Error while evaluating uicontrol Callback
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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 comentario
Rik
el 1 de Dic. de 2020
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.
Respuestas (1)
Furkan DEMIR
el 10 de Dic. de 2020
Hello.
load Trainset.mat has two file. one of these meas and label.
When I see meas files. I saw 20*13 matrix. what is the meaning. Why the file is 20*13 matrix
1 comentario
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