code: def compute_recall_scene(
      scene_name, list_pair, list_trans, list_tau1, list_tau2, res_path
  ):
      """
      evaluate the recall for each scene
      """
      list_frac_correct = []
      list_dico = []
      for i, pair in enumerate(list_pair):
          dico = pair_evaluation(
              pair[0], pair[1], list_trans[i], list_tau1, res_path
          )
          list_frac_correct.append(dico["frac_correct"])
          list_dico.append(dico)
  
      list_recall = compute_mean_correct_matches(
          np.asarray(list_frac_correct), list_tau2, is_leq=False
      )
      print("Save the matches")
      df_matches = pd.DataFrame(list_dico)
      df_matches.to_csv(osp.join(res_path, "matches.csv"))
      dico = dict(
          scene_name=scene_name,
          list_tau2=list(list_tau2),
          list_recall=list(list_recall),
      )
      df_res = pd.DataFrame([dico])
      df_res.to_csv(osp.join(res_path, "res_recall.csv"))
      return dico
  
file_path: torch-points3d/scripts/test_registration_scripts/descriptor_matcher.py
imports: import pandas
  import hydra
  import numpy
  from sklearn.neighbors import KDTree
  import os
  import os.path
  from omegaconf import OmegaConf
  import sys
  import matplotlib.pyplot
label_desc: Use image node_id 6 for calling a global function with example usage: compute_recall_scene(scene_name, list_pair, list_trans, list_tau1, list_tau2, res_path) and returns: dico