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from __future__ import (print_function, division, unicode_literals,
absolute_import)
from sympathy.api import node
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
from sympathy.utils import port
from skimage import io, data
from sylib.imageprocessing.image import Image
import sylib.imageprocessing
import numpy as np
import os.path
[docs]class LoadImage(node.Node):
"""
Loads an image from a file given by a datasource.
"""
name = 'Load Image'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'image_load.svg'
description = 'Loads an image from a datasource'
nodeid = 'syip.loadimage'
tags = Tags(Tag.ImageProcessing.IO)
parameters = node.parameters()
parameters.set_boolean(
'as_greyscale', label='As greyscale',
description='Transforms image to greyscale if not already such',
value=False)
inputs = Ports(
[Port.Datasource('Source of image data. Must be a file on disk',
name='source')])
outputs = Ports(
[Image('Output image', name='image')])
def execute(self, node_context):
source = node_context.input['source']
if source.decode_type() != 'FILE':
raise NotImplementedError(
'Image loading from databases not implemented.')
source_path = source.decode_path()
as_greyscale = node_context.parameters['as_greyscale'].value
image = io.imread(source_path, as_gray=as_greyscale)
if image.dtype == 'uint8':
image = image / 256.0
node_context.output['image'].set_image(image)
[docs]class LoadImageList(node.Node):
"""
Loads a list of images from a file given a list of datasources.
"""
name = 'Load Image List'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'image_load.svg'
description = 'Loads an image from a datasource'
nodeid = 'syip.loadimage_list'
tags = Tags(Tag.ImageProcessing.IO)
parameters = node.parameters()
parameters.set_boolean(
'as_greyscale', label='As greyscale',
description='Transforms image to greyscale if not already such',
value=False)
inputs = Ports(
[port.CustomPort('[datasource]', 'Source of image data. Must be a file on disk',
name='source')])
outputs = Ports(
[port.CustomPort('[image]','Output image', name='image')])
def execute(self, node_context):
for idx, source in enumerate(node_context.input['source']):
self.set_progress((100*idx) / len(node_context.input['source']))
if source.decode_type() != 'FILE':
raise NotImplementedError(
'Image loading from databases not implemented.')
source_path = source.decode_path()
as_greyscale = node_context.parameters['as_greyscale'].value
image = io.imread(source_path, as_gray=as_greyscale)
if image.dtype == 'uint8':
image = image / 256.0
image_obj = sylib.imageprocessing.image.File()
image_obj.set_image(image)
node_context.output['image'].append(image_obj)
[docs]class ExampleImage(node.Node):
"""
Loads an image from the built-in default example images in scikit-image
"""
name = 'Example Image'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'image_examples.svg'
description = (
'Loads an image from the built-in default example images in '
'scikit-image')
nodeid = 'syip.exampleimage'
tags = Tags(Tag.ImageProcessing.IO)
parameters = node.parameters()
parameters.set_string(
'source', value='coins', label='Image',
description='Selected predefined image',
editor=node.Util.combo_editor(
sorted([
'astronaut', 'camera', 'candy', 'checkerboard', 'chelsea (cat)', 'clock',
'coffee', 'coins', 'horse', 'hubble deep field', 'page',
'motorcycle_left', 'motorcycle_right',
'immunohistochemistry', 'moon', 'rocket', 'text']))
)
outputs = Ports([Image('Output image', name='output')])
def execute(self, node_context):
source_name = node_context.parameters['source'].value
if source_name == 'astronaut':
im = data.astronaut()
elif source_name == 'camera':
im = data.camera()
elif source_name == 'checkerboard':
im = data.checkerboard()
elif source_name == 'chelsea (cat)':
im = data.chelsea()
elif source_name == 'clock':
im = data.clock()
elif source_name == 'coffee':
im = data.coffee()
elif source_name == 'coins':
im = data.coins()
elif source_name == 'horse':
im = data.horse()
elif source_name == 'hubble deep field':
im = data.hubble_deep_field()
elif source_name == 'immunohistochemistry':
im = data.immunohistochemistry()
elif source_name == 'moon':
im = data.moon()
elif source_name == 'rocket':
im = data.rocket()
elif source_name == 'text':
im = data.text()
elif source_name == 'page':
im = data.page()
elif source_name == 'motorcycle_left':
im = data.stereo_motorcycle()[0]
elif source_name == 'motorcycle_right':
im = data.stereo_motorcycle()[1]
elif source_name == 'candy':
path = os.path.join(
os.path.dirname(sylib.imageprocessing.__file__),
"candy.png")
im = io.imread(path, as_gray=False)
else:
im = np.zeros((512, 512, 3))
if im.dtype == 'uint8':
im = im/256.0
node_context.output['output'].set_image(im)
