class_docstr: !
  @brief Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results.
docstr: !
  @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.
docstr_tok: ["!","@","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","."]
code: 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
  
imports: from math import floor
  from PIL import Image
  from pyclustering.cluster.syncnet import syncnet
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.nnet import solve_type, initial_type
  from pyclustering.nnet.sync import sync_visualizer
  from pyclustering.utils import read_image
label_desc: Use image node_id 1 to create a new syncsegm_analyser object  with example: obj = syncsegm_analyser(color_analyser, object_segment_analysers)