# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# SYMPATHY FOR DATA COMMERCIAL LICENSE
# You should have received a link to the License with Sympathy for Data.
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)
