docstr: Parameters:
  image: file path or numpy-array or a byte stream object
  When sending a list of images, they all must of the same size,
  the following parameters will automatically resize if they are not None
  n_width: int, new width
  n_height: int, new height
docstr_tok: ["Parameters",":","image",":","file","path","or","numpy-array","or","a","byte","stream","object","When","sending","a","list","of","images",",","they","all","must","of","the","same","size",",","the","following","parameters","will","automatically","resize","if","they","are","not","None","n_width",":","int",",","new","width","n_height",":","int",",","new","height"]
code: def readtext_batched(
      self,
      image,
      n_width=None,
      n_height=None,
      decoder="greedy",
      beamWidth=5,
      batch_size=1,
      workers=0,
      allowlist=None,
      blocklist=None,
      detail=1,
      rotation_info=None,
      paragraph=False,
      min_size=20,
      contrast_ths=0.1,
      adjust_contrast=0.5,
      filter_ths=0.003,
      text_threshold=0.7,
      low_text=0.4,
      link_threshold=0.4,
      canvas_size=2560,
      mag_ratio=1.0,
      slope_ths=0.1,
      ycenter_ths=0.5,
      height_ths=0.5,
      width_ths=0.5,
      y_ths=0.5,
      x_ths=1.0,
      add_margin=0.1,
      threshold=0.2,
      bbox_min_score=0.2,
      bbox_min_size=3,
      max_candidates=0,
      output_format="standard",
  ):
      """
      Parameters:
      image: file path or numpy-array or a byte stream object
      When sending a list of images, they all must of the same size,
      the following parameters will automatically resize if they are not None
      n_width: int, new width
      n_height: int, new height
      """
      img, img_cv_grey = reformat_input_batched(
          image, n_width, n_height
      )
  
      horizontal_list_agg, free_list_agg = self.detect(
          img,
          min_size=min_size,
          text_threshold=text_threshold,
          low_text=low_text,
          link_threshold=link_threshold,
          canvas_size=canvas_size,
          mag_ratio=mag_ratio,
          slope_ths=slope_ths,
          ycenter_ths=ycenter_ths,
          height_ths=height_ths,
          width_ths=width_ths,
          add_margin=add_margin,
          reformat=False,
          threshold=threshold,
          bbox_min_score=bbox_min_score,
          bbox_min_size=bbox_min_size,
          max_candidates=max_candidates,
      )
      result_agg = []
      # put img_cv_grey in a list if its a single img
      img_cv_grey = (
          [img_cv_grey] if len(img_cv_grey.shape) == 2 else img_cv_grey
      )
      for grey_img, horizontal_list, free_list in zip(
          img_cv_grey, horizontal_list_agg, free_list_agg
      ):
          result_agg.append(
              self.recognize(
                  grey_img,
                  horizontal_list,
                  free_list,
                  decoder,
                  beamWidth,
                  batch_size,
                  workers,
                  allowlist,
                  blocklist,
                  detail,
                  rotation_info,
                  paragraph,
                  contrast_ths,
                  adjust_contrast,
                  filter_ths,
                  y_ths,
                  x_ths,
                  False,
                  output_format,
              )
          )
  
      return result_agg
  
imports: from .recognition import get_recognizer, get_text
  from .utils import group_text_box, get_image_list, calculate_md5, get_paragraph, download_and_unzip, printProgressBar, diff, reformat_input, make_rotated_img_list, set_result_with_confidence, reformat_input_batched, merge_to_free
  from .config import *
  from bidi.algorithm import get_display
  import numpy
  import cv2
  import torch
  import os
  import sys
  from PIL import Image
  from logging import getLogger
  import yaml
  import json
label_desc: Use image node_id 12 for calling the Reader obj's underlying member method code with example usage: obj.readtext_batched(image, n_width, n_height, decoder, beamWidth, batch_size, workers, allowlist, blocklist, detail, rotation_info, paragraph, min_size, contrast_ths, adjust_contrast, filter_ths, text_threshold, low_text, link_threshold, canvas_size, mag_ratio, slope_ths, ycenter_ths, height_ths, width_ths, y_ths, x_ths, add_margin, threshold, bbox_min_score, bbox_min_size, max_candidates, output_format) and returns: result_agg