comparison auto_threshold.py @ 3:0c777d708acc draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/2d_auto_threshold/ commit b1b3c63ab021aa77875c3b04127f6836024812f9"

2:81f0cbca04a7

import argparse
import numpy as np
import sys 
import skimage.io
import skimage.filters
import skimage.util

threshOptions = {
    'otsu': lambda img_raw: skimage.filters.threshold_otsu(img_raw),
    'gaussian_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='gaussian'),
    'mean_adaptive': lambda img_raw: skimage.filters.threshold_local(img_raw, 3, method='mean'),
    'isodata': lambda img_raw: skimage.filters.threshold_isodata(img_raw),
    'li': lambda img_raw: skimage.filters.threshold_li(img_raw),
    'yen': lambda img_raw: skimage.filters.threshold_yen(img_raw),
}

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Segment Foci')
    parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file')
    parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
    parser.add_argument('thresh_type', choices=threshOptions.keys(), help='thresholding method')
    parser.add_argument('dark_background', default=True, type=bool, help='True if background is dark')
    args = parser.parse_args()

    img_in = skimage.io.imread(args.input_file.name)
    img_in = np.reshape(img_in, [img_in.shape[0], img_in.shape[1]])
    thresh = threshOptions[args.thresh_type](img_in)

    if args.dark_background:
        res = img_in > thresh
    else: 
        res = img_in <= thresh

    res = skimage.util.img_as_uint(res)
    skimage.io.imsave(args.out_file.name, res, plugin="tifffile")

3:0c777d708acc

"""
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)