#!D:\bld\medpy_1636629887875\_h_env\python.exe

"""
Execute a graph cut on a region image based on some foreground and background markers.

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
from argparse import RawTextHelpFormatter
import argparse
import logging
import os

# third-party modules
import scipy

# path changes

# own modules
from medpy.core import ArgumentError, Logger
from medpy.io import load, save
from medpy import graphcut
from medpy import filter
from medpy.graphcut.wrapper import split_marker



# information
__author__ = "Oskar Maier"
__version__ = "d0.2.1, 2012-07-31"
__email__ = "oskar.maier@googlemail.com"
__status__ = "Development"
__description__ = """
                  Perform a binary graph cut using Boykov's max-flow/min-cut algorithm.
                  
                  This implementation does not only compute a boundary term but also a
                  regional term which. The only available implementation up till now is
                  the use of an atalas (i.e. a probability image of float values). The
                  pixel values have to lie between 0 and 1, whereas 1 denounces a sure
                  probability that the object is situated at this position.  The desired
                  boundary term can be selected via the --boundary argument. Depending on
                  the selected term, an additional image has to be supplied as badditional.
                  
                  In the case of the stawiaski boundary term, this is the gradient image.
                  In the case of the difference of means, it is the original image.
                  
                  Furthermore the algorithm requires the region map of the original
                  image and an integer image with foreground and background markers.
                  
                  Additionally a filename for the created binary mask marking foreground
                  and background has to be supplied.
                  
                  Note that the input images must be of the same dimensionality,
                  otherwise an exception is thrown.
                  Note to take into account the input images orientation.
                  Note that the quality of the resulting segmentations depends also on
                  the quality of the supplied markers.
                  
                  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
    if not args.force:
        if os.path.exists(args.output):
            logger.warning('The output image {} already exists. Exiting.'.format(args.output))
            exit(-1)
            
    # select boundary term
    if args.boundary == 'stawiaski':
        boundary_term = graphcut.energy_label.boundary_stawiaski
        logger.info('Selected boundary term: stawiaski')
    else:
        boundary_term = graphcut.energy_label.boundary_difference_of_means
        logger.info('Selected boundary term: difference of means')
        
    # select regional term
    if args.regional == 'atlas':
        regional_term = graphcut.energy_label.regional_atlas
    else:
        regional_term = None

    # load input images
    region_image_data, reference_header = load(args.region)
    markers_image_data, _ = load(args.markers)
    
    # loading and splitting the marker image
    fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data)
    
    badditional_image_data, _ = load(args.badditional)
    
    if 'radditional' in args:
        radditional_image_data, _ = load(args.radditional)
    else:
        radditional_image_data = False
    
       
    # check if all images dimensions are the same
    if not (badditional_image_data.shape == region_image_data.shape == fgmarkers_image_data.shape == bgmarkers_image_data.shape):
        logger.critical('Not all of the supplied images are of the same shape.')
        raise ArgumentError('Not all of the supplied images are of the same shape.')
    if not bool == type(radditional_image_data):
        if not (badditional_image_data.shape == radditional_image_data.shape):
            logger.critical('Not all of the supplied images are of the same shape.')
            raise ArgumentError('Not all of the supplied images are of the same shape.')
       
    # recompute the label ids to start from id = 1
    logger.info('Relabel input image...')
    region_image_data = filter.relabel(region_image_data)

    # generate graph
    logger.info('Preparing graph...')
    gcgraph = graphcut.graph_from_labels(region_image_data,
                                    fgmarkers_image_data,
                                    bgmarkers_image_data,
                                    regional_term = regional_term,
                                    boundary_term = boundary_term,
                                    regional_term_args = (radditional_image_data, args.alpha),
                                    boundary_term_args = (badditional_image_data)) # second (optional) parameter is directedness of graph , 0)

    logger.info('Removing images that are not longer required from memory...')
    del fgmarkers_image_data
    del bgmarkers_image_data
    del radditional_image_data
    del badditional_image_data
    
    # execute min-cut
    logger.info('Executing min-cut...')
    maxflow = gcgraph.maxflow()
    logger.debug('Maxflow is {}'.format(maxflow))
    
    # apply results to the region image
    logger.info('Applying results...')
    mapping = [0] # no regions with id 1 exists in mapping, entry used as padding
    mapping.extend([0 if gcgraph.termtype.SINK == gcgraph.what_segment(int(x) - 1) else 1 for x in scipy.unique(region_image_data)])
    region_image_data = filter.relabel_map(region_image_data, mapping)
    
    # save resulting mask
    save(region_image_data.astype(scipy.bool_), args.output, reference_header, 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__, formatter_class=RawTextHelpFormatter)
    
    parser.add_argument('badditional', help='The additional image required by the boundary term. See there for details.')
    parser.add_argument('region', help='The region image of the image to segment.')
    parser.add_argument('markers', help='Binary image containing the foreground (=1) and background (=2) markers.')
    parser.add_argument('output', help='The output image containing the segmentation.')
    parser.add_argument('--boundary', default='stawiaski', help='The boundary term to use. Note that difference of means (means) requires the original image, while stawiaski requires the gradient image of the original image to be passed to badditional.', choices=['means', 'stawiaski'])
    parser.add_argument('--regional', default='none', help='The regional term to use. Note that the atlas requires to provide an atlas image.', choices=['none', 'atlas'])
    parser.add_argument('--radditional', help='The additional image required by the regional term. See there for details.')
    parser.add_argument('--alpha', type=float, help='The weight of the regional term compared to the boundary term.')
    parser.add_argument('-f', dest='force', action='store_true', help='Set this flag to silently override files that exist.')
    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.')
    
    return parser    

if __name__ == "__main__":
    main()
