import numpy as np

import argparse

import os

import cv2

import sys

from glob import glob

from our_model import OurModel

import hyperparameters as hp

from skimage.transform import rescale

import PIL.Image

from helpers import load_image, save_image, my_imfilter

#to be imported whne tensorflow is implemented

# from tensorpack import *

# from tensorpack.tfutils.sessinit import get_model_loader

# from tensorpack.tfutils.symbolic_functions import *

# from tensorpack.tfutils.summary import *

# from tensorpack.utils.gpu import get_nr_gpu

# from tensorpack.dataflow.base import RNGDataFlow

# from vgg_model import VGGModel

def load_data():

return data

"""

Program argument parsing, data setup, and training

"""

if __name__ == '__main__':

data = load_data()

model = OurModel()

# model.load_data()

model.style_transfer(data)

# model.style_transfer(data[0], data[1])

# parser = argparse.ArgumentParser()

# parser.add_argument(

# '--task',

# required=True,

# choices=['1', '2'],

# help='Which task of the assignment to run - training from scratch (1), or fine tuning VGG-16 (2).')

# # Set GPU to -1 to not use a GPU.

# parser.add_argument('--gpu', help='Comma-separated list of GPU(s) to use.')

# parser.add_argument(

# '--load',

# # Location of pre-trained model

# # - As a relative path to the student distribution

# default='vgg16.npy',

# # - As an absolute path to the location on the Brown CS filesystem

# #default='/course/cs1430/pretrained_weights/vgg16.npy',

# help='Load VGG-16 model.')

# parser.add_argument(

# '--data',

# # Location of 15 Scenes dataset

# # - As a relative path to the student distribution

# default=os.getcwd() + '/../data/',

# # - As an absolute path to the location on the Brown CS filesystem

# #default='/course/cs1430/datasets/15SceneData/',

# help='Location where the dataset is stored.')

# args = parser.parse_args()

# if args.gpu:

# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu

# logger.auto_set_dir()

# dataset_train = get_data(args.data, args.task, 'train')

# dataset_test = get_data(args.data, args.task, 'test')