# This file is part of pylibczi.
# Copyright (c) 2018 Center of Advanced European Studies and Research (caesar)
#
# pylibczi is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# pylibczi is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with pylibczi. If not, see <https://www.gnu.org/licenses/>.
# Parent class for python wrapper to libczi file for accessing Zeiss czi image and metadata.
import numpy as np
import time
#import os
from lxml import etree as etree
[docs]class CziFile(object):
"""Zeiss CZI file object.
Args:
| czi_filename (str): Filename of czifile to access.
Kwargs:
| metafile_out (str): Filename of xml file to optionally export czi meta data to.
| use_pylibczi (bool): Set to false to use Christoph Gohlke's czifile reader instead of libCZI.
| verbose (bool): Print information and times during czi file access.
.. note::
Utilizes compiled wrapper to libCZI for accessing the CZI file.
"""
# how many calibration markers to read in, this should essentially be a constant
nmarkers = 3
# xxx - likely this is a Zeiss bug,
# units for the scale in the xml file are not correct (says microns, given in meters)
scale_units = 1e6
def __init__(self, czi_filename, metafile_out='', use_pylibczi=True, verbose=False):
self.czi_filename = czi_filename
self.metafile_out = metafile_out
self.czifile_verbose = verbose
# whether to use czifile or pylibczi for reading the czi file.
self.use_pylibczi = use_pylibczi
if use_pylibczi:
import _pylibczi
self.czilib = _pylibczi
else:
# https://www.lfd.uci.edu/~gohlke/code/czifile.py.html
import czifile
self.czilib = czifile
[docs] def read_image(self):
"""Read image data from all subblocks and create single montaged image.
Returns:
| (m,n ndarray): Montaged image from all subblocks.
| (nimages, 2): Integer subscripts where subblocks were placed in montaged image.
"""
if self.czifile_verbose:
print('Loading czi image for all scenes'); t = time.time()
if self.use_pylibczi:
imgs, coords = self.czilib.cziread_allsubblocks(self.czi_filename)
img, _ = CziFile._montage(imgs, coords)
# xxx - this does not work for czifiles for which the subblocks have no dimension label.
# additionally it seems not possible to create an accessor without specifying a dimension label / index.
#img = self.czilib.cziread_scene(self.czi_filename, -np.ones((1,), dtype=np.int64))
else:
# (?, scenes, ?, xdim, ydim, colors?)
img = np.squeeze(self.czilib.CziFile(self.czi_filename).asarray())
assert( img.ndim == 2 ) # multiple colors or other dims?
if self.czifile_verbose:
print('\tdone in %.4f s' % (time.time() - t, ))
print('\tAll scenes size is %d x %d' % (img.shape[0], img.shape[1]))
return img
[docs] @staticmethod
def plot_image(image, figno=1, doplots_ds=8, reduce=np.mean, interp_string='nearest', show=True):
"""Generic image plot using matplotlib.
Kwargs:
| figno (int): Figure number to use.
| doplots_ds (int): Downsampling reduce factor before plotting.
| reduce (func): Function to use for block-reduce downsampling.
| interp_string (str): Interpolation string for matplotlib imshow.
| show (bool): Whether to show images or return immediately.
"""
from matplotlib import pylab as pl
import skimage.measure as measure
img_ds = measure.block_reduce(image, block_size=(doplots_ds, doplots_ds),
func=reduce).astype(image.dtype) if doplots_ds > 1 else image
pl.figure(figno)
ax = pl.subplot(1,1,1)
ax.imshow(img_ds,interpolation=interp_string, cmap='gray')
pl.axis('off')
if show: pl.show()
@staticmethod
def _montage(images, coords, bg=0):
nimages = len(images)
# images should be the same datatype, get the datatype.
sel = np.array([x is None for x in images])
fnz = np.nonzero(np.logical_not(sel))[0][0]
img_dtype = images[fnz].dtype
# get the sizes of all the images.
img_shapes = np.vstack(np.array([x.shape if x is not None else (0,0) for x in images]))
# calculate size and allocate output image.
corners = coords.astype(np.double, copy=True)
corners -= np.nanmin(corners, axis=0)
sz_out = np.ceil(np.nanmax(corners + img_shapes[:,::-1], axis=0)).astype(np.int64)[::-1]
image = np.empty(sz_out, dtype=img_dtype); image.fill(bg)
# convert the image coorners to subscripts.
corners = np.round(corners).astype(np.int64)
c = corners[:,::-1]
for i in range(nimages):
if images[i] is None: continue
s = img_shapes[i,:]
image[c[i,0]:c[i,0]+s[0],c[i,1]:c[i,1]+s[1]] = images[i]
return image, corners