code: def _ocr(self, img):
      h, w = img.shape[:2]
      dt_boxes, det_elapse = self.text_detector(copy.deepcopy(img))
      dt_boxes = sorted_boxes(dt_boxes)
  
      r_boxes = []
      for box in dt_boxes:
          x_min = max(0, box[:, 0].min() - 1)
          x_max = min(w, box[:, 0].max() + 1)
          y_min = max(0, box[:, 1].min() - 1)
          y_max = min(h, box[:, 1].max() + 1)
          box = [x_min, y_min, x_max, y_max]
          r_boxes.append(box)
      dt_boxes = np.array(r_boxes)
      logger.debug(
          "dt_boxes num : {}, elapse : {}".format(
              len(dt_boxes), det_elapse
          )
      )
      if dt_boxes is None:
          return None, None
  
      img_crop_list = []
      for i in range(len(dt_boxes)):
          det_box = dt_boxes[i]
          x0, y0, x1, y1 = expand(2, det_box, img.shape)
          text_rect = img[int(y0) : int(y1), int(x0) : int(x1), :]
          img_crop_list.append(text_rect)
      rec_res, rec_elapse = self.text_recognizer(img_crop_list)
      logger.debug(
          "rec_res num  : {}, elapse : {}".format(
              len(rec_res), rec_elapse
          )
      )
      return dt_boxes, rec_res, det_elapse, rec_elapse
  
imports: import os
  import sys
  import cv2
  import copy
  import logging
  import numpy
  import time
  import tools.infer.predict_rec
  import tools.infer.predict_det
  import tools.infer.utility
  from tools.infer.predict_system import sorted_boxes
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppocr.utils.logging import get_logger
  from ppstructure.table.matcher import TableMatch
  from ppstructure.table.table_master_match import TableMasterMatcher
  from ppstructure.utility import parse_args
  import ppstructure.table.predict_structure
label_desc: Use image node_id 4 for calling the TableSystem obj's underlying member method code with example usage: obj._ocr(img) and returns: dt_boxes,  rec_res,  det_elapse,  rec_elapse, None,  None