code: def __init__(self, args, cfg):
      self.args = args
      self.pre_process_list = {
          "Resize_Mult32": {
              "limit_side_len": cfg["det_limit_side_len"],
              "limit_type": cfg["det_limit_type"],
          },
          "NormalizeImage": {
              "mean": [0.485, 0.456, 0.406],
              "std": [0.229, 0.224, 0.225],
              "is_scale": True,
          },
          "Permute": {},
      }
      postprocess_params = {}
      postprocess_params["name"] = "DBPostProcess"
      postprocess_params["thresh"] = 0.3
      postprocess_params["box_thresh"] = 0.6
      postprocess_params["max_candidates"] = 1000
      postprocess_params["unclip_ratio"] = 1.5
      postprocess_params["use_dilation"] = False
      postprocess_params["score_mode"] = "fast"
  
      self.postprocess_op = build_post_process(postprocess_params)
      (
          self.predictor,
          self.input_tensor,
          self.output_tensors,
          self.config,
      ) = create_predictor(args, cfg, "det")
  
imports: import os
  import yaml
  import glob
  from functools import reduce
  import time
  import cv2
  import numpy
  import math
  import paddle
  import sys
  from python.infer import get_test_images
  from python.preprocess import preprocess, NormalizeImage, Permute, Resize_Mult32
  from pipeline.ppvehicle.vehicle_plateutils import create_predictor, get_infer_gpuid, get_rotate_crop_image, draw_boxes
  from pipeline.ppvehicle.vehicleplate_postprocess import build_post_process
  from pipeline.cfg_utils import merge_cfg, print_arguments, argsparser
label_desc: Use image node_id 1 to create a new PlateDetector object from inherited base classes: object with example: obj = PlateDetector(args, cfg)