# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# Sympathy for Data 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, version 3 of the License.
#
# Sympathy for Data 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 Sympathy for Data. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from sympathy.api import node
from sympathy.api.nodeconfig import Ports
from skimage import morphology
from sylib.imageprocessing.image import ImagePort
from sylib.imageprocessing.algorithm_selector import ImageFiltering_abstract
from sylib.imageprocessing.generic_filtering import GenericImageFiltering
API_URL = 'http://scikit-image.org/docs/0.13.x/api/'
CONVEX_HULL_DESC = (
'The convex hull is the set of pixels included in the smallest convex '
'polygon that surround all white pixels in the input image.'
)
def _remove_small_holes(im, area_threshold, connectivity):
"""
Handling for renamed parameter in remove_small_holes.
"""
try:
return morphology.remove_small_holes(
im, area_threshold=area_threshold, connectivity=connectivity)
except TypeError:
# scikit-image <= 0.13.
return morphology.remove_small_holes(
im, min_size=area_threshold, connectivity=connectivity)
MORPHOLOGY_ALGS = {
'labeling': {
'description': (
'Creates a unique integer label for each connected component '
'in an integer valued or binary image.'),
'diagonal neighborhood': (
'If true then also consider diagonals for connectivity'),
'multi_chromatic': False,
'url': API_URL+'skimage.morphology.html#skimage.morphology.label',
'algorithm': lambda im, par: morphology.label(
im,
connectivity=2 if par['diagonal neighborhood'].value else 1)
},
'remove small holes': {
'description': (
'Removes small holes from an integer or boolean image.'),
'diagonal neighborhood': (
'If true then also consider diagonals for connectivity'),
'n': 'Maximum size in pixels of areas to remove (default 64)',
'multi_chromatic': False,
'url': (
API_URL+'skimage.morphology.html'
'#skimage.morphology.remove_small_holes'),
'algorithm': lambda im, par: _remove_small_holes(
im, area_threshold=par['n'].value,
connectivity=2 if par['diagonal neighborhood'].value else 1)
},
'remove small objects': {
'description': (
'Removes connected components smaller than the given size.'),
'diagonal neighborhood': (
'If true then also consider diagonals for connectivity'),
'n': 'Maximum size in pixels of areas to remove (default 64)',
'multi_chromatic': False,
'url': (
API_URL+'skimage.morphology.html'
'#skimage.morphology.remove_small_objects'),
'algorithm': lambda im, par: morphology.remove_small_objects(
im, min_size=par['n'].value,
connectivity=2 if par['diagonal neighborhood'].value else 1)
},
'skeletonize': {
'description': (
'Returns the skeleton of a binary image. '
'Thinning is used to reduce each connected component in a '
'binary image to a single-pixel wide skeleton.'),
'multi_chromatic': False,
'url': (
API_URL+'skimage.morphology.html#skimage.morphology.skeletonize'),
'algorithm': lambda im, par: morphology.skeletonize(np.copy(im))
},
'convex hull, image': {
'description': (
'Computes the convex hull of a binary image.\n' +
CONVEX_HULL_DESC),
'multi_chromatic': False,
'url': (
API_URL+'skimage.morphology.html'
'#skimage.morphology.convex_hull_image'),
'algorithm': lambda im, par: morphology.convex_hull_image(im)
},
'convex hull, objects': {
'description': (
'Computes the convex hull of each object in a binary image.\n' +
CONVEX_HULL_DESC +
'\nThis function uses labeling to define unique objects, finds'
'the convex hull of each using convex_hull_image,\nand '
'combines these regions with logical OR. Be aware the convex'
'hulls of unconnected objects may overlap in the result'),
'multi_chromatic': False,
'url': (
API_URL + 'skimage.morphology.html'
'#skimage.morphology.convex_hull_object'),
'algorithm': lambda im, par: morphology.convex_hull_object(im)
},
}
MORPHOLOGY_PARAMETERS = ['diagonal neighborhood', 'n']
MORPHOLOGY_TYPES = {
'n': int,
'diagonal neighborhood': bool,
}
MORPHOLOGY_DEFAULTS = {
'n': 12,
'diagonal neighborhood': False,
}
