# 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, Tag, Tags
from skimage import filters
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/'
def alg_auto_threshold(im, params):
method = params['auto threshold method'].value
fns = {
'otsu': filters.threshold_otsu,
'yen': filters.threshold_yen,
'isodata': filters.threshold_isodata,
'li': filters.threshold_li,
'minimum': filters.threshold_minimum,
'mean': filters.threshold_mean,
'triangle': filters.threshold_triangle,
'median': lambda x: np.median(x)
}
fn = fns[method]
return im > fn(im)
THRESHOLD_ALGS = {
'basic': {
'description': 'Compares each channel with a threshold',
'threshold': 'Threshold value to compare with',
'multi_chromatic': False,
'algorithm': lambda im, par: im >= par['threshold'].value
},
'automatic': {
'description': (
'Performs global thresholding based a selection of automatic '
'algorithms with none or few parameters'),
'auto threshold method': (
'Method used for calculating threshold'),
'url': (
API_URL+'skimage.filters.html'),
'algorithm': alg_auto_threshold,
'multi_chromatic': False,
},
'adaptive': {
'description': (
'Applies an adaptive threshold to an array.\n\n'
'Also known as local or dynamic thresholding where the '
'threshold value is the weighted mean for the local '
'neighborhood of a pixel subtracted by a constant.'),
'kernel size': (
'Size of blocks used during threshold check.\n'
'Must be an odd number. (default 3)'),
'threshold method': (
'Method used for calculating adaptive threshold'),
'offset': (
'Constant subtracted from weighted mean of neighborhood '
'to calculate the local threshold value. (default 0.0)'),
'sigma': (
'Standard deviation of gaussian kernel when method '
'gaussian is used.'),
'multi_chromatic': False,
'url': (
API_URL+'skimage.filters.html#skimage.filters.threshold_local'),
'algorithm': lambda im, par: im > filters.threshold_local(
im, par['kernel size'].value,
method=par['threshold method'].value,
offset=par['offset'].value,
param=par['sigma'].value)
},
}
THRESHOLD_PARAMETERS = [
'threshold method', 'auto threshold method', 'threshold',
'kernel size', 'offset', 'sigma',
]
THRESHOLD_TYPES = {
'threshold method': ['gaussian', 'mean', 'median'],
'auto threshold method': [
'otsu', 'yen', 'isodata', 'li', 'minimum', 'mean', 'triangle', 'median'
],
'offset': float, 'threshold': float, 'kernel size': int, 'sigma': float
}
THRESHOLD_DEFAULTS = {
'threshold': 0.15, 'kernel size': 21, 'sigma': 21.0,
'auto threshold method': 'otsu', 'offset': 0.0
}
[docs]
class ThresholdImage(ImageFiltering_abstract, GenericImageFiltering,
node.Node):
name = 'Threshold image'
icon = 'image_threshold.svg'
description = (
'Applies a threshold to an image giving a boolean output')
nodeid = 'syip.threshold'
tags = Tags(Tag.ImageProcessing.Segmentation)
algorithms = THRESHOLD_ALGS
options_list = THRESHOLD_PARAMETERS
options_types = THRESHOLD_TYPES
options_default = THRESHOLD_DEFAULTS
parameters = node.parameters()
parameters.set_string('algorithm', value=next(iter(algorithms)),
description='', label='Algorithm')
ImageFiltering_abstract.generate_parameters(parameters, options_types,
options_default)
inputs = Ports([
ImagePort('source image to filter', name='source'),
])
outputs = Ports([
ImagePort('result after filtering', name='result'),
])
__doc__ = ImageFiltering_abstract.generate_docstring(
description, algorithms, options_list, inputs, outputs)
