code: def line_cluster(self, linesP):
      points = []
      for i in range(0, len(linesP)):
          l = linesP[i]
          x_center = float(
              (max(l[2], l[0]) - min(l[2], l[0]))
          ) / 2.0 + min(l[2], l[0])
          y_center = float(
              (max(l[3], l[1]) - min(l[3], l[1]))
          ) / 2.0 + min(l[3], l[1])
          points.append([x_center, y_center])
  
      dbscan = DBSCAN(
          eps=50, min_samples=2, metric=self.get_distance
      ).fit(points)
  
      labels = dbscan.labels_
      n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
      cluster_list = list([] for i in range(n_clusters_))
      if linesP is not None:
          for i in range(0, len(linesP)):
              if labels[i] == -1:
                  continue
              l = linesP[i]
              x1, y1, x2, y2 = l
              if y2 >= y1:
                  cluster_list[labels[i]].append([x1, y1, x2, y2])
              else:
                  ll = [x2, y2, x1, y1]
                  cluster_list[labels[i]].append(ll)
  
      return cluster_list
  
imports: import argparse
  import codecs
  import os
  import yaml
  import numpy
  import cv2
  from sklearn.cluster import DBSCAN
  from pptracking.python.det_infer import load_predictor
label_desc: Use image node_id 9 for calling the LaneSegPredictor obj's underlying member method code with example usage: obj.line_cluster(linesP) and returns: cluster_list