import numpy as np
from keras.models import *
from keras.layers import *
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint
import cv2

def train_generator(batch_size=32):
    data_gen_args = dict(featurewise_center=True,
                         rotation_range=90.,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         fill_mode="constant",
                         cval=255,
                         horizontal_flip=True,
                         vertical_flip=True,
                         zoom_range=0.2)
    image_datagen = ImageDataGenerator(**data_gen_args)
    mask_datagen = ImageDataGenerator(**data_gen_args)

    seed = 1
    image_generator = image_datagen.flow_from_directory(
        'data/train/images',
        class_mode=None,
        batch_size=batch_size,
        color_mode='rgb',
        target_size=(512,512),
        #save_to_dir='./data/gen/images',
        seed=seed)

    mask_generator = mask_datagen.flow_from_directory(
        'data/train/masks',
        class_mode=None,
        color_mode='grayscale',
        target_size=(512,512),
        batch_size=batch_size,
        #save_to_dir='./data/gen/masks',
        seed=seed)

    # combine generators into one which yields image and masks
    train_generator = zip(image_generator, mask_