code: def __call__(self, img, return_ocr_result_in_table=False, img_idx=0):
      time_dict = {
          "image_orientation": 0,
          "layout": 0,
          "table": 0,
          "table_match": 0,
          "det": 0,
          "rec": 0,
          "kie": 0,
          "all": 0,
      }
      start = time.time()
      if self.image_orientation_predictor is not None:
          tic = time.time()
          cls_result = self.image_orientation_predictor.predict(
              input_data=img
          )
          cls_res = next(cls_result)
          angle = cls_res[0]["label_names"][0]
          cv_rotate_code = {
              "90": cv2.ROTATE_90_COUNTERCLOCKWISE,
              "180": cv2.ROTATE_180,
              "270": cv2.ROTATE_90_CLOCKWISE,
          }
          if angle in cv_rotate_code:
              img = cv2.rotate(img, cv_rotate_code[angle])
          toc = time.time()
          time_dict["image_orientation"] = toc - tic
      if self.mode == "structure":
          ori_im = img.copy()
          if self.layout_predictor is not None:
              layout_res, elapse = self.layout_predictor(img)
              time_dict["layout"] += elapse
          else:
              h, w = ori_im.shape[:2]
              layout_res = [dict(bbox=None, label="table")]
          res_list = []
          for region in layout_res:
              res = ""
              if region["bbox"] is not None:
                  x1, y1, x2, y2 = region["bbox"]
                  x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
                  roi_img = ori_im[y1:y2, x1:x2, :]
              else:
                  x1, y1, x2, y2 = 0, 0, w, h
                  roi_img = ori_im
              if region["label"] == "table":
                  if self.table_system is not None:
                      res, table_time_dict = self.table_system(
                          roi_img, return_ocr_result_in_table
                      )
                      time_dict["table"] += table_time_dict["table"]
                      time_dict["table_match"] += table_time_dict[
                          "match"
                      ]
                      time_dict["det"] += table_time_dict["det"]
                      time_dict["rec"] += table_time_dict["rec"]
              else:
                  if self.text_system is not None:
                      if self.recovery:
                          wht_im = np.ones(
                              ori_im.shape, dtype=ori_im.dtype
                          )
                          wht_im[y1:y2, x1:x2, :] = roi_img
                          (
                              filter_boxes,
                              filter_rec_res,
                              ocr_time_dict,
                          ) = self.text_system(wht_im)
                      else:
                          (
                              filter_boxes,
                              filter_rec_res,
                              ocr_time_dict,
                          ) = self.text_system(roi_img)
                      time_dict["det"] += ocr_time_dict["det"]
                      time_dict["rec"] += ocr_time_dict["rec"]
  
                      # remove style char,
                      # when using the recognition model trained on the PubtabNet dataset,
                      # it will recognize the text format in the table, such as <b>
                      style_token = [
                          "<strike>",
                          "<strike>",
                          "<sup>",
                          "</sub>",
                          "<b>",
                          "</b>",
                          "<sub>",
                          "</sup>",
                          "<overline>",
                          "</overline>",
                          "<underline>",
                          "</underline>",
                          "<i>",
                          "</i>",
                      ]
                      res = []
                      for box, rec_res in zip(
                          filter_boxes, filter_rec_res
                      ):
                          rec_str, rec_conf = rec_res
                          for token in style_token:
                              if token in rec_str:
                                  rec_str = rec_str.replace(token, "")
                          if not self.recovery:
                              box += [x1, y1]
                          res.append(
                              {
                                  "text": rec_str,
                                  "confidence": float(rec_conf),
                                  "text_region": box.tolist(),
                              }
                          )
              res_list.append(
                  {
                      "type": region["label"].lower(),
                      "bbox": [x1, y1, x2, y2],
                      "img": roi_img,
                      "res": res,
                      "img_idx": img_idx,
                  }
              )
          end = time.time()
          time_dict["all"] = end - start
          return res_list, time_dict
      elif self.mode == "kie":
          re_res, elapse = self.kie_predictor(img)
          time_dict["kie"] = elapse
          time_dict["all"] = elapse
          return re_res[0], time_dict
      return None, None
  
imports: import os
  import sys
  import subprocess
  import cv2
  import json
  import numpy
  import time
  import logging
  from copy import deepcopy
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppocr.utils.logging import get_logger
  from ppocr.utils.visual import draw_ser_results, draw_re_results
  from tools.infer.predict_system import TextSystem
  from ppstructure.layout.predict_layout import LayoutPredictor
  from ppstructure.table.predict_table import TableSystem, to_excel
  from ppstructure.utility import parse_args, draw_structure_result
label_desc: Use image node_id 2 for calling the StructureSystem obj's underlying member method code with example usage: obj.__call__(img, return_ocr_result_in_table, img_idx) and returns: None,  None, res_list,  time_dict, re_res,  time_dict