Source code for node_imagelists

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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

import numpy as np
from sylib.imageprocessing.image import Image
from sylib.imageprocessing.image import File as ImageFile
from sylib.imageprocessing.algorithm_selector import (
    ImageFiltering_abstract)


[docs]class ImageToList(ImageFiltering_abstract, node.Node): name = 'Image to List' author = 'Mathias Broxvall' copyright = '(C) 2017 System Engineering Software Society' version = '0.1' icon = 'image_list.svg' description = ('Generates a list of images based on algorithms operating ' 'on one image') nodeid = 'syip.image2list' tags = Tags(Tag.ImageProcessing.Layers) def alg_from_labels(im, results, par): if not np.issubdtype(im.dtype, np.integer): # Early return, input is not a labeled image return if len(im.shape) == 3: im = im[:, :, 0] for value in range(np.min(im), np.max(im)): mask = im == value if value == 0 and not par['do background'].value: pass elif np.sum(mask) > 0: new_image = ImageFile() new_image.set_image(mask) results.append(new_image) def alg_from_channels(im, results, par): if len(im.shape) < 3: im = im.reshape(im.shape + (1,)) channels = im.shape[2] for channel in range(channels): new_image = ImageFile() new_image.set_image(im[:, :, channel]) results.append(new_image) algorithms = { 'from labels': { 'description': ('Generates an image mask selecting each label in ' 'an image once'), 'do background': 'If true include the background object (ID=0)', 'multi_chromatic': False, 'algorithm': alg_from_labels}, 'from channels': { 'description': ('Generates a list of grayscale images from each ' 'channel in input image'), 'multi_chromatic': True, 'algorithm': alg_from_channels}} options_list = ['do background'] options_types = {'do background': bool} options_default = {'do background': True} 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([ Image('source image', name='source')]) outputs = Ports([ Port.Custom('[image]', 'Resulting list of images', name='results')]) __doc__ = ImageFiltering_abstract.generate_docstring( description, algorithms, options_list, inputs, outputs) def execute(self, node_context): image = node_context.input['source'].get_image() params = node_context.parameters alg_name = params['algorithm'].value results = node_context.output['results'] alg = self.algorithms[alg_name]['algorithm'] alg(image, results, params)
[docs]class ListToImage(ImageFiltering_abstract, node.Node): name = 'List to Image' author = 'Mathias Broxvall' copyright = '(C) 2017 System Engineering Software Society' version = '0.1' icon = 'image_list2image.svg' description = 'Generates an image based on a list of images' nodeid = 'syip.list2image' tags = Tags(Tag.ImageProcessing.Layers) def alg_concatenate(images, par): max_x, max_y = 0, 0 channels = 0 if len(images) == 0: return np.zeros((1, 1, 1)) dtype = images[0].get_image().dtype for im_obj in images: im = im_obj.get_image() max_x = max(max_x, im.shape[1]) max_y = max(max_y, im.shape[0]) if len(im.shape) > 2: channels += im.shape[2] else: channels += 1 result = np.zeros((max_y, max_x, channels), dtype=dtype) ch = 0 for im_obj in images: im = im_obj.get_image() if len(im.shape) < 3: im = im.reshape(im.shape + (1,)) result[:im.shape[0], :im.shape[1], ch:ch+im.shape[2]] = im ch += im.shape[2] return result algorithms = { 'concatenate channels': { 'description': ('Generates an image by concatenating all channels ' 'in the input images. Resulting datatype given by ' 'first image in the list'), 'multi_chromatic': True, 'algorithm': alg_concatenate}} options_list = ['do background'] options_types = {'do background': bool} options_default = {'do background': True} 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([ Port.Custom('[image]', 'Input list of images', name='inputs') ]) outputs = Ports([ Image('result image', name='result'), ]) __doc__ = ImageFiltering_abstract.generate_docstring( description, algorithms, options_list, inputs, outputs) def execute(self, node_context): params = node_context.parameters output = node_context.output['result'] inputs = node_context.input['inputs'] alg_name = params['algorithm'].value alg = self.algorithms[alg_name]['algorithm'] im = alg(inputs, params) output.set_image(im)