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# -*- coding: utf-8 -*-
#
# Copyright (C) 2001-2005 Ichiro Fujinaga, Michael Droettboom, and Karl MacMillan
#
# 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 2
# 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 this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
from gamera.plugin import *
import _thinning
class Thinning(PluginFunction):
self_type = ImageType([ONEBIT])
return_type = ImageType([ONEBIT])
doc_examples = [(ONEBIT,)]
class thin_zs(Thinning):
"""
Thins (skeletonizes) a ONEBIT image using the Zhang and Suen
algorithm.
The resulting skeleton is not a medial axis transformation, and
the ends of the skeleton will not extend to the edges of the
original image.
T. Y. Zhang and C. Y. Suen. 1984. A Fast Parallel Algorithm for
Thinning Digital Patterns., *Communications of ACM*, 2(3).
R. C. Gonzalez and P. Wintz. 1987 *Digital Image Processing.*,
2. edition. 398-402.
"""
pass
class thin_hs(Thinning):
"""
Derives the medial axis transformation from a ONEBIT image using the
Haralick and Shapiro algorithm.
Unlike thin_zs_ and thin_lc_, this function performs a medial axis
transformation, and the ends of the skeleton extend to the corners of
the original image.
Consider using thin_hs_large_image_ instead, for faster performance on
large images with a lot of connected components.
R. M. Haralick and L. G. Shapiro. 1992. *Computer and Robot Vision*,
Vol. 1, Chapter 5 (especially 5.10.1). Reading, MA: Addison-Wesley.
"""
pass
class medial_axis_transform_hs(Thinning):
""""This function is an alias for thin_hs_."""
pure_python = True
def __call__(self):
return _thinning.thin_hs(self)
class thin_hs_large_image(Thinning):
"""
Thins (skeletonizes) a ONEBIT image using the Haralick and Shapiro
algorithm.
Unlike thin_hs_, this algorithm performs skeletonization on one
connected component at a time. On large images with a lot of
connected components, this can be significantly faster. However,
for small images with a single connected component, this has
unnecessary overhead, which is why both versions are included.
Please note cc_analysis results in a labelled image, which you can
reset afterwards with reset_onebit_image().
"""
pure_python = True
def __call__(self):
copy = self.image_copy()
ccs = copy.cc_analysis()
for cc in ccs:
thin_cc = cc.thin_hs()
cc.and_image(thin_cc, in_place=True)
return copy
__call__ = staticmethod(__call__)
class medial_axis_transform_large_image_hs(Thinning):
"""
This function is an alias for thin_hs_large_image_.
"""
pure_python = True
def __call__(self):
return thin_hs_large_image()(self)
class thin_lc(Thinning):
"""
Thins (skeletonizes) a ONEBIT image using the Lee and Chen
algorithm.
This function is a simple extension to the Zhang and Suen
algorithm in thin_zs_ that ensure that no two pixels are more than
4-connected.
The resulting skeleton is not a medial axis transformation, and
the ends of the skeleton will not extend to the edges of the
original image.
H.-J. Lee and B. Chen. 1992. Recognition of handwritten Chinese
characters via short line segments. *Pattern Recognition*. 25(5)
543-552.
"""
pass
class ThinningModule(PluginModule):
category = "Thinning"
functions = [thin_zs, thin_hs, medial_axis_transform_hs,
thin_hs_large_image, medial_axis_transform_large_image_hs,
thin_lc]
cpp_headers = ["thinning.hpp"]
author = u"Michael Droettboom and Karl MacMillan (based on code by \u00d8ivind Due Trier and Qian Huang)"
url = "http://gamera.sourceforge.net/"
module = ThinningModule()
del Thinning
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