,:);
%Feat = getmswpfeat(signal,winsize,wininc,J,'matlab');
%Features = getmswpfeat(signal,winsize,wininc,J,'matlab');
[cA1,cH1,cV1,cD1] = dwt2(signal1,'db4');
[cA2,cH2,cV2,cD2] = dwt2(cA1,'db4');
[cA3,cH3,cV3,cD3] = dwt2(cA2,'db4');
DWT_feat = [cA3,cH3,cV3,cD3];
G = pca(DWT_feat);
whos DWT_feat
whos G
g = graycomatrix(G);
stats = graycoprops(g,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
Mean = mean2(G);
Standard_Deviation = std2(G);
Entropy = entropy(G);
RMS = mean2(rms(G));
%Skewness = skewness(img)
Variance = mean2(var(double(G)));
a = sum(double(G(:)));
Smoothness = 1-(1/(1+a));
Kurtosis = kurtosis(double(G(:)));
Skewness = skewness(double(G(:)));
% Inverse Difference Movement
m = size(G,1);
n = size(G,2);
in_diff = 0;
for i = 1:m
    for j = 1:n
        temp = G(i,j)./(1+(i-j).^2);
        in_diff = in_diff+temp;
    end
end
IDM = double(in_diff);
    
feat = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
load Trainset.mat
 xdata = meas;
 group = label;
 svmStruct1 = svmtrain(xdata,group,'kernel_function', 'linear');
 species = svmclassify(svmStruct1,feat,'showplot',false);
 
 if strcmpi(species,'MALIGNANT')
     helpdlg(' Malignant Tumor ');
     disp(' Malignant Tumor ');
 else
     helpdlg(' Benign Tumor ');
     disp(' Benign Tumor ');
 end
 set(handles.edit4,'string',species);
 % Put the features in GUI
set(handles.edit5,'string',Mean);
set(handles.edit6,'string',Standard_Deviation);
set(handles.edit7,'string',Entropy);
set(handles.edit8,'string',RMS);
set(handles.edit9,'string',Variance);
set(handles.edit10,'string',Smoothness);
set(handles.edit11,'string',Kurtosis);
set(handles.edit12,'string',Skewness);
set(handles.edit13,'string',IDM);
set(handles.edit14,'string',Contrast);
set(handles.edit15,'string',Correlation);
set(handles.edit16,'string',Energy);
set(handles.edit17,'string',Homogeneity);
end
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load Trainset.mat
%data   = [meas(:,1), meas(:,2)];
Accuracy_Percent= zeros(200,1);
itr = 80;
hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 100 iterations');
for i = 1:itr
data = meas;
%groups = ismember(label,'BENIGN   ');
groups = ismember(label,'MALIGNANT');
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
%svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
svmStruct_RBF = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
classes2 = svmclassify(svmStruct_RBF,data(test,:),'showplot',false);
classperf(cp,classes2,test);
%Accuracy_Classification_RBF = cp.CorrectRate.*100;
Accuracy_Percent(i) = cp.CorrectRate.*100;
sprintf('Accuracy of RBF Kernel is: %g%%',Accuracy_Percent(i))
waitbar(i/itr);
end
delete(hWaitBar);
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of RBF kernel is: %g%%',Max_Accuracy)
set(handles.edit1,'string',Max_Accuracy);
guidata(hObject,handles);
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton4 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load Trainset.mat
%data   = [meas(:,1), meas(:,2)];
Accuracy_Percent= zeros(200,1);
itr = 100;
hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 100 iterations');
for i = 1:itr
data = meas;
%groups = ismember(label,'BENIGN   ');
groups = ismember(label,'MALIGNANT');
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = svmclassify(svmStruct,data(test,:),'showplot',false);
classperf(cp,classes,test);
%Accuracy_Classification = cp.CorrectRate.*100;
Accuracy_Percent(i) = cp.CorrectRate.*100;
sprintf('Accuracy of Linear Kernel is: %g%%',Accuracy_Percent(i))
waitbar(i/itr);
end
delete(hWaitBar);
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of Linear kernel is: %g%%',Max_Accuracy)
set(handles.edit2,'string',Max_Accuracy);
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton5 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load Trainset.mat
%data   = [meas(:,1), meas(:,2)];
Accuracy_Percent= zeros(200,1);
itr = 100;
hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 100 iterations');
for i = 1:itr
data = meas;
groups = ismember(label,'BENIGN   ');
groups = ismember(label,'MALIGNANT');
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
svmStruct_Poly = svmtrain(data(train,:),groups(train),'Polyorder',2,'Kernel_Function','polynomial');
classes3 = svmclassify(svmStruct_Poly,data(test,:),'showplot',false);
classperf(cp,classes3,test);
Accuracy_Percent(i) = cp.CorrectRate.*100;
sprintf('Accuracy of Polynomial Kernel is: %g%%',Accuracy_Percent(i))
waitbar(i/itr);
end
delete(hWaitBar);
Max_Accuracy = max(Accuracy_Percent);
%Accuracy_Classification_Poly = cp.CorrectRate.*100;
sprintf('Accuracy of Polynomial kernel is: %g%%',Max_Accuracy)
set(handles.edit3,'string',Max_Accuracy);
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton6 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load Trainset.mat
%data   = [meas(:,1), meas(:,2)];
Accuracy_Percent= zeros(200,1);
itr = 100;
hWaitBar = waitbar(0,'Evaluating Maximum Accuracy with 100 iterations');
for i = 1:itr
data = meas;
groups = ismember(label,'BENIGN   ');
groups = ismember(label,'MALIGNANT');
[train,test] = crossvalind('HoldOut',groups);
cp = classperf(groups);
svmStruct4 = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','quadratic');
classes4 = svmclassify(svmStruct4,data(test,:),'showplot',false);
classperf(cp,classes4,test);
%Accuracy_Classification_Quad = cp.CorrectRate.*100;
Accuracy_Percent(i) = cp.CorrectRate.*100;
sprintf('Accuracy of Quadratic Kernel is: %g%%',Accuracy_Percent(i))
waitbar(i/itr);
end
delete(hWaitBar);
Max_Accuracy = max(Accuracy_Percent);
sprintf('Accuracy of Quadratic kernel is: %g%%',Max_Accuracy)
set(handles.edit19,'string',Max_Accuracy);

1 个评论
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Rik 2020-12-1

This time I edited your question for you. Next time, please use the tools explained on this page to make your question more readable.

I also removed most of the irrelevant code that GUIDE inserts.

The error message you posted is incomplete. Do you expect people to read this wall of mostly uncommented and non-indented code? You should try to make it easy for people to answer your question. Have a read here and here. It will greatly improve your chances of getting an answer.

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