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 个评论
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.
回答(1 个)
Furkan DEMIR
2020-12-10
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 个评论
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