#!/usr/bin/python

"""
Executes the watershed algorithm over images.
requires the skimage package to be installed.

Copyright (C) 2013 Oskar Maier

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""

# build-in modules
import argparse
import logging
import os

# third-party modules
import numpy
from scipy.ndimage import label
from skimage.segmentation import watershed

from medpy.core import ArgumentError, Logger
from medpy.filter import local_minima

# own modules
from medpy.io import load, save

# path changes


# information
__author__ = "Oskar Maier"
__version__ = "r0.1.2, 2013-12-11"
__email__ = "oskar.maier@googlemail.com"
__status__ = "Release"
__description__ = """
                  Applies the watershed segmentation an image using the supplied
                  parameters.
                  Note that this version does not take the voxel-spacing into account.

                  Copyright (C) 2013 Oskar Maier
                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
                  and you are welcome to redistribute it under certain conditions; see
                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
                  """


# code
def main():
    # parse cmd arguments
    parser = getParser()
    parser.parse_args()
    args = getArguments(parser)

    # prepare logger
    logger = Logger.getInstance()
    if args.debug:
        logger.setLevel(logging.DEBUG)
    elif args.verbose:
        logger.setLevel(logging.INFO)

    # check if output image exists (will also be performed before saving, but as the watershed might be very time intensity, a initial check can save frustration)
    if not args.force:
        if os.path.exists(args.output):
            raise ArgumentError(
                "The output image {} already exists.".format(args.output)
            )

    # loading images
    data_input, header_input = load(args.input)
    if args.mask:
        mask = load(args.mask)[0].astype(numpy.bool_)
    else:
        mask = None

    # extract local minima and convert to markers
    logger.info(
        "Extract local minima with minimum distance of {}...".format(args.mindist)
    )
    lm, _ = local_minima(data_input, args.mindist)
    lm_indices = tuple([numpy.asarray(x) for x in lm.T])
    minima_labels = numpy.zeros(data_input.shape, dtype=numpy.uint64)
    minima_labels[lm_indices] = 1
    if not None == mask:
        minima_labels[~mask] = 0
    minima_labels, _ = label(minima_labels)

    # apply the watershed
    logger.info("Watershedding...")
    data_output = watershed(data_input, minima_labels, mask=mask)

    # save file
    save(data_output, args.output, header_input, args.force)

    logger.info("Successfully terminated.")


def getArguments(parser):
    "Provides additional validation of the arguments collected by argparse."
    return parser.parse_args()


def getParser():
    "Creates and returns the argparse parser object."
    parser = argparse.ArgumentParser(description=__description__)
    parser.add_argument("input", help="Source volume (usually a gradient image).")
    parser.add_argument("output", help="Target volume.")
    parser.add_argument(
        "--mindist",
        type=int,
        default=2,
        help="The minimum distance between local minima in voxel units.",
    )
    parser.add_argument(
        "--mask",
        help="Optional binary mask image denoting the area over which to compute the watershed.",
    )
    parser.add_argument(
        "-v", dest="verbose", action="store_true", help="Display more information."
    )
    parser.add_argument(
        "-d", dest="debug", action="store_true", help="Display debug information."
    )
    parser.add_argument(
        "-f",
        dest="force",
        action="store_true",
        help="Silently override existing output images.",
    )

    return parser


if __name__ == "__main__":
    main()
