I have an Image which is 1944(R) x 2592 (C). I would like to take one column at a time; treating each column as an Image and calculate how many pixels among each row of that column contains value > half(max) value of that column. Those number of pixels are to be written to the excel sheet corresponding to it's respective column.
Image: the image
Here is what I have tried so far, I am not able to write it successfully.
clc;
sig = rgb2gray(imread('1.bmp')); % read in the image.
% imshow(sig);
ArraySize = size(sig); %1944(R) x 2592 (C)
[maxval, maxloc] = max(sig(:)); % Gives the max and the location
maxval; % max value
[maxloc_row, maxloc_col] = ind2sub(size(sig), maxloc); % convert logical
%-------------------------------------------------------------------%
% Count pixels through each column > half(max) value of that column
%-------------------------------------------------------------------%
newfilename = 'Results.csv'; % write new values to .csv files
Array = zeros(2592,2);
% % R = Array(:,1);
% y = Array(:,2);
for a = 1: 2592% maxloc_row = 635 maxloc_col = 1094
[a_maxval, a_maxloc] = max(sig(:,a)); % search max among every column.
% maxloc is the row at which maxval is.
newval = a_maxval/2; % averaged max value
% New structure for find width
x = 0;
x = Array(:, 1);
for b = 1: 1944 % maxloc_row = 635 maxloc_col = 1094
% R = b;
if sig(b,a) > newval
x=x+1;
end
end % End row search
x;
% y = x*(2.2); % pixels into microns
output = [num2cell(x)];
xlswrite(newfilename, output);
end % End column search
I think csvwrite can write data to csv file. Here's what you can try:
place output = []; somewhrere at top, says below clc;
then, replace
output = [num2cell(x)]; with output = [output x];
and
xlswrite(newfilename, output); with csvwrite(newfilename, output); but place it at the end of program because output has all x inside.
I have tried and it works.
I am trying to write a program which acquires an image from a webcam, averages over several of the vertical pixel rows, displays the result and then applies a Gaussian fit to locate the peak position with a high degree of accuracy. All of this is to be done in real time. I used a timer object to run the function (called datain) which triggers the camera and acquires the data. To iterate and update the data the function includes a while loop. My code worked perfectly until I added a command inside the while loop to call another function (called GaussianFit) which preformed the fit. When this function is added I receive an error which says the callback function in the timer does not have enough inputs.
"Error while evaluating TimerFcn for timer 'timer-1' Not enough input arguments."
This seems odd since the data acquisition function provides all the inputs needed by the fitting function. My code is as follows:
function WaveAQ()
global webcamin
%%%%%%%%%%%%%%%%%%%%%%%
% Create video object %
%%%%%%%%%%%%%%%%%%%%%%%
dID = 2; vForm = 'MJPG_1280x720';
% Search for existing video objects to avoid creating multiple handles
vObj = imaqfind('DeviceID', dID, 'VideoFormat', vForm);
if isempty(vObj)
webcamin = videoinput('winvideo',dID,vForm);
else
webcamin = vObj{1};
end
% Configure # datasets to be averaged per measurement
set(webcamin,'FramesPerTrigger',1);
% Repeat until stopped
set(webcamin,'TriggerRepeat',Inf);
% Set to grayscale
set(webcamin,'ReturnedColorSpace','grayscale');
% Allow webcam to be triggered by timer
triggerconfig(webcamin,'manual')
% % View the properties for the selected video source object.
% src_obj = getselectedsource(webcamin);
% get(src_obj)
% Prompt for dark reading
j = 1;
j = input('Would you like to take a dark reading? (y/n): ','s');
switch j
case 'y'
j = 1;
case 'n'
j = 2;
end
if j == 1
prompt = input('Block input and enter any number to continue: ');
end
% We now define a function which will acquire data from the camera to be
% executed by the timer function
fig1 = figure(1);
function datain(~,event,input)
persistent image
h = 1; % Initialize Loop
bg = 0;
avgroi = 0;
% Calibration and fitting constants
tol = 1;
calib_slope = .003126;
calib_offset = 781.8;
spect = (1:1280)*calib_slope + calib_offset;
while h == 1
% Check if figure is open
h = ishandle(fig1);
trigger(input);
% First run finds dark reading
if j == 1
bg = getdata(input,1,'uint8');
j = 2;
else
image = getdata(input,1,'uint8');
image = image - bg; % Correct for background
npxy = length(image(:,1));
npxx = length(image(1,:));
% Region of interest for spectral data
dy_roi = 200;
y_roi = npxy/2;
% Region of interest for beam position
dx_roi = npxx/2;
x_roi = npxx/2;
avgroi = sum(image((y_roi-dy_roi):(y_roi+dy_roi),:))/(2*dy_roi);
avgroi = avgroi/256; % Normalize units
avg_y = (sum(image(:,(x_roi-dx_roi+1):(x_roi+dx_roi))).'/(2*dx_roi)).';
[cx,sx,Ipeak] = GaussianFit(avgroi,tol);
% cx = max(avgroi);
% sx = 2;
% Ipeak = max(avgroi);
subplot(2,1,1)
plot(spect,avgroi)
title('Spectrogram')
xlabel('Wavelength (nm)')
ylabel('Intensity (arb.)')
legend(['\lambda_P_e_a_k = ',num2str(cx),'nm'])
subplot(2,1,2)
plot(1:length(avg_y),avg_y/256)
title('Beam Profile in Y')
xlabel('Pixel #')
ylabel('Intensity (arb.)')
end
end
end
%%%%%%%%%%%%%%%%%%%
% Define timer object to execute daq %
%%%%%%%%%%%%%%%%%%%
% Set callback properies
timerfcn = #datain;
% Image acquisition rate
Raq = 5;
timer_exec = timer('TimerFcn',{timerfcn,webcamin},'period',1/Raq,'BusyMode','drop');% Maybe change busy mode to queue if regular acquisition interval needed.
% timer_exec2 = timer('TimerFcn',#GaussianFit,'period',1/Raq,'BusyMode','drop');
% Execute acquistion
start(webcamin);
preview(webcamin);
start(timer_exec);
% start(timer_exec2);
% Clean system
stop(timer_exec);
% stop(timer_exec2);
delete(timer);
stop(webcamin);
delete(webcamin);
clear functions;
end
Can anyone tell me why the timer needs more inputs when GaussianFit is called inside the loop?
In cwt() I can specify which wavelet function to use. How does that impact the speed of cwt()?
Here is a benchmark, which I run with the -singleCompThread option when starting MATLAB to force it to use a single computational thread. cwt() was passed a 1,000,000-sample signal and asked to compute scales 1 to 10. My CPU is an i7-3610QM.
Code used:
clear all
%% Benchmark parameters
results_file_name = 'results_scale1-10.csv';
number_of_random_runs = 10;
scales = 1:10;
number_of_random_samples = 1000000;
%% Construct a cell array containing all the wavelet names
wavelet_haar_names = {'haar'};
wavelet_db_names = {'db1'; 'db2'; 'db3'; 'db4'; 'db5'; 'db6'; 'db7'; 'db8'; 'db9'; 'db10'};
wavelet_sym_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
wavelet_coif_names = {'coif1'; 'coif2'; 'coif3'; 'coif4'; 'coif5'};
wavelet_bior_names = {'bior1.1'; 'bior1.3'; 'bior1.5'; 'bior2.2'; 'bior2.4'; 'bior2.6'; 'bior2.8'; 'bior3.1'; 'bior3.3'; 'bior3.5'; 'bior3.7'; 'bior3.9'; 'bior4.4'; 'bior5.5'; 'bior6.8'};
wavelet_rbior_names = {'rbio1.1'; 'rbio1.3'; 'rbio1.5'; 'rbio2.2'; 'rbio2.4'; 'rbio2.6'; 'rbio2.8'; 'rbio3.1'; 'rbio3.3'; 'rbio3.5'; 'rbio3.7'; 'rbio3.9'; 'rbio4.4'; 'rbio5.5'; 'rbio6.8'};
wavelet_meyer_names = {'meyr'};
wavelet_dmeyer_names = {'dmey'};
wavelet_gaus_names = {'gaus1'; 'gaus2'; 'gaus3'; 'gaus4'; 'gaus5'; 'gaus6'; 'gaus7'; 'gaus8'};
wavelet_mexh_names = {'mexh'};
wavelet_morl_names = {'morl'};
wavelet_cgau_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_shan_names = {'shan1-1.5'; 'shan1-1'; 'shan1-0.5'; 'shan1-0.1'; 'shan2-3'};
wavelet_fbsp_names = {'fbsp1-1-1.5'; 'fbsp1-1-1'; 'fbsp1-1-0.5'; 'fbsp2-1-1'; 'fbsp2-1-0.5'; 'fbsp2-1-0.1'};
wavelet_cmor_names = {'cmor1-1.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-0.1'};
% Concatenate all wavelet names into a single cell array
wavelet_categories_names = who('wavelet*names');
wavelet_names = {};
for wavelet_categories_number=1:size(wavelet_categories_names,1)
temp = wavelet_categories_names(wavelet_categories_number);
temp = eval(temp{1});
wavelet_names = vertcat(wavelet_names, temp);
end
%% Prepare data
random_signal = rand(number_of_random_runs,number_of_random_samples);
%% Run benchmarks
result_file_ID = fopen(results_file_name, 'w');
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names(wavelet_number,:)
% Compute wavelet on a random signal
tic
for run = 1:number_of_random_runs
cwt(random_signal(run, :),scales,wavelet_name{1});
end
run_time_random_test = toc
fprintf(result_file_ID, '%s,', wavelet_name{1})
fprintf(result_file_ID, '%d\n', run_time_random_test)
end
size(wavelet_names,1)
fclose(result_file_ID);
If you want to see the impact of the choice of the scale:
Code used:
clear all
%% Benchmark parameters
results_file_name = 'results_sym2_change_scale.csv';
number_of_random_runs = 10;
scales = 1:10;
number_of_random_samples = 10000000;
% wavelet_names = {'sym2', 'sym3'}%, 'sym4'};
output_directory = 'output';
wavelet_names = get_all_wavelet_names();
%% Prepare data
random_signal = rand(number_of_random_runs,number_of_random_samples);
%% Prepare result folder
if ~exist(output_directory, 'dir')
mkdir(output_directory);
end
%% Run benchmarks
result_file_ID = fopen(results_file_name, 'w');
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
if wavelet_number > 1
fprintf(result_file_ID, '%s\n', '');
end
fprintf(result_file_ID, '%s', wavelet_name)
run_time_random_test_scales = zeros(size(scales,2),1);
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
% Compute wavelet on a random signal
tic
for run = 1:number_of_random_runs
cwt(random_signal(run, :),scale,wavelet_name);
end
run_time_random_test = toc
fprintf(result_file_ID, ',%d', run_time_random_test)
run_time_random_test_scales(scale_number) = run_time_random_test;
end
figure
bar(run_time_random_test_scales)
title(['Run time on random signal for ' wavelet_name])
xlabel('Scale')
ylabel('Run time (seconds)')
save_figure( fullfile(output_directory, ['run_time_random_test_' wavelet_name]) )
close all
end
size(wavelet_names,1)
fclose(result_file_ID);
With 3 functions:
get_all_wavelet_names.m:
function [ wavelet_names ] = get_all_wavelet_names( )
%GET_ALL_WAVELET_NAMES Get a list of available wavelet functions
%% Construct a cell array containing all the wavelet names
wavelet_haar_names = {'haar'};
wavelet_db_names = {'db1'; 'db2'; 'db3'; 'db4'; 'db5'; 'db6'; 'db7'; 'db8'; 'db9'; 'db10'};
wavelet_sym_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
wavelet_coif_names = {'coif1'; 'coif2'; 'coif3'; 'coif4'; 'coif5'};
wavelet_bior_names = {'bior1.1'; 'bior1.3'; 'bior1.5'; 'bior2.2'; 'bior2.4'; 'bior2.6'; 'bior2.8'; 'bior3.1'; 'bior3.3'; 'bior3.5'; 'bior3.7'; 'bior3.9'; 'bior4.4'; 'bior5.5'; 'bior6.8'};
wavelet_rbior_names = {'rbio1.1'; 'rbio1.3'; 'rbio1.5'; 'rbio2.2'; 'rbio2.4'; 'rbio2.6'; 'rbio2.8'; 'rbio3.1'; 'rbio3.3'; 'rbio3.5'; 'rbio3.7'; 'rbio3.9'; 'rbio4.4'; 'rbio5.5'; 'rbio6.8'};
wavelet_meyer_names = {'meyr'};
wavelet_dmeyer_names = {'dmey'};
wavelet_gaus_names = {'gaus1'; 'gaus2'; 'gaus3'; 'gaus4'; 'gaus5'; 'gaus6'; 'gaus7'; 'gaus8'};
wavelet_mexh_names = {'mexh'};
wavelet_morl_names = {'morl'};
wavelet_cgau_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_shan_names = {'shan1-1.5'; 'shan1-1'; 'shan1-0.5'; 'shan1-0.1'; 'shan2-3'};
wavelet_fbsp_names = {'fbsp1-1-1.5'; 'fbsp1-1-1'; 'fbsp1-1-0.5'; 'fbsp2-1-1'; 'fbsp2-1-0.5'; 'fbsp2-1-0.1'};
wavelet_cmor_names = {'cmor1-1.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-1'; 'cmor1-0.5'; 'cmor1-0.1'};
% Concatenate all wavelet names into a single cell array
wavelet_categories_names = who('wavelet*names');
wavelet_names = {};
for wavelet_categories_number=1:size(wavelet_categories_names,1)
temp = wavelet_categories_names(wavelet_categories_number);
temp = eval(temp{1});
wavelet_names = vertcat(wavelet_names, temp);
end
end
save_figure.m:
function [ ] = save_figure( output_graph_filename )
% Record aa figure as PNG and fig files
% Create the folder if it doesn't exist already.
[pathstr, name, ext] = fileparts(output_graph_filename);
if ~exist(pathstr, 'dir')
mkdir(pathstr);
end
h = gcf;
set(0,'defaultAxesFontSize',18) % http://www.mathworks.com/support/solutions/en/data/1-8XOW94/index.html?solution=1-8XOW94
boldify(h);
print('-dpng','-r600', [output_graph_filename '.png']);
print(h,[output_graph_filename '.pdf'],'-dpdf','-r600')
saveas(gcf,[output_graph_filename '.fig'], 'fig')
end
and boldify.m:
function boldify(h,g)
%BOLDIFY Make lines and text bold for standard viewgraph style.
% BOLDIFY boldifies the lines and text of the current figure.
% BOLDIFY(H) applies to the graphics handle H.
%
% BOLDIFY(X,Y) specifies an X by Y inch graph of the current
% figure. If text labels have their 'UserData' data property
% set to 'slope = ...', then the 'Rotation' property is set to
% account for changes in the graph's aspect ratio. The
% default is MATLAB's default.
% S. T. Smith
% The name of this function does not represent an endorsement by the author
% of the egregious grammatical trend of verbing nouns.
if nargin < 1, h = gcf;, end
% Set (and get) the default MATLAB paper size and position
set(gcf,'PaperPosition','default');
units = get(gcf,'PaperUnits');
set(gcf,'PaperUnits','inches');
fsize = get(gcf,'PaperPosition');
fsize = fsize(3:4); % Figure size (X" x Y") on paper.
psize = get(gcf,'PaperSize');
if nargin == 2 % User specified graph size
fsize = [h,g];
h = gcf;
end
% Set the paper position of the current figure
set(gcf,'PaperPosition', ...
[(psize(1)-fsize(1))/2 (psize(2)-fsize(2))/2 fsize(1) fsize(2)]);
fsize = get(gcf,'PaperPosition');
fsize = fsize(3:4); % Graph size (X" x Y") on paper.
set(gcf,'PaperUnits',units); % Back to original
% Get the normalized axis position of the current axes
units = get(gca,'Units');
set(gca,'Units','normalized');
asize = get(gca,'Position');
asize = asize(3:4);
set(gca,'Units',units);
ha = get(h,'Children');
for i=1:length(ha)
% if get(ha(i),'Type') == 'axes'
% changed by B. A. Miller
if strcmp(get(ha(i), 'Type'), 'axes') == 1
units = get(ha(i),'Units');
set(ha(i),'Units','normalized');
asize = get(ha(i),'Position'); % Axes Position (normalized)
asize = asize(3:4);
set(ha(i),'Units',units);
[m,j] = max(asize); j = j(1);
scale = 1/(asize(j)*fsize(j)); % scale*inches -normalized units
set(ha(i),'FontWeight','Bold');
set(ha(i),'LineWidth',2);
[m,k] = min(asize); k = k(1);
if asize(k)*fsize(k) > 1/2
set(ha(i),'TickLength',[1/8 1.5*1/8]*scale); % Gives 1/8" ticks
else
set(ha(i),'TickLength',[3/32 1.5*3/32]*scale); % Gives 3/32" ticks
end
set(get(ha(i),'XLabel'),'FontSize',18); % 14-pt labels
set(get(ha(i),'XLabel'),'FontWeight','Bold');
set(get(ha(i),'XLabel'),'VerticalAlignment','top');
set(get(ha(i),'YLabel'),'FontSize',18); % 14-pt labels
set(get(ha(i),'YLabel'),'FontWeight','Bold');
%set(get(ha(i),'YLabel'),'VerticalAlignment','baseline');
set(get(ha(i),'Title'),'FontSize',18); % 16-pt titles
set(get(ha(i),'Title'),'FontWeight','Bold');
% set(get(ha(i), 'FontSize',20, 'XTick',[]));
end
hc = get(ha(i),'Children');
for j=1:length(hc)
chtype = get(hc(j),'Type');
if chtype(1:4) == 'text'
set(hc(j),'FontSize',17); % 12 pt descriptive labels
set(hc(j),'FontWeight','Bold');
ud = get(hc(j),'UserData'); % User data
if length(ud) 8
if ud(1:8) == 'slope = ' % Account for change in actual slope
slope = sscanf(ud,'slope = %g');
slope = slope*(fsize(2)/fsize(1))/(asize(2)/asize(1));
set(hc(j),'Rotation',atan(slope)/pi*180);
end
end
elseif chtype(1:4) == 'line'
set(hc(j),'LineWidth',2);
end
end
end
Bonus: correlation between all wavelets on a random signal with 1000000 samples with the first 10 scales:
Code used:
%% PRE-REQUISITE: You need to download http://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps , which gives the function heatmap()
%% Benchmark parameters
scales = 1:10;
number_of_random_samples = 1000000;
% wavelet_names = {'sym2'; 'sym3'; 'sym4'; 'sym5'; 'sym6'; 'sym7'; 'sym8'};
% wavelet_names = {'cgau1'; 'cgau2'; 'cgau3'; 'cgau4'; 'cgau5'};
wavelet_names = {'db2'; 'sym2'};
OUTPUT_FOLDER = 'output_corr';
% wavelet_names = get_all_wavelet_names(); % WARNING: you need to remove all complex wavelets, viz. cgau1, shan, fbsp and cmor, and the heatmap will be pissed to see complex values coming to her.
%% Prepare data
random_signal = rand(1,number_of_random_samples);
results = zeros(size(wavelet_names,1), number_of_random_samples);
%% Prepare result folder
if ~exist(OUTPUT_FOLDER, 'dir')
mkdir(OUTPUT_FOLDER);
end
%% Run benchmarks
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
% Compute wavelet on a random signal
run = 1;
results(wavelet_number, :) = cwt(random_signal(run, :),scale,wavelet_name);
if wavelet_number == 999
break
end
end
correlation_results = corrcoef(results')
heatmap(correlation_results, [], [], '%0.2f', 'MinColorValue', -1.0, 'MaxColorValue', 1.0, 'Colormap', 'jet',...
'Colorbar', true, 'ColorLevels', 64, 'UseFigureColormap', false);
title(['Correlation matrix for scale ' num2str(scale)]);
xlabel(['Wavelet 1 to ' num2str(size(wavelet_names,1)) ' for scale ' num2str(scale)]);
ylabel(['Wavelet 1 to ' num2str(size(wavelet_names,1)) ' for scale ' num2str(scale)]);
snapnow
print('-dpng','-r600',fullfile(OUTPUT_FOLDER, ['scalecorr' num2str(scale) '.png']))
end
Correlation for each wavelet between different scales (1 to 100):
Code used:
%% PRE-REQUISITE: You need to download http://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps , which gives the function heatmap()
%% Benchmark parameters
scales = 1:100;
number_of_random_samples = 1000000;
% wavelet_name = 'gaus2';
% wavelet_names = {'sym2', 'sym3'}%, 'sym4'};
OUTPUT_FOLDER = 'output_corr';
wavelet_names = get_all_wavelet_names(); % WARNING: you need to remove all complex wavelets, viz. cgau1, shan, fbsp and cmor, and the heatmap will be pissed to see complex values coming to her.
%% Prepare data
random_signal = rand(1,number_of_random_samples);
results = zeros(size(scales,2), number_of_random_samples);
%% Prepare result folder
if ~exist(OUTPUT_FOLDER, 'dir')
mkdir(OUTPUT_FOLDER);
end
%% Run benchmarks
for wavelet_number = 1:size(wavelet_names,1)
wavelet_name = wavelet_names{wavelet_number}
run_time_random_test_scales = zeros(size(scales,2),1);
for scale_number = 1:size(scales,2)
scale = scales(scale_number);
run = 1;
% Compute wavelet on a random signal
results(scale_number, :) = cwt(random_signal(run, :),scale,wavelet_name);
end
correlation_results = corrcoef(results')
heatmap(correlation_results, [], [], '%0.2f', 'MinColorValue', -1.0, 'MaxColorValue', 1.0, 'Colormap', 'jet',...
'Colorbar', true, 'ColorLevels', 64, 'UseFigureColormap', false);
title(['Correlation matrix for wavelet ' wavelet_name]);
xlabel(['Scales 1 to ' num2str(max(scales)) ' for wavelet ' wavelet_name]);
ylabel(['Scales 1 to ' num2str(max(scales)) ' for wavelet ' wavelet_name]);
snapnow
print('-dpng','-r600',fullfile(OUTPUT_FOLDER, [wavelet_name '_scalecorr_scale1to' num2str(max(scales)) '.png']))
end
I am importing wordnet library in matlab code given below but it takes almost 5-10 seconds for giving me synonyms of each word.
can its time be reduced in some way because I have to test it on numerous words
Thanks
Code:
function [status,varargout] = dictionary(text)
%DICTIONARY checks the spelling status of word(s) and returns synonyms if found.
%
% [status,synonyms] = dictionary(text);
% status = dictionary(text);
%
% text: word(s) separated by a single space.
% status: returns '1' if word(s) exist in dictionary or '0' otherwise.
% synonyms: an array of synonyms of word(s) or otherwise returns
% a message 'No Synonyms Found!' or 'Incorrect Spelling!'.
%
% Examples:
% [status,synonyms] = dictionary('peddler');
% [status,synonyms] = dictionary('walk match ground');
% status = dictionary('hysteria');
%
% Separating string of words into arrays of words.
k=1;
temp='';
% t=fopen('Crime.txt');
% text=textscan(t,'%s');
% text=texts{1,1};
% fclose(t);
%text='ignorevbnnn';
for n=1:length(text);
if ~isspace(text(n))
temp = [temp text(n)];
else
if ~isspace(text(n-1))
words{k} = temp;
end
temp='';
k=k+1;
end
end
words{k} = temp;
% Opening MS Word and Starting the Spelling Check & Finding Synonyms
Doc = actxserver('Word.Application');
m=1;
for n=1:length(words)
if ~isempty(words{n})
status(m) = invoke(Doc,'CheckSpelling',words{n},[],1,1);
if nargout==2
X = invoke(Doc,'SynonymInfo',words{n});
Synonyms = get(X,'MeaningList');
Meanings{m,1} = words{n};
if length(Synonyms)==0 & status(m)==1
Meanings{m,2} = 'No Synonyms Found!';
elseif status(m)==0
Meanings{m,2} = 'Incorrect Spelling!';
else
for k=2:length(Synonyms)+1
Meanings{m,k} = Synonyms{k-1};
end
end
end
m=m+1;
end
end
if exist('Meanings','var')
varargout = {Meanings};
end
status = all(status);
invoke(Doc,'Quit');
delete(Doc);