class_docstr: Interface for object detection evalution classes.
  
  Example usage of the Evaluator:
  ------------------------------
  evaluator = DetectionEvaluator(categories)
  
  # Detections and groundtruth for image 1.
  evaluator.add_single_groundtruth_image_info(...)
  evaluator.add_single_detected_image_info(...)
  
  # Detections and groundtruth for image 2.
  evaluator.add_single_groundtruth_image_info(...)
  evaluator.add_single_detected_image_info(...)
  
  metrics_dict = evaluator.evaluate()
docstr: Constructor.
  
  Args:
    categories: A list of dicts, each of which has the following keys -
      'id': (required) an integer id uniquely identifying this category.
      'name': (required) string representing category name e.g., 'cat', 'dog'.
docstr_tok: ["Constructor",".","Args",":","categories",":","A","list","of","dicts",",","each","of","which","has","the","following","keys","-","'id","'",":","(","required",")","an","integer","id","uniquely","identifying","this","category",".","'name","'",":","(","required",")","string","representing","category","name","e.g.",",","'cat","'",",","'dog","'","."]
code: def __init__(self, categories):
      """Constructor.
  
      Args:
        categories: A list of dicts, each of which has the following keys -
          'id': (required) an integer id uniquely identifying this category.
          'name': (required) string representing category name e.g., 'cat', 'dog'.
      """
      self._categories = categories
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import collections
  import logging
  import numpy
  from abc import ABCMeta, abstractmethod
  from .None import label_map_util, metrics, per_image_evaluation, standard_fields
label_desc: Use image node_id 1 to create a new DetectionEvaluator object  with example: obj = DetectionEvaluator(categories)