function varargout = image_dbr(varargin)%IMAGE_DBR M-file for image_dbr.fig%      IMAGE_DBR, by itself, creates a new IMAGE_DBR or raises the existing%      singleton*.%%      H = IMAGE_DBR returns the handle to a new IMAGE_DBR or the handle to%      the existing singleton*.%%      IMAGE_DBR('Property','Value',...) creates a new IMAGE_DBR using the%      given property value pairs. Unrecognized properties are passed via%      varargin to image_dbr_OpeningFcn.  This calling syntax produces a%      warning when there is an existing singleton*.%%      IMAGE_DBR('CALLBACK') and IMAGE_DBR('CALLBACK',hObject,...) call the%      local function named CALLBACK in IMAGE_DBR.M with the given input%      arguments.%%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one%      instance to run (singleton)".%% See also: GUIDE, GUIDATA, GUIHANDLES% Edit the above text to modify the response to help image_dbr% Last Modified by GUIDE v2.5 16-Aug-2008 23:50:43% Begin initialization code - DO NOT EDITgui_Singleton = 1;gui_State = struct('gui_Name',       mfilename, ...                   'gui_Singleton',  gui_Singleton, ...                   'gui_OpeningFcn', @image_dbr_OpeningFcn, ...                   'gui_OutputFcn',  @image_dbr_OutputFcn, ...                   'gui_LayoutFcn',  [], ...                   'gui_Callback',   []);if nargin && ischar(varargin{1})   gui_State.gui_Callback = str2func(varargin{1});endif nargout    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});else    gui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDITfunction image_dbr_OpeningFcn(hObject, eventdata, handles, varargin)% Main routine, this starts after creation of the guide                              % Handling of input parameters	handles.output = hObject;	h = my_handles(handles);	if length(varargin) < 4		cclasf = meanc;           % default combiner	else 		cclasf = varargin{4};	end	                              	if length(varargin) < 3		clasf = knnc([],1);       % default classifier	else		clasf = varargin{3};	end		featsets = varargin{2};      % arrange feature sets in cells	if ~iscell(featsets), featsets = {featsets}; end	if ~iscell(clasf)		clasf = repmat({clasf},1,length(featsets));	else		if length(clasf) ~= length(featsets)			error('Number of classifiers and number of feature sets do not match')		end	end		database = varargin{1};       % dataset or datafile, just used for image display	m = size(database,1);	labels = zeros(1,m);          % user give labels: 0 (no), 1 (target, 2 (outlier)	S = randperm(m);              % initial set of images to be shown	for j=1:4                     % feature weight slider bars default invisible		set(h.feat_weight(j),'visible','off');		set(handles.(['text' num2str(j+2)]),'visible','off');	end	                              % optimize location sliders in case of 2 or 3 sliders 	if length(featsets) == 1, feath = 2; end	if length(featsets) == 2, feath = [2 3]; end	if length(featsets) == 3, feath = [1 2 3]; end	if length(featsets) == 4, feath = [1 2 3 4]; end	                        	for j=1:length(featsets)      % initialize feature sets and related sliders		if size(featsets{j},1) ~= m			error('Feature set(s) have wrong size')		end		set(handles.(['text' num2str(feath(j)+2)]),'visible','on');		set(handles.(['text' num2str(feath(j)+2)]),'string',getname(featsets{j}));		% re-arrange datasets with features, use 'target' and 'outlier' as labels		featsets{j} = dataset(featsets{j});		featsets{j} = featsets{j}*scalem(featsets{j},'variance');		featsets{j} = dataset(+featsets{j});		featsets{j} = setlablist(featsets{j},char('target','outlier'));		featsets{j} = setnlab(featsets{j},labels);		set(h.feat_weight(feath(j)),'value',1);       % initial weights: 1		set(h.feat_weight(feath(j)),'visible','on');	end		if length(featsets) == 1 % no feature slider bar in case of one feature set		set(h.feat_weight(feath(1)),'visible','off');		set(handles.(['text' num2str(feath(j)+2)]),'visible','off');	end		% store useful data in figure user data field	guidata(hObject,{h,database,featsets,clasf,cclasf}); 		set(h.classify,'userdata','reset'); % initial procedure: reset	set(h.all,'value',0);               % initial ranking:	set(h.unlabeled,'value',2);         % unlabeled objects	labels_im = zeros(1,10);            % labels of shown images	weights   = zeros(1,4);             % weights of feature sets	Targets = [];	Outliers= [];	show_targ_out(database,Targets,1,h,'Targets');  % initialise target /	show_targ_out(database,Outliers,1,h,'Outliers');% outlier images	S = randperm(m);	waitstate = prwaitbar;	prwaitbar off	                            % main loop	while(1)                               % S, images to be shown		show_images(database,S,1,h);         % show images on axes								                           % wait for the user 		                                     % ..........		uiwait                               % uiresume is activated by one of the 		                                     % CLASSIFY / LABEL / RESET / QUIT buttonS.		                                     % get labels and weights		Targets = get(h.targets,'userdata'); % get Tagerts		Outliers = get(h.outliers,'userdata');%abd Outliers		if isempty(guidata(hObject))         % we need to stop (QUIT button is pressed)			guidata(hObject,{S,Targets,Outliers});% 			prwaitbar(waitstate);			return                             % return ranking S, Targets and Outliers.		end				proch = proc(h);                   % classify / label / reset				if ~strcmp(proch,'reset');					for j=1:10                         % labels are 1-2, convert from 0-1 check boxes				labels_im(j) = 2 - get(h.handlab(j),'value');			end			for j=1:length(featsets)           % get feature weights from sliders				weights(j) = get(h.feat_weight(feath(j)),'value');			end			T = [1:m];                         % potential trainingset	%		Targets = get(h.targets,'userdata');	%		Outliers = get(h.outliers,'userdata');			t = get(h.next,'userdata'); n = t{2};       			SS = [];                           % training set indices in case of 'label' procedure			for j=1:10                         % update labeling				SAct = S(j-1+n);                 % object number j in S				if labels_im(j) == 1					if isempty(find(Targets==SAct)), Targets = [Targets SAct]; end				else					if isempty(find(Outliers==SAct)), Outliers = [Outliers SAct]; end				end				SS = [SS SAct];			end			set(h.targets,'userdata',Targets);             % store indices of Targets			set(h.outliers,'userdata',Outliers);           % and outliers			labels = zeros(1,m);                           % construct labels training set			labels(Targets)  = ones(1,length(Targets));			labels(Outliers) = 2*ones(1,length(Outliers));			if strcmp(proch,'label')                   % Label: learn from present set only				if all(labels_im == labels_im(1))        % present set is uniformly labeled					if labels_im(1) == 1 % no outliers, use remaining unlabeled dataset as outlier						labels_train = ones(size(labels))*2; % Label entire set as outlier						labels_train(SS) = ones(size(SS));   % Label present set as target					else                 % no targets, use remaining dataset as target						labels_train = ones(size(labels));   % Label entire set as target						labels_train(SS) = 2*ones(size(SS)); % Label present set as outlier					end				else					T = SS;					labels_train = labels_im;				end				[S,W] = train_classify(featsets,T,labels_train,clasf,cclasf,weights);			else                                       % Classify procedure: 				T = find(labels > 0);                    % learn from all Targets and Outliers				labels_train = labels(T);				[S,W] = train_classify(featsets,T,labels_train,clasf,cclasf,weights);			end			U = find(labels(S) == 0);			set(h.all,'userdata',{S,U});			if (get(h.all,'value') == 0)				S = S(U);			end		else                 % reset			S = randperm(m);		end		% S is the ranked set of indices, either for all objects, or for just		% the unlabeled objects	endreturn		function h = my_handles(handles)% construct meaningfull handle names / numbers	T = [1 2 3 4 7 8 9 10 11 12]; % correct checkbox numbering	h.image   = zeros(1,10);	h.handlab = zeros(1,10);	h.feat_weight = zeros(1,4);	for j=1:10		h.image(j) = handles.(['axes' num2str(j)]);		h.imnum(j) = handles.(['text' num2str(j+8)]);		h.obnum(j) = handles.(['text' num2str(j+18)]);		h.handlab(j) = handles.(['checkbox' num2str(T(j))]);	end	for j=1:4		h.feat_weight(j) = handles.(['slider' num2str(j)]);	end	h.all  = handles.radiobutton4;	h.unlabeled  = handles.radiobutton5;	h.classify  = handles.pushbutton1;	h.targets = handles.axes11;	h.outliers = handles.axes12;	h.target_slider = handles.slider5;	h.outliert_slider = handles.slider6;	h.target_title = handles.text1;	h.outlier_title = handles.text2;	h.target_obnum = handles.text29;	h.outlier_obnum = handles.text30;	h.target_delete = handles.pushbutton2;	h.target_move = handles.pushbutton3;	h.outlier_delete = handles.pushbutton5;	h.outlier_move = handles.pushbutton4;	h.next = handles.pushbutton8;	h.previous = handles.pushbutton9;returnfunction varargout = image_dbr_OutputFcn(hObject, eventdata, handles)% takes care of final return of main return	varargout = guidata(hObject);	delete(hObject);returnfunction pushbutton1_Callback(hObject, eventdata, handles)% CLASSIFY button, this reactivates the UIWAIT function in the main loop	s = guidata(hObject);	h = s{1};	set(h.classify,'userdata','classify');	uiresumereturnfunction show_images(database,S,n,h)% show images on axes	Targets = get(h.targets,'userdata');	Outliers = get(h.outliers,'userdata');	im = data2im(database,S(n:n+9));	               % the new images	for j=1:10		show_im(im,j,h.image(j));		set(h.handlab(j),'value',1); % set target default		set(h.imnum(j),'string',num2str(n+j-1));		set(h.obnum(j),'string',num2str(S(n+j-1)));	end	set(h.next,'userdata',{S,n});	              % set visibility of next / previous buttons	if n > 1		set(h.previous,'visible','on');	else		set(h.previous,'visible','off');	end	if n < (length(S)-9)		set(h.next,'visible','on');	else		set(h.next,'visible','off');	end	               % the targets, show most recent one	if ~isempty(Targets)		show_targ_out(database,Targets,length(Targets),h,'Targets'); 		set(h.target_obnum,'string',num2str(Targets(end)));	end	               % the outliers, show most recent one	if ~isempty(Outliers)		show_targ_out(database,Outliers,length(Outliers),h,'Outliers');		set(h.outlier_obnum,'string',num2str(Outliers(end)));	endreturnfunction show_targ_out(database,Pointers,n,h,name)% show targets or outliers and update properties	if strcmp(name,'Targets') % create single handle variable for targets and outliers		h_axes = h.targets;		h_slider = h.target_slider;		h_title = h.target_title;