%IM_SKEL_MEASURE Fixed mapping measuring skeleton-based features
%
% F = IM_SKEL_MEASURE(A,FEATURES)
% F = A*IM_SKEL_MEASURE([],FEATURES)
% F = A*IM_SKEL_MEASURE(FEATURES)
%
% INPUT
% A Dataset with binary object images dataset
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with computed features
%
% DESCRIPTION
% The following features may be computed on the skeleton images in A:
% 'branch', 'end', 'link', 'single'. They may be combined in a cell
% array or given separately.
%
% Use FEATURES = 'all' for computing all features (default).
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, DATAFILES, IM_SKEL, DIP_IMAGE, BSKELETON
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function b = im_skel_meas(varargin)
argin = shiftargin(varargin,{'char','cell'});
argin = setdefaults(argin,[],'all');
if mapping_task(argin,'definition')
b = define_mapping(argin,'fixed');
b = setname(b,'Skeleton features');
else
features = cell(1,numel(argin)-1);
[a,features{:}] = deal(argin{:});
if iscell(features{1})
features = features{1};
elseif strcmp(features{1},'all')
features = {'branch', 'end', 'link', 'single'};
end
if isa(a,'prdataset') % allows datafiles too
isobjim(a);
b = filtim(a,mfilename,{features});
b = setfeatlab(b,features);
elseif isa(a,'double') || isa(a,'dip_image') % here we have a single image
a = double(a);
b = [];
if ~iscell(features), features = {features}; end
cleanp = bwmorph(a,'clean');
endp = bwmorph(cleanp,'endpoints');
branchp = bwmorph(cleanp,'branchpoints');
se = sum(endp(:));
sb = sum(branchp(:));
st = sum(a(:));
ss = st-sum(cleanp(:));
sl = st-se-sb-ss;
for i = 1:length(features)
switch features{i}
case 'branch'
b = [ b sb ];
case 'end'
b = [ b se ];
case 'link'
b = [ b sl ];
case 'single'
cleanp = bwmorph(a,'clean');
b = [ b ss ];
otherwise
error('Unknown feature desired: %s',features{i})
end
end
end
end
return