code: def __call__(self, img):
      ori_im = img.copy()
      data = {"image": img}
  
      st = time.time()
  
      if self.args.benchmark:
          self.autolog.times.start()
  
      data = transform(data, self.preprocess_op)
      img, shape_list = data
      if img is None:
          return None, 0
      img = np.expand_dims(img, axis=0)
      shape_list = np.expand_dims(shape_list, axis=0)
      img = img.copy()
  
      if self.args.benchmark:
          self.autolog.times.stamp()
      if self.use_onnx:
          input_dict = {}
          input_dict[self.input_tensor.name] = img
          outputs = self.predictor.run(self.output_tensors, input_dict)
      else:
          self.input_tensor.copy_from_cpu(img)
          self.predictor.run()
          outputs = []
          for output_tensor in self.output_tensors:
              output = output_tensor.copy_to_cpu()
              outputs.append(output)
          if self.args.benchmark:
              self.autolog.times.stamp()
  
      preds = {}
      if self.det_algorithm == "EAST":
          preds["f_geo"] = outputs[0]
          preds["f_score"] = outputs[1]
      elif self.det_algorithm == "SAST":
          preds["f_border"] = outputs[0]
          preds["f_score"] = outputs[1]
          preds["f_tco"] = outputs[2]
          preds["f_tvo"] = outputs[3]
      elif self.det_algorithm in ["DB", "PSE", "DB++"]:
          preds["maps"] = outputs[0]
      elif self.det_algorithm == "FCE":
          for i, output in enumerate(outputs):
              preds["level_{}".format(i)] = output
      elif self.det_algorithm == "CT":
          preds["maps"] = outputs[0]
          preds["score"] = outputs[1]
      else:
          raise NotImplementedError
  
      post_result = self.postprocess_op(preds, shape_list)
      dt_boxes = post_result[0]["points"]
  
      if self.args.det_box_type == "poly":
          dt_boxes = self.filter_tag_det_res_only_clip(
              dt_boxes, ori_im.shape
          )
      else:
          dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
  
      if self.args.benchmark:
          self.autolog.times.end(stamp=True)
      et = time.time()
      return dt_boxes, et - st
  
imports: import os
  import sys
  import cv2
  import numpy
  import time
  import sys
  import tools.infer.utility
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppocr.data import create_operators, transform
  from ppocr.postprocess import build_post_process
  import json
label_desc: Use image node_id 6 for calling the TextDetector obj's underlying member method code with example usage: obj.__call__(img) and returns: dt_boxes,  unknown, None,  int