code: def visualize_attention(
      filename,
      output_dir,
      attentions,
      pred,
      pad_width,
      pad_height,
      threshold=1,
      normalize=False,
      binarize=True,
      ground=None,
      flag=None,
  ):
      """Visualize the focus of the attention mechanism on an image.
  
      Parameters
      ----------
      filename : string
          Input filename.
      output_dir : string
          Output directory for visualizations.
      attentions : array of shape [len(pred), attention_size]
          Attention weights.
      pred : string
          Predicted output.
      pad_width : int
          Padded image width in pixels used as model input.
      pad_height : int
          Padded image height in pixels used as model input.
      threshold : int or float, optional (default=1)
          Threshold of maximum attention weight to display.
      normalize : bool, optional (default=False)
          Normalize the attention values to the [0, 1] range.
      binarize : bool, optional (default=True)
          If normalized, set attention values below `threshold` to 0.
          If not normalized, set maximum attention values to 1 and others to 0.
      ground : string or None, optional (default=None)
          Ground truth label.
      flag : bool or None, optional (default=None)
          Incorrect prediction flag.
  
      """
      if not os.path.exists(output_dir):
          os.makedirs(output_dir)
      if flag is None:
          filestring = "predict-{}".format(str(pred))
          idx = 2
          while filestring in os.listdir(output_dir):
              filestring = "predict-{}-{}".format(str(pred), idx)
              idx += 1
          out_dir = output_dir
      elif flag:
          filestring = os.path.splitext(os.path.basename(filename))[0]
          out_dir = os.path.join(output_dir, "incorrect")
      else:
          filestring = os.path.splitext(os.path.basename(filename))[0]
          out_dir = os.path.join(output_dir, "correct")
      out_dir = os.path.join(out_dir, filestring.replace("/", "_"))
  
      if not os.path.exists(out_dir):
          os.makedirs(out_dir)
  
      with open(os.path.join(out_dir, "word.txt"), "w") as fword:
          fword.write(pred + "\n")
          if ground is not None:
              fword.write(ground)
  
          if isinstance(filename, str):
              img_file = open(filename, "rb")
              img = Image.open(img_file)
          else:
              img = Image.open(BytesIO(filename))
  
          # Get image sequence with attention applied.
          img_data = np.asarray(img, dtype=np.uint8)
          img_out_frames, _ = map_attentions(
              img_data,
              attentions,
              pred,
              pad_width,
              pad_height,
              threshold=threshold,
              normalize=normalize,
              binarize=binarize,
          )
  
          # Create initial image frame.
          img_out_init = (img_data * 0.3).astype(np.uint8)
          img_out_init = Image.fromarray(img_out_init)
          img_out_init = img_out_init.convert("RGB")
  
          # Save cropped and animated images.
          output_animation = os.path.join(out_dir, "image.gif")
  
          # Assemble animation frames.
          img_out_frames = [img_out_init] + img_out_frames
          img_out_frames[0].save(
              output_animation,
              format="gif",
              save_all=True,
              loop=999,
              duration=500,
              append_images=img_out_frames[1:],
          )
  
          if isinstance(filename, str):
              img_file.close()
          img.close()
  
file_path: attention-ocr/aocr/util/visualizations.py
imports: from __future__ import absolute_import
  from __future__ import division
  import math
  import os
  from io import BytesIO
  import numpy
  from PIL import Image
label_desc: Use image node_id 1 for calling a global function with example usage: visualize_attention(filename, output_dir, attentions, pred, pad_width, pad_height, threshold, normalize, binarize, ground, flag) without return types