www.gusucode.com > 声音的处理有：LPC，FFT，共振峰，频谱源码程序 > siganlandsystemusingMatlab/SSUM/models/modelexpofn.m
%
function modelexpofn(action,datastruct)
if nargin < 1
action='init';
end
name = mfilename;
figname = [name(1:end-2) '_fig'];
f=findobj('Tag',figname);
handles = get(f,'UserData');
% If main window closes, close all other window
switch action
case 'help'
display_help(figname);
case 'init'
setdefaults('menu');
movegui(f,'center');
case 'draw'
imgsize = str2double(get(handles.imgsize,'String'));
imgdata.data = zeros(imgsize,imgsize);
imgdata.map = colormap('gray');
if get(handles.twostate,'Value'),
bits = 8;
prev_val = round(rand*(2^bits-1));
p = str2double(get(handles.p,'String'));
q = str2double(get(handles.q,'String'));
if ~get(handles.animate,'Value')
h = waitbar(0,'Computing...');
for i=1:imgsize,
for j=1:imgsize,
% Roll dice
dice = rand;
if prev_val == 0,
if dice < p
imgdata.data(i,j) = 2^bits-1;
else
imgdata.data(i,j) = 0;
end
else
if dice < q
imgdata.data(i,j) = 0;
else
imgdata.data(i,j) = 2^bits-1;
end
end
prev_val = imgdata.data(i,j);
end
%prev_val = imgdata.data(i,1);
waitbar(i/imgsize,h);
end
close(h);
%imgdata.data = uint8(imgdata.data);
handles.figwin = showimage(imgdata,0);
else
handles.figwin = showimage(imgdata,0);
for i=1:imgsize,
for j=1:imgsize,
imgdata.data(i,j) = 2^bits-1;
showimage(imgdata,0);
% Roll dice
dice = rand;
if prev_val == 0,
if dice < p
imgdata.data(i,j) = 2^bits-1;
else
imgdata.data(i,j) = 0;
end
else
if dice < q
imgdata.data(i,j) = 0;
else
imgdata.data(i,j) = 2^bits-1;
end
end
drawnow;
showimage(imgdata,0);
prev_val = imgdata.data(i,j);
end
end
end
handles.imagedata = imgdata;
elseif get(handles.ar,'Value'),
bits = 8;
prev_val = 2^bits-1;
alpha = str2double(get(handles.alpha,'String'));
stdev = str2double(get(handles.std,'String'));
meanval = (2^bits)/2-1;
scale = get(handles.scale,'Value');
if ~get(handles.animate,'Value')
h = waitbar(0,'Computing...');
for i=1:imgsize,
for j=1:imgsize,
pixel_val = round(prev_val*alpha + ...
randn*stdev*(2^bits-1));
if pixel_val > 2^bits-1,
pixel_val = 2^bits-1;
elseif pixel_val < 0
pixel_val = 0;
end
%imgdata.data(i,j) = pixel_val+meanval;
imgdata.data(i,j) = pixel_val;
prev_val = pixel_val;
end
waitbar(i/imgsize,h);
end
close(h);
handles.figwin = showimage(imgdata,scale);
else
handles.figwin = showimage(imgdata,scale);
for i=1:imgsize,
for j=1:imgsize,
imgdata.data(i,j) = 2^(bits-1);
showimage(imgdata,scale);
pixel_val = round(prev_val*alpha + ...
randn*stdev*(2^bits-1));
if pixel_val > 2^bits-1,
pixel_val = 2^bits-1;
elseif pixel_val < 0
pixel_val = 0;
end
%imgdata.data(i,j) = pixel_val+meanval;
imgdata.data(i,j) = pixel_val;
drawnow;
showimage(imgdata,scale);
prev_val = pixel_val;
end
end
end
elseif get(handles.us,'Value'),
bits = str2num(get(handles.bits,'String'));
prev_val = 2^bits-1;
alpha = str2double(get(handles.us_a,'String'));
stdev = str2double(get(handles.us_std,'String'));
contents = get(handles.distribution,'String');
distribution = lower(contents{get(handles.distribution,'Value')});
meanval = (2^bits-1)/2;
scale = get(handles.us_scale,'Value');
if ~get(handles.animate,'Value')
h = waitbar(0,'Computing...');
for i=1:imgsize,
for j=1:imgsize,
switch distribution,
case 'gaussian'
pixel_val = round(prev_val*alpha + ...
randn*stdev*(2^bits-1)/2+meanval);
case 'uniform'
pixel_val = round(prev_val*alpha + ...
rand*(2^bits-1));
end
if pixel_val > 2^bits-1,
pixel_val = 2^bits-1;
elseif pixel_val < 0
pixel_val = 0;
end
imgdata.data(i,j) = pixel_val;
prev_val = pixel_val;
end
waitbar(i/imgsize,h);
end
close(h);
handles.figwin = showimage(imgdata,scale);
else
handles.figwin = showimage(imgdata,scale);
for i=1:imgsize,
for j=1:imgsize,
imgdata.data(i,j) = 2^(bits-1);
showimage(imgdata,scale);
switch distribution,
case 'gaussian'
pixel_val = round(prev_val*alpha + ...
randn*stdev*(2^bits-1)+meanval);
case 'uniform'
pixel_val = round(prev_val*alpha + ...
rand*(2^bits-1));
end
if pixel_val > 2^bits-1,
pixel_val = 2^bits-1;
elseif pixel_val < 0
pixel_val = 0;
end
imgdata.data(i,j) = pixel_val;
drawnow;
showimage(imgdata,scale);
prev_val = pixel_val;
end
end
end
end
handles.imagedata = imgdata;
handles.entropy = calc_entropy(imgdata.data, bits);
axes(get(handles.figwin,'Children'));
title(['Entropy = ' num2str(handles.entropy)]);
case 'scale'
if get(handles.ar,'Value')
scale = get(handles.scale,'Value');
elseif get(handles.us,'Value')
scale = get(handles.us_scale,'Value');
end
if isfield(handles,'figwin')
axes(get(handles.figwin,'Children'));
showimage(handles.imagedata,scale);
title(['Entropy = ' num2str(handles.entropy)]);
end
case 'save'
save_imagedata(handles.imagedata);
case 'print'
print_figure(f);
case 'close'
close_figure(f,figname(1:end-4));
if isfield(handles, 'figwin'),
close_figure(handles.figwin);
end
return;
end
set(f,'UserData',handles);
function entropy = calc_entropy(data, bits);
[m,n] = size(data);
% for each level find probability
entropy = 0;
for i=0:2^bits-1,
A = data==i;
p = sum(sum(A))/(m*n);
if p > 0
entropy = entropy - p*log2(p);
end
end
function figwin = showimage(imagedata, varargin)
if nargin < 2
scale = 1;
inverse = 0;
fignum = 2;
elseif nargin < 3
scale = varargin{1};
inverse = 0;
fignum = 2;
elseif nargin < 4
scale = varargin{1};
inverse = varargin{2};
fignum = 2;
else
scale = varargin{1};
inverse = varargin{2};
fignum = varargin{3};
end
figwin = figure(fignum);
cla;
set(figwin,'MenuBar','none','DoubleBuffer','on','Units','Pixels');
if ~isempty(imagedata.map)
cmap = colormap(imagedata.map);
else
cmap = colormap('gray');
end
if inverse,
colormap(flipud(cmap));
end
if scale,
imagesc(imagedata.data);
else
image(imagedata.data);
end
axis image;