class_docstr: Internal implementation of Pascal object detection metrics.
class_docstr_tok: ["Internal","implementation","of","Pascal","object","detection","metrics","."]
docstr: Update grouth truth statitistics.
  
  1. Difficult boxes are ignored when counting the number of ground truth
  instances as done in Pascal VOC devkit.
  2. Difficult boxes are treated as normal boxes when computing CorLoc related
  statitistics.
  
  Args:
    groundtruth_class_labels: An integer numpy array of length M,
        representing M class labels of object instances in ground truth
    groundtruth_is_difficult_list: A boolean numpy array of length M denoting
        whether a ground truth box is a difficult instance or not
    groundtruth_is_group_of_list: A boolean numpy array of length M denoting
        whether a ground truth box is a group-of box or not
code: def _update_ground_truth_statistics(
      self,
      groundtruth_class_labels,
      groundtruth_is_difficult_list,
      groundtruth_is_group_of_list,
  ):
      """Update grouth truth statitistics.
  
      1. Difficult boxes are ignored when counting the number of ground truth
      instances as done in Pascal VOC devkit.
      2. Difficult boxes are treated as normal boxes when computing CorLoc related
      statitistics.
  
      Args:
        groundtruth_class_labels: An integer numpy array of length M,
            representing M class labels of object instances in ground truth
        groundtruth_is_difficult_list: A boolean numpy array of length M denoting
            whether a ground truth box is a difficult instance or not
        groundtruth_is_group_of_list: A boolean numpy array of length M denoting
            whether a ground truth box is a group-of box or not
      """
      for class_index in range(self.num_class):
          num_gt_instances = np.sum(
              groundtruth_class_labels[
                  ~groundtruth_is_difficult_list
                  & ~groundtruth_is_group_of_list
              ]
              == class_index
          )
          self.num_gt_instances_per_class[
              class_index
          ] += num_gt_instances
          if np.any(groundtruth_class_labels == class_index):
              self.num_gt_imgs_per_class[class_index] += 1
  
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 6 for calling the ObjectDetectionEvaluation obj's underlying member method code with example usage: obj._update_ground_truth_statistics(groundtruth_class_labels, groundtruth_is_difficult_list, groundtruth_is_group_of_list) without return types