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"""!
@brief Double-layer oscillatory network with phase oscillator for image segmentation.
@details Implementation based on paper @cite inproceedings::nnet::syncsegm::1.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import warnings
from math import floor
try:
from PIL import Image
except Exception as error_instance:
warnings.warn("Impossible to import PIL (please, install 'PIL'), pyclustering's visualization "
"functionality is partially not available (details: '%s')." % str(error_instance))
from pyclustering.cluster.syncnet import syncnet
from pyclustering.nnet import solve_type, initial_type
from pyclustering.nnet.sync import sync_visualizer
from pyclustering.utils import read_image
class syncsegm_visualizer:
"""!
@brief Result visualizer of double-layer oscillatory network 'syncsegm'.
"""
@staticmethod
def show_first_layer_dynamic(analyser):
"""!
@brief Shows output dynamic of the first layer.
@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
"""
sync_visualizer.show_output_dynamic(analyser.get_first_layer_analyser());
@staticmethod
def show_second_layer_dynamic(analyser):
"""!
@brief Shows output dynamic of the second layer.
@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
"""
second_layer_analysers = analyser.get_second_layer_analysers();
analysers_sequence = [ object_segment_analyser['analyser'] for object_segment_analyser in second_layer_analysers ]
sync_visualizer.show_output_dynamics(analysers_sequence);
class syncsegm_analyser:
"""!
@brief Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results.
"""
def __init__(self, color_analyser, object_segment_analysers = None):
"""!
@brief Constructor of the analyser.
@param[in] color_analyser (list): Analyser of coloring segmentation results of the first layer.
@param[in] object_segment_analysers (list): Analysers of objects on image segments - results of the second layer.
"""
self.__color_analyser = color_analyser;
self.__object_segment_analysers = object_segment_analysers;
def get_first_layer_analyser(self):
"""!
@brief Returns analyser of coloring segmentation of the first layer.
"""
return self.__color_analyser;
def get_second_layer_analysers(self):
"""!
@brief Returns analysers of object segmentation of the second layer.
"""
return self.__object_segment_analysers;
def allocate_colors(self, eps = 0.01, noise_size = 1):
"""!
@brief Allocates color segments.
@param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
@param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
@return (list) Color segments where each color segment consists of indexes of pixels that forms color segment.
"""
segments = self.__color_analyser.allocate_clusters(eps);
real_segments = [cluster for cluster in segments if len(cluster) > noise_size];
return real_segments;
def allocate_objects(self, eps = 0.01, noise_size = 1):
"""!
@brief Allocates object segments.
@param[in] eps (double): Tolerance level that define maximal difference between phases of oscillators in one segment.
@param[in] noise_size (uint): Threshold that defines noise - segments size (in pixels) that is less then the threshold is considered as a noise.
@return (list) Object segments where each object segment consists of indexes of pixels that forms object segment.
"""
if (self.__object_segment_analysers is None):
return [];
segments = [];
for object_segment_analyser in self.__object_segment_analysers:
indexes = object_segment_analyser['color_segment'];
analyser = object_segment_analyser['analyser'];
segments += analyser.allocate_clusters(eps, indexes);
real_segments = [segment for segment in segments if len(segment) > noise_size];
return real_segments;
class syncsegm:
"""!
@brief Class represents segmentation algorithm syncsegm.
@details syncsegm is a bio-inspired algorithm that is based on double-layer oscillatory network that uses modified Kuramoto model.
Algorithm extracts colors and colored objects. It uses only CCORE (C++ implementation of pyclustering) parts to implement the algorithm.
CCORE option is True by default to use sync network in the pyclustering core - C/C++ shared library for processing that significantly increases performance.
Example:
@code
# create oscillatory for image segmentaion - extract colors (radius 128) and objects (radius 4),
# and ignore noise (segments with size that is less than 10 pixels)
algorithm = syncsegm(128, 4, 10);
# extract segments (colors and objects)
analyser = algorithm(path_to_file);
# obtain segmentation results (only colors - from the first layer)
color_segments = analyser.allocate_colors(0.01, 10);
draw_image_mask_segments(path_to_file, color_segments);
# obtain segmentation results (objects - from the second layer)
object_segments = analyser.allocate_objects(0.01, 10);
draw_image_mask_segments(path_to_file, object_segments);
@endcode
"""
def __init__(self, color_radius, object_radius, noise_size = 0, ccore = True):
"""!
@brief Contructor of the oscillatory network SYNC for cluster analysis.
@param[in] color_radius (double): Radius of color connectivity (color similarity) for the first layer.
@param[in] object_radius (double): Radius of object connectivity (object similarity) for the second layer,
if 'None' then object segmentation is not performed (only color segmentation).
@param[in] noise_size (double): Size of segment that should be considered as a noise and ignored by the second layer.
@param[in] ccore (bool): If 'True' then C/C++ implementation is used to increase performance.
"""
self.__color_radius = color_radius;
self.__object_radius = object_radius;
self.__noise_size = noise_size;
self.__order_color = 0.9995;
self.__order_object = 0.999;
self.__network = None;
self.__ccore = ccore;
def process(self, image_source, collect_dynamic = False, order_color = 0.9995, order_object = 0.999):
"""!
@brief Performs image segmentation.
@param[in] image_source (string): Path to image file that should be processed.
@param[in] collect_dynamic (bool): If 'True' then whole dynamic of each layer of the network is collected.
@param[in] order_color (double): Local synchronization order for the first layer - coloring segmentation.
@param[in] order_object (double): Local synchronization order for the second layer - object segmentation.
@return (syncsegm_analyser) Analyser of segmentation results by the network.
"""
self.__order_color = order_color
self.__order_object = order_object
data = read_image(image_source)
color_analyser = self.__analyse_colors(data, collect_dynamic)
if self.__object_radius is None:
return syncsegm_analyser(color_analyser, None)
object_segment_analysers = self.__analyse_objects(image_source, color_analyser, collect_dynamic)
return syncsegm_analyser(color_analyser, object_segment_analysers)
def __analyse_colors(self, image_data, collect_dynamic):
"""!
@brief Performs color segmentation by the first layer.
@param[in] image_data (array_like): Image sample as a array-like structure.
@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
@return (syncnet_analyser) Analyser of color segmentation results of the first layer.
"""
network = syncnet(image_data, self.__color_radius, initial_phases = initial_type.RANDOM_GAUSSIAN, ccore = self.__ccore);
analyser = network.process(self.__order_color, solve_type.FAST, collect_dynamic);
return analyser;
def __analyse_objects(self, image_source, color_analyser, collect_dynamic):
"""!
@brief Performs object segmentation by the second layer.
@param[in] image_source (string): Path to image file that should be processed.
@param[in] color_analyser (syncnet_analyser): Analyser of color segmentation results.
@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the first layer of the network is collected.
@return (map) Analysers of object segments.
"""
# continue analysis
pointer_image = Image.open(image_source);
image_size = pointer_image.size;
object_analysers = [];
color_segments = color_analyser.allocate_clusters();
for segment in color_segments:
object_analyser = self.__analyse_color_segment(image_size, segment, collect_dynamic);
if (object_analyser is not None):
object_analysers.append( { 'color_segment': segment, 'analyser': object_analyser } );
pointer_image.close();
return object_analysers;
def __analyse_color_segment(self, image_size, color_segment, collect_dynamic):
"""!
@brief Performs object segmentation of separate segment.
@param[in] image_size (list): Image size presented as a [width x height].
@param[in] color_segment (list): Image segment that should be processed.
@param[in] collect_dynamic (bool): If 'True' then whole dynamic of the second layer of the network is collected.
@return (syncnet_analyser) Analyser of object segmentation results of the second layer.
"""
coordinates = self.__extract_location_coordinates(image_size, color_segment);
if (len(coordinates) < self.__noise_size):
return None;
network = syncnet(coordinates, self.__object_radius, initial_phases = initial_type.EQUIPARTITION, ccore = True);
analyser = network.process(self.__order_object, solve_type.FAST, collect_dynamic);
return analyser;
def __extract_location_coordinates(self, image_size, color_segment):
"""!
@brief Extracts coordinates of specified image segment.
@param[in] image_size (list): Image size presented as a [width x height].
@param[in] color_segment (list): Image segment whose coordinates should be extracted.
@return (list) Coordinates of each pixel.
"""
coordinates = [];
for index in color_segment:
y = floor(index / image_size[0]);
x = index - y * image_size[0];
coordinates.append([x, y]);
return coordinates;
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