class_docstr: Abstract class to postprocess the raw output of the model
  
  Args:
  ----
      box_thresh (float): minimal objectness score to consider a box
      bin_thresh (float): threshold to apply to segmentation raw heatmap
      assume straight_pages (bool): if True, fit straight boxes only
class_docstr_tok: ["Abstract","class","to","postprocess","the","raw","output","of","the","model","Args",":","--","--","box_thresh","(","float",")",":","minimal","objectness","score","to","consider","a","box","bin_thresh","(","float",")",":","threshold","to","apply","to","segmentation","raw","heatmap","assume","straight_pages","(","bool",")",":","if","True",",","fit","straight","boxes","only"]
code: def bitmap_to_boxes(
      self,
      pred: np.ndarray,
      bitmap: np.ndarray,
  ) -> np.ndarray:
      raise NotImplementedError
  
file_path: doctr/doctr/models/detection/core.py
imports: from typing import List
  import cv2
  import numpy
  from doctr.utils.repr import NestedObject
label_desc: Use image node_id 4 for calling the DetectionPostProcessor obj's underlying member method code with example usage: obj.bitmap_to_boxes(pred, bitmap) without return types