code: def get_image_feature(
      img_path, img_list_path, model_path, epoch, gpu_id
  ):
      img_list = open(img_list_path)
      embedding = Embedding(model_path, epoch, gpu_id)
      files = img_list.readlines()
      print("files:", len(files))
      faceness_scores = []
      img_feats = []
      for img_index, each_line in enumerate(files):
          if img_index % 500 == 0:
              print("processing", img_index)
          name_lmk_score = each_line.strip().split(" ")
          img_name = os.path.join(img_path, name_lmk_score[0])
          img = cv2.imread(img_name)
          lmk = np.array(
              [float(x) for x in name_lmk_score[1:-1]], dtype=np.float32
          )
          lmk = lmk.reshape((5, 2))
          img_feats.append(embedding.get(img, lmk))
          faceness_scores.append(name_lmk_score[-1])
      img_feats = np.array(img_feats).astype(np.float32)
      faceness_scores = np.array(faceness_scores).astype(np.float32)
  
      # img_feats = np.ones( (len(files), 1024), dtype=np.float32) * 0.01
      # faceness_scores = np.ones( (len(files), ), dtype=np.float32 )
      return img_feats, faceness_scores
  
imports: import os
  import numpy
  import pickle
  import pandas
  import matplotlib
  import matplotlib.pyplot
  import timeit
  import sklearn
  import argparse
  from sklearn.metrics import roc_curve, auc
  from sklearn import preprocessing
  import cv2
  import sys
  import glob
  from embedding import Embedding
  from menpo.visualize import print_progress
  from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
  from prettytable import PrettyTable
  from pathlib import Path
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: get_image_feature(img_path, img_list_path, model_path, epoch, gpu_id) and returns: img_feats,  faceness_scores