code: def hough_line(
      self, binary_img, min_line=50, min_line_points=50, max_line_gap=10
  ):
      linesP = cv2.HoughLinesP(
          binary_img,
          1,
          np.pi / 180,
          min_line,
          None,
          min_line_points,
          max_line_gap,
      )
      if linesP is None:
          return [], None
  
      coarse_cluster_list = self.line_cluster(linesP[:, 0])
      filter_lines_output, direction = self.filter_lines(
          coarse_cluster_list
      )
  
      return filter_lines_output, direction
  
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 10 for calling the LaneSegPredictor obj's underlying member method code with example usage: obj.hough_line(binary_img, min_line, min_line_points, max_line_gap) and returns: filter_lines_output,  direction, list,  None