code: def evaluate_detections(
      predicted_segments,
      gt_segments,
      splits,
      iou_thresholds=(0.3, 0.5, 0.7, 0.9),
  ):
      """Compute the mean P/R between the predicted and ground truth segments.
  
      Args:
        predicted_segments: A numpy array of shape [K x 2] containing the predicted
          segments.
        gt_segments: A numpy array of shape [S x 2] containing the ground truth
          segments.
        splits: A numpy array of shape [S] indicating the annotation set.
        iou_thresholds: The IOU thresholds to use for Precision/Recall calculations.
  
      Returns:
        precision: The mean precision of the predictions over the IOU thresholds.
        recall: The mean recall of the predictions over the IOU thresholds.
        best_miou: The mIoU.
        iou_matrices: dictionary mapping each split to the corresponding iou matrix.
      """
      # Recall is the percentage of ground truth that is covered by the predictions.
      # Precision is the percentage of predictions that are valid.
  
      best_recall = []
      best_precision = []
      iou_matrices = {}
  
      predicted_shape = predicted_segments.shape[0]
  
      for split in set(splits):
          metrics = {}
          for threshold in iou_thresholds:
              metrics[str(threshold)] = {
                  "gt_covered": set(),
                  "pred_covered": set(),
              }
          split_idx = np.where(splits == split)[0]
          split_gt_segments = np.array(
              [gt_segments[idx] for idx in split_idx]
          )
  
          gt_shape = split_gt_segments.shape[0]
  
          # Compute the IOUs for the segments.
          iou_matrix = np.zeros((gt_shape, max(predicted_shape, 1)))
          for idx_g, gt_segment in enumerate(split_gt_segments):
              cur_max_iou = 0
              for idx_p, segment in enumerate(predicted_segments):
                  sample_iou = iou(segment, gt_segment)
                  iou_matrix[idx_g, idx_p] = sample_iou
                  cur_max_iou = max(cur_max_iou, sample_iou)
                  for threshold in iou_thresholds:
                      if sample_iou > threshold:
                          metrics[str(threshold)]["pred_covered"].add(
                              idx_p
                          )
                          metrics[str(threshold)]["gt_covered"].add(
                              idx_g
                          )
  
          # Compute the precisions and recalls for each IOU threshold.
          for threshold, m in metrics.items():
              pred_covered = m["pred_covered"]
              gt_covered = m["gt_covered"]
  
              # Avoid dividing by 0 for precision
              m["precision"] = float(len(pred_covered)) / max(
                  float(predicted_shape), 1.0
              )
              m["recall"] = float(len(gt_covered)) / float(gt_shape)
  
          precision = [m["precision"] for m in metrics.values()]
          recall = [m["recall"] for m in metrics.values()]
          if best_precision:
              best_precision = [
                  max(precision[i], best_precision[i])
                  for i in range(len(precision))
              ]
              best_recall = [
                  max(recall[i], best_recall[i])
                  for i in range(len(recall))
              ]
          else:
              best_precision, best_recall = precision, recall
          iou_matrices[int(split)] = iou_matrix
  
      return best_precision, best_recall, iou_matrices
  
imports: import collections
  import logging
  import random
  import re
  import string
  import numpy
  from scenic.projects.vid2seq.metrics.cider import Cider
  from scenic.projects.vid2seq.metrics.meteor import Meteor
  from scenic.projects.vid2seq.metrics.ptbtokenizer import PTBTokenizer
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate_detections(predicted_segments, gt_segments, splits, iou_thresholds) and returns: best_precision,  best_recall,  iou_matrices