import argparse

from model_ENET_SAD import ENet_SAD
from utils.prob2lines import getLane
from utils.transforms import *

img_size = (800, 288)

net = ENet_SAD(img_size, sad=False)
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
transform_img = Resize(img_size)
transform_to_net = Compose(ToTensor(), Normalize(mean=mean, std=std))


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--img_path", '-i', type=str, default=r"demo/demo.jpg",
                        help="Path to demo image")
    parser.add_argument("--weight_path", '-w', type=str, default=r"exp1_best.pth",
                        help="Path to model weights")
    args = parser.parse_args()
    return args


def main():
    ########## Begin ##########

    ########## End ##########



if __name__ == "__main__":
    main()