Source code for node_extractdata
# Copyright (c) 2017, System Engineering Software Society
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from __future__ import (print_function, division, unicode_literals,
absolute_import)
from sympathy.api import node, ParameterView
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
import string
import numpy as np
from skimage import transform
from skimage import io
from sylib.imageprocessing.image import Image
from sylib.imageprocessing.algorithm_selector import ImageFiltering_abstract
from sylib.imageprocessing.algorithm_selector import AlgorithmParameterWidget
from sylib.imageprocessing.color import grayscale_transform
[docs]class ExtractData(ImageFiltering_abstract, node.Node):
name = 'Extract Image Data'
author = 'Mathias Broxvall'
copyright = '(C) 2017 System Engineering Software Society'
version = '0.1'
icon = 'image_and_table_to_table.svg'
description = 'Extracts table data from an image based on tabular input data'
nodeid = 'syip.extractdata'
tags = Tags(Tag.ImageProcessing.Extract)
def alg_integrate(im, table, params, result):
def clamp(arr, axis):
return np.maximum(0, np.minimum(arr.astype('int'), im.shape[axis]-1))
result.set_name("Image integrals")
channels = 1 if len(im.shape)<3 else im.shape[2]
for channel in range(channels):
start_coords = zip(
clamp(table.get_column_to_array(params['start y'].value), 0),
clamp(table.get_column_to_array(params['start x'].value), 1)
)
end_coords = zip(
clamp(table.get_column_to_array(params['end y'].value), 0),
clamp(table.get_column_to_array(params['end x'].value), 1)
)
start_coords = list(start_coords)
end_coords = list(end_coords)
integrals = transform.integrate(
im[:,:,channel], start_coords, end_coords
)
result.set_column_from_array(
"ch{0}_integral".format(channel), integrals
)
def alg_pixelvalue(im, table, params, result):
result.set_name("Image pixels")
channels = 1 if len(im.shape)<3 else im.shape[2]
for channel in range(channels):
xs = table.get_column_to_array(params['x'].value).astype('int')
ys = table.get_column_to_array(params['y'].value).astype('int')
values = im[ys,xs,channel]
result.set_column_from_array("ch{0}_values".format(channel), values)
algorithms = {
'integrate': {
'description': (
'Computes the integral on all points in a square between two'
'corner points,\nmust have an integral image as input.'
'Operates on each channel separately'
),
'start x': (
'Column containing starting points on X axis for integral'
),
'start y': (
'Column containing starting points on Y axis for integral'
),
'end x': 'Column containing ending points on X axis for integral',
'end y': 'Column containing ending points on X axis for integral',
'algorithm': alg_integrate
},
'pixel values': {
'description': (
'Extracts the pixel values at positions given by X and Y'
'table rows'
),
'x': 'Column containing X coordinates of the points to extract',
'y': 'Column containing Y coordinates of the points to extract',
'algorithm': alg_pixelvalue
},
}
options_list = [
'start x', 'start y', 'end x', 'end y', 'x', 'y',
]
options_types = {
'x': str,
'y': str,
'start x': str,
'start y': str,
'end x': str,
'end y': str,
}
options_default = {
'x': 'x',
'y': 'y',
'start x': 'x0',
'start y': 'y0',
'end x': 'x1',
'end y': 'y1',
}
parameters = node.parameters()
parameters.set_string(
'algorithm', value=next(iter(algorithms)),
description='', label=''
)
ImageFiltering_abstract.generate_parameters(
parameters, options_types, options_default
)
inputs = Ports([
Image('Source image to extract data from', name='source_im'),
Port.Table(
'Table with parameters for data extraction', name='source_table'
),
])
outputs = Ports([
Port.Table('Table with results', name='result'),
])
__doc__ = ImageFiltering_abstract.generate_docstring(
description, algorithms, options_list, inputs, outputs
)
def execute(self, node_context):
source_im = node_context.input['source_im'].get_image()
source_table = node_context.input['source_table']
params = node_context.parameters
alg_name = params['algorithm'].value
if len(source_im.shape) < 3:
source_im = source_im.reshape(source_im.shape+(1,))
alg = self.algorithms[alg_name]['algorithm']
result = node_context.output['result']
result.set_name('Statistics')
alg(source_im, source_table, params, result)