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 __init__(
      self,
      box_thresh: float = 0.5,
      bin_thresh: float = 0.5,
      assume_straight_pages: bool = True,
  ) -> None:
      self.box_thresh = box_thresh
      self.bin_thresh = bin_thresh
      self.assume_straight_pages = assume_straight_pages
      self._opening_kernel: np.ndarray = np.ones((3, 3), dtype=np.uint8)
  
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 1 to create a new DetectionPostProcessor object from inherited base classes: NestedObject with example: obj = DetectionPostProcessor(box_thresh, bin_thresh, assume_straight_pages)