#!/usr/bin/python

"""Blurs a land-sea-mask

blur_lsm infile.png outfile.png blur_radius

Applies a square uniform filter to a land-sea-mask.
The resulting mask will be:
- 0 if the full filter was land
- 255 if the full filter was sea
- 1 <= x <= 254 if part of the filter was land, part sea

This is particularly useful if applying the land-sea-mask for sensors with
large footprints, such as AMSU-A; then the literally gray areas can be
excluded from either land-based or sea-based pixels."""

# $Id$

from __future__ import division

import sys
import PIL.Image
##import PIL.ImageFilter
import numpy
import datetime
now = datetime.datetime.now

import scipy.ndimage.io
import scipy.ndimage.filters

def main():
    try:
        f_in, f_out, size = sys.argv[1:4]
    except ValueError:
        print __doc__
        sys.exit(1)
    size = int(size)
    im = PIL.Image.open(sys.argv[1])
    # convert to uint8 or numpy.array will crash
    im = im.convert('L')
    # apply uniform (averaging) filter
    imd = numpy.array(im, dtype='float32')
    imd = scipy.ndimage.filters.uniform_filter(imd, size)
    # correct for rounding errors
    verysmall = imd<1e-4
    verylarge = imd>(255-1e-4)
    imd[verysmall] = 0
    imd[verylarge] = 255
    # make sure small and large don't get clipped to none and all
    small = imd<1
    imd[small] = numpy.ceil(imd[small])
    large = imd>254
    imd[large] = numpy.floor(imd[large])
    # convert back to uint8
    imd = numpy.round(imd)
    imd = numpy.uint8(imd)
    im.fromstring(imd.tostring())
    im.save(f_out)

if __name__ == "__main__":
    main()