class_docstr: !
  @brief Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results.
docstr: !
  @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.
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code: 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
  
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 4 for calling the syncsegm_analyser obj's underlying member method code with example usage: obj.allocate_colors(eps, noise_size) and returns: real_segments