from cvargparse import BaseParser, Arg

from nabirds.annotations import AnnotationType

def parse_args():

	parser = BaseParser([

		Arg("data",
			help="Folder containing the dataset with images and annotation files or dataset info file",
			type=str),

		AnnotationType.as_arg("dataset",
			help_text="Type of the annotation"),

		Arg("--parts", "-p",
			choices=["GT", "GT2", "NAC", "UNI", "L1_pred", "L1_full"]),


		Arg("--feature_model", "-fm",
			choices=["inception", "inception_tf", "resnet"]),


		Arg("--subset", "-sub",
			help="Possible subsets: train, test",
			choices=["train", "test"],
			default="train", type=str),


		Arg("--start", "-s",
			help="Image id to start with",
			type=int, default=0),

		Arg("--n_images", "-n",
			help="Number of images to display",
			type=int, default=10),


		Arg("--rnd",
			help="select random subset of present parts",
			action="store_true"),

		Arg("--crop_to_bb",
			help="Crop image to the bounding box",
			action="store_true"),

		Arg("--crop_uniform",
			help="Try to extend the bounding box to same height and width",
			action="store_true"),

		Arg("--parts_in_bb",
			help="Only display parts, that are inside the bounding box",
			action="store_true"),


		Arg("--features",
			help="pre-extracted train and test features",
			default=[None, None],
			nargs=2, type=str),


		Arg("--ratio",
			help="Part extraction ratio",
			type=float, default=.2),

		Arg("--rescale_size",
			help="rescales the part positions from this size to original image size",
			type=int, default=-1),

		Arg("--uniform_parts", "-u",
			help="Do not use GT parts, but sample parts uniformly from the image",
			action="store_true"),


		Arg('--seed', type=int, default=12311123,
			help='random seed'),

	])

	parser.init_logger()
	return parser.parse_args()