Ggtcf

i having trouble to call the function from the generated code and i want to display the output in command window.

function varargout = ipwebcamGUI(varargin)
% IPWEBCAMGUI MATLAB code for ipwebcamGUI.fig
% IPWEBCAMGUI, by itself, creates a new IPWEBCAMGUI or raises the existing
% singleton*.
%
% H = IPWEBCAMGUI returns the handle to a new IPWEBCAMGUI or the handle to
% the existing singleton*.
%
% IPWEBCAMGUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in IPWEBCAMGUI.M with the given input arguments.
%
% IPWEBCAMGUI('Property','Value',...) creates a new IPWEBCAMGUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before ipwebcamGUI_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to ipwebcamGUI_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 ipwebcamGUI

% Last Modified by GUIDE v2.5 07-Mar-2019 22:03:50

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
 'gui_Singleton', gui_Singleton, ...
 'gui_OpeningFcn', @ipwebcamGUI_OpeningFcn, ...
 'gui_OutputFcn', @ipwebcamGUI_OutputFcn, ...
 'gui_LayoutFcn', [] , ...
 'gui_Callback', []);
if nargin && ischar(varargin1)
 gui_State.gui_Callback = str2func(varargin1);
end

if nargout
 [varargout1:nargout] = gui_mainfcn(gui_State, varargin:);
else
 gui_mainfcn(gui_State, varargin:);
end
% End initialization code - DO NOT EDIT


% --- Executes just before ipwebcamGUI is made visible.
function ipwebcamGUI_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 ipwebcamGUI (see VARARGIN)

% Choose default command line output for ipwebcamGUI
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes ipwebcamGUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = ipwebcamGUI_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
varargout1 = 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)


axes(handles.axes1);
im=imread('c1.jpg');
k=rgb2gray(im);

% Threshold image - global threshold
BW = imbinarize(k);

% Invert mask
BW = imcomplement(BW);

%clear border
BW = imclearborder(BW);

% Create masked image.
maskedImage = k;
maskedImage(~BW) = 0;

%count the white pixel in image
whitepix = sum(BW(:));

axis off;
imshow(BW);
clear


% --- 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)

T = trainClassifier2(trainingData);
yfit = trainedClassifier.predictFcn(T);
fprintf('result %d n',yfit );
% set(handles.text2,'String',T);
% drawnow();

this file named ipwebcamGUI.m
and the second trainClassifier2.m

function [trainedClassifier, validationAccuracy] = trainClassifier2(trainingData)
% trainClassifier(trainingData)
% returns a trained classifier and its accuracy.
% This code recreates the classification model trained in
% Classification Learner app.
%
% Input:
% trainingData: the training data of same data type as imported
% in the app (table or matrix).
%
% Output:
% trainedClassifier: a struct containing the trained classifier.
% The struct contains various fields with information about the
% trained classifier.
%
% trainedClassifier.predictFcn: a function to make predictions
% on new data. It takes an input of the same form as this training
% code (table or matrix) and returns predictions for the response.
% If you supply a matrix, include only the predictors columns (or
% rows).
%
% validationAccuracy: a double containing the accuracy in
% percent. In the app, the History list displays this
% overall accuracy score for each model.
%
% Use the code to train the model with new data.
% To retrain your classifier, call the function from the command line
% with your original data or new data as the input argument trainingData.
%
% For example, to retrain a classifier trained with the original data set
% T, enter:
% [trainedClassifier, validationAccuracy] = trainClassifier(T)
%
% To make predictions with the returned 'trainedClassifier' on new data T,
% use
% yfit = trainedClassifier.predictFcn(T)
%
% To automate training the same classifier with new data, or to learn how
% to programmatically train classifiers, examine the generated code.

% Auto-generated by MATLAB on 03-Mar-2019 14:35:31


% Extract predictors and response
% This code processes the data into the right shape for training the
% classifier.
inputTable = trainingData;
predictorNames = 'VarName1';
predictors = inputTable(:, predictorNames);
response = inputTable.A;
isCategoricalPredictor = [false];

% Train a classifier
% This code specifies all the classifier options and trains the classifier.
classificationTree = fitctree(...
 predictors, ...
 response, ...
 'SplitCriterion', 'gdi', ...
 'MaxNumSplits', 100, ...
 'Surrogate', 'off', ...
 'ClassNames', 'A'; 'B'; 'C');

% Create the result struct with predict function
predictorExtractionFcn = @(t) t(:, predictorNames);
treePredictFcn = @(x) predict(classificationTree, x);
trainedClassifier.predictFcn = @(x) treePredictFcn(predictorExtractionFcn(x));

% Add additional fields to the result struct
trainedClassifier.RequiredVariables = 'VarName1';
trainedClassifier.ClassificationTree = classificationTree;
trainedClassifier.About = 'This struct is a trained classifier exported from Classification Learner R2016a.';
trainedClassifier.HowToPredict = sprintf('To make predictions on a new table, T, use: n yfit = c.predictFcn(T) nreplacing ''c'' with the name of the variable that is this struct, e.g. ''trainedClassifier''. n nThe table, T, must contain the variables returned by: n c.RequiredVariables nVariable formats (e.g. matrix/vector, datatype) must match the original training data. nAdditional variables are ignored. n nFor more information, see <a href="matlab:helpview(fullfile(docroot, ''stats'', ''stats.map''), ''appclassification_exportmodeltoworkspace'')">How to predict using an exported model</a>.');

% Extract predictors and response
% This code processes the data into the right shape for training the
% classifier.
inputTable = trainingData;
predictorNames = 'VarName1';
predictors = inputTable(:, predictorNames);
response = inputTable.A;
isCategoricalPredictor = [false];

% Perform cross-validation
partitionedModel = crossval(trainedClassifier.ClassificationTree, 'KFold', 5);

% Compute validation accuracy
validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');

% Compute validation predictions and scores
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);

the program counts the white pixel in the image.And the "whitepix" variable would be the expected input..the output will be A,B,C..

this part is where i am having a trouble..i want to display the output when button is clicked.im new to this please help me.

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)

T = trainClassifier2(trainingData);
yfit = trainedClassifier.predictFcn(T);
fprintf('result %d n',yfit );