class_docstr: Evaluate detection result of a single image.
class_docstr_tok: ["Evaluate","detection","result","of","a","single","image","."]
docstr: Returns numpy arrays belonging to class with index `class_index`.
  
  Args:
    detected_boxes: A numpy array containing detected boxes.
    detected_scores: A numpy array containing detected scores.
    detected_masks: A numpy array containing detected masks.
    detected_class_labels: A numpy array containing detected class labels.
    groundtruth_boxes: A numpy array containing groundtruth boxes.
    groundtruth_masks: A numpy array containing groundtruth masks.
    groundtruth_class_labels: A numpy array containing groundtruth class
      labels.
    class_index: An integer index.
  
  Returns:
    gt_boxes_at_ith_class: A numpy array containing groundtruth boxes labeled
      as ith class.
    gt_masks_at_ith_class: A numpy array containing groundtruth masks labeled
      as ith class.
    detected_boxes_at_ith_class: A numpy array containing detected boxes
      corresponding to the ith class.
    detected_scores_at_ith_class: A numpy array containing detected scores
      corresponding to the ith class.
    detected_masks_at_ith_class: A numpy array containing detected masks
      corresponding to the ith class.
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code: def _get_ith_class_arrays(
      self,
      detected_boxes,
      detected_scores,
      detected_masks,
      detected_class_labels,
      groundtruth_boxes,
      groundtruth_masks,
      groundtruth_class_labels,
      class_index,
  ):
      """Returns numpy arrays belonging to class with index `class_index`.
  
      Args:
        detected_boxes: A numpy array containing detected boxes.
        detected_scores: A numpy array containing detected scores.
        detected_masks: A numpy array containing detected masks.
        detected_class_labels: A numpy array containing detected class labels.
        groundtruth_boxes: A numpy array containing groundtruth boxes.
        groundtruth_masks: A numpy array containing groundtruth masks.
        groundtruth_class_labels: A numpy array containing groundtruth class
          labels.
        class_index: An integer index.
  
      Returns:
        gt_boxes_at_ith_class: A numpy array containing groundtruth boxes labeled
          as ith class.
        gt_masks_at_ith_class: A numpy array containing groundtruth masks labeled
          as ith class.
        detected_boxes_at_ith_class: A numpy array containing detected boxes
          corresponding to the ith class.
        detected_scores_at_ith_class: A numpy array containing detected scores
          corresponding to the ith class.
        detected_masks_at_ith_class: A numpy array containing detected masks
          corresponding to the ith class.
      """
      selected_groundtruth = groundtruth_class_labels == class_index
      gt_boxes_at_ith_class = groundtruth_boxes[selected_groundtruth]
      if groundtruth_masks is not None:
          gt_masks_at_ith_class = groundtruth_masks[
              selected_groundtruth
          ]
      else:
          gt_masks_at_ith_class = None
      selected_detections = detected_class_labels == class_index
      detected_boxes_at_ith_class = detected_boxes[selected_detections]
      detected_scores_at_ith_class = detected_scores[
          selected_detections
      ]
      if detected_masks is not None:
          detected_masks_at_ith_class = detected_masks[
              selected_detections
          ]
      else:
          detected_masks_at_ith_class = None
      return (
          gt_boxes_at_ith_class,
          gt_masks_at_ith_class,
          detected_boxes_at_ith_class,
          detected_scores_at_ith_class,
          detected_masks_at_ith_class,
      )
  
file_path: SlowFast/ava_evaluation/per_image_evaluation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import numpy
  from .None import np_box_list, np_box_list_ops, np_box_mask_list, np_box_mask_list_ops
label_desc: Use image node_id 6 for calling the PerImageEvaluation obj's underlying member method code with example usage: obj._get_ith_class_arrays(detected_boxes, detected_scores, detected_masks, detected_class_labels, groundtruth_boxes, groundtruth_masks, groundtruth_class_labels, class_index) and returns: gt_boxes_at_ith_class,  gt_masks_at_ith_class,  detected_boxes_at_ith_class,  detected_scores_at_ith_class,  detected_masks_at_ith_class