"""
Copyright 2017-2022 Biomedical Computer Vision Group, Heidelberg University.

Distributed under the MIT license.
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT

"""

import argparse

import skimage.filters
import skimage.io
import skimage.util
import tifffile

thOptions = {
    'otsu': lambda img_raw, bz: skimage.filters.threshold_otsu(img_raw),
    'li': lambda img_raw, bz: skimage.filters.threshold_li(img_raw),
    'yen': lambda img_raw, bz: skimage.filters.threshold_yen(img_raw),
    'isodata': lambda img_raw, bz: skimage.filters.threshold_isodata(img_raw),

    'loc_gaussian': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='gaussian'),
    'loc_median': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='median'),
    'loc_mean': lambda img_raw, bz: skimage.filters.threshold_local(img_raw, bz, method='mean')
}


def auto_thresholding(in_fn, out_fn, th_method, block_size=5, dark_bg=True):
    img = skimage.io.imread(in_fn)
    th = thOptions[th_method](img, block_size)
    if dark_bg:
        res = img > th
    else:
        res = img <= th
    tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Automatic Image Thresholding')
    parser.add_argument('im_in', help='Path to the input image')
    parser.add_argument('im_out', help='Path to the output image (TIFF)')
    parser.add_argument('th_method', choices=thOptions.keys(), help='Thresholding method')
    parser.add_argument('block_size', type=int, default=5, help='Odd size of pixel neighborhood for calculating the threshold')
    parser.add_argument('dark_bg', default=True, type=bool, help='True if background is dark')
    args = parser.parse_args()

    auto_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.dark_bg)