text: """Helper for evaluation on the Labeled Faces in the Wild dataset
  """
  
  # MIT License
  #
  # Copyright (c) 2016 David Sandberg
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  import tensorflow as tf
  import numpy as np
  from sklearn.model_selection import KFold
  from sklearn.decomposition import PCA
  import sklearn
  from scipy import interpolate
  import datetime
  
  
  def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0):
      assert (embeddings1.shape[0] == embeddings2.shape[0])
      assert (embeddings1.shape[1] == embeddings2.shape[1])
      nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
      nrof_thresholds = len(thresholds)
      k_fold = KFold(n_splits=nrof_folds, shuffle=False)
  
      tprs = np.zeros((nrof_folds, nrof_thresholds))
      fprs = np.zeros((nrof_folds, nrof_thresholds))
      accuracy = np.zeros((nrof_folds))
      indices = np.arange(nrof_pairs)
      # print('pca', pca)
  
      if pca == 0:
          diff = np.subtract(embeddings1, embeddings2)
          dist = np.sum(np.square(diff), 1)
  
      for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
          # print('train_set', train_set)
          # print('test_set', test_set)
          if pca > 0:
              print('doing pca on', fold_idx)
              embed1_train = embeddings1[train_set]
              embed2_train = embeddings2[train_set]
              _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
              # print(_embed_train.shape)
              pca_model = PCA(n_components=pca)
              pca_model.fit(_embed_train)
              embed1 = pca_model.transform(embeddings1)
              embed2 = pca_model.transform(embeddings2)
              embed1 = sklearn.preprocessing.normalize(embed1)
              embed2 = sklearn.preprocessing.normalize(embed2)
              # print(embed1.shape, embed2.shape)
              diff = np.subtract(embed1, embed2)
              dist = np.sum(np.square(diff), 1)
  
          # Find the best threshold for the fold
          acc_train = np.zeros((nrof_thresholds))
          for threshold_idx, threshold in enumerate(thresholds):
              _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])
          best_threshold_index = np.argmax(acc_train)
          print('best_threshold_index', best_threshold_index, acc_train[best_threshold_index])
          for threshold_idx, threshold in enumerate(thresholds):
              tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold,
                                                                                                   dist[test_set],
                                                                                                   actual_issame[
                                                                                                       test_set])
          _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set],
                                                        actual_issame[test_set])
  
      tpr = np.mean(tprs, 0)
      fpr = np.mean(fprs, 0)
      return tpr, fpr, accuracy
  
  
  def calculate_accuracy(threshold, dist, actual_issame):
      predict_issame = np.less(dist, threshold)
      tp = np.sum(np.logical_and(predict_issame, actual_issame))
      fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
      tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
      fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
  
      tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
      fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
      acc = float(tp + tn) / dist.size
      return tpr, fpr, acc
  
  
  def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
      '''
      Copy from [insightface](https://github.com/deepinsight/insightface)
      :param thresholds:
      :param embeddings1:
      :param embeddings2:
      :param actual_issame:
      :param far_target:
      :param nrof_folds:
      :return:
      '''
      assert (embeddings1.shape[0] == embeddings2.shape[0])
      assert (embeddings1.shape[1] == embeddings2.shape[1])
      nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
      nrof_thresholds = len(thresholds)
      k_fold = KFold(n_splits=nrof_folds, shuffle=False)
  
      val = np.zeros(nrof_folds)
      far = np.zeros(nrof_folds)
  
      diff = np.subtract(embeddings1, embeddings2)
      dist = np.sum(np.square(diff), 1)
      indices = np.arange(nrof_pairs)
  
      for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
  
          # Find the threshold that gives FAR = far_target
          far_train = np.zeros(nrof_thresholds)
          for threshold_idx, threshold in enumerate(thresholds):
              _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
          if np.max(far_train) >= far_target:
              f = interpolate.interp1d(far_train, thresholds, kind='slinear')
              threshold = f(far_target)
          else:
              threshold = 0.0
  
          val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
      val_mean = np.mean(val)
      far_mean = np.mean(far)
      val_std = np.std(val)
      return val_mean, val_std, far_mean
  
  
  def calculate_val_far(threshold, dist, actual_issame):
      predict_issame = np.less(dist, threshold)
      true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
      false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
      n_same = np.sum(actual_issame)
      n_diff = np.sum(np.logical_not(actual_issame))
      val = float(true_accept) / float(n_same)
      far = float(false_accept) / float(n_diff)
      return val, far
  
  
  def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
      # Calculate evaluation metrics
      thresholds = np.arange(0, 4, 0.01)
      embeddings1 = embeddings[0::2]
      embeddings2 = embeddings[1::2]
      tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,
                                         np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca)
      thresholds = np.arange(0, 4, 0.001)
      val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,
                                        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)
      return tpr, fpr, accuracy, val, val_std, far
  
  
  def data_iter(datasets, batch_size):
      data_num = datasets.shape[0]
      for i in range(0, data_num, batch_size):
          yield datasets[i:min(i+batch_size, data_num), ...]
  
  
  def test(data_set, sess, embedding_tensor, batch_size, label_shape=None, feed_dict=None, input_placeholder=None):
      '''
      referenc official implementation [insightface](https://github.com/deepinsight/insightface)
      :param data_set:
      :param sess:
      :param embedding_tensor:
      :param batch_size:
      :param label_shape:
      :param feed_dict:
      :param input_placeholder:
      :return:
      '''
      print('testing verification..')
      data_list = data_set[0]
      issame_list = data_set[1]
      embeddings_list = []
      time_consumed = 0.0
      for i in range(len(data_list)):
          datas = data_list[i]
          embeddings = None
          feed_dict.setdefault(input_placeholder, None)
          for idx, data in enumerate(data_iter(datas, batch_size)):
              data_tmp = data.copy()    # fix issues #4
              data_tmp -= 127.5
              data_tmp *= 0.0078125
              feed_dict[input_placeholder] = data_tmp
              time0 = datetime.datetime.now()
              _embeddings = sess.run(embedding_tensor, feed_dict)
              time_now = datetime.datetime.now()
              diff = time_now - time0
              time_consumed += diff.total_seconds()
              if embeddings is None:
                  embeddings = np.zeros((datas.shape[0], _embeddings.shape[1]))
              try:
                  embeddings[idx*batch_size:min((idx+1)*batch_size, datas.shape[0]), ...] = _embeddings
              except ValueError:
                  print('idx*batch_size value is %d min((idx+1)*batch_size, datas.shape[0]) %d, batch_size %d, data.shape[0] %d' %
                        (idx*batch_size, min((idx+1)*batch_size, datas.shape[0]), batch_size, datas.shape[0]))
                  print('embedding shape is ', _embeddings.shape)
          embeddings_list.append(embeddings)
  
      _xnorm = 0.0
      _xnorm_cnt = 0
      for embed in embeddings_list:
          for i in range(embed.shape[0]):
              _em = embed[i]
              _norm = np.linalg.norm(_em)
              # print(_em.shape, _norm)
              _xnorm += _norm
              _xnorm_cnt += 1
      _xnorm /= _xnorm_cnt
  
      acc1 = 0.0
      std1 = 0.0
      embeddings = embeddings_list[0] + embeddings_list[1]
      embeddings = sklearn.preprocessing.normalize(embeddings)
      print(embeddings.shape)
      print('infer time', time_consumed)
      _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)
      acc2, std2 = np.mean(accuracy), np.std(accuracy)
      return acc1, std1, acc2, std2, _xnorm, embeddings_list
  
  
  def ver_test(ver_list, ver_name_list, nbatch, sess, embedding_tensor, batch_size, feed_dict, input_placeholder):
      results = []
      for i in range(len(ver_list)):
          acc1, std1, acc2, std2, xnorm, embeddings_list = test(data_set=ver_list[i], sess=sess, embedding_tensor=embedding_tensor,
                                                                batch_size=batch_size, feed_dict=feed_dict,
                                                                input_placeholder=input_placeholder)
          print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))
          print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))
          results.append(acc2)
      return results
  