pyclustering/nnet/syncsegm.py
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 __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 __analyse_color_segment ( self , image_size , color_segment , collect_dynamic ) : 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
!
        @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.
!