generator.py

from keras.utils import Sequence, np_utils
import numpy as np 
import cv2, os
import preprocess
def loadImage(db, paths, size):
	images = [cv2.imread(os.path.join('{}_aligned'.format(db), img_path)) 
				for (db, img_path) in zip(db,paths)]
	images = [cv2.resize(image, (size, size), interpolation = cv2.INTER_CUBIC) for image in images]
	return np.array(images, dtype='uint8')

class DataGenerator(Sequence):
	def __init__(self, model, db, paths, age, gender, batch_size, input_size, categorical):
		self.db = db 
		self.paths = paths
		self.age = age
		self.gender = gender
		self.batch_size = batch_size
		self.model = model
		self.input_size = input_size
		self.categorical = categorical

	def __len__(self):
		return int(np.ceil(len(self.db) / float(self.batch_size)))

	def __getitem__(self, idx):
		db = self.db[idx * self.batch_size:(idx + 1) * self.batch_size]
		paths = self.paths[idx * self.batch_size:(idx + 1) * self.batch_size]
		batch_x = loadImage(db, paths, self.input_size)
		X = self.model.prepImg(batch_x)
		del db, paths, batch_x

		batch_age = self.age[idx * self.batch_size:(idx + 1) * self.batch_size]
		Age = batch_age
		if self.categorical:
			Age = np_utils.to_categorical(batch_age, 101)
		del batch_age

		batch_gender = self.gender[idx * self.batch_size:(idx + 1) * self.batch_size]
		Gender = batch_gender
		if self.categorical :
			Gender = np_utils.to_categorical(batch_gender, 2)
		del batch_gender

		Y = {'age_prediction': Age, 
			'gender_prediction': Gender}

		return X, Y