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github
sunhongfu/scripts-master
jpg_write.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/jpg_write.m
898
utf_8
2ec215dcac320f380bff814c62d09c54
function jpg_write(x, file, varargin) %function jpg_write(x, file, varargin) arg.clim = []; arg.qual = 99; arg = vararg_pair(arg, varargin); if isempty(arg.clim) arg.clim = minmax(x); end x = floor(255*(x-arg.clim(1))/(arg.clim(2)-arg.clim(1))); x = max(x,0); x = min(x,255); x = uint8(x); x = x.'; file = file_check(file, '.jpg'); if ~isempty(file) imwrite(x, file, 'quality', arg.qual) end function file = file_check(base, suff) keepask = 1; while (keepask) keepask = 0; file = [base suff]; if exist(file, 'file') prompt = sprintf('overwrite figure "%s"? y/a/[n]: ', file); else prompt = sprintf('print new figure "%s"? y/n: ', file); end ans = input(prompt, 's'); % alternate filename if streq(ans, 'a') t = sprintf('enter alternate basename (no %s): ', suff); base = input(t, 's'); keepask = 1; end end if ~isempty(ans) && ans(1) == 'y' return end file = [];
github
sunhongfu/scripts-master
xtick.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/xtick.m
482
utf_8
cf4f7a0e298bed9f6e9808cbf5baafd2
function xtick(arg) %|function xtick(arg) %| set axis xticks to just end points if ~nargin lim = get(gca, 'xlim'); if lim(1) == -lim(2) lim = [lim(1) 0 lim(2)]; end set(gca, 'xtick', lim) elseif nargin == 1 if ischar(arg) switch arg case 'off' set(gca, 'xtick', []) set(gca, 'xticklabel', []) case 'test' xtick_test otherwise fail 'bug' end else set(gca, 'xtick', sort(arg)) end else error arg end function xtick_test im clf, im(eye(7)) %xtick
github
sunhongfu/scripts-master
jf_mip3.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/jf_mip3.m
1,511
utf_8
ee392bfcbeae0ee1437753a1d74904a1
function out = jf_mip3(vol, varargin) %|function out = jf_mip3(vol, varargin) %| %| create a MIP (maximum intensity projection) mosaic from a 3D volume %| in %| vol [] [nx ny nz] 3d %| option %| 'show' bool default: 1 if no output, 0 else %| 'type' char 'mip' (default) | 'sum' | 'mid' %| out %| out [] [nx+nz ny+nz] 2d %| %| if no output, then display it using im() %| %| Copyright 2010-02-22, Jeff Fessler, University of Michigan if nargin < 1, help(mfilename), error(mfilename), end if nargin == 1 && streq(vol, 'test'), jf_mip3_test, return, end arg.show = ~nargout; arg.type = 'mip'; arg = vararg_pair(arg, varargin); if ndims(vol) > 3, fail '3D only', end switch arg.type case 'mip' fun = @(v,d) jf_mip3_max(v, d); case 'sum' fun = @(v,d) sum(v, d); case 'mid' pn = jf_protected_names; fun = @(v,d) pn.mid3(v, d); otherwise fail('unknown type %s', arg.type) end xy = fun(vol, 3); % [nx ny] xz = fun(vol, 2); % [nx nz] yz = fun(vol, 1); % [ny nz] xz = permute(xz, [1 3 2]); zy = permute(yz, [2 3 1])'; nz = size(vol,3); out = [ xy, xz; zy, zeros(nz,nz)]; if arg.show im(out) end if ~nargout clear out end function out = jf_mip3_max(in, d) if isreal(in) out = max(in, [], d); else out = max(abs(in), [], d); end function jf_mip3_test ig = image_geom('nx', 64, 'ny', 60, 'nz', 32, 'dx', 1); vol = ellipsoid_im(ig, [0 0 0 ig.nx/3 ig.ny/4 ig.nz/3 20 0 1], 'oversample', 2); mip = jf_mip3(vol); %im(vol), prompt im(mip), prompt jf_mip3(vol, 'type', 'sum') jf_mip3(vol, 'type', 'mid')
github
sunhongfu/scripts-master
subplot_stack.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/subplot_stack.m
1,219
utf_8
d36068356f2b48aac3c75d89a4584eac
function subplot_stack(x, ys, str_title, colors) %function subplot_stack(x, ys, str_title, colors) % a tight stack of subplots to show L signal components % in % x [N,1] % ys [N,L] or ? if nargin == 1 & streq(x, 'test'), subplot_stack_test, return, end if nargin < 2, help(mfilename), error(mfilename), end if ~isvar('colors') | isempty(colors), colors = {'c', 'y'}; end if ~isvar('str_title') | isempty(str_title) str_title = ''; end if ~iscell(ys) ys = {ys}; end L = size(ys{1},2); apos = get(gca, 'position'); % current axes position for ll=1:L % pos = [0.1 0.1+0.8/L*(L-ll) 0.8 0.8/L]; pos = [apos(1) apos(2)+apos(4)/L*(L-ll) apos(3) apos(4)/L]; subplot('position', pos) % l b w h for ip=1:length(ys) plot( x, real(ys{ip}(:,ll)), colors{1+2*(ip-1)}, ... x, imag(ys{ip}(:,ll)), colors{2+2*(ip-1)}) if ip == 1, hold on, end end hold off axis tight ytick(0), set(gca, 'yticklabel', '') fontsize = 10; fontweight = 'normal'; texts(1.02, 0.8, sprintf('%d.', ll), ... 'fontsize', fontsize, 'fontweight', fontweight) if ll==1, title(str_title), end if ll<L xtick off end end function subplot_stack_test x = linspace(0,1,101)'; y = exp(2i*pi*x*[1:5]); clf, subplot(121) subplot_stack(x, y)
github
sunhongfu/scripts-master
movie2.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/movie2.m
2,123
utf_8
cbbaf61f83e762896df3850e8d12ae20
function [mov avi] = movie2(x, varargin) %|function [mov avi] = movie2(x, [options]) %| in %| x [nx ny nz] sequence of 2d frames %| option %| clim "color" limits default: minmax(x) %| file name of avi file default: [test_dir 'tmp.avi'] %| cmap colormap default: gray(256) %| fps frames per second in avifile default: 15 %| out %| mov movie object %| avi avifile object %| make matlab movie from 3d array (e.g., iterations) %| make avi movie from 3d array (e.g., iterations) %| %| If no output arguments, then display movie %| Copyright Aug 2000, Jeff Fessler, University of Michigan if nargin < 1, help(mfilename), error(mfilename), end if streq(x, 'test'), movie2_test, return, end arg.clim = []; arg.file = [test_dir 'tmp.avi']; arg.cmap = gray(256); arg.fps = 15; % default for avifile arg = vararg_pair(arg, varargin); if isempty(arg.clim) arg.clim = minmax(x); end %clf %fig = figure; %fig = gcf; %set(fig, 'DoubleBuffer', 'on'); %set(gca, 'NextPlot', 'replace', 'Visible', 'off') x = max(x, arg.clim(1)); x = min(x, arg.clim(2)); scale = 255. / single(arg.clim(2) - arg.clim(1)); x = scale * single(x - arg.clim(1)); % need single for uint16 inputs avi = avifile(arg.file, 'fps', arg.fps); F.colormap = []; nz = size(x,3); for iz=1:nz ticker(mfilename, iz, nz) % im(x(:,:,iz), clim, sprintf('%d', iz-1)) % drawnow % F = getframe(gca); t = x(:,:,iz)'; t = uint8(t); mov(iz) = im2frame(t, arg.cmap); F.cdata = repmat(t, [1 1 3]); avi = addframe(avi,F); end avi = close(avi); if ~nargout if im movie(mov, 2); end clear mov avi end % % movie2_test() % function movie2_test down = 30; cg = ct_geom('fan', 'ns', round(888/down), 'nt', 64, 'na', round(984/down), ... 'ds', 1.0*down, 'down', 1, ... % only downsample s and beta 'offset_s', 0.25, ... % quarter detector 'offset_t', 0.0, ... 'ztrans', 1*300, ... % stress test with helix 'dsd', 949, 'dod', 408, 'dfs', inf); % flat detector % 'dsd', 949, 'dod', 408, 'dfs', 0); % 3rd gen CT ell = [0*50 0*50 0*50 200 100 100 90 0 10]; proj = ellipsoid_proj(cg, ell, 'oversample', 2); [mov avi] = movie2(proj); if im movie(mov) % keyboard end
github
sunhongfu/scripts-master
yaxis_pi.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/yaxis_pi.m
1,124
utf_8
e129c4b8ad40edc7a47f5b0cab53ebd6
function yaxis_pi(varargin) %function yaxis_pi(varargin) % label y axis with various forms of "pi" % the argument can be a string with p's in it, or fractions of pi: % [0 1/2 1] or '0 p/2 p' -> [0 pi/2 pi] % [-1 0 1] or '-p 0 p' -> [-pi 0 pi] % etc. % Jeff Fessler if length(varargin) == 0 ticks = '0 p'; elseif length(varargin) == 1 ticks = varargin{1}; else error 'only one arg allowed' end if ischar(ticks) str = ticks; str = strrep(str, ' ', ' | '); str = strrep(str, '*', ''); % we don't need the "*" in label ticks = strrep(ticks, '2p', '2*p'); ticks = strrep(ticks, 'p', 'pi'); ticks = eval(['[' ticks ']']); else if same(ticks, [0]) str = '0'; elseif same(ticks, [0 1]) str = '0 | p'; elseif same(ticks, [0 1/2 1]) str = '0 | p/2 | p'; elseif same(ticks, [-1 0 1]) str = '-p | 0 | p'; elseif same(ticks, [0 1 2]) str = '0 | p | 2p'; else error 'this ticks not done' end end % here is the main part axisy(min(ticks), max(ticks)) ytick(ticks) set(gca, 'yticklabel', str, 'fontname', 'symbol') function is = same(x,y) if length(x) ~= length(y) is = 0; return end is = all(x == y);
github
sunhongfu/scripts-master
jf_add_slider.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/jf_add_slider.m
2,254
utf_8
202a6292a4880b44dee34b2b4c51096e
function hs = jf_add_slider(varargin) %|function hs = jf_add_slider([options]) %| %| add a slider control to the current figure to enable interaction %| %| option %| 'callback' @ (value,data{:}) function to call with %| slider value [0,1] and data %| 'pos' [l b w h] slider position %| 'data' {} data to be passed to callback %| 'value' 0 <= x <= 1 initial slider value, default 0 %| %| Copyright 2010-1-11, Jeff Fessler, University of Michigan if ~nargin && ~nargout, help(mfilename), error(mfilename), end if nargin && streq(varargin{1}, 'test'), jf_add_slider_test, return, end arg.callback = @jf_add_slider_call; arg.pos = [0.1, 0.0, 0.8, 0.03]; arg.data = {}; arg.name = ''; arg.value = 0; arg.min = []; arg.max = []; arg.style = 'slider'; arg.sliderstep = [0.01 0.10]; arg.string = 'button'; arg = vararg_pair(arg, varargin); args = {}; if ~isempty(arg.min) args = {args{:}, 'min', arg.min}; end if ~isempty(arg.max) args = {args{:}, 'max', arg.max}; end % construct gui components hs = uicontrol('style', arg.style, 'string', arg.string, ... 'units', 'normalized', 'position', arg.pos, ... 'value', arg.value, ... args{:}, ... 'sliderstep', arg.sliderstep, ... 'callback', {@jf_add_slider_callback}); if ~isempty(arg.name) set(gcf, 'name', arg.name) % assign GUI a name for window title end if ~nargout clear hs end % callbacks: these automatically have access to component handles % and initialized data because they are nested at a lower level. % slider callback function jf_add_slider_callback(source, eventdata) val = get(source, 'value'); arg.callback(val, arg.data{:}) end % jf_add_slider_callback end % jf_add_slider function jf_add_slider_call(value) pr value end % jf_add_slider_call function jf_add_slider_test jf_add_slider_test_call(0.5); jf_add_slider('callback', @jf_add_slider_test_call, 'data', {gca}, ... 'value', 0.5) % jf_add_slider_test_call(0); jf_add_slider('callback', @jf_add_slider_test_call, 'data', {gca}, ... 'pos', [0.1 0.04 0.8 0.03], ... 'value', 0, 'style', 'togglebutton', 'string', 'push me') end % jf_add_slider_test function jf_add_slider_test_call(value, h) if isvar('h') axes(h) end plot([0 value], [0 value], '-') axis([0 1 0 1]) end % jf_add_slider_test_call
github
sunhongfu/scripts-master
jf_slicer.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/jf_slicer.m
1,594
utf_8
bccf2da9064af86ffb306ec118277cce
function jf_slicer(data, varargin) %|function jf_slicer(data, [options]) %| %| slice 3d data interactively (along 3rd dimension) %| uses scroll wheel to sweep through all slices %| %| in %| data [nx ny nz] %| %| options %| clim [1 2] clim arg to im() %| iz [1] initial slice (default: nz/2+1) %| %| Jeff Fessler, University of Michigan if ~nargin, help(mfilename), error(mfilename), end if streq(data, 'test'), jf_slicer_test, return, end arg.clim = []; arg.iz = []; arg = vararg_pair(arg, varargin); if isempty(arg.clim) arg.clim = minmax(data)'; end nz = size(data, 3); if isempty(arg.iz) iz = ceil((nz+1)/2); else iz = arg.iz; end clamp = @(iz) max(min(iz, nz), 1); stuff.data = data; stuff.arg = arg; %jf_slicer_call(iz, stuff) %drawnow jf_slicer_show set(gcf, 'WindowScrollWheelFcn', @jf_slicer_scroll) %h = jf_add_slider('callback', @jf_slicer_call, 'data', {stuff}, ... % 'min', 1, 'max', nz, 'sliderstep', [1 1]/(nz-1), 'value', iz); function jf_slicer_scroll(src, event) iz = iz + event.VerticalScrollCount; iz = clamp(iz); jf_slicer_show end % jf_slicer_scroll function jf_slicer_show im(data(:,:,iz), arg.clim), cbar xlabelf('%d / %d', iz, nz) end % jf_slicer_show end % jf_slicer function jf_slicer_call(iz, stuff) persistent iz_save if isempty(iz_save) iz_save = -1; end iz = round(iz); if iz ~= iz_save iz_save = iz; arg = stuff.arg; im(stuff.data(:,:,iz), arg.clim), cbar end end % jf_slicer_call function jf_slicer_test data = reshape(1:7*8*9, 7, 8, 9); im plc 1 2 im subplot 2 if im jf_slicer(data, 'clim', [0 500]) end end % jf_slicer_test
github
sunhongfu/scripts-master
label.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/label.m
654
utf_8
6ae74a9fa1ebfb4616a56849758663e3
function label(varargin) % usage: % label x string % label y string % label tex ... todo narg = length(varargin); if narg < 2 error 'need x or y' end props = {'interpreter', 'none'}; if streq(varargin{1}, 'x') arg2 = varargin{2}; xlabel(arg2, props{:}) elseif streq(varargin{1}, 'y') arg2 = varargin{2}; ylabel(arg2, props{:}) else error 'not done' end function xlabeltex(arg) %function xlabeltex(arg) % xlabel embedded in \tex{} for psfrag xlabel(['\tex[t][t]{' arg '}'], 'interpreter', 'none') function ylabeltex(arg) %function ylabeltex(arg) % ylabel embedded in \tex{} for psfrag ylabel(['\tex[B][B]{' arg '}'], 'interpreter', 'none')
github
sunhongfu/scripts-master
fig_text.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/fig_text.m
1,398
utf_8
20a59fa6097354c841c2f7e7fbf80e9b
function hh = fig_text(varargin) %|function hh = fig_text(...) %| add text to figure, using coordinates relative to entire window %| options: %| '-date' prepend date %| '-tex' tex parse string %| x,y coordinates relative to [0 0 1 1] %| {args} style arguments for text() command %| jeff fessler comment = ''; thedate = ''; interpret = 'none'; textarg = {'fontsize', 11}; x = 0.01; y = 0.01; while length(varargin) arg = varargin{1}; if ischar(arg) if streq(arg, '-date') thedate = [mydate ' ']; elseif streq(arg, '-tex') interpret = 'tex'; else comment = arg; end varargin = {varargin{2:end}}; elseif isnumeric(arg) if length(varargin) < 2, error 'need x, y', end x = varargin{1}; y = varargin{2}; varargin = {varargin{3:end}}; elseif iscell(arg) textarg = {textarg{:}, arg{:}}; varargin = {varargin{2:end}}; else help(mfilename), error 'bad args' end end hh = gca; hold on h = axes('position', [0 0 1 1]); axis off h = text(x, y, [thedate comment], 'interpreter', interpret, textarg{:}); hold off if nargout hh = h; else axes(hh) % reset axes to the one before the text added clear hh end function s = mydate %s = datestr(now, 'yyyy-mm-dd HH:MM:SS'); %s = datestr(now, 'yy-mm-dd HH:MM:SS'); t1 = datestr(now, 'yy'); t2 = datestr(now, 'mm'); t3 = datestr(now, 'dd'); t4 = datestr(now, 'HH:MM:SS'); s = sprintf('%s-%s-%s %s', t1, t2, t3, t4);
github
sunhongfu/scripts-master
im_toggle.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/im_toggle.m
2,525
utf_8
dc930a7b6f4278cde007dcf7464e52a0
function im_toggle(i1, i2, varargin) %|function im_toggle(i1, i2, [..., options for im()]) %| toggle between two or more images via keypress if nargin == 1 && streq(i1, 'test'), im_toggle_test, return, end if nargin < 2, help(mfilename), error(mfilename), end % find leading additional arguments corresponding to images iall = {i1, i2}; names = {inputname(1), inputname(2)}; ii = 3; while length(varargin) && isequal(size(i2), size(varargin{1})) iall = {iall{:}, varargin{1}}; varargin = {varargin{2:end}}; names{ii} = inputname(ii); ii = ii + 1; end for ii=1:length(names) names{ii} = sprintf(['toggle i%d: ' names{ii}], ii); end if length(varargin) && ( ... streq(varargin{1}, 'mip') || ... streq(varargin{1}, 'sum') || ... streq(varargin{1}, 'mid') ) for ii=1:length(iall) iall{ii} = jf_mip3(iall{ii}, 'type', varargin{1}); end varargin = {varargin{2:end}}; end if ~im, return, end % toggle between two or more images ft.args = {{}, {'horiz', 'right'}}; ft.pos = [0.01, 0.99]; im clf ht = uicontrol('style', 'text', 'string', 'test', ... 'units', 'normalized', 'position', [0.1 0.03 0.8 0.03]); data = {iall, ft, varargin, names, ht}; im_toggle_call(0, data{:}, true) jf_add_slider('callback', @im_toggle_call, 'data', data, ... 'sliderstep', [0.999 1] / (length(iall)-1)); return while (1) % old way for ii=1:length(iall) % im_toggle_call(ii/length(iall), iall, ft, varargin, names) im clf im(iall{ii}, varargin{:}) fig_text(ft.pos(2-mod(ii,2)), 0.01, ... names{ii}, ... ft.args{2-mod(ii,2)}) % pause in = input('hit enter for next image, or "q" to quit ', 's'); if streq(in, 'q') set(gca, 'nextplot', 'replace') return end end end function im_toggle_call(value, iall, ft, im_args, names, ht, reset) persistent counter persistent last_value if isempty(last_value) last_value = value; end if isempty(counter) || isvar('reset') counter = 0; end if value == last_value % click counter = 1 + counter; if counter > length(iall) counter = 1; end else % slide counter = 1 + floor(0.9999 * length(iall) * value); last_value = value; end im(iall{counter}, im_args{:}) set(ht, 'string', names{counter}) function im_toggle_test if 0 % 2d nx = 20; i1 = eye(20); i2 = flipud(i1); i3 = 1 - i1; im_toggle(i1, i2, i3, [0 2]) end if 1 % 3d mip3 ig = image_geom('nx', 2^5, 'ny', 2^5-1', 'nz', 2^5-3, 'dx', 1); t1 = ellipsoid_im(ig, [4 3 2 ig.fovs ./ [3 3 4] 0 0 1]); t2 = flipdim(t1, 1); t3 = flipdim(t1, 2); im_toggle(t1, t2, t3, 'sum') end
github
sunhongfu/scripts-master
texts.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/texts.m
1,224
utf_8
855769813da86ffc24b870bc0514a55a
function ho = texts(x, y, str, varargin) %function ho = texts(x, y, str [, center], options) % put text on current plot using screen coordinates % user can supply optional name/value object properties. % for horizontalalign, only the value is needed (e.g., 'center') % if it is the first option. if nargin == 1 && streq(x, 'test'), texts_test, return, end if nargin < 3, help(mfilename), error(mfilename), end xlim = get(gca, 'xlim'); ylim = get(gca, 'ylim'); xscale = get(gca, 'xscale'); yscale = get(gca, 'yscale'); if streq(xscale, 'log') xlim = log10(xlim); end if streq(yscale, 'log') ylim = log10(ylim); end x = xlim(1) + x * (xlim(2)-xlim(1)); y = ylim(1) + y * (ylim(2)-ylim(1)); if streq(xscale, 'log') x = 10 ^ x; end if streq(yscale, 'log') y = 10 ^ y; end if isfreemat args = {}; else args = {'buttondownfcn', 'textmove'}; end if length(varargin) arg1 = varargin{1}; if streq(arg1, 'left') | streq(arg1, 'center') | streq(arg1, 'right') args = {args{:}, 'horizontalalign', varargin{:}}; else args = {args{:}, varargin{:}}; end end h = text(x, y, str, args{:}); if nargout > 0 ho = h; end function texts_test if im plot(rand(3)) texts(0.5, 0.5, 'test', 'center', 'color', 'blue') end
github
sunhongfu/scripts-master
im.m
.m
scripts-master/MRF/recon/_src/_NUFFT/graph/im.m
14,093
utf_8
842e9abbdd9c15e8fc82cb53a7faa718
function h = im(varargin) %|function h = im([options,] [xx,] [yy,] zz, [scale|clim,] [title]) %| show matrix zz as an image, possibly with (x,y) axes labeled by xx,yy %| %| options: %| subplot an integer for subplot %| 'notick' no axis tick marks %| 'black0' make sure image value of 0 is black (max white) %| 'blue0' show zeros as blue %| 'mid3' show middle 3 planes of 3D volume %| 'mip3' show 3 MIP (max. intens. proj.) views of 3D volume %| 'row', n # of rows for 3D montages, for this call %| 'col', n # of cols for 3D montages, for this call %| %| after options: %| scale scale image by this factor %| clim limits for colorbar %| title axis title %| 'cbar' add colorbar %| %| one argument commands: %| 'off' disable display, %| 'off-quiet' disable display, and do not print 'disabled' messages %| 'on' enable display, 'ison' report if enabled %| 'clf' clear display (if enabled) %| 'colorneg' show negatives as red %| 'db40' log grayscale over 40dB range %| 'nan-warn' warning if nan value(s), 'nan-fail' fail if nan %| 'drawnow' 'drawnot' toggle immediate drawing %| 'tickon' 'tickoff' toggle default tick drawing %| 'state' report state %| %| other commands: %| 'pl' sub_m sub_n specify subplot layout for subsequent plots %| 'plc' like 'pl' but first clf %| 'pl-tight' like 'pl' but tight plots with little (if any) spacing %| 'subplot' plotindex choose a subplot %| n2min n <= this min # for dim2 we plot instead (def: 1) %| zero blue replace 1st colormap entry (zero?) with blue %| row n # of rows for 3D montages - setting state %| col n # of cols for 3D montages - setting state %| %| Copyright 1997, Jeff Fessler, University of Michigan % % handle states % persistent state if ~isvar('state') || isempty(state) state = im_reset; end % default is to give help if ~nargin & ~nargout help(mfilename) if im, printm('im enabled'), else, printm('im disabled'), end error(mfilename) end % for conditional of the form 'if im, ..., end' if ~nargin & nargout h = state.display; return end % im row 2 or im col 3 if nargin == 2 && (streq(varargin{1}, 'row') | streq(varargin{1}, 'row')) tmp = varargin{2}; if ischar(tmp), tmp = str2num(tmp); end state.montage = {varargin{1}, tmp}; return end % % process single string command arguments % if nargin == 1 && ischar(varargin{1}) arg = varargin{1}; if streq(arg, 'on') state.display = true; state.display_quiet = false; disp 'enabling images' elseif streq(arg, 'off') state.display = false; state.display_quiet = false; disp 'disabling images' elseif streq(arg, 'off-quiet') state.display = false; state.display_quiet = true; elseif streq(arg, 'clf') if state.display clf end elseif streq(arg, 'drawnow') state.drawnow = true; elseif streq(arg, 'drawnot') state.drawnow = false; elseif streq(arg, 'tickon') state.tick = true; elseif streq(arg, 'tickoff') state.tick = false; elseif streq(arg, 'ison') % query if state.display h = true; else h = false; end elseif streq(arg, 'state') % query h = state; elseif streq(arg, 'nan-fail') state.nan_fail = true; elseif streq(arg, 'nan-warn') state.nan_fail = false; elseif streq(arg, 'blue0') state.blue0 = true; elseif streq(arg, 'colorneg') state.colorneg = true; elseif streq(arg, 'db', 2) state.db = sscanf(arg, 'db%g'); elseif streq(arg, 'reset') state = im_reset; elseif streq(arg, 'test') im_test, return else error(['unknown argument: ' arg]) end return end % 'pl' 'plc' 'pl-tight' option if streq(varargin{1}, 'pl') || streq(varargin{1}, 'pl-tight') ... || streq(varargin{1}, 'plc') if streq(varargin{1}, 'plc'), im clf, end if nargin == 3 state.sub_m = ensure_num(varargin{2}); state.sub_n = ensure_num(varargin{3}); elseif nargin == 1 state.sub_m = []; state.sub_n = []; else error 'bad pl usage' end state.pl_tight = streq(varargin{1}, 'pl-tight'); return end % 'subplot' option if streq(varargin{1}, 'subplot') im_subplot(state, ensure_num(varargin{2})) return end % 'n2min' option if streq(varargin{1}, 'n2min') state.n2min = ensure_num(varargin{2}); return end % 'zero' or 'zero blue' if streq(varargin{1}, 'zero') cmap = get(gcf, 'colormap'); cmap(1,:) = [0 0 1]; set(gcf, 'colormap', cmap); return end scale = 1; titlearg = {}; opt.cbar = false; clim = []; xx = []; yy = []; opt.blue0 = false; % 1 to color 0 as blue opt.colorneg = false; % 1 to put negatives in color and 0=blue opt.db = 0; % nonzero to use a log scale isxy = false; % 1 if user provides x and y coordinates isplot = false; tick = state.tick; opt.black0 = false; opt.mid3 = false; % 1 if show mid-plane slices of 3D object opt.mip3 = false; % 1 if show 3 MIP views of 3D object opt.montage = state.montage; if length(varargin) == 1 && isempty(varargin{1}) fail 'empty argument?' end % % optional arguments % zz_arg_index = 1; while length(varargin) arg = varargin{1}; if isempty(arg) 0; % do nothing elseif max(size(arg)) == 1 if state.display arg1 = varargin{1}; if arg1 > 99 % reset state.sub_m = []; state.sub_n = []; end if isempty(state.sub_m) if arg1 >= 111 subplot(arg1) else printm('ignoring subplot %d', arg1) end else im_subplot(state, arg1) end end elseif streq(arg, 'notick') tick = false; elseif streq(arg, 'colorneg') opt.colorneg = true; elseif streq(arg, 'db', 2) opt.db = sscanf(arg, 'db%g'); elseif streq(arg, 'black0') opt.black0 = true; elseif streq(arg, 'blue0') opt.blue0 = true; elseif streq(arg, 'mid3') opt.mid3 = true; elseif streq(arg, 'mip3') opt.mip3 = true; elseif streq(arg, 'row') | streq(arg, 'col') opt.montage = {arg, varargin{2}}; varargin = {varargin{2:end}}; zz_arg_index = 1 + zz_arg_index; else break end varargin = {varargin{2:end}}; zz_arg_index = 1 + zz_arg_index; end if length(varargin) < 1, help(mfilename), error args, end % % plotting vector(s) % if ndims(varargin{1}) <= 2 && min(size(varargin{1})) <= state.n2min ... && length(varargin) < 3 isplot = true; plot_data = varargin{1}; % % xx, yy % elseif ndims(varargin{1}) <= 2 & min(size(varargin{1})) == 1 xx = col(varargin{1}); if length(varargin) < 2, help(mfilename), error 'need both xx,yy', end if min(size(varargin{2})) ~= 1, error 'both xx,yy need to be 1D', end yy = col(varargin{2}); varargin = {varargin{3:end}}; isxy = 1; zz_arg_index = 2 + zz_arg_index; end if ~state.display if ~state.display_quiet printm(['disabled: ' inputname(zz_arg_index)]) end return end % % zz % if length(varargin) zz = varargin{1}; if iscell(zz); zz = stackup(zz{:}); isplot = 0; end % trick zz = double(zz); if ndims(zz) > 2 & min(size(zz)) == 1 zz = squeeze(zz); % handle [1 n2 n3] case as [n2 n3] end varargin = {varargin{2:end}}; else error 'no image?' end if any(isnan(zz(:))) tmp = sprintf('image contains %d NaN values of %d!?', ... sum(isnan(zz(:))), numel(zz)); if state.nan_fail, error(tmp), else, warning(tmp), end end if any(isinf(zz(:))) tmp = sprintf('image contains %d Inf values of %d!?', ... sum(isinf(zz(:))), numel(zz)); if state.nan_fail, error(tmp), else, warning(tmp), end end if opt.mid3 pn = jf_protected_names; zz = pn.mid3(zz); end if opt.mip3 mip3_size = size(zz); zz = jf_mip3(zz); end % title, scale, clim, cbar while length(varargin) arg = varargin{1}; if isempty(arg) % do nothing elseif ischar(arg) if streq(arg, 'cbar') opt.cbar = true; else titlearg = {arg}; if ~isempty(strfind(arg, '\tex')) titlearg = {arg, 'interpreter', 'none'}; end end elseif isnumeric(arg) % isa(arg, 'double') if max(size(arg)) == 1 scale = arg; elseif all(size(arg) == [1 2]) clim = arg; else error 'nonscalar scale / nonpair clim?' end % pr scale else error 'unknown arg' end varargin = {varargin{2:end}}; end if isplot plot(plot_data, state.line1type) if ~isempty(titlearg), titlef(titlearg{:}), end return end if issparse(zz), zz = full(zz); end if ~isreal(zz) zz = abs(zz); printf('warn %s: magnitude of complex image', mfilename) end if opt.db | state.db if ~opt.db & state.db, opt.db = state.db, end if max(zz(:)) <= 0, error 'db for negatives?', end zz = abs(zz); zz = max(zz, eps); zz = 10*log10(abs(zz) / max(abs(zz(:)))); zz(zz < -opt.db) = -opt.db; end zmax = max(zz(:)); zmin = min(zz(:)); if opt.black0 if ~isempty(clim) warning 'black0 overrules clim' end clim = [0 zmax]; end if scale ~= 1 if scale == 0 zmin = 0; scale = 1; elseif scale < 0 zmin = 0; scale = -scale; end end % unfortunately, colormaps affect the entire figure, not just the axes if opt.blue0 | state.blue0 cmap = [[0 0 1]; gray(256)]; colormap_gca(cmap) zt = zz; zz(zt > 0) = 1+floor(255 * zz(zt > 0) / (abs(max(zt(:))) + eps)); zz(zt == 0) = 1; elseif opt.colorneg | state.colorneg if 1 % original cmap = hot(512); cmap = flipud(cmap(1:256,:)); cmap = [cmap; [0 0 1]; gray(256)]; else % +green -red tmp = [0:255]'/255; cmap = [flipud(tmp)*[1 0 0]; [0 0 1]; tmp*[0 1 0]]; end colormap_gca(cmap) zt = zz; zz(zt > 0) = 257+1+floor(255 * zz(zt > 0) / (abs(max(zt(:))) + eps)); zz(zt == 0) = 257; zz(zt < 0) = 1+floor(-255 * zz(zt < 0) / (abs(min(zt(:))) + eps)); else colormap_gca(gray(256)) end if scale ~= 1 % fix: use clim? n = size(colormap,1); if zmax ~= zmin zz = (n - 1) * (zz - zmin) / (zmax - zmin); % [0,n-1] else if zmin == 0 zz(:) = 0; clim = [0 1]; else zz(:) = n - 1; end end zz = 1 + round(scale * zz); zz = min(zz,n); zz = max(zz,1); elseif zmin == zmax if zmin == 0 clim = [0 1]; else zz(:) = 1; clim = [0 1]; end end if ndims(zz) < 3 % 2d zz = zz'; % trick: transpose for consistency with C programs if isxy if length(xx) ~= size(zz,2), fail 'xx size', end if length(yy) ~= size(zz,1), fail 'yy size', end hh = image(xx, yy, zz, 'CDataMapping', 'scaled'); else hh = image(zz, 'CDataMapping', 'scaled'); % setgca('CLimMode','auto') % unclutter axes by only showing end limits n1 = size(zz,2); n2 = size(zz,1); % setgca('xtick', [1 n1], 'ytick', [1 n2]) if tick setgca('xtick', [], 'ytick', [1 n2]) if is_pre_v7 text(1, 1.04*(n2+0.5), '1', ... 'horizontalalign', 'left', ... 'verticalalign', 'top') text(n1, 1.04*(n2+0.5), num2str(n1), ... 'horizontalalign', 'right', ... 'verticalalign', 'top') elseif opt.mip3 tmp = [1 mip3_size(1)+[0 mip3_size(3)]]; setgca('xtick', tmp) tmp = [1 mip3_size(2)+[0 mip3_size(3)]]; setgca('ytick', tmp) else setgca('xtick', [1 n1]) end end % problem with this is it fails to register % space used for 'ylabel' % so i stick with manual xtick since that is what overlaps % with the colorbar if 0 & tick text(-0.01*n1, n2, '1', ... 'verticalalign', 'bottom', ... 'horizontalalign', 'right') text(-0.01*n1, 1, num2str(n2), ... 'verticalalign', 'top', ... 'horizontalalign', 'right') end end else % 3d n1 = size(zz,1); n2 = size(zz,2); if 0 % white border zz(:,end+1,:) = max(zz(:)); zz(end+1,:,:) = max(zz(:)); end dimz = size(zz); zz = montager(zz, opt.montage{:}); if isxy xx3 = [0:size(zz,1)/dimz(1)-1]*(xx(2)-xx(1))*dimz(1); xx3 = col(outer_sum(xx, xx3)); yy3 = [0:size(zz,2)/dimz(2)-1]*(yy(2)-yy(1))*dimz(2); yy3 = col(outer_sum(yy, yy3)); hh = image(xx3, yy3, zz', 'CDataMapping', 'scaled'); if tick & n1 > 1 & n2 > 1 % unclutter setgca('xtick', [xx(1) xx(end)], 'ytick', ... sort([yy(1) yy(end)])) end axis xy else hh = image(zz', 'CDataMapping', 'scaled'); if tick & n1 > 1 & n2 > 1 % unclutter setgca('xtick', [1 n1], 'ytick', [1 n2]) end if state.line3plot % lines around each sub image m1 = size(zz,1) / n1; m2 = size(zz,2) / n2; plot_box = @(ox,oy,arg) plot(ox+[0 1 1 0 0]*n1+0.5, ... oy+[0 0 1 1 0]*n2+0.5, state.line3type, arg{:}); n3 = dimz(3); for ii=0:n3-1 i1 = mod(ii, m1); i2 = floor(ii / m1); hold on plot_box(i1*n1, i2*n2, ... {'linewidth', state.line3width}) hold off end end end end if (zmax == zmin) % fprintf('Uniform image %g [', zmin) % fprintf(' %g', size(zz)) % fprintf(' ]\n') texts(0.5, 0.5, sprintf('Uniform: %g', zmin), 'center', 'color', 'blue') end if opt.colorneg | state.colorneg set(hh, 'CDataMapping', 'direct') else if ~isempty(clim), setgca('CLim', clim) elseif ~ishold setgca('CLimMode', 'auto') end end setgca('TickDir', 'out') if nargout > 0 h = hh; end % axis type depends on whether user provides x,y coordinates if isxy if length(xx) > 1 && length(yy) > 1 && ... abs(xx(2)-xx(1)) == abs(yy(2)-yy(1)) % square pixels axis image end axis xy else axis image end if ~isempty(titlearg) title(titlearg{:}) else tmp = sprintf('%s range: [%g %g]', inputname(zz_arg_index), zmin, zmax); title(tmp, 'interpreter', 'none') end if ~tick setgca('xtick', [], 'ytick', []) end if opt.cbar, cbar, end if state.drawnow, drawnow, end % im_reset() % reset state to its defaults function state = im_reset state.display = true; state.display_quiet = false; state.colorneg = false; state.db = 0; state.blue0 = false; state.nan_fail = false; state.drawnow = false; state.tick = true; state.montage = {}; % for montager state.sub_m = []; % for subplot state.sub_n = []; state.n2min = 1; state.pl_tight = false; state.line3plot = true; state.line3type = 'y-'; state.line3width = 1; state.line1type = '-'; % for 1D plots function x = ensure_num(x) if ischar(x), x = str2num(x); end function colormap_gca(cmap) if isfreemat colormap(cmap) else colormap(gca, cmap) end function im_test im clf, im(rand(6)) im clf, im pl-tight 2 3 im(1, rand(3)) im(2, rand(3)) im(5, ones(3)) prompt im clf, im(rand(2^7,2^7-2,4)) function im_subplot(state, num) if state.display if state.pl_tight num = num - 1; x = 1 / state.sub_n; y = 1 / state.sub_m; ny = floor(num / state.sub_n); nx = num - ny * state.sub_n; ny = state.sub_m - ny - 1; subplot('position', [nx*x ny*y x y]) else subplot(state.sub_m, state.sub_n, num) end end function setgca(varargin) set(gca, varargin{:})
github
sunhongfu/scripts-master
mtimes_block.m
.m
scripts-master/MRF/recon/_src/_NUFFT/@Fatrix/mtimes_block.m
1,276
utf_8
11f89a7167f3f66a965c297a8620dcd5
function y = mtimes_block(ob, x, istart, nblock) %function y = mtimes_block(ob, x, istart, nblock) % y = G(i'th block) * x or y = G'(i'th block) * x % in either case the projection data will be "small" % istart is 1,...,nblock % support 'exists' option for seeing if this routine is available if nargin == 2 & ischar(x) & streq(x, 'exists') y = ~isempty(ob.handle_mtimes_block); return end if nargin ~= 4 error(mfilename) end if isempty(ob.handle_mtimes_block) error 'bug: no mtimes_block() method for this object' end if ob.is_transpose y = Fatrix_mtimes_block_back(ob, x, istart, nblock); else y = Fatrix_mtimes_block_forw(ob, x, istart, nblock); end % caution: cascade_* may not work except for scalars here function y = Fatrix_mtimes_block_forw(ob, x, istart, nblock); x = do_cascade(ob.cascade_before, x, false, istart, nblock, true); y = feval(ob.handle_mtimes_block, ob.arg, ob.is_transpose, x, istart, nblock); y = do_cascade(ob.cascade_after, y, false, istart, nblock, false); function x = Fatrix_mtimes_block_back(ob, y, istart, nblock); y = do_cascade(ob.cascade_after, y, true, istart, nblock, false); x = feval(ob.handle_mtimes_block, ob.arg, ob.is_transpose, y, istart, nblock); x = do_cascade(ob.cascade_before, x, true, istart, nblock, true);
github
sunhongfu/scripts-master
mtimes.m
.m
scripts-master/MRF/recon/_src/_NUFFT/@Fatrix/mtimes.m
1,374
utf_8
b01e213ce10f513c5f91f944f5e7d1f4
function y = mtimes(ob, x) %function y = mtimes(ob, x) % y = M * x or x = M' * y if ~isa(ob, 'Fatrix') error 'only multiplication on right is done' end if isa(x, 'Fatrix') % object1 * object2, tested in Gcascade y = Gcascade(ob, x); return end % % partial multiplication? % if ob.is_subref error 'subref not done' end % % block multiplication (if needed) % if ~isempty(ob.nblock) % block object if ~isempty(ob.handle_mtimes_block) % proper block object if ~isempty(ob.iblock) % Gb{?} * ? y = mtimes_block(ob, x, ob.iblock, ob.nblock); return % else: % Gb * ? end % else should be a 1-block object elseif ob.nblock > 1 error 'nblock > 1 but no mtimes_block?' end end % % ordinary multiplication % if ob.is_transpose y = Fatrix_mtimes_back(ob, x); else y = Fatrix_mtimes_forw(ob, x); end % % full forward multiplication ("projection") % y = after * ob * before * x; % function y = Fatrix_mtimes_forw(ob, x); x = do_cascade(ob.cascade_before, x, false, 0, 1, true); y = feval(ob.handle_forw, ob.arg, x); y = do_cascade(ob.cascade_after, y, false, 0, 1, false); % % full transposed multiplication ("back-projection") % x = before' * ob' * after' * y; % function x = Fatrix_mtimes_back(ob, y); y = do_cascade(ob.cascade_after, y, true, 0, 1, false); x = feval(ob.handle_back, ob.arg, y); x = do_cascade(ob.cascade_before, x, true, 0, 1, true);
github
sunhongfu/scripts-master
do_cascade.m
.m
scripts-master/MRF/recon/_src/_NUFFT/@Fatrix/private/do_cascade.m
870
utf_8
c5e864c129913ce1841a0ce0b0ddacda
function y = do_cascade(cascade, x, is_transpose, istart, nblock, is_before) %function y = do_cascade(cascade, x, is_transpose, istart, nblock, is_before) % do cascade * x or cascade' * x if isempty(cascade) y = x; elseif isa(cascade, 'function_handle') | isa(cascade, 'inline') if nargin(cascade) == 4 y = feval(cascade, x, is_transpose, istart, nblock); elseif nargin(cascade) == 2 | nblock == 1 y = feval(cascade, x, is_transpose); else error 'cascade_* should have 2 or 4 arguments?' end else % matrix or object if is_transpose if is_before do_cascade_warn(cascade, nblock) end y = cascade' * x; else if ~is_before do_cascade_warn(cascade, nblock) end y = cascade * x; end end function do_cascade_warn(cascade, nblock) if max(size(cascade)) > 1 & nblock ~= 1 warning 'non scalar array/object cascade not tested with block!' end
github
sunhongfu/scripts-master
recon_arc_asset.m
.m
scripts-master/GERecon/recon_arc_asset.m
11,061
utf_8
eb77545d89bf56e33f277cbd7081dd35
function recon_arc_asset(pfilePath, calibrationPfile, outputDir) % % mac % cd '/Users/hongfusun/DATA/p-files/oct7/raw/' % pfilePath='/Users/hongfusun/DATA/p-files/oct7/raw/P31232.7'; % calibrationPfile='/Users/hongfusun/DATA/p-files/oct7/raw/P32328.7'; % pfilePath='/Users/hongfusun/DATA/p-files/oct12/P44544.7'; % pfilePath='/Users/hongfusun/DATA/p-files/oct12/P32256.7'; % linux % pfilePath='/media/data/p-files/oct12/t1/P44544.7' % Load Pfile clear GERecon pfile = GERecon('Pfile.Load', pfilePath); GERecon('Pfile.SetActive',pfile); header = GERecon('Pfile.Header', pfile); % if length(varargin) == 1 % % Load Arc Sampling Pattern (kacq_yz.txt) % GERecon('Arc.LoadKacq', varargin{1}); % end % Load KSpace. Since 3D Arc Pfiles contain space for the zipped % slices (even though the data is irrelevant), only pull out % the true acquired K-Space. Z-transform will zip the slices % out to the expected extent. acquiredSlices = pfile.slicesPerPass / header.RawHeader.zip_factor; % 3D Scaling Factor scaleFactor = header.RawHeader.user0; if header.RawHeader.a3dscale > 0 scaleFactor = scaleFactor * header.RawHeader.a3dscale; end scaleFactor = pfile.slicesPerPass / scaleFactor; % extract the kSpace kSpace = zeros(pfile.xRes, pfile.yRes, acquiredSlices, pfile.channels, pfile.echoes, pfile.passes); for pass = 1:pfile.passes for echo = 1:pfile.echoes for slice = 1:acquiredSlices sliceInfo.pass = pass; sliceInfo.sliceInPass = slice; for channel = 1:pfile.channels % Load K-Space kSpace(:,:,slice,channel,echo,pass) = GERecon('Pfile.KSpace', sliceInfo, echo, channel, pfile); end end end end % Synthesize KSpace to get full kSpace kSpace_full = zeros(pfile.xRes, pfile.yRes, acquiredSlices, pfile.channels, pfile.echoes, pfile.passes); for pass = 1:pfile.passes for echo = 1:pfile.echoes kSpace_full(:,:,:,:,echo,pass) = GERecon('Arc.Synthesize', kSpace(:,:,:,:,echo,pass)); end end clear kSpace % image recon % Scale kSpace_full = kSpace_full * scaleFactor; %%%%% 1 %%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Default GE method %%%%%%%%%%%%%%%%%%%%%%% % Transform Across Slices kSpace_full = ifft(kSpace_full, pfile.slicesPerPass, 3); for pass = 1:pfile.passes for echo = 1:pfile.echoes for slice = 1:pfile.slicesPerPass for channel = 1:pfile.channels % Transform K-Space channelImages(:,:,slice,channel,echo,pass) = GERecon('Transform', kSpace_full(:,:,slice,channel,echo,pass)); end end end end nii=make_nii(abs(channelImages)); save_nii(nii,'channelImage_mag.nii'); nii=make_nii(angle(channelImages)); save_nii(nii,'channelImage_pha.nii'); clear kSpace_full %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%% 2 %%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%% IFFT method %%%%%%%%%%%%%%%%%%%%%%% % kSpace_full = padarray(kSpace_full,[18, 18], 'both'); % channelImages = ifft(ifft(ifft(kSpace_full,[],1),[],2),[],3); % channelImages = fftshift(fftshift(channelImages,1),2); % channelImages = channelImages*256*sqrt(40); % nii=make_nii(abs(channelImages(:,:,:,:,4))); % save_nii(nii,'channelImage_mag_e4.nii'); % nii=make_nii(angle(channelImages(:,:,:,:,4))); % save_nii(nii,'channelImage_pha_e4.nii'); % clear kSpace_full % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%% 3 %%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%% Hongfu method %%%%%%%%%%%%%%%%%%%%%%%%%%% % kSpace_full = padarray(kSpace_full,[18, 18], 'both'); % kSpace_full = fftshift(kSpace_full,1); % kSpace_full = fftshift(kSpace_full,2); % kSpace_full = fftshift(kSpace_full,3); % channelImages = fft(fft(fft(kSpace_full,[],1),[],2),[],3); % channelImages = fftshift(fftshift(channelImages,1),2); % channelImages = channelImages/256/sqrt(40); % %%% seems like need flip to be the same as method 1 and 3, also circshift % channelImages = flipdim(flipdim(flipdim(channelImages,1),2),3); % channelImages = circshift(channelImages,[1 1 1]); % nii=make_nii(abs(channelImages(:,:,:,:,4))); % save_nii(nii,'channelImage_mag_e4.nii'); % nii=make_nii(angle(channelImages(:,:,:,:,4))); % save_nii(nii,'channelImage_pha_e4.nii'); % clear kSpace_full % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%% 4 %%%%%%% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%% Hongfu method no phase checkerboard %%%%%%%%%%%%%%%%%%%%%%%%%%% % kSpace_full = fftshift(kSpace_full,1); % kSpace_full = fftshift(kSpace_full,2); % kSpace_full = fftshift(kSpace_full,3); % channelImages = fft(fft(fft(kSpace_full,[],1),[],2),[],3); % channelImages = fftshift(fftshift(channelImages,1),2); % channelImages = channelImages/256/sqrt(40); % % %%% seems like need flip to be the same as method 1 and 3, also circshift % % channelImages = flipdim(flipdim(flipdim(channelImages,1),2),3); % % channelImages = circshift(channelImages,[1 1 1]); % nii=make_nii(single(abs(channelImages))); % save_nii(nii,'channelImage_mag.nii'); % nii=make_nii(single(angle(channelImages))); % save_nii(nii,'channelImage_pha.nii'); % clear kSpace_full % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % SVD coil combination % img_cmb = zeros(pfile.xRes, pfile.yRes, acquiredSlices); % for slice = 1:pfile.slicesPerPass % [img_cmb(:,:,slice)] = adaptive_cmb_2d(squeeze(channelImages(:,:,slice,:))); % end % nii=make_nii(abs(img_cmb)); % save_nii(nii,'combinedImage_mag.nii'); % nii=make_nii(angle(img_cmb)); % save_nii(nii,'combinedImage_pha.nii'); % % combine magnitude images with SoS % % Apply Channel Combination % imsize = size(channelImages); % combinedImage = zeros([imsize(1:3), pfile.echoes, pfile.passes]); % finalImage = combinedImage; % for pass = 1:pfile.passes % for echo = 1:pfile.echoes % for slice = 1:acquiredSlices % % Get slice information (corners and orientation) for this slice location % sliceInfo.pass = pass; % sliceInfo.sliceInPass = slice; % info = GERecon('Pfile.Info', sliceInfo); % combinedImage(:,:,slice,echo,pass) = GERecon('SumOfSquares', squeeze(channelImages(:,:,slice,:,echo,pass))); % % Create Magnitude Image % magnitudeImage = abs(combinedImage(:,:,slice,echo,pass)); % % Apply Gradwarp % gradwarpedImage = GERecon('Gradwarp', magnitudeImage, info.Corners); % % Orient the image % finalImage(:,:,slice,echo,pass) = GERecon('Orient', gradwarpedImage, info.Orientation); % end % end % end % nii=make_nii(abs(combinedImage(:,:,:,4))); % save_nii(nii,'combinedImage_mag_e4.nii'); % nii=make_nii(abs(finalImage(:,:,:,4))); % save_nii(nii,'finalImage_mag_e4.nii'); % ASSET recon % change the p-file header of ASSET setenv('pfilePath',pfilePath); unix('/Users/hongfusun/bin/orchestra-sdk-1.7-1/build/BuildOutputs/bin/HS_ModHeader --pfile $pfilePath') pfilePath=[pfilePath '.mod']; % Load Pfile clear GERecon pfile = GERecon('Pfile.Load', pfilePath); GERecon('Pfile.SetActive',pfile); header = GERecon('Pfile.Header', pfile); % calibrationPfile GERecon('Calibration.Process', calibrationPfile); imsize = size(channelImages); unaliasedImage = zeros([imsize(1:3), pfile.echoes, pfile.passes]); for pass = 1:pfile.passes for echo = 1:pfile.echoes for slice = 1:acquiredSlices % Get slice information (corners and orientation) for this slice location sliceInfo.pass = pass; sliceInfo.sliceInPass = slice; info = GERecon('Pfile.Info', slice); unaliasedImage(:,:,slice,echo,pass) = GERecon('Asset.Unalias', squeeze(channelImages(:,:,slice,:,echo,pass)), info); end end end % correct for phase chopping unaliasedImage = fft(fft(fft(fftshift(fftshift(fftshift(ifft(ifft(ifft(unaliasedImage,[],1),[],2),[],3),1),2),3),[],1),[],2),[],3); nii=make_nii(abs(unaliasedImage)); save_nii(nii,'unaliasedImage_mag.nii'); nii=make_nii(angle(unaliasedImage)); save_nii(nii,'unaliasedImage_pha.nii'); % save DICOMs for QSM inputs for pass = 1:pfile.passes for echo = 1:pfile.echoes for slice = 1:acquiredSlices % Get slice information (corners and orientation) for this slice location sliceInfo.pass = pass; sliceInfo.sliceInPass = slice; info = GERecon('Pfile.Info', sliceInfo); realImage = real(unaliasedImage_new(:,:,slice,echo,pass)); imagImage = imag(unaliasedImage_new(:,:,slice,echo,pass)); % Apply Gradwarp gradwarpedRealImage = GERecon('Gradwarp', realImage, info.Corners); gradwarpedImagImage = GERecon('Gradwarp', imagImage, info.Corners); % Orient the image finalRealImage = GERecon('Orient', gradwarpedRealImage, info.Orientation); finalImagImage = GERecon('Orient', gradwarpedImagImage, info.Orientation); % Save DICOMs imageNumber = ImageNumber(pass, info.Number, echo, pfile); filename = ['DICOMs_real/realImage' num2str(imageNumber) '.dcm']; GERecon('Dicom.Write', filename, finalRealImage, imageNumber, info.Orientation, info.Corners); filename = ['DICOMs_imag/imagImage' num2str(imageNumber) '.dcm']; GERecon('Dicom.Write', filename, finalImagImage, imageNumber, info.Orientation, info.Corners); end end end end function number = ImageNumber(pass, slice, echo, pfile) % Image numbering scheme (P = Phase; S = Slice; E = Echo): % P0S0E0, P0S0E1, ... P0S0En, P0S1E0, P0S1E1, ... P0S1En, ... P0SnEn, ... % P1S0E0, P1S0E1, ... PnSnEn % Need to map the legacy "pass" number to a phase number numPassesPerPhase = fix(pfile.passes / pfile.phases); phase = fix(pass / numPassesPerPhase); slicesPerPhase = pfile.slicesPerPass * numPassesPerPhase * pfile.echoes; number = (phase-1) * slicesPerPhase + (slice-1) * pfile.echoes + (echo-1) + 1; end
github
sunhongfu/scripts-master
deleteoutliers.m
.m
scripts-master/fMRI-QSM/deleteoutliers.m
3,493
utf_8
a97518a30ca50a19dcfb5e180b3089c7
function [b,idx,outliers] = deleteoutliers(a,alpha,rep); % [B, IDX, OUTLIERS] = DELETEOUTLIERS(A, ALPHA, REP) % % For input vector A, returns a vector B with outliers (at the significance % level alpha) removed. Also, optional output argument idx returns the % indices in A of outlier values. Optional output argument outliers returns % the outlying values in A. % % ALPHA is the significance level for determination of outliers. If not % provided, alpha defaults to 0.05. % % REP is an optional argument that forces the replacement of removed % elements with NaNs to presereve the length of a. (Thanks for the % suggestion, Urs.) % % This is an iterative implementation of the Grubbs Test that tests one % value at a time. In any given iteration, the tested value is either the % highest value, or the lowest, and is the value that is furthest % from the sample mean. Infinite elements are discarded if rep is 0, or % replaced with NaNs if rep is 1 (thanks again, Urs). % % Appropriate application of the test requires that data can be reasonably % approximated by a normal distribution. For reference, see: % 1) "Procedures for Detecting Outlying Observations in Samples," by F.E. % Grubbs; Technometrics, 11-1:1--21; Feb., 1969, and % 2) _Outliers in Statistical Data_, by V. Barnett and % T. Lewis; Wiley Series in Probability and Mathematical Statistics; % John Wiley & Sons; Chichester, 1994. % A good online discussion of the test is also given in NIST's Engineering % Statistics Handbook: % http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm % % ex: % [B,idx,outliers] = deleteoutliers([1.1 1.3 0.9 1.2 -6.4 1.2 0.94 4.2 1.3 1.0 6.8 1.3 1.2], 0.05) % returns: % B = 1.1000 1.3000 0.9000 1.2000 1.2000 0.9400 1.3000 1.0000 1.3000 1.2000 % idx = 5 8 11 % outliers = -6.4000 4.2000 6.8000 % % ex: % B = deleteoutliers([1.1 1.3 0.9 1.2 -6.4 1.2 0.94 4.2 1.3 1.0 6.8 1.3 1.2 % Inf 1.2 -Inf 1.1], 0.05, 1) % returns: % B = 1.1000 1.3000 0.9000 1.2000 NaN 1.2000 0.9400 NaN 1.3000 1.0000 NaN 1.3000 1.2000 NaN 1.2000 NaN 1.1000 % Written by Brett Shoelson, Ph.D. % [email protected] % 9/10/03 % Modified 9/23/03 to address suggestions by Urs Schwartz. % Modified 10/08/03 to avoid errors caused by duplicate "maxvals." % (Thanks to Valeri Makarov for modification suggestion.) if nargin == 1 alpha = 0.05; rep = 0; elseif nargin == 2 rep = 0; elseif nargin == 3 if ~ismember(rep,[0 1]) error('Please enter a 1 or a 0 for optional argument rep.') end elseif nargin > 3 error('Requires 1,2, or 3 input arguments.'); end if isempty(alpha) alpha = 0.05; end b = a; b(isinf(a)) = NaN; %Delete outliers: outlier = 1; while outlier tmp = b(~isnan(b)); meanval = mean(tmp); maxval = tmp(find(abs(tmp-mean(tmp))==max(abs(tmp-mean(tmp))))); maxval = maxval(1); sdval = std(tmp); tn = abs((maxval-meanval)/sdval); critval = zcritical(alpha,length(tmp)); outlier = tn > critval; if outlier tmp = find(a == maxval); b(tmp) = NaN; end end if nargout >= 2 idx = find(isnan(b)); end if nargout > 2 outliers = a(idx); end if ~rep b=b(~isnan(b)); end return function zcrit = zcritical(alpha,n) %ZCRIT = ZCRITICAL(ALPHA,N) % Computes the critical z value for rejecting outliers (GRUBBS TEST) tcrit = tinv(alpha/(2*n),n-2); zcrit = (n-1)/sqrt(n)*(sqrt(tcrit^2/(n-2+tcrit^2)));
github
sunhongfu/scripts-master
Get_mrd_3D5.m
.m
scripts-master/XiangFeng/Get_mrd_3D5.m
10,379
utf_8
ba0ba6f33bf17effeafe8a956b00c067
% Description: Function to open multidimensional MRD/SUR files given a filename with PPR-parsing % Inputs: string filename, reordering1, reordering2 % Outputs: complex data, raw dimension [no_expts,no_echoes,no_slices,no_views,no_views_2,no_samples], MRD/PPR parameters % Author: Ruslan Garipov % Date: 01/03/2014 - swapped views and views2 dimension - now correct % 30 April 2014 - support for reading orientations added % 11 September 2014 - swapped views and views2 in the array (otherwise the images are rotated) % 13 October 2015 - scaling added as a parameter % 19 October 2018 - image tags added function [im,dim,par] = Get_mrd_3D5(filename,reordering1, reordering2) % Read in MRD and SUR file formats % reordering1, 2 is 'seq' or 'cen' % reordering1 is for 2D (views) % reordering2 is for 3D (views2) fid = fopen(filename,'r'); % Define the file id val = fread(fid,4,'int32'); xdim = val(1); ydim = val(2); zdim = val(3); dim4 = val(4); fseek(fid,18,'bof'); datatype=fread(fid,1, 'uint16'); datatype = dec2hex(datatype); fseek(fid,48,'bof'); scaling=fread(fid,1, 'float32'); bitsperpixel=fread(fid,1, 'uchar'); fseek(fid,152,'bof'); val = fread(fid,2, 'int32'); dim5 = val(1); dim6 = val(2); fseek(fid,256,'bof'); text=fread(fid,256); no_samples = xdim; no_views = ydim; no_views_2=zdim; no_slices = dim4; no_echoes = dim5; no_expts = dim6; % Read in the complex image data dim = [no_expts,no_echoes,no_slices,no_views_2,no_views,no_samples]; if size(datatype,2)>1 onlydatatype = datatype(2); iscomplex = 2; else onlydatatype = datatype(1); iscomplex = 1; end switch onlydatatype case '0' dataformat = 'uchar'; datasize = 1; % size in bytes case '1' dataformat = 'schar'; datasize = 1; % size in bytes case '2' dataformat = 'short'; datasize = 2; % size in bytes case '3' dataformat = 'int16'; datasize = 2; % size in bytes case '4' dataformat = 'int32'; datasize = 4; % size in bytes case '5' dataformat = 'float32'; datasize = 4; % size in bytes case '6' dataformat = 'double'; datasize = 8; % size in bytes otherwise dataformat = 'int32'; datasize = 4; % size in bytes end num2read = no_expts*no_echoes*no_slices*no_views_2*no_views*no_samples*iscomplex; %*datasize; [m_total, count] = fread(fid,num2read,dataformat); % reading all the data at once if (count~=num2read) h = msgbox('We have a problem...'); end if iscomplex == 2 a=1:count/2; m_real = m_total(2*a-1); m_imag = m_total(2*a); clear m_total; m_C = m_real+m_imag*1i; clear m_real m_imag; else m_C = m_total; clear m_total; end n=0; % shaping the data manually: ord=1:no_views; if reordering1 == 'cen' for g=1:no_views/2 ord(2*g-1)=no_views/2+g; ord(2*g)=no_views/2-g+1; end end ord1 = 1:no_views_2; ord2 = ord1; if reordering2 == 'cen' for g=1:no_views_2/2 ord2(2*g-1)=no_views_2/2+g; ord2(2*g)=no_views_2/2-g+1; end end for a=1:no_expts for b=1:no_echoes for c=1:no_slices for d=1:no_views for e=1:no_views_2 m_C_1(a,b,c,ord(d),ord2(e),:) = m_C(1+n:no_samples+n); % sequential ordering n=n+no_samples; end end end end end clear ord; clear ord2; m_C = squeeze(m_C_1); clear m_C_1; im=m_C; sample_filename = char(fread(fid,120,'uchar')'); ppr_text = char(fread(fid,Inf,'uchar')'); fclose(fid); %% parse fields in ppr section of the MRD file if numel(ppr_text)>0 cell_text = textscan(ppr_text,'%s','delimiter',char(13)); PPR_keywords = {'BUFFER_SIZE','DATA_TYPE','DECOUPLE_FREQUENCY','DISCARD','DSP_ROUTINE','EDITTEXT','EXPERIMENT_ARRAY','FOV','FOV_READ_OFF','FOV_PHASE_OFF','FOV_SLICE_OFF','GRADIENT_STRENGTH','MULTI_ORIENTATION','Multiple Receivers','NO_AVERAGES','NO_ECHOES','NO_RECEIVERS','NO_SAMPLES','NO_SLICES','NO_VIEWS','NO_VIEWS_2','OBLIQUE_ORIENTATION','OBSERVE_FREQUENCY','ORIENTATION','PHASE_CYCLE','READ/PHASE/SLICE_SELECTION','RECEIVER_FILTER','SAMPLE_PERIOD','SAMPLE_PERIOD_2','SCROLLBAR','SLICE_BLOCK','SLICE_FOV','SLICE_INTERLEAVE','SLICE_THICKNESS','SLICE_SEPARATION','SPECTRAL_WIDTH','SWEEP_WIDTH','SWEEP_WIDTH_2','VAR_ARRAY','VIEW_BLOCK','VIEWS_PER_SEGMENT','SMX','SMY','SWX','SWY','SMZ','SWZ','VAR','PHASE_ORIENTATION','X_ANGLE','Y_ANGLE','Z_ANGLE','PPL','IM_ORIENTATION','IM_OFFSETS','IM_SLICE', 'IM_ECHO', 'IM_EXPERIMENT', 'FOV_OFFSETS', 'READ_VAR'}; %PPR_type_0 keywords have text fields only, e.g. ":PPL C:\ppl\smisim\1ge_tagging2_1.PPL" PPR_type_0 = [23 53]; %PPR_type_1 keywords have single value, e.g. ":FOV 300" and 'IM_SLICE', 'IM_ECHO', 'IM_EXPERIMENT' SUR tags PPR_type_1 = [8 42:47 56 57]; %PPR_type_2 keywords have single variable and single value, e.g. ":NO_SAMPLES no_samples, 16" PPR_type_2 = [4 7 9:11 15:21 25 31 33 41 49 60]; PPR_type_3 = 48; % VAR keyword only (syntax same as above) PPR_type_4 = [28 29]; % :SAMPLE_PERIOD sample_period, 300, 19, "33.3 KHz 30 ?s" and SAMPLE_PERIOD_2 - read the first number=timeincrement in 100ns %PPR_type_5 keywords have single variable and two values, e.g. ":SLICE_THICKNESS gs_var, -799, 100" PPR_type_5 = [34 35]; % KEYWORD [pre-prompt,] [post-prompt,] [min,] [max,] default, variable [,scale] [,further parameters ...]; PPR_type_6 = [39 50:52]; % VAR_ARRAY and angles keywords PPR_type_7 = [54 55]; % IM_ORIENTATION and IM_OFFSETS (SUR only) PPR_type_8 = 12; % GRADIENT_STRENGTH keyword PPR_type_9 = 59; % FOV_OFFSETS keyword par = struct('filename',filename); for j=1:size(cell_text{1},1) char1 = char(cell_text{1}(j,:)); field_ = ''; if ~isempty(char1) C = textscan(char1, '%*c%s %s', 1); field_ = char(C{1}); end % find the corresponding number in PPR_keyword array: num = find(strcmp(field_,PPR_keywords)); if num>0 if find(PPR_type_3==num) % :VAR keyword C = textscan(char1, '%*s %s %f'); field_title = char(C{1}); field_title(numel(field_title)) = []; numeric_field = C{2}; par = setfield(par, field_title, numeric_field); elseif find(PPR_type_1==num) C = textscan(char1, '%*s %f'); numeric_field = C{1}; par = setfield(par, field_, numeric_field); elseif find(PPR_type_2==num) C = textscan(char1, '%*s %s %f'); numeric_field = C{2}; par = setfield(par, field_, numeric_field); elseif find(PPR_type_4==num) C = textscan(char1, '%*s %s %n %n %s'); field_title = char(C{1}); field_title(numel(field_title)) = []; numeric_field = C{2}; par = setfield(par, field_, numeric_field); elseif find(PPR_type_0==num) C = textscan(char1, '%*s %[^\n]'); text_field = char(C{1}); %text_field = reshape(text_field,1,[]); par = setfield(par, field_, text_field); elseif find(PPR_type_5==num) C = textscan(char1, '%*s %s %f %c %f'); numeric_field = C{4}; par = setfield(par, field_, numeric_field); elseif find(PPR_type_6==num) C = textscan(char1, '%*s %s %f %c %f', 200); field_ = char(C{1}); field_(end) = [];% the name of the array num_elements = C{2}; % the number of elements of the array numeric_field = C{4}; multiplier = []; for l=4:numel(C) multiplier = [multiplier C{l}]; end pattern = ':'; k=1; tline = char(cell_text{1}(j+k,:)); while (isempty(strfind(tline, pattern))) tline = char(cell_text{1}(j+k,:)); arr = textscan(tline, '%*s %f', num_elements); multiplier = [multiplier, arr{1}']; k = k+1; tline = char(cell_text{1}(j+k,:)); end par = setfield(par, field_, multiplier); elseif find(PPR_type_7==num) % :IM_ORIENTATION keyword C = textscan(char1, '%s %f %f %f'); field_title = char(C{1}); field_title(1) = []; numeric_field = [C{2}, C{3}, C{4}]; par = setfield(par, field_title, numeric_field); elseif find(PPR_type_8==num) % GRADIENT_STRENGTH keyword C=textscan(char1(20:end), '%s %d %d %d %d %d', 'Delimiter', ',', 'ReturnOnError', 0); field_ = char(C{1}); multiplier(1) = C{3}; multiplier(2) = C{4}; multiplier(3) = C{5}; multiplier(4) = C{6}; par = setfield(par, field_, multiplier); elseif find(PPR_type_9==num) % FOV_OFFSETS keyword C = textscan(char1, '%s %d', 200); field_ = char(C{1}); field_(1) = [];% the name of the array num_elements = C{2}; % the number of elements of the array multiplier = []; for l=1:num_elements tline = char(cell_text{1}(j+l,:)); arr = textscan(tline, '%*s %f', 3); multiplier(l,1) = arr{1}(1); multiplier(l,2) = arr{1}(2); multiplier(l,3) = arr{1}(3); end par = setfield(par, field_, multiplier); end end end if isfield('OBSERVE_FREQUENCY','par') C = textscan(par.OBSERVE_FREQUENCY, '%q'); text_field = char(C{1}); par.Nucleus = text_field(1,:); else par.Nucleus = 'Unspecified'; end par.datatype = datatype; file_pars = dir(filename); par.date = file_pars.date; else par = []; end par.scaling = scaling;
github
yhexie/particle-filter-localization-master
bresenham.m
.m
particle-filter-localization-master/bresenham.m
2,568
utf_8
dc4f9f7fd627abd3521074f046e9271e
function [myline,mycoords,outmat,X,Y] = bresenham(mymat,mycoordinates,dispFlag, threshold) %#codegen % BRESENHAM: Generate a line profile of a 2d image % using Bresenham's algorithm % [myline,mycoords] = bresenham(mymat,mycoordinates,dispFlag) % % - For a demo purpose, try >> bresenham(); % % - mymat is an input image matrix. % % - mycoordinates is coordinate of the form: [x1, y1; x2, y2] % which can be obtained from ginput function % % - dispFlag will show the image with a line if it is 1 % % - myline is the output line % % - mycoords is the same as mycoordinates if provided. % if not it will be the output from ginput() % Author: N. Chattrapiban % % Ref: nprotech: Chackrit Sangkaew; Citec % Ref: http://en.wikipedia.org/wiki/Bresenham's_line_algorithm % % See also: tut_line_algorithm % if nargin < 1, % for demo purpose % pxl = 20; % mymat = 1:pxl^2; % mymat = reshape(mymat,pxl,pxl); % disp('This is a demo.') % end % % if nargin < 2, % if no coordinate provided % imagesc(mymat); axis image; % disp('Click two points on the image.') % %[mycoordinates(1:2),mycoordinates(3:4)] = ginput(2); % mycoordinates = ginput(2); % end % % if nargin < 3, dispFlag = 1; end outmat = mymat; mycoords = mycoordinates; [xmax, ymax] = size(outmat); x = round(mycoords(:,1)); y = round(mycoords(:,2)); steep = (abs(y(2)-y(1)) > abs(x(2)-x(1))); if steep, [x,y] = swap(x,y); [ymax, xmax] = swap(ymax, xmax); end delx = abs(x(2)-x(1)); dely = abs(y(2)-y(1)); myline = zeros(1, delx+dely+1); X = zeros(1, delx+dely+1); Y = zeros(1, delx+dely+1); error = zeros(1, 'uint8'); x_n = x(1); y_n = y(1); if y(1) < y(2), ystep = 1; else ystep = -1; end if x(1) < x(2), xstep = 1; else xstep = -1; end for n = 1:delx+1 if steep, myline(n) = mymat(y_n,x_n); outmat(y_n,x_n) = 0; X(n) = x_n; Y(n) = y_n; else myline(n) = mymat(x_n,y_n); outmat(x_n,y_n) = 0; X(n) = y_n; Y(n) = x_n; end x_n = x_n + xstep; error = uint8(error + dely); if bitshift(error,1) >= delx, % same as -> if 2*error >= delx, y_n = y_n + ystep; error = error - delx; end if myline(n) < threshold break end if (y_n > ymax || y_n < 0 || x_n > xmax || x_n < 0) break end end % -> a(y,x) % if dispFlag, imagesc(outmat); % end function [q,r] = swap(s,t) % function SWAP q = t; r = s;
github
ccaicedo/SCMBAT-master
PropMap_find_piece.m
.m
SCMBAT-master/Octave/PropMap_find_piece.m
4,556
utf_8
4d3a8b78a9f94ba1b7303735d59cd194
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ PropMap_find_piece.m Function to find a power given at an azimuth, elevation angle and minimum distance for a piecewise propagation map. p: power at given orientation. theta_0: azimuth angle phi_0: elevation angle Min_Dist: breakpoint distance. PropMap: propagation map for the given transmitter/reciever. %} function [n0,d_break,n1] = PropMap_find_piece(theta_0,phi_0,PropMap,Min_Dist) %disp("Inside the PropMap_find_piece function"), disp("theta_0 is: "), disp(theta_0), disp("phi_0 is:"), disp(phi_0), disp("PropMap is : "), disp(PropMap); P=PropMap; c=length(P); ind_phi_s=0; ind_phi_e=0; ind_theta_s=0; ind_theta_e=0; %Finding all the indexes of 360 in PropogationMap, the vector index360 shall contain index of all theh 360s present in PowerMap index360=find(P==360.0); %This part identifies and marks the phi_s and phi_e, i.e. the block where required elevation resides index=1; ind_phi_curr=index; ind_phi_nxt=index360(index)+1; while true if(phi_0>=P(ind_phi_curr) && phi_0<P(ind_phi_nxt)) ind_phi_s=ind_phi_curr; ind_phi_e=ind_phi_nxt; break; end ind_phi_curr=index360(index)+1; ind_phi_nxt=index360(index+1)+1; if(index==c) break; end index++; end %This part identifies and marks the theta_s and theta_e indexes i.e. the piece where the required azimuth resides n0=0; d_break=0; n1=0; %disp("the phi at start is : "), disp(P(ind_phi_s)); %disp("the phi at end is : "), disp(P(ind_phi_e)); %disp("diff is : "), disp(ind_phi_e - ind_phi_s); if(ind_phi_e - ind_phi_s == 5 || ind_phi_e - ind_phi_s == 7) ind_theta_curr=ind_phi_s+1; %disp("the theta current is : "), disp(P(ind_theta_curr)); %disp("the theta current + 1 is : "), disp(P(ind_theta_curr+1)); if(P(ind_theta_curr+1)==0) % 0 refers to linear type %disp("it's linear type"); n0=P(ind_theta_curr+2); d_break=0; n1=0; end if(P(ind_theta_curr+1)==1) % 1 refers to piecewise linear %disp("it's piecewise linear"); n0=P(ind_theta_curr+2); d_break=P(ind_theta_curr+3); n1=P(ind_theta_curr+4); end %disp("the values are: " ), disp(n0), disp(d_break), disp(n1); else ind_theta_next=ind_phi_s+1; while index <= ind_phi_e-2 ind_theta_curr=ind_theta_next; %disp("ind_theta_curr is: "), disp(P(ind_theta_curr)); % Finding next theta depends on whether we have linear or piecewise linear if (P(ind_theta_curr + 1) == 0) % 0 refers to linear type %disp("setting theta next for linear type"); ind_theta_next=ind_theta_curr+3; elseif (P(ind_theta_curr + 1) == 1) % 1 refers to piecewise linear %disp("setting theta next for piecewise linear"); ind_theta_next=ind_theta_curr+5; endif index=index+2; %disp("ind_theta_next is: "), disp(P(ind_theta_next)); if(theta_0 >= P(ind_theta_curr) && theta_0 < P(ind_theta_next)) if(P(ind_theta_curr + 1) == 0) % 0 refers to linear type %disp("it's linear type"); n0 = P(ind_theta_curr + 2); d_break = 0; n1 = 0; end if(P(ind_theta_curr + 1) == 1) % 1 refers to piecewise linear %disp("it's piecewise linear"); n0 = P(ind_theta_curr + 2); d_break = P(ind_theta_curr + 3); n1 = P(ind_theta_curr + 4); end %disp("the values are: " ), disp(n0), disp(d_break), disp(n1); break; end end %disp("Exiting the PropMap_find_piece function") end
github
ccaicedo/SCMBAT-master
BWRatedCompliance.m
.m
SCMBAT-master/Octave/BWRatedCompliance.m
1,425
utf_8
0bf79533fef231c4f5e1861208309fc3
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ BWRatedCompliance Function to perform Bandwidth Rated Compatibility analysis %} function [epsd,compatBWMask_List] = BWRatedCompliance (bw, maxPSD) compatBWMask_List=0; load SCM_receiver_java.txt; BWRated_BW_List=Rx_BWRatedList(1:2:end-1); BWRated_Power_List=Rx_BWRatedList(2:2:end); [ef_bw,epsd] = calculateEPSD (bw, maxPSD); for i=1:length(BWRated_BW_List) MaskPowerCriterion = min(Rx_UnderlayMask(2:2:end)+BWRated_Power_List(i)); MaskPowerCriterion=MaskPowerCriterion(1); [baepsd] = calculateBAEPSD (bw, BWRated_BW_List(i), epsd); if(baepsd<MaskPowerCriterion) compatBWMask_List(i) = BWRated_BW_List(i); else end end endfunction
github
ccaicedo/SCMBAT-master
Coupler.m
.m
SCMBAT-master/Octave/Coupler.m
8,330
utf_8
ccd504c09378a7084ab7bd214abedccc
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ OctaveLauncher.m Launcher file to handle all the calculations and processing related to Octave code. Instead of interacting direct with the specific Octave files, the Java code will inreract with this file and from here, all the required code will be launched. execPattern - defines the pattern in which bandwidth and frequency components have to be executed in case of BTP and DC %} function [retVal1, retVal2, retVal3, retVal4] = Coupler(report_directory, method, logging, specTag, numOfTransmitterString, execPattern) disp('displaying from the coupler file.'); disp(report_directory), disp(method), disp(logging), disp(numOfTransmitterString); %converting string to decimal, so that we may loop over it. numOfTransmitter = str2num(numOfTransmitterString) switch (method) case 'TotalPower' rename('SCM_transmitter_java1.txt', 'SCM_transmitter_java.txt') [Spec_mask_new,Underlay_mask] = DSA_TotPow(report_directory); return; case 'MaxPower' rename('SCM_transmitter_java1.txt', 'SCM_transmitter_java.txt') DSA_MaxPow(report_directory); return; case 'PlotBWRated' disp('inside bandwidth switch case'); fig1=figure; retval = plotBWRated(); saveas(fig1, 'Analysis_Figure_1.png'); movefile('Analysis_Figure_1.png', report_directory); disp('inside RatedBW'); %initializing the return values retVal1 = [] retVal2 = [] retVal3 = [] retVal4 = [] specTagIndex = 1 for i = 1:numOfTransmitter %changing the name of the file fileToBeReplaced = strcat('SCM_transmitter_java', num2str(i), '.txt') disp('file to be replaced is: '), disp(fileToBeReplaced); rename(fileToBeReplaced, 'SCM_transmitter_java.txt') disp('rename done'); disp('spec Tag is : '), disp(specTag); %fetch the specTagName from the specTagList %get the length of next name disp('trying to fetch length of specNameTag'); len = str2num(substr(specTag, specTagIndex, 2)); disp('len is '), disp(len); specTagName = substr(specTag, specTagIndex+2, len); disp('specTagName is: '), disp(specTagName); %update the specTagIndex to point to next name specTagIndex = specTagIndex+2+len [SpecMask,PSD,BW,compatBWList] = TxMPSD(); plot(SpecMask(1:2:end-1),SpecMask(2:2:end),'r.-','LineWidth',2); xpoint=(SpecMask(length(SpecMask)/2-1)+SpecMask(length(SpecMask)/2+1))/2; minSpecPow=min(SpecMask(2:2:end)); text(xpoint,minSpecPow-1,specTagName); #setting all the values to standard return value variable names retVal1 = [retVal1, SpecMask] retVal2 = [retVal2, PSD] retVal3 = [retVal3, BW] retVal4 = [retVal4, compatBWList, 123456.789] disp('compatBWList is:'), disp(compatBWList); disp('retval4 is:'), disp(retVal4); endfor saveas(fig1,'BWRatedAnalysis.png'); movefile('BWRatedAnalysis.png', report_directory); return; case 'PlotBTPRated' disp('inside BTP switch case'); fig2=figure; retval = plotBTPRated(); saveas(fig2, 'Analysis_Figure_1.png'); movefile('Analysis_Figure_1.png', report_directory); %initializing the return values retVal1 = [] retVal2 = [] retVal3 = [] retVal4 = [] %initialize specTagName index specTagIndex = 1 for i = 1:numOfTransmitter %changing the name of the file fileToBeReplaced = strcat('SCM_transmitter_java', num2str(i), '.txt') disp('file to be replaced is: '), disp(fileToBeReplaced); rename(fileToBeReplaced, 'SCM_transmitter_java.txt') disp('spec Tag is : '), disp(specTag); %fetch the specTagName from the specTagList %get the length of next name disp('trying to fetch length of specNameTag'); len = str2num(substr(specTag, specTagIndex, 2)); disp('len is '), disp(len); specTagName = substr(specTag, specTagIndex+2, len); disp('specTagName is: '), disp(specTagName); %update the specTagIndex to point to next name specTagIndex = specTagIndex+2+len %fetching whether it's frequency based or bandwidth based system ch = substr(execPattern, i, 1) disp('the char is:'), disp(ch); %in case of freq if(ch == "f") [Spec_BTP,ExtSpecMask,compatBTPList] = TxHop_FreqList(); plot(ExtSpecMask(1:2:end-1),ExtSpecMask(2:2:end),'r.-','LineWidth',2); xpoint=ExtSpecMask(1); minSpecPow=min(ExtSpecMask(2:2:end)); text(xpoint,minSpecPow-1,specTagName); #setting all the values to standard return value variable names retVal1 = [retVal1, Spec_BTP] retVal2 = [retVal2, ExtSpecMask] retVal3 = [retVal3, compatBTPList, 123456.789] endif %in case of bandwidth if(ch == "b") [Spec_BTP,NewBandList,Spec_MaxPower,compatBTPList] = TxHop_BandList(); plot(NewBandList,Spec_MaxPower*ones(1,length(NewBandList)),'r.-','LineWidth',2); xpoint=NewBandList(1); minSpecPow=Spec_MaxPower; text(xpoint,minSpecPow-1,specTagName); #setting all the values to standard return value variable names retVal1 = [retVal1, Spec_BTP] retVal2 = [retVal2, NewBandList] retVal3 = [retVal3, Spec_MaxPower] retVal4 = [retVal4, compatBTPList, 123456.789] endif endfor saveas(fig2,'BTPRatedAnalysis.png'); movefile('BTPRatedAnalysis.png', report_directory); return; case 'PlotDCRated' disp('inside PlotDCRated'); fig3=figure; %initializing the return values retVal1 = [] retVal2 = [] retVal3 = [] retVal4 = [] for i = 1:numOfTransmitter % changing the name of the file fileToBeReplaced = strcat('SCM_transmitter_java', num2str(i), '.txt') disp('file to be replaced is: '), disp(fileToBeReplaced); rename(fileToBeReplaced, 'SCM_transmitter_java.txt') %fetching whether it's frequency based or bandwidth based system ch = substr(execPattern, i, 1) disp('the char is:'), disp(ch); %in case of freq if(ch == "f") [Spec_mask_new,p_Tx_new,compatDutyList] = TxDuty_FreqList(); #setting all the values to standard return value variable names retVal1 = [retVal1, Spec_mask_new] retVal2 = [retVal2, p_Tx_new] retVal3 = [retVal3, compatDutyList, 123456.789] endif %in case of bandwidth if(ch == "b") [Spec_mask_new,p_Tx_new,compatDutyList] = TxDuty_BandList(); #setting all the values to standard return value variable names retVal1 = [retVal1, Spec_mask_new] retVal2 = [retVal2, p_Tx_new] retVal3 = [retVal3, compatDutyList, 123456.789] endif endfor saveas(fig3,'DCRatedAnalysis.png'); movefile('DCRatedAnalysis.png', report_directory); return; otherwise %nothing to do!! We don't have a default case!! return; endswitch
github
ccaicedo/SCMBAT-master
TxDuty_FreqList.m
.m
SCMBAT-master/Octave/TxDuty_FreqList.m
5,179
utf_8
3da5aeb176c3a93dd680333ec55a4a76
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ TxDuty_FreqList.m Function to perform compatibility computation if the underlay mask is duty cycle rated and the spectrum mask is frequency listed %} function [Spec_mask_new,p_Tx_new,compatDutyList] = TxDuty_FreqList() load SCM_transmitter_java.txt; load SCM_receiver_java.txt; status=0; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); Power=Power+(10*log10(Rx_ResBW/1e-3)); Power=Power+p2; Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; Underlay_mask=Rx_UnderlayMask; Underlay_freq_list=Underlay_mask(1:2:end-1); Underlay_power_list=Underlay_mask(2:2:end); Underlay_min_pow_freq=Underlay_freq_list( find(Underlay_power_list==min(Underlay_power_list)) ); %--- Start BTP and compatibility Analysis --- %Compare if the SCMs are operating in the same time duration %if((Tx_Start<Rx_Start && Tx_End<Rx_Start) || (Tx_Start>Rx_End && Tx_End>Rx_End) ) % disp('System is compatible') % break; % else %end %Compare frequency range i0=1; FreqList=0; for i=1:length(Tx_FreqList) if((Tx_SpecMask(1)+Tx_FreqList(i)<=Rx_UnderlayMask(1) && Tx_SpecMask(end-1)+Tx_FreqList(i)<=Rx_UnderlayMask(1)) || (Tx_SpecMask(1)+Tx_FreqList(i)>=Rx_UnderlayMask(end-1) && Tx_SpecMask(end-1)+Tx_FreqList(i)>=Rx_UnderlayMask(end-1)) ) else FreqList(i0)=Tx_FreqList(i); i0=i0+1; end end if(FreqList==0) disp(strcat('result: ', 'System is compatible')); return; end DutyCylceList = Rx_DutyList(1:3:end-2); DwellList = Rx_DutyList(2:3:end-1); PowerAdj = Rx_DutyList(3:3:end); Center_UnderlayFreq=(Underlay_freq_list(end)+Underlay_freq_list(1))/2; diff=abs(Center_UnderlayFreq-FreqList); ind = find(diff==min(diff)); Center_UnderlayFreq FreqList(ind(1)) Spec_mask_new(1:2:end-1)=Spec_mask_new(1:2:end-1)+FreqList(ind(1)); Spec_freq_list=Spec_mask_new(1:2:end-1); Spec_power_list=Spec_mask_new(2:2:end); Spec_cent_freq=(Spec_freq_list(1)+Spec_freq_list(end))/2; Spec_BW=Spec_mask_new(end-1)-Spec_mask_new(1); Spec_MaxPower=max(Spec_power_list); Spec_DutyCycle=Tx_DwellTime/Tx_RevisitPeriod; % Execute total power method with each Duty cycle mask. compatDutyList=0; i1=1; p_Tx_new=0; for i=1:length(DutyCylceList) if(Spec_DutyCycle<DutyCylceList(i) && Tx_DwellTime<(DwellList(i)*1000)) DutyUnderlayMask = Underlay_mask; DutyUnderlayMask(2:2:end)=Underlay_mask(2:2:end)+PowerAdj(i); %SCM compatibility [p_Tx_new] = new_spectrum(Spec_mask_new,DutyUnderlayMask); plot(p_Tx_new(1:2:end-1),p_Tx_new(2:2:end),'LineWidth',2); hold all [Power_Tx,Power_Tx_dB] = calculate_power(p_Tx_new,Rx_ResBW); [p_Rx_new] = calculate_power_3dB(DutyUnderlayMask,Rx_ResBW); [Power_Rx,Power_Rx_dB] = calculate_power(p_Rx_new,Rx_ResBW); %Power_Tx_dB AllowablePower=Power_Rx_dB; Power_Margin_Difference = AllowablePower-Power_Tx_dB if(AllowablePower>Power_Tx_dB) compatDutyList(i1)=DutyCylceList(i); i1=i1+1; else end else end end if(compatDutyList==0) disp(strcat('result: ', 'System is not at all compatible')); return; else disp(strcat('result: ', 'System compatible with: ')); disp(strcat('result: ', num2str(compatDutyList))); return; end %figure %plot(Tx_SpecMask(1:2:end-1),Tx_SpecMask(2:2:end),'b.-','LineWidth',2) %hold all %plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %fig1=figure; %for i=1:length(BTP_BW_List) %plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+BTP_Power_List(i),'b.-','LineWidth',2) %hold all %end %plot(ExtSpecMask(1:2:end-1),ExtSpecMask(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %saveas(fig1,'BTPRatedFreqList.png') end
github
ccaicedo/SCMBAT-master
calcNarrowBW.m
.m
SCMBAT-master/Octave/calcNarrowBW.m
1,658
utf_8
c1ccf20fe3468e32e84452cc6cf24472
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ calcNarrowBW.m Function to calculate the banwidth of narrow band signals BW: Bandwidth p0: spectrum mask representation: [f0,p0,f1,p1....fn,pn]; %} function [BW] = calcNarrowBW(p0) fv=p0(1:2:end-1); pv=p0(2:2:end); f1 = ( fv(1) + fv(end) )/2; f2 = ( fv(1) + fv(end) )/2; maxP=max(pv); p1=find_power(f1,p0); delP1 = maxP-p1; while(abs(delP1-20)>0.1) if(delP1>20) f1= f1+0.0001; p1=find_power(f1,p0); else f1= f1-0.0001; p1=find_power(f1,p0); end delP1=maxP-p1; end maxP=max(pv); p2=find_power(f2,p0); delP2 = maxP-p2; while(abs(delP2-20)>0.1) if(delP2>20) f2= f2-0.0001; p2=find_power(f2,p0); else f2= f2+0.0001; p2=find_power(f2,p0); end delP2=maxP-p2; end BW=f2-f1; %fig1 = figure; %plot(p0(1:2:end-1),p0(2:2:end),'r.-','LineWidth',2) %hold all; %stem(f1,p1,'b'); %stem(f2,p2,'b'); %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); end
github
ccaicedo/SCMBAT-master
DSA_MaxPow.m
.m
SCMBAT-master/Octave/DSA_MaxPow.m
4,172
utf_8
dc43fe82b08a7f8b719bf331417d4c1b
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ DSA_MaxPow.m Function to perform maximum power compatibility computations %} function [Spec_mask_new,Underlay_mask] = DSA_MaxPow(report_directory) disp("the report directory is: " ), disp(report_directory); load SCM_transmitter_java.txt; load SCM_receiver_java.txt; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R) %Compare if the SCMs are operating in the same time duration %if((Tx_Start<Rx_Start && Tx_End<Rx_Start) || (Tx_Start>Rx_End && Tx_End>Rx_End) ) % disp('System is compatible') % break; % else %end %Compare frequency range if((Tx_SpecMask(1)<Rx_UnderlayMask(1) && Tx_SpecMask(end-1)<Rx_UnderlayMask(1)) || (Tx_SpecMask(1)>Rx_UnderlayMask(end-1) && Tx_SpecMask(end-1)>Rx_SpecMask(end-1)) ) disp('System is compatible'); return; end %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; %%---- Computations for the Receiver Model ------- rx_PowAdj = Rx_TotPow; %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); rx_PowAdj=rx_PowAdj+(10*log10(1e-3/Rx_ResBW)); rx_PowAdj=rx_PowAdj+p2; Underlay_mask=Rx_UnderlayMask; Underlay_mask(2:2:end) = Rx_UnderlayMask(2:2:end) + rx_PowAdj; Underlay_freq_list=Underlay_mask(1:2:end-1); Spec_freq_list=Spec_mask_new(1:2:end-1); list_1=find(Underlay_freq_list(end)<Spec_freq_list); if(isempty(list_1)==0) Spec_freq_list=[Spec_freq_list(1:list_1(1)-1),Underlay_freq_list(end)]; p = find_power(Underlay_freq_list(end),Spec_mask_new); Spec_mask_new=[Spec_mask_new(1:2*list_1(1)-2),Underlay_freq_list(end),p]; end list_2=find(Spec_freq_list<Underlay_freq_list(1)) if(isempty(list_2)==0) Spec_freq_list=[Underlay_freq_list(1),Spec_freq_list(list_2(end)+1:end)]; p2 = find_power(Underlay_freq_list(1),Spec_mask_new); Spec_mask_new=[Underlay_freq_list(1),p2,Spec_mask_new(2*list_2(end)+1:end)]; end fig1 = figure; plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end),'b.-','LineWidth',2) hold all plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); saveas(fig1,'CompatAnalysis.png') movefile('CompatAnalysis.png', report_directory) %SCM compatibility [P_diff] = MaxPow_Diff(Underlay_mask,Spec_mask_new); ind=find(P_diff<0); %Power_Tx_dB PowerMargin = min((P_diff)); % the actual power margin is the negative of this value if(isempty(ind)==1) disp('System is compatible'); disp(PowerMargin * -1); % the system is compaitable when the power margin is negative else disp('System is not compatible'); disp(PowerMargin * -1); end end
github
ccaicedo/SCMBAT-master
freq_sort.m
.m
SCMBAT-master/Octave/freq_sort.m
1,375
utf_8
9132e23b975d0d792c1289e80d9abd1e
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ freq_sort.m Function to sort two lists of frequencies f1 and f2 and return the sorted list in f_new. %} function [f_new] = freq_sort(f1,f2) f=sort([f1,f2]); u=unique(f); for i=1:length(u) ind=find(f==u(i)); switch length(ind) case 2 ind2=find(f1==u(i)); if(length(ind2)==1) f(ind(1))=[]; else end case 3 f(ind(1))=[]; case 4 f(ind(1))=[]; f(ind(2))=[]; otherwise end end f_new=f; end
github
ccaicedo/SCMBAT-master
calculate_power_3dB.m
.m
SCMBAT-master/Octave/calculate_power_3dB.m
1,965
utf_8
6f8a072779b1f31d264978fdccdf4810
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ calculate_power_3dB.m Function to calculate underlay mask within the 3dB region p_new: new underlay mask, p0: underlay mask representation: [f0,p0,f1,p1....fn,pn]; RBW: resolution bandwidth. %} function [p_new] = calculate_power_3dB(p0,RBW) %disp("Inside the function calculate_power_3dB"); %disp("p0 is : ") , disp(p0); fv=p0(1:2:end-1)*1e+6; pv=p0(2:2:end); %plot(fv,pv,'r-','LineWidth',2); P_net=0; pl=min(pv); %po=pl*(10^(.3)); % unused variable ind=find(pv==pl); i1=ind(1); i2=ind(end); if(fv(i1)~=fv(i1-1)) b1=( pv(i1)-pv(i1-1) )./( fv(i1)-fv(i1-1) ); b0=pv(i1-1) - (b1*fv(i1-1)); f1=((pl+3)-b0)/b1; p1=pl+3; else f1=fv(i1-1); p1=pv(i1-1); end if(fv(i2)~=fv(i2+1)) b1=( pv(i2+1)-pv(i2) )./( fv(i2+1)-fv(i2) ); b0=pv(i2) - (b1*fv(i2)); f2=((pl+3)-b0)/b1; p2=pl+3; else f2=fv(i2+1); p2=pv(i2+1); end p_new=zeros(1,8); p_new(1:2:end-1)=[f1,fv(i1),fv(i2),f2].*1e-6; p_new(2:2:end)=[p1,pl,pl,p2]; %figure %plot(p_new(1:2:end-1),p_new(2:2:end),'b-','LineWidth',2); %disp("Exiting calculate_power_3dB function"); end
github
ccaicedo/SCMBAT-master
Hop_Analysis.m
.m
SCMBAT-master/Octave/Hop_Analysis.m
1,628
utf_8
90dab15cd51acf2f80a3ad9dd9a2c35d
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ Hop_Analysis.m Function to perform BTP analysis for spectrum hopping systems. %} function [Hop_Result] = Hop_Analysis(Tx_Spec_mask,Rx_Spec_mask,Rx_BTP,Tx_FreqList,Tx_RevisitPeriod,Tx_DwellTime) BTP_Data=Rx_BTP(1:2:end-1); Tx_BW=Tx_Spec_mask(end-1)-Tx_Spec_mask(1); Rx_BW=Rx_Spec_mask(end-1)-Rx_Spec_mask(1); Tx_minFreq=Tx_FreqList(find(Tx_FreqList>Rx_Spec_mask(1))); if(isempty(Tx_minFreq)==1) Hop_Result=1; else Tx_NewFreqList=Tx_minFreq; Tx_maxFreq=Tx_FreqList(find(Tx_NewFreqList<Rx_Spec_mask(end-1))); if(isempty(Tx_maxFreq)==1) Hop_Result=1; else Tx_NewFreqList=Tx_maxFreq; n=length(Tx_NewFreqList); Tx_BTP = n*Tx_BW*Tx_DwellTime*(1/Tx_RevisitPeriod); BTP_index=find(BTP_Data>Tx_BTP); Hop_Result=zeros(1,2*length(BTP_index)); Hop_Result(1:2:end-1)=Rx_BTP((2*BTP_index)-1); Hop_Result(2:2:end)=Rx_BTP(2*BTP_index); end end end
github
ccaicedo/SCMBAT-master
TxDuty_BandList.m
.m
SCMBAT-master/Octave/TxDuty_BandList.m
5,195
utf_8
b66c749cd23d988131f81a25f14b01f1
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ TxDuty_BandList.m Function to perform compatibility computations if the underlay mask is duty cycle rated and the spectrum mask uses a Band list %} function [Spec_mask_new,p_Tx_new,compatDutyList] = TxDuty_BandList() load SCM_transmitter_java.txt; load SCM_receiver_java.txt; status=0; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); Power=Power+(10*log10(Rx_ResBW/1e-3)); Power=Power+p2; Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; Underlay_mask=Rx_UnderlayMask; Underlay_freq_list=Underlay_mask(1:2:end-1); Underlay_power_list=Underlay_mask(2:2:end); Underlay_min_pow_freq=Underlay_freq_list( find(Underlay_power_list==min(Underlay_power_list)) ); %--- Start BTP and compatibility Analysis --- %Compare if the SCMs are operating in the same time duration %if((Tx_Start<Rx_Start && Tx_End<Rx_Start) || (Tx_Start>Rx_End && Tx_End>Rx_End) ) % disp('System is compatible') % break; % else %end %Compare frequency range i0=1; NewBandList=sort([Tx_BandList,Underlay_freq_list(1),Underlay_freq_list(end)]); ind0=find(NewBandList==Underlay_freq_list(1)); ind1=find(NewBandList==Underlay_freq_list(end)); NewBandList=NewBandList(ind0:ind1); Center_UnderlayFreq=(Underlay_freq_list(end)+Underlay_freq_list(1))/2; if(Center_UnderlayFreq>NewBandList(end)) Spec_mask_new(1:2:end-1)=Spec_mask_new(1:2:end-1)+NewBandList(end); else if(Center_UnderlayFreq<NewBandList(1)) Spec_mask_new(1:2:end-1)=Spec_mask_new(1:2:end-1)+NewBandList(1); else Spec_mask_new(1:2:end-1)=Spec_mask_new(1:2:end-1)+Center_UnderlayFreq; end end if(NewBandList==0) disp(strcat('result: ', 'System is compatible')); return; end DutyCylceList = Rx_DutyList(1:3:end-2); DwellList = Rx_DutyList(2:3:end-1); PowerAdj = Rx_DutyList(3:3:end); Spec_freq_list=Spec_mask_new(1:2:end-1); Spec_power_list=Spec_mask_new(2:2:end); Spec_cent_freq=(Spec_freq_list(1)+Spec_freq_list(end))/2; Spec_BW=Spec_mask_new(end-1)-Spec_mask_new(1); Spec_MaxPower=max(Spec_power_list); Spec_DutyCycle=Tx_DwellTime/Tx_RevisitPeriod; % Execute total power method with each Duty cycle mask. compatDutyList=0; i1=1; p_Tx_new=0; for i=1:length(DutyCylceList) if(Spec_DutyCycle<DutyCylceList(i) && Tx_DwellTime<(DwellList(i)*1000)) DutyUnderlayMask = Underlay_mask; DutyUnderlayMask(2:2:end)=Underlay_mask(2:2:end)+PowerAdj(i); %SCM compatibility [p_Tx_new] = new_spectrum(Spec_mask_new,DutyUnderlayMask); %plot(p_Tx_new(1:2:end-1),p_Tx_new(2:2:end),'LineWidth',2); [Power_Tx,Power_Tx_dB] = calculate_power(p_Tx_new,Rx_ResBW); [p_Rx_new] = calculate_power_3dB(DutyUnderlayMask,Rx_ResBW); [Power_Rx,Power_Rx_dB] = calculate_power(p_Rx_new,Rx_ResBW); %Power_Tx_dB AllowablePower=Power_Rx_dB; Power_Margin_Difference = AllowablePower-Power_Tx_dB if(AllowablePower>Power_Tx_dB) compatDutyList(i1)=DutyCylceList(i); i1=i1+1; else end else end end if(compatDutyList==0) disp(strcat('result: ', 'System is not at all compatible')); return; else disp(strcat('result: ', 'System compatible with: ')); disp(strcat('result: ', num2str(compatDutyList))); return; end %figure %plot(Tx_SpecMask(1:2:end-1),Tx_SpecMask(2:2:end),'b.-','LineWidth',2) %hold all %plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %fig1=figure; %for i=1:length(BTP_BW_List) %plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+BTP_Power_List(i),'b.-','LineWidth',2) %hold all %end %plot(ExtSpecMask(1:2:end-1),ExtSpecMask(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %saveas(fig1,'BTPRatedFreqList.png') end
github
ccaicedo/SCMBAT-master
plotBWRated.m
.m
SCMBAT-master/Octave/plotBWRated.m
1,493
utf_8
16bd8da1a24f69c79fb45500fbabc310
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ plotBWRated.m Function to plot BW rated underlay masks %} function [retval] = plotBWRated () load SCM_receiver_java.txt; BWRated_BW_List=Rx_BWRatedList(1:2:end-1); BWRated_Power_List=Rx_BWRatedList(2:2:end); plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end),'b-.','LineWidth',2) hold all for i=1:length(BWRated_BW_List) newPowerList = Rx_UnderlayMask(2:2:end)+BWRated_Power_List(i); xpoint=(Rx_UnderlayMask((length(Rx_UnderlayMask)/2)-1)+Rx_UnderlayMask((length(Rx_UnderlayMask)/2)+1))/2 plot(Rx_UnderlayMask(1:2:end-1),newPowerList,'b.-','LineWidth',2) text(xpoint,min(newPowerList)+5,strcat(num2str(BWRated_BW_List(i)*1000), ' KHz')) hold all end grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); retval=0; endfunction
github
ccaicedo/SCMBAT-master
calculate_power.m
.m
SCMBAT-master/Octave/calculate_power.m
1,840
utf_8
95de70ec56d9271e4aa1521cf86918d0
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ calculate_power.m Function to calculate power under a spectrum/underlay mask P_net: power under the spectrum/underlay mask, P_net_dB: power under the spectrum/underlay mask in dB, p0: spectrum/underlay mask representation: [f0,p0,f1,p1....fn,pn], RBW: Resolution Bandwidth %} function [P_net,P_net_dB] = calculate_power(p0,RBW) %disp("************* Inside calculate_power function **************"); RBW = RBW*1e+6; % adjusting RBW to Hertz (previously MHz) fv=p0(1:2:end-1)*1e+6; pv=p0(2:2:end); %plot(fv,pv,'r-','LineWidth',2); P_net=0; for i=1:length(fv)-1 if(fv(i)~=fv(i+1)) b1=( pv(i+1)-pv(i) )./( fv(i+1)-fv(i) ); b0=pv(i) - (b1*fv(i)); if(b1~=0) P_net=P_net + ( 10*( (10^((b0 + (b1*fv(i+1)))/10)) - (10^((b0+(b1*fv(i)))/10)) )./(RBW*log(10)*b1) ); else P_net= P_net + ( (10^(b0/10)).*((fv(i+1)-fv(i))./RBW) ); end else end P_net_dB=10*log10(P_net); end %disp("************* Exiting calculate_power function **************"); end
github
ccaicedo/SCMBAT-master
find_power.m
.m
SCMBAT-master/Octave/find_power.m
2,027
utf_8
475ad5565b5b74fd112c969fb7cdcf62
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ find_power.m Function to find power at a given frequency in a spectrum/underlay mask. p: power at the provided frequency. f: frequency where power is to be found. p0: spectrum/underlay mask representation : [f0,p0,f1,p1,...fn,pn] %} function [p] = find_power(fo,p0) fv=p0(1:2:end-1); pv=p0(2:2:end); %disp("fv is: "), disp(fv); %disp("pv is: "), disp(pv); %plot(fv,pv,'r-','LineWidth',2); ind=find(fo==fv); %disp("ind is : " ), disp(ind); if(isempty(ind)==1) ind2=find(fv>fo); %disp("ind2 is: "), disp(ind2); if(isempty(ind2)==0) i=min(ind2); if(i==1) p=pv(1); %disp("i==1, and the power is :"), disp(p); else if(pv(i)==pv(i-1)) p=pv(i); %disp("inside if, the power is :"), disp(p); else b1=(pv(i)-pv(i-1))./( fv(i)-fv(i-1) ); b0=pv(i-1)-(b1*fv(i-1)); p=b0+(b1*fo); %disp("inside else, the power is :"), disp(p); end end else p=pv(end); end else p=pv(ind); end %disp("the value of p is: "), disp(p); end
github
ccaicedo/SCMBAT-master
PowerMap_find.m
.m
SCMBAT-master/Octave/PowerMap_find.m
2,380
utf_8
5bd83161e7f1b937e3a9d547e33dc599
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ PowerMap_find.m Function to compute a power level given an azimuth and elevation angle for a power map. p: power at given orientation. theta_0: azimuth angle phi_0: elevation angle PowerMap: power map for the given transmitter/reciever. %} function [p] = PowerMap_find(theta_0,phi_0,PowerMap) P=PowerMap; c=length(P); ind_phi_s=0; ind_phi_e=0; ind_theta_s=0; ind_theta_e=0; %Finding all the indexes of 360 in PowerMap, the vector index360 shall contain index of all theh 360s present in PowerMap index360=find(P==360.0); %This part identifies and marks the phi_s and phi_e, i.e. the block where required elevation resides index=1; ind_phi_curr=index; ind_phi_nxt=index360(index)+1; while true if(phi_0>=P(ind_phi_curr) && phi_0<P(ind_phi_nxt)) ind_phi_s=ind_phi_curr; ind_phi_e=ind_phi_nxt; break; end ind_phi_curr=index360(index)+1; ind_phi_nxt=index360(index+1)+1; if(index==c) break; end index++; end %This part identifies and marks the theta_s and theta_e indexes i.e. the piece where the required azimuth resides index=ind_phi_s+1; if(ind_phi_e - ind_phi_s==3) p=P(ind_phi_s+2); %disp("the value of p(powermap) is: " ), disp(p); else while index <= ind_phi_e-2 ind_theta_curr=index; ind_theta_next=index+2; if(theta_0 >= P(ind_theta_curr) && theta_0 < P(ind_theta_next)) p=P(ind_theta_curr+1); %disp("the value of p(powermap) is: " ), disp(p); break; end index=index+2; end end
github
ccaicedo/SCMBAT-master
DSA_TotPow.m
.m
SCMBAT-master/Octave/DSA_TotPow.m
9,255
utf_8
bce88b50b4f3f40260032f4d3cc13ce0
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ DSA_TotPow.m Function to perform total power method compatibility computations %} function [Spec_mask_new,Underlay_mask] = DSA_TotPow(report_directory) load SCM_transmitter_java.txt; disp("SCM_transmitter_java.txt loaded successfully"); load SCM_receiver_java.txt; disp("SCM_receiver_java.txt loaded successfully"); %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; disp("Getting the the min distance, elevation and azimuth angle: "); disp("Calling function find_Compare"); [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); disp("Values returned from find_direction are: "); disp("phi_Txo: "), disp(phi_Txo), disp("theta_Txo: "), disp(theta_Txo), disp("Min_Dist: "), disp(Min_Dist); %Compare frequency range disp("Comparing Frequency range."); if((Tx_SpecMask(1)<Rx_UnderlayMask(1) && Tx_SpecMask(end-1)<Rx_UnderlayMask(1)) || (Tx_SpecMask(1)>Rx_UnderlayMask(end-1) && Tx_SpecMask(end-1)>Rx_UnderlayMask(end-1)) ) disp('System is compatible'); return; end %Transmitter Power map attenuation to Total Power; disp("Transmitter Power map attenuation to Total Power"); disp("Calling function PowerMap_find for Transmitter model with values:"); disp("phi_Txo: "), disp(phi_Txo), disp("theta_Txo: "), disp(theta_Txo), disp("Tx_PowerMap"), disp(Tx_PowerMap); p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); disp("the p (gain) value for transmitter is : "), disp(p); disp("the total power value is: "), disp(Tx_TotPow); Power=Tx_TotPow+p; disp("the total power @ 'Power=Tx_TotPow+p' is : " ), disp(Power); Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; disp("Bringing the Reference Bandwidth to 1 Khz"); disp("the total power after adjustment (10*log10(1e-3/Tx_ResBW)) : " ), disp(Power); %Attenuation due to Propagation Map disp("Calling function PropMap_find_piece with values: "); disp("theta_Txo: "), disp(theta_Txo), disp("phi_Txo: "), disp(phi_Txo), disp("Min_Dist"), disp(Min_Dist); [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); disp("the returned values for n0 is : " ), disp(n0), disp("for d_break: "), disp(d_break), disp("for n1: "), disp(n1); disp("Checking if(Min_Dist>d_break && d_break!=0.0)"); if(Min_Dist>d_break && d_break!=0.0) disp("condition is true, calculating 'Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break))'"); Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else disp("Condition is false; calculating 'Power = Power - (10*n0*log(Min_Dist))'"); Power = Power - (10*n0*log(Min_Dist)); end disp("After calculations the Power is : " ), disp(Power); Spec_mask_new=Tx_SpecMask; disp("Spec_mask @ 'Spec_mask_new=Tx_SpecMask' is : "), disp(Spec_mask_new); Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; disp("Spec_mask_new(2:2:end) @ 'Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power' is: "), disp(Spec_mask_new(2:2:end)); %%---- Computations for the Receiver Model ------- rx_PowAdj = Rx_TotPow; %Attenuation due to the Receiver Power Map disp("Attenuation due to the Receiver Power Map"); phi_Rxo=-phi_Txo; disp("phi_Rxo after phi_Rxo=-phi_Txo is : "), disp(phi_Rxo); disp("checking if(theta_Txo>180)"); if(theta_Txo>180) disp("Condition true calculating 'theta_Rxo=theta_Txo-180'"); theta_Rxo=theta_Txo-180; else disp("Condition true, calculating theta_Rxo=theta_Txo+180"); theta_Rxo=theta_Txo+180; end disp("Calling function PowerMap_find for Receiver model."); p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); disp("the p (gain) power value for receiver model is : " ), disp(p2); %Power=Power+(10*log10(Rx_ResBW/1e-3)); rx_PowAdj=rx_PowAdj+(10*log10(1e-3/Rx_ResBW)); %disp("Power @ Power=Power+(10*log10(Rx_ResBW/1e-3)): "), disp(Power); disp("Power @ Power=Power+(10*log10(1e-3/Rx_ResBW)): "), disp(Power); rx_PowAdj=rx_PowAdj+p2; disp("Power @ 'Power=Power+p2' is : "), disp(Power); % Adjusted Underlay Mask Underlay_mask=Rx_UnderlayMask; disp("Underlay_mask is: "), disp(Underlay_mask); Underlay_mask(2:2:end) = Rx_UnderlayMask(2:2:end) + rx_PowAdj; disp("Adjusted Underlay_mask is: "), disp(Underlay_mask(2:2:end)); Underlay_freq_list=Underlay_mask(1:2:end-1); disp("Underlay_freq_list @ 'Underlay_freq_list=Underlay_mask(1:2:end-1)' is "), disp(Underlay_freq_list); Spec_freq_list=Spec_mask_new(1:2:end-1); disp("Spec_freq_list @ 'Spec_freq_list=Spec_mask_new(1:2:end-1)' " ), disp(Spec_freq_list); list_1=find(Underlay_freq_list(end)<Spec_freq_list); disp("list_1 @ 'list_1=find(Underlay_freq_list(end)<Spec_freq_list)'is: "), disp(list_1); if(isempty(list_1)==0) Spec_freq_list=[Spec_freq_list(1:list_1(1)-1),Underlay_freq_list(end)]; disp("Spec_freq_list @ 'Spec_freq_list=[Spec_freq_list(1:list_1(1)-1),Underlay_freq_list(end)'] is: "), disp(Spec_freq_list); disp("calling find_power with the values: "), disp("Underlay_freq_list(end) :"), disp(Underlay_freq_list(end)), disp("Spec_mask_new"), disp(Spec_mask_new); p = find_power(Underlay_freq_list(end),Spec_mask_new); disp("returning from find_power, the value of p is: "), disp(p); Spec_mask_new=[Spec_mask_new(1:2*list_1(1)-2),Underlay_freq_list(end),p]; disp("Spec_mask_new @ 'Spec_mask_new=[Spec_mask_new(1:2*list_1(1)-2),Underlay_freq_list(end),p]' is : "), disp(Spec_mask_new); end list_2=find(Spec_freq_list<Underlay_freq_list(1)); disp("list_2 @ 'list_2=find(Spec_freq_list<Underlay_freq_list(1))' is: "), disp(list_2); if(isempty(list_2)==0) Spec_freq_list=[Underlay_freq_list(1),Spec_freq_list(list_2(end)+1:end)]; disp("Spec_freq_list @ 'Spec_freq_list=[Underlay_freq_list(1),Spec_freq_list(list_2(end)+1:end)]"), disp(Spec_freq_list); disp("calling find_power with the values: "), disp("Underlay_freq_list(1) :"), disp(Underlay_freq_list(1)), disp("Spec_mask_new"), disp(Spec_mask_new); p2 = find_power(Underlay_freq_list(1),Spec_mask_new); disp("returning from find_power, the value of p is: "), disp(p2); Spec_mask_new=[Underlay_freq_list(1),p2,Spec_mask_new(2*list_2(end)+1:end)]; disp("Spec_mask_new @ 'Spec_mask_new=[Underlay_freq_list(1),p2,Spec_mask_new(2*list_2(end)+1:end)]' is : "), disp(Spec_mask_new); end disp("Plotting Analysis_Figure_1.png"); fig1 = figure (); plot(Tx_SpecMask(1:2:end-1),Tx_SpecMask(2:2:end),'b.-','LineWidth',2) hold all plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); title("Analysis Figure 1"); saveas(fig1, 'Analysis_Figure_1.png'); movefile('Analysis_Figure_1.png', report_directory); %this pause value is used to allow the octave code to flush the first figure and create the second figure, in the absense of this pause, the second figure might not get generated. %this pause value might need to be increased for the slower systems. (initially set to .4 seconds) pause (.4); disp("Plotting CompatAnalysis.png"); fig2 = figure (); plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end),'b.-','LineWidth',2) hold all plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); title("Compat Analysis"); saveas(fig2,'CompatAnalysis.png'); movefile('CompatAnalysis.png', report_directory); %SCM compatibility [p_Tx_new] = new_spectrum(Spec_mask_new,Underlay_mask); disp("[p_Tx_new] @ '[p_Tx_new] = new_spectrum(Spec_mask_new,Underlay_mask)' is : "), disp([p_Tx_new]); [Power_Tx,Power_Tx_dB] = calculate_power(p_Tx_new,Rx_ResBW); disp("[Power_Tx,Power_Tx_dB] @ [Power_Tx,Power_Tx_dB] = calculate_power(p_Tx_new,Rx_ResBW) is: "), disp([Power_Tx,Power_Tx_dB]); [p_Rx_new] = calculate_power_3dB(Underlay_mask,Rx_ResBW); disp("[p_Rx_new] @ [p_Rx_new] = calculate_power_3dB(Underlay_mask,Rx_ResBW)' is : "), disp([p_Rx_new]); [Power_Rx,Power_Rx_dB] = calculate_power(p_Rx_new,Rx_ResBW); disp("[Power_Rx,Power_Rx_dB] @ '[Power_Rx,Power_Rx_dB] = calculate_power(p_Rx_new,Rx_ResBW)' is : "), disp([Power_Rx,Power_Rx_dB]); %Power_Tx_dB AllowablePower=Power_Rx_dB; Power_Margin_Difference = Power_Tx_dB-AllowablePower; if(AllowablePower>Power_Tx_dB) disp('System is compatible'); disp(Power_Margin_Difference); else disp('System is not compatible'); disp(Power_Margin_Difference); end end
github
ccaicedo/SCMBAT-master
calculateEPSD.m
.m
SCMBAT-master/Octave/calculateEPSD.m
1,209
utf_8
5748ff810dea09f6e62c94d935369742
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ calculateEPSD.m Function to calculate Effective power spectral density of narrow band signals ef_bw: Effective Bandwidth, epsd: Effective power spectral density, bw: vector of Bandwidth of narrowband signals [bw_0,bw_1,....bw_n], maxPSD: vector of Maximum power density of narrow band signals [MPSD0,MPSD1,... MPSDn]. %} function [ef_bw,epsd] = calculateEPSD (bw, maxPSD) ef_bw=sum(bw); epsd= 10*log10( sum(bw.*(10.^(maxPSD./10)))/sum(bw) ); endfunction
github
ccaicedo/SCMBAT-master
new_spectrum.m
.m
SCMBAT-master/Octave/new_spectrum.m
2,533
utf_8
bec979afc4bff8aa6dba4d48d96e0614
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ new_spectrum.m Function to generate adjusted spectrum mask pv_new: new spectrum mask [f0,p0,f1,p1,f2,p2...fn,pn] ps: spectrum mask representation: [f0,p0,f1,p1....fn,pn]; pr: underlay mask representation: [f0,p0,f1,p1....fn,pn]; %} function [pv_new] = new_spectrum(ps,pr) %disp("Inside new_spectrum function"); fs=ps(1:2:end-1); PS=ps(2:2:end); fr=pr(1:2:end-1); PR=pr(2:2:end); %disp("fs is " ), disp(fs); %disp("PS is "), disp(PS); %disp("fr is "), disp(fr); %disp("PR is "), disp(PR); f_new=freq_sort(fs,fr); %disp("f_new is :"), disp(f_new); pl=min(PR); %disp("pl is " ), disp(pl); for i=1:length(f_new) %disp("the value of i is: "), disp(i); fo=f_new(i); ps_v=find_power(fo,ps); if(length(ps_v)>1) %Given that there can't be more than two same frequencies in the frequency vector if(f_new(i)==f_new(i+1)) ps_i=ps_v(1); else ps_i=ps_v(2); end else ps_i=ps_v; end %disp("f0 is :"), disp(fo); %disp("ps_v is :"), disp(ps_v); %disp("ps_i is :"), disp(ps_i); pr_v=find_power(fo,pr); if(length(pr_v)>1) %Given that there can't be more than two same frequencies in the frequency vector if(f_new(i)==f_new(i+1)) pr_i=pr_v(1); else pr_i=pr_v(2); end else pr_i=pr_v; end %disp("pr_v is : "), disp(pr_v); %disp("pr_i is : "), disp(pr_i); p_new(i)=ps_i+pl-pr_i; %disp("p_new is : "), disp(p_new); end %figure %plot(f_new,p_new,'r.-','LineWidth',2); %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); pv_new=zeros(1,2*length(f_new)); pv_new(1:2:end-1)=f_new; pv_new(2:2:end)=p_new; %disp("Exiting new_spectrum function"); end
github
ccaicedo/SCMBAT-master
compat.m
.m
SCMBAT-master/Octave/compat.m
1,942
utf_8
3acb27129a17270c97de12952378ac09
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ compat.m Function to generate adjusted spectrum mask. p_new: power list for new spectrum mask: [p0,p1,p2...pn] f_new: frequency list for new spectrum mask: [f0,f1,f2...fn] ps: spectrum mask representation: [f0,p0,f1,p1....fn,pn]; pr: underlay mask representation: [f0,p0,f1,p1....fn,pn]; %} function [p_new,f_new] = compat(ps,pr) fs=ps(1:2:end-1); PS=ps(2:2:end); fr=pr(1:2:end-1); PR=pr(2:2:end); [f_new] = freq_sort(fs,fr); pl=min(PR); for i=1:length(f_new) fo=f_new(i); ps_v=find_power(fo,ps); if(length(ps_v)>1) %Given that there can't be more than two same frequencies in the frequency vector if(f_new(i)==f_new(i+1)) ps_i=ps_v(1); else ps_i=ps_v(2); end else ps_i=ps_v; end pr_v=find_power(fo,pr); if(length(pr_v)>1) %Given that there can't be more than two same frequencies in the frequency vector if(f_new(i)==f_new(i+1)) pr_i=pr_v(1); else pr_i=pr_v(2); end else pr_i=pr_v; end p_new(i)=ps_i+pl-pr_i; end figure plot(f_new,p_new,'r-','LineWidth',2); end
github
ccaicedo/SCMBAT-master
plotBTPRated.m
.m
SCMBAT-master/Octave/plotBTPRated.m
1,606
utf_8
7ba0ec5f03f638f1b6a7d547a4c1df20
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ plotBTPRated.m Function to plot BTP rated underlay masks %} function [retval] = plotBTPRated () load SCM_receiver_java.txt; BTPRated_BW_List=Rx_BTPRatedList(1:2:end-1); BTPRated_Power_List=Rx_BTPRatedList(2:2:end); plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end),'b-.','LineWidth',2) hold all for i=1:length(BTPRated_BW_List) %plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+BTPRated_Power_List(i),'b.-','LineWidth',2) newPowerList = Rx_UnderlayMask(2:2:end)+BTPRated_Power_List(i); xpoint=(Rx_UnderlayMask((length(Rx_UnderlayMask)/2)-1)+Rx_UnderlayMask((length(Rx_UnderlayMask)/2)+1))/2; plot(Rx_UnderlayMask(1:2:end-1),newPowerList,'b.-','LineWidth',2) text(xpoint,min(newPowerList)+5,strcat(num2str(BTPRated_BW_List(i)), ' MHz.sec')) hold all end grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); retval=0; endfunction
github
ccaicedo/SCMBAT-master
TxMPSD.m
.m
SCMBAT-master/Octave/TxMPSD.m
4,454
utf_8
16d7efcfa51e5d81835e1db0e94904ac
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ TxMPSD.m Function to calculate the maximum power spectral density for narrow band spectrum masks %} function [Spec_mask_new,MaxPSD,Spec_BW,compatBWRatedList] = TxMPSD () load SCM_transmitter_java.txt; load SCM_receiver_java.txt; status=0; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); disp('orientation'); [phi_Txo,theta_Txo,Min_Dist] %Compare frequency range if((Tx_SpecMask(1)<Rx_UnderlayMask(1) && Tx_SpecMask(end-1)<Rx_UnderlayMask(1)) || (Tx_SpecMask(1)>Rx_UnderlayMask(end-1) && Tx_UnderlayMask(end-1)>Rx_SpecMask(end-1)) ) disp(strcat('result: ', 'System is compatible')); return; end %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); Power=Power+(10*log10(Rx_ResBW/1e-3)); Power=Power+p2; Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; Underlay_mask=Rx_UnderlayMask Underlay_freq_list=Underlay_mask(1:2:end-1); Underlay_power_list=Underlay_mask(2:2:end); Underlay_min_pow_freq=Underlay_freq_list( find(Underlay_power_list==min(Underlay_power_list)) ); BWRated_BW_List=Rx_BWRatedList(1:2:end-1); BWRated_Power_List=Rx_BWRatedList(2:2:end); Spec_freq_list=Spec_mask_new(1:2:end-1); Spec_power_list=Spec_mask_new(2:2:end); Spec_cent_freq=(Spec_freq_list(1)+Spec_freq_list(end))/2; Spec_BW=calcNarrowBW(Spec_mask_new); %Spec_freq_list(end)-Spec_freq_list(1); Spec_MaxPower=max(Spec_power_list); Compat_BWRated_Masks1=[]; Compat_BWRated_Masks2=[]; i0=1; i1=1; for i=1:length(BWRated_BW_List) BWRatedMask=Underlay_mask; BWRatedMask(2:2:end)=BWRatedMask(2:2:end)+BWRated_Power_List(i); MaxPSD=Spec_MaxPower; MaxPSD Underlay_pow_cent=find_power(Spec_cent_freq,BWRatedMask); if(Spec_MaxPower<Underlay_pow_cent && Spec_BW<=BWRated_BW_List(i)) if(Spec_cent_freq<Underlay_min_pow_freq(1) || Spec_cent_freq>Underlay_min_pow_freq(end)) Compat_BWRated_Masks1((2*i0)-1:(2*i0))=[BWRated_BW_List(i),BWRated_Power_List(i)]; MaxPSD=min(BWRatedMask(2:2:end))-(Underlay_pow_cent-Spec_MaxPower); i0=i0+1; else Compat_BWRated_Masks2((2*i1)-1:(2*i1))=[BWRated_BW_List(i),BWRated_Power_List(i)]; MaxPSD=Spec_MaxPower; i1=i1+1; end else end end MaxPSD=min(MaxPSD); if(length(Compat_BWRated_Masks1)==length(Rx_BWRatedList) || length(Compat_BWRated_Masks2)==length(Rx_BWRatedList)) compatBWRatedList=1; disp('1'); disp(MaxPSD); disp(Spec_BW); disp(Spec_mask_new); else if(isempty(Compat_BWRated_Masks1)==0) compatBWRatedList=Compat_BWRated_Masks1; disp(Compat_BWRated_Masks1); disp(MaxPSD); disp(Spec_BW); disp(Spec_mask_new); else if(isempty(Compat_BWRated_Masks2)==0) compatBWRatedList=Compat_BWRated_Masks2; disp(Compat_BWRated_Masks2); disp(MaxPSD); disp(Spec_BW); disp(Spec_mask_new); else compatBWRatedList=0; disp('0'); disp(MaxPSD); disp(Spec_BW); disp(Spec_mask_new); end end end disp('Spec BW') Spec_BW MaxPSD end
github
ccaicedo/SCMBAT-master
TxHop_FreqList.m
.m
SCMBAT-master/Octave/TxHop_FreqList.m
4,842
utf_8
5f34647f595fb1e083895f40f609afa0
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ TxHop_FreqList.m Function to perform compatibility calculation if the underlay mask is BTP rated and the spectrum mask is frequency listed. %} function [Spec_BTP,ExtSpecMask,compatBWList] = TxHop_FreqList() load SCM_transmitter_java.txt; load SCM_receiver_java.txt; status=0; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); Power=Power+(10*log10(Rx_ResBW/1e-3)); Power=Power+p2; Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; Underlay_mask=Rx_UnderlayMask; Underlay_freq_list=Underlay_mask(1:2:end-1); Underlay_power_list=Underlay_mask(2:2:end); Underlay_min_pow_freq=Underlay_freq_list( find(Underlay_power_list==min(Underlay_power_list)) ); %--- Start BTP and compatibility Analysis --- %Compare if the SCMs are operating in the same time duration %if((Tx_Start<Rx_Start && Tx_End<Rx_Start) || (Tx_Start>Rx_End && Tx_End>Rx_End) ) % disp('System is compatible') % break; % else %end %Compare frequency range i0=1; FreqList=0; ExtSpecMask=zeros(1,length(Spec_mask_new)); for i=1:length(Tx_FreqList) if((Tx_SpecMask(1)+Tx_FreqList(i)<=Rx_UnderlayMask(1) && Tx_SpecMask(end-1)+Tx_FreqList(i)<=Rx_UnderlayMask(1)) || (Tx_SpecMask(1)+Tx_FreqList(i)>=Rx_UnderlayMask(end-1) && Tx_SpecMask(end-1)+Tx_FreqList(i)>=Rx_UnderlayMask(end-1)) ) else FreqList(i0)=Tx_FreqList(i); ExtSpecMask(i0,2:2:end)=Spec_mask_new(2:2:end); ExtSpecMask(i0,1:2:end-1)=Spec_mask_new(1:2:end-1)+Tx_FreqList(i); i0=i0+1; end end matrixSize=size(ExtSpecMask); ExtSpecMask=reshape(ExtSpecMask',1,matrixSize(1)*matrixSize(2)); if(FreqList==0) disp('FreqList is 0'); disp(strcat('result: ', 'System is compatible')); return; end BTP_BW_List=Rx_BTPRatedList(1:2:end-1); BTP_Power_List=Rx_BTPRatedList(2:2:end); Underlay_BW = Underlay_freq_list(end)-Underlay_freq_list(1); Spec_freq_list=Spec_mask_new(1:2:end-1); Spec_power_list=Spec_mask_new(2:2:end); Spec_cent_freq=(Spec_freq_list(1)+Spec_freq_list(end))/2; Spec_BW=Spec_mask_new(end-1)-Spec_mask_new(1); Spec_MaxPower=max(Spec_power_list); bwList=ones(1,length(FreqList))*Spec_BW; td=ones(1,length(FreqList))*Tx_DwellTime; tr=ones(1,length(FreqList))*Tx_RevisitPeriod; Spec_BTP=findBTP(bwList,td,tr); compatBWList=0; i2=1; for i=1:length(BTP_BW_List) BTPMask=Underlay_mask; BTPMask(2:2:end)=Underlay_mask(2:2:end)+BTP_Power_List(i); minBTPMask=min(BTPMask(2:2:end)); if(Spec_MaxPower<minBTPMask && Spec_BTP<BTP_BW_List(i)) compatBWList(i2)=BTP_BW_List(i); i2=i2+1; else end end if(compatBWList==0) disp(strcat('result: ', 'System is not at all compatible')); return; else disp(strcat('result: ', 'System is compatible')); disp(compatBWList); return; end %figure %plot(Tx_SpecMask(1:2:end-1),Tx_SpecMask(2:2:end),'b.-','LineWidth',2) %hold all %plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %fig1=figure; %for i=1:length(BTP_BW_List) %plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+BTP_Power_List(i),'b.-','LineWidth',2) %hold all %end %plot(ExtSpecMask(1:2:end-1),ExtSpecMask(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %saveas(fig1,'BTPRatedFreqList.png') end function BTP = findBTP(bw,td,tr) BTP = sum(bw.*td./tr)*1e+6; end
github
ccaicedo/SCMBAT-master
plotDutyRated.m
.m
SCMBAT-master/Octave/plotDutyRated.m
1,190
utf_8
1dfa2e97051ccd11e14fcd557a188e25
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ plotDutyRated.m Function to plot Duty cycle rated underlay masks. %} function [retval] = plotDutyRated () load SCM_receiver_java.txt; DutyRated_List=Rx_DutyList(1:2:end-1); DutyRated_PowerList=Rx_DutyList(2:2:end); for i=1:length(DutyRated_List) plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+DutyRated_PowerList(i),'b.-','LineWidth',2) hold all end grid on xlabel('Frequency (MHz)'); ylabel('Power (dB)'); retval=0; endfunction
github
ccaicedo/SCMBAT-master
find_direction.m
.m
SCMBAT-master/Octave/find_direction.m
2,349
utf_8
10cf744c1dbbb51537d71471489fa466
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ find_direction.m Function to find distance, azimuth angle and elevation based on Transmitter and Receiver location (geographical coordinates) e: elevation angle az: azimuth angle d: distance T: Geographical coordinates for the transmitter R: Geographical coordinates for the receiver. %} function [e,az,d] = find_direction(T,R) % T(1)-Lat T(2)-Long T(3)-Height T(1)=T(1)*pi/180; T(2)=T(2)*pi/180; R(1)=R(1)*pi/180; R(2)=R(2)*pi/180; a=6378137; E=0.0818191908426; er=6367495; v_t=a/( (1-((E*sin(T(1))).^2)).^0.5); x_t=(v_t+T(3))*cos(T(1))*cos(T(2)); y_t=(v_t+T(3))*cos(T(1))*sin(T(2)); z_t=( (v_t*(1-(E.^2))) + T(3) )*sin(T(1)); v_r=a/( (1-((E*sin(R(1))).^2)).^0.5); x_r=(v_r+R(3))*cos(R(1))*cos(R(2)); y_r=(v_r+R(3))*cos(R(1))*sin(R(2)); z_r=( (v_r*(1-(E.^2))) + R(3) )*sin(R(1)); %Finding Coordinates %C_r=[x_r,y_r,z_r] %C_t=[x_t,y_t,z_t] %Plotting Coordinates %figure %plot3(x_r-x_t,y_r-y_t,z_r-z_t,'rx','Linewidth',2) %hold all %plot3(0,0,0,'bo','Linewidth',2) d=( ((x_t-x_r).^2) + ((y_t-y_r).^2) + ((z_t-z_r).^2) ).^0.5; % Finding the angle and curvature d_t=( (x_t^2) + (y_t^2) + (z_t^2) ).^0.5; d_r=( (x_r^2) + (y_r^2) + (z_r^2) ).^0.5; w= acos( ( (x_t*x_r)+(y_t*y_r)+(z_t*z_r) )/(d_t*d_r) ); %d= er*w; tan_theta=(y_r-y_t)./(x_r-x_t); sin_phi=(z_r-z_t)./d; if(y_r>y_t && x_r>x_t) theta=atan(tan_theta)*180/pi; elseif(y_r>y_t && x_r<x_t) theta=180 + (atan(tan_theta)*180/pi) ; elseif(y_r<y_t && x_r<x_t) theta=180 + (atan(tan_theta)*180/pi); elseif(y_r<y_t && x_r>x_t) theta=360 + (atan(tan_theta)*180/pi); end phi=asin(sin_phi)*180/pi; e=phi; az=theta; end
github
ccaicedo/SCMBAT-master
TxHop_BandList.m
.m
SCMBAT-master/Octave/TxHop_BandList.m
4,478
utf_8
31f1fac8825eacd42ba320bb8dddd616
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ TxHop_BandList.m Function to perform compatibility if the underlay mask is BTP rated, and the spectrum mask is Band listed. %} function [Spec_BTP,NewBandList,Spec_MaxPower,compatBWList] = TxHop_BandList() load SCM_transmitter_java.txt; load SCM_receiver_java.txt; status=0; %Gets the min distance, elevation and azimuth angle T=[Tx_Lat,Tx_Long,Tx_Alt]; R=[Rx_Lat,Rx_Long,Rx_Alt]; [phi_Txo,theta_Txo,Min_Dist]=find_direction(T,R); %Transmitter Power map attenuation to Total Power; p=PowerMap_find(theta_Txo,phi_Txo,Tx_PowerMap); Power=Tx_TotPow+p; Power=Power+(10*log10(1e-3/Tx_ResBW)); % Bringing the Reference Bandwidth to 1 Khz; %Attenuation due to Propagation Map [n0,d_break,n1]=PropMap_find_piece(theta_Txo,phi_Txo,Tx_PropMap,Min_Dist); if(Min_Dist>d_break && d_break!=0.0) Power = Power - (10*n0*log10(d_break)) - (10*n1*log10(Min_Dist/d_break)); else Power = Power - (10*n0*log(Min_Dist)); end %Attenuation due to the Receiver Power Map phi_Rxo=-phi_Txo; if(theta_Txo>180) theta_Rxo=theta_Txo-180; else theta_Rxo=theta_Txo+180; end p2=PowerMap_find(theta_Rxo,phi_Rxo,Rx_PowerMap); Power=Power+(10*log10(Rx_ResBW/1e-3)); Power=Power+p2; Spec_mask_new=Tx_SpecMask; Spec_mask_new(2:2:end)=Tx_SpecMask(2:2:end)+Power; Underlay_mask=Rx_UnderlayMask; Underlay_freq_list=Underlay_mask(1:2:end-1); Underlay_power_list=Underlay_mask(2:2:end); Underlay_min_pow_freq=Underlay_freq_list( find(Underlay_power_list==min(Underlay_power_list)) ); %--- Start BTP and compatibility Analysis --- %Compare if the SCMs are operating in the same time duration %if((Tx_Start<Rx_Start && Tx_End<Rx_Start) || (Tx_Start>Rx_End && Tx_End>Rx_End) ) % disp('System is compatible') % break; % else %end %Compare frequency range i0=1; ExtSpecMask=zeros(1,length(Spec_mask_new)); NewBandList=sort([Tx_BandList,Underlay_freq_list(1),Underlay_freq_list(end)]); ind0=find(NewBandList==Underlay_freq_list(1)); ind1=find(NewBandList==Underlay_freq_list(end)); NewBandList=NewBandList(ind0:ind1); BTP_BW_List=Rx_BTPRatedList(1:2:end-1); BTP_Power_List=Rx_BTPRatedList(2:2:end); Underlay_BW = Underlay_freq_list(end)-Underlay_freq_list(1); Spec_freq_list=Spec_mask_new(1:2:end-1); Spec_power_list=Spec_mask_new(2:2:end); Spec_cent_freq=(Spec_freq_list(1)+Spec_freq_list(end))/2; Spec_BW=Spec_mask_new(end-1)-Spec_mask_new(1); Spec_MaxPower=max(Spec_power_list); Band_high=Tx_BandList(2:2:end); Band_low=Tx_BandList(1:2:end-1); BW_total=sum(Band_high-Band_low); NewBand_high=NewBandList(2:2:end); NewBand_low=NewBandList(1:2:end-1); NewBW_total=sum(NewBand_high-NewBand_low); Spec_BTP=NewBW_total*Spec_BW*Tx_DwellTime*1e+6/(BW_total*Tx_RevisitPeriod); compatBWList=0; i2=1; for i=1:length(BTP_BW_List) BTPMask=Underlay_mask; BTPMask(2:2:end)=Underlay_mask(2:2:end)+BTP_Power_List(i); minBTPMask=min(BTPMask(2:2:end)); if(Spec_MaxPower<minBTPMask && Spec_BTP<BTP_BW_List(i)) compatBWList(i2)=BTP_BW_List(i); i2=i2+1; else end end if(compatBWList==0) disp(strcat('result: ', 'System is not at all compatible')); return; else disp(strcat('result: ', 'System compatible with: ')); disp(strcat('result: ', num2str(compatBWList))); return; end %figure %plot(Tx_SpecMask(1:2:end-1),Tx_SpecMask(2:2:end),'b.-','LineWidth',2) %hold all %plot(Spec_mask_new(1:2:end-1),Spec_mask_new(2:2:end),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %fig1=figure; %for i=1:length(BTP_BW_List) %plot(Rx_UnderlayMask(1:2:end-1),Rx_UnderlayMask(2:2:end)+BTP_Power_List(i),'b.-','LineWidth',2) %hold all %end %plot(NewBandList,Spec_MaxPower*ones(1,length(NewBandList)),'r.-','LineWidth',2) %grid on %xlabel('Frequency (MHz)'); %ylabel('Power (dB)'); %saveas(fig1,'BTPRatedFreqList.png') end
github
ccaicedo/SCMBAT-master
MaxPow_Diff.m
.m
SCMBAT-master/Octave/MaxPow_Diff.m
1,561
utf_8
410524f5e50cff3f1e96809b3fc73628
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ MaxPow_Diff.m Function to find the power difference between a spectrum mask and and underlay mask using the Max Power Density method. P_diff: Power difference between the spectrum and underlay mask pv_s: spectrum mask representation: [f0,p0,f1,p1....fn,pn]; pv_r: underlay mask representation: [f0,p0,f1,p1....fn,pn]; %} function P_diff = MaxPow_Diff(pv_r,pv_s) fr=pv_r(1:2:end-1); fs=pv_s(1:2:end-1); f_new = freq_sort(fr,fs); f_new(end+1) = 0; i=1; while(i<length(f_new)) if(f_new(i)==f_new(i+1)) P_r=find_power(f_new(i),pv_r); P_s=find_power(f_new(i),pv_s); P_diff(i:i+1)=P_r-P_s; i=i+2; else P_r=find_power(f_new(i),pv_r); P_s=find_power(f_new(i),pv_s); P_diff(i)=P_r-P_s; i=i+1; end end f_new(end) =[]; end
github
ccaicedo/SCMBAT-master
calculateBAEPSD.m
.m
SCMBAT-master/Octave/calculateBAEPSD.m
1,189
utf_8
68a2a4f5cd7024e07751d51cb0245ac8
%{ Copyright (C) 2016 Syracuse University This file is part of the Spectrum Consumption Model Builder and Analysis Tool This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with program. If not, see <http://www.gnu.org/licenses/>. %} %{ calculateBAEPSD.m Function to calculate the Bandwidth adjusted effective power spectral density (BAEPSD) for a combination of narrowband spectrum masks. baepsd: BAEPSD value, bw: vector containing bandwidths of narrowband signals bwMask: bandwidth of the underlay mask. epsd: Effective Power spectral density. %} function [baepsd] = calculateBAEPSD (bw, bwMask, epsd) baepsd=10*log10( (10.^(epsd/10))*sum(bw)/bwMask); endfunction
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
edison_wrapper.m
.m
Segmentation-Using-Superpixels-master/msseg/edison_wrapper.m
6,071
utf_8
e38b90d4d1c4979c05527087208324c4
function [varargout] = edison_wrapper(rgbim, featurefun, varargin) % % Performing mean_shift operation on image % % Usage: % [fimage labels modes regSize grad conf] = edison_wrapper(rgbim, featurefunc, ...) % % Inputs: % rgbim - original image in RGB space % featurefunc - converting RGB to some feature space in which to perform % the segmentation, like @RGB2Lab etc. % % Allowed parameters: % 'steps' - What steps the algorithm should perform: % 1 - only mean shift filtering % 2 - filtering and region fusion [default] % 'synergistic' - perform synergistic segmentation [true]|false % 'SpatialBandWidth' - segmentation spatial radius (integer) [7] % 'RangeBandWidth' - segmentation feature space radius (float) [6.5] % 'MinimumRegionArea'- minimum segment area (integer) [20] % 'SpeedUp' - algorithm speed up {1,2,3} [2] % 'GradientWindowRadius' - synergistic parameters (integer) [2] % 'MixtureParameter' - synergistic parameter (float 0,1) [.3] % 'EdgeStrengthThreshold'- synergistic parameter (float 0,1) [.3] % % Outputs: % fimage - the result in feature space % labels - labels of regions [if steps==2] % modes - list of all modes [if steps==2] % regSize - size, in pixels, of each region [if steps==2] % grad - gradient map [if steps==2 and synergistic] % conf - confidence map [if steps==2 and synergistic] % % rgbim must be of type uint8 if ~isa(rgbim,'uint8'), if max(rgbim(:)) <= 1, rgbim = im2uint8(rgbim); else rgbim = uint8(rgbim); end end imsiz = size(rgbim); fim = im2single(rgbim); fim = single(featurefun(fim)); rgbim = permute(rgbim,[3 2 1]); fim = permute(fim, [3 2 1]); p = parse_inputs(varargin); labels = []; modes =[]; regSize = []; grad = []; conf = []; if p.steps == 1 [fimage] = edison_wrapper_mex(fim, rgbim, p); else if p.synergistic [fimage labels modes regSize grad conf] = edison_wrapper_mex(fim, rgbim, p); else [fimage labels modes regSize] = edison_wrapper_mex(fim, rgbim, p); end grad = reshape(grad,imsiz([2 1]))'; conf = reshape(conf',imsiz([2 1]))'; end fimage = permute(fimage, [3 2 1]); if nargout >= 1, varargout{1} = fimage; end; if nargout >= 2, varargout{2} = labels'; end; if nargout >= 3, varargout{3} = modes; end; if nargout >= 4, varargout{4} = regSize; end; if nargout >= 5, varargout{5} = grad; end; if nargout >= 6, varargout{6} = conf; end; %--------------------------------------------------------% function [p] = parse_inputs(args) % Allowed parameters % 'steps' - What steps the algorithm should perform: % 1 - only mean shift filtering % 2 - filtering and region fusion [defualt] % 'synergistic' - perform synergistic segmentation [true]|false % 'SpatialBandWidth' - segmentation spatial radius (integer) [7] % 'RangeBandWidth' - segmentation feature space radius (float) [6.5] % 'MinimumRegionArea'- minimum segment area (integer) [20] % 'SpeedUp' - algorithm speed up {0,1,2} [1] % 'GradientWindowRadius' - synergistic parameters (integer) [2] % 'MixtureParameter' - synergistic parameter (float 0,1) [.3] % 'EdgeStrengthThreshold'- synergistic parameter (float 0,1) [.3] % convert ars to parameters - then init all the rest according to defualts try p = struct(args{:}); catch error('edison_wrapper:parse_inputs','Cannot parse arguments'); end % % modes of operation % -. edge detection -- currently unsupported % 1. Filtering % 2. Fusing regions % 3. Segmentation if ~isfield(p,'steps') p.steps = 2; end if p.steps ~= 1 && p.steps ~=2 error('edison_wrapper:parse_inputs','steps must be either 1 or 2'); end % % parameters % Flags % 1. synergistic if ~isfield(p,'synergistic') p.synergistic = true; end p.synergistic = logical(p.synergistic); % Mean Shift Segmentation parameters % SpatialBandWidth [integer] if ~isfield(p,'SpatialBandWidth') p.SpatialBandWidth = 7; end if p.SpatialBandWidth < 0 || p.SpatialBandWidth ~= round(p.SpatialBandWidth) error('edison_wrapper:parse_inputs','SpatialBandWidth must be a positive integer'); end % RangeBandWidth [float] if ~isfield(p,'RangeBandWidth') p.RangeBandWidth = 6.5; end if p.RangeBandWidth < 0 error('edison_wrapper:parse_inputs','RangeBandWidth must be positive'); end % MinimumRegionArea [integer] if ~isfield(p,'MinimumRegionArea') p.MinimumRegionArea = 20; end if p.MinimumRegionArea < 0 || p.MinimumRegionArea ~= round(p.MinimumRegionArea) error('edison_wrapper:parse_inputs','MinimumRegionArea must be a positive integer'); end % SpeedUp if ~isfield(p,'SpeedUp') p.SpeedUp = 2; end if p.SpeedUp ~=1 && p.SpeedUp ~= 2 && p.SpeedUp ~= 3 error('edison_wrapper:parse_inputs','SpeedUp must be either 1, 2 or 3'); end % Synergistic Segmentation parameters % GradientWindowRadius [integer] if ~isfield(p,'GradientWindowRadius') p.GradientWindowRadius = 2; end if p.GradientWindowRadius < 0 || p.GradientWindowRadius ~= round(p.GradientWindowRadius) error('edison_wrapper:parse_inputs','GradientWindowRadius must be a positive integer'); end % MixtureParameter [float (0,1)] if ~isfield(p,'MixtureParameter') p.MixtureParameter = .3; end if p.MixtureParameter < 0 || p.MixtureParameter > 1 error('edison_wrapper:parse_inputs','MixtureParameter must be between zero and one'); end % EdgeStrengthThreshold [float (0,1)] if ~isfield(p,'EdgeStrengthThreshold') p.EdgeStrengthThreshold = .3; end if p.EdgeStrengthThreshold < 0 || p.EdgeStrengthThreshold > 1 error('edison_wrapper:parse_inputs','MixtureParameter must be between zero and one'); end % % Currently unsupported % Edge Detection Parameters % GradientWindowRadius [integer] % MinimumLength [integer] % NmxRank [float (0,1)] % NmxConf [float (0,1)] % NmxType % HysterisisHighRank [float (0,1)] % HysterisisHighConf [float (0,1)] % HysterisisHighType % HysterisisLowRank [float (0,1)] % HysterisisLowConf [float (0,1)] % HysterisisLowType % %
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
msseg.m
.m
Segmentation-Using-Superpixels-master/msseg/msseg.m
2,216
utf_8
704da001d36390de9568b3942400295b
% Performing mean_shift image segmentation using EDISON code implementation % of Comaniciu's paper with a MEX wrapper from Shai Bagon. links at bottom % of help % % Usage: % [S L] = msseg(I,hs,hr,M) % % Inputs: % I - original image in RGB or grayscale % hs - spatial bandwith for mean shift analysis % hr - range bandwidth for mean shift analysis % M - minimum size of final output regions % % Outputs: % S - segmented image % L - resulting label map % % Links: % Comaniciu's Paper % http://www.caip.rutgers.edu/riul/research/papers/abstract/mnshft.html % EDISON code % http://www.caip.rutgers.edu/riul/research/code/EDISON/index.html % Shai's mex wrapper code % http://www.wisdom.weizmann.ac.il/~bagon/matlab.html % % Author: % This file and re-wrapping by Shawn Lankton (www.shawnlankton.com) % Nov. 2007 %------------------------------------------------------------------------ function [S L seg seg_vals seg_lab_vals seg_edges] = msseg(img,lab_vals,hs,hr,M,full) gray = 0; if(size(img,3)==1) gray = 1; I = repmat(img,[1 1 3]); end if(nargin < 5) hs = 10; hr = 7; M = 30; end if(nargin < 6) full = 0; end [fimg labels modes regsize grad conf] = edison_wrapper(img,@RGB2Luv,... 'SpatialBandWidth',hs,'RangeBandWidth',hr,... 'MinimumRegionArea',M,'speedup',3); S = fimg; %Luv2RGB(fimg); L = labels + 1; if(gray == 1) S = rgb2gray(S); end [X,Y,Z] = size(img); nseg = max(L(:)); vals = reshape(img,X*Y,Z); if full == 1, [x y] = meshgrid(1:nseg,1:nseg); seg_edges = [x(:) y(:)]; else [points edges]=lattice(X,Y,0); clear points; d_edges = edges(find(L(edges(:,1))~=L(edges(:,2))),:); all_seg_edges = [L(d_edges(:,1)) L(d_edges(:,2))]; all_seg_edges = sort(all_seg_edges,2); tmp = zeros(nseg,nseg); tmp(nseg*(all_seg_edges(:,1)-1)+all_seg_edges(:,2)) = 1; [edges_x edges_y] = find(tmp==1); seg_edges = [edges_x edges_y]; end; seg_vals = zeros(nseg,Z); seg_lab_vals = zeros(nseg,size(lab_vals,2)); for i=1:nseg seg{i} = find(L(:)==i); seg_vals(i,:) = mean(vals(seg{i},:)); seg_lab_vals(i,:) = mean(lab_vals(seg{i},:)); end;
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
compare_segmentations.m
.m
Segmentation-Using-Superpixels-master/evals/compare_segmentations.m
4,879
utf_8
9894ac0153bc6ab19440e99ea9a875b0
%A MATLAB Toolbox % %Compare two segmentation results using %1. Probabilistic Rand Index %2. Variation of Information %3. Global Consistency Error % %IMPORTANT: The two input images must have the same size! % %Authors: John Wright, and Allen Y. Yang %Contact: Allen Y. Yang <[email protected]> % %(c) Copyright. University of California, Berkeley. 2007. % %Notice: The packages should NOT be used for any commercial purposes %without direct consent of their author(s). The authors are not responsible %for any potential property loss or damage caused directly or indirectly by the usage of the software. function [ri,gce,vi]=compare_segmentations(sampleLabels1,sampleLabels2) % compare_segmentations % % Computes several simple segmentation benchmarks. Written for use with % images, but works for generic segmentation as well (i.e. if the % sampleLabels inputs are just lists of labels, rather than rectangular % arrays). % % The measures: % Rand Index % Global Consistency Error % Variation of Information % % The Rand Index can be easily extended to the Probabilistic Rand Index % by averaging the result across all human segmentations of a given % image: % PRI = 1/K sum_1^K RI( seg, humanSeg_K ). % With a little more work, this can also be extended to the Normalized % PRI. % % Inputs: % sampleLabels1 - n x m array whose entries are integers between 1 % and K1 % sampleLabels2 - n x m (sample size as sampleLabels1) array whose % entries are integers between 1 and K2 (not % necessarily the same as K1). % Outputs: % ri - Rand Index % gce - Global Consistency Error % vi - Variation of Information % % NOTE: % There are a few formulas here that look less-straightforward (e.g. % the log2_quotient function). These exist to handle corner cases % where some of the groups are empty, and quantities like 0 * % log(0/0) arise... % % Oct. 2006 % Questions? John Wright - [email protected] [imWidth,imHeight]=size(sampleLabels1); [imWidth2,imHeight2]=size(sampleLabels2); N=imWidth*imHeight; if (imWidth~=imWidth2)||(imHeight~=imHeight2) disp( 'Input sizes: ' ); disp( size(sampleLabels1) ); disp( size(sampleLabels2) ); error('Input sizes do not match in compare_segmentations.m'); end; % make the group indices start at 1 if min(min(sampleLabels1)) < 1 sampleLabels1 = sampleLabels1 - min(min(sampleLabels1)) + 1; end if min(min(sampleLabels2)) < 1 sampleLabels2 = sampleLabels2 - min(min(sampleLabels2)) + 1; end segmentcount1=max(max(sampleLabels1)); segmentcount2=max(max(sampleLabels2)); % compute the count matrix % from this we can quickly compute rand index, GCE, VOI, ect... n=zeros(segmentcount1,segmentcount2); for i=1:imWidth for j=1:imHeight u=sampleLabels1(i,j); v=sampleLabels2(i,j); n(u,v)=n(u,v)+1; end; end; ri = rand_index(n); gce = global_consistancy_error(n); vi = variation_of_information(n); return; % the performance measures % the rand index, in [0,1] ... higher => better % fast computation is based on equation (2.2) of Rand's paper. function ri = rand_index(n) N = sum(sum(n)); n_u=sum(n,2); n_v=sum(n,1); N_choose_2=N*(N-1)/2; ri = 1 - ( sum(n_u .* n_u)/2 + sum(n_v .* n_v)/2 - sum(sum(n.*n)) )/N_choose_2; % global consistancy error (from BSDS ICCV 01 paper) ... lower => better function gce = global_consistancy_error(n) N = sum(sum(n)); marginal_1 = sum(n,2); marginal_2 = sum(n,1); % the hackery is to protect against cases where some of the marginals are % zero (should never happen, but seems to...) E1 = 1 - sum( sum(n.*n,2) ./ (marginal_1 + (marginal_1 == 0)) ) / N; E2 = 1 - sum( sum(n.*n,1) ./ (marginal_2 + (marginal_2 == 0)) ) / N; gce = min( E1, E2 ); % variation of information a "distance", in (0,vi_max] ... lower => better function vi = variation_of_information(n) N = sum(sum(n)); joint = n / N; % the joint pmf of the two labels marginal_2 = sum(joint,1); % row vector marginal_1 = sum(joint,2); % column vector H1 = - sum( marginal_1 .* log2(marginal_1 + (marginal_1 == 0) ) ); % entropy of the first label H2 = - sum( marginal_2 .* log2(marginal_2 + (marginal_2 == 0) ) ); % entropy of the second label MI = sum(sum( joint .* log2_quotient( joint, marginal_1*marginal_2 ) )); % mutual information vi = H1 + H2 - 2 * MI; % log2_quotient % helper function for computing the mutual information % returns a matrix whose ij entry is % log2( a_ij / b_ij ) if a_ij, b_ij > 0 % 0 if a_ij is 0 % log2( a_ij + 1 ) if a_ij > 0 but b_ij = 0 (this behavior should % not be encountered in practice! function lq = log2_quotient( A, B ) lq = log2( (A + ((A==0).*B) + (B==0)) ./ (B + (B==0)) );
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
compare_image_boundary_error.m
.m
Segmentation-Using-Superpixels-master/evals/compare_image_boundary_error.m
2,214
utf_8
df0574848dc09e9cb1903235da501e92
%A MATLAB Toolbox % %Compare two segmentation results using the Boundary Displacement Error % %IMPORTANT: The input two images must have the same size! % %Authors: John Wright, and Allen Y. Yang %Contact: Allen Y. Yang <[email protected]> % %(c) Copyright. University of California, Berkeley. 2007. % %Notice: The packages should NOT be used for any commercial purposes %without direct consent of their author(s). The authors are not responsible %for any potential property loss or damage caused directly or indirectly by the usage of the software. function [averageError, returnStatus] = compare_image_boundary_error(imageLabels1, imageLabels2); returnStatus = 0; [imageX, imageY] = size(imageLabels1); if imageX~=size(imageLabels2,1) | imageY~=size(imageLabels2,2) error('The sizes of the two comparing images must be the same.'); end if isempty(find(imageLabels1~=imageLabels1(1))) % imageLabels1 only has one group boundary1 = zeros(size(imageLabels1)); boundary1(1,:) = 1; boundary1(:,1) = 1; boundary1(end,:) = 1; boundary1(:,end) = 1; else % Generate boundary maps [cx,cy] = gradient(imageLabels1); [boundaryPixelX{1},boundaryPixelY{1}] = find((abs(cx)+abs(cy))~=0); boundary1 = abs(cx) + abs(cy) > 0; end if isempty(find(imageLabels2~=imageLabels2(1))) % imageLabels2 only has one group boundary2 = zeros(size(imageLabels2)); boundary2(1,:) = 1; boundary2(:,1) = 1; boundary2(end,:) = 1; boundary2(:,end) = 1; else % Generate boundary maps [cx,cy] = gradient(imageLabels2); [boundaryPixelX{2},boundaryPixelY{2}] = find((abs(cx)+abs(cy))~=0); boundary2 = abs(cx) + abs(cy) > 0; end % boundary1 and boundary2 are now binary boundary masks. compute their % distance transforms: D1 = bwdist(boundary1); D2 = bwdist(boundary2); % compute the distance of the pixels in boundary1 to the nearest pixel in % boundary2: dist_12 = sum(sum(boundary1 .* D2 )); dist_21 = sum(sum(boundary2 .* D1 )); avgError_12 = dist_12 / sum(sum(boundary1)); avgError_21 = dist_21 / sum(sum(boundary2)); averageError = (avgError_12 + avgError_21) / 2;
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
Rand_index.m
.m
Segmentation-Using-Superpixels-master/evals/Rand_index.m
3,073
utf_8
1908dff37c5ef0f12f9077404f6ed054
function ri=Rand_index(sampleLabels1,sampleLabels2) % compare_segmentations % % Computes several simple segmentation benchmarks. Written for use with % images, but works for generic segmentation as well (i.e. if the % sampleLabels inputs are just lists of labels, rather than rectangular % arrays). % % The measures: % Rand Index % % The Rand Index can be easily extended to the Probabilistic Rand Index % by averaging the result across all human segmentations of a given % image: % PRI = 1/K sum_1^K RI( seg, humanSeg_K ). % With a little more work, this can also be extended to the Normalized % PRI. % % Inputs: % sampleLabels1 - n x m array whose entries are integers between 1 % and K1 % sampleLabels2 - n x m (sample size as sampleLabels1) array whose % entries are integers between 1 and K2 (not % necessarily the same as K1). % Outputs: % ri - Rand Index % gce - Global Consistency Error % vi - Variation of Information % % NOTE: % There are a few formulas here that look less-straightforward (e.g. % the log2_quotient function). These exist to handle corner cases % where some of the groups are empty, and quantities like 0 * % log(0/0) arise... % % Oct. 2006 % Questions? John Wright - [email protected] [imWidth,imHeight]=size(sampleLabels1); [imWidth2,imHeight2]=size(sampleLabels2); N=imWidth*imHeight; if (imWidth~=imWidth2)||(imHeight~=imHeight2) disp( 'Input sizes: ' ); disp( size(sampleLabels1) ); disp( size(sampleLabels2) ); error('Input sizes do not match in compare_segmentations.m'); end; % make the group indices start at 1 if min(min(sampleLabels1)) < 1 sampleLabels1 = sampleLabels1 - min(min(sampleLabels1)) + 1; end if min(min(sampleLabels2)) < 1 sampleLabels2 = sampleLabels2 - min(min(sampleLabels2)) + 1; end segmentcount1=max(max(sampleLabels1)); segmentcount2=max(max(sampleLabels2)); % compute the count matrix % from this we can quickly compute rand index, GCE, VOI, ect... n=zeros(segmentcount1,segmentcount2); for i=1:imWidth for j=1:imHeight u=sampleLabels1(i,j); v=sampleLabels2(i,j); n(u,v)=n(u,v)+1; end; end; ri = rand_index(n); return; % the performance measures % the rand index, in [0,1] ... higher => better % fast computation is based on equation (2.2) of Rand's paper. function ri = rand_index(n) N = sum(sum(n)); n_u=sum(n,2); n_v=sum(n,1); N_choose_2=N*(N-1)/2; ri = 1 - ( sum(n_u .* n_u)/2 + sum(n_v .* n_v)/2 - sum(sum(n.*n)) )/N_choose_2; % log2_quotient % helper function for computing the mutual information % returns a matrix whose ij entry is % log2( a_ij / b_ij ) if a_ij, b_ij > 0 % 0 if a_ij is 0 % log2( a_ij + 1 ) if a_ij > 0 but b_ij = 0 (this behavior should % not be encountered in practice! function lq = log2_quotient( A, B ) lq = log2( (A + ((A==0).*B) + (B==0)) ./ (B + (B==0)) );
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
colorspace.m
.m
Segmentation-Using-Superpixels-master/others/colorspace.m
14,019
utf_8
c8d23ed54d9745d5e49e7d99ee1ed95f
function varargout = colorspace(Conversion,varargin) %COLORSPACE Convert a color image between color representations. % B = COLORSPACE(S,A) converts the color representation of image A % where S is a string specifying the conversion. S tells the % source and destination color spaces, S = 'dest<-src', or % alternatively, S = 'src->dest'. Supported color spaces are % % 'RGB' R'G'B' Red Green Blue (ITU-R BT.709 gamma-corrected) % 'YPbPr' Luma (ITU-R BT.601) + Chroma % 'YCbCr'/'YCC' Luma + Chroma ("digitized" version of Y'PbPr) % 'YUV' NTSC PAL Y'UV Luma + Chroma % 'YIQ' NTSC Y'IQ Luma + Chroma % 'YDbDr' SECAM Y'DbDr Luma + Chroma % 'JPEGYCbCr' JPEG-Y'CbCr Luma + Chroma % 'HSV'/'HSB' Hue Saturation Value/Brightness % 'HSL'/'HLS'/'HSI' Hue Saturation Luminance/Intensity % 'XYZ' CIE XYZ % 'Lab' CIE L*a*b* (CIELAB) % 'Luv' CIE L*u*v* (CIELUV) % 'Lch' CIE L*ch (CIELCH) % % All conversions assume 2 degree observer and D65 illuminant. Color % space names are case insensitive. When R'G'B' is the source or % destination, it can be omitted. For example 'yuv<-' is short for % 'yuv<-rgb'. % % MATLAB uses two standard data formats for R'G'B': double data with % intensities in the range 0 to 1, and uint8 data with integer-valued % intensities from 0 to 255. As MATLAB's native datatype, double data is % the natural choice, and the R'G'B' format used by colorspace. However, % for memory and computational performance, some functions also operate % with uint8 R'G'B'. Given uint8 R'G'B' color data, colorspace will % first cast it to double R'G'B' before processing. % % If A is an Mx3 array, like a colormap, B will also have size Mx3. % % [B1,B2,B3] = COLORSPACE(S,A) specifies separate output channels. % COLORSPACE(S,A1,A2,A3) specifies separate input channels. % Pascal Getreuer 2005-2006 %%% Input parsing %%% if nargin < 2, error('Not enough input arguments.'); end [SrcSpace,DestSpace] = parse(Conversion); if nargin == 2 Image = varargin{1}; elseif nargin >= 3 Image = cat(3,varargin{:}); else error('Invalid number of input arguments.'); end FlipDims = (size(Image,3) == 1); if FlipDims, Image = permute(Image,[1,3,2]); end if ~isa(Image,'double'), Image = double(Image)/255; end if size(Image,3) ~= 3, error('Invalid input size.'); end SrcT = gettransform(SrcSpace); DestT = gettransform(DestSpace); if ~ischar(SrcT) & ~ischar(DestT) % Both source and destination transforms are affine, so they % can be composed into one affine operation T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)]; Temp = zeros(size(Image)); Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10); Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11); Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12); Image = Temp; elseif ~ischar(DestT) Image = rgb(Image,SrcSpace); Temp = zeros(size(Image)); Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10); Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11); Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12); Image = Temp; else Image = feval(DestT,Image,SrcSpace); end %%% Output format %%% if nargout > 1 varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)}; else if FlipDims, Image = permute(Image,[1,3,2]); end varargout = {Image}; end return; function [SrcSpace,DestSpace] = parse(Str) % Parse conversion argument if isstr(Str) Str = lower(strrep(strrep(Str,'-',''),' ','')); k = find(Str == '>'); if length(k) == 1 % Interpret the form 'src->dest' SrcSpace = Str(1:k-1); DestSpace = Str(k+1:end); else k = find(Str == '<'); if length(k) == 1 % Interpret the form 'dest<-src' DestSpace = Str(1:k-1); SrcSpace = Str(k+1:end); else error(['Invalid conversion, ''',Str,'''.']); end end SrcSpace = alias(SrcSpace); DestSpace = alias(DestSpace); else SrcSpace = 1; % No source pre-transform DestSpace = Conversion; if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end end return; function Space = alias(Space) Space = strrep(Space,'cie',''); if isempty(Space) Space = 'rgb'; end switch Space case {'ycbcr','ycc'} Space = 'ycbcr'; case {'hsv','hsb'} Space = 'hsv'; case {'hsl','hsi','hls'} Space = 'hsl'; case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'} return; end return; function T = gettransform(Space) % Get a colorspace transform: either a matrix describing an affine transform, % or a string referring to a conversion subroutine switch Space case 'ypbpr' T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0]; case 'yuv' % R'G'B' to NTSC/PAL YUV % Wikipedia: http://en.wikipedia.org/wiki/YUV T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0]; case 'ydbdr' % R'G'B' to SECAM YDbDr % Wikipedia: http://en.wikipedia.org/wiki/YDbDr T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0]; case 'yiq' % R'G'B' in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591]; % Wikipedia: http://en.wikipedia.org/wiki/YIQ T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0]; case 'ycbcr' % R'G'B' (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr % Wikipedia: http://en.wikipedia.org/wiki/YCbCr % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128]; case 'jpegycbcr' % Wikipedia: http://en.wikipedia.org/wiki/YCbCr T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255; case {'rgb','xyz','hsv','hsl','lab','luv','lch'} T = Space; otherwise error(['Unknown color space, ''',Space,'''.']); end return; function Image = rgb(Image,SrcSpace) % Convert to Rec. 709 R'G'B' from 'SrcSpace' switch SrcSpace case 'rgb' return; case 'hsv' % Convert HSV to R'G'B' Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1)); case 'hsl' % Convert HSL to R'G'B' L = Image(:,:,3); Delta = Image(:,:,2).*min(L,1-L); Image = huetorgb(L-Delta,L+Delta,Image(:,:,1)); case {'xyz','lab','luv','lch'} % Convert to CIE XYZ Image = xyz(Image,SrcSpace); % Convert XYZ to RGB T = [3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311]; R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B % Desaturate and rescale to constrain resulting RGB values to [0,1] AddWhite = -min(min(min(R,G),B),0); Scale = max(max(max(R,G),B)+AddWhite,1); R = (R + AddWhite)./Scale; G = (G + AddWhite)./Scale; B = (B + AddWhite)./Scale; % Apply gamma correction to convert RGB to Rec. 709 R'G'B' Image(:,:,1) = gammacorrection(R); % R' Image(:,:,2) = gammacorrection(G); % G' Image(:,:,3) = gammacorrection(B); % B' otherwise % Conversion is through an affine transform T = gettransform(SrcSpace); temp = inv(T(:,1:3)); T = [temp,-temp*T(:,4)]; R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10); G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11); B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12); AddWhite = -min(min(min(R,G),B),0); Scale = max(max(max(R,G),B)+AddWhite,1); R = (R + AddWhite)./Scale; G = (G + AddWhite)./Scale; B = (B + AddWhite)./Scale; Image(:,:,1) = R; Image(:,:,2) = G; Image(:,:,3) = B; end % Clip to [0,1] Image = min(max(Image,0),1); return; function Image = xyz(Image,SrcSpace) % Convert to CIE XYZ from 'SrcSpace' WhitePoint = [0.950456,1,1.088754]; switch SrcSpace case 'xyz' return; case 'luv' % Convert CIE L*uv to XYZ WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); L = Image(:,:,1); Y = (L + 16)/116; Y = invf(Y)*WhitePoint(2); U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU; V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV; Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X Image(:,:,2) = Y; % Y Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z case {'lab','lch'} Image = lab(Image,SrcSpace); % Convert CIE L*ab to XYZ fY = (Image(:,:,1) + 16)/116; fX = fY + Image(:,:,2)/500; fZ = fY - Image(:,:,3)/200; Image(:,:,1) = WhitePoint(1)*invf(fX); % X Image(:,:,2) = WhitePoint(2)*invf(fY); % Y Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z otherwise % Convert from some gamma-corrected space % Convert to Rec. 701 R'G'B' Image = rgb(Image,SrcSpace); % Undo gamma correction R = invgammacorrection(Image(:,:,1)); G = invgammacorrection(Image(:,:,2)); B = invgammacorrection(Image(:,:,3)); % Convert RGB to XYZ T = inv([3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311]); Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z end return; function Image = hsv(Image,SrcSpace) % Convert to HSV Image = rgb(Image,SrcSpace); V = max(Image,[],3); S = (V - min(Image,[],3))./(V + (V == 0)); Image(:,:,1) = rgbtohue(Image); Image(:,:,2) = S; Image(:,:,3) = V; return; function Image = hsl(Image,SrcSpace) % Convert to HSL switch SrcSpace case 'hsv' % Convert HSV to HSL MaxVal = Image(:,:,3); MinVal = (1 - Image(:,:,2)).*MaxVal; L = 0.5*(MaxVal + MinVal); temp = min(L,1-L); Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0)); Image(:,:,3) = L; otherwise Image = rgb(Image,SrcSpace); % Convert to Rec. 701 R'G'B' % Convert R'G'B' to HSL MinVal = min(Image,[],3); MaxVal = max(Image,[],3); L = 0.5*(MaxVal + MinVal); temp = min(L,1-L); S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0)); Image(:,:,1) = rgbtohue(Image); Image(:,:,2) = S; Image(:,:,3) = L; end return; function Image = lab(Image,SrcSpace) % Convert to CIE L*a*b* (CIELAB) WhitePoint = [0.950456,1,1.088754]; switch SrcSpace case 'lab' return; case 'lch' % Convert CIE L*CH to CIE L*ab C = Image(:,:,2); Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a* Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b* otherwise Image = xyz(Image,SrcSpace); % Convert to XYZ % Convert XYZ to CIE L*a*b* X = Image(:,:,1)/WhitePoint(1); Y = Image(:,:,2)/WhitePoint(2); Z = Image(:,:,3)/WhitePoint(3); fX = f(X); fY = f(Y); fZ = f(Z); Image(:,:,1) = 116*fY - 16; % L* Image(:,:,2) = 500*(fX - fY); % a* Image(:,:,3) = 200*(fY - fZ); % b* end return; function Image = luv(Image,SrcSpace) % Convert to CIE L*u*v* (CIELUV) WhitePoint = [0.950456,1,1.088754]; WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); Image = xyz(Image,SrcSpace); % Convert to XYZ U = (4*Image(:,:,1))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3)); V = (9*Image(:,:,2))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3)); Y = Image(:,:,2)/WhitePoint(2); L = 116*f(Y) - 16; Image(:,:,1) = L; % L* Image(:,:,2) = 13*L.*(U - WhitePointU); % u* Image(:,:,3) = 13*L.*(V - WhitePointV); % v* return; function Image = lch(Image,SrcSpace) % Convert to CIE L*ch Image = lab(Image,SrcSpace); % Convert to CIE L*ab H = atan2(Image(:,:,3),Image(:,:,2)); H = H*180/pi + 360*(H < 0); Image(:,:,2) = sqrt(Image(:,:,2).^2 + Image(:,:,3).^2); % C Image(:,:,3) = H; % H return; function Image = huetorgb(m0,m2,H) % Convert HSV or HSL hue to RGB N = size(H); H = min(max(H(:),0),360)/60; m0 = m0(:); m2 = m2(:); F = H - round(H/2)*2; M = [m0, m0 + (m2-m0).*abs(F), m2]; Num = length(m0); j = [2 1 0;1 2 0;0 2 1;0 1 2;1 0 2;2 0 1;2 1 0]*Num; k = floor(H) + 1; Image = reshape([M(j(k,1)+(1:Num).'),M(j(k,2)+(1:Num).'),M(j(k,3)+(1:Num).')],[N,3]); return; function H = rgbtohue(Image) % Convert RGB to HSV or HSL hue [M,i] = sort(Image,3); i = i(:,:,3); Delta = M(:,:,3) - M(:,:,1); Delta = Delta + (Delta == 0); R = Image(:,:,1); G = Image(:,:,2); B = Image(:,:,3); H = zeros(size(R)); k = (i == 1); H(k) = (G(k) - B(k))./Delta(k); k = (i == 2); H(k) = 2 + (B(k) - R(k))./Delta(k); k = (i == 3); H(k) = 4 + (R(k) - G(k))./Delta(k); H = 60*H + 360*(H < 0); H(Delta == 0) = nan; return; function Rp = gammacorrection(R) Rp = real(1.099*R.^0.45 - 0.099); i = (R < 0.018); Rp(i) = 4.5138*R(i); return; function R = invgammacorrection(Rp) R = real(((Rp + 0.099)/1.099).^(1/0.45)); i = (R < 0.018); R(i) = Rp(i)/4.5138; return; function fY = f(Y) fY = real(Y.^(1/3)); i = (Y < 0.008856); fY(i) = Y(i)*(841/108) + (4/29); return; function Y = invf(fY) Y = fY.^3; i = (Y < 0.008856); Y(i) = (fY(i) - 4/29)*(108/841); return;
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
k_means.m
.m
Segmentation-Using-Superpixels-master/others/k_means.m
1,984
utf_8
031bae601547fa2a4821a4657bdf547c
function [R, M] = k_means(X, K, seed) %KMEANS: K-means clustering % idx = KMEANS(X, K) returns M with K columns, one for each mean. Each % column of X is a datapoint. K is the number of clusters % [idx, mu] = KMEANS(X, K) also returns mu, a row vector, R(i) is the % index of the cluster datapoint X(:, i) is assigned to. % idx = KMEANS(X,K) returns idx where idx(i) is the index of the cluster % that datapoint X(:,i) is assigned to. % [idx,mu] = KMEANS(X,K) also returns mu, the K cluster centers. % % KMEANS(X, K, SEED) uses SEED (default 1) to randomise initial assignments. if ~exist('seed', 'var'), seed = 1; end % % Initialization % [D,N] = size(X); % if D>N, warning(sprintf('K-means running on %d points in %d dimensions\n',N,D)); end; M = zeros(D, K); Dist = zeros(N, K); M(:, 1) = X(:,seed); Dist(:, 1) = sum((X - repmat(M(:, 1), 1, N)).^2, 1)'; for ii = 2:K % maximum, minimum dist mindist = min(Dist(:,1:ii-1), [], 2); [junk, jj] = max(mindist); M(:, ii) = X(:, jj); Dist(:, ii) = sum((X - repmat(M(:, ii), 1, N)).^2, 1)'; end % plotfig(X,M); X2 = sum(X.^2,1)'; converged = 0; R = zeros(N, 1); while ~converged distance = repmat(X2,1,K) - 2 * X' * M + repmat(sum(M.^2, 1), N, 1); [junk, newR] = min(distance, [], 2); if norm(R-newR) == 0 converged = 1; else R = newR; end total = 0; for ii = 1:K ix = find(R == ii); M(:, ii) = mean(X(:, ix), 2); total = total + sum(distance(ix, ii)); end % plotfig(X,M); % fprintf('Distance %f\n', total); end % pause; close all; return function plotfig(x,M), figure; plot(x(1,:),x(2,:),'go', 'MarkerFaceColor','g', 'LineWidth',1.5); hold on; plot(M(1,:),M(2,:),'rx','MarkerSize',12, 'LineWidth',2); w = 2.15; h = 2; for k=1:size(M,2), rectangle('Position',[M(1,k) M(2,k) 0 0]+w*[-1 -1 +2 +2], 'Curvature',[1 1], 'EdgeColor','r', 'LineWidth',2); end; xlim([floor(min(x(1,:))) ceil(max(x(1,:)))]); ylim([floor(min(x(2,:))) ceil(max(x(2,:)))]); return
github
ColumbiaDVMM/Segmentation-Using-Superpixels-master
sc.m
.m
Segmentation-Using-Superpixels-master/others/sc.m
39,501
utf_8
33abd391190c650fbdfca77620408f4e
function I = sc(I, varargin) %SC Display/output truecolor images with a range of colormaps % % Examples: % sc(image) % sc(image, limits) % sc(image, map) % sc(image, limits, map) % sc(image, map, limits) % sc(..., col1, mask1, col2, mask2,...) % out = sc(...) % sc % % Generates a truecolor RGB image based on the input values in 'image' and % any maximum and minimum limits specified, using the colormap specified. % The image is displayed on screen if there is no output argument. % % SC has these advantages over MATLAB image rendering functions: % - images can be displayed or output; makes combining/overlaying images % simple. % - images are rendered/output in truecolor (RGB [0,1]); no nasty % discretization of the input data. % - many special, built-in colormaps for viewing various types of data. % - linearly interpolates user defined linear and non-linear colormaps. % - no border and automatic, integer magnification (unless figure is % docked or maximized) for better display. % - multiple images can be generated for export simultaneously. % % For a demonstration, simply call SC without any input arguments. % % IN: % image - MxNxCxP or 3xMxNxP image array. MxN are the dimensions of the % image(s), C is the number of channels, and P the number of % images. If P > 1, images can only be exported, not displayed. % limits - [min max] where values in image less than min will be set to % min and values greater than max will be set to max. % map - Kx3 or Kx4 user defined colormap matrix, where the optional 4th % column is the relative distance between colours along the scale, % or a string containing the name of the colormap to use to create % the output image. Default: 'none', which is RGB for 3-channel % images, grayscale otherwise. Conversion of multi-channel images % to intensity for intensity-based colormaps is done using the L2 % norm. Most MATLAB colormaps are supported. All named colormaps % can be reversed by prefixing '-' to the string. This maintains % integrity of the colorbar. Special, non-MATLAB colormaps are: % 'contrast' - a high contrast colormap for intensity images that % maintains intensity scale when converted to grayscale, % for example when printing in black & white. % 'prob' - first channel is plotted as hue, and the other channels % modulate intensity. Useful for laying probabilites over % images. % 'prob_jet' - first channel is plotted as jet colormap, and the other % channels modulate intensity. % 'diff' - intensity values are marked blue for > 0 and red for < 0. % Darker colour means larger absolute value. For multi- % channel images, the L2 norm of the other channels sets % green level. 3 channel images are converted to YUV and % images with more that 3 channels are projected onto the % principle components first. % 'compress' - compress many channels to RGB while maximizing % variance. % 'flow' - display two channels representing a 2d Cartesian vector as % hue for angle and intensity for magnitude (darker colour % indicates a larger magnitude). % 'phase' - first channel is intensity, second channel is phase in % radians. Darker colour means greater intensity, hue % represents phase from 0 to 2 pi. % 'stereo' - pair of concatenated images used to generate a red/cyan % anaglyph. % 'stereo_col' - pair of concatenated RGB images used to generate a % colour anaglyph. % 'rand' - gives an index image a random colormap. Useful for viewing % segmentations. % 'rgb2gray' - converts an RGB image to grayscale in the same fashion % as MATLAB's rgb2gray (in the image processing toolbox). % col/mask pairs - Pairs of parameters for coloring specific parts of the % image differently. The first (col) parameter can be % a MATLAB color specifier, e.g. 'b' or [0.5 0 1], or % one of the colormaps named above, or an MxNx3 RGB % image. The second (mask) paramater should be an MxN % logical array indicating those pixels (true) whose % color should come from the specified color parameter. % If there is only one col parameter, without a mask % pair, then mask = any(isnan(I, 3)), i.e. the mask is % assumed to indicate the location of NaNs. Note that % col/mask pairs are applied in order, painting over % previous pixel values. % % OUT: % out - MxNx3xP truecolour (double) RGB image array in range [0, 1] % % See also IMAGE, IMAGESC, IMSHOW, COLORMAP, COLORBAR. % $Id: sc.m,v 1.84 2009/03/17 10:38:54 ojw Exp $ % Copyright: Oliver Woodford, 2007 %% Check for arguments if nargin == 0 % If there are no input arguments then run the demo if nargout > 0 error('Output expected from no inputs!'); end demo; % Run the demo return end %% Size our image(s) [y x c n] = size(I); I = reshape(I, y, x, c, n); %% Check if image is given with RGB colour along the first dimension if y == 3 && c > 3 % Flip colour to 3rd dimension I = permute(I, [2 3 1 4]); [y x c n] = size(I); end %% Don't do much if I is empty if isempty(I) if nargout == 0 % Clear the current axes if we were supposed to display the image cla; axis off; else % Create an empty array with the correct dimensions I = zeros(y, x, (c~=0)*3, n); end return end %% Check for multiple images % If we have a non-singleton 4th dimension we want to display the images in % a 3x4 grid and use buttons to cycle through them if n > 1 if nargout > 0 % Return transformed images in an YxXx3xN array A = zeros(y, x, 3, n); for a = 1:n A(:,:,:,a) = sc(I(:,:,:,a), varargin{:}); end I = A; else % Removed functionality fprintf([' SC no longer supports the display of multiple images. The\n'... ' functionality has been incorporated into an improved version\n'... ' of MONTAGE, available on the MATLAB File Exchange at:\n'... ' http://www.mathworks.com/matlabcentral/fileexchange/22387\n']); clear I; end return end %% Parse the input arguments coming after I (1st input) [map limits mask] = parse_inputs(I, varargin, y, x); %% Call the rendering function I = reshape(double(real(I)), y*x, c); % Only work with real doubles if ~ischar(map) % Table-based colormap reverseMap = false; [I limits] = interp_map(I, limits, reverseMap, map); else % If map starts with a '-' sign, invert the colourmap reverseMap = map(1) == '-'; map = lower(map(reverseMap+1:end)); % Predefined colormap [I limits] = colormap_switch(I, map, limits, reverseMap, c); end %% Update any masked pixels I = reshape(I, y*x, 3); for a = 1:size(mask, 2) I(mask{2,a},1) = mask{1,a}(:,1); I(mask{2,a},2) = mask{1,a}(:,2); I(mask{2,a},3) = mask{1,a}(:,3); end I = reshape(I, [y x 3]); % Reshape to correct size %% Only display if the output isn't used if nargout == 0 display_image(I, map, limits, reverseMap); % Don't print out the matrix if we've forgotten the ";" clear I end return %% Colormap switch function [I limits] = colormap_switch(I, map, limits, reverseMap, c) % Large switch statement for all the colourmaps switch map %% Prism case 'prism' % Similar to the MATLAB internal prism colormap, but only works on % index images, assigning each index (or rounded float) to a % different colour [I limits] = index_im(I); % Generate prism colourmap map = prism(6); if reverseMap map = map(end:-1:1,:); % Reverse the map end % Lookup the colours I = mod(I, 6) + 1; I = map(I,:); %% Rand case 'rand' % Assigns a random colour to each index [I limits num_vals] = index_im(I); % Generate random colourmap map = rand(num_vals, 3); % Lookup the colours I = map(I,:); %% Diff case 'diff' % Show positive as blue and negative as red, white is 0 switch c case 1 I(:,2:3) = 0; case 2 % Second channel can only have absolute value I(:,3) = abs(I(:,2)); case 3 % Diff of RGB images - convert to YUV first I = rgb2yuv(I); I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(2); otherwise % Use difference along principle component, and other % channels to modulate second channel I = calc_prin_comps(I); I(:,3) = sqrt(sum(I(:,2:end) .^ 2, 2)) ./ sqrt(c - 1); I(:,4:end) = []; end % Generate limits if isempty(limits) limits = [min(I(:,1)) max(I(:,1))]; end limits = max(abs(limits)); if limits % Scale if c > 1 I(:,[1 3]) = I(:,[1 3]) / limits; else I = I / (limits * 0.5); end end % Colour M = I(:,1) > 0; I(:,2) = -I(:,1) .* ~M; I(:,1) = I(:,1) .* M; if reverseMap % Swap first two channels I = I(:,[2 1 3]); end %I = 1 - I * [1 0.4 1; 0.4 1 1; 1 1 0.4]; % (Green/Red) I = 1 - I * [1 1 0.4; 0.4 1 1; 1 0.4 1]; % (Blue/Red) I = min(max(I, 0), 1); limits = [-limits limits]; % For colourbar %% Flow case 'flow' % Calculate amplitude and phase, and use 'phase' if c ~= 2 error('''flow'' requires two channels'); end A = sqrt(sum(I .^ 2, 2)); if isempty(limits) limits = [min(A) max(A)*2]; else limits = [0 max(abs(limits)*sqrt(2))*2]; end I(:,1) = atan2(I(:,2), I(:,1)); I(:,2) = A; if reverseMap % Invert the amplitude I(:,2) = -I(:,2); limits = -limits([2 1]); end I = phase_helper(I, limits, 2); % Last parameter tunes how saturated colors can get % Set NaNs (unknown flow) to 0 I(isnan(I)) = reverseMap; limits = []; % This colourmap doesn't have a valid colourbar %% Phase case 'phase' % Plot amplitude as intensity and angle as hue if c < 2 error('''phase'' requires two channels'); end if isempty(limits) limits = [min(I(:,1)) max(I(:,1))]; end if reverseMap % Invert the phase I(:,2) = -I(:,2); end I = I(:,[2 1]); if diff(limits) I = phase_helper(I, limits, 1.3); % Last parameter tunes how saturated colors can get else % No intensity - just cycle hsv I = hsv_helper(mod(I(:,1) / (2 * pi), 1)); end limits = []; % This colourmap doesn't have a valid colourbar %% RGB2Grey case {'rgb2grey', 'rgb2gray'} % Compress RGB to greyscale [I limits] = rgb2grey(I, limits, reverseMap); %% RGB2YUV case 'rgb2yuv' % Convert RGB to YUV - not for displaying or saving to disk! [I limits] = rgb2yuv(I); %% YUV2RGB case 'yuv2rgb' % Convert YUV to RGB - undo conversion of rgb2yuv if c ~= 3 error('''yuv2rgb'' requires a 3 channel image'); end I = reshape(I, y*x, 3); I = I * [1 1 1; 0, -0.39465, 2.03211; 1.13983, -0.58060 0]; I = reshape(I, y, x, 3); I = sc(I, limits); limits = []; % This colourmap doesn't have a valid colourbar %% Prob case 'prob' % Plot first channel as grey variation of 'bled' and modulate % according to other channels if c > 1 A = rgb2grey(I(:,2:end), [], false); I = I(:,1); else A = 0.5; end [I limits] = bled(I, limits, reverseMap); I = normalize(A + I, [-0.1 1.3]); %% Prob_jet case 'prob_jet' % Plot first channel as 'jet' and modulate according to other % channels if c > 1 A = rgb2grey(I(:,2:end), [], false); I = I(:,1); else A = 0.5; end [I limits] = jet_helper(I, limits, reverseMap); I = normalize(A + I, [0.2 1.8]); %% Compress case 'compress' % Compress to RGB, maximizing variance % Determine and scale to limits I = normalize(I, limits); if reverseMap % Invert after everything I = 1 - I; end % Zero mean meanCol = mean(I, 1); isBsx = exist('bsxfun', 'builtin'); if isBsx I = bsxfun(@minus, I, meanCol); else I = I - meanCol(ones(x*y, 1, 'uint8'),:); end % Calculate top 3 principle components I = calc_prin_comps(I, 3); % Normalize each channel independently if isBsx I = bsxfun(@minus, I, min(I, [], 1)); I = bsxfun(@times, I, 1./max(I, [], 1)); else for a = 1:3 I(:,a) = I(:,a) - min(I(:,a)); I(:,a) = I(:,a) / max(I(:,a)); end end % Put components in order of human eyes' response to channels I = I(:,[2 1 3]); limits = []; % This colourmap doesn't have a valid colourbar %% Stereo (anaglyph) case 'stereo' % Convert 2 colour images to intensity images % Show first channel as red and second channel as cyan A = rgb2grey(I(:,1:floor(end/2)), limits, false); I = rgb2grey(I(:,floor(end/2)+1:end), limits, false); if reverseMap I(:,2:3) = A(:,1:2); % Make first image cyan else I(:,1) = A(:,1); % Make first image red end limits = []; % This colourmap doesn't have a valid colourbar %% Coloured anaglyph case 'stereo_col' if c ~= 6 error('''stereo_col'' requires a 6 channel image'); end I = normalize(I, limits); % Red channel from one image, green and blue from the other if reverseMap I(:,1) = I(:,4); % Make second image red else I(:,2:3) = I(:,5:6); % Make first image red end I = I(:,1:3); limits = []; % This colourmap doesn't have a valid colourbar %% None case 'none' % No colour map - just output the image if c ~= 3 [I limits] = grey(I, limits, reverseMap); else I = intensity(I(:), limits, reverseMap); limits = []; end %% Grey case {'gray', 'grey'} % Greyscale [I limits] = grey(I, limits, reverseMap); %% Jet case 'jet' % Dark blue to dark red, through green [I limits] = jet_helper(I, limits, reverseMap); case 'jet2' % Like jet, but starts in black and goes to saturated red [I limits] = interp_map(I, limits, reverseMap, [0 0 0; 0.5 0 0.5; 0 0 0.9; 0 1 1; 0 1 0; 1 1 0; 1 0 0]); %% Hot case 'hot' % Black to white through red and yellow [I limits] = interp_map(I, limits, reverseMap, [0 0 0 3; 1 0 0 3; 1 1 0 2; 1 1 1 0]); case 'hot2' % Like hot, but equally spaced [I limits] = intensity(I, limits, reverseMap); % Intensity map I = I * 3; I = [I, I-1, I-2]; I = min(max(I, 0), 1); % Truncate case {'hotter', 'hot*'} % Converts to linear greyscale [I limits] = interp_map(I, limits, reverseMap, [0 0 0 299; 1 0 0 587; 1 1 0 114; 1 1 1 0]); %% Thermal case 'thermal' % Black, purple, red, orange, yellow - typical for thermal imaging [I limits] = interp_map(I, limits, reverseMap, [0 0 0; 0.3 0 0.7; 1 0.2 0; 1 1 0; 1 1 1]); case 'thermal*' % Converts to linear greyscale [I limits] = interp_map(I, limits, reverseMap, [0 0 0 1695; 0.3 0 0.7 2469; 1 0.2 0 4696; 1 1 0 1140; 1 1 1 0]); %% Contrast case 'contrast' % A high contrast, full-colour map that goes from black to white % linearly when converted to greyscale, and passes through all the % corners of the RGB colour cube [I limits] = interp_map(I, limits, reverseMap, [0 0 0 114; 0 0 1 185; 1 0 0 114; 1 0 1 174;... 0 1 0 114; 0 1 1 185; 1 1 0 114; 1 1 1 0]); %% HSV case 'hsv' % Cycle through hues [I limits] = intensity(I, limits, reverseMap); % Intensity map I = hsv_helper(I); %% Bone case 'bone' % Greyscale with a blue tint [I limits] = interp_map(I, limits, reverseMap, [0 0 0 3; 21 21 29 3; 42 50 50 2; 64 64 64 1]/64); case 'bone2' % Like bone, but equally spaced [I limits] = intensity(I, limits, reverseMap); % Intensity map J = [I-2/3, I-1/3, I]; J = max(min(J, 1/3), 0) * (2 / 5); I = I * (13 / 15); I = J + I(:,[1 1 1]); %% Colourcube case {'colorcube', 'colourcube'} % Psychedelic colourmap inspired by MATLAB's version [I limits] = intensity(I, limits, reverseMap); % Intensity map step = 4; I = I * (step * (1 - eps)); J = I * step; K = floor(J); I = cat(3, mod(K, step)/(step-1), J - floor(K), mod(floor(I), step)/(step-1)); %% Cool case 'cool' % Cyan through to magenta [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [I, 1-I, ones(size(I))]; %% Spring case 'spring' % Magenta through to yellow [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [ones(size(I)), I, 1-I]; %% Summer case 'summer' % Darkish green through to pale yellow [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [I, 0.5+I*0.5, 0.4*ones(size(I))]; %% Autumn case 'autumn' % Red through to yellow [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [ones(size(I)), I, zeros(size(I))]; %% Winter case 'winter' % Blue through to turquoise [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [zeros(size(I)), I, 1-I*0.5]; %% Copper case 'copper' % Black through to copper [I limits] = intensity(I, limits, reverseMap); % Intensity map I = [I*(1/0.8), I*0.78, I*0.5]; I = min(max(I, 0), 1); % Truncate case {'copper2', 'copper*'} % Converts to greyscale [I limits] = interp_map(I, limits, reverseMap, [0 0 0; 0.2651 0.2426 0.2485; 0.666 0.4399 0.3738; 0.8118 0.7590 0.5417; 1 1 1]); %% Pink case 'pink' % Greyscale with a pink tint [I limits] = intensity(I, limits, reverseMap); % Intensity map J = I * (2 / 3); I = [I, I-1/3, I-2/3]; I = max(min(I, 1/3), 0); I = I + J(:,[1 1 1]); I = sqrt(I); %% Sepia case 'sepia' % Greyscale with a brown (sepia) tint [I limits] = interp_map(I, limits, reverseMap, [0 0 0 5; 0.1 0.05 0 85; 1 0.9 0.8 10; 1 1 1 0]); %% Bled case 'bled' % Black to red, through blue [I limits] = bled(I, limits, reverseMap); %% Earth case 'earth' % High contrast, converts to linear scale in grey, strong % shades of green table = [0 0 0; 0 0.1104 0.0583; 0.1661 0.1540 0.0248; 0.1085 0.2848 0.1286;... 0.2643 0.3339 0.0939; 0.2653 0.4381 0.1808; 0.3178 0.5053 0.3239;... 0.4858 0.5380 0.3413; 0.6005 0.5748 0.4776; 0.5698 0.6803 0.6415;... 0.5639 0.7929 0.7040; 0.6700 0.8626 0.6931; 0.8552 0.8967 0.6585;... 1 0.9210 0.7803; 1 1 1]; [I limits] = interp_map(I, limits, reverseMap, table); %% Pinker case 'pinker' % High contrast, converts to linear scale in grey, strong % shades of pink table = [0 0 0; 0.0455 0.0635 0.1801; 0.2425 0.0873 0.1677;... 0.2089 0.2092 0.2546; 0.3111 0.2841 0.2274; 0.4785 0.3137 0.2624;... 0.5781 0.3580 0.3997; 0.5778 0.4510 0.5483; 0.5650 0.5682 0.6047;... 0.6803 0.6375 0.5722; 0.8454 0.6725 0.5855; 0.9801 0.7032 0.7007;... 1 0.7777 0.8915; 0.9645 0.8964 1; 1 1 1]; [I limits] = interp_map(I, limits, reverseMap, table); %% Pastel case 'pastel' % High contrast, converts to linear scale in grey, strong % pastel shades table = [0 0 0; 0.4709 0 0.018; 0 0.3557 0.6747; 0.8422 0.1356 0.8525; 0.4688 0.6753 0.3057; 1 0.6893 0.0934; 0.9035 1 0; 1 1 1]; [I limits] = interp_map(I, limits, reverseMap, table); %% Bright case 'bright' % High contrast, converts to linear scale in grey, strong % saturated shades table = [0 0 0; 0.3071 0.0107 0.3925; 0.007 0.289 1; 1 0.0832 0.7084; 1 0.4447 0.1001; 0.5776 0.8360 0.4458; 0.9035 1 0; 1 1 1]; [I limits] = interp_map(I, limits, reverseMap, table); %% Unknown colourmap otherwise error('Colormap ''%s'' not recognised.', map); end return %% Display image function display_image(I, map, limits, reverseMap) % Clear the axes cla(gca, 'reset'); % Display the image - using image() is fast hIm = image(I); % Get handles to the figure and axes (now, as the axes may have % changed) hFig = gcf; hAx = gca; % Axes invisible and equal set(hFig, 'Units', 'pixels'); set(hAx, 'Visible', 'off', 'DataAspectRatio', [1 1 1], 'DrawMode', 'fast'); % Make title and axis labels visible set(get(hAx, 'XLabel'), 'Visible', 'on'); set(get(hAx, 'YLabel'), 'Visible', 'on'); set(get(hAx, 'Title'), 'Visible', 'on'); % Set data for a colorbar if ~isempty(limits) && limits(1) ~= limits(2) colBar = (0:255) * ((limits(2) - limits(1)) / 255) + limits(1); colBar = squeeze(sc(colBar, map, limits)); if reverseMap colBar = colBar(end:-1:1,:); end set(hFig, 'Colormap', colBar); set(hAx, 'CLim', limits); set(hIm, 'CDataMapping', 'scaled'); end % Only resize image if it is alone in the figure if numel(findobj(get(hFig, 'Children'), 'Type', 'axes')) > 1 return end % Could still be the first subplot - do another check axesPos = get(hAx, 'Position'); if isequal(axesPos, get(hFig, 'DefaultAxesPosition')) % Default position => not a subplot % Fill the window set(hAx, 'Units', 'normalized', 'Position', [0 0 1 1]); axesPos = [0 0 1 1]; end if ~isequal(axesPos, [0 0 1 1]) || strcmp(get(hFig, 'WindowStyle'), 'docked') % Figure not alone, or docked. Either way, don't resize. return end % Get the size of the monitor we're on figPosCur = get(hFig, 'Position'); MonSz = get(0, 'MonitorPositions'); MonOn = size(MonSz, 1); if MonOn > 1 figCenter = figPosCur(1:2) + figPosCur(3:4) / 2; figCenter = MonSz - repmat(figCenter, [MonOn 2]); MonOn = all(sign(figCenter) == repmat([-1 -1 1 1], [MonOn 1]), 2); MonOn(1) = MonOn(1) | ~any(MonOn); MonSz = MonSz(MonOn,:); end MonSz(3:4) = MonSz(3:4) - MonSz(1:2) + 1; % Check if the window is maximized % This is a hack which may only work on Windows! No matter, though. if isequal(MonSz([1 3]), figPosCur([1 3])) % Leave maximized return end % Compute the size to set the window MaxSz = MonSz(3:4) - [20 120]; ImSz = [size(I, 2) size(I, 1)]; RescaleFactor = min(MaxSz ./ ImSz); if RescaleFactor > 1 % Integer scale for enlarging, but don't make too big MaxSz = min(MaxSz, [1000 680]); RescaleFactor = max(floor(min(MaxSz ./ ImSz)), 1); end figPosNew = ceil(ImSz * RescaleFactor); % Don't move the figure if the size isn't changing if isequal(figPosCur(3:4), figPosNew) return end % Keep the centre of the figure stationary figPosNew = [max(1, floor(figPosCur(1:2)+(figPosCur(3:4)-figPosNew)/2)) figPosNew]; % Ensure the figure bar is in bounds figPosNew(1:2) = min(figPosNew(1:2), MonSz(1:2)+MonSz(3:4)-[6 101]-figPosNew(3:4)); set(hFig, 'Position', figPosNew); return %% Parse input variables function [map limits mask] = parse_inputs(I, inputs, y, x) % Check the first two arguments for the colormap and limits ninputs = numel(inputs); map = 'none'; limits = []; mask = 1; for a = 1:min(2, ninputs) if ischar(inputs{a}) && numel(inputs{a}) > 1 % Name of colormap map = inputs{a}; elseif isnumeric(inputs{a}) [p q r] = size(inputs{a}); if (p * q * r) == 2 % Limits limits = double(inputs{a}); elseif p > 1 && (q == 3 || q == 4) && r == 1 % Table-based colormap map = inputs{a}; else break; end else break; end mask = mask + 1; end % Check for following inputs if mask > ninputs mask = cell(2, 0); return end % Following inputs must either be colour/mask pairs, or a colour for NaNs if ninputs - mask == 0 mask = cell(2, 1); mask{1} = inputs{end}; mask{2} = ~all(isfinite(I), 3); elseif mod(ninputs-mask, 2) == 1 mask = reshape(inputs(mask:end), 2, []); else error('Error parsing inputs'); end % Go through pairs and generate for a = 1:size(mask, 2) % Generate any masks from functions if isa(mask{2,a}, 'function_handle') mask{2,a} = mask{2,a}(I); end if ~islogical(mask{2,a}) error('Mask is not a logical array'); end if ~isequal(size(mask{2,a}), [y x]) error('Mask does not match image size'); end if ischar(mask{1,a}) if numel(mask{1,a}) == 1 % Generate colours from MATLAB colour strings mask{1,a} = rem(floor((strfind('kbgcrmyw', mask{1,a}) - 1) * [0.25 0.5 1]), 2); else % Assume it's a colormap name mask{1,a} = sc(I, mask{1,a}); end end mask{1,a} = reshape(mask{1,a}, [], 3); if size(mask{1,a}, 1) ~= y*x && size(mask{1,a}, 1) ~= 1 error('Replacement color/image of unexpected dimensions'); end if size(mask{1,a}, 1) ~= 1 mask{1,a} = mask{1,a}(mask{2,a},:); end end return %% Grey function [I limits] = grey(I, limits, reverseMap) % Greyscale [I limits] = intensity(I, limits, reverseMap); I = I(:,[1 1 1]); return %% RGB2grey function [I limits] = rgb2grey(I, limits, reverseMap) % Compress RGB to greyscale if size(I, 2) == 3 I = I * [0.299; 0.587; 0.114]; end [I limits] = grey(I, limits, reverseMap); return %% RGB2YUV function [I limits] = rgb2yuv(I) % Convert RGB to YUV - not for displaying or saving to disk! if size(I, 2) ~= 3 error('rgb2yuv requires a 3 channel image'); end I = I * [0.299, -0.14713, 0.615; 0.587, -0.28886, -0.51498; 0.114, 0.436, -0.10001]; limits = []; % This colourmap doesn't have a valid colourbar return %% Phase helper function I = phase_helper(I, limits, n) I(:,1) = mod(I(:,1)/(2*pi), 1); I(:,2) = I(:,2) - limits(1); I(:,2) = I(:,2) * (n / (limits(2) - limits(1))); I(:,3) = n - I(:,2); I(:,[2 3]) = min(max(I(:,[2 3]), 0), 1); I = hsv2rgb(reshape(I, [], 1, 3)); return %% Jet helper function [I limits] = jet_helper(I, limits, reverseMap) % Dark blue to dark red, through green [I limits] = intensity(I, limits, reverseMap); I = I * 4; I = [I-3, I-2, I-1]; I = 1.5 - abs(I); I = min(max(I, 0), 1); return %% HSV helper function I = hsv_helper(I) I = I * 6; I = abs([I-3, I-2, I-4]); I(:,1) = I(:,1) - 1; I(:,2:3) = 2 - I(:,2:3); I = min(max(I, 0), 1); return %% Bled function [I limits] = bled(I, limits, reverseMap) % Black to red through blue [I limits] = intensity(I, limits, reverseMap); J = reshape(hsv_helper(I), [], 3); if exist('bsxfun', 'builtin') I = bsxfun(@times, I, J); else I = J .* I(:,[1 1 1]); end return %% Normalize function [I limits] = normalize(I, limits) if isempty(limits) limits = isfinite(I); if ~any(reshape(limits, numel(limits), 1)) % All NaNs, Infs or -Infs I = double(I > 0); limits = [0 1]; return end limits = [min(I(limits)) max(I(limits))]; I = I - limits(1); if limits(2) ~= limits(1) I = I * (1 / (limits(2) - limits(1))); end else I = I - limits(1); if limits(2) ~= limits(1) I = I * (1 / (limits(2) - limits(1))); end I = min(max(I, 0), 1); end return %% Intensity maps function [I limits] = intensity(I, limits, reverseMap) % Squash to 1d using L2 norm if size(I, 2) > 1 I = sqrt(sum(I .^ 2, 2)); end % Determine and scale to limits [I limits] = normalize(I, limits); if reverseMap % Invert after everything I = 1 - I; end return %% Interpolate table-based map function [I limits] = interp_map(I, limits, reverseMap, map) % Convert to intensity [I limits] = intensity(I, limits, reverseMap); % Compute indices and offsets if size(map, 2) == 4 bins = map(1:end-1,4); cbins = cumsum(bins); bins = bins ./ cbins(end); cbins = cbins(1:end-1) ./ cbins(end); if exist('bsxfun', 'builtin') ind = bsxfun(@gt, I(:)', cbins(:)); else ind = repmat(I(:)', [numel(cbins) 1]) > repmat(cbins(:), [1 numel(I)]); end ind = min(sum(ind), size(map, 1) - 2) + 1; bins = 1 ./ bins; cbins = [0; cbins]; I = (I - cbins(ind)) .* bins(ind); else n = size(map, 1) - 1; I = I(:) * n; ind = min(floor(I), n-1); I = I - ind; ind = ind + 1; end if exist('bsxfun', 'builtin') I = bsxfun(@times, map(ind,1:3), 1-I) + bsxfun(@times, map(ind+1,1:3), I); else I = map(ind,1:3) .* repmat(1-I, [1 3]) + map(ind+1,1:3) .* repmat(I, [1 3]); end I = min(max(I, 0), 1); % Rounding errors can make values slip outside bounds return %% Index images function [J limits num_vals] = index_im(I) % Returns an index image if size(I, 2) ~= 1 error('Index maps only work on single channel images'); end J = round(I); rescaled = any(abs(I - J) > 0.01); if rescaled % Appears not to be an index image. Rescale over 256 indices m = min(I); m = m * (1 - sign(m) * eps); I = I - m; I = I * (256 / max(I(:))); J = ceil(I); num_vals = 256; elseif nargout > 2 % Output the number of values J = J - (min(J) - 1); num_vals = max(J); end % These colourmaps don't have valid colourbars limits = []; return %% Calculate principle components function I = calc_prin_comps(I, numComps) if nargin < 2 numComps = size(I, 2); end % Do SVD [I S] = svd(I, 0); % Calculate projection of data onto components S = diag(S(1:numComps,1:numComps))'; if exist('bsxfun', 'builtin') I = bsxfun(@times, I(:,1:numComps), S); else I = I(:,1:numComps) .* S(ones(size(I, 1), 1, 'uint8'),:); end return %% Demo function to show capabilities of sc function demo %% Demo gray & lack of border figure; fig = gcf; Z = peaks(256); sc(Z); display_text([... ' Lets take a standard, MATLAB, real-valued function:\n\n peaks(256)\n\n'... ' Calling:\n\n figure\n Z = peaks(256);\n sc(Z)\n\n'... ' gives (see figure). SC automatically scales intensity to fill the\n'... ' truecolor range of [0 1].\n\n'... ' If your figure isn''t docked, then the image will have no border, and\n'... ' will be magnified by an integer factor (in this case, 2) so that the\n'... ' image is a reasonable size.']); %% Demo colour image display figure(fig); clf; load mandrill; mandrill = ind2rgb(X, map); sc(mandrill); display_text([... ' That wasn''t so interesting. The default colormap is ''none'', which\n'... ' produces RGB images given a 3-channel input image, otherwise it produces\n'... ' a grayscale image. So calling:\n\n load mandrill\n'... ' mandrill = ind2rgb(X, map);\n sc(mandrill)\n\n gives (see figure).']); %% Demo discretization figure(fig); clf; subplot(121); sc(Z, 'jet'); label(Z, 'sc(Z, ''jet'')'); subplot(122); imagesc(Z); axis image off; colormap(jet(64)); % Fix the fact we change the default depth label(Z, 'imagesc(Z); axis image off; colormap(''jet'');'); display_text([... ' However, if we want to display intensity images in color we can use any\n'... ' of the MATLAB colormaps implemented (most of them) to give truecolor\n'... ' images. For example, to use ''jet'' simply call:\n\n'... ' sc(Z, ''jet'')\n\n'... ' The MATLAB alternative, shown on the right, is:\n\n'... ' imagesc(Z)\n axis equal off\n colormap(jet)\n\n'... ' which generates noticeable discretization artifacts.']); %% Demo intensity colourmaps figure(fig); clf; subplot(221); sc(Z, 'hsv'); label(Z, 'sc(Z, ''hsv'')'); subplot(222); sc(Z, 'colorcube'); label(Z, 'sc(Z, ''colorcube'')'); subplot(223); sc(Z, 'contrast'); label(Z, 'sc(Z, ''contrast'')'); subplot(224); sc(Z-round(Z), 'diff'); label(Z, 'sc(Z-round(Z), ''diff'')'); display_text([... ' There are several other intensity colormaps to choose from. Calling:\n\n'... ' help sc\n\n'... ' will give you a list of them. Here are several others demonstrated.']); %% Demo saturation limits & colourmap reversal figure(fig); clf; subplot(121); sc(Z, [0 max(Z(:))], '-hot'); label(Z, 'sc(Z, [0 max(Z(:))], ''-hot'')'); subplot(122); sc(mandrill, [-0.5 0.5]); label(mandrill, 'sc(mandrill, [-0.5 0.5])'); display_text([... ' SC can also rescale intensity, given an upper and lower bound provided\n'... ' by the user, and invert most colormaps simply by prefixing a ''-'' to the\n'... ' colormap name. For example:\n\n'... ' sc(Z, [0 max(Z(:))], ''-hot'');\n'... ' sc(mandrill, [-0.5 0.5]);\n\n'... ' Note that the order of the colormap and limit arguments are\n'... ' interchangable.']); %% Demo prob load gatlin; gatlin = X; figure(fig); clf; im = cat(3, abs(Z)', gatlin(1:256,end-255:end)); sc(im, 'prob'); label(im, 'sc(cat(3, prob, gatlin), ''prob'')'); display_text([... ' SC outputs the recolored data as a truecolor RGB image. This makes it\n'... ' easy to combine colormaps, either arithmetically, or by masking regions.\n'... ' For example, we could combine an image and a probability map\n'... ' arithmetically as follows:\n\n'... ' load gatlin\n'... ' gatlin = X(1:256,end-255:end);\n'... ' prob = abs(Z)'';\n'... ' im = sc(prob, ''hsv'') .* sc(prob, ''gray'') + sc(gatlin, ''rgb2gray'');\n'... ' sc(im, [-0.1 1.3]);\n\n'... ' In fact, that particular colormap has already been implemented in SC.\n'... ' Simply call:\n\n'... ' sc(cat(3, prob, gatlin), ''prob'');']); %% Demo colorbar colorbar; display_text([... ' SC also makes possible the generation of a colorbar in the normal way, \n'... ' with all the colours and data values correct. Simply call:\n\n'... ' colorbar\n\n'... ' The colorbar doesn''t work with all colormaps, but when it does,\n'... ' inverting the colormap (using ''-map'') maintains the integrity of the\n'... ' colorbar (i.e. it works correctly) - unlike if you invert the input data.']); %% Demo combine by masking figure(fig); clf; sc(Z, [0 max(Z(:))], '-hot', sc(Z-round(Z), 'diff'), Z < 0); display_text([... ' It''s just as easy to combine generated images by masking too. Here''s an\n'... ' example:\n\n'... ' im = cat(4, sc(Z, [0 max(Z(:))], ''-hot''), sc(Z-round(Z), ''diff''));\n'... ' mask = repmat(Z < 0, [1 1 3]);\n'... ' mask = cat(4, mask, ~mask);\n'... ' im = sum(im .* mask, 4);\n'... ' sc(im)\n\n'... ' In fact, SC can also do this for you, by adding image/colormap and mask\n'... ' pairs to the end of the argument list, as follows:\n\n'... ' sc(Z, [0 max(Z(:))], ''-hot'', sc(Z-round(Z), ''diff''), Z < 0);\n\n'... ' A benefit of the latter approach is that you can still display a\n'... ' colorbar for the first colormap.']); %% Demo texture map figure(fig); clf; surf(Z, sc(Z, 'contrast'), 'edgecolor', 'none'); display_text([... ' Other benefits of SC outputting the image as an array are that the image\n'... ' can be saved straight to disk using imwrite() (if you have the image\n'... ' processing toolbox), or can be used to texture map a surface, thus:\n\n'... ' tex = sc(Z, ''contrast'');\n'... ' surf(Z, tex, ''edgecolor'', ''none'');']); %% Demo compress load mri; mri = D; close(fig); % Only way to get round loss of focus (bug?) figure(fig); clf; sc(squeeze(mri(:,:,:,1:6)), 'compress'); display_text([... ' For images with more than 3 channels, SC can compress these images to RGB\n'... ' while maintaining the maximum amount of variance in the data. For\n'... ' example, this 6 channel image:\n\n'... ' load mri\n mri = D;\n sc(squeeze(mri(:,:,:,1:6), ''compress'')']); %% Demo multiple images figure(fig); clf; im = sc(mri, 'bone'); for a = 1:12 subplot(3, 4, a); sc(im(:,:,:,a)); end display_text([... ' SC can process multiple images for export when passed in as a 4d array.\n'... ' For example:\n\n'... ' im = sc(mri, ''bone'')\n'... ' for a = 1:12\n'... ' subplot(3, 4, a);\n'... ' sc(im(:,:,:,a));\n'... ' end']); %% Demo user defined colormap figure(fig); clf; sc(abs(Z), rand(10, 3)); colorbar; display_text([... ' Finally, SC can use user defined colormaps to display indexed images.\n'... ' These can be defined as a linear colormap. For example:\n\n'... ' sc(abs(Z), rand(10, 3))\n colorbar;\n\n'... ' Note that the colormap is automatically linearly interpolated.']); %% Demo non-linear user defined colormap figure(fig); clf; sc(abs(Z), [rand(10, 3) exp((1:10)/2)']); colorbar; display_text([... ' Non-linear colormaps can also be defined by the user, by including the\n'... ' relative distance between the given colormap points on the colormap\n'... ' scale in the fourth column of the colormap matrix. For example:\n\n'... ' sc(abs(Z), [rand(10, 3) exp((1:10)/2)''])\n colorbar;\n\n'... ' Note that the colormap is still linearly interpolated between points.']); clc; fprintf('End of demo.\n'); return %% Some helper functions for the demo function display_text(str) clc; fprintf([str '\n\n']); fprintf('Press a key to go on.\n'); figure(gcf); waitforbuttonpress; return function label(im, str) text(size(im, 2)/2, size(im, 1)+12, str,... 'Interpreter', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle'); return
github
ma-xu/2PTWSVM-master
psvm.m
.m
2PTWSVM-master/AccuracyPSVM/psvm.m
5,947
utf_8
484a5de502317215d57c1fc439c57533
function [w,gamma, trainCorr, testCorr, cpu_time, nu]=psvm(A,d,k,nu,output,bal); % version 1.1 % last revision: 01/24/03 %=============================================================================== % Usage: [w,gamma,trainCorr, testCorr,cpu_time,nu]=psvm(A,d,k,nu,output,bal) % % A and d are both required, everything else has a default % An example: [w gamma train test time nu] = psvm(A,d,10); % % Input: % A is a matrix containing m data in n dimensions each. % d is a m dimensional vector of 1's or -1's containing % the corresponding labels for each example in A. % k is k-fold for correctness purpose % nu - the weighting factor. % -1 - easy estimation % 0 - hard estimation % any other value - used as nu by the algorithm % default - 0 % output - indicates whether you want output % % If the input parameter bal is 1 % the algorithm weighs the classes depending on the % number of points in each class and balance them. % It is useful when the number of point in each class % is very unbalanced. % % Output: % w,gamma are the values defining the separating % Hyperplane w'x-gamma=0 such that: % % w'x-gamma>0 => x belongs to A+ % w'x-gamma<0 => x belongs to A- % w'x-gamma=0 => x can belongs to both classes % nu - the estimated or specified value of nu % % For details refer to the paper: % "Proximal Support Vector Machine Classifiers" % available at: www.cs.wisc.edu/~gfung % For questions or suggestions, please email: % Glenn Fung, [email protected] % Sept 2001. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tic; [m,n]=size(A); r=randperm(size(d,1));d=d(r,:);A=A(r,:); % random permutation %move one point in A a little if perfectly balanced AA=A;dd=d; ma=A(find(d==1),:); mb=A(find(d==-1),:); [s1 s2]=size(ma); c1=sum(ma)/s1; [s1 s2]=size(mb); c2=sum(mb)/s1; if (c1==c2) nu=1; A(3,:)=A(3,:)+0.01*norm(A(3,:)-c1,inf)*ones(1,n); end % default values for input parameters if nargin<6 bal=0; end if nargin<5 output=0; end if (nargin<4) nu=0; % default is hard estimation end if (nargin<3) k=0; end [H,v]=HV(A,d,bal); % calculate H and v trainCorr = 0; testCorr = 0; if (nu==0) nu = EstNuLong(H,d,m); elseif nu==-1 % easy estimation nu = EstNuShort(H,d); end % if k=0 no correctness is calculated, just run the algorithm if k==0 [w, gamma] = core(H,v,nu,n); cpu_time = toc; return end %if k==1 only training set correctness is calculated if k==1 [w, gamma] = core(H,v,nu,n); trainCorr = correctness(AA,dd,w,gamma); cpu_time = toc; if output == 1 fprintf(1,'\nTraining set correctness: %3.2f%% \n',trainCorr); fprintf(1,'\nElapse time: %10.2f\n',toc); end return end [sm sn]=size(A); accuIter = 0; lastToc=0; % used for calculating time indx = [0:k]; indx = floor(sm*indx/k); %last row numbers for all 'segments' % split trainining set from test set tic; for i = 1:k Ctest = []; dtest = [];Ctrain = []; dtrain = []; Ctest = A((indx(i)+1:indx(i+1)),:); dtest = d(indx(i)+1:indx(i+1)); Ctrain = A(1:indx(i),:); Ctrain = [Ctrain;A(indx(i+1)+1:sm,:)]; dtrain = [d(1:indx(i));d(indx(i+1)+1:sm,:)]; [H, v] = HV(Ctrain,dtrain,bal); [w, gamma] = core(H,v,nu,n); tmpTrainCorr = correctness(Ctrain,dtrain,w,gamma); trainCorr = trainCorr + tmpTrainCorr; tmpTestCorr = correctness(Ctest,dtest,w,gamma); testCorr = testCorr + tmpTestCorr; if output==1 fprintf(1,'________________________________________________\n'); fprintf(1,'Fold %d\n',i); fprintf(1,'Training set correctness: %3.2f%%\n',tmpTrainCorr); fprintf(1,'Testing set correctness: %3.2f%%\n',tmpTestCorr); fprintf(1,'Elapse time: %10.2f\n',toc); end end % trainCorr = trainCorr/k; testCorr = testCorr/k; cpu_time = toc/k; if output == 1 fprintf(1,'___________________________________________________\n'); fprintf(1,'\nTraining set correctness: %3.2f%% \n',trainCorr); fprintf(1,'\nTesting set correctness: %3.2f%% \n',testCorr); fprintf(1,'\nAverage CPU time is: %3.2f \n',cpu_time); end %y=spdiags(d,0,m,m)*((A*w-gamma)-ones(m,1)); return %%%%%%%%%%%%%%%% core function to calcuate w and gamma %%%%%%%% function [w, gamma]=core(H,v,nu,n) v=(speye(n+1)/nu+H'*H)\v; w=v(1:n);gamma=v(n+1); return %%%%%%%%%%%%%%% correctness calculation %%%%%%%%%%%%%%%%%%%% function corr = correctness(AA,dd,w,gamma) p=sign(AA*w-gamma); corr=length(find(p==dd))/size(AA,1)*100; return %%%%%%%%%%%%% EstNuLong %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % use to estimate nu function lamda=EstNuLong(H,d,m) if m<201 H2=H;d2=d; else r=rand(m,1); [s1,s2]=sort(r); H2=H(s2(1:200),:); d2=d(s2(1:200)); end lamda=1; [vu,u]=eig(H2*H2');u=diag(u);p=length(u); yt=d2'*vu; lamdaO=lamda+1; cnt=0; while (abs(lamdaO-lamda)>10e-4)&(cnt<100) cnt=cnt+1; nu1=0;pr=0;ee=0;waw=0; lamdaO=lamda; for i=1:p nu1= nu1 + lamda/(u(i)+lamda); pr= pr + u(i)/(u(i)+lamda)^2; ee= ee + u(i)*yt(i)^2/(u(i)+lamda)^3; waw= waw + lamda^2*yt(i)^2/(u(i)+lamda)^2; end lamda=nu1*ee/(pr*waw); end value=lamda; if cnt==100 value=1; end %%%%%%%%%%%%%%%%%EstNuShort%%%%%%%%%%%%%%%%%%%%%%% % easy way to estimate nu if not specified by the user function value = EstNuShort(C,d) value = 1/(sum(sum(C.^2))/size(C,2)); return %%% function to calculate H and v %%%%%%%%%%%%% function [H,v]=HV(A,d,bal); [m,n]=size(A);e=ones(m,1); if (bal==0) H=[A -e]; v=(d'*H)'; else H=[A -e]; mm=e; m1=find(d==-1); mm(m1)=(1/length(m1)); m2=find(d==1); mm(m2)=(1/length(m2)); mm=sqrt(mm); N=spdiags(mm,0,m,m); H=N*H; %keyboard v=(d'*N*H)'; end
github
ma-xu/2PTWSVM-master
psvm2.m
.m
2PTWSVM-master/AccuracyPSVM/psvm2.m
6,063
utf_8
a401eae3c716fd9565e4fb0c51d9b9d6
function [w,gamma, trainCorr, testCorr, cpu_time, nu,testcorrstd]=psvm2(A,d,k,nu,output,bal); % version 1.1 % last revision: 01/24/03 %=============================================================================== % Usage: [w,gamma,trainCorr, testCorr,cpu_time,nu]=psvm(A,d,k,nu,output,bal) % % A and d are both required, everything else has a default % An example: [w gamma train test time nu] = psvm(A,d,10); % % Input: % A is a matrix containing m data in n dimensions each. % d is a m dimensional vector of 1's or -1's containing % the corresponding labels for each example in A. % k is k-fold for correctness purpose % nu - the weighting factor. % -1 - easy estimation % 0 - hard estimation % any other value - used as nu by the algorithm % default - 0 % output - indicates whether you want output % % If the input parameter bal is 1 % the algorithm weighs the classes depending on the % number of points in each class and balance them. % It is useful when the number of point in each class % is very unbalanced. % % Output: % w,gamma are the values defining the separating % Hyperplane w'x-gamma=0 such that: % % w'x-gamma>0 => x belongs to A+ % w'x-gamma<0 => x belongs to A- % w'x-gamma=0 => x can belongs to both classes % nu - the estimated or specified value of nu % % For details refer to the paper: % "Proximal Support Vector Machine Classifiers" % available at: www.cs.wisc.edu/~gfung % For questions or suggestions, please email: % Glenn Fung, [email protected] % Sept 2001. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tic; [m,n]=size(A); r=randperm(size(d,1));d=d(r,:);A=A(r,:); % random permutation %move one point in A a little if perfectly balanced AA=A;dd=d; ma=A(find(d==1),:); mb=A(find(d==-1),:); [s1 s2]=size(ma); c1=sum(ma)/s1; [s1 s2]=size(mb); c2=sum(mb)/s1; if (c1==c2) nu=1; A(3,:)=A(3,:)+0.01*norm(A(3,:)-c1,inf)*ones(1,n); end % default values for input parameters if nargin<6 bal=0; end if nargin<5 output=0; end if (nargin<4) nu=0; % default is hard estimation end if (nargin<3) k=0; end [H,v]=HV(A,d,bal); % calculate H and v trainCorr = 0; testCorr = 0; if (nu==0) nu = EstNuLong(H,d,m); elseif nu==-1 % easy estimation nu = EstNuShort(H,d); end % if k=0 no correctness is calculated, just run the algorithm if k==0 [w, gamma] = core(H,v,nu,n); cpu_time = toc; return end %if k==1 only training set correctness is calculated if k==1 [w, gamma] = core(H,v,nu,n); trainCorr = correctness(AA,dd,w,gamma); cpu_time = toc; if output == 1 fprintf(1,'\nTraining set correctness: %3.2f%% \n',trainCorr); fprintf(1,'\nElapse time: %10.2f\n',toc); end return end [sm sn]=size(A); accuIter = 0; lastToc=0; % used for calculating time indx = [0:k]; indx = floor(sm*indx/k); %last row numbers for all 'segments' % split trainining set from test set testCorrList=[]; tic; for i = 1:k Ctest = []; dtest = [];Ctrain = []; dtrain = []; Ctest = A((indx(i)+1:indx(i+1)),:); dtest = d(indx(i)+1:indx(i+1)); Ctrain = A(1:indx(i),:); Ctrain = [Ctrain;A(indx(i+1)+1:sm,:)]; dtrain = [d(1:indx(i));d(indx(i+1)+1:sm,:)]; [H, v] = HV(Ctrain,dtrain,bal); [w, gamma] = core(H,v,nu,n); tmpTrainCorr = correctness(Ctrain,dtrain,w,gamma); trainCorr = trainCorr + tmpTrainCorr; tmpTestCorr = correctness(Ctest,dtest,w,gamma); testCorrList=[testCorrList;tmpTestCorr]; testCorr = testCorr + tmpTestCorr; if output==1 fprintf(1,'________________________________________________\n'); fprintf(1,'Fold %d\n',i); fprintf(1,'Training set correctness: %3.2f%%\n',tmpTrainCorr); fprintf(1,'Testing set correctness: %3.2f%%\n',tmpTestCorr); fprintf(1,'Elapse time: %10.2f\n',toc); end end % trainCorr = trainCorr/k; testCorr = testCorr/k; cpu_time = toc/k; testcorrstd=std(testCorrList,1); if output == 1 fprintf(1,'___________________________________________________\n'); fprintf(1,'\nTraining set correctness: %3.2f%% \n',trainCorr); fprintf(1,'\nTesting set correctness: %3.2f%% \n',testCorr); fprintf(1,'\nAverage CPU time is: %3.2f \n',cpu_time); end %y=spdiags(d,0,m,m)*((A*w-gamma)-ones(m,1)); return %%%%%%%%%%%%%%%% core function to calcuate w and gamma %%%%%%%% function [w, gamma]=core(H,v,nu,n) v=(speye(n+1)/nu+H'*H)\v; w=v(1:n);gamma=v(n+1); return %%%%%%%%%%%%%%% correctness calculation %%%%%%%%%%%%%%%%%%%% function corr = correctness(AA,dd,w,gamma) p=sign(AA*w-gamma); corr=length(find(p==dd))/size(AA,1)*100; return %%%%%%%%%%%%% EstNuLong %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % use to estimate nu function lamda=EstNuLong(H,d,m) if m<201 H2=H;d2=d; else r=rand(m,1); [s1,s2]=sort(r); H2=H(s2(1:200),:); d2=d(s2(1:200)); end lamda=1; [vu,u]=eig(H2*H2');u=diag(u);p=length(u); yt=d2'*vu; lamdaO=lamda+1; cnt=0; while (abs(lamdaO-lamda)>10e-4)&(cnt<100) cnt=cnt+1; nu1=0;pr=0;ee=0;waw=0; lamdaO=lamda; for i=1:p nu1= nu1 + lamda/(u(i)+lamda); pr= pr + u(i)/(u(i)+lamda)^2; ee= ee + u(i)*yt(i)^2/(u(i)+lamda)^3; waw= waw + lamda^2*yt(i)^2/(u(i)+lamda)^2; end lamda=nu1*ee/(pr*waw); end value=lamda; if cnt==100 value=1; end %%%%%%%%%%%%%%%%%EstNuShort%%%%%%%%%%%%%%%%%%%%%%% % easy way to estimate nu if not specified by the user function value = EstNuShort(C,d) value = 1/(sum(sum(C.^2))/size(C,2)); return %%% function to calculate H and v %%%%%%%%%%%%% function [H,v]=HV(A,d,bal); [m,n]=size(A);e=ones(m,1); if (bal==0) H=[A -e]; v=(d'*H)'; else H=[A -e]; mm=e; m1=find(d==-1); mm(m1)=(1/length(m1)); m2=find(d==1); mm(m2)=(1/length(m2)); mm=sqrt(mm); N=spdiags(mm,0,m,m); H=N*H; %keyboard v=(d'*N*H)'; end
github
ma-xu/2PTWSVM-master
monitormatlab.m
.m
2PTWSVM-master/NEW_TWSVM/monitormatlab.m
30,148
utf_8
702d3e62f38bce4f5547affd49f0de96
function monitormatlab(action) %MONITORMATLAB Displays runtime diagnostic information % This task manager like tool displays real time memory % state of MATLAB, HG, and Java using time based strip charts. % % The following information is displayed: % * Memory allocated by MATLAB % * Memory allocated by Java % * Memory allocted by the O/S % * Number of MFiles in MATLAB memory % * Size of m-file parsing stack % % To see real time MATLAB memory allocation, start MATLAB with % the O/S environment flag "MATLAB_MEM_MGR" set to a "debug" % as in: set MATLAB_MEM_MGR = debug. % % Example: % % monitormatlab % bench if str2num(version('-release'))<14 error('MATLAB version 14sp2 or later required') end if nargin==0 action = 'start'; end if strcmp(action,'start') info = localStart; elseif strcmp(action,'stop') localStop; else error('Invalid input') end %----------------------------------------------------------% function [info] = localStart drawnow; [info] = localShowUI; drawnow; localSetInfo(info); localStartPause; [info] = localStartTimer(info); localSetInfo(info); drawnow; localStopPause; %----------------------------------------------------------% function [info] = localShowUI % Singleton: Only create new UI if current one is stale or empty info = localGetInfo; if isempty(info.figure) || ~ishandle(info.figure); [info] = localRegisterPanels(info); [info] = localCreateUI(info); end %----------------------------------------------------------% function localOpenRecording(obj,evd) localStartPause; info = localGetInfo; doload = true; doreset = false; [filename, pathname] = uigetfile('monitor.mat', 'Open mat file'); if filename ~= 0 s = load(fullfile(pathname,filename),'-mat'); % Loop through and set view for n = 1:length(info.panel) % Update each panel hfunc = info.panel(n).update; info.panel(n) = feval(hfunc,info.panel(n),s.info.panel(n),doload,doreset); end else localStopPause; end localSetInfo(info); %----------------------------------------------------------% function localStartPause h = findall(0,'type','uicontrol','Tag','Pause'); set(h,'Value',1); %----------------------------------------------------------% function localStopPause h = findall(0,'type','uicontrol','Tag','Pause'); set(h,'Value',0); %----------------------------------------------------------% function localSaveRecording(obj,evd) localStartPause [filename, pathname] = uiputfile('monitor.mat', 'Open mat file'); if filename ~= 0 info = localGetInfo; info.dorecord = true; localSetInfo(info); localUpdateRecording(fullfile(pathname,filename)); end info = localGetInfo; info.dorecord = false; localSetInfo(info); localStopPause; %----------------------------------------------------------% function localRecord(obj,evd) info = localGetInfo; p = get(obj,'parent'); ubegin = findall(p,'tag','BeginRecording'); ustop = findall(p,'tag','StopRecording'); if isequal(obj,ustop) info.dorecord = false; set(ubegin,'Enable','on'); set(ustop,'Enable','off'); elseif isequal(obj,ubegin) info.dorecord = true; set(ubegin,'Enable','off'); set(ustop,'Enable','on'); end localSetInfo(info); %----------------------------------------------------------% function [info] = localCreateUI(info) fig = figure('resize','off','handlevis','off','toolbar','none',... 'name','MATLAB Monitoring Tool','NumberTitle','off','units','pixels',... 'pos',[100 100 500 340],'DeleteFcn',@localShutDown); info.figure = fig; % Create figure menus delete(findall(fig,'type','uimenu')); % File Menu u = uimenu('Parent',fig,'Label','File'); uimenu('Parent',u,'Tag','Load','Label','Open...',... 'Callback',@localOpenRecording); uimenu('Parent',u,'Tag','Load','Label','Save',... 'Callback',@localSaveRecording); % Tools menu u = uimenu('Parent',fig,'Label','Tools'); uimenu('Parent',u,'Tag','BeginRecording',... 'Callback',@localRecord,... 'Label','Begin Recording to ''monitor.mat'' File'); uimenu('Callback',@localRecord,... 'Parent',u,'Tag','StopRecording',... 'Label','Stop Recording','Enable','off'); info.frame_center = uipanel('title','','titleposition','centertop',... 'parent',fig,'units','norm','pos',[0 0 1 1 ]); info.frame_left = uibuttongroup('parent',info.frame_center,... 'units','norm',... 'pos',[0 0 .3 1 ],... 'BackgroundColor',[.4 .4 .4],... 'SelectionChangeFcn',@localButtonCallback); uicontrol('style','togglebutton','Parent',info.frame_center,... 'units','norm','pos',[.03 .02 .2 .07],'string','Pause',... 'Tag','Pause'); uicontrol('style','pushbutton','Parent',info.frame_center,... 'units','norm','pos',[.03 .12 .2 .07],'string','Reset',... 'Tag','Reset','Callback',@localReset); % Toggle button properties props.style = 'togglebutton'; props.parent = info.frame_left; props.units = 'norm'; props.position = [.1 1 .66 .07]; % Create each panel and button for n = 1:length(info.panel) panel_name = info.panel(n).name; % Create panel h = uipanel('parent',info.frame_center,... 'units','norm','pos',[.25 0 1 1],'Visible','off'); if (n==1) set(h,'Visible','on'); end info.panel(n).panel = h; % Add a button for each panel props.string = panel_name; props.position(2) = props.position(2) - .1; u(n) = uicontrol(props); % Add panel contents hfunc = info.panel(n).create; info.panel(n).data = feval(hfunc,info.panel(n)); end % Set first button to on by default if length(u)>0 set(u(1),'Value',1); end %----------------------------------------------------------% function [info] = localRegisterPanels(info) ind = 0; ind = ind + 1; info.panel(ind).name = 'General'; info.panel(ind).update = @localUpdatePanelGeneral; info.panel(ind).create = @localCreatePanelGeneral; info.panel(ind).data = []; info.panel(ind).panel = []; ind = ind + 1; info.panel(ind).name = 'MATLAB Memory'; info.panel(ind).update = @localUpdatePanelMalloc; info.panel(ind).create = @localCreatePanelMalloc; info.panel(ind).data = []; info.panel(ind).panel = []; % Don't show this panel on unix if ispc try evalc('feature memstats'); doshow = true; catch doshow = false; end if(doshow) ind = ind + 1; info.panel(ind).name = 'O/S Memory'; info.panel(ind).update = @localUpdatePanelMemory; info.panel(ind).create = @localCreatePanelMemory; info.panel(ind).data = []; info.panel(ind).panel = []; end end % ind = ind + 1; % info.panel(ind).name = 'Handle Graphics'; % info.panel(ind).update = @localUpdatePanelHG; % info.panel(ind).create = @localCreatePanelHG; % info.panel(ind).data = []; % info.panel(ind).panel = []; ind = ind + 1; info.panel(ind).name = 'Java Memory'; info.panel(ind).update = @localUpdatePanelJava; info.panel(ind).create = @localCreatePanelJava; info.panel(ind).data = []; info.panel(ind).panel = []; %----------------------------------------------------------% function localButtonCallback(obj,evd) obj = evd.NewValue; str = get(obj,'String'); localSetCurrentPanel(str); %----------------------------------------------------------% function localSetCurrentPanel(str) info = localGetInfo; % Only make the selected panel visible for n = 1:length(info.panel) panel_name = info.panel(n).name; if strcmp(panel_name,str) set(info.panel(n).panel,'Visible','on'); else set(info.panel(n).panel,'Visible','off'); end end %----------------------------------------------------------% function [info] = localStartTimer(info) t = timer('TimerFcn',@localTimerCallback, 'Period', .5); set(t,'ExecutionMode','fixedRate'); start(t); info.timer = t; %localSetHGCreateFcn(@localHGCreateCallback); %----------------------------------------------------------% function localTimerCallback(obj,evd) localUpdateRecording('monitor.mat'); %----------------------------------------------------------% function localUpdateRecording(filename) info = localGetInfo; doload = false; doreset = info.doreset; h = findall(0,'type','uicontrol','Tag','Pause'); if get(h,'Value')==0 for n = 1:length(info.panel) % Update each panel hfunc = info.panel(n).update; info.panel(n) = feval(hfunc,info.panel(n),[],doload,doreset); end % Save state to file if info.dorecord fname = filename; save(fname,'info','-mat'); end end info = localGetInfo; info.doreset = false; localSetInfo(info); %----------------------------------------------------------% function [retval] = localCreatePanelMalloc(panel_info) ax = localMakeStripChart(panel_info.panel,'top'); pos = get(ax,'Position'); set(ax,'Position',[pos(1),.2,pos(3),.5]); retval.ax_malloc = ax; uicontrol('parent',panel_info.panel,... 'style','pushbutton',... 'string','''clear all''',... 'fontsize',13,... 'units','norm',... 'position',[.23 .05 .3 .09],... 'callback','clear all'); retval.line_malloc = localMakeLine(retval.ax_malloc); if ~system_dependent('CheckMalloc') ax = retval.ax_malloc; axis(ax,'off'); text('Parent',ax,... 'String',... {'To see MATLAB memory allocation information',... 'Create an O/S environment variable using ''set''',... 'set MATLAB_MEM_MGR = debug',... 'then restart MATLAB.',... 'Remove this O/S variable to restore original settings.'},... 'Units','Norm',... 'Interpreter','None',... 'Position',[.5 .5],... 'HorizontalAlignment','Center',... 'Color','k',... 'FontWeight','Bold'); end %----------------------------------------------------------% function [panel_info] = localUpdatePanelMalloc(panel_info, serialize_info, doload, doreset) if system_dependent('CheckMalloc') MB = 1048576; % megabyte hLine = panel_info.data.line_malloc; hAxes = panel_info.data.ax_malloc; if doload ydata = serialize_info.data.data_malloc; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else dispval = system_dependent('CheckMallocMemoryUsage'); dispval = dispval/MB; system_dependent('CheckMallocClear'); localUpdateLineData(hLine,dispval,doreset); panel_info.data.data_malloc = get(hLine,'ydata'); str = ['Memory Allocated: ',num2str(dispval,4), ' (MB)']; title(hAxes,str); end end %----------------------------------------------------------% function [retval] = localCreatePanelMemory(panel_info) % Swap Space str = 'Swap Space Consumed (red = available)'; retval.ax_swap_memory = localMakeStripChart(panel_info.panel,'top',str,'MB'); retval.line_swap_memory = localMakeLine(retval.ax_swap_memory); retval.line_max_swap_memory = localMakeLine(retval.ax_swap_memory,'r'); % Physical Memory str = 'Physical Memory Consumed (red = available)'; retval.ax_physical_memory = localMakeStripChart(panel_info.panel,'middle',str,'MB'); retval.line_physical_memory = localMakeLine(retval.ax_physical_memory); retval.line_max_physical_memory = localMakeLine(retval.ax_physical_memory,'r'); % Block memory str = 'Largest Contiguous Memory Block'; retval.ax_block_memory = localMakeStripChart(panel_info.panel,'bottom',str,'MB'); retval.line_block_memory = localMakeLine(retval.ax_block_memory); %----------------------------------------------------------% function [panel_info] = localUpdatePanelMemory(panel_info,serialize_info,doload,doreset) mem_info = localGetMemoryInfo; % Swap space hLine = panel_info.data.line_swap_memory; if doload ydata = serialize_info.data.data_swap_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.PageFile.InUse; localUpdateLineData(hLine,val,doreset); panel_info.data.data_swap_memory = get(hLine,'ydata'); end hLine = panel_info.data.line_max_swap_memory; hAxes = panel_info.data.ax_swap_memory; if doload ydata = serialize_info.data.data_max_swap_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val2 = mem_info.PageFile.Total; localUpdateLineData(hLine,val2,doreset); panel_info.data.data_max_swap_memory = get(hLine,'ydata'); title(hAxes,{'Page File',['Consumed: ', num2str(val),'(MB) Available: ',num2str(val2),'(MB)']}); end % Physical Memory hLine = panel_info.data.line_physical_memory; if doload ydata = serialize_info.data.data_physical_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.PhysicalMemory.InUse; localUpdateLineData(hLine,val,doreset); panel_info.data.data_physical_memory = get(hLine,'ydata'); end hLine = panel_info.data.line_max_physical_memory; hAxes = panel_info.data.ax_physical_memory; if doload ydata = serialize_info.data.data_max_physical_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val2 = mem_info.PhysicalMemory.Total; localUpdateLineData(hLine,val2,doreset); panel_info.data.data_max_physical_memory = get(hLine,'ydata'); title(hAxes,{'Physical Memory',['Consumed: ', num2str(val),' (MB) Available: ',num2str(val2),' (MB)']}); end % Memory Block hLine = panel_info.data.line_block_memory; hAxes = panel_info.data.ax_block_memory; if doload ydata = serialize_info.data.data_block_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.LargestFreeBlock; localUpdateLineData(hLine,val,doreset); panel_info.data.data_block_memory = get(hLine,'ydata'); title(hAxes,['Largest Contiguous Memory Block: ',num2str(val),' (MB)']); end %----------------------------------------------------------% function [retval] = localCreatePanelJava(panel_info) ax = localMakeStripChart(panel_info.panel,... 'top','Java Memory Consumed (red = available)','MB'); pos = get(ax,'Position'); set(ax,'Position',[pos(1),.2,pos(3),.5]); uicontrol('parent',panel_info.panel,... 'style','pushbutton',... 'string','Garbage Collect',... 'fontsize',13,... 'units','norm',... 'position',[.23 .05 .3 .09],... 'callback','java.lang.System.gc'); retval.ax_java_memory = ax; % Create lines retval.line_java_max_memory = localMakeLine(retval.ax_java_memory,'r'); set(retval.line_java_max_memory,'LineStyle',':'); retval.line_java_total_memory = localMakeLine(retval.ax_java_memory,'r'); retval.line_java_used_memory = localMakeLine(retval.ax_java_memory); %----------------------------------------------------------% function [panel_info] = localUpdatePanelJava(panel_info, serialize_info, doload, doreset) [free_mem,total_mem,max_mem] = localGetJavaMemory; consumed = total_mem-free_mem; % used memory hLine = panel_info.data.line_java_used_memory; if doload ydata = serialize_info.data.data_java_used_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,consumed,doreset); panel_info.data.data_java_used_memory = get(hLine,'ydata'); end % total memory hLine = panel_info.data.line_java_total_memory; hAxes = panel_info.data.ax_java_memory; if doload ydata = serialize_info.data.data_java_total_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,total_mem,doreset); panel_info.data.data_java_total_memory = get(hLine,'ydata'); title(hAxes,{'Java Memory, Garbage collection will fluctuate',... ['Total: ',num2str(total_mem,3),' (MB), Maximum: ',num2str(max_mem,3),' (MB)'],... ['Consumed: ',num2str(consumed,3),' (MB) ']}); end % max memory hLine = panel_info.data.line_java_max_memory; if doload ydata = serialize_info.data.data_java_max_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,max_mem,doreset); panel_info.data.data_java_max_memory = get(hLine,'ydata'); end % %----------------------------------------------------------% % function [retval] = localCreatePanelHG(panel_info) % % retval.ax_hg_objects = localMakeStripChart(panel_info.panel,'top','Number of HG Objects in Memory'); % retval.line_hg_objects = localMakeLine(retval.ax_hg_objects); % % retval.ax_hg_created_objects = localMakeStripChart(panel_info.panel,'middle'); % retval.line_hg_created_objects = localMakeLine(retval.ax_hg_created_objects); % % %----------------------------------------------------------% % function [panel_info] = localUpdatePanelHG(panel_info, serialize_info, doload,doreset) % % info = localGetInfo; % hgmem = info.HGObjectInMemory; % ind = find(ishandle(hgmem)~=true); % hgmem(ind) = []; % info.HGObjectInMemory = hgmem; % % hLine = panel_info.data.line_hg_objects; % hAxes = panel_info.data.ax_hg_objects; % if doload % ydata = serialize_info.data.data_hg_objects; % xdata = 1:length(ydata); % set(hLine,'xdata',xdata,'ydata',ydata); % else % val = length(findall(0)); % localUpdateLineData(hLine,val,doreset); % panel_info.data.data_hg_objects = get(hLine,'YData'); % title(hAxes,['Number of HG Objects in Memory: ',num2str(val)]); % end % % hLine = panel_info.data.line_hg_created_objects; % hAxes = panel_info.data.ax_hg_created_objects; % if doload % ydata = serialize_info.data.data_hg_created_objects; % xdata = 1:length(ydata); % set(hLine,'xdata',xdata,'ydata',ydata); % else % val = info.HGObjectCreatedCount; % localUpdateLineData(hLine,val,doreset); % panel_info.data.data_hg_created_objects = get(hLine,'YData'); % title(hAxes,... % { ['HG Objects Created per Unit Time: ', num2str(val)],... % 'Performance sensitive plotting code should',... % 'resuse and not create objects during animations.'}); % end % info.HGObjectCreatedCount = 0; % localSetInfo(info); %----------------------------------------------------------% function [retval] = localCreatePanelGeneral(panel_info) retval.ax_mfiles = localMakeStripChart(panel_info.panel,'top'); retval.line_mfiles = localMakeLine(retval.ax_mfiles); retval.ax_dbstack = localMakeStripChart(panel_info.panel,'middle'); retval.line_dbstack = localMakeLine(retval.ax_dbstack); %----------------------------------------------------------% function [panel_info] = localUpdatePanelGeneral(panel_info, serialize_info, doload, doreset) hLine = panel_info.data.line_mfiles; hAxes = panel_info.data.ax_mfiles; if doload ydata = serialize_info.data.data_mfiles; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else [val] = inmem; val = length(val); localUpdateLineData(hLine,val,doreset); panel_info.data.data_mfiles = get(hLine,'ydata'); title(hAxes,['Number of M-Files in Memory (''inmem''): ',num2str(val)]); end hLine = panel_info.data.line_dbstack; hAxes = panel_info.data.ax_dbstack; if doload ydata = serialize_info.data.data_dbstack; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = length(dbstack); localUpdateLineData(hLine,val,doreset); panel_info.data.data_dbstack = get(hLine,'ydata'); title(hAxes,['M-File Call-Stack Size (''dbstack''): ',num2str(val),' ']); end %----------------------------------------------------------% function [retval] = localCreatePanelObjects(panel_info) retval.ax_objects = localMakeStripChart(panel_info.panel,'top','Number of Oops Objects in Memory'); retval.line_objects = localMakeLine(retval.ax_objects); %----------------------------------------------------------% function [panel_info] = localUpdatePanelObjects(panel_info, serialize_info, doload,doreset) hLine = panel_info.data.line_objects; hAxes = panel_info.data.ax_objects; if doload ydata = serialize_info.data.data_objects; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else [val] = localGetOopsCount; localUpdateLineData(hLine,val,doreset); panel_info.data.data_objects = get(hLine,'ydata'); title(hAxes,['Number of Oops Objects in Memory: ',num2str(val)]); end %----------------------------------------------------------% function [retval] = localCreatePanelAWT(panel_info) retval.ax_java_awt = localMakeStripChart(panel_info.panel,'top','Number of Events in the AWT Queue'); retval.line_java_awt_events = localMakeLine(retval.ax_java_awt); retval.ax_java_paint = localMakeStripChart(panel_info.panel,'middle','Number of Paint Events in the AWT Queue'); retval.line_java_paint_events = localMakeLine(retval.ax_java_paint); %----------------------------------------------------------% function [panel_info] = localUpdatePanelAWT(panel_info, serialize_info, doload, doreset) h = MonitorEventQueue.getInstance; val = h.getTotalEventCount; hLine = panel_info.data.line_java_awt_events; if doload ydata = serialize_info.data.data_java_awt_events; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,val,doreset); panel_info.data.data.data_java_awt_events = get(hLine,'ydata'); end val = h.getPaintEventCount; hLine = panel_info.data.line_java_paint_events; if doload ydata = serialize_info.data.data_java_paint_events; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,val,doreset); panel_info.data.data.data_java_paint_events = get(hLine,'ydata'); end %----------------------------------------------------------% function [val] = localGetCustom % val: numeric scalar % Change this to output scalar value of interest val = rand(1,1); % %----------------------------------------------------------% % function localHGCreateCallback(obj,evd) % % Callback fires when HG object is created % % info = localGetInfo; % count = info.HGObjectCreatedCount; % info.HGObjectCreatedCount = count + 1; % % list = info.HGObjectInMemory; % info.HGObjectInMemory = [list,obj]; % localSetInfo(info); %----------------------------------------------------- function [ax] = localMakeStripChart(frame,loc,titl,ylab) pos = [.14 .3 .5 .15]; gray = [.4 .4 .4]; switch(loc) case 'bottom' pos(2) = .09; case 'middle' pos(2) = .35; case 'top' pos(2) = .66; end ax = axes('Parent',frame,'units','norm',... 'position',pos,'XLim',[1 200],... 'YScale','linear','XTickLabel','','XMinorGrid','off','XGrid','on',... 'Color','w','XColor',[.4 .4 .4],'YColor',[.4 .4 .4],'box','on'); if nargin>2 title(ax,titl,'FontSize',8); end set(ax,'fontsize',8); if nargin>3 ylabel(ax,ylab); end %----------------------------------------------------------% function [hLine] = localMakeLine(ax,color) if nargin==1 color= 'b'; end hLine = line('xdata',nan,'ydata',nan,'Parent',ax,'Color',color,'LineWidth',1.5); %----------------------------------------------------------% function localReset(obj,evd) % reset line data info = localGetInfo; info.doreset = true; localSetInfo(info); %----------------------------------------------------------% function localUpdateLineData(hLine,newval,doreset) if ~ishandle(hLine) return; end if doreset set(hLine,'xdata',nan,'ydata',nan); return; end MAX = 200; ydata = get(hLine,'ydata'); % Remove startup noise if length(ydata)<2 newval = nan; end ydata = [ydata,newval]; % Clip out front of data len = length(ydata); if len>MAX ydata = ydata((len-MAX+1):MAX+1); end xdata = (MAX-length(ydata)+1):1:MAX; set(hLine,'xdata',xdata,'ydata',ydata,'Visible','on'); %----------------------------------------------------------% function localShutDown(obj,evd) info = localGetInfo; try, localStop(info); end localSetInfo([]); %----------------------------------------------------------% function localStop(info); try, t = info.timer; stop(t); delete(t); end %----------------------------------------------------------% function val = localGetOopsCount % Get number of oops objects in memory val = 0; s = objectdirectory; if ~isempty(s) c = struct2cell(s); count = 0; for n = 1:length(c) count = c{n} + count; end val = count; end %----------------------------------------------------------% function val = localGetBaseWorkspaceSize val = 0; ret = evalin('base','whos'); for n = 1:length(ret) val = ret(n).bytes + val; end val = val/1e6; % %----------------------------------------------------------% % function localSetHGCreateFcn(val) % Commenting out the code below. This is causing problems % if a user saves a fig file while the memory tool is up. % set(0,'DefaultFigureCreateFcn', val); % set(0,'DefaultAxesCreateFcn', val); % set(0,'DefaultUIPanelCreateFcn', val); % set(0,'DefaultPatchCreateFcn', val); % set(0,'DefaultRectangleCreateFcn', val); % set(0,'DefaultSurfaceCreateFcn', val); % set(0,'DefaultImageCreateFcn', val); % set(0,'DefaultTextCreateFcn', val); % set(0,'DefaultLineCreateFcn', val); % set(0,'DefaultUIControlCreateFcn', val); % set(0,'DefaultUIMenuCreateFcn', val); %----------------------------------------------------------% function localUpdateAxesLimits(hAxes,hLine) min_data = min(get(hLine,'ydata')); max_data = max(get(hLine,'ydata')); if max_data<10 newlim = [min_data 10]; else delta = abs(max_data); newlim = [min_data - .1*delta, max_data + .1*delta]; end set(hAxes,'YLim',newlim); %----------------------------------------------------------% function [names] = localGetClassLoadingNames names = []; try h = com.mathworks.jmi.ClassLoaderManager.getClassLoaderManager; cloader = h.getCustomClassLoader; if ~isempty(cloader) names = char(cloader.debugGetClassNames); end end %----------------------------------------------------------% function [val] = localGetClassLoadingSize val = 0; try h = com.mathworks.jmi.ClassLoaderManager.getClassLoaderManager; cloader = h.getCustomClassLoader; if ~isempty(cloader) val = cloader.debugGetCacheSize; end end % hack to keep line visible on log plot if (val<1) val = 1; end %----------------------------------------------------------% function [free_memory,total_memory, max_memory] = localGetJavaMemory MB = 1048576; % megabyte free_memory = java.lang.Runtime.getRuntime.freeMemory/MB; total_memory = java.lang.Runtime.getRuntime.totalMemory/MB; max_memory = java.lang.Runtime.getRuntime.maxMemory/MB; %----------------------------------------------------- function retval = localGetIconPath retval = []; persistent iconpath; if isempty(iconpath) [iconpath,filename,ext] = fileparts(which(mfilename)); iconpath = fullfile(iconpath,'private'); end retval = iconpath; %----------------------------------------------------- function info = localGetMemoryInfo % Big hack here, parse output of memstats since there % are no output variables. str = evalc('feature memstats'); ind = findstr(str,'MB'); LEN = 20; % Parse output % Physical Memory (RAM): % In Use: 483 MB (1e305000) % Free: 539 MB (21ba3000) % Total: 1022 MB (3fea8000) retval = str((ind(1)-2):-1:ind(1)-LEN); info.PhysicalMemory.InUse = str2num(fliplr(retval)); %retval = str((ind(2)-2):-1:ind(2)-LEN); %info.PhysicalMemory.Free = str2num(fliplr(retval)); retval = str((ind(3)-2):-1:ind(3)-LEN); info.PhysicalMemory.Total = str2num(fliplr(retval)); % Page File (Swap space): % In Use: 571 MB (23b57000) % Free: 1890 MB (76220000) % Total: 2461 MB (99d77000) retval = str((ind(4)-2):-1:ind(4)-LEN); info.PageFile.InUse = str2num(fliplr(retval)); retval = str((ind(5)-2):-1:ind(5)-LEN); info.PageFile.Free = str2num(fliplr(retval)); retval = str((ind(6)-2):-1:ind(6)-LEN); info.PageFile.Total = str2num(fliplr(retval)); % Virtual Memory (Address Space): % In Use: 536 MB (21851000) % Free: 1511 MB (5e78f000) % Total: 2047 MB (7ffe0000) %retval = str((ind(7)-2):-1:ind(7)-LEN); %info.VirtualMemory.InUse = str2num(fliplr(retval)); %retval = str((ind(8)-2):-1:ind(8)-LEN); %info.VirtualMemory.Free = str2num(fliplr(retval)); %retval = str((ind(9)-2):-1:ind(9)-LEN); %info.VirtualMemory.Total = str2num(fliplr(retval)); % Largest Contiguous Free Blocks: % 1. [at 25d20000] 859 MB (35b40000) retval = str((ind(10)-2):-1:ind(10)-LEN); info.LargestFreeBlock = str2num(fliplr(retval)); %----------------------------------------------------------% function info = localGetInfo info = getappdata(0,'MonitorMATLAB'); if isempty(info) info = struct; info.figure = []; %info.HGObjectCreatedCount = []; %info.HGObjectInMemory = []; info.dorecord = false; info.doreset = false; end %----------------------------------------------------------% function localSetInfo(info) setappdata(0,'MonitorMATLAB',info);
github
ma-xu/2PTWSVM-master
monitormatlab.m
.m
2PTWSVM-master/NEW_GEPSVM/monitormatlab.m
30,148
utf_8
702d3e62f38bce4f5547affd49f0de96
function monitormatlab(action) %MONITORMATLAB Displays runtime diagnostic information % This task manager like tool displays real time memory % state of MATLAB, HG, and Java using time based strip charts. % % The following information is displayed: % * Memory allocated by MATLAB % * Memory allocated by Java % * Memory allocted by the O/S % * Number of MFiles in MATLAB memory % * Size of m-file parsing stack % % To see real time MATLAB memory allocation, start MATLAB with % the O/S environment flag "MATLAB_MEM_MGR" set to a "debug" % as in: set MATLAB_MEM_MGR = debug. % % Example: % % monitormatlab % bench if str2num(version('-release'))<14 error('MATLAB version 14sp2 or later required') end if nargin==0 action = 'start'; end if strcmp(action,'start') info = localStart; elseif strcmp(action,'stop') localStop; else error('Invalid input') end %----------------------------------------------------------% function [info] = localStart drawnow; [info] = localShowUI; drawnow; localSetInfo(info); localStartPause; [info] = localStartTimer(info); localSetInfo(info); drawnow; localStopPause; %----------------------------------------------------------% function [info] = localShowUI % Singleton: Only create new UI if current one is stale or empty info = localGetInfo; if isempty(info.figure) || ~ishandle(info.figure); [info] = localRegisterPanels(info); [info] = localCreateUI(info); end %----------------------------------------------------------% function localOpenRecording(obj,evd) localStartPause; info = localGetInfo; doload = true; doreset = false; [filename, pathname] = uigetfile('monitor.mat', 'Open mat file'); if filename ~= 0 s = load(fullfile(pathname,filename),'-mat'); % Loop through and set view for n = 1:length(info.panel) % Update each panel hfunc = info.panel(n).update; info.panel(n) = feval(hfunc,info.panel(n),s.info.panel(n),doload,doreset); end else localStopPause; end localSetInfo(info); %----------------------------------------------------------% function localStartPause h = findall(0,'type','uicontrol','Tag','Pause'); set(h,'Value',1); %----------------------------------------------------------% function localStopPause h = findall(0,'type','uicontrol','Tag','Pause'); set(h,'Value',0); %----------------------------------------------------------% function localSaveRecording(obj,evd) localStartPause [filename, pathname] = uiputfile('monitor.mat', 'Open mat file'); if filename ~= 0 info = localGetInfo; info.dorecord = true; localSetInfo(info); localUpdateRecording(fullfile(pathname,filename)); end info = localGetInfo; info.dorecord = false; localSetInfo(info); localStopPause; %----------------------------------------------------------% function localRecord(obj,evd) info = localGetInfo; p = get(obj,'parent'); ubegin = findall(p,'tag','BeginRecording'); ustop = findall(p,'tag','StopRecording'); if isequal(obj,ustop) info.dorecord = false; set(ubegin,'Enable','on'); set(ustop,'Enable','off'); elseif isequal(obj,ubegin) info.dorecord = true; set(ubegin,'Enable','off'); set(ustop,'Enable','on'); end localSetInfo(info); %----------------------------------------------------------% function [info] = localCreateUI(info) fig = figure('resize','off','handlevis','off','toolbar','none',... 'name','MATLAB Monitoring Tool','NumberTitle','off','units','pixels',... 'pos',[100 100 500 340],'DeleteFcn',@localShutDown); info.figure = fig; % Create figure menus delete(findall(fig,'type','uimenu')); % File Menu u = uimenu('Parent',fig,'Label','File'); uimenu('Parent',u,'Tag','Load','Label','Open...',... 'Callback',@localOpenRecording); uimenu('Parent',u,'Tag','Load','Label','Save',... 'Callback',@localSaveRecording); % Tools menu u = uimenu('Parent',fig,'Label','Tools'); uimenu('Parent',u,'Tag','BeginRecording',... 'Callback',@localRecord,... 'Label','Begin Recording to ''monitor.mat'' File'); uimenu('Callback',@localRecord,... 'Parent',u,'Tag','StopRecording',... 'Label','Stop Recording','Enable','off'); info.frame_center = uipanel('title','','titleposition','centertop',... 'parent',fig,'units','norm','pos',[0 0 1 1 ]); info.frame_left = uibuttongroup('parent',info.frame_center,... 'units','norm',... 'pos',[0 0 .3 1 ],... 'BackgroundColor',[.4 .4 .4],... 'SelectionChangeFcn',@localButtonCallback); uicontrol('style','togglebutton','Parent',info.frame_center,... 'units','norm','pos',[.03 .02 .2 .07],'string','Pause',... 'Tag','Pause'); uicontrol('style','pushbutton','Parent',info.frame_center,... 'units','norm','pos',[.03 .12 .2 .07],'string','Reset',... 'Tag','Reset','Callback',@localReset); % Toggle button properties props.style = 'togglebutton'; props.parent = info.frame_left; props.units = 'norm'; props.position = [.1 1 .66 .07]; % Create each panel and button for n = 1:length(info.panel) panel_name = info.panel(n).name; % Create panel h = uipanel('parent',info.frame_center,... 'units','norm','pos',[.25 0 1 1],'Visible','off'); if (n==1) set(h,'Visible','on'); end info.panel(n).panel = h; % Add a button for each panel props.string = panel_name; props.position(2) = props.position(2) - .1; u(n) = uicontrol(props); % Add panel contents hfunc = info.panel(n).create; info.panel(n).data = feval(hfunc,info.panel(n)); end % Set first button to on by default if length(u)>0 set(u(1),'Value',1); end %----------------------------------------------------------% function [info] = localRegisterPanels(info) ind = 0; ind = ind + 1; info.panel(ind).name = 'General'; info.panel(ind).update = @localUpdatePanelGeneral; info.panel(ind).create = @localCreatePanelGeneral; info.panel(ind).data = []; info.panel(ind).panel = []; ind = ind + 1; info.panel(ind).name = 'MATLAB Memory'; info.panel(ind).update = @localUpdatePanelMalloc; info.panel(ind).create = @localCreatePanelMalloc; info.panel(ind).data = []; info.panel(ind).panel = []; % Don't show this panel on unix if ispc try evalc('feature memstats'); doshow = true; catch doshow = false; end if(doshow) ind = ind + 1; info.panel(ind).name = 'O/S Memory'; info.panel(ind).update = @localUpdatePanelMemory; info.panel(ind).create = @localCreatePanelMemory; info.panel(ind).data = []; info.panel(ind).panel = []; end end % ind = ind + 1; % info.panel(ind).name = 'Handle Graphics'; % info.panel(ind).update = @localUpdatePanelHG; % info.panel(ind).create = @localCreatePanelHG; % info.panel(ind).data = []; % info.panel(ind).panel = []; ind = ind + 1; info.panel(ind).name = 'Java Memory'; info.panel(ind).update = @localUpdatePanelJava; info.panel(ind).create = @localCreatePanelJava; info.panel(ind).data = []; info.panel(ind).panel = []; %----------------------------------------------------------% function localButtonCallback(obj,evd) obj = evd.NewValue; str = get(obj,'String'); localSetCurrentPanel(str); %----------------------------------------------------------% function localSetCurrentPanel(str) info = localGetInfo; % Only make the selected panel visible for n = 1:length(info.panel) panel_name = info.panel(n).name; if strcmp(panel_name,str) set(info.panel(n).panel,'Visible','on'); else set(info.panel(n).panel,'Visible','off'); end end %----------------------------------------------------------% function [info] = localStartTimer(info) t = timer('TimerFcn',@localTimerCallback, 'Period', .5); set(t,'ExecutionMode','fixedRate'); start(t); info.timer = t; %localSetHGCreateFcn(@localHGCreateCallback); %----------------------------------------------------------% function localTimerCallback(obj,evd) localUpdateRecording('monitor.mat'); %----------------------------------------------------------% function localUpdateRecording(filename) info = localGetInfo; doload = false; doreset = info.doreset; h = findall(0,'type','uicontrol','Tag','Pause'); if get(h,'Value')==0 for n = 1:length(info.panel) % Update each panel hfunc = info.panel(n).update; info.panel(n) = feval(hfunc,info.panel(n),[],doload,doreset); end % Save state to file if info.dorecord fname = filename; save(fname,'info','-mat'); end end info = localGetInfo; info.doreset = false; localSetInfo(info); %----------------------------------------------------------% function [retval] = localCreatePanelMalloc(panel_info) ax = localMakeStripChart(panel_info.panel,'top'); pos = get(ax,'Position'); set(ax,'Position',[pos(1),.2,pos(3),.5]); retval.ax_malloc = ax; uicontrol('parent',panel_info.panel,... 'style','pushbutton',... 'string','''clear all''',... 'fontsize',13,... 'units','norm',... 'position',[.23 .05 .3 .09],... 'callback','clear all'); retval.line_malloc = localMakeLine(retval.ax_malloc); if ~system_dependent('CheckMalloc') ax = retval.ax_malloc; axis(ax,'off'); text('Parent',ax,... 'String',... {'To see MATLAB memory allocation information',... 'Create an O/S environment variable using ''set''',... 'set MATLAB_MEM_MGR = debug',... 'then restart MATLAB.',... 'Remove this O/S variable to restore original settings.'},... 'Units','Norm',... 'Interpreter','None',... 'Position',[.5 .5],... 'HorizontalAlignment','Center',... 'Color','k',... 'FontWeight','Bold'); end %----------------------------------------------------------% function [panel_info] = localUpdatePanelMalloc(panel_info, serialize_info, doload, doreset) if system_dependent('CheckMalloc') MB = 1048576; % megabyte hLine = panel_info.data.line_malloc; hAxes = panel_info.data.ax_malloc; if doload ydata = serialize_info.data.data_malloc; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else dispval = system_dependent('CheckMallocMemoryUsage'); dispval = dispval/MB; system_dependent('CheckMallocClear'); localUpdateLineData(hLine,dispval,doreset); panel_info.data.data_malloc = get(hLine,'ydata'); str = ['Memory Allocated: ',num2str(dispval,4), ' (MB)']; title(hAxes,str); end end %----------------------------------------------------------% function [retval] = localCreatePanelMemory(panel_info) % Swap Space str = 'Swap Space Consumed (red = available)'; retval.ax_swap_memory = localMakeStripChart(panel_info.panel,'top',str,'MB'); retval.line_swap_memory = localMakeLine(retval.ax_swap_memory); retval.line_max_swap_memory = localMakeLine(retval.ax_swap_memory,'r'); % Physical Memory str = 'Physical Memory Consumed (red = available)'; retval.ax_physical_memory = localMakeStripChart(panel_info.panel,'middle',str,'MB'); retval.line_physical_memory = localMakeLine(retval.ax_physical_memory); retval.line_max_physical_memory = localMakeLine(retval.ax_physical_memory,'r'); % Block memory str = 'Largest Contiguous Memory Block'; retval.ax_block_memory = localMakeStripChart(panel_info.panel,'bottom',str,'MB'); retval.line_block_memory = localMakeLine(retval.ax_block_memory); %----------------------------------------------------------% function [panel_info] = localUpdatePanelMemory(panel_info,serialize_info,doload,doreset) mem_info = localGetMemoryInfo; % Swap space hLine = panel_info.data.line_swap_memory; if doload ydata = serialize_info.data.data_swap_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.PageFile.InUse; localUpdateLineData(hLine,val,doreset); panel_info.data.data_swap_memory = get(hLine,'ydata'); end hLine = panel_info.data.line_max_swap_memory; hAxes = panel_info.data.ax_swap_memory; if doload ydata = serialize_info.data.data_max_swap_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val2 = mem_info.PageFile.Total; localUpdateLineData(hLine,val2,doreset); panel_info.data.data_max_swap_memory = get(hLine,'ydata'); title(hAxes,{'Page File',['Consumed: ', num2str(val),'(MB) Available: ',num2str(val2),'(MB)']}); end % Physical Memory hLine = panel_info.data.line_physical_memory; if doload ydata = serialize_info.data.data_physical_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.PhysicalMemory.InUse; localUpdateLineData(hLine,val,doreset); panel_info.data.data_physical_memory = get(hLine,'ydata'); end hLine = panel_info.data.line_max_physical_memory; hAxes = panel_info.data.ax_physical_memory; if doload ydata = serialize_info.data.data_max_physical_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val2 = mem_info.PhysicalMemory.Total; localUpdateLineData(hLine,val2,doreset); panel_info.data.data_max_physical_memory = get(hLine,'ydata'); title(hAxes,{'Physical Memory',['Consumed: ', num2str(val),' (MB) Available: ',num2str(val2),' (MB)']}); end % Memory Block hLine = panel_info.data.line_block_memory; hAxes = panel_info.data.ax_block_memory; if doload ydata = serialize_info.data.data_block_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = mem_info.LargestFreeBlock; localUpdateLineData(hLine,val,doreset); panel_info.data.data_block_memory = get(hLine,'ydata'); title(hAxes,['Largest Contiguous Memory Block: ',num2str(val),' (MB)']); end %----------------------------------------------------------% function [retval] = localCreatePanelJava(panel_info) ax = localMakeStripChart(panel_info.panel,... 'top','Java Memory Consumed (red = available)','MB'); pos = get(ax,'Position'); set(ax,'Position',[pos(1),.2,pos(3),.5]); uicontrol('parent',panel_info.panel,... 'style','pushbutton',... 'string','Garbage Collect',... 'fontsize',13,... 'units','norm',... 'position',[.23 .05 .3 .09],... 'callback','java.lang.System.gc'); retval.ax_java_memory = ax; % Create lines retval.line_java_max_memory = localMakeLine(retval.ax_java_memory,'r'); set(retval.line_java_max_memory,'LineStyle',':'); retval.line_java_total_memory = localMakeLine(retval.ax_java_memory,'r'); retval.line_java_used_memory = localMakeLine(retval.ax_java_memory); %----------------------------------------------------------% function [panel_info] = localUpdatePanelJava(panel_info, serialize_info, doload, doreset) [free_mem,total_mem,max_mem] = localGetJavaMemory; consumed = total_mem-free_mem; % used memory hLine = panel_info.data.line_java_used_memory; if doload ydata = serialize_info.data.data_java_used_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,consumed,doreset); panel_info.data.data_java_used_memory = get(hLine,'ydata'); end % total memory hLine = panel_info.data.line_java_total_memory; hAxes = panel_info.data.ax_java_memory; if doload ydata = serialize_info.data.data_java_total_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,total_mem,doreset); panel_info.data.data_java_total_memory = get(hLine,'ydata'); title(hAxes,{'Java Memory, Garbage collection will fluctuate',... ['Total: ',num2str(total_mem,3),' (MB), Maximum: ',num2str(max_mem,3),' (MB)'],... ['Consumed: ',num2str(consumed,3),' (MB) ']}); end % max memory hLine = panel_info.data.line_java_max_memory; if doload ydata = serialize_info.data.data_java_max_memory; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,max_mem,doreset); panel_info.data.data_java_max_memory = get(hLine,'ydata'); end % %----------------------------------------------------------% % function [retval] = localCreatePanelHG(panel_info) % % retval.ax_hg_objects = localMakeStripChart(panel_info.panel,'top','Number of HG Objects in Memory'); % retval.line_hg_objects = localMakeLine(retval.ax_hg_objects); % % retval.ax_hg_created_objects = localMakeStripChart(panel_info.panel,'middle'); % retval.line_hg_created_objects = localMakeLine(retval.ax_hg_created_objects); % % %----------------------------------------------------------% % function [panel_info] = localUpdatePanelHG(panel_info, serialize_info, doload,doreset) % % info = localGetInfo; % hgmem = info.HGObjectInMemory; % ind = find(ishandle(hgmem)~=true); % hgmem(ind) = []; % info.HGObjectInMemory = hgmem; % % hLine = panel_info.data.line_hg_objects; % hAxes = panel_info.data.ax_hg_objects; % if doload % ydata = serialize_info.data.data_hg_objects; % xdata = 1:length(ydata); % set(hLine,'xdata',xdata,'ydata',ydata); % else % val = length(findall(0)); % localUpdateLineData(hLine,val,doreset); % panel_info.data.data_hg_objects = get(hLine,'YData'); % title(hAxes,['Number of HG Objects in Memory: ',num2str(val)]); % end % % hLine = panel_info.data.line_hg_created_objects; % hAxes = panel_info.data.ax_hg_created_objects; % if doload % ydata = serialize_info.data.data_hg_created_objects; % xdata = 1:length(ydata); % set(hLine,'xdata',xdata,'ydata',ydata); % else % val = info.HGObjectCreatedCount; % localUpdateLineData(hLine,val,doreset); % panel_info.data.data_hg_created_objects = get(hLine,'YData'); % title(hAxes,... % { ['HG Objects Created per Unit Time: ', num2str(val)],... % 'Performance sensitive plotting code should',... % 'resuse and not create objects during animations.'}); % end % info.HGObjectCreatedCount = 0; % localSetInfo(info); %----------------------------------------------------------% function [retval] = localCreatePanelGeneral(panel_info) retval.ax_mfiles = localMakeStripChart(panel_info.panel,'top'); retval.line_mfiles = localMakeLine(retval.ax_mfiles); retval.ax_dbstack = localMakeStripChart(panel_info.panel,'middle'); retval.line_dbstack = localMakeLine(retval.ax_dbstack); %----------------------------------------------------------% function [panel_info] = localUpdatePanelGeneral(panel_info, serialize_info, doload, doreset) hLine = panel_info.data.line_mfiles; hAxes = panel_info.data.ax_mfiles; if doload ydata = serialize_info.data.data_mfiles; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else [val] = inmem; val = length(val); localUpdateLineData(hLine,val,doreset); panel_info.data.data_mfiles = get(hLine,'ydata'); title(hAxes,['Number of M-Files in Memory (''inmem''): ',num2str(val)]); end hLine = panel_info.data.line_dbstack; hAxes = panel_info.data.ax_dbstack; if doload ydata = serialize_info.data.data_dbstack; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else val = length(dbstack); localUpdateLineData(hLine,val,doreset); panel_info.data.data_dbstack = get(hLine,'ydata'); title(hAxes,['M-File Call-Stack Size (''dbstack''): ',num2str(val),' ']); end %----------------------------------------------------------% function [retval] = localCreatePanelObjects(panel_info) retval.ax_objects = localMakeStripChart(panel_info.panel,'top','Number of Oops Objects in Memory'); retval.line_objects = localMakeLine(retval.ax_objects); %----------------------------------------------------------% function [panel_info] = localUpdatePanelObjects(panel_info, serialize_info, doload,doreset) hLine = panel_info.data.line_objects; hAxes = panel_info.data.ax_objects; if doload ydata = serialize_info.data.data_objects; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else [val] = localGetOopsCount; localUpdateLineData(hLine,val,doreset); panel_info.data.data_objects = get(hLine,'ydata'); title(hAxes,['Number of Oops Objects in Memory: ',num2str(val)]); end %----------------------------------------------------------% function [retval] = localCreatePanelAWT(panel_info) retval.ax_java_awt = localMakeStripChart(panel_info.panel,'top','Number of Events in the AWT Queue'); retval.line_java_awt_events = localMakeLine(retval.ax_java_awt); retval.ax_java_paint = localMakeStripChart(panel_info.panel,'middle','Number of Paint Events in the AWT Queue'); retval.line_java_paint_events = localMakeLine(retval.ax_java_paint); %----------------------------------------------------------% function [panel_info] = localUpdatePanelAWT(panel_info, serialize_info, doload, doreset) h = MonitorEventQueue.getInstance; val = h.getTotalEventCount; hLine = panel_info.data.line_java_awt_events; if doload ydata = serialize_info.data.data_java_awt_events; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,val,doreset); panel_info.data.data.data_java_awt_events = get(hLine,'ydata'); end val = h.getPaintEventCount; hLine = panel_info.data.line_java_paint_events; if doload ydata = serialize_info.data.data_java_paint_events; xdata = 1:length(ydata); set(hLine,'xdata',xdata,'ydata',ydata); else localUpdateLineData(hLine,val,doreset); panel_info.data.data.data_java_paint_events = get(hLine,'ydata'); end %----------------------------------------------------------% function [val] = localGetCustom % val: numeric scalar % Change this to output scalar value of interest val = rand(1,1); % %----------------------------------------------------------% % function localHGCreateCallback(obj,evd) % % Callback fires when HG object is created % % info = localGetInfo; % count = info.HGObjectCreatedCount; % info.HGObjectCreatedCount = count + 1; % % list = info.HGObjectInMemory; % info.HGObjectInMemory = [list,obj]; % localSetInfo(info); %----------------------------------------------------- function [ax] = localMakeStripChart(frame,loc,titl,ylab) pos = [.14 .3 .5 .15]; gray = [.4 .4 .4]; switch(loc) case 'bottom' pos(2) = .09; case 'middle' pos(2) = .35; case 'top' pos(2) = .66; end ax = axes('Parent',frame,'units','norm',... 'position',pos,'XLim',[1 200],... 'YScale','linear','XTickLabel','','XMinorGrid','off','XGrid','on',... 'Color','w','XColor',[.4 .4 .4],'YColor',[.4 .4 .4],'box','on'); if nargin>2 title(ax,titl,'FontSize',8); end set(ax,'fontsize',8); if nargin>3 ylabel(ax,ylab); end %----------------------------------------------------------% function [hLine] = localMakeLine(ax,color) if nargin==1 color= 'b'; end hLine = line('xdata',nan,'ydata',nan,'Parent',ax,'Color',color,'LineWidth',1.5); %----------------------------------------------------------% function localReset(obj,evd) % reset line data info = localGetInfo; info.doreset = true; localSetInfo(info); %----------------------------------------------------------% function localUpdateLineData(hLine,newval,doreset) if ~ishandle(hLine) return; end if doreset set(hLine,'xdata',nan,'ydata',nan); return; end MAX = 200; ydata = get(hLine,'ydata'); % Remove startup noise if length(ydata)<2 newval = nan; end ydata = [ydata,newval]; % Clip out front of data len = length(ydata); if len>MAX ydata = ydata((len-MAX+1):MAX+1); end xdata = (MAX-length(ydata)+1):1:MAX; set(hLine,'xdata',xdata,'ydata',ydata,'Visible','on'); %----------------------------------------------------------% function localShutDown(obj,evd) info = localGetInfo; try, localStop(info); end localSetInfo([]); %----------------------------------------------------------% function localStop(info); try, t = info.timer; stop(t); delete(t); end %----------------------------------------------------------% function val = localGetOopsCount % Get number of oops objects in memory val = 0; s = objectdirectory; if ~isempty(s) c = struct2cell(s); count = 0; for n = 1:length(c) count = c{n} + count; end val = count; end %----------------------------------------------------------% function val = localGetBaseWorkspaceSize val = 0; ret = evalin('base','whos'); for n = 1:length(ret) val = ret(n).bytes + val; end val = val/1e6; % %----------------------------------------------------------% % function localSetHGCreateFcn(val) % Commenting out the code below. This is causing problems % if a user saves a fig file while the memory tool is up. % set(0,'DefaultFigureCreateFcn', val); % set(0,'DefaultAxesCreateFcn', val); % set(0,'DefaultUIPanelCreateFcn', val); % set(0,'DefaultPatchCreateFcn', val); % set(0,'DefaultRectangleCreateFcn', val); % set(0,'DefaultSurfaceCreateFcn', val); % set(0,'DefaultImageCreateFcn', val); % set(0,'DefaultTextCreateFcn', val); % set(0,'DefaultLineCreateFcn', val); % set(0,'DefaultUIControlCreateFcn', val); % set(0,'DefaultUIMenuCreateFcn', val); %----------------------------------------------------------% function localUpdateAxesLimits(hAxes,hLine) min_data = min(get(hLine,'ydata')); max_data = max(get(hLine,'ydata')); if max_data<10 newlim = [min_data 10]; else delta = abs(max_data); newlim = [min_data - .1*delta, max_data + .1*delta]; end set(hAxes,'YLim',newlim); %----------------------------------------------------------% function [names] = localGetClassLoadingNames names = []; try h = com.mathworks.jmi.ClassLoaderManager.getClassLoaderManager; cloader = h.getCustomClassLoader; if ~isempty(cloader) names = char(cloader.debugGetClassNames); end end %----------------------------------------------------------% function [val] = localGetClassLoadingSize val = 0; try h = com.mathworks.jmi.ClassLoaderManager.getClassLoaderManager; cloader = h.getCustomClassLoader; if ~isempty(cloader) val = cloader.debugGetCacheSize; end end % hack to keep line visible on log plot if (val<1) val = 1; end %----------------------------------------------------------% function [free_memory,total_memory, max_memory] = localGetJavaMemory MB = 1048576; % megabyte free_memory = java.lang.Runtime.getRuntime.freeMemory/MB; total_memory = java.lang.Runtime.getRuntime.totalMemory/MB; max_memory = java.lang.Runtime.getRuntime.maxMemory/MB; %----------------------------------------------------- function retval = localGetIconPath retval = []; persistent iconpath; if isempty(iconpath) [iconpath,filename,ext] = fileparts(which(mfilename)); iconpath = fullfile(iconpath,'private'); end retval = iconpath; %----------------------------------------------------- function info = localGetMemoryInfo % Big hack here, parse output of memstats since there % are no output variables. str = evalc('feature memstats'); ind = findstr(str,'MB'); LEN = 20; % Parse output % Physical Memory (RAM): % In Use: 483 MB (1e305000) % Free: 539 MB (21ba3000) % Total: 1022 MB (3fea8000) retval = str((ind(1)-2):-1:ind(1)-LEN); info.PhysicalMemory.InUse = str2num(fliplr(retval)); %retval = str((ind(2)-2):-1:ind(2)-LEN); %info.PhysicalMemory.Free = str2num(fliplr(retval)); retval = str((ind(3)-2):-1:ind(3)-LEN); info.PhysicalMemory.Total = str2num(fliplr(retval)); % Page File (Swap space): % In Use: 571 MB (23b57000) % Free: 1890 MB (76220000) % Total: 2461 MB (99d77000) retval = str((ind(4)-2):-1:ind(4)-LEN); info.PageFile.InUse = str2num(fliplr(retval)); retval = str((ind(5)-2):-1:ind(5)-LEN); info.PageFile.Free = str2num(fliplr(retval)); retval = str((ind(6)-2):-1:ind(6)-LEN); info.PageFile.Total = str2num(fliplr(retval)); % Virtual Memory (Address Space): % In Use: 536 MB (21851000) % Free: 1511 MB (5e78f000) % Total: 2047 MB (7ffe0000) %retval = str((ind(7)-2):-1:ind(7)-LEN); %info.VirtualMemory.InUse = str2num(fliplr(retval)); %retval = str((ind(8)-2):-1:ind(8)-LEN); %info.VirtualMemory.Free = str2num(fliplr(retval)); %retval = str((ind(9)-2):-1:ind(9)-LEN); %info.VirtualMemory.Total = str2num(fliplr(retval)); % Largest Contiguous Free Blocks: % 1. [at 25d20000] 859 MB (35b40000) retval = str((ind(10)-2):-1:ind(10)-LEN); info.LargestFreeBlock = str2num(fliplr(retval)); %----------------------------------------------------------% function info = localGetInfo info = getappdata(0,'MonitorMATLAB'); if isempty(info) info = struct; info.figure = []; %info.HGObjectCreatedCount = []; %info.HGObjectInMemory = []; info.dorecord = false; info.doreset = false; end %----------------------------------------------------------% function localSetInfo(info) setappdata(0,'MonitorMATLAB',info);
github
stoman/MachineLearningKernelMethods-master
gradient.m
.m
MachineLearningKernelMethods-master/functions/gradient.m
260
utf_8
a74bbc7c4ac0e82b40fa580599439977
%A gradient computation used for the fminunc function in mnist/learning.m %for the gradient descent. %Author: Stefan Toman ([email protected]) function [L, grad] = gradient(w, x, y) L = 1/2*norm(x*w-y); if nargout > 1 grad = x'*(x*w-y); end; end
github
stoman/MachineLearningKernelMethods-master
euclideankernel.m
.m
MachineLearningKernelMethods-master/functions/euclideankernel.m
700
utf_8
770d7b069d4fe145d4eeb48087d03f02
%This function returns a function handle for a Euclidean kernel. The %argument nrinputs is optional and defaults to 2. If nrinputs is 1 the %function will take one argument, if nrinputs is 2 it will take two %arguments and call the function for one argument with the difference of %the arguments. %Author: Stefan Toman ([email protected]) function K = euclideankernel(nrinputs) %optional argument if nargin == 0 nrinputs = 2; end %create function if nrinputs == 1 K = @(x) sum(x*x',2); elseif nrinputs == 2 K1 = euclideankernel(1); K = @(x,z) K1(bsxfun(@minus, x, z)); else error('parameter nrinputs should be 1 or 2'); end end
github
stoman/MachineLearningKernelMethods-master
gaussiankernel.m
.m
MachineLearningKernelMethods-master/functions/gaussiankernel.m
1,120
utf_8
f155a3486cb007982465902a74eb5c72
%This function returns a function handle for a Gaussian kernel using a %given parameter gamma. The argument nrinputs is optional and defaults to %2. If nrinputs is 1 the function will take one argument, if nrinputs is 2 %it will take two arguments and call the function for one argument with the %difference of the arguments. The argument nonormalization is also optional %and defaults to false. If it is true then the factor (2*pi)^(-size(x,2)/2) %is ignored. % Author: Stefan Toman ([email protected]) function K = gaussiankernel(gamma, nrinputs, nonormalization) %optional arguments if nargin < 2 nrinputs = 2; end if nargin < 3 nonormalization = false; end %create function if nrinputs == 1 && nonormalization K = @(x) exp(-gamma/2.*sum(x.^2,2)); elseif nrinputs == 1 K1 = gaussiankernel(gamma, 1, true); K = @(x) (2*pi)^(-size(x,2)/2)*K1(x); elseif nrinputs == 2 K1 = gaussiankernel(gamma, 1, nonormalization); K = @(x,z) K1(bsxfun(@minus, x, z)); else error('parameter nrinputs should be 1 or 2'); end end
github
stoman/MachineLearningKernelMethods-master
funpredict.m
.m
MachineLearningKernelMethods-master/functions/funpredict.m
790
utf_8
b9833b2b5c971ba6e44bfd00184cab59
%This function returns a handle to a prediction function using kernels %given a training set X and Y, a regularization parameter lambda and a %kernel function K. The resulting function can predict single data as row %vectors or several vectors simultaneously given as a matrix. %Author: Stefan Toman ([email protected]) function predict = funpredict(X, Y, lambda, K) %make assertion for inputs assert(size(X,1) == size(Y,1), 'number of rows of X and Y do not match'); assert(sum(Y.^2 ~= 1) == 0, 'Y contains entries other than +1 and -1'); %solve the regular dual linear regression problem with the given kernel a = (pdist2(X, X, K) + lambda*eye(size(X, 1)))\Y; %results can be predicted by multiplying with the vector a predict = @(Xt) (pdist2(Xt, X, K) * a); end
github
stoman/MachineLearningKernelMethods-master
defaultkernel.m
.m
MachineLearningKernelMethods-master/functions/defaultkernel.m
175
utf_8
d3836fdf1ebdd60c998aa84c69161ec6
%This function returns a function handle for a default kernel (the inner %product). %Author: Stefan Toman ([email protected]) function K = defaultkernel() K = @(x,z) x*z'; end
github
stoman/MachineLearningKernelMethods-master
printresults.m
.m
MachineLearningKernelMethods-master/functions/printresults.m
522
utf_8
cdb1adc5a9b9500164e21a66d04fcbef
%This function evaluates the quality of predicted results on a test set as %used in mnist/learning.m. x and y are the test data, w are the parameters %of the prediction function, name is the name of the method in use and a %and b are the labels of the two classes of objects. %Author: Stefan Toman ([email protected]) function printresults(x, y, w, name, a, b) yy = x*w; s = sum(abs(sign(y-(a+b)/2)-sign(yy-(a+b)/2)))/2; percent = 100*(1-s/size(y,1)); fprintf('quality of method %s: %f%%\n', name, percent); end
github
stoman/MachineLearningKernelMethods-master
predictionquality.m
.m
MachineLearningKernelMethods-master/functions/predictionquality.m
378
utf_8
b8484f32a6393ed5a1910fc72ee870c3
%This function predicts the classes of some objects using a given %prediction function. predict is the prediction function and X and Y are %the test data. The return value is the number of correct estimates. %Author: Stefan Toman ([email protected]) function correct = predictionquality(predict, X, Y) predictions = predict(X); correct = sum(Y - sign(predictions) == 0); end
github
stoman/MachineLearningKernelMethods-master
naivekernel.m
.m
MachineLearningKernelMethods-master/functions/naivekernel.m
828
utf_8
d038124cec473315638d6f6176b98082
%This function returns a function handle for a naive kernel using a %parameter width. The kernel will create a box above each data point of %size 1 with length 2*width. The argument nrinputs is optional and %defaults to 2. If nrinputs is 1 the function will take one argument, if %nrinputs is 2 it will take two arguments and call the function for one %argument with the difference of the arguments. %Author: Stefan Toman ([email protected]) function K = naivekernel(width, nrinputs) %optional argument if nargin == 1 nrinputs = 2; end %create function if nrinputs == 1 K = @(x) (sum(x.^2,2)<width)./(2*width); elseif nrinputs == 2 K1 = naivekernel(gamma, 1); K = @(x,z) K1(bsxfun(@minus, x, z)); else error('parameter nrinputs should be 1 or 2'); end end
github
stoman/MachineLearningKernelMethods-master
testdataset.m
.m
MachineLearningKernelMethods-master/functions/testdataset.m
440
utf_8
bf12d5a9d596571fe5fe42bf3e5088d4
%This function creates a data set given two types of objects. X is the %concatenation of the rows of both sets, Y is a vector containing 1 for all %objects in the first set and -1 for all objects in the second set. The %test data are also converted to doubles. %Author: Stefan Toman ([email protected]) function [X, Y] = testdataset(seta, setb) X = [double(seta); double(setb)]; Y = [ones(size(seta,1),1)*-1; ones(size(setb,1),1)*1]; end
github
siam1251/Fast-SeqSLAM-master
patchNormalize.m
.m
Fast-SeqSLAM-master/fast_seqSlam/patchNormalize.m
1,483
iso_8859_1
bd4717ee5240260998e3b2043d5a7090
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function img = patchNormalize(img, params) s = params.normalization.sideLength; n = 1:s:size(img,1)+1; m = 1:s:size(img,2)+1; for i=1:length(n)-1 for j=1:length(m)-1 p = img(n(i):n(i+1)-1, m(j):m(j+1)-1); pp=p(:); if params.normalization.mode ~=0 pp=double(pp); img(n(i):n(i+1)-1, m(j):m(j+1)-1) = 127+reshape(round((pp-mean(pp))/std(pp)), s, s); else f = 255.0/double(max(pp) - min(pp)); img(n(i):n(i+1)-1, m(j):m(j+1)-1) = round(f * (p-min(pp))); end end end end
github
siam1251/Fast-SeqSLAM-master
doFindMatchesModified.m
.m
Fast-SeqSLAM-master/fast_seqSlam/doFindMatchesModified.m
6,615
utf_8
22903cb2aa235535aede7f5513322e82
% % function results = doFindMatchesModified(results, params) filename = sprintf('%s/matches-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.matching.load && exist(filename, 'file') display(sprintf('Loading matchings from file %s ...', filename)); m = load(filename); results.matches = m.matches; results.seqValues = m.seqValues; else %matches = NaN(size(results.DD,2),2); display('Searching for matching images ...'); % h_waitbar = waitbar(0, 'Searching for matching images.'); % make sure ds is dividable by two params.matching.ds = params.matching.ds + mod(params.matching.ds,2); [matches, seqValues] = findMatchingMatrix(results, params); % save it if params.matching.save save(filename, 'matches','seqValues'); end results.matches = matches; results.seqValues = seqValues;% for debugging purpose end end function [matches, seqValues] = findMatchingMatrix(results, params) DD = (results.DD); max_val = max(DD(:)); seqMaxValue = max_val*(params.matching.ds+1); % We shall search for matches using velocities between % params.matching.vmin and params.matching.vmax. % However, not every vskip may be neccessary to check. So we first find % out, which v leads to different trajectories: move_min = params.matching.vmin * params.matching.ds; move_max = params.matching.vmax * params.matching.ds; move = move_min:move_max; v = move / params.matching.ds; %adding velocity 1 will go in the next location if % since all the trajectories will have same difference score % and it will return the first one v = [1, v]; %v(1) = 1 ds = params.matching.ds; idy_add = repmat([-ds:0], size(v,2),1); % idy_add is y axis indices % -1 0 1 % 0 0 1 % -1 -1 0 % length(idy_add) idy_add = round(idy_add .* repmat(v', 1, size(idy_add,2))); %score = zeros(2,size(DD,1)); % add a line of inf costs so that we penalize running out of data %score = zeros(1,size(DD,1)); %[id, vls] = min(DD); %row padding DD=[DD]; num_cols = size(DD,2); row_padding = ones(ds,num_cols)*max_val; DD=[row_padding;DD]; %col padding %col padding is 1 more than ds/2 because if we have higher velcity than %1 then it will create problem num_rows = size(DD,1); col_padding = ones(num_rows,ds); DD = [col_padding, DD]; maxRow = size(DD,1); maxCol = size(DD,2); matchingMatrix = ones(size(DD))*seqMaxValue; y_max = size(DD,1); % [sortedValues,sortIndex] = sort(results.DD,'ascend'); % max_index = 5; next_states = []; if params.N < params.K K = params.N else K =params.K end for Col = 2*ds : size(DD,2) % this is where our trajectory starts % n_start = Col - ds/2; % %x is in x axis indices, % x= repmat([n_start : n_start+ds], length(v), 1); %indices = find(DD(:,Col) < max_val); %indices of n lowest values in a column [sortedValues,sortIndex] = sort(DD(:,Col),'ascend'); %# Sort the values in increasing order indices = sortIndex(1:K); %# Get a linear index into A of the smallest values indices = union(indices,next_states); if size(indices,1) > 1 indices = indices'; end %indices = sortIndex(1:10); %lf = find(DD(:,Col) > params.initial_distance) next_states = []; for Row=indices if Col > maxCol || Row > maxRow break; end % score is zero for entering in the while loop if matchingMatrix(Row,Col) < seqMaxValue continue; end score = 0; n_start = Col; %x is in x axis indices, or column indices x= repmat([n_start-ds : n_start], length(v), 1); xx = (x-1) * y_max; %row indices y = min(idy_add+Row, y_max); %adding row indices and column indices idy = xx + y; [score, velocity_index] = min(sum(DD(idy),2)); %idy = indices are always accessed row wise %Since we made the indices using colunm by column or assume %indices will be column by column, %in sum function, for option 2, column wise indices summation %otherwise row wise indices summation %matchingMatrix(R,C) = score; matchingMatrix(Row,Col) = score; %DD(R,C) = score/(ds+1); %[R,C,score,velocity_index] %current_velocity = v(velocity_index); %matchingMatrix R = Row + idy_add(velocity_index,ds+1)-idy_add(velocity_index,ds); if score < seqMaxValue*.9 next_states = [next_states; R]; end end % waitbar(N / size(results.DD,2), h_waitbar); %break; end %normA = matchingMatrix - min(matchingMatrix(:)); %normA = normA ./ max(normA(:)) % %figure, imshow(normA); %matchingMatrix = normc(matchingMatrix); %[scores, id] = min(matchingMatrix) %matches = [id'+params.matching.ds/2, scores']; %give up the padding of machingMatrix %since col padding was 1 more than ds/2 matchingMatrix = matchingMatrix(ds+1:end,ds+1:end); matches = NaN(size(matchingMatrix,2),2); for Col = 1: size(matchingMatrix,2) score= matchingMatrix(1:end,Col); %score = normc(score); [min_value, min_idx] = min(score); window = max(1, min_idx-params.matching.Rwindow/2):min(length(score), min_idx+params.matching.Rwindow/2); not_window = setxor(1:length(score), window); min_value_2nd = min(score(not_window)); if min_value >= seqMaxValue min_idx = NaN; end matches(Col,:) = [min_idx; min_value/min_value_2nd ]; %if min_value < params.matching.seqMaxValue % matches(Col,:) = [min_idx ; min_value]; %end end seqValues = matchingMatrix; end
github
siam1251/Fast-SeqSLAM-master
previous stable_doFindMatchesModified.m
.m
Fast-SeqSLAM-master/fast_seqSlam/previous stable_doFindMatchesModified.m
5,841
utf_8
02d08b5e18097163ec0485e94f34dfcb
% % function results = doFindMatchesModified(results, params) filename = sprintf('%s/matches-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.matching.load && exist(filename, 'file') display(sprintf('Loading matchings from file %s ...', filename)); m = load(filename); results.matches = m.matches; else %matches = NaN(size(results.DD,2),2); display('Searching for matching images ...'); % h_waitbar = waitbar(0, 'Searching for matching images.'); % make sure ds is dividable by two params.matching.ds = params.matching.ds + mod(params.matching.ds,2); [matches, seqValues] = findMatchingMatrix(results, params); % save it if params.matching.save save(filename, 'matches'); end results.matches = matches; results.seqValues = seqValues;% for debugging purpose end end function [matches, seqValues] = findMatchingMatrix(results, params) DD = (results.DD); max_val = max(DD(:)); seqMaxValue = max_val*(params.matching.ds+1); % We shall search for matches using velocities between % params.matching.vmin and params.matching.vmax. % However, not every vskip may be neccessary to check. So we first find % out, which v leads to different trajectories: move_min = params.matching.vmin * params.matching.ds; move_max = params.matching.vmax * params.matching.ds; move = move_min:move_max; v = move / params.matching.ds; %v(1) = 1 ds = params.matching.ds; idy_add = repmat([-ds/2:ds/2], size(v,2),1); % idy_add = floor(idy_add.*v); % idy_add is y axis indices % -1 0 1 % 0 0 1 % -1 -1 0 % length(idy_add) idy_add = floor(idy_add .* repmat(v', 1, size(idy_add,2))); %score = zeros(2,size(DD,1)); % add a line of inf costs so that we penalize running out of data %score = zeros(1,size(DD,1)); %[id, vls] = min(DD); DD=[DD; inf(1,size(DD,2))]; maxRow = size(DD,1); matchingMatrix = ones(size(DD))*seqMaxValue; y_max = size(DD,1); % [sortedValues,sortIndex] = sort(results.DD,'ascend'); % max_index = 5; for Col = 2+ds/2 : size(DD,2)-ds/2 % this is where our trajectory starts % n_start = Col - ds/2; % %x is in x axis indices, % x= repmat([n_start : n_start+ds], length(v), 1); indices = find(DD(:,Col) < max_val); % indices of n lowest values in a column %[sortedValues,sortIndex] = sort(DD(:,Col),'ascend'); %# Sort the values in %# descending order %indices = sortIndex(1:10); %# Get a linear index into A of the 5 largest values %lf = find(DD(:,Col) > params.initial_distance) for Row=indices' C = Col; R = Row; % score is zero for entering in the while loop if matchingMatrix(R,C) < seqMaxValue continue; end score = 0; while score < seqMaxValue %score = findSingleMatch(DD,x,idy_add,y_max, Col,Row, params); % if matchingMatrix(R,C) < seqMaxValue % break; % end if C > size(DD,2)-ds/2|| R > size(DD,1)-ds/2||C < ds/2 break; end n_start = C; %x is in x axis indices, x= repmat([n_start-ds/2 : n_start+ds/2], length(v), 1); xx = (x-1) * y_max; y = min(idy_add+R, y_max); idy = xx + y; [score, velocity_index] = min(sum(DD(idy),2)); %idy = indices are always accessed row wise %Since we made the indices using colunm by column or assume %indices will be column by column, %in sum function, for option 2, column wise indices summation %otherwise row wise indices summation %matchingMatrix(R,C) = score; if matchingMatrix(R,C) > score matchingMatrix(R,C) = score; %[R,C,score,velocity_index] else break; end %current_velocity = v(velocity_index); %matchingMatrix C = C+1; R = R + idy_add(velocity_index,2+ds/2)-idy_add(velocity_index,1+ds/2); R end end % waitbar(N / size(results.DD,2), h_waitbar); %break; end %normA = matchingMatrix - min(matchingMatrix(:)); %normA = normA ./ max(normA(:)) % %figure, imshow(normA); %matchingMatrix = normc(matchingMatrix); %[scores, id] = min(matchingMatrix) %matches = [id'+params.matching.ds/2, scores']; matches = NaN(size(DD,2),2); for Col = 2+ds/2 : size(DD,2)-ds/2 score= matchingMatrix(1:maxRow,Col); %score = normc(score); [min_value, min_idx] = min(score); %window = max(1, min_idx-params.matching.Rwindow/2):min(length(score), min_idx+params.matching.Rwindow/2); %not_window = setxor(1:length(score), window); %min_value_2nd = min(score(not_window)); matches(Col,:) = [min_idx; min_value ]; %if min_value < params.matching.seqMaxValue % matches(Col,:) = [min_idx ; min_value]; %end end seqValues = matchingMatrix; end
github
siam1251/Fast-SeqSLAM-master
b_doDifferenceMatrix.m
.m
Fast-SeqSLAM-master/fast_seqSlam/b_doDifferenceMatrix.m
4,516
utf_8
53f2379567ce2965dee54bf1163a7d93
% % function results = doDifferenceMatrix(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % 'checks',64)); tic; [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); toc %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); column1 = result2(:); S2 = table(column1, column2); [S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1) + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); tic; similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; toc %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D'; % --------M1---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M2 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 % save it if params.differenceMatrix.save save(filename, 'D'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
doPreprocessing.m
.m
Fast-SeqSLAM-master/fast_seqSlam/doPreprocessing.m
3,144
utf_8
88439f0df15bc850c247e25f9338587a
% function results = doPreprocessing(params) for i = 1:length(params.dataset) % shall we just load it? filename = sprintf('%s/preprocessing-%s%s.mat', params.dataset(i).savePath, params.dataset(i).saveFile, params.saveSuffix); if params.dataset(i).preprocessing.load && exist(filename, 'file'); r = load(filename); display(sprintf('Loading file %s ...', filename)); results.dataset(i).preprocessing = r.results_preprocessing; else % or shall we actually calculate it? p = params; p.dataset=params.dataset(i); results.dataset(i).preprocessing = single(preprocessing(p)); if params.dataset(i).preprocessing.save results_preprocessing = single(results.dataset(i).preprocessing); save(filename, 'results_preprocessing'); end end end end %% function dhog = preprocessing(params) display(sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % h_waitbar = waitbar(0,sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % allocate memory for all the processed images % n = length(params.dataset.imageIndices); % m = params.downsample.size(1)*params.downsample.size(2); % % if ~isempty(params.dataset.crop) % c = params.dataset.crop; % m = (c(3)-c(1)+1) * (c(4)-c(2)+1); % end % % images = zeros(m,n, 'uint8'); % j=1; l = length( params.dataset.imageIndices); dhog = []*l; readFormat = strcat('%s/%s%0',num2str(params.dataset.numberFormat),'d%s%s') % for every image .... indices = params.dataset.imageIndices; j = 1; for i = indices filename = sprintf(readFormat, params.dataset.imagePath, ... params.dataset.prefix, ... i, ... params.dataset.suffix, ... params.dataset.extension); im = imread(filename); % convert to grayscale if params.DO_GRAYLEVEL im = rgb2gray(im); end % resize the image if params.DO_RESIZE im = imresize(im, params.downsample.size, params.downsample.method); end % do patch normalization % it didn't work well with hog descriptor if params.DO_PATCHNORMALIZATION im = patchNormalize(im, params); end [d, visualization] = extractHOGFeatures(im,'CellSize',params.dataset.cellSize); dhog(j,:)= d(:); j=j+1; % waitbar((i-params.dataset.imageIndices(1)) / (params.dataset.imageIndices(end)-params.dataset.imageIndices(1))); end dhog = dhog'; % close(h_waitbar); size(dhog) end
github
siam1251/Fast-SeqSLAM-master
b_doPreprocessing.m
.m
Fast-SeqSLAM-master/fast_seqSlam/b_doPreprocessing.m
3,811
iso_8859_1
4c79c1ead929d54cc2610f05b48630a1
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doPreprocessing(params) for i = 1:length(params.dataset) % shall we just load it? filename = sprintf('%s/preprocessing-%s%s.mat', params.dataset(i).savePath, params.dataset(i).saveFile, params.saveSuffix); if params.dataset(i).preprocessing.load && exist(filename, 'file'); r = load(filename); display(sprintf('Loading file %s ...', filename)); results.dataset(i).preprocessing = r.results_preprocessing; else % or shall we actually calculate it? p = params; p.dataset=params.dataset(i); results.dataset(i).preprocessing = single(preprocessing(p)); if params.dataset(i).preprocessing.save results_preprocessing = single(results.dataset(i).preprocessing); save(filename, 'results_preprocessing'); end end end end %% function images = preprocessing(params) display(sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % h_waitbar = waitbar(0,sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % allocate memory for all the processed images n = length(params.dataset.imageIndices); m = params.downsample.size(1)*params.downsample.size(2); if ~isempty(params.dataset.crop) c = params.dataset.crop; m = (c(3)-c(1)+1) * (c(4)-c(2)+1); end images = zeros(m,n, 'uint8'); j=1; readFormat = strcat('%s/%s%0',num2str(params.dataset.numberFormat),'d%s%s') % for every image .... for i = params.dataset.imageIndices filename = sprintf(readFormat, params.dataset.imagePath, ... params.dataset.prefix, ... i, ... params.dataset.suffix, ... params.dataset.extension); filename img = imread(filename); % convert to grayscale if params.DO_GRAYLEVEL img = rgb2gray(img); end % resize the image if params.DO_RESIZE img = imresize(img, params.downsample.size, params.downsample.method); end % crop the image if necessary if ~isempty(params.dataset.crop) img = img(params.dataset.crop(2):params.dataset.crop(4), params.dataset.crop(1):params.dataset.crop(3)); end % do patch normalization if params.DO_PATCHNORMALIZATION img = patchNormalize(img, params); end images(:,j) = img(:); j=j+1; % waitbar((i-params.dataset.imageIndices(1)) / (params.dataset.imageIndices(end)-params.dataset.imageIndices(1))); end % close(h_waitbar); end
github
siam1251/Fast-SeqSLAM-master
doContrastEnhancement.m
.m
Fast-SeqSLAM-master/fast_seqSlam/doContrastEnhancement.m
2,015
iso_8859_1
0fe5d2f80ba01e5d10f64177d2e7695d
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doContrastEnhancement(results, params) filename = sprintf('%s/differenceEnhanced-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.contrastEnhanced.load && exist(filename, 'file') display(sprintf('Loading contrast-enhanced image distance matrix from file %s ...', filename)); dd = load(filename); results.DD = dd.DD; else display('Performing local contrast enhancement on difference matrix ...'); % h_waitbar = waitbar(0,'Local contrast enhancement on difference matrix'); %DD = zeros(size(results.D), 'single'); D=results.D; max_v = max(D(:)); cols = size(D,2); for i = 1:size(results.D,1) cnt = sum(D(i,:)< max_v); if cnt > cols*.2 D(i,:) = ones(cols,1)*max_v; end end % let the minimum distance be 0 %DD = DD-min(min(DD)); results.DD = D; % save it? if params.contrastEnhanced.save DD = results.DD; save(filename, 'DD'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
b_doContrastEnhancement.m
.m
Fast-SeqSLAM-master/fast_seqSlam/b_doContrastEnhancement.m
2,015
iso_8859_1
0fe5d2f80ba01e5d10f64177d2e7695d
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doContrastEnhancement(results, params) filename = sprintf('%s/differenceEnhanced-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.contrastEnhanced.load && exist(filename, 'file') display(sprintf('Loading contrast-enhanced image distance matrix from file %s ...', filename)); dd = load(filename); results.DD = dd.DD; else display('Performing local contrast enhancement on difference matrix ...'); % h_waitbar = waitbar(0,'Local contrast enhancement on difference matrix'); %DD = zeros(size(results.D), 'single'); D=results.D; max_v = max(D(:)); cols = size(D,2); for i = 1:size(results.D,1) cnt = sum(D(i,:)< max_v); if cnt > cols*.2 D(i,:) = ones(cols,1)*max_v; end end % let the minimum distance be 0 %DD = DD-min(min(DD)); results.DD = D; % save it? if params.contrastEnhanced.save DD = results.DD; save(filename, 'DD'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
doDifferenceMatrix_noMutualConstraint.m
.m
Fast-SeqSLAM-master/fast_seqSlam/doDifferenceMatrix_noMutualConstraint.m
4,792
utf_8
39d2472da02c8305260087d5fef5d1be
% % function results = doDifferenceMatrix_noMutualConstraint(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); tic; if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; size(dhog1) dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1,speedup ] = flann_build_index(dhog1, struct('algorithm',params.algorithm, 'target_precision',... params.target_precision));%, 'trees',params.trees)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); toc [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2, speedup ] = flann_build_index(dhog2, struct('algorithm',params.algorithm,'target_precision',... params.target_precision));%, 'trees',params.trees)); results.speedup = speedup; % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % s0e 'checks',64)); [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); toc %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); column1 = result2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); S2 = table(column1, column2); column1 = [1]; column1 = column1'; column2 = column1; S2 = table(column1,column2); S = S1; i1 = 1:height(S1); %[S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1));% + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D'; % --------M1---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M2 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 % save it if params.differenceMatrix.save save(filename, 'D'); end toc % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
showPrecisionCurve.m
.m
Fast-SeqSLAM-master/fast_seqSlam/showPrecisionCurve.m
3,688
utf_8
9a33792a410fba51873fd621e129b0d0
% Sayem Mohammad Siam % University of Alberta % Date 20th March 2016 % Computes empirical statistics based on classification output. % Modified the following matlab functions prc_stats_empirical(targs, dvs) to compute % Precision recall for SLAM function showPrecisionCurve(matches,targs,range,imageSkip,filename) % Compute empirical curves dvs = matches(:,2)'; predicted = matches(:,1)'; [TPR_emp, FPR_emp, PPV_emp] = precision_recall(targs, dvs, predicted,range,imageSkip); points = [TPR_emp;PPV_emp]; filename = strcat('prcurve/',filename) save(filename,'points'); cols = [200 45 43; 37 64 180; 0 176 80; 0 0 0]/255; figure,hold on; plot(TPR_emp, PPV_emp, '-o', 'color', cols(1,:), 'linewidth', 2); axis([0 1 0 1]); xlabel('TPR (recall)'); ylabel('PPV (precision)'); title('PR curves'); set(gca, 'box', 'on'); end % Arguments: % targs: true image Number which should match(targets) % dvs: decision values output by the classifier % predicted: image number which matched by our algorithm % range: No of images may differ % imageSkip: if you didn't skip images then it should be 1 % Return values: % TPR: true positive rate (recall) % FPR: false positive rate % PPV: positive predictive value (precision) % AUC: area under the ROC curve % AP: area under the PR curve (average precision) % % Each of these return vectors has length(desireds)+1 elements. % % Literature: % K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann (2010). The % binormal assumption on precision-recall curves. In: Proceedings of % the 20th International Conference on Pattern Recognition (ICPR). % Modified code of % Kay H. Brodersen & Cheng Soon Ong, ETH Zurich, Switzerland % $Id: prc_stats_empirical.m 5529 2010-04-22 21:10:32Z bkay $ % ------------------------------------------------------------------------- function [TPR, FPR, PPV] = precision_recall(targs, dvs,predicted,range,imageSkip) % Check input %assert(all(size(targs)==size(dvs))); %assert(all(targs==-1 | targs==1)); % Sort decision values and true labels according to decision values n = length(dvs); [dvs_sorted,idx] = sort(dvs,'descend'); find(idx>645) idx(166) targs_sorted = targs(idx*imageSkip); predicted_sorted = predicted(idx)*imageSkip; % Inititalize accumulators TPR = repmat(NaN,1,n+1); FPR = repmat(NaN,1,n+1); PPV = repmat(NaN,1,n+1); % Now slide the threshold along the decision values (the threshold % always lies in between two values; here, the threshold represents the % decision value immediately to the right of it) fn = zeros(size(predicted_sorted)); for thr = 1:length(dvs_sorted)+1 % values greater than thr are positive and smaller than thr are NaN % TP = sum((abs(targs_sorted(thr:end)-predicted_sorted(thr:end))<range)&( ~isnan(predicted_sorted(thr:end))));%you have to match whether trgs(i) == dvs(i) FN = sum(~isnan(targs_sorted(1:thr-1)));% 1 to thr-1 all should be NaN if not NaN they are false negative TN = sum(isnan(targs_sorted(1:thr-1)));% 1 to thr-1 all should be NaN if they are NaN they are true Negative FP = sum(abs(targs_sorted(thr:end)-predicted_sorted(thr:end))>=range | isnan(predicted_sorted(thr:end))); TPR(thr) = TP/(TP+FN);%recall FPR(thr) = FP/(FP+TN); PPV(thr) = TP/(TP+FP);%precision end % Compute empirical AUC %[tmp,tmp,tmp,AUC] = perfcurve(targs,dvs,1); % Compute empirical AP %AP = abs(trapz(TPR(~isnan(PPV)),PPV(~isnan(PPV)))); end
github
siam1251/Fast-SeqSLAM-master
openSeqSLAM.m
.m
Fast-SeqSLAM-master/fast_seqSlam/openSeqSLAM.m
1,826
iso_8859_1
a02a94c886b776a5bcb33c971460a2fa
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = openSeqSLAM(params) results=[]; % try to allocate 3 CPU cores (adjust to your machine) for parallel % processing tic; % begin with preprocessing of the images if params.DO_PREPROCESSING results = doPreprocessing(params); end results.time_preprocessing = toc; tic; % image difference matrix if params.DO_DIFF_MATRIX results = doDifferenceMatrix_noMutualConstraint(results, params); end results.time_differenceMatrix = toc; tic; % contrast enhancement if params.DO_CONTRAST_ENHANCEMENT results = doContrastEnhancement(results, params); else if params.DO_DIFF_MATRIX results.DD = results.D; end end results.time_contrastEnhancement = toc; tic; % find the matches if params.DO_FIND_MATCHES results = doFindMatchesModified(results, params); end results.time_findMatches = toc; end
github
siam1251/Fast-SeqSLAM-master
doDifferenceMatrix.m
.m
Fast-SeqSLAM-master/fast_seqSlam/doDifferenceMatrix.m
4,590
utf_8
2cd561e80b84d7b6d45246cb95eec6e1
% % function results = doDifferenceMatrix(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); tic; if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1,speedup ] = flann_build_index(dhog1, struct('algorithm',params.algorithm, 'target_precision',... params.target_precision));%, 'trees',params.trees)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); toc [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2, speedup ] = flann_build_index(dhog2, struct('algorithm',params.algorithm,'target_precision',... params.target_precision));%, 'trees',params.trees)); results.speedup = speedup; % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % s0e 'checks',64)); [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); toc %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); column1 = result2(:); S2 = table(column1, column2); [S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1) + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; D = D'; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D; % --------M1---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M2 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 % save it if params.differenceMatrix.save save(filename, 'D'); end toc % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
doFindMatchesModified.m
.m
Fast-SeqSLAM-master/fast_seqSlam/back_fast/doFindMatchesModified.m
6,617
utf_8
aa817af8acf450d798153c3da4b69fb0
% % function results = doFindMatchesModified(results, params) filename = sprintf('%s/matches-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.matching.load && exist(filename, 'file') display(sprintf('Loading matchings from file %s ...', filename)); m = load(filename); results.matches = m.matches; results.seqValues = m.seqValues; else %matches = NaN(size(results.DD,2),2); display('Searching for matching images ...'); % h_waitbar = waitbar(0, 'Searching for matching images.'); % make sure ds is dividable by two params.matching.ds = params.matching.ds + mod(params.matching.ds,2); [matches, seqValues] = findMatchingMatrix(results, params); % save it if params.matching.save save(filename, 'matches','seqValues'); end results.matches = matches; results.seqValues = seqValues;% for debugging purpose end end function [matches, seqValues] = findMatchingMatrix(results, params) DD = (results.DD); max_val = max(DD(:)); seqMaxValue = max_val*(params.matching.ds+1); % We shall search for matches using velocities between % params.matching.vmin and params.matching.vmax. % However, not every vskip may be neccessary to check. So we first find % out, which v leads to different trajectories: move_min = params.matching.vmin * params.matching.ds; move_max = params.matching.vmax * params.matching.ds; move = move_min:move_max; v = move / params.matching.ds; %v(1) = 1 ds = params.matching.ds; idy_add = repmat([-ds/2:ds/2], size(v,2),1); % idy_add is y axis indices % -1 0 1 % 0 0 1 % -1 -1 0 % length(idy_add) idy_add = floor(idy_add .* repmat(v', 1, size(idy_add,2))); %score = zeros(2,size(DD,1)); % add a line of inf costs so that we penalize running out of data %score = zeros(1,size(DD,1)); %[id, vls] = min(DD); %row padding DD=[DD]; num_cols = size(DD,2); row_padding = ones(ds/2,num_cols)*max_val; DD=[row_padding;DD;row_padding]; %col padding %col padding is 1 more than ds/2 because if we have higher velcity than %1 then it will create problem num_rows = size(DD,1); col_padding = ones(num_rows,1+ds/2); DD = [col_padding, DD, col_padding]; maxRow = size(DD,1); matchingMatrix = ones(size(DD))*seqMaxValue; y_max = size(DD,1); % [sortedValues,sortIndex] = sort(results.DD,'ascend'); % max_index = 5; for Col = 2+ds/2 : size(DD,2)-1-ds/2 % this is where our trajectory starts % n_start = Col - ds/2; % %x is in x axis indices, % x= repmat([n_start : n_start+ds], length(v), 1); indices = find(DD(:,Col) < max_val); %indices of n lowest values in a column [sortedValues,sortIndex] = sort(DD(:,Col),'ascend'); %# Sort the values in increasing order indices = intersect(indices,sortIndex(1:10)); %# Get a linear index into A of the smallest values %indices = sortIndex(1:10); %lf = find(DD(:,Col) > params.initial_distance) for Row=indices' C = Col; R = Row; % score is zero for entering in the while loop if matchingMatrix(R,C) < seqMaxValue continue; end score = 0; while score < seqMaxValue % at least 1-.x %score = findSingleMatch(DD,x,idy_add,y_max, Col,Row, params); % if matchingMatrix(R,C) < seqMaxValue % break; % end if C > size(DD,2)-ds/2|| R > size(DD,1)-ds/2||C < ds/2 break; end n_start = C; %x is in x axis indices, or column indices x= repmat([n_start-ds/2 : n_start+ds/2], length(v), 1); xx = (x-1) * y_max; %row indices y = min(idy_add+R, y_max); %adding row indices and column indices idy = xx + y; [score, velocity_index] = min(sum(DD(idy),2)); %idy = indices are always accessed row wise %Since we made the indices using colunm by column or assume %indices will be column by column, %in sum function, for option 2, column wise indices summation %otherwise row wise indices summation %matchingMatrix(R,C) = score; if matchingMatrix(R,C) > score matchingMatrix(R,C) = score; %DD(R,C) = score/(ds+1); %[R,C,score,velocity_index] else break; end %current_velocity = v(velocity_index); %matchingMatrix C = C+1; R = R + idy_add(velocity_index,2+ds/2)-idy_add(velocity_index,1+ds/2); end end % waitbar(N / size(results.DD,2), h_waitbar); %break; end %normA = matchingMatrix - min(matchingMatrix(:)); %normA = normA ./ max(normA(:)) % %figure, imshow(normA); %matchingMatrix = normc(matchingMatrix); %[scores, id] = min(matchingMatrix) %matches = [id'+params.matching.ds/2, scores']; %give up the padding of machingMatrix %since col padding was 1 more than ds/2 matchingMatrix = matchingMatrix(1+ds/2:end-ds/2,2+ds/2:end-1-ds/2); matches = NaN(size(matchingMatrix,2),2); for Col = 1: size(matchingMatrix,2) score= matchingMatrix(1:end,Col); %score = normc(score); [min_value, min_idx] = min(score); window = max(1, min_idx-params.matching.Rwindow/2):min(length(score), min_idx+params.matching.Rwindow/2); not_window = setxor(1:length(score), window); min_value_2nd = min(score(not_window)); if min_value >= seqMaxValue min_idx = NaN; end matches(Col,:) = [min_idx; min_value/min_value_2nd ]; %if min_value < params.matching.seqMaxValue % matches(Col,:) = [min_idx ; min_value]; %end end seqValues = matchingMatrix; end
github
siam1251/Fast-SeqSLAM-master
doPreprocessing.m
.m
Fast-SeqSLAM-master/fast_seqSlam/back_fast/doPreprocessing.m
3,144
utf_8
88439f0df15bc850c247e25f9338587a
% function results = doPreprocessing(params) for i = 1:length(params.dataset) % shall we just load it? filename = sprintf('%s/preprocessing-%s%s.mat', params.dataset(i).savePath, params.dataset(i).saveFile, params.saveSuffix); if params.dataset(i).preprocessing.load && exist(filename, 'file'); r = load(filename); display(sprintf('Loading file %s ...', filename)); results.dataset(i).preprocessing = r.results_preprocessing; else % or shall we actually calculate it? p = params; p.dataset=params.dataset(i); results.dataset(i).preprocessing = single(preprocessing(p)); if params.dataset(i).preprocessing.save results_preprocessing = single(results.dataset(i).preprocessing); save(filename, 'results_preprocessing'); end end end end %% function dhog = preprocessing(params) display(sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % h_waitbar = waitbar(0,sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % allocate memory for all the processed images % n = length(params.dataset.imageIndices); % m = params.downsample.size(1)*params.downsample.size(2); % % if ~isempty(params.dataset.crop) % c = params.dataset.crop; % m = (c(3)-c(1)+1) * (c(4)-c(2)+1); % end % % images = zeros(m,n, 'uint8'); % j=1; l = length( params.dataset.imageIndices); dhog = []*l; readFormat = strcat('%s/%s%0',num2str(params.dataset.numberFormat),'d%s%s') % for every image .... indices = params.dataset.imageIndices; j = 1; for i = indices filename = sprintf(readFormat, params.dataset.imagePath, ... params.dataset.prefix, ... i, ... params.dataset.suffix, ... params.dataset.extension); im = imread(filename); % convert to grayscale if params.DO_GRAYLEVEL im = rgb2gray(im); end % resize the image if params.DO_RESIZE im = imresize(im, params.downsample.size, params.downsample.method); end % do patch normalization % it didn't work well with hog descriptor if params.DO_PATCHNORMALIZATION im = patchNormalize(im, params); end [d, visualization] = extractHOGFeatures(im,'CellSize',params.dataset.cellSize); dhog(j,:)= d(:); j=j+1; % waitbar((i-params.dataset.imageIndices(1)) / (params.dataset.imageIndices(end)-params.dataset.imageIndices(1))); end dhog = dhog'; % close(h_waitbar); size(dhog) end
github
siam1251/Fast-SeqSLAM-master
doDifferenceMatrix.m
.m
Fast-SeqSLAM-master/fast_seqSlam/CMakeFiles/doDifferenceMatrix.m
4,276
utf_8
df153f88a48c06590dd35c17112304ce
% % function results = doDifferenceMatrix(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % 'checks',64)); [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); column1 = result2(:); S2 = table(column1, column2); [S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1) + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D; % save it if params.differenceMatrix.save save(filename, 'D'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
doDifferenceMatrix.m
.m
Fast-SeqSLAM-master/fast_seqSlam/CMakeFiles/mex_nearest_neighbors.dir/doDifferenceMatrix.m
4,216
utf_8
dac48ceba7e3fcece6fa922ed9d95c1b
% % function results = doDifferenceMatrix(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','kdtree', 'trees',8,... 'checks',64)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','kdtree', 'trees',8,... 'checks',64)); % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % 'checks',64)); [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); column1 = result2(:); S2 = table(column1, column2); [S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1) + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D; % save it if params.differenceMatrix.save save(filename, 'D'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
flann_search.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/flann_search.m
3,506
utf_8
cffd29579f0290f0f680a3f12cb7a671
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function [indices, dists] = flann_search(data, testset, n, search_params) %NN_SEARCH Fast approximate nearest neighbors search % % Performs a fast approximate nearest neighbor search using an % index constructed using flann_build_index or directly a % dataset. % Marius Muja, January 2008 algos = struct( 'linear', 0, 'kdtree', 1, 'kmeans', 2, 'composite', 3, 'saved', 254, 'autotuned', 255 ); center_algos = struct('random', 0, 'gonzales', 1, 'kmeanspp', 2 ); log_levels = struct('none', 0, 'fatal', 1, 'error', 2, 'warning', 3, 'info', 4); function value = id2value(map, id) fields = fieldnames(map); for i = 1:length(fields), val = cell2mat(fields(i)); if map.(val) == id value = val; break; end end end function id = value2id(map,value) id = map.(value); end default_params = struct('algorithm', 'kdtree' ,'checks', 32, 'trees', 4, 'branching', 32, 'iterations', 5, 'centers_init', 'random', 'cb_index', 0.4, 'target_precision', -1, 'build_weight', 0.01, 'memory_weight', 0, 'sample_fraction', 0.1, 'log_level', 'warning', 'random_seed', 0); if ~isstruct(search_params) error('The "search_params" argument must be a structure'); end params = default_params; fn = fieldnames(search_params); for i = [1:length(fn)], name = cell2mat(fn(i)); params.(name) = search_params.(name); end if ~isnumeric(params.algorithm), params.algorithm = value2id(algos,params.algorithm); end if ~isnumeric(params.centers_init), params.centers_init = value2id(center_algos,params.centers_init); end if ~isnumeric(params.log_level), params.log_level = value2id(log_levels,params.log_level); end if (size(data,1)==1 && size(data,2)==1) % we already have an index [indices,dists] = nearest_neighbors('index_find_nn', data, testset, n, params); else % create the index and search [indices,dists] = nearest_neighbors('find_nn', data, testset, n, params); end end
github
siam1251/Fast-SeqSLAM-master
flann_load_index.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/flann_load_index.m
1,578
utf_8
f9bcc41fd5972c5c987d6a4d41bdc796
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function index = flann_load_index(filename, dataset) %FLANN_LOAD_INDEX Loads an index from disk % % Marius Muja, March 2009 index = nearest_neighbors('load_index', filename, dataset); end
github
siam1251/Fast-SeqSLAM-master
test_flann.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/test_flann.m
10,100
utf_8
d65a8eac8c411227a355b4ddbe6de38a
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function test_flann data_path = './'; outcome = {'FAILED!!!!!!!!!', 'PASSED'}; failed = 0; passed = 0; cnt = 0; ok = 1; function assert(condition) if (~condition) ok = 0; end end function run_test(name, test) ok = 1; cnt = cnt + 1; tic; fprintf('Test %d: %s...',cnt,name); test(); time = toc; if (ok) passed = passed + 1; else failed = failed + 1; end fprintf('done (%g sec) : %s\n',time,cell2mat(outcome(ok+1))) end function status fprintf('-----------------\n'); fprintf('Passed: %d/%d\nFailed: %d/%d\n',passed,cnt,failed,cnt); end dataset = []; testset = []; function test_load_data % load the datasets and testsets % use single precision for better memory efficiency % store the features one per column because MATLAB % uses column major ordering dataset = single(load([data_path 'dataset.dat']))'; testset = single(load([data_path 'testset.dat']))'; assert(size(dataset,1) == size(testset,1)); end run_test('Load data',@test_load_data); match = []; dists = []; function test_linear_search [match,dists] = flann_search(dataset, testset, 10, struct('algorithm','linear')); assert(size(match,1) ==10 && size(match,2) == size(testset,2)); end run_test('Linear search',@test_linear_search); function test_kdtree_search [result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','kdtree',... 'trees',8,... 'checks',64)); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('kd-tree search',@test_kdtree_search); function test_kmeans_search [result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','kmeans',... 'branching',32,... 'iterations',3,... 'checks',120)); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('k-means search',@test_kmeans_search); function test_composite_search [result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','composite',... 'branching',32,... 'iterations',3,... 'trees', 1,... 'checks',64)); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('composite search',@test_composite_search); function test_autotune_search [result, ndists] = flann_search(dataset, testset, 10, struct('algorithm','autotuned',... 'target_precision',0.95,... 'build_weight',0.01,... 'memory_weight',0)); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('search with autotune',@test_autotune_search); function test_index_kdtree_search [index, search_params ] = flann_build_index(dataset, struct('algorithm','kdtree', 'trees',8,... 'checks',64)); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index kd-tree search',@test_index_kdtree_search); function test_index_kmeans_search [index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',... 'branching',32,... 'iterations',3,... 'checks',120)); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index kmeans search',@test_index_kmeans_search); function test_index_kmeans_search_gonzales [index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',... 'branching',32,... 'iterations',3,... 'checks',120,... 'centers_init','gonzales')); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index kmeans search gonzales',@test_index_kmeans_search_gonzales); function test_index_kmeans_search_kmeanspp [index, search_params ] = flann_build_index(dataset, struct('algorithm','kmeans',... 'branching',32,... 'iterations',3,... 'checks',120,... 'centers_init','kmeanspp')); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index kmeans search kmeanspp',@test_index_kmeans_search_kmeanspp); function test_index_composite_search [index, search_params ] = flann_build_index(dataset,struct('algorithm','composite',... 'branching',32,... 'iterations',3,... 'trees', 1,... 'checks',64)); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index composite search',@test_index_composite_search); function test_index_autotune_search [index, search_params, speedup ] = flann_build_index(dataset,struct('algorithm','autotuned',... 'target_precision',0.95,... 'build_weight',0.01,... 'memory_weight',0)); [result, ndists] = flann_search(index, testset, 10, search_params); n = size(match,2); precision = (n-sum(abs(result(1,:)-match(1,:))>0))/n; assert(precision>0.9); assert(sum(~(match(1,:)-result(1,:)).*(dists(1,:)-ndists(1,:)))==0); end run_test('index autotune search',@test_index_autotune_search); status(); end
github
siam1251/Fast-SeqSLAM-master
flann_free_index.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/flann_free_index.m
1,614
utf_8
5d719d8d60539b6c90bee08d01e458b5
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function flann_free_index(index_id) %FLANN_FREE_INDEX Deletes the nearest-neighbors index % % Deletes an index constructed using flann_build_index. % Marius Muja, January 2008 nearest_neighbors('free_index',index_id); end
github
siam1251/Fast-SeqSLAM-master
flann_save_index.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/flann_save_index.m
1,563
utf_8
5a44d911827fba5422041529b3c01cf6
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function flann_save_index(index_id, filename) %FLANN_SAVE_INDEX Saves an index to disk % % Marius Muja, March 2010 nearest_neighbors('save_index',index_id, filename); end
github
siam1251/Fast-SeqSLAM-master
flann_set_distance_type.m
.m
Fast-SeqSLAM-master/fast_seqSlam/flann/flann_set_distance_type.m
1,926
utf_8
8ba72989a4ac1bd6b30bec841b9def25
%Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. %Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. % %THE BSD LICENSE % %Redistribution and use in source and binary forms, with or without %modification, are permitted provided that the following conditions %are met: % %1. Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. %2. Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in the % documentation and/or other materials provided with the distribution. % %THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR %IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES %OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. %IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, %INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT %NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, %DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY %THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT %(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF %THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. function flann_set_distance_type(type, order) %FLANN_LOAD_INDEX Loads an index from disk % % Marius Muja, March 2009 distances = struct('euclidean', 1, 'manhattan', 2, 'minkowski', 3, 'max_dist', 4, 'hik', 5, 'hellinger', 6, 'chi_square', 7, 'cs', 7, 'kullback_leibler', 8, 'kl', 8); function id = value2id(map,value) id = map.(value); end if ~isnumeric(type), type = value2id(distances,type); end if type~=3 order = 0; end nearest_neighbors('set_distance_type', type, order); end
github
siam1251/Fast-SeqSLAM-master
doPreprocessing.m
.m
Fast-SeqSLAM-master/fast_seqSlam/back_original/doPreprocessing.m
3,898
iso_8859_1
7519d4254e7bd4ce8189bf94ae9117a9
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doPreprocessing(params) for i = 1:length(params.dataset) % shall we just load it? filename = sprintf('%s/preprocessing-%s%s.mat', params.dataset(i).savePath, params.dataset(i).saveFile, params.saveSuffix); if params.dataset(i).preprocessing.load && exist(filename, 'file'); r = load(filename); display(sprintf('Loading file %s ...', filename)); results.dataset(i).preprocessing = r.results_preprocessing; else % or shall we actually calculate it? p = params; p.dataset=params.dataset(i); results.dataset(i).preprocessing = single(preprocessing(p)); if params.dataset(i).preprocessing.save results_preprocessing = single(results.dataset(i).preprocessing); save(filename, 'results_preprocessing'); end end end end %% function images = preprocessing(params) display(sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % h_waitbar = waitbar(0,sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % allocate memory for all the processed images n = length(params.dataset.imageIndices); m = params.downsample.size(1)*params.downsample.size(2); if ~isempty(params.dataset.crop) c = params.dataset.crop; m = (c(3)-c(1)+1) * (c(4)-c(2)+1); end images = zeros(m,n, 'uint8'); j=1; readFormat = strcat('%s/%s%0',num2str(params.dataset.numberFormat),'d%s%s') % for every image .... for i = params.dataset.imageIndices filename = sprintf(readFormat, params.dataset.imagePath, ... params.dataset.prefix, ... i, ... params.dataset.suffix, ... params.dataset.extension); img = imread(filename); % convert to grayscale if params.DO_GRAYLEVEL img = rgb2gray(img); end % resize the image if params.DO_RESIZE img = imresize(img, params.downsample.size, params.downsample.method); end % crop the image if necessary if ~isempty(params.dataset.crop) img = img(params.dataset.crop(2):params.dataset.crop(4), params.dataset.crop(1):params.dataset.crop(3)); end % do patch normalization if params.DO_PATCHNORMALIZATION img = patchNormalize(img, params); end % shall we save the result? if params.DO_SAVE_PREPROCESSED_IMG end images(:,j) = img(:); j=j+1; % waitbar((i-params.dataset.imageIndices(1)) / (params.dataset.imageIndices(end)-params.dataset.imageIndices(1))); end % close(h_waitbar); end
github
siam1251/Fast-SeqSLAM-master
defaultParameters.m
.m
Fast-SeqSLAM-master/demo/defaultParameters.m
1,714
utf_8
82fece418707dd09e59815fb69b91339
% % function params=defaultParameters() % switches params.DO_PREPROCESSING = 1; params.DO_RESIZE = 1; params.DO_GRAYLEVEL = 1; params.DO_PATCHNORMALIZATION = 0; params.DO_SAVE_PREPROCESSED_IMG = 1; params.DO_DIFF_MATRIX = 1; params.DO_CONTRAST_ENHANCEMENT = 0;%make it 0!! params.DO_FIND_MATCHES = 1; %parameters for Flann and building similarity matrix params.N = 10; params.initial_distance = 9999; params.mul_unit = 2; %%ANN parameters params.distance_type = 'manhattan'; params.algorithm = 'kdtree'; params.trees = 8; params.checks = 64; % parameters for preprocessing params.downsample.size = [32 32]; % height, width params.downsample.method = 'lanczos3'; params.normalization.sideLength = 8; params.normalization.mode = 1; % parameters regarding the matching between images params.matching.ds = 30; %always even number %params.matching.seqMaxValue = params.initial_distance*(params.matching.ds+1); params.thresh = .90; params.matching.Rrecent=5; params.matching.vmin = .8; %params.matching.vskip = 0.1; params.matching.vmax = 1.2; params.matching.Rwindow = 10; params.matching.save = 1; params.matching.load = 1; % parameters for contrast enhancement on difference matrix params.contrastEnhancement.R = 10; % load old results or re-calculate? save results? params.differenceMatrix.save = 0; params.differenceMatrix.load = 0; params.contrastEnhanced.save = 1; params.contrastEnhanced.load = 0; % suffix appended on files containing the results params.saveSuffix=''; end
github
siam1251/Fast-SeqSLAM-master
patchNormalize.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/patchNormalize.m
1,483
iso_8859_1
bd4717ee5240260998e3b2043d5a7090
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function img = patchNormalize(img, params) s = params.normalization.sideLength; n = 1:s:size(img,1)+1; m = 1:s:size(img,2)+1; for i=1:length(n)-1 for j=1:length(m)-1 p = img(n(i):n(i+1)-1, m(j):m(j+1)-1); pp=p(:); if params.normalization.mode ~=0 pp=double(pp); img(n(i):n(i+1)-1, m(j):m(j+1)-1) = 127+reshape(round((pp-mean(pp))/std(pp)), s, s); else f = 255.0/double(max(pp) - min(pp)); img(n(i):n(i+1)-1, m(j):m(j+1)-1) = round(f * (p-min(pp))); end end end end
github
siam1251/Fast-SeqSLAM-master
doFindMatchesModified.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/doFindMatchesModified.m
5,881
utf_8
fa185462c4ee7aa944de7689bb43d7c6
% % function results = doFindMatchesModified(results, params) filename = sprintf('%s/matches-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.matching.load && exist(filename, 'file') display(sprintf('Loading matchings from file %s ...', filename)); m = load(filename); results.matches = m.matches; else %matches = NaN(size(results.DD,2),2); display('Searching for matching images ...'); % h_waitbar = waitbar(0, 'Searching for matching images.'); % make sure ds is dividable by two params.matching.ds = params.matching.ds + mod(params.matching.ds,2); [matches, seqValues] = findMatchingMatrix(results, params); % save it if params.matching.save save(filename, 'matches'); end results.matches = matches; results.seqValues = seqValues;% for debugging purpose end end function [matches, seqValues] = findMatchingMatrix(results, params) DD = (results.DD); max_val = max(DD(:)); seqMaxValue = max_val*(params.matching.ds+1); %seqMaxValue = 9999999999; % We shall search for matches using velocities between % params.matching.vmin and params.matching.vmax. % However, not every vskip may be neccessary to check. So we first find % out, which v leads to different trajectories: move_min = params.matching.vmin * params.matching.ds; move_max = params.matching.vmax * params.matching.ds; move = move_min:move_max; v = move / params.matching.ds; %v(1) = 1 ds = params.matching.ds; idy_add = repmat([-ds/2:ds/2], size(v,2),1); % idy_add = floor(idy_add.*v); % idy_add is y axis indices % -1 0 1 % 0 0 1 % -1 -1 0 % length(idy_add) idy_add = floor(idy_add .* repmat(v', 1, size(idy_add,2))); %score = zeros(2,size(DD,1)); % add a line of inf costs so that we penalize running out of data %score = zeros(1,size(DD,1)); %[id, vls] = min(DD); DD=[DD; inf(1,size(DD,2))]; maxRow = size(DD,1); matchingMatrix = ones(size(DD))*seqMaxValue; y_max = size(DD,1); % [sortedValues,sortIndex] = sort(results.DD,'ascend'); % max_index = 5; for Col = 2+ds/2 : size(DD,2)-ds/2 % this is where our trajectory starts % n_start = Col - ds/2; % %x is in x axis indices, % x= repmat([n_start : n_start+ds], length(v), 1); indices = find(DD(:,Col) < max_val); % indices of n lowest values in a column %[sortedValues,sortIndex] = sort(DD(:,Col),'ascend'); %# Sort the values in %# descending order %indices = sortIndex(1:50); %# Get a linear index into A of the 5 largest values %lf = find(DD(:,Col) > params.initial_distance) for Row=indices' C = Col; R = Row; % score is zero for entering in the while loop if matchingMatrix(R,C) < seqMaxValue continue; end score = 0; while score < seqMaxValue %score = findSingleMatch(DD,x,idy_add,y_max, Col,Row, params); % if matchingMatrix(R,C) < seqMaxValue % break; % end if C > size(DD,2)-ds/2|| R > size(DD,1)-ds/2||C < ds/2 break; end n_start = C; %x is in x axis indices, x= repmat([n_start-ds/2 : n_start+ds/2], length(v), 1); xx = (x-1) * y_max; y = min(idy_add+R, y_max); idy = xx + y; [score, velocity_index] = min(sum(DD(idy),2)); %idy = indices are always accessed row wise %Since we made the indices using colunm by column or assume %indices will be column by column, %in sum function, for option 2, column wise indices summation %otherwise row wise indices summation %matchingMatrix(R,C) = score; if matchingMatrix(R,C) > score matchingMatrix(R,C) = score; %[R,C,score,velocity_index] else break; end %current_velocity = v(velocity_index); %matchingMatrix C = C+1; R = R + idy_add(velocity_index,2+ds/2)-idy_add(velocity_index,1+ds/2); R end end % waitbar(N / size(results.DD,2), h_waitbar); %break; end %normA = matchingMatrix - min(matchingMatrix(:)); %normA = normA ./ max(normA(:)) % %figure, imshow(normA); %matchingMatrix = normc(matchingMatrix); %[scores, id] = min(matchingMatrix) %matches = [id'+params.matching.ds/2, scores']; matches = NaN(size(DD,2),2); for Col = 1 : size(DD,2)-params.matching.ds score= matchingMatrix(1:maxRow,Col); %score = normc(score); [min_value, min_idx] = min(score); %window = max(1, min_idx-params.matching.Rwindow/2):min(length(score), min_idx+params.matching.Rwindow/2); %not_window = setxor(1:length(score), window); %min_value_2nd = min(score(not_window)); matches(Col,:) = [min_idx; min_value ]; %if min_value < params.matching.seqMaxValue % matches(Col,:) = [min_idx ; min_value]; %end end seqValues = matchingMatrix; end
github
siam1251/Fast-SeqSLAM-master
doFindMatches.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/doFindMatches.m
3,543
iso_8859_1
d58b1f0451dacccc9bd8fcd447712c2a
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doFindMatches(results, params) filename = sprintf('%s/matches-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.matching.load && exist(filename, 'file') display(sprintf('Loading matchings from file %s ...', filename)); m = load(filename); results.matches = m.matches; else matches = NaN(size(results.DD,2),2); display('Searching for matching images ...'); % h_waitbar = waitbar(0, 'Searching for matching images.'); % make sure ds is dividable by two params.matching.ds = params.matching.ds + mod(params.matching.ds,2); DD = results.DD; parfor N = params.matching.ds/2+1 : size(results.DD,2)-params.matching.ds/2 matches(N,:) = findSingleMatch(DD, N, params); % waitbar(N / size(results.DD,2), h_waitbar); end % save it if params.matching.save save(filename, 'matches'); end results.matches = matches; end end %% function match = findSingleMatch(DD, N, params) % We shall search for matches using velocities between % params.matching.vmin and params.matching.vmax. % However, not every vskip may be neccessary to check. So we first find % out, which v leads to different trajectories: move_min = params.matching.vmin * params.matching.ds; move_max = params.matching.vmax * params.matching.ds; move = move_min:move_max; v = move / params.matching.ds; idx_add = repmat([0:params.matching.ds], size(v,2),1); % idx_add = floor(idx_add.*v); idx_add = floor(idx_add .* repmat(v', 1, length(idx_add))); % this is where our trajectory starts n_start = N - params.matching.ds/2; x= repmat([n_start : n_start+params.matching.ds], length(v), 1); score = zeros(1,size(DD,1)); % add a line of inf costs so that we penalize running out of data DD=[DD; inf(1,size(DD,2))]; y_max = size(DD,1); xx = (x-1) * y_max; for s=1:size(DD,1) y = min(idx_add+s, y_max); idx = xx + y; score(s) = min(sum(DD(idx),2)); end % find min score and 2nd smallest score outside of a window % around the minimum [min_value, min_idx] = min(score); window = max(1, min_idx-params.matching.Rwindow/2):min(length(score), min_idx+params.matching.Rwindow/2); not_window = setxor(1:length(score), window); min_value_2nd = min(score(not_window)); match = [min_idx + params.matching.ds/2; min_value / min_value_2nd]; end
github
siam1251/Fast-SeqSLAM-master
doPreprocessing.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/doPreprocessing.m
3,385
utf_8
8fe0631458856f931c18a870de22ec6d
% function results = doPreprocessing(params) for i = 1:length(params.dataset) % shall we just load it? filename = sprintf('%s/preprocessing-%s%s.mat', params.dataset(i).savePath, params.dataset(i).saveFile, params.saveSuffix); if params.dataset(i).preprocessing.load && exist(filename, 'file'); r = load(filename); display(sprintf('Loading file %s ...', filename)); results.dataset(i).preprocessing = r.results_preprocessing; else % or shall we actually calculate it? p = params; p.dataset=params.dataset(i); results.dataset(i).preprocessing = single(preprocessing(p)); if params.dataset(i).preprocessing.save results_preprocessing = single(results.dataset(i).preprocessing); save(filename, 'results_preprocessing'); end end end end %% function dhog = preprocessing(params) display(sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % h_waitbar = waitbar(0,sprintf('Preprocessing dataset %s, indices %d - %d ...', params.dataset.name, params.dataset.imageIndices(1), params.dataset.imageIndices(end))); % allocate memory for all the processed images % n = length(params.dataset.imageIndices); % m = params.downsample.size(1)*params.downsample.size(2); % % if ~isempty(params.dataset.crop) % c = params.dataset.crop; % m = (c(3)-c(1)+1) * (c(4)-c(2)+1); % end % % images = zeros(m,n, 'uint8'); % j=1; l = length( params.dataset.imageIndices); dhog = []*l; readFormat = strcat('%s/%s%0',num2str(params.dataset.numberFormat),'d%s%s') % for every image .... indices = params.dataset.imageIndices; j = 1; for i = indices filename = sprintf(readFormat, params.dataset.imagePath, ... params.dataset.prefix, ... i, ... params.dataset.suffix, ... params.dataset.extension); filename im = imread(filename); % convert to grayscale if params.DO_GRAYLEVEL im = rgb2gray(im); end % resize the image if params.DO_RESIZE im = imresize(im, params.downsample.size, params.downsample.method); end % do patch normalization % it didn't work well with hog descriptor if params.DO_PATCHNORMALIZATION im = patchNormalize(im, params); end %h = size(im, 1); %w = size(im, 2); %scale = 1; % im = imresize(im,scale,'bicubic','AntiAliasing',false); %reshape = [h/scale, w/scale]; %im = cv.resize(im, reshape, 'Interpolation', 'Cubic'); [d, visualization] = extractHOGFeatures(im,'CellSize',params.dataset.cellSize); dhog(j,:)= d(:); j=j+1; % waitbar((i-params.dataset.imageIndices(1)) / (params.dataset.imageIndices(end)-params.dataset.imageIndices(1))); end dhog = dhog'; % close(h_waitbar); size(dhog) end
github
siam1251/Fast-SeqSLAM-master
doContrastEnhancement.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/doContrastEnhancement.m
2,164
iso_8859_1
3a5452345b7790f53cc34d31bc80b80d
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = doContrastEnhancement(results, params) filename = sprintf('%s/differenceEnhanced-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.contrastEnhanced.load && exist(filename, 'file') display(sprintf('Loading contrast-enhanced image distance matrix from file %s ...', filename)); dd = load(filename); results.DD = dd.DD; else display('Performing local contrast enhancement on difference matrix ...'); % h_waitbar = waitbar(0,'Local contrast enhancement on difference matrix'); DD = zeros(size(results.D), 'single'); D=results.D; parfor i = 1:size(results.D,1) a=max(1, i-params.contrastEnhancement.R/2); b=min(size(D,1), i+params.contrastEnhancement.R/2); v = D(a:b, :); DD(i,:) = (D(i,:) - mean(v)) ./ std(v); % waitbar(i/size(results.D, 1)); end % let the minimum distance be 0 results.DD = DD-min(min(DD)); % save it? if params.contrastEnhanced.save DD = results.DD; save(filename, 'DD'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
showPrecisionCurve.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/showPrecisionCurve.m
3,142
utf_8
a08bcb7a982e8b06ae0f9695de25d40f
function showPrecisionCurve(matches,targs,range,imageSkip,filename) % Compute empirical curves dvs = matches(:,2)'; predicted = matches(:,1)'; [TPR_emp, FPR_emp, PPV_emp] = precision_recall(targs, dvs, predicted,range,imageSkip); points = [TPR_emp,PPV_emp]; filename = strcat('prcurve/',filename) save(filename,'points'); cols = [200 45 43; 37 64 180; 0 176 80; 0 0 0]/255; figure,hold on; plot(TPR_emp, PPV_emp, '-o', 'color', cols(1,:), 'linewidth', 2); axis([0 1 0 1]); xlabel('TPR (recall)'); ylabel('PPV (precision)'); title('PR curves'); set(gca, 'box', 'on'); end % Computes empirical statistics based on classification output. % % Usage: % [TPR, FPR, PPV, AUC, AP] = prc_stats_empirical(targs, dvs) % % Arguments: % targs: true class labels (targets) % dvs: decision values output by the classifier % % Return values: % TPR: true positive rate (recall) % FPR: false positive rate % PPV: positive predictive value (precision) % AUC: area under the ROC curve % AP: area under the PR curve (average precision) % % Each of these return vectors has length(desireds)+1 elements. % % Literature: % K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann (2010). The % binormal assumption on precision-recall curves. In: Proceedings of % the 20th International Conference on Pattern Recognition (ICPR). % Kay H. Brodersen & Cheng Soon Ong, ETH Zurich, Switzerland % $Id: prc_stats_empirical.m 5529 2010-04-22 21:10:32Z bkay $ % ------------------------------------------------------------------------- function [TPR, FPR, PPV, AUC, AP] = precision_recall(targs, dvs,predicted,range,imageSkip) % Check input %assert(all(size(targs)==size(dvs))); %assert(all(targs==-1 | targs==1)); % Sort decision values and true labels according to decision values n = length(dvs); [dvs_sorted,idx] = sort(dvs,'descend'); targs_sorted = targs(idx*imageSkip); predicted_sorted = predicted(idx)*imageSkip; % Inititalize accumulators TPR = repmat(NaN,1,n+1); FPR = repmat(NaN,1,n+1); PPV = repmat(NaN,1,n+1); % Now slide the threshold along the decision values (the threshold % always lies in between two values; here, the threshold represents the % decision value immediately to the right of it) fn = zeros(size(predicted_sorted)); for thr = 1:length(dvs_sorted)+1 % values greater than thr are positive and smaller than thr are NaN TP = sum(abs(targs_sorted(thr:end)-predicted_sorted(thr:end))<range);%you have to match whether trgs(i) == dvs(i) FN = sum(~isnan(targs_sorted(1:thr-1))); TN = sum(isnan(targs_sorted(1:thr-1))); FP = sum(abs(targs_sorted(thr:end)-predicted_sorted(thr:end))>=range); TPR(thr) = TP/(TP+FN);%recall FPR(thr) = FP/(FP+TN); PPV(thr) = TP/(TP+FP);%precision end % Compute empirical AUC %[tmp,tmp,tmp,AUC] = perfcurve(targs,dvs,1); % Compute empirical AP %AP = abs(trapz(TPR(~isnan(PPV)),PPV(~isnan(PPV)))); end
github
siam1251/Fast-SeqSLAM-master
openSeqSLAM.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/openSeqSLAM.m
1,771
iso_8859_1
5dbda8263e359c610136532c81968d9a
% % Copyright 2013, Niko Sünderhauf % [email protected] % % This file is part of OpenSeqSLAM. % % OpenSeqSLAM is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % OpenSeqSLAM is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with OpenSeqSLAM. If not, see <http://www.gnu.org/licenses/>. function results = openSeqSLAM(params) results=[]; % try to allocate 3 CPU cores (adjust to your machine) for parallel % processing try if matlabpool('size')==0 matlabpool 3 end catch display('No parallel computing toolbox installed.'); end % begin with preprocessing of the images if params.DO_PREPROCESSING results = doPreprocessing(params); end % image difference matrix if params.DO_DIFF_MATRIX results = doDifferenceMatrix(results, params); end % contrast enhancement if params.DO_CONTRAST_ENHANCEMENT results = doContrastEnhancement(results, params); else if params.DO_DIFF_MATRIX results.DD = results.D; end end % find the matches if params.DO_FIND_MATCHES results = doFindMatchesModified(results, params); end end
github
siam1251/Fast-SeqSLAM-master
doDifferenceMatrix.m
.m
Fast-SeqSLAM-master/demo/prcurve/local_fast_seqSlam/doDifferenceMatrix.m
4,276
utf_8
df153f88a48c06590dd35c17112304ce
% % function results = doDifferenceMatrix(results, params) addpath(genpath('./flann')); filename = sprintf('%s/difference-%s-%s%s.mat', params.savePath, params.dataset(1).saveFile, params.dataset(2).saveFile, params.saveSuffix); if params.differenceMatrix.load && exist(filename, 'file') display(sprintf('Loading image difference matrix from file %s ...', filename)); d = load(filename); results.D = d.D; else if length(results.dataset)<2 display('Error: Cannot calculate difference matrix with less than 2 datasets.'); return; end display('Calculating image difference matrix ...'); % h_waitbar = waitbar(0,'Calculating image difference matrix'); dhog1 = results.dataset(1).preprocessing; dhog2 = results.dataset(2).preprocessing; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%calcualtion of similarity matrix using FLANN nearest neighbour%%%%%%%%%%%%%%%%%%%%%%%% flann_set_distance_type(params.distance_type, 0); N = params.N; [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % % [index1, search_params1 ] = flann_build_index(dhog1, struct('algorithm','linear'... % )); [result1, ndists1] = flann_search(index1, dhog2, N, search_params1); result1 = result1'; ndists1 = ndists1'; d1 = ndists1(:); tmp = [1: length(result1)]'; column1 = repmat(tmp, N, 1); column2 = result1(:); size(column2) S1 = table(column1, column2); %S1 '------------' %% cross check [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm',params.algorithm, 'trees',params.trees,... 'checks',params.checks)); % [index2, search_params2 ] = flann_build_index(dhog2, struct('algorithm','linear', 'trees',8,... % 'checks',64)); [result2, ndists2] = flann_search(index2, dhog1, N, search_params2); %result2 contains nodes of M2 for each node 1, 2, 3, .. of M1 %result1 contains nodes of M1 for each node 1, 2, 3, .. of M2 result2 = result2'; ndists2 = ndists2'; d2 = ndists2(:); tmp = [1: length(result2)]'; column2 = repmat(tmp, N, 1); column1 = result2(:); S2 = table(column1, column2); [S, i1, i2] = intersect(S1, S2); %S = (N1_i, N2_j) S_arr = table2array(S); %[S_arr d1(i1)] ini_dist = params.initial_distance; similarity_matrix = (-ini_dist)*ones(length(result1), length(result2)); %S_arr(:,1) = nodes from map 2 %S_arr(:,2) = nodes from map 1 % --------M2---------- % | 4 0 0 0 0 0 % | 1 0 0 0 0 0 % S= M1 0 0 0 3 0 0 % | 0 4 0 0 0 0 % | 0 0 2 0 0 0 % | 0 1 0 0 0 0 ind = sub2ind(size(similarity_matrix), S_arr(:,1), S_arr(:,2)); %similarity_matrix(ind) = 30; %[S_arr d1(i1)] %ind = sub2ind(size(similarity_matrix), S_arr(:,2), S_arr(:,1)); similarity_matrix(ind) = (d1(i1) + d2(i2))/2; %%%%fill the values by max value max_val = max(similarity_matrix(:)); similarity_matrix(similarity_matrix == -params.initial_distance) = params.mul_unit*max_val; %params.matching.seqMaxValue = params.mul_unit*max_val*(params.matching.ds+1); D = similarity_matrix; %%%%%%%%calculation of similarity matrix%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% results.D=D; % save it if params.differenceMatrix.save save(filename, 'D'); end % close(h_waitbar); end end
github
siam1251/Fast-SeqSLAM-master
pdftops.m
.m
Fast-SeqSLAM-master/graphs/altmany-export_fig-113e357/pdftops.m
5,528
utf_8
1042ce73e979a940784d5ea5f57f1ffc
function varargout = pdftops(cmd) %PDFTOPS Calls a local pdftops executable with the input command % % Example: % [status result] = pdftops(cmd) % % Attempts to locate a pdftops executable, finally asking the user to % specify the directory pdftops was installed into. The resulting path is % stored for future reference. % % Once found, the executable is called with the input command string. % % This function requires that you have pdftops (from the Xpdf package) % installed on your system. You can download this from: % http://www.foolabs.com/xpdf % % IN: % cmd - Command string to be passed into pdftops (e.g. '-help'). % % OUT: % status - 0 iff command ran without problem. % result - Output from pdftops. % Copyright: Oliver Woodford, 2009-2010 % Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS. % Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path under linux. % 23/01/2014 - Add full path to pdftops.txt in warning. % 27/05/2015 - Fixed alert in case of missing pdftops; fixed code indentation % 02/05/2016 - Added possible error explanation suggested by Michael Pacer (issue #137) % 02/05/2016 - Search additional possible paths suggested by Jonas Stein (issue #147) % 03/05/2016 - Display the specific error message if pdftops fails for some reason (issue #148) % Call pdftops [varargout{1:nargout}] = system(sprintf('"%s" %s', xpdf_path, cmd)); end function path_ = xpdf_path % Return a valid path % Start with the currently set path path_ = user_string('pdftops'); % Check the path works if check_xpdf_path(path_) return end % Check whether the binary is on the path if ispc bin = 'pdftops.exe'; else bin = 'pdftops'; end if check_store_xpdf_path(bin) path_ = bin; return end % Search the obvious places if ispc paths = {'C:\Program Files\xpdf\pdftops.exe', 'C:\Program Files (x86)\xpdf\pdftops.exe'}; else paths = {'/usr/bin/pdftops', '/usr/local/bin/pdftops'}; end for a = 1:numel(paths) path_ = paths{a}; if check_store_xpdf_path(path_) return end end % Ask the user to enter the path errMsg1 = 'Pdftops not found. Please locate the program, or install xpdf-tools from '; url1 = 'http://foolabs.com/xpdf'; fprintf(2, '%s\n', [errMsg1 '<a href="matlab:web(''-browser'',''' url1 ''');">' url1 '</a>']); errMsg1 = [errMsg1 url1]; %if strncmp(computer,'MAC',3) % Is a Mac % % Give separate warning as the MacOS uigetdir dialogue box doesn't have a title % uiwait(warndlg(errMsg1)) %end % Provide an alternative possible explanation as per issue #137 errMsg2 = 'If you have pdftops installed, perhaps Matlab is shaddowing it as described in '; url2 = 'https://github.com/altmany/export_fig/issues/137'; fprintf(2, '%s\n', [errMsg2 '<a href="matlab:web(''-browser'',''' url2 ''');">issue #137</a>']); errMsg2 = [errMsg2 url1]; state = 0; while 1 if state option1 = 'Install pdftops'; else option1 = 'Issue #137'; end answer = questdlg({errMsg1,'',errMsg2},'Pdftops error',option1,'Locate pdftops','Cancel','Cancel'); drawnow; % prevent a Matlab hang: http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem switch answer case 'Install pdftops' web('-browser',url1); case 'Issue #137' web('-browser',url2); state = 1; case 'Locate pdftops' base = uigetdir('/', errMsg1); if isequal(base, 0) % User hit cancel or closed window break end base = [base filesep]; %#ok<AGROW> bin_dir = {'', ['bin' filesep], ['lib' filesep]}; for a = 1:numel(bin_dir) path_ = [base bin_dir{a} bin]; if exist(path_, 'file') == 2 break end end if check_store_xpdf_path(path_) return end otherwise % User hit Cancel or closed window break end end error('pdftops executable not found.'); end function good = check_store_xpdf_path(path_) % Check the path is valid good = check_xpdf_path(path_); if ~good return end % Update the current default path to the path found if ~user_string('pdftops', path_) warning('Path to pdftops executable could not be saved. Enter it manually in %s.', fullfile(fileparts(which('user_string.m')), '.ignore', 'pdftops.txt')); return end end function good = check_xpdf_path(path_) % Check the path is valid [good, message] = system(sprintf('"%s" -h', path_)); %#ok<ASGLU> % system returns good = 1 even when the command runs % Look for something distinct in the help text good = ~isempty(strfind(message, 'PostScript')); % Display the error message if the pdftops executable exists but fails for some reason if ~good && exist(path_,'file') % file exists but generates an error fprintf('Error running %s:\n', path_); fprintf(2,'%s\n\n',message); end end