code: def load_ground_truth(
      gt_file, labels, class_names, mid_step, plot_results
  ):
      accuracy = 0
      cm = np.array([])
      labels_gt = np.array([])
      if os.path.isfile(gt_file):
          # load ground truth and class names
          labels_gt, class_names_gt = load_ground_truth_segments(
              gt_file, mid_step
          )
  
          # map predicted labels to ground truth class names
          # Note: if a predicted label does not belong to the ground truth
          #       classes --> -1
          labels_new = []
          for il, l in enumerate(labels):
              if class_names[int(l)] in class_names_gt:
                  labels_new.append(
                      class_names_gt.index(class_names[int(l)])
                  )
              else:
                  labels_new.append(-1)
          labels_new = np.array(labels_new)
          cm = calculate_confusion_matrix(
              labels_new, labels_gt, class_names_gt
          )
  
          accuracy = plot_segmentation_results(
              labels_new,
              labels_gt,
              class_names_gt,
              mid_step,
              not plot_results,
          )
          if accuracy >= 0:
              print("Overall Accuracy: {0:.2f}".format(accuracy))
  
      return labels_gt, class_names, accuracy, cm
  
imports: from __future__ import print_function
  import os
  import csv
  import glob
  import scipy
  import sklearn
  import numpy
  import hmmlearn.hmm
  import sklearn.cluster
  import pickle
  import matplotlib.pyplot
  from scipy.spatial import distance
  import sklearn.discriminant_analysis
  from sklearn.preprocessing import StandardScaler
  import sys
  import pyAudioAnalysis.audioBasicIO
  import pyAudioAnalysis.audioTrainTest
  import pyAudioAnalysis.MidTermFeatures
  import pyAudioAnalysis.ShortTermFeatures
label_desc: Use image node_id 17 for calling a global function with example usage: load_ground_truth(gt_file, labels, class_names, mid_step, plot_results) and returns: labels_gt,  class_names,  accuracy,  cm