text: from config import DatasetName, AffectnetConf, InputDataSize, LearningConfig, ExpressionCodesAffectnet
  from config import LearningConfig, InputDataSize, DatasetName, RafDBConf, DatasetType
  from sklearn.metrics import precision_recall_fscore_support as score
  
  import numpy as np
  import os
  import matplotlib.pyplot as plt
  import math
  from datetime import datetime
  from sklearn.utils import shuffle
  from sklearn.model_selection import train_test_split
  from numpy import save, load, asarray, savez_compressed
  import csv
  from skimage.io import imread
  import pickle
  import csv
  from tqdm import tqdm
  from PIL import Image
  from skimage.transform import resize
  import tensorflow as tf
  import random
  import cv2
  from skimage.feature import hog
  from skimage import data, exposure
  from matplotlib.path import Path
  from scipy import ndimage, misc
  from data_helper import DataHelper
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import confusion_matrix
  from shutil import copyfile
  from dataset_class import CustomDataset
  
  
  class RafDB:
      def __init__(self, ds_type):
          """we set the parameters needed during the whole class:
          """
          self.ds_type = ds_type
          if ds_type == DatasetType.train:
              self.img_path = RafDBConf.no_aug_train_img_path
              self.anno_path = RafDBConf.no_aug_train_annotation_path
              self.img_path_aug = RafDBConf.aug_train_img_path
              self.anno_path_aug = RafDBConf.aug_train_annotation_path
              self.masked_img_path = RafDBConf.aug_train_masked_img_path
  
          elif ds_type == DatasetType.test:
              self.img_path = RafDBConf.test_img_path
              self.anno_path = RafDBConf.test_annotation_path
              self.img_path_aug = RafDBConf.test_img_path
              self.anno_path_aug = RafDBConf.test_annotation_path
              self.masked_img_path = RafDBConf.test_masked_img_path
  
      def test_accuracy(self, model, print_samples=False):
          dhp = DataHelper()
          batch_size = LearningConfig.batch_size
          # exp_pr_glob = []
          # exp_gt_glob = []
          # acc_per_label = []
          '''create batches'''
          img_filenames, exp_filenames = dhp.create_generator_full_path(
              img_path=self.img_path,
              annotation_path=self.anno_path, label=None)
          print(len(img_filenames))
          step_per_epoch = int(len(img_filenames) // batch_size)
          exp_pr_lbl = []
          exp_gt_lbl = []
  
          cds = CustomDataset()
          ds = cds.create_dataset(img_filenames=img_filenames,
                                  anno_names=exp_filenames,
                                  is_validation=True,
                                  ds=DatasetName.rafdb)
  
          batch_index = 0
          for img_batch, exp_gt_b in tqdm(ds):
              '''predict on batch'''
              exp_gt_b = exp_gt_b[:, -1]
              img_batch = img_batch[:, -1, :, :]
  
              pr_data = model.predict_on_batch([img_batch])
  
              probab_exp_pr_b = pr_data[0]
              exp_pr_b = np.array([np.argmax(probab_exp_pr_b[i]) for i in range(len(probab_exp_pr_b))])
  
              if print_samples:
                  for i in range(len(exp_pr_b)):
                      dhp.test_image_print_exp(str(i) + str(batch_index + 1), np.array(img_batch[i]),
                                               np.int8(exp_gt_b[i]), np.int8(exp_pr_b[i]))
  
              exp_pr_lbl += np.array(exp_pr_b).tolist()
              exp_gt_lbl += np.array(exp_gt_b).tolist()
              batch_index += 1
          exp_pr_lbl = np.float64(np.array(exp_pr_lbl))
          exp_gt_lbl = np.float64(np.array(exp_gt_lbl))
  
          global_accuracy = accuracy_score(exp_gt_lbl, exp_pr_lbl)
          precision, recall, fscore, support = score(exp_gt_lbl, exp_pr_lbl)
  
          conf_mat = confusion_matrix(exp_gt_lbl, exp_pr_lbl, normalize='true')
          avg_acc = np.mean([conf_mat[i, i] for i in range(7)])
  
          ds = None
          face_img_filenames = None
          eyes_img_filenames = None
          nose_img_filenames = None
          mouth_img_filenames = None
          exp_filenames = None
  
          return global_accuracy, conf_mat, avg_acc, precision, recall, fscore, support