Laboratoire RDP » Biophysics Team » FLORIVAR

#!/usr/bin/env python

# before lounching the executable activate the timagetk conda environement

# conda activate florivar

# general imports :

from sys import path, argv

import os

import time

import numpy as np

from skimage.filters import threshold_otsu

from titk_tools.io import imread, imsave, SpatialImage

#from timagetk.plugins import linear_filtering

from scipy.ndimage import gaussian_filter

# multiprocessing

from multiprocessing import Pool

nproc = 20 # number of processors

print('=======================================')

def gauss_titk(img, radius):

        '''

        gaussian filter with radius in real units

        - if radius=a number : isotropic

        - if radius= a list of numbers : anisotropic

        '''

        img_filtered = linear_filtering(img, std_dev=radius, method='gaussian_smoothing')#, real=True)

        return img_filtered

def gauss_nd(img, radius):

        '''

        gaussian filter with radius in real units

        - if radius=a number : isotropic

        - if radius= a list of numbers : anisotropic

        '''

        img_filtered = gaussian_filter(img, radius)

        return img_filtered

def iterated_gauss_2d(img):

        vs=img.voxelsize

        rad=2*vs[2]/vs[0]

        #rad=10

        radius = [rad, rad, 0]

        img_filtered=gaussian_filter(img, radius)

        rad=rad/2

        while rad>1:

                print(rad)

                radius=[rad,rad,0]

                img_filtered = gaussian_filter(img_filtered, radius)

                rad=rad/2

        return SpatialImage(img_filtered, dtype='uint8', voxelsize=vs, origin=[0, 0, 0])

indir = argv[1]

T = int(argv[2])

newdir=indir+'_normalised-OtsuT'+str(T)

os.system("mkdir -p "+newdir)

print('Normalising images in the folder:',indir)

print('Common Otsu threshold for normalisation:',T)

print('Results will be written in the folder:',newdir)

pathdir=indir

listfiles=os.listdir(pathdir)

def treat_file(i):

        filename = listfiles[i]

        extension = filename.split('.')[-1]

        if extension=='tif':

                print('--------------------------')

                print(filename)

                im = imread(pathdir+'/'+filename)

                # filter for computing Otsu

                #imf = iterated_gauss_2d(im)

                imf=im

                if len(np.unique(imf))>0:#20:

                        iotsu = threshold_otsu(imf)

                        # normalise the original image

                        imin = np.min(im)+0.0

                        imax= np.max(im)+0.0

                        a = T/(iotsu-imin)

                        b = - imin*a

                        transformed_im = SpatialImage(a*im+b, dtype='uint16', voxelsize=im.voxelsize, origin=[0, 0, 0])

                        transformed_im[transformed_im>255]=255                 

                        transformed_im = SpatialImage(transformed_im, dtype='uint8', voxelsize=im.voxelsize, origin=[0, 0, 0])

                        tiotsu = threshold_otsu(transformed_im)

                        imf = iterated_gauss_2d(transformed_im)

                        tiotsuf = threshold_otsu(imf)

                        print('   otsu=',iotsu,'  otsu normalised=',tiotsu,'  otsu normalised filtered=',tiotsuf)

                        newname = 'norm'+str(T)+'-'+filename

                        newnamef = 'filtered-'+newname

                        #imsave(newdir+'/'+newname,transformed_im)

                        imsave(newdir+'/'+newnamef,imf)

        return 0

inputs = range(len(listfiles))

for i in range(len(inputs)):

        treat_file(i)

'''

pool = Pool(processes=nproc)

pool.map(treat_file, inputs)

pool.close()

pool.join()

'''