filtm.m

The pattern recognition matlab toolbox

%FILTM Mapping to filter objects in datasets and datafiles
%
%    B = FILTM(A,FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
%    B = A*FILTM([],FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
%
% INPUT
%    A               Dataset or datafile
%    FILTER_COMMAND  String with function name
%    {PAR1, ...  }   Cell array with optional parameters to FILTER_COMMAND
%    SIZE            Output size of the mapping (default: input size)
%
% OUTPUT
%    B               Dataset containing images processed by FILTER_COMMAND
%
% DESCRIPTION
% For each object stored in A a filter operation is performed as
%
%    OBJECT_OUT = FILTER_COMMAND(OBJECT_IN,PAR1,PAR2,....)
%
% The results are collected and stored in B. In case A (and thereby B) is
% a datafile, execution is postponed until conversion into a dataset, or a
% call to SAVEDATAFILE.
%
% EXAMPLES
% B = FILTM(A,'CONV2',{[-1 0 1; -1 0 1; -1 0 1],'same'});
% Performs a convolution with a horizontal gradient filter (see CONV2).
%
% There is a similar command FILTIM that is recommended for handling
% multi-band images.
%
% SEE ALSO
% DATASETS, DATAFILES, IM2OBJ, DATA2IM, IM2FEAT, DATGAUSS, DATFILT, FILTIM

% Copyright: R.P.W. Duin, [e-mail]
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands

function b = filtm(a,command,pars,outsize)

	prtrace(mfilename);

	if nargin < 4, outsize = []; end
	if nargin < 3, pars = {}; end
	if nargin < 2
		error('No command given')
	end
	if ~iscell(pars), pars = {pars}; end
	
	mapname = 'dataset image processing';
	
	if isempty(a)                    % no data, so just mapping definition
		b = mapping(mfilename,'fixed',{command,pars});
		if ~isempty(outsize)
			b = setsize_out(b,outsize);
		end
		b = setname(b,mapname);
		return
	end

  if isdatafile(a)                 % for datafiles filters are stored
    if isempty(getpostproc(a))     % as preprocessing (if no preproc defined)
      b = addpreproc(a,command,pars,outsize);
    else                           % or as mapping as postprocessing
		  v = mapping(mfilename,'fixed',{command,pars});
		  if ~isempty(outsize)
			  v = setsize_out(v,outsize);
		  end
		  v = setname(v,mapname);
		  b = addpostproc(a,v);
    end
		if ~isempty(outsize)
			b = setfeatsize(b,outsize);
		end
		return
    
	elseif isdataset(a)
   
    m = size(a,1);                 
    d = +a;
    imsize = getfeatsize(a);
    
	  % Perform command on first image to check whether image size stays equal
		if length(imsize) == 1
			imsize = [1 imsize];
		end
	  first = feval(command,reshape(d(1,:),imsize),pars{:});
    first = double(first); % for DipLib users
	  if isempty(outsize)
		  outsize = size(first);
	  end
    % process all other images
    
		out = repmat(first(:)',m,1);
		for i = 2:m
			ima = double(feval(command,reshape(d(i,:),imsize),pars{:}));
			sima = size(ima);
			if (any(outsize ~= sima(1:2)))
				error('All image sizes should be the same')
		  end
			out(i,:) = ima(:)';
		end
    
    % store processed images in dataset
    
		b = setdata(a,out);
		b = setfeatsize(b,outsize);
    
  end
  
return