from keras.preprocessing.image import ImageDataGenerator,  array_to_img, img_to_array, load_img
from keras import backend as K
K.set_image_dim_ordering('th')

#the path of images to apply augmentation on them
images_path='train'
#create an instance of ImageDataGenerator
datagen = ImageDataGenerator(width_shift_range=0.2,
        height_shift_range=0.2)
datagen.flow_from_directory(directory=images_path, target_size=(480,752),color_mode='grayscale', class_mode=None, save_to_dir='saved',save_prefix='keras_')
img = load_img('train/images/photon10.png')
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
datagen.flow(x,batch_size=1,save_to_dir='saved',save_format='png')