Files
ndbioimage/ndbioimage/readers/tifread.py

109 lines
4.4 KiB
Python

import re
import warnings
from abc import ABC
from functools import cached_property
from itertools import product
from pathlib import Path
import numpy as np
import tifffile
import yaml
from lfdfiles import TiffFile
from ome_types import from_xml, model
from .. import AbstractReader, try_default
class Reader(AbstractReader, ABC):
priority = 0
do_not_pickle = 'reader'
@staticmethod
def _can_open(path):
if isinstance(path, Path) and path.suffix in ('.tif', '.tiff'):
with tifffile.TiffFile(path) as tif:
return tif.is_imagej and tif.pages[-1]._nextifd() == 0 # noqa
else:
return False
@cached_property
def metadata(self):
return {key: try_default(yaml.safe_load, value, value) if isinstance(value, str) else value
for key, value in self.reader.imagej_metadata.items()}
def get_ome(self):
if self.reader.is_ome:
match = re.match(r'^(.*)(pos.*)$', self.path.stem, flags=re.IGNORECASE)
if match is not None and len(match.groups()) == 2:
a, b = match.groups()
file0 = TiffFile(self.path.with_stem(a + re.sub(r'\d', '0', b)))
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=UserWarning)
ome = from_xml(file0.ome_metadata)
ome.images = [image for image in ome.images if self.path.stem[:len(image.name)] == image.name]
return ome
page = self.reader.pages[0]
size_y = page.imagelength
size_x = page.imagewidth
if self.p_ndim == 3:
size_c = page.samplesperpixel
size_t = self.metadata.get('frames', 1) # // C
else:
size_c = self.metadata.get('channels', 1)
size_t = self.metadata.get('frames', 1)
size_z = self.metadata.get('slices', 1)
if 282 in page.tags and 296 in page.tags and page.tags[296].value == 1:
f = page.tags[282].value
pxsize = f[1] / f[0]
else:
pxsize = None
dtype = page.dtype.name
if dtype not in ('int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32',
'float', 'double', 'complex', 'double-complex', 'bit'):
dtype = 'float'
interval_t = self.metadata.get('interval', 0)
ome = model.OME()
ome.instruments.append(model.Instrument(id='Instrument:0'))
ome.instruments[0].objectives.append(model.Objective(id='Objective:0'))
ome.images.append(
model.Image(
id='Image:0',
pixels=model.Pixels(
id='Pixels:0',
size_c=size_c, size_z=size_z, size_t=size_t, size_x=size_x, size_y=size_y,
dimension_order='XYCZT', type=dtype, # type: ignore
physical_size_x=pxsize, physical_size_y=pxsize),
objective_settings=model.ObjectiveSettings(id='Objective:0')))
for c, z, t in product(range(size_c), range(size_z), range(size_t)):
ome.images[0].pixels.planes.append(model.Plane(the_c=c, the_z=z, the_t=t, delta_t=interval_t * t))
return ome
def open(self):
self.reader = tifffile.TiffFile(self.path)
page = self.reader.pages.first
self.p_ndim = page.ndim # noqa
if self.p_ndim == 3:
self.p_transpose = [i for i in [page.axes.find(j) for j in 'SYX'] if i >= 0] # noqa
else:
self.p_transpose = [i for i in [page.axes.find(j) for j in 'YX'] if i >= 0] # noqa
def close(self):
self.reader.close()
def __frame__(self, c: int, z: int, t: int):
dimension_order = self.ome.images[0].pixels.dimension_order.value
if self.p_ndim == 3:
axes = ''.join([ax.lower() for ax in dimension_order if ax.lower() in 'zt'])
ct = {'z': z, 't': t}
n = sum([ct[ax] * np.prod(self.base_shape[axes[:i]]) for i, ax in enumerate(axes)])
return np.transpose(self.reader.asarray(int(n)), self.p_transpose)[int(c)]
else:
axes = ''.join([ax.lower() for ax in dimension_order if ax.lower() in 'czt'])
czt = {'c': c, 'z': z, 't': t}
n = sum([czt[ax] * np.prod(self.base_shape[axes[:i]]) for i, ax in enumerate(axes)])
return np.transpose(self.reader.asarray(int(n)), self.p_transpose)