#!/usr/bin/env python2 # -*- coding: utf-8 -*- import numpy as np import tifffile as tiff from scipy.ndimage.filters import gaussian_filter, median_filter import os from stackAlign.deconv import create_psf, deconvolve import gc import phenotastic.file_processing as fp from phenotastic.misc import autocrop, listdir, mkdir fname = '/home/henrik/data/from-marcus/R2D2 4DAS segmentation and quantification trial_for sending to Henrik_20 august 2018.lif - 5.3 4DAS-manually_cropped.tif' f = fp.tiffload(fname) f.data = autocrop(f.data, fct=np.max, threshold=100) f.data = f.data.astype(np.float64) res = fp.get_resolution(f) data = f.data del f gc.collect() MAGNIFICATION = 25 NA = 0.95 iterations = [7, 8, 8] nchannels = len(iterations) iopair = (fname, fname[:-4] + '_deconv.tif') fin = iopair[0] fout = iopair[1]
''' FILE INPUT '''
home = os.path.expanduser('~')
dir_ = '/home/henrik/ross_hypo/'
files = listdir(dir_, include='.tif')
files.sort()
#files = files[20:]
file_ = files[0]
import gc
for file_ in files:
try:
f = fp.tiffload(file_)
meta = f.metadata
resolution = [meta['spacing'], 0.7568361, 0.7568361]
data = f.data[:140, [0], 290:700, :400]
data[data < mh.otsu(data, True) / 10] = 0
data = autocrop(data, 2500, fct=np.max)
data = data[:,0]
del f
gc.collect()
# resolution = np.array((meta['spacing'], 0.3045961, 0.3045961))
resolution[0] *=2
data = data[::2]
# if resolution[0] < 2.:
# downfac = np.floor(2. / resolution[0]).astype(np.int)
# resolution[0] *= downfac
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import numpy as np
import tifffile as tiff
from pycostanza.steepest import get_footprint, steepest_ascent
from pycostanza.labels import erode, dilate, merge_labels_distance, remove_labels_intensity
from pycostanza.labels import remove_labels_size, relabel, merge_labels_depth
from scipy.ndimage.filters import gaussian_filter, median_filter
import mahotas as mh
import phenotastic.file_processing as fp
from phenotastic.misc import autocrop
### Import example inage
fname = '/home/henrik/pWUS-3XVENUS-pCLV3-mCherry-on_npa-6-72h_deconv.tif'
f = fp.tiffload(
'/home/henrik/pWUS-3XVENUS-pCLV3-mCherry-on_npa-6-72h_deconv.tif')
f.data[:, 2] = 0
f.data = autocrop(f.data, fct=np.max, threshold=100)
int_img = f.data[:, 0]
del f
### Preprocessing
smooth_img = int_img.copy()
int_img[int_img < mh.otsu(int_img, True) / 2.] = 0
smooth_img = median_filter(smooth_img, footprint=get_footprint(3, 3))
smooth_img = median_filter(smooth_img, footprint=get_footprint(3, 3))
smooth_img = gaussian_filter(smooth_img, sigma=[1, 2, 2])
smooth_img = gaussian_filter(smooth_img, sigma=[1, 2, 2])
smooth_img = gaussian_filter(smooth_img, sigma=[1, 2, 2])