annotate color_deconvolution.py @ 3:be70a57d7174 draft default tip

planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/color_deconvolution/ commit 07aba15c8f753d650fee9cdd455896a77086a615

import argparse
import sys
import warnings

import numpy as np
import skimage.color
import skimage.io
import skimage.util
from sklearn.decomposition import FactorAnalysis, FastICA, NMF, PCA

convOptions = {
    'hed2rgb': lambda img_raw: skimage.color.hed2rgb(img_raw),
    'hsv2rgb': lambda img_raw: skimage.color.hsv2rgb(img_raw),
    'lab2lch': lambda img_raw: skimage.color.lab2lch(img_raw),
    'lab2rgb': lambda img_raw: skimage.color.lab2rgb(img_raw),
    'lab2xyz': lambda img_raw: skimage.color.lab2xyz(img_raw),
    'lch2lab': lambda img_raw: skimage.color.lch2lab(img_raw),
    'luv2rgb': lambda img_raw: skimage.color.luv2rgb(img_raw),
    'luv2xyz': lambda img_raw: skimage.color.luv2xyz(img_raw),
    'rgb2hed': lambda img_raw: skimage.color.rgb2hed(img_raw),
    'rgb2hsv': lambda img_raw: skimage.color.rgb2hsv(img_raw),
    'rgb2lab': lambda img_raw: skimage.color.rgb2lab(img_raw),
    'rgb2luv': lambda img_raw: skimage.color.rgb2luv(img_raw),
    'rgb2rgbcie': lambda img_raw: skimage.color.rgb2rgbcie(img_raw),
    'rgb2xyz': lambda img_raw: skimage.color.rgb2xyz(img_raw),
    'rgbcie2rgb': lambda img_raw: skimage.color.rgbcie2rgb(img_raw),
    'xyz2lab': lambda img_raw: skimage.color.xyz2lab(img_raw),
    'xyz2luv': lambda img_raw: skimage.color.xyz2luv(img_raw),
    'xyz2rgb': lambda img_raw: skimage.color.xyz2rgb(img_raw),

    'rgb_from_hed': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hed),
    'rgb_from_hdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hdx),
    'rgb_from_fgx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_fgx),
    'rgb_from_bex': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bex),
    'rgb_from_rbd': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_rbd),
    'rgb_from_gdx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_gdx),
    'rgb_from_hax': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hax),
    'rgb_from_bro': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bro),
    'rgb_from_bpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_bpx),
    'rgb_from_ahx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_ahx),
    'rgb_from_hpx': lambda img_raw: skimage.color.combine_stains(img_raw, skimage.color.rgb_from_hpx),

    'hed_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hed_from_rgb),
    'hdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hdx_from_rgb),
    'fgx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.fgx_from_rgb),
    'bex_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bex_from_rgb),
    'rbd_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.rbd_from_rgb),
    'gdx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.gdx_from_rgb),
    'hax_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hax_from_rgb),
    'bro_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bro_from_rgb),
    'bpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.bpx_from_rgb),
    'ahx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.ahx_from_rgb),
    'hpx_from_rgb': lambda img_raw: skimage.color.separate_stains(img_raw, skimage.color.hpx_from_rgb),

    'pca': lambda img_raw: np.reshape(PCA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
                                      [img_raw.shape[0], img_raw.shape[1], -1]),
    'nmf': lambda img_raw: np.reshape(NMF(n_components=3, init='nndsvda').fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
                                      [img_raw.shape[0], img_raw.shape[1], -1]),
    'ica': lambda img_raw: np.reshape(FastICA(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
                                      [img_raw.shape[0], img_raw.shape[1], -1]),
    'fa': lambda img_raw: np.reshape(FactorAnalysis(n_components=3).fit_transform(np.reshape(img_raw, [-1, img_raw.shape[2]])),
                                     [img_raw.shape[0], img_raw.shape[1], -1])
}

parser = argparse.ArgumentParser()
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('conv_type', choices=convOptions.keys(), help='conversion type')
args = parser.parse_args()

img_in = skimage.io.imread(args.input_file.name)[:, :, 0:3]
res = convOptions[args.conv_type](img_in)
res[res < -1] = -1
res[res > +1] = +1

with warnings.catch_warnings():
    warnings.simplefilter('ignore')
    res = skimage.util.img_as_uint(res)  # Attention: precision loss
    skimage.io.imsave(args.out_file.name, res, plugin='tifffile')