class_docstr: Image windows are retrieved and analysed by the
  tensorflow graph, this windows aggregator receives
  output window data in numpy array. To access image-level
  information the reader is needed.
class_docstr_tok: ["Image","windows","are","retrieved","and","analysed","by","the","tensorflow","graph",",","this","windows","aggregator","receives","output","window","data","in","numpy","array",".","To","access","image-level","information","the","reader","is","needed","."]
code: def __init__(self, image_reader=None, output_path="."):
      self.reader = image_reader
      self._image_id = None
      self.postfix = ""
      self.output_path = os.path.abspath(output_path)
      if not os.path.exists(self.output_path):
          os.makedirs(self.output_path)
      self.inferred_cleared = False
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_base.py
imports: from __future__ import absolute_import, print_function, division
  import os
  import numpy
  import tensorflow
  from niftynet.engine.image_window import N_SPATIAL
label_desc: Use image node_id 1 to create a new ImageWindowsAggregator object from inherited base classes: object with example: obj = ImageWindowsAggregator(image_reader, output_path)