class_docstr: Evaluate detection result of a single image.
class_docstr_tok: ["Evaluate","detection","result","of","a","single","image","."]
docstr: Computes overlaps and scores between detected and groudntruth boxes.
  
  Args:
    detected_boxes: A numpy array of shape [N, 4] representing detected box
        coordinates
    detected_scores: A 1-d numpy array of length N representing classification
        score
    groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
        box coordinates
    groundtruth_is_group_of_list: A boolean numpy array of length M denoting
        whether a ground truth box has group-of tag. If a groundtruth box
        is group-of box, every detection matching this box is ignored.
  
  Returns:
    iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
        gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
    ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
        gt_group_of_boxlist.num_boxes() == 0 it will be None.
    scores: The score of the detected boxlist.
    num_boxes: Number of non-maximum suppressed detected boxes.
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code: def _get_overlaps_and_scores_box_mode(
      self,
      detected_boxes,
      detected_scores,
      groundtruth_boxes,
      groundtruth_is_group_of_list,
  ):
      """Computes overlaps and scores between detected and groudntruth boxes.
  
      Args:
        detected_boxes: A numpy array of shape [N, 4] representing detected box
            coordinates
        detected_scores: A 1-d numpy array of length N representing classification
            score
        groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth
            box coordinates
        groundtruth_is_group_of_list: A boolean numpy array of length M denoting
            whether a ground truth box has group-of tag. If a groundtruth box
            is group-of box, every detection matching this box is ignored.
  
      Returns:
        iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
            gt_non_group_of_boxlist.num_boxes() == 0 it will be None.
        ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If
            gt_group_of_boxlist.num_boxes() == 0 it will be None.
        scores: The score of the detected boxlist.
        num_boxes: Number of non-maximum suppressed detected boxes.
      """
      detected_boxlist = np_box_list.BoxList(detected_boxes)
      detected_boxlist.add_field("scores", detected_scores)
      gt_non_group_of_boxlist = np_box_list.BoxList(
          groundtruth_boxes[~groundtruth_is_group_of_list]
      )
      iou = np_box_list_ops.iou(
          detected_boxlist, gt_non_group_of_boxlist
      )
      scores = detected_boxlist.get_field("scores")
      num_boxes = detected_boxlist.num_boxes()
      return iou, None, scores, num_boxes
  
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 4 for calling the PerImageEvaluation obj's underlying member method code with example usage: obj._get_overlaps_and_scores_box_mode(detected_boxes, detected_scores, groundtruth_boxes, groundtruth_is_group_of_list) and returns: iou,  None,  scores,  num_boxes