old_contents: import numpy as np
  from scipy.io import loadmat, savemat
  from keras.preprocessing.image import load_img, list_pictures, img_to_array
  
  
  def load_label(data_path_file):
      meta = loadmat(data_path_file)
  
      name              = meta['wiki'][0, 0]['name'][0]
      full_path         = meta['wiki'][0, 0]['full_path'][0]
      gender            = meta['wiki'][0, 0]['gender'][0]
      face_score        = meta['wiki'][0, 0]['face_score'][0]
      face_location     = meta['wiki'][0, 0]['face_location'][0]
      dob               = meta['wiki'][0, 0]['dob'][0]               # Day of birth
      photo_taken       = meta['wiki'][0, 0]['photo_taken'][0]
      second_face_score = meta['wiki'][0, 0]['second_face_score'][0]
  
      return name, full_path, gender, face_score, face_location, dob, photo_taken, second_face_score
  
  
  def load_img_label_asarray(data_dir, ext):
      full_paths = []
      genders = []
  
      name_list, full_path_list, gender_list, face_score_list, face_location_list, dob_list, photo_taken_list, second_face_score_list = load_label(data_dir + '/wiki.mat')
  
      for name, full_path, gender, face_score, second_face_score in zip(name_list, full_path_list, gender_list, face_location_list, second_face_score_list):
          if gender != 0 and gender != 1:
              continue
          if np.isnan(second_face_score):
              continue
          # if face_score > 1.0:
          #     continue
          full_paths.append(full_path)
          genders.append(gender)
          # print(name, full_path, gender, face_score, second_face_score)
  
      savemat('modified_wiki.mat', { "full_path": full_paths, "gender": genders })
  
  
  # load_label('data2/wiki.mat')
  load_img_label_asarray('data2', 'jpg')
  
  # X = load_img_asarray('data2', 'jpg')
  # print(X)
  
  
new_contents: import numpy as np
  from os.path import isfile
  from scipy.io import loadmat, savemat
  from keras.preprocessing.image import load_img, list_pictures, img_to_array
  
  
  def load_label(data_path_file):
      meta = loadmat(data_path_file)
  
      name              = meta['wiki'][0, 0]['name'][0]
      full_path         = meta['wiki'][0, 0]['full_path'][0]
      gender            = meta['wiki'][0, 0]['gender'][0]
      face_score        = meta['wiki'][0, 0]['face_score'][0]
      face_location     = meta['wiki'][0, 0]['face_location'][0]
      dob               = meta['wiki'][0, 0]['dob'][0]               # Day of birth
      photo_taken       = meta['wiki'][0, 0]['photo_taken'][0]
      second_face_score = meta['wiki'][0, 0]['second_face_score'][0]
  
      return name, full_path, gender, face_score, face_location, dob, photo_taken, second_face_score
  
  
  def load_img_label_asarray(data_dir, ext):
      full_paths = []
      genders = []
  
      name_list, full_path_list, gender_list, face_score_list, face_location_list, dob_list, photo_taken_list, second_face_score_list = load_label(data_dir + '/wiki.mat')
  
      for name, full_path, gender, face_score, second_face_score in zip(name_list, full_path_list, gender_list, face_location_list, second_face_score_list):
          if gender != 0 and gender != 1:
              print('Pass gender NaN:', full_path[0])
              continue
          if not np.isnan(second_face_score):
              print('Pass second face score not NaN:', full_path[0])
              continue
          if not isfile(data_dir + '/' + full_path[0].strip()):
              print('Pass not exist:', full_path[0])
              continue
          # if face_score > 1.0:
          #     continue
          full_paths.append( full_path[0] )
          genders.append( gender )
          # print(name, full_path, gender, face_score, second_face_score)
  
      savemat('modified_wiki.mat', { "full_path": np.array(full_paths), "gender": np.array(genders) })
  
  
  # load_label('data2/wiki.mat')
  load_img_label_asarray('data', 'jpg')
  
  # X = load_img_asarray('data', 'jpg')
  # print(X)
  
  
subject: Update modification