code: def norm_img_can(self, img, image_shape):
      img = cv2.cvtColor(
          img, cv2.COLOR_BGR2GRAY
      )  # CAN only predict gray scale image
  
      if self.inverse:
          img = 255 - img
  
      if self.rec_image_shape[0] == 1:
          h, w = img.shape
          _, imgH, imgW = self.rec_image_shape
          if h < imgH or w < imgW:
              padding_h = max(imgH - h, 0)
              padding_w = max(imgW - w, 0)
              img_padded = np.pad(
                  img,
                  ((0, padding_h), (0, padding_w)),
                  "constant",
                  constant_values=(255),
              )
              img = img_padded
  
      img = np.expand_dims(img, 0) / 255.0  # h,w,c -> c,h,w
      img = img.astype("float32")
  
      return img
  
imports: import os
  import sys
  from PIL import Image
  import cv2
  import numpy
  import math
  import time
  import traceback
  import paddle
  import tools.infer.utility
  from ppocr.postprocess import build_post_process
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read
label_desc: Use image node_id 11 for calling the TextRecognizer obj's underlying member method code with example usage: obj.norm_img_can(img, image_shape) and returns: img