code: def __init__(
      self,
      num_classes=2,
      ori_shape=(720, 1280),
      cut_height=0,
      thresh=0.6,
      test_gt_json=None,
      save_dir="output/",
  ):
      super(TusimpleProcessor, self).__init__()
      self.num_classes = num_classes
      self.dump_to_json = []
      self.save_dir = save_dir
      self.test_gt_json = test_gt_json
      self.color_map = [
          (255, 0, 0),
          (0, 255, 0),
          (0, 0, 255),
          (255, 255, 0),
          (255, 0, 255),
          (0, 255, 125),
          (50, 100, 50),
          (100, 50, 100),
      ]
      self.laneProcessor = LaneProcessor(
          num_classes=self.num_classes,
          ori_shape=ori_shape,
          cut_height=cut_height,
          y_pixel_gap=10,
          points_nums=56,
          thresh=thresh,
          smooth=True,
      )
  
file_path: PaddleHub/modules/image/semantic_segmentation/bisenet_lane_segmentation/lane_processor/tusimple_processor.py
imports: import os
  import cv2
  import json
  import paddle.nn
  from .lane import LaneEval
  from .get_lane_coords import LaneProcessor
label_desc: Use image node_id 1 to create a new TusimpleProcessor object  with example: obj = TusimpleProcessor(num_classes, ori_shape, cut_height, thresh, test_gt_json, save_dir)