#!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py
# Copyright (c) 2008-2014, Christoph Gohlke
# Copyright (c) 2008-2014, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holders nor the names of any
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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"""Read and write image data from and to TIFF files.
Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH,
SGI, ImageJ, MicroManager, FluoView, SEQ and GEL files.
Only a subset of the TIFF specification is supported, mainly uncompressed
and losslessly compressed 2**(0 to 6) bit integer, 16, 32 and 64-bit float,
grayscale and RGB(A) images, which are commonly used in bio-scientific imaging.
Specifically, reading JPEG and CCITT compressed image data or EXIF, IPTC, GPS,
and XMP metadata is not implemented.
Only primary info records are read for STK, FluoView, MicroManager, and
NIH image formats.
TIFF, the Tagged Image File Format, is under the control of Adobe Systems.
BigTIFF allows for files greater than 4 GB. STK, LSM, FluoView, SGI, SEQ, GEL,
and OME-TIFF, are custom extensions defined by Molecular Devices (Universal
Imaging Corporation), Carl Zeiss MicroImaging, Olympus, Silicon Graphics
International, Media Cybernetics, Molecular Dynamics, and the Open Microscopy
Environment consortium respectively.
For command line usage run ``python tifffile.py --help``
:Author:
`Christoph Gohlke `_
:Organization:
Laboratory for Fluorescence Dynamics, University of California, Irvine
:Version: 2014.08.24
Requirements
------------
* `CPython 2.7 or 3.4 `_
* `Numpy 1.8.2 `_
* `Matplotlib 1.4 `_ (optional for plotting)
* `Tifffile.c 2013.11.05 `_
(recommended for faster decoding of PackBits and LZW encoded strings)
Notes
-----
The API is not stable yet and might change between revisions.
Tested on little-endian platforms only.
Other Python packages and modules for reading bio-scientific TIFF files:
* `Imread `_
* `PyLibTiff `_
* `SimpleITK `_
* `PyLSM `_
* `PyMca.TiffIO.py `_ (same as fabio.TiffIO)
* `BioImageXD.Readers `_
* `Cellcognition.io `_
* `CellProfiler.bioformats
`_
Acknowledgements
----------------
* Egor Zindy, University of Manchester, for cz_lsm_scan_info specifics.
* Wim Lewis for a bug fix and some read_cz_lsm functions.
* Hadrien Mary for help on reading MicroManager files.
References
----------
(1) TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated.
http://partners.adobe.com/public/developer/tiff/
(2) TIFF File Format FAQ. http://www.awaresystems.be/imaging/tiff/faq.html
(3) MetaMorph Stack (STK) Image File Format.
http://support.meta.moleculardevices.com/docs/t10243.pdf
(4) Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010).
Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011
(5) File Format Description - LSM 5xx Release 2.0.
http://ibb.gsf.de/homepage/karsten.rodenacker/IDL/Lsmfile.doc
(6) The OME-TIFF format.
http://www.openmicroscopy.org/site/support/file-formats/ome-tiff
(7) UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf
(8) Micro-Manager File Formats.
http://www.micro-manager.org/wiki/Micro-Manager_File_Formats
(9) Tags for TIFF and Related Specifications. Digital Preservation.
http://www.digitalpreservation.gov/formats/content/tiff_tags.shtml
Examples
--------
>>> data = numpy.random.rand(5, 301, 219)
>>> imsave('temp.tif', data)
>>> image = imread('temp.tif')
>>> numpy.testing.assert_array_equal(image, data)
>>> with TiffFile('temp.tif') as tif:
... images = tif.asarray()
... for page in tif:
... for tag in page.tags.values():
... t = tag.name, tag.value
... image = page.asarray()
"""
from __future__ import division, print_function
import sys
import os
import re
import glob
import math
import zlib
import time
import json
import struct
import warnings
import tempfile
import datetime
import collections
from fractions import Fraction
from xml.etree import cElementTree as etree
import numpy
try:
from . import _tifffile
except ImportError:
warnings.warn(
"failed to import the optional _tifffile C extension module.\n"
"Loading of some compressed images will be slow.\n"
"Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/")
__version__ = '2014.08.24'
__docformat__ = 'restructuredtext en'
__all__ = ('imsave', 'imread', 'imshow', 'TiffFile', 'TiffWriter',
'TiffSequence')
def imsave(filename, data, **kwargs):
"""Write image data to TIFF file.
Refer to the TiffWriter class and member functions for documentation.
Parameters
----------
filename : str
Name of file to write.
data : array_like
Input image. The last dimensions are assumed to be image depth,
height, width, and samples.
kwargs : dict
Parameters 'byteorder', 'bigtiff', and 'software' are passed to
the TiffWriter class.
Parameters 'photometric', 'planarconfig', 'resolution',
'description', 'compress', 'volume', and 'extratags' are passed to
the TiffWriter.save function.
Examples
--------
>>> data = numpy.random.rand(2, 5, 3, 301, 219)
>>> description = u'{"shape": %s}' % str(list(data.shape))
>>> imsave('temp.tif', data, compress=6,
... extratags=[(270, 's', 0, description, True)])
"""
tifargs = {}
for key in ('byteorder', 'bigtiff', 'software', 'writeshape'):
if key in kwargs:
tifargs[key] = kwargs[key]
del kwargs[key]
if 'writeshape' not in kwargs:
kwargs['writeshape'] = True
if 'bigtiff' not in tifargs and data.size*data.dtype.itemsize > 2000*2**20:
tifargs['bigtiff'] = True
with TiffWriter(filename, **tifargs) as tif:
tif.save(data, **kwargs)
class TiffWriter(object):
"""Write image data to TIFF file.
TiffWriter instances must be closed using the close method, which is
automatically called when using the 'with' statement.
Examples
--------
>>> data = numpy.random.rand(2, 5, 3, 301, 219)
>>> with TiffWriter('temp.tif', bigtiff=True) as tif:
... for i in range(data.shape[0]):
... tif.save(data[i], compress=6)
"""
TYPES = {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6,
'h': 8, 'i': 9, 'f': 11, 'd': 12, 'Q': 16, 'q': 17}
TAGS = {
'new_subfile_type': 254, 'subfile_type': 255,
'image_width': 256, 'image_length': 257, 'bits_per_sample': 258,
'compression': 259, 'photometric': 262, 'fill_order': 266,
'document_name': 269, 'image_description': 270, 'strip_offsets': 273,
'orientation': 274, 'samples_per_pixel': 277, 'rows_per_strip': 278,
'strip_byte_counts': 279, 'x_resolution': 282, 'y_resolution': 283,
'planar_configuration': 284, 'page_name': 285, 'resolution_unit': 296,
'software': 305, 'datetime': 306, 'predictor': 317, 'color_map': 320,
'tile_width': 322, 'tile_length': 323, 'tile_offsets': 324,
'tile_byte_counts': 325, 'extra_samples': 338, 'sample_format': 339,
'image_depth': 32997, 'tile_depth': 32998}
def __init__(self, filename, bigtiff=False, byteorder=None,
software='tifffile.py'):
"""Create a new TIFF file for writing.
Use bigtiff=True when creating files greater than 2 GB.
Parameters
----------
filename : str
Name of file to write.
bigtiff : bool
If True, the BigTIFF format is used.
byteorder : {'<', '>'}
The endianness of the data in the file.
By default this is the system's native byte order.
software : str
Name of the software used to create the image.
Saved with the first page only.
"""
if byteorder not in (None, '<', '>'):
raise ValueError("invalid byteorder %s" % byteorder)
if byteorder is None:
byteorder = '<' if sys.byteorder == 'little' else '>'
self._byteorder = byteorder
self._software = software
self._fh = open(filename, 'wb')
self._fh.write({'<': b'II', '>': b'MM'}[byteorder])
if bigtiff:
self._bigtiff = True
self._offset_size = 8
self._tag_size = 20
self._numtag_format = 'Q'
self._offset_format = 'Q'
self._val_format = '8s'
self._fh.write(struct.pack(byteorder+'HHH', 43, 8, 0))
else:
self._bigtiff = False
self._offset_size = 4
self._tag_size = 12
self._numtag_format = 'H'
self._offset_format = 'I'
self._val_format = '4s'
self._fh.write(struct.pack(byteorder+'H', 42))
# first IFD
self._ifd_offset = self._fh.tell()
self._fh.write(struct.pack(byteorder+self._offset_format, 0))
def save(self, data, photometric=None, planarconfig=None, resolution=None,
description=None, volume=False, writeshape=False, compress=0,
extratags=()):
"""Write image data to TIFF file.
Image data are written in one stripe per plane.
Dimensions larger than 2 to 4 (depending on photometric mode, planar
configuration, and SGI mode) are flattened and saved as separate pages.
The 'sample_format' and 'bits_per_sample' TIFF tags are derived from
the data type.
Parameters
----------
data : array_like
Input image. The last dimensions are assumed to be image depth,
height, width, and samples.
photometric : {'minisblack', 'miniswhite', 'rgb'}
The color space of the image data.
By default this setting is inferred from the data shape.
planarconfig : {'contig', 'planar'}
Specifies if samples are stored contiguous or in separate planes.
By default this setting is inferred from the data shape.
'contig': last dimension contains samples.
'planar': third last dimension contains samples.
resolution : (float, float) or ((int, int), (int, int))
X and Y resolution in dots per inch as float or rational numbers.
description : str
The subject of the image. Saved with the first page only.
compress : int
Values from 0 to 9 controlling the level of zlib compression.
If 0, data are written uncompressed (default).
volume : bool
If True, volume data are stored in one tile (if applicable) using
the SGI image_depth and tile_depth tags.
Image width and depth must be multiple of 16.
Few software can read this format, e.g. MeVisLab.
writeshape : bool
If True, write the data shape to the image_description tag
if necessary and no other description is given.
extratags: sequence of tuples
Additional tags as [(code, dtype, count, value, writeonce)].
code : int
The TIFF tag Id.
dtype : str
Data type of items in 'value' in Python struct format.
One of B, s, H, I, 2I, b, h, i, f, d, Q, or q.
count : int
Number of data values. Not used for string values.
value : sequence
'Count' values compatible with 'dtype'.
writeonce : bool
If True, the tag is written to the first page only.
"""
if photometric not in (None, 'minisblack', 'miniswhite', 'rgb'):
raise ValueError("invalid photometric %s" % photometric)
if planarconfig not in (None, 'contig', 'planar'):
raise ValueError("invalid planarconfig %s" % planarconfig)
if not 0 <= compress <= 9:
raise ValueError("invalid compression level %s" % compress)
fh = self._fh
byteorder = self._byteorder
numtag_format = self._numtag_format
val_format = self._val_format
offset_format = self._offset_format
offset_size = self._offset_size
tag_size = self._tag_size
data = numpy.asarray(data, dtype=byteorder+data.dtype.char, order='C')
data_shape = shape = data.shape
data = numpy.atleast_2d(data)
# normalize shape of data
samplesperpixel = 1
extrasamples = 0
if volume and data.ndim < 3:
volume = False
if photometric is None:
if planarconfig:
photometric = 'rgb'
elif data.ndim > 2 and shape[-1] in (3, 4):
photometric = 'rgb'
elif volume and data.ndim > 3 and shape[-4] in (3, 4):
photometric = 'rgb'
elif data.ndim > 2 and shape[-3] in (3, 4):
photometric = 'rgb'
else:
photometric = 'minisblack'
if planarconfig and len(shape) <= (3 if volume else 2):
planarconfig = None
photometric = 'minisblack'
if photometric == 'rgb':
if len(shape) < 3:
raise ValueError("not a RGB(A) image")
if len(shape) < 4:
volume = False
if planarconfig is None:
if shape[-1] in (3, 4):
planarconfig = 'contig'
elif shape[-4 if volume else -3] in (3, 4):
planarconfig = 'planar'
elif shape[-1] > shape[-4 if volume else -3]:
planarconfig = 'planar'
else:
planarconfig = 'contig'
if planarconfig == 'contig':
data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
samplesperpixel = data.shape[-1]
else:
data = data.reshape(
(-1,) + shape[(-4 if volume else -3):] + (1,))
samplesperpixel = data.shape[1]
if samplesperpixel > 3:
extrasamples = samplesperpixel - 3
elif planarconfig and len(shape) > (3 if volume else 2):
if planarconfig == 'contig':
data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
samplesperpixel = data.shape[-1]
else:
data = data.reshape(
(-1,) + shape[(-4 if volume else -3):] + (1,))
samplesperpixel = data.shape[1]
extrasamples = samplesperpixel - 1
else:
planarconfig = None
# remove trailing 1s
while len(shape) > 2 and shape[-1] == 1:
shape = shape[:-1]
if len(shape) < 3:
volume = False
if False and (
len(shape) > (3 if volume else 2) and shape[-1] < 5 and
all(shape[-1] < i
for i in shape[(-4 if volume else -3):-1])):
# DISABLED: non-standard TIFF, e.g. (220, 320, 2)
planarconfig = 'contig'
samplesperpixel = shape[-1]
data = data.reshape((-1, 1) + shape[(-4 if volume else -3):])
else:
data = data.reshape(
(-1, 1) + shape[(-3 if volume else -2):] + (1,))
if samplesperpixel == 2:
warnings.warn("writing non-standard TIFF (samplesperpixel 2)")
if volume and (data.shape[-2] % 16 or data.shape[-3] % 16):
warnings.warn("volume width or length are not multiple of 16")
volume = False
data = numpy.swapaxes(data, 1, 2)
data = data.reshape(
(data.shape[0] * data.shape[1],) + data.shape[2:])
# data.shape is now normalized 5D or 6D, depending on volume
# (pages, planar_samples, (depth,) height, width, contig_samples)
assert len(data.shape) in (5, 6)
shape = data.shape
bytestr = bytes if sys.version[0] == '2' else (
lambda x: bytes(x, 'utf-8') if isinstance(x, str) else x)
tags = [] # list of (code, ifdentry, ifdvalue, writeonce)
if volume:
# use tiles to save volume data
tag_byte_counts = TiffWriter.TAGS['tile_byte_counts']
tag_offsets = TiffWriter.TAGS['tile_offsets']
else:
# else use strips
tag_byte_counts = TiffWriter.TAGS['strip_byte_counts']
tag_offsets = TiffWriter.TAGS['strip_offsets']
def pack(fmt, *val):
return struct.pack(byteorder+fmt, *val)
def addtag(code, dtype, count, value, writeonce=False):
# Compute ifdentry & ifdvalue bytes from code, dtype, count, value.
# Append (code, ifdentry, ifdvalue, writeonce) to tags list.
code = int(TiffWriter.TAGS.get(code, code))
try:
tifftype = TiffWriter.TYPES[dtype]
except KeyError:
raise ValueError("unknown dtype %s" % dtype)
rawcount = count
if dtype == 's':
value = bytestr(value) + b'\0'
count = rawcount = len(value)
value = (value, )
if len(dtype) > 1:
count *= int(dtype[:-1])
dtype = dtype[-1]
ifdentry = [pack('HH', code, tifftype),
pack(offset_format, rawcount)]
ifdvalue = None
if count == 1:
if isinstance(value, (tuple, list)):
value = value[0]
ifdentry.append(pack(val_format, pack(dtype, value)))
elif struct.calcsize(dtype) * count <= offset_size:
ifdentry.append(pack(val_format,
pack(str(count)+dtype, *value)))
else:
ifdentry.append(pack(offset_format, 0))
ifdvalue = pack(str(count)+dtype, *value)
tags.append((code, b''.join(ifdentry), ifdvalue, writeonce))
def rational(arg, max_denominator=1000000):
# return nominator and denominator from float or two integers
try:
f = Fraction.from_float(arg)
except TypeError:
f = Fraction(arg[0], arg[1])
f = f.limit_denominator(max_denominator)
return f.numerator, f.denominator
if self._software:
addtag('software', 's', 0, self._software, writeonce=True)
self._software = None # only save to first page
if description:
addtag('image_description', 's', 0, description, writeonce=True)
elif writeshape and shape[0] > 1 and shape != data_shape:
addtag('image_description', 's', 0,
"shape=(%s)" % (",".join('%i' % i for i in data_shape)),
writeonce=True)
addtag('datetime', 's', 0,
datetime.datetime.now().strftime("%Y:%m:%d %H:%M:%S"),
writeonce=True)
addtag('compression', 'H', 1, 32946 if compress else 1)
addtag('orientation', 'H', 1, 1)
addtag('image_width', 'I', 1, shape[-2])
addtag('image_length', 'I', 1, shape[-3])
if volume:
addtag('image_depth', 'I', 1, shape[-4])
addtag('tile_depth', 'I', 1, shape[-4])
addtag('tile_width', 'I', 1, shape[-2])
addtag('tile_length', 'I', 1, shape[-3])
addtag('new_subfile_type', 'I', 1, 0 if shape[0] == 1 else 2)
addtag('sample_format', 'H', 1,
{'u': 1, 'i': 2, 'f': 3, 'c': 6}[data.dtype.kind])
addtag('photometric', 'H', 1,
{'miniswhite': 0, 'minisblack': 1, 'rgb': 2}[photometric])
addtag('samples_per_pixel', 'H', 1, samplesperpixel)
if planarconfig and samplesperpixel > 1:
addtag('planar_configuration', 'H', 1, 1
if planarconfig == 'contig' else 2)
addtag('bits_per_sample', 'H', samplesperpixel,
(data.dtype.itemsize * 8, ) * samplesperpixel)
else:
addtag('bits_per_sample', 'H', 1, data.dtype.itemsize * 8)
if extrasamples:
if photometric == 'rgb' and extrasamples == 1:
addtag('extra_samples', 'H', 1, 1) # associated alpha channel
else:
addtag('extra_samples', 'H', extrasamples, (0,) * extrasamples)
if resolution:
addtag('x_resolution', '2I', 1, rational(resolution[0]))
addtag('y_resolution', '2I', 1, rational(resolution[1]))
addtag('resolution_unit', 'H', 1, 2)
addtag('rows_per_strip', 'I', 1,
shape[-3] * (shape[-4] if volume else 1))
# use one strip or tile per plane
strip_byte_counts = (data[0, 0].size * data.dtype.itemsize,) * shape[1]
addtag(tag_byte_counts, offset_format, shape[1], strip_byte_counts)
addtag(tag_offsets, offset_format, shape[1], (0, ) * shape[1])
# add extra tags from users
for t in extratags:
addtag(*t)
# the entries in an IFD must be sorted in ascending order by tag code
tags = sorted(tags, key=lambda x: x[0])
if not self._bigtiff and (fh.tell() + data.size*data.dtype.itemsize
> 2**31-1):
raise ValueError("data too large for non-bigtiff file")
for pageindex in range(shape[0]):
# update pointer at ifd_offset
pos = fh.tell()
fh.seek(self._ifd_offset)
fh.write(pack(offset_format, pos))
fh.seek(pos)
# write ifdentries
fh.write(pack(numtag_format, len(tags)))
tag_offset = fh.tell()
fh.write(b''.join(t[1] for t in tags))
self._ifd_offset = fh.tell()
fh.write(pack(offset_format, 0)) # offset to next IFD
# write tag values and patch offsets in ifdentries, if necessary
for tagindex, tag in enumerate(tags):
if tag[2]:
pos = fh.tell()
fh.seek(tag_offset + tagindex*tag_size + offset_size + 4)
fh.write(pack(offset_format, pos))
fh.seek(pos)
if tag[0] == tag_offsets:
strip_offsets_offset = pos
elif tag[0] == tag_byte_counts:
strip_byte_counts_offset = pos
fh.write(tag[2])
# write image data
data_offset = fh.tell()
if compress:
strip_byte_counts = []
for plane in data[pageindex]:
plane = zlib.compress(plane, compress)
strip_byte_counts.append(len(plane))
fh.write(plane)
else:
# if this fails try update Python/numpy
data[pageindex].tofile(fh)
fh.flush()
# update strip and tile offsets and byte_counts if necessary
pos = fh.tell()
for tagindex, tag in enumerate(tags):
if tag[0] == tag_offsets: # strip or tile offsets
if tag[2]:
fh.seek(strip_offsets_offset)
strip_offset = data_offset
for size in strip_byte_counts:
fh.write(pack(offset_format, strip_offset))
strip_offset += size
else:
fh.seek(tag_offset + tagindex*tag_size +
offset_size + 4)
fh.write(pack(offset_format, data_offset))
elif tag[0] == tag_byte_counts: # strip or tile byte_counts
if compress:
if tag[2]:
fh.seek(strip_byte_counts_offset)
for size in strip_byte_counts:
fh.write(pack(offset_format, size))
else:
fh.seek(tag_offset + tagindex*tag_size +
offset_size + 4)
fh.write(pack(offset_format, strip_byte_counts[0]))
break
fh.seek(pos)
fh.flush()
# remove tags that should be written only once
if pageindex == 0:
tags = [t for t in tags if not t[-1]]
def close(self):
self._fh.close()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def imread(files, **kwargs):
"""Return image data from TIFF file(s) as numpy array.
The first image series is returned if no arguments are provided.
Parameters
----------
files : str or list
File name, glob pattern, or list of file names.
key : int, slice, or sequence of page indices
Defines which pages to return as array.
series : int
Defines which series of pages in file to return as array.
multifile : bool
If True (default), OME-TIFF data may include pages from multiple files.
pattern : str
Regular expression pattern that matches axes names and indices in
file names.
kwargs : dict
Additional parameters passed to the TiffFile or TiffSequence asarray
function.
Examples
--------
>>> im = imread('test.tif', key=0)
>>> im.shape
(256, 256, 4)
>>> ims = imread(['test.tif', 'test.tif'])
>>> ims.shape
(2, 256, 256, 4)
"""
kwargs_file = {}
if 'multifile' in kwargs:
kwargs_file['multifile'] = kwargs['multifile']
del kwargs['multifile']
else:
kwargs_file['multifile'] = True
kwargs_seq = {}
if 'pattern' in kwargs:
kwargs_seq['pattern'] = kwargs['pattern']
del kwargs['pattern']
if isinstance(files, basestring) and any(i in files for i in '?*'):
files = glob.glob(files)
if not files:
raise ValueError('no files found')
if len(files) == 1:
files = files[0]
if isinstance(files, basestring):
with TiffFile(files, **kwargs_file) as tif:
return tif.asarray(**kwargs)
else:
with TiffSequence(files, **kwargs_seq) as imseq:
return imseq.asarray(**kwargs)
class lazyattr(object):
"""Lazy object attribute whose value is computed on first access."""
__slots__ = ('func', )
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
if instance is None:
return self
value = self.func(instance)
if value is NotImplemented:
return getattr(super(owner, instance), self.func.__name__)
setattr(instance, self.func.__name__, value)
return value
class TiffFile(object):
"""Read image and metadata from TIFF, STK, LSM, and FluoView files.
TiffFile instances must be closed using the close method, which is
automatically called when using the 'with' statement.
Attributes
----------
pages : list
All TIFF pages in file.
series : list of Records(shape, dtype, axes, TiffPages)
TIFF pages with compatible shapes and types.
micromanager_metadata: dict
Extra MicroManager non-TIFF metadata in the file, if exists.
All attributes are read-only.
Examples
--------
>>> with TiffFile('test.tif') as tif:
... data = tif.asarray()
... data.shape
(256, 256, 4)
"""
def __init__(self, arg, name=None, offset=None, size=None,
multifile=True, multifile_close=True):
"""Initialize instance from file.
Parameters
----------
arg : str or open file
Name of file or open file object.
The file objects are closed in TiffFile.close().
name : str
Optional name of file in case 'arg' is a file handle.
offset : int
Optional start position of embedded file. By default this is
the current file position.
size : int
Optional size of embedded file. By default this is the number
of bytes from the 'offset' to the end of the file.
multifile : bool
If True (default), series may include pages from multiple files.
Currently applies to OME-TIFF only.
multifile_close : bool
If True (default), keep the handles of other files in multifile
series closed. This is inefficient when few files refer to
many pages. If False, the C runtime may run out of resources.
"""
self._fh = FileHandle(arg, name=name, offset=offset, size=size)
self.offset_size = None
self.pages = []
self._multifile = bool(multifile)
self._multifile_close = bool(multifile_close)
self._files = {self._fh.name: self} # cache of TiffFiles
try:
self._fromfile()
except Exception:
self._fh.close()
raise
@property
def filehandle(self):
"""Return file handle."""
return self._fh
@property
def filename(self):
"""Return name of file handle."""
return self._fh.name
def close(self):
"""Close open file handle(s)."""
for tif in self._files.values():
tif._fh.close()
self._files = {}
def _fromfile(self):
"""Read TIFF header and all page records from file."""
self._fh.seek(0)
try:
self.byteorder = {b'II': '<', b'MM': '>'}[self._fh.read(2)]
except KeyError:
raise ValueError("not a valid TIFF file")
version = struct.unpack(self.byteorder+'H', self._fh.read(2))[0]
if version == 43: # BigTiff
self.offset_size, zero = struct.unpack(self.byteorder+'HH',
self._fh.read(4))
if zero or self.offset_size != 8:
raise ValueError("not a valid BigTIFF file")
elif version == 42:
self.offset_size = 4
else:
raise ValueError("not a TIFF file")
self.pages = []
while True:
try:
page = TiffPage(self)
self.pages.append(page)
except StopIteration:
break
if not self.pages:
raise ValueError("empty TIFF file")
if self.is_micromanager:
# MicroManager files contain metadata not stored in TIFF tags.
self.micromanager_metadata = read_micromanager_metadata(self._fh)
if self.is_lsm:
self._fix_lsm_strip_offsets()
self._fix_lsm_strip_byte_counts()
def _fix_lsm_strip_offsets(self):
"""Unwrap strip offsets for LSM files greater than 4 GB."""
for series in self.series:
wrap = 0
previous_offset = 0
for page in series.pages:
strip_offsets = []
for current_offset in page.strip_offsets:
if current_offset < previous_offset:
wrap += 2**32
strip_offsets.append(current_offset + wrap)
previous_offset = current_offset
page.strip_offsets = tuple(strip_offsets)
def _fix_lsm_strip_byte_counts(self):
"""Set strip_byte_counts to size of compressed data.
The strip_byte_counts tag in LSM files contains the number of bytes
for the uncompressed data.
"""
if not self.pages:
return
strips = {}
for page in self.pages:
assert len(page.strip_offsets) == len(page.strip_byte_counts)
for offset, bytecount in zip(page.strip_offsets,
page.strip_byte_counts):
strips[offset] = bytecount
offsets = sorted(strips.keys())
offsets.append(min(offsets[-1] + strips[offsets[-1]], self._fh.size))
for i, offset in enumerate(offsets[:-1]):
strips[offset] = min(strips[offset], offsets[i+1] - offset)
for page in self.pages:
if page.compression:
page.strip_byte_counts = tuple(
strips[offset] for offset in page.strip_offsets)
@lazyattr
def series(self):
"""Return series of TiffPage with compatible shape and properties."""
if not self.pages:
return []
series = []
page0 = self.pages[0]
if self.is_ome:
series = self._omeseries()
elif self.is_fluoview:
dims = {b'X': 'X', b'Y': 'Y', b'Z': 'Z', b'T': 'T',
b'WAVELENGTH': 'C', b'TIME': 'T', b'XY': 'R',
b'EVENT': 'V', b'EXPOSURE': 'L'}
mmhd = list(reversed(page0.mm_header.dimensions))
series = [Record(
axes=''.join(dims.get(i[0].strip().upper(), 'Q')
for i in mmhd if i[1] > 1),
shape=tuple(int(i[1]) for i in mmhd if i[1] > 1),
pages=self.pages, dtype=numpy.dtype(page0.dtype))]
elif self.is_lsm:
lsmi = page0.cz_lsm_info
axes = CZ_SCAN_TYPES[lsmi.scan_type]
if page0.is_rgb:
axes = axes.replace('C', '').replace('XY', 'XYC')
axes = axes[::-1]
shape = tuple(getattr(lsmi, CZ_DIMENSIONS[i]) for i in axes)
pages = [p for p in self.pages if not p.is_reduced]
series = [Record(axes=axes, shape=shape, pages=pages,
dtype=numpy.dtype(pages[0].dtype))]
if len(pages) != len(self.pages): # reduced RGB pages
pages = [p for p in self.pages if p.is_reduced]
cp = 1
i = 0
while cp < len(pages) and i < len(shape)-2:
cp *= shape[i]
i += 1
shape = shape[:i] + pages[0].shape
axes = axes[:i] + 'CYX'
series.append(Record(axes=axes, shape=shape, pages=pages,
dtype=numpy.dtype(pages[0].dtype)))
elif self.is_imagej:
shape = []
axes = []
ij = page0.imagej_tags
if 'frames' in ij:
shape.append(ij['frames'])
axes.append('T')
if 'slices' in ij:
shape.append(ij['slices'])
axes.append('Z')
if 'channels' in ij and not self.is_rgb:
shape.append(ij['channels'])
axes.append('C')
remain = len(self.pages) // (product(shape) if shape else 1)
if remain > 1:
shape.append(remain)
axes.append('I')
shape.extend(page0.shape)
axes.extend(page0.axes)
axes = ''.join(axes)
series = [Record(pages=self.pages, shape=tuple(shape), axes=axes,
dtype=numpy.dtype(page0.dtype))]
elif self.is_nih:
if len(self.pages) == 1:
shape = page0.shape
axes = page0.axes
else:
shape = (len(self.pages),) + page0.shape
axes = 'I' + page0.axes
series = [Record(pages=self.pages, shape=shape, axes=axes,
dtype=numpy.dtype(page0.dtype))]
elif page0.is_shaped:
# TODO: shaped files can contain multiple series
shape = page0.tags['image_description'].value[7:-1]
shape = tuple(int(i) for i in shape.split(b','))
series = [Record(pages=self.pages, shape=shape,
axes='Q' * len(shape),
dtype=numpy.dtype(page0.dtype))]
# generic detection of series
if not series:
shapes = []
pages = {}
for page in self.pages:
if not page.shape:
continue
shape = page.shape + (page.axes,
page.compression in TIFF_DECOMPESSORS)
if shape not in pages:
shapes.append(shape)
pages[shape] = [page]
else:
pages[shape].append(page)
series = [Record(pages=pages[s],
axes=(('I' + s[-2])
if len(pages[s]) > 1 else s[-2]),
dtype=numpy.dtype(pages[s][0].dtype),
shape=((len(pages[s]), ) + s[:-2]
if len(pages[s]) > 1 else s[:-2]))
for s in shapes]
# remove empty series, e.g. in MD Gel files
series = [s for s in series if sum(s.shape) > 0]
return series
def asarray(self, key=None, series=None, memmap=False):
"""Return image data from multiple TIFF pages as numpy array.
By default the first image series is returned.
Parameters
----------
key : int, slice, or sequence of page indices
Defines which pages to return as array.
series : int
Defines which series of pages to return as array.
memmap : bool
If True, return an array stored in a binary file on disk
if possible.
"""
if key is None and series is None:
series = 0
if series is not None:
pages = self.series[series].pages
else:
pages = self.pages
if key is None:
pass
elif isinstance(key, int):
pages = [pages[key]]
elif isinstance(key, slice):
pages = pages[key]
elif isinstance(key, collections.Iterable):
pages = [pages[k] for k in key]
else:
raise TypeError("key must be an int, slice, or sequence")
if not len(pages):
raise ValueError("no pages selected")
if self.is_nih:
if pages[0].is_palette:
result = stack_pages(pages, colormapped=False, squeeze=False)
result = numpy.take(pages[0].color_map, result, axis=1)
result = numpy.swapaxes(result, 0, 1)
else:
result = stack_pages(pages, memmap=memmap,
colormapped=False, squeeze=False)
elif len(pages) == 1:
return pages[0].asarray(memmap=memmap)
elif self.is_ome:
assert not self.is_palette, "color mapping disabled for ome-tiff"
if any(p is None for p in pages):
# zero out missing pages
firstpage = next(p for p in pages if p)
nopage = numpy.zeros_like(
firstpage.asarray(memmap=False))
s = self.series[series]
if memmap:
with tempfile.NamedTemporaryFile() as fh:
result = numpy.memmap(fh, dtype=s.dtype, shape=s.shape)
result = result.reshape(-1)
else:
result = numpy.empty(s.shape, s.dtype).reshape(-1)
index = 0
class KeepOpen:
# keep Tiff files open between consecutive pages
def __init__(self, parent, close):
self.master = parent
self.parent = parent
self._close = close
def open(self, page):
if self._close and page and page.parent != self.parent:
if self.parent != self.master:
self.parent.filehandle.close()
self.parent = page.parent
self.parent.filehandle.open()
def close(self):
if self._close and self.parent != self.master:
self.parent.filehandle.close()
keep = KeepOpen(self, self._multifile_close)
for page in pages:
keep.open(page)
if page:
a = page.asarray(memmap=False, colormapped=False,
reopen=False)
else:
a = nopage
try:
result[index:index + a.size] = a.reshape(-1)
except ValueError as e:
warnings.warn("ome-tiff: %s" % e)
break
index += a.size
keep.close()
else:
result = stack_pages(pages, memmap=memmap)
if key is None:
try:
result.shape = self.series[series].shape
except ValueError:
try:
warnings.warn("failed to reshape %s to %s" % (
result.shape, self.series[series].shape))
# try series of expected shapes
result.shape = (-1,) + self.series[series].shape
except ValueError:
# revert to generic shape
result.shape = (-1,) + pages[0].shape
else:
result.shape = (-1,) + pages[0].shape
return result
def _omeseries(self):
"""Return image series in OME-TIFF file(s)."""
root = etree.fromstring(self.pages[0].tags['image_description'].value)
uuid = root.attrib.get('UUID', None)
self._files = {uuid: self}
dirname = self._fh.dirname
modulo = {}
result = []
for element in root:
if element.tag.endswith('BinaryOnly'):
warnings.warn("ome-xml: not an ome-tiff master file")
break
if element.tag.endswith('StructuredAnnotations'):
for annot in element:
if not annot.attrib.get('Namespace',
'').endswith('modulo'):
continue
for value in annot:
for modul in value:
for along in modul:
if not along.tag[:-1].endswith('Along'):
continue
axis = along.tag[-1]
newaxis = along.attrib.get('Type', 'other')
newaxis = AXES_LABELS[newaxis]
if 'Start' in along.attrib:
labels = range(
int(along.attrib['Start']),
int(along.attrib['End']) + 1,
int(along.attrib.get('Step', 1)))
else:
labels = [label.text for label in along
if label.tag.endswith('Label')]
modulo[axis] = (newaxis, labels)
if not element.tag.endswith('Image'):
continue
for pixels in element:
if not pixels.tag.endswith('Pixels'):
continue
atr = pixels.attrib
dtype = atr.get('Type', None)
axes = ''.join(reversed(atr['DimensionOrder']))
shape = list(int(atr['Size'+ax]) for ax in axes)
size = product(shape[:-2])
ifds = [None] * size
for data in pixels:
if not data.tag.endswith('TiffData'):
continue
atr = data.attrib
ifd = int(atr.get('IFD', 0))
num = int(atr.get('NumPlanes', 1 if 'IFD' in atr else 0))
num = int(atr.get('PlaneCount', num))
idx = [int(atr.get('First'+ax, 0)) for ax in axes[:-2]]
try:
idx = numpy.ravel_multi_index(idx, shape[:-2])
except ValueError:
# ImageJ produces invalid ome-xml when cropping
warnings.warn("ome-xml: invalid TiffData index")
continue
for uuid in data:
if not uuid.tag.endswith('UUID'):
continue
if uuid.text not in self._files:
if not self._multifile:
# abort reading multifile OME series
# and fall back to generic series
return []
fname = uuid.attrib['FileName']
try:
tif = TiffFile(os.path.join(dirname, fname))
except (IOError, ValueError):
tif.close()
warnings.warn(
"ome-xml: failed to read '%s'" % fname)
break
self._files[uuid.text] = tif
if self._multifile_close:
tif.close()
pages = self._files[uuid.text].pages
try:
for i in range(num if num else len(pages)):
ifds[idx + i] = pages[ifd + i]
except IndexError:
warnings.warn("ome-xml: index out of range")
# only process first uuid
break
else:
pages = self.pages
try:
for i in range(num if num else len(pages)):
ifds[idx + i] = pages[ifd + i]
except IndexError:
warnings.warn("ome-xml: index out of range")
if all(i is None for i in ifds):
# skip images without data
continue
dtype = next(i for i in ifds if i).dtype
result.append(Record(axes=axes, shape=shape, pages=ifds,
dtype=numpy.dtype(dtype)))
for record in result:
for axis, (newaxis, labels) in modulo.items():
i = record.axes.index(axis)
size = len(labels)
if record.shape[i] == size:
record.axes = record.axes.replace(axis, newaxis, 1)
else:
record.shape[i] //= size
record.shape.insert(i+1, size)
record.axes = record.axes.replace(axis, axis+newaxis, 1)
record.shape = tuple(record.shape)
# squeeze dimensions
for record in result:
record.shape, record.axes = squeeze_axes(record.shape, record.axes)
return result
def __len__(self):
"""Return number of image pages in file."""
return len(self.pages)
def __getitem__(self, key):
"""Return specified page."""
return self.pages[key]
def __iter__(self):
"""Return iterator over pages."""
return iter(self.pages)
def __str__(self):
"""Return string containing information about file."""
result = [
self._fh.name.capitalize(),
format_size(self._fh.size),
{'<': 'little endian', '>': 'big endian'}[self.byteorder]]
if self.is_bigtiff:
result.append("bigtiff")
if len(self.pages) > 1:
result.append("%i pages" % len(self.pages))
if len(self.series) > 1:
result.append("%i series" % len(self.series))
if len(self._files) > 1:
result.append("%i files" % (len(self._files)))
return ", ".join(result)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
@lazyattr
def fstat(self):
try:
return os.fstat(self._fh.fileno())
except Exception: # io.UnsupportedOperation
return None
@lazyattr
def is_bigtiff(self):
return self.offset_size != 4
@lazyattr
def is_rgb(self):
return all(p.is_rgb for p in self.pages)
@lazyattr
def is_palette(self):
return all(p.is_palette for p in self.pages)
@lazyattr
def is_mdgel(self):
return any(p.is_mdgel for p in self.pages)
@lazyattr
def is_mediacy(self):
return any(p.is_mediacy for p in self.pages)
@lazyattr
def is_stk(self):
return all(p.is_stk for p in self.pages)
@lazyattr
def is_lsm(self):
return self.pages[0].is_lsm
@lazyattr
def is_imagej(self):
return self.pages[0].is_imagej
@lazyattr
def is_micromanager(self):
return self.pages[0].is_micromanager
@lazyattr
def is_nih(self):
return self.pages[0].is_nih
@lazyattr
def is_fluoview(self):
return self.pages[0].is_fluoview
@lazyattr
def is_ome(self):
return self.pages[0].is_ome
class TiffPage(object):
"""A TIFF image file directory (IFD).
Attributes
----------
index : int
Index of page in file.
dtype : str {TIFF_SAMPLE_DTYPES}
Data type of image, colormapped if applicable.
shape : tuple
Dimensions of the image array in TIFF page,
colormapped and with one alpha channel if applicable.
axes : str
Axes label codes:
'X' width, 'Y' height, 'S' sample, 'I' image series|page|plane,
'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
'T' time, 'R' region|tile, 'A' angle, 'P' phase, 'H' lifetime,
'L' exposure, 'V' event, 'Q' unknown, '_' missing
tags : TiffTags
Dictionary of tags in page.
Tag values are also directly accessible as attributes.
color_map : numpy array
Color look up table, if exists.
cz_lsm_scan_info: Record(dict)
LSM scan info attributes, if exists.
imagej_tags: Record(dict)
Consolidated ImageJ description and metadata tags, if exists.
uic_tags: Record(dict)
Consolidated MetaMorph STK/UIC tags, if exists.
All attributes are read-only.
Notes
-----
The internal, normalized '_shape' attribute is 6 dimensional:
0. number planes (stk)
1. planar samples_per_pixel
2. image_depth Z (sgi)
3. image_length Y
4. image_width X
5. contig samples_per_pixel
"""
def __init__(self, parent):
"""Initialize instance from file."""
self.parent = parent
self.index = len(parent.pages)
self.shape = self._shape = ()
self.dtype = self._dtype = None
self.axes = ""
self.tags = TiffTags()
self._fromfile()
self._process_tags()
def _fromfile(self):
"""Read TIFF IFD structure and its tags from file.
File cursor must be at storage position of IFD offset and is left at
offset to next IFD.
Raises StopIteration if offset (first bytes read) is 0.
"""
fh = self.parent.filehandle
byteorder = self.parent.byteorder
offset_size = self.parent.offset_size
fmt = {4: 'I', 8: 'Q'}[offset_size]
offset = struct.unpack(byteorder + fmt, fh.read(offset_size))[0]
if not offset:
raise StopIteration()
# read standard tags
tags = self.tags
fh.seek(offset)
fmt, size = {4: ('H', 2), 8: ('Q', 8)}[offset_size]
try:
numtags = struct.unpack(byteorder + fmt, fh.read(size))[0]
except Exception:
warnings.warn("corrupted page list")
raise StopIteration()
tagcode = 0
for _ in range(numtags):
try:
tag = TiffTag(self.parent)
# print(tag)
except TiffTag.Error as e:
warnings.warn(str(e))
continue
if tagcode > tag.code:
# expected for early LSM and tifffile versions
warnings.warn("tags are not ordered by code")
tagcode = tag.code
if tag.name not in tags:
tags[tag.name] = tag
else:
# some files contain multiple IFD with same code
# e.g. MicroManager files contain two image_description
i = 1
while True:
name = "%s_%i" % (tag.name, i)
if name not in tags:
tags[name] = tag
break
pos = fh.tell()
if self.is_lsm or (self.index and self.parent.is_lsm):
# correct non standard LSM bitspersample tags
self.tags['bits_per_sample']._correct_lsm_bitspersample(self)
if self.is_lsm:
# read LSM info subrecords
for name, reader in CZ_LSM_INFO_READERS.items():
try:
offset = self.cz_lsm_info['offset_'+name]
except KeyError:
continue
if offset < 8:
# older LSM revision
continue
fh.seek(offset)
try:
setattr(self, 'cz_lsm_'+name, reader(fh))
except ValueError:
pass
elif self.is_stk and 'uic1tag' in tags and not tags['uic1tag'].value:
# read uic1tag now that plane count is known
uic1tag = tags['uic1tag']
fh.seek(uic1tag.value_offset)
tags['uic1tag'].value = Record(
read_uic1tag(fh, byteorder, uic1tag.dtype, uic1tag.count,
tags['uic2tag'].count))
fh.seek(pos)
def _process_tags(self):
"""Validate standard tags and initialize attributes.
Raise ValueError if tag values are not supported.
"""
tags = self.tags
for code, (name, default, dtype, count, validate) in TIFF_TAGS.items():
if not (name in tags or default is None):
tags[name] = TiffTag(code, dtype=dtype, count=count,
value=default, name=name)
if name in tags and validate:
try:
if tags[name].count == 1:
setattr(self, name, validate[tags[name].value])
else:
setattr(self, name, tuple(
validate[value] for value in tags[name].value))
except KeyError:
raise ValueError("%s.value (%s) not supported" %
(name, tags[name].value))
tag = tags['bits_per_sample']
if tag.count == 1:
self.bits_per_sample = tag.value
else:
# LSM might list more items than samples_per_pixel
value = tag.value[:self.samples_per_pixel]
if any((v-value[0] for v in value)):
self.bits_per_sample = value
else:
self.bits_per_sample = value[0]
tag = tags['sample_format']
if tag.count == 1:
self.sample_format = TIFF_SAMPLE_FORMATS[tag.value]
else:
value = tag.value[:self.samples_per_pixel]
if any((v-value[0] for v in value)):
self.sample_format = [TIFF_SAMPLE_FORMATS[v] for v in value]
else:
self.sample_format = TIFF_SAMPLE_FORMATS[value[0]]
if 'photometric' not in tags:
self.photometric = None
if 'image_depth' not in tags:
self.image_depth = 1
if 'image_length' in tags:
self.strips_per_image = int(math.floor(
float(self.image_length + self.rows_per_strip - 1) /
self.rows_per_strip))
else:
self.strips_per_image = 0
key = (self.sample_format, self.bits_per_sample)
self.dtype = self._dtype = TIFF_SAMPLE_DTYPES.get(key, None)
if 'image_length' not in self.tags or 'image_width' not in self.tags:
# some GEL file pages are missing image data
self.image_length = 0
self.image_width = 0
self.image_depth = 0
self.strip_offsets = 0
self._shape = ()
self.shape = ()
self.axes = ''
if self.is_palette:
self.dtype = self.tags['color_map'].dtype[1]
self.color_map = numpy.array(self.color_map, self.dtype)
dmax = self.color_map.max()
if dmax < 256:
self.dtype = numpy.uint8
self.color_map = self.color_map.astype(self.dtype)
#else:
# self.dtype = numpy.uint8
# self.color_map >>= 8
# self.color_map = self.color_map.astype(self.dtype)
self.color_map.shape = (3, -1)
# determine shape of data
image_length = self.image_length
image_width = self.image_width
image_depth = self.image_depth
samples_per_pixel = self.samples_per_pixel
if self.is_stk:
assert self.image_depth == 1
planes = self.tags['uic2tag'].count
if self.is_contig:
self._shape = (planes, 1, 1, image_length, image_width,
samples_per_pixel)
if samples_per_pixel == 1:
self.shape = (planes, image_length, image_width)
self.axes = 'YX'
else:
self.shape = (planes, image_length, image_width,
samples_per_pixel)
self.axes = 'YXS'
else:
self._shape = (planes, samples_per_pixel, 1, image_length,
image_width, 1)
if samples_per_pixel == 1:
self.shape = (planes, image_length, image_width)
self.axes = 'YX'
else:
self.shape = (planes, samples_per_pixel, image_length,
image_width)
self.axes = 'SYX'
# detect type of series
if planes == 1:
self.shape = self.shape[1:]
elif numpy.all(self.uic2tag.z_distance != 0):
self.axes = 'Z' + self.axes
elif numpy.all(numpy.diff(self.uic2tag.time_created) != 0):
self.axes = 'T' + self.axes
else:
self.axes = 'I' + self.axes
# DISABLED
if self.is_palette:
assert False, "color mapping disabled for stk"
if self.color_map.shape[1] >= 2**self.bits_per_sample:
if image_depth == 1:
self.shape = (3, planes, image_length, image_width)
else:
self.shape = (3, planes, image_depth, image_length,
image_width)
self.axes = 'C' + self.axes
else:
warnings.warn("palette cannot be applied")
self.is_palette = False
elif self.is_palette:
samples = 1
if 'extra_samples' in self.tags:
samples += len(self.extra_samples)
if self.is_contig:
self._shape = (1, 1, image_depth, image_length, image_width,
samples)
else:
self._shape = (1, samples, image_depth, image_length,
image_width, 1)
if self.color_map.shape[1] >= 2**self.bits_per_sample:
if image_depth == 1:
self.shape = (3, image_length, image_width)
self.axes = 'CYX'
else:
self.shape = (3, image_depth, image_length, image_width)
self.axes = 'CZYX'
else:
warnings.warn("palette cannot be applied")
self.is_palette = False
if image_depth == 1:
self.shape = (image_length, image_width)
self.axes = 'YX'
else:
self.shape = (image_depth, image_length, image_width)
self.axes = 'ZYX'
elif self.is_rgb or samples_per_pixel > 1:
if self.is_contig:
self._shape = (1, 1, image_depth, image_length, image_width,
samples_per_pixel)
if image_depth == 1:
self.shape = (image_length, image_width, samples_per_pixel)
self.axes = 'YXS'
else:
self.shape = (image_depth, image_length, image_width,
samples_per_pixel)
self.axes = 'ZYXS'
else:
self._shape = (1, samples_per_pixel, image_depth,
image_length, image_width, 1)
if image_depth == 1:
self.shape = (samples_per_pixel, image_length, image_width)
self.axes = 'SYX'
else:
self.shape = (samples_per_pixel, image_depth,
image_length, image_width)
self.axes = 'SZYX'
if False and self.is_rgb and 'extra_samples' in self.tags:
# DISABLED: only use RGB and first alpha channel if exists
extra_samples = self.extra_samples
if self.tags['extra_samples'].count == 1:
extra_samples = (extra_samples, )
for exs in extra_samples:
if exs in ('unassalpha', 'assocalpha', 'unspecified'):
if self.is_contig:
self.shape = self.shape[:-1] + (4,)
else:
self.shape = (4,) + self.shape[1:]
break
else:
self._shape = (1, 1, image_depth, image_length, image_width, 1)
if image_depth == 1:
self.shape = (image_length, image_width)
self.axes = 'YX'
else:
self.shape = (image_depth, image_length, image_width)
self.axes = 'ZYX'
if not self.compression and 'strip_byte_counts' not in tags:
self.strip_byte_counts = (
product(self.shape) * (self.bits_per_sample // 8), )
assert len(self.shape) == len(self.axes)
def asarray(self, squeeze=True, colormapped=True, rgbonly=False,
scale_mdgel=False, memmap=False, reopen=True):
"""Read image data from file and return as numpy array.
Raise ValueError if format is unsupported.
If any of 'squeeze', 'colormapped', or 'rgbonly' are not the default,
the shape of the returned array might be different from the page shape.
Parameters
----------
squeeze : bool
If True, all length-1 dimensions (except X and Y) are
squeezed out from result.
colormapped : bool
If True, color mapping is applied for palette-indexed images.
rgbonly : bool
If True, return RGB(A) image without additional extra samples.
memmap : bool
If True, use numpy.memmap to read arrays from file if possible.
For use on 64 bit systems and files with few huge contiguous data.
reopen : bool
If True and the parent file handle is closed, the file is
temporarily re-opened (and closed if no exception occurs).
scale_mdgel : bool
If True, MD Gel data will be scaled according to the private
metadata in the second TIFF page. The dtype will be float32.
"""
if not self._shape:
return
if self.dtype is None:
raise ValueError("data type not supported: %s%i" % (
self.sample_format, self.bits_per_sample))
if self.compression not in TIFF_DECOMPESSORS:
raise ValueError("cannot decompress %s" % self.compression)
tag = self.tags['sample_format']
if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
raise ValueError("sample formats don't match %s" % str(tag.value))
fh = self.parent.filehandle
closed = fh.closed
if closed:
if reopen:
fh.open()
else:
raise IOError("file handle is closed")
dtype = self._dtype
shape = self._shape
image_width = self.image_width
image_length = self.image_length
image_depth = self.image_depth
typecode = self.parent.byteorder + dtype
bits_per_sample = self.bits_per_sample
if self.is_tiled:
if 'tile_offsets' in self.tags:
byte_counts = self.tile_byte_counts
offsets = self.tile_offsets
else:
byte_counts = self.strip_byte_counts
offsets = self.strip_offsets
tile_width = self.tile_width
tile_length = self.tile_length
tile_depth = self.tile_depth if 'tile_depth' in self.tags else 1
tw = (image_width + tile_width - 1) // tile_width
tl = (image_length + tile_length - 1) // tile_length
td = (image_depth + tile_depth - 1) // tile_depth
shape = (shape[0], shape[1],
td*tile_depth, tl*tile_length, tw*tile_width, shape[-1])
tile_shape = (tile_depth, tile_length, tile_width, shape[-1])
runlen = tile_width
else:
byte_counts = self.strip_byte_counts
offsets = self.strip_offsets
runlen = image_width
if any(o < 2 for o in offsets):
raise ValueError("corrupted page")
if memmap and self._is_memmappable(rgbonly, colormapped):
result = fh.memmap_array(typecode, shape, offset=offsets[0])
elif self.is_contiguous:
fh.seek(offsets[0])
result = fh.read_array(typecode, product(shape))
result = result.astype('=' + dtype)
else:
if self.is_contig:
runlen *= self.samples_per_pixel
if bits_per_sample in (8, 16, 32, 64, 128):
if (bits_per_sample * runlen) % 8:
raise ValueError("data and sample size mismatch")
def unpack(x):
try:
return numpy.fromstring(x, typecode)
except ValueError as e:
# strips may be missing EOI
warnings.warn("unpack: %s" % e)
xlen = ((len(x) // (bits_per_sample // 8))
* (bits_per_sample // 8))
return numpy.fromstring(x[:xlen], typecode)
elif isinstance(bits_per_sample, tuple):
def unpack(x):
return unpackrgb(x, typecode, bits_per_sample)
else:
def unpack(x):
return unpackints(x, typecode, bits_per_sample, runlen)
decompress = TIFF_DECOMPESSORS[self.compression]
if self.compression == 'jpeg':
table = self.jpeg_tables if 'jpeg_tables' in self.tags else b''
decompress = lambda x: decodejpg(x, table, self.photometric)
if self.is_tiled:
result = numpy.empty(shape, dtype)
tw, tl, td, pl = 0, 0, 0, 0
for offset, bytecount in zip(offsets, byte_counts):
fh.seek(offset)
tile = unpack(decompress(fh.read(bytecount)))
tile.shape = tile_shape
if self.predictor == 'horizontal':
numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile)
result[0, pl, td:td+tile_depth,
tl:tl+tile_length, tw:tw+tile_width, :] = tile
del tile
tw += tile_width
if tw >= shape[4]:
tw, tl = 0, tl + tile_length
if tl >= shape[3]:
tl, td = 0, td + tile_depth
if td >= shape[2]:
td, pl = 0, pl + 1
result = result[...,
:image_depth, :image_length, :image_width, :]
else:
strip_size = (self.rows_per_strip * self.image_width *
self.samples_per_pixel)
result = numpy.empty(shape, dtype).reshape(-1)
index = 0
for offset, bytecount in zip(offsets, byte_counts):
fh.seek(offset)
strip = fh.read(bytecount)
strip = decompress(strip)
strip = unpack(strip)
size = min(result.size, strip.size, strip_size,
result.size - index)
result[index:index+size] = strip[:size]
del strip
index += size
result.shape = self._shape
if self.predictor == 'horizontal' and not (self.is_tiled and not
self.is_contiguous):
# work around bug in LSM510 software
if not (self.parent.is_lsm and not self.compression):
numpy.cumsum(result, axis=-2, dtype=dtype, out=result)
if colormapped and self.is_palette:
if self.color_map.shape[1] >= 2**bits_per_sample:
# FluoView and LSM might fail here
result = numpy.take(self.color_map,
result[:, 0, :, :, :, 0], axis=1)
elif rgbonly and self.is_rgb and 'extra_samples' in self.tags:
# return only RGB and first alpha channel if exists
extra_samples = self.extra_samples
if self.tags['extra_samples'].count == 1:
extra_samples = (extra_samples, )
for i, exs in enumerate(extra_samples):
if exs in ('unassalpha', 'assocalpha', 'unspecified'):
if self.is_contig:
result = result[..., [0, 1, 2, 3+i]]
else:
result = result[:, [0, 1, 2, 3+i]]
break
else:
if self.is_contig:
result = result[..., :3]
else:
result = result[:, :3]
if squeeze:
try:
result.shape = self.shape
except ValueError:
warnings.warn("failed to reshape from %s to %s" % (
str(result.shape), str(self.shape)))
if scale_mdgel and self.parent.is_mdgel:
# MD Gel stores private metadata in the second page
tags = self.parent.pages[1]
if tags.md_file_tag in (2, 128):
scale = tags.md_scale_pixel
scale = scale[0] / scale[1] # rational
result = result.astype('float32')
if tags.md_file_tag == 2:
result **= 2 # squary root data format
result *= scale
if closed:
# TODO: file remains open if an exception occurred above
fh.close()
return result
def _is_memmappable(self, rgbonly, colormapped):
"""Return if image data in file can be memory mapped."""
if not self.parent.filehandle.is_file or not self.is_contiguous:
return False
return not (self.predictor or
(rgbonly and 'extra_samples' in self.tags) or
(colormapped and self.is_palette) or
({'big': '>', 'little': '<'}[sys.byteorder] !=
self.parent.byteorder))
@lazyattr
def is_contiguous(self):
"""Return offset and size of contiguous data, else None.
Excludes prediction and colormapping.
"""
if self.compression or self.bits_per_sample not in (8, 16, 32, 64):
return
if self.is_tiled:
if (self.image_width != self.tile_width or
self.image_length % self.tile_length or
self.tile_width % 16 or self.tile_length % 16):
return
if ('image_depth' in self.tags and 'tile_depth' in self.tags and
(self.image_length != self.tile_length or
self.image_depth % self.tile_depth)):
return
offsets = self.tile_offsets
byte_counts = self.tile_byte_counts
else:
offsets = self.strip_offsets
byte_counts = self.strip_byte_counts
if len(offsets) == 1:
return offsets[0], byte_counts[0]
if self.is_stk or all(offsets[i] + byte_counts[i] == offsets[i+1]
or byte_counts[i+1] == 0 # no data/ignore offset
for i in range(len(offsets)-1)):
return offsets[0], sum(byte_counts)
def __str__(self):
"""Return string containing information about page."""
s = ', '.join(s for s in (
' x '.join(str(i) for i in self.shape),
str(numpy.dtype(self.dtype)),
'%s bit' % str(self.bits_per_sample),
self.photometric if 'photometric' in self.tags else '',
self.compression if self.compression else 'raw',
'|'.join(t[3:] for t in (
'is_stk', 'is_lsm', 'is_nih', 'is_ome', 'is_imagej',
'is_micromanager', 'is_fluoview', 'is_mdgel', 'is_mediacy',
'is_sgi', 'is_reduced', 'is_tiled',
'is_contiguous') if getattr(self, t))) if s)
return "Page %i: %s" % (self.index, s)
def __getattr__(self, name):
"""Return tag value."""
if name in self.tags:
value = self.tags[name].value
setattr(self, name, value)
return value
raise AttributeError(name)
@lazyattr
def uic_tags(self):
"""Consolidate UIC tags."""
if not self.is_stk:
raise AttributeError("uic_tags")
tags = self.tags
result = Record()
result.number_planes = tags['uic2tag'].count
if 'image_description' in tags:
result.plane_descriptions = self.image_description.split(b'\x00')
if 'uic1tag' in tags:
result.update(tags['uic1tag'].value)
if 'uic3tag' in tags:
result.update(tags['uic3tag'].value) # wavelengths
if 'uic4tag' in tags:
result.update(tags['uic4tag'].value) # override uic1 tags
uic2tag = tags['uic2tag'].value
result.z_distance = uic2tag.z_distance
result.time_created = uic2tag.time_created
result.time_modified = uic2tag.time_modified
try:
result.datetime_created = [
julian_datetime(*dt) for dt in
zip(uic2tag.date_created, uic2tag.time_created)]
result.datetime_modified = [
julian_datetime(*dt) for dt in
zip(uic2tag.date_modified, uic2tag.time_modified)]
except ValueError as e:
warnings.warn("uic_tags: %s" % e)
return result
@lazyattr
def imagej_tags(self):
"""Consolidate ImageJ metadata."""
if not self.is_imagej:
raise AttributeError("imagej_tags")
tags = self.tags
if 'image_description_1' in tags:
# MicroManager
result = imagej_description(tags['image_description_1'].value)
else:
result = imagej_description(tags['image_description'].value)
if 'imagej_metadata' in tags:
try:
result.update(imagej_metadata(
tags['imagej_metadata'].value,
tags['imagej_byte_counts'].value,
self.parent.byteorder))
except Exception as e:
warnings.warn(str(e))
return Record(result)
@lazyattr
def is_rgb(self):
"""True if page contains a RGB image."""
return ('photometric' in self.tags and
self.tags['photometric'].value == 2)
@lazyattr
def is_contig(self):
"""True if page contains a contiguous image."""
return ('planar_configuration' in self.tags and
self.tags['planar_configuration'].value == 1)
@lazyattr
def is_palette(self):
"""True if page contains a palette-colored image and not OME or STK."""
try:
# turn off color mapping for OME-TIFF and STK
if self.is_stk or self.is_ome or self.parent.is_ome:
return False
except IndexError:
pass # OME-XML not found in first page
return ('photometric' in self.tags and
self.tags['photometric'].value == 3)
@lazyattr
def is_tiled(self):
"""True if page contains tiled image."""
return 'tile_width' in self.tags
@lazyattr
def is_reduced(self):
"""True if page is a reduced image of another image."""
return bool(self.tags['new_subfile_type'].value & 1)
@lazyattr
def is_mdgel(self):
"""True if page contains md_file_tag tag."""
return 'md_file_tag' in self.tags
@lazyattr
def is_mediacy(self):
"""True if page contains Media Cybernetics Id tag."""
return ('mc_id' in self.tags and
self.tags['mc_id'].value.startswith(b'MC TIFF'))
@lazyattr
def is_stk(self):
"""True if page contains UIC2Tag tag."""
return 'uic2tag' in self.tags
@lazyattr
def is_lsm(self):
"""True if page contains LSM CZ_LSM_INFO tag."""
return 'cz_lsm_info' in self.tags
@lazyattr
def is_fluoview(self):
"""True if page contains FluoView MM_STAMP tag."""
return 'mm_stamp' in self.tags
@lazyattr
def is_nih(self):
"""True if page contains NIH image header."""
return 'nih_image_header' in self.tags
@lazyattr
def is_sgi(self):
"""True if page contains SGI image and tile depth tags."""
return 'image_depth' in self.tags and 'tile_depth' in self.tags
@lazyattr
def is_ome(self):
"""True if page contains OME-XML in image_description tag."""
return ('image_description' in self.tags and self.tags[
'image_description'].value.startswith(b' parent.offset_size or code in CUSTOM_TAGS:
pos = fh.tell()
tof = {4: 'I', 8: 'Q'}[parent.offset_size]
self.value_offset = offset = struct.unpack(byteorder+tof, value)[0]
if offset < 0 or offset > parent.filehandle.size:
raise TiffTag.Error("corrupt file - invalid tag value offset")
elif offset < 4:
raise TiffTag.Error("corrupt value offset for tag %i" % code)
fh.seek(offset)
if code in CUSTOM_TAGS:
readfunc = CUSTOM_TAGS[code][1]
value = readfunc(fh, byteorder, dtype, count)
if isinstance(value, dict): # numpy.core.records.record
value = Record(value)
elif code in TIFF_TAGS or dtype[-1] == 's':
value = struct.unpack(fmt, fh.read(size))
else:
value = read_numpy(fh, byteorder, dtype, count)
fh.seek(pos)
else:
value = struct.unpack(fmt, value[:size])
if code not in CUSTOM_TAGS and code not in (273, 279, 324, 325):
# scalar value if not strip/tile offsets/byte_counts
if len(value) == 1:
value = value[0]
if (dtype.endswith('s') and isinstance(value, bytes)
and self._type != 7):
# TIFF ASCII fields can contain multiple strings,
# each terminated with a NUL
value = stripascii(value)
self.code = code
self.name = name
self.dtype = dtype
self.count = count
self.value = value
def _correct_lsm_bitspersample(self, parent):
"""Correct LSM bitspersample tag.
Old LSM writers may use a separate region for two 16-bit values,
although they fit into the tag value element of the tag.
"""
if self.code == 258 and self.count == 2:
# TODO: test this. Need example file.
warnings.warn("correcting LSM bitspersample tag")
fh = parent.filehandle
tof = {4: '')
def __str__(self):
"""Return string containing information about tag."""
return ' '.join(str(getattr(self, s)) for s in self.__slots__)
class TiffSequence(object):
"""Sequence of image files.
The data shape and dtype of all files must match.
Properties
----------
files : list
List of file names.
shape : tuple
Shape of image sequence.
axes : str
Labels of axes in shape.
Examples
--------
>>> tifs = TiffSequence("test.oif.files/*.tif")
>>> tifs.shape, tifs.axes
((2, 100), 'CT')
>>> data = tifs.asarray()
>>> data.shape
(2, 100, 256, 256)
"""
_patterns = {
'axes': r"""
# matches Olympus OIF and Leica TIFF series
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
_?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
"""}
class ParseError(Exception):
pass
def __init__(self, files, imread=TiffFile, pattern='axes',
*args, **kwargs):
"""Initialize instance from multiple files.
Parameters
----------
files : str, or sequence of str
Glob pattern or sequence of file names.
imread : function or class
Image read function or class with asarray function returning numpy
array from single file.
pattern : str
Regular expression pattern that matches axes names and sequence
indices in file names.
By default this matches Olympus OIF and Leica TIFF series.
"""
if isinstance(files, basestring):
files = natural_sorted(glob.glob(files))
files = list(files)
if not files:
raise ValueError("no files found")
#if not os.path.isfile(files[0]):
# raise ValueError("file not found")
self.files = files
if hasattr(imread, 'asarray'):
# redefine imread
_imread = imread
def imread(fname, *args, **kwargs):
with _imread(fname) as im:
return im.asarray(*args, **kwargs)
self.imread = imread
self.pattern = self._patterns.get(pattern, pattern)
try:
self._parse()
if not self.axes:
self.axes = 'I'
except self.ParseError:
self.axes = 'I'
self.shape = (len(files),)
self._start_index = (0,)
self._indices = tuple((i,) for i in range(len(files)))
def __str__(self):
"""Return string with information about image sequence."""
return "\n".join([
self.files[0],
'* files: %i' % len(self.files),
'* axes: %s' % self.axes,
'* shape: %s' % str(self.shape)])
def __len__(self):
return len(self.files)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def close(self):
pass
def asarray(self, memmap=False, *args, **kwargs):
"""Read image data from all files and return as single numpy array.
If memmap is True, return an array stored in a binary file on disk.
The args and kwargs parameters are passed to the imread function.
Raise IndexError or ValueError if image shapes don't match.
"""
im = self.imread(self.files[0], *args, **kwargs)
shape = self.shape + im.shape
if memmap:
with tempfile.NamedTemporaryFile() as fh:
result = numpy.memmap(fh, dtype=im.dtype, shape=shape)
else:
result = numpy.zeros(shape, dtype=im.dtype)
result = result.reshape(-1, *im.shape)
for index, fname in zip(self._indices, self.files):
index = [i-j for i, j in zip(index, self._start_index)]
index = numpy.ravel_multi_index(index, self.shape)
im = self.imread(fname, *args, **kwargs)
result[index] = im
result.shape = shape
return result
def _parse(self):
"""Get axes and shape from file names."""
if not self.pattern:
raise self.ParseError("invalid pattern")
pattern = re.compile(self.pattern, re.IGNORECASE | re.VERBOSE)
matches = pattern.findall(self.files[0])
if not matches:
raise self.ParseError("pattern doesn't match file names")
matches = matches[-1]
if len(matches) % 2:
raise self.ParseError("pattern doesn't match axis name and index")
axes = ''.join(m for m in matches[::2] if m)
if not axes:
raise self.ParseError("pattern doesn't match file names")
indices = []
for fname in self.files:
matches = pattern.findall(fname)[-1]
if axes != ''.join(m for m in matches[::2] if m):
raise ValueError("axes don't match within the image sequence")
indices.append([int(m) for m in matches[1::2] if m])
shape = tuple(numpy.max(indices, axis=0))
start_index = tuple(numpy.min(indices, axis=0))
shape = tuple(i-j+1 for i, j in zip(shape, start_index))
if product(shape) != len(self.files):
warnings.warn("files are missing. Missing data are zeroed")
self.axes = axes.upper()
self.shape = shape
self._indices = indices
self._start_index = start_index
class Record(dict):
"""Dictionary with attribute access.
Can also be initialized with numpy.core.records.record.
"""
__slots__ = ()
def __init__(self, arg=None, **kwargs):
if kwargs:
arg = kwargs
elif arg is None:
arg = {}
try:
dict.__init__(self, arg)
except (TypeError, ValueError):
for i, name in enumerate(arg.dtype.names):
v = arg[i]
self[name] = v if v.dtype.char != 'S' else stripnull(v)
def __getattr__(self, name):
return self[name]
def __setattr__(self, name, value):
self.__setitem__(name, value)
def __str__(self):
"""Pretty print Record."""
s = []
lists = []
for k in sorted(self):
try:
if k.startswith('_'): # does not work with byte
continue
except AttributeError:
pass
v = self[k]
if isinstance(v, (list, tuple)) and len(v):
if isinstance(v[0], Record):
lists.append((k, v))
continue
elif isinstance(v[0], TiffPage):
v = [i.index for i in v if i]
s.append(
("* %s: %s" % (k, str(v))).split("\n", 1)[0]
[:PRINT_LINE_LEN].rstrip())
for k, v in lists:
l = []
for i, w in enumerate(v):
l.append("* %s[%i]\n %s" % (k, i,
str(w).replace("\n", "\n ")))
s.append('\n'.join(l))
return '\n'.join(s)
class TiffTags(Record):
"""Dictionary of TiffTag with attribute access."""
def __str__(self):
"""Return string with information about all tags."""
s = []
for tag in sorted(self.values(), key=lambda x: x.code):
typecode = "%i%s" % (tag.count * int(tag.dtype[0]), tag.dtype[1])
line = "* %i %s (%s) %s" % (
tag.code, tag.name, typecode, tag.as_str())
s.append(line[:PRINT_LINE_LEN].lstrip())
return '\n'.join(s)
class FileHandle(object):
"""Binary file handle.
* Handle embedded files (for CZI within CZI files).
* Allow to re-open closed files (for multi file formats such as OME-TIFF).
* Read numpy arrays and records from file like objects.
Only binary read, seek, tell, and close are supported on embedded files.
When initialized from another file handle, do not use it unless this
FileHandle is closed.
Attributes
----------
name : str
Name of the file.
path : str
Absolute path to file.
size : int
Size of file in bytes.
is_file : bool
If True, file has a filno and can be memory mapped.
All attributes are read-only.
"""
__slots__ = ('_fh', '_arg', '_mode', '_name', '_dir',
'_offset', '_size', '_close', 'is_file')
def __init__(self, arg, mode='rb', name=None, offset=None, size=None):
"""Initialize file handle from file name or another file handle.
Parameters
----------
arg : str, File, or FileHandle
File name or open file handle.
mode : str
File open mode in case 'arg' is a file name.
name : str
Optional name of file in case 'arg' is a file handle.
offset : int
Optional start position of embedded file. By default this is
the current file position.
size : int
Optional size of embedded file. By default this is the number
of bytes from the 'offset' to the end of the file.
"""
self._fh = None
self._arg = arg
self._mode = mode
self._name = name
self._dir = ''
self._offset = offset
self._size = size
self._close = True
self.is_file = False
self.open()
def open(self):
"""Open or re-open file."""
if self._fh:
return # file is open
if isinstance(self._arg, basestring):
# file name
self._arg = os.path.abspath(self._arg)
self._dir, self._name = os.path.split(self._arg)
self._fh = open(self._arg, self._mode)
self._close = True
if self._offset is None:
self._offset = 0
elif isinstance(self._arg, FileHandle):
# FileHandle
self._fh = self._arg._fh
if self._offset is None:
self._offset = 0
self._offset += self._arg._offset
self._close = False
if not self._name:
if self._offset:
name, ext = os.path.splitext(self._arg._name)
self._name = "%s@%i%s" % (name, self._offset, ext)
else:
self._name = self._arg._name
self._dir = self._arg._dir
else:
# open file object
self._fh = self._arg
if self._offset is None:
self._offset = self._arg.tell()
self._close = False
if not self._name:
try:
self._dir, self._name = os.path.split(self._fh.name)
except AttributeError:
self._name = "Unnamed stream"
if self._offset:
self._fh.seek(self._offset)
if self._size is None:
pos = self._fh.tell()
self._fh.seek(self._offset, 2)
self._size = self._fh.tell()
self._fh.seek(pos)
try:
self._fh.fileno()
self.is_file = True
except Exception:
self.is_file = False
def read(self, size=-1):
"""Read 'size' bytes from file, or until EOF is reached."""
if size < 0 and self._offset:
size = self._size
return self._fh.read(size)
def memmap_array(self, dtype, shape, offset=0, mode='r', order='C'):
"""Return numpy.memmap of data stored in file."""
if not self.is_file:
raise ValueError("Can not memory map file without fileno.")
return numpy.memmap(self._fh, dtype=dtype, mode=mode,
offset=self._offset + offset,
shape=shape, order=order)
def read_array(self, dtype, count=-1, sep=""):
"""Return numpy array from file.
Work around numpy issue #2230, "numpy.fromfile does not accept
StringIO object" https://github.com/numpy/numpy/issues/2230.
"""
try:
return numpy.fromfile(self._fh, dtype, count, sep)
except IOError:
if count < 0:
size = self._size
else:
size = count * numpy.dtype(dtype).itemsize
data = self._fh.read(size)
return numpy.fromstring(data, dtype, count, sep)
def read_record(self, dtype, shape=1, byteorder=None):
"""Return numpy record from file."""
try:
rec = numpy.rec.fromfile(self._fh, dtype, shape,
byteorder=byteorder)
except Exception:
dtype = numpy.dtype(dtype)
if shape is None:
shape = self._size // dtype.itemsize
size = product(sequence(shape)) * dtype.itemsize
data = self._fh.read(size)
return numpy.rec.fromstring(data, dtype, shape,
byteorder=byteorder)
return rec[0] if shape == 1 else rec
def tell(self):
"""Return file's current position."""
return self._fh.tell() - self._offset
def seek(self, offset, whence=0):
"""Set file's current position."""
if self._offset:
if whence == 0:
self._fh.seek(self._offset + offset, whence)
return
elif whence == 2:
self._fh.seek(self._offset + self._size + offset, 0)
return
self._fh.seek(offset, whence)
def close(self):
"""Close file."""
if self._close and self._fh:
self._fh.close()
self._fh = None
self.is_file = False
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def __getattr__(self, name):
"""Return attribute from underlying file object."""
if self._offset:
warnings.warn(
"FileHandle: '%s' not implemented for embedded files" % name)
return getattr(self._fh, name)
@property
def name(self):
return self._name
@property
def dirname(self):
return self._dir
@property
def path(self):
return os.path.join(self._dir, self._name)
@property
def size(self):
return self._size
@property
def closed(self):
return self._fh is None
def read_bytes(fh, byteorder, dtype, count):
"""Read tag data from file and return as byte string."""
dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
return fh.read_array(dtype, count).tostring()
def read_numpy(fh, byteorder, dtype, count):
"""Read tag data from file and return as numpy array."""
dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
return fh.read_array(dtype, count)
def read_json(fh, byteorder, dtype, count):
"""Read JSON tag data from file and return as object."""
data = fh.read(count)
try:
return json.loads(unicode(stripnull(data), 'utf-8'))
except ValueError:
warnings.warn("invalid JSON `%s`" % data)
def read_mm_header(fh, byteorder, dtype, count):
"""Read MM_HEADER tag from file and return as numpy.rec.array."""
return fh.read_record(MM_HEADER, byteorder=byteorder)
def read_mm_stamp(fh, byteorder, dtype, count):
"""Read MM_STAMP tag from file and return as numpy.array."""
return fh.read_array(byteorder+'f8', 8)
def read_uic1tag(fh, byteorder, dtype, count, plane_count=None):
"""Read MetaMorph STK UIC1Tag from file and return as dictionary.
Return empty dictionary if plane_count is unknown.
"""
assert dtype in ('2I', '1I') and byteorder == '<'
result = {}
if dtype == '2I':
# pre MetaMorph 2.5 (not tested)
values = fh.read_array(' structure_size:
break
cz_lsm_info.append((name, dtype))
else:
cz_lsm_info = CZ_LSM_INFO
return fh.read_record(cz_lsm_info, byteorder=byteorder)
def read_cz_lsm_floatpairs(fh):
"""Read LSM sequence of float pairs from file and return as list."""
size = struct.unpack(' 0:
esize, etime, etype = struct.unpack(''}[fh.read(2)]
except IndexError:
raise ValueError("not a MicroManager TIFF file")
results = {}
fh.seek(8)
(index_header, index_offset, display_header, display_offset,
comments_header, comments_offset, summary_header, summary_length
) = struct.unpack(byteorder + "IIIIIIII", fh.read(32))
if summary_header != 2355492:
raise ValueError("invalid MicroManager summary_header")
results['summary'] = read_json(fh, byteorder, None, summary_length)
if index_header != 54773648:
raise ValueError("invalid MicroManager index_header")
fh.seek(index_offset)
header, count = struct.unpack(byteorder + "II", fh.read(8))
if header != 3453623:
raise ValueError("invalid MicroManager index_header")
data = struct.unpack(byteorder + "IIIII"*count, fh.read(20*count))
results['index_map'] = {
'channel': data[::5], 'slice': data[1::5], 'frame': data[2::5],
'position': data[3::5], 'offset': data[4::5]}
if display_header != 483765892:
raise ValueError("invalid MicroManager display_header")
fh.seek(display_offset)
header, count = struct.unpack(byteorder + "II", fh.read(8))
if header != 347834724:
raise ValueError("invalid MicroManager display_header")
results['display_settings'] = read_json(fh, byteorder, None, count)
if comments_header != 99384722:
raise ValueError("invalid MicroManager comments_header")
fh.seek(comments_offset)
header, count = struct.unpack(byteorder + "II", fh.read(8))
if header != 84720485:
raise ValueError("invalid MicroManager comments_header")
results['comments'] = read_json(fh, byteorder, None, count)
return results
def imagej_metadata(data, bytecounts, byteorder):
"""Return dict from ImageJ metadata tag value."""
_str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252')
def read_string(data, byteorder):
return _str(stripnull(data[0 if byteorder == '<' else 1::2]))
def read_double(data, byteorder):
return struct.unpack(byteorder+('d' * (len(data) // 8)), data)
def read_bytes(data, byteorder):
#return struct.unpack('b' * len(data), data)
return numpy.fromstring(data, 'uint8')
metadata_types = { # big endian
b'info': ('info', read_string),
b'labl': ('labels', read_string),
b'rang': ('ranges', read_double),
b'luts': ('luts', read_bytes),
b'roi ': ('roi', read_bytes),
b'over': ('overlays', read_bytes)}
metadata_types.update( # little endian
dict((k[::-1], v) for k, v in metadata_types.items()))
if not bytecounts:
raise ValueError("no ImageJ metadata")
if not data[:4] in (b'IJIJ', b'JIJI'):
raise ValueError("invalid ImageJ metadata")
header_size = bytecounts[0]
if header_size < 12 or header_size > 804:
raise ValueError("invalid ImageJ metadata header size")
ntypes = (header_size - 4) // 8
header = struct.unpack(byteorder+'4sI'*ntypes, data[4:4+ntypes*8])
pos = 4 + ntypes * 8
counter = 0
result = {}
for mtype, count in zip(header[::2], header[1::2]):
values = []
name, func = metadata_types.get(mtype, (_str(mtype), read_bytes))
for _ in range(count):
counter += 1
pos1 = pos + bytecounts[counter]
values.append(func(data[pos:pos1], byteorder))
pos = pos1
result[name.strip()] = values[0] if count == 1 else values
return result
def imagej_description(description):
"""Return dict from ImageJ image_description tag."""
def _bool(val):
return {b'true': True, b'false': False}[val.lower()]
_str = str if sys.version_info[0] < 3 else lambda x: str(x, 'cp1252')
result = {}
for line in description.splitlines():
try:
key, val = line.split(b'=')
except Exception:
continue
key = key.strip()
val = val.strip()
for dtype in (int, float, _bool, _str):
try:
val = dtype(val)
break
except Exception:
pass
result[_str(key)] = val
return result
def _replace_by(module_function, package=None, warn=False):
"""Try replace decorated function by module.function."""
try:
from importlib import import_module
except ImportError:
warnings.warn('could not import module importlib')
return lambda func: func
def decorate(func, module_function=module_function, warn=warn):
try:
module, function = module_function.split('.')
if not package:
module = import_module(module)
else:
module = import_module('.' + module, package=package)
func, oldfunc = getattr(module, function), func
globals()['__old_' + func.__name__] = oldfunc
except Exception:
if warn:
warnings.warn("failed to import %s" % module_function)
return func
return decorate
def decodejpg(encoded, tables=b'', photometric=None,
ycbcr_subsampling=None, ycbcr_positioning=None):
"""Decode JPEG encoded byte string (using _czifile extension module)."""
import _czifile
image = _czifile.decodejpg(encoded, tables)
if photometric == 'rgb' and ycbcr_subsampling and ycbcr_positioning:
# TODO: convert YCbCr to RGB
pass
return image.tostring()
@_replace_by('_tifffile.decodepackbits')
def decodepackbits(encoded):
"""Decompress PackBits encoded byte string.
PackBits is a simple byte-oriented run-length compression scheme.
"""
func = ord if sys.version[0] == '2' else lambda x: x
result = []
result_extend = result.extend
i = 0
try:
while True:
n = func(encoded[i]) + 1
i += 1
if n < 129:
result_extend(encoded[i:i+n])
i += n
elif n > 129:
result_extend(encoded[i:i+1] * (258-n))
i += 1
except IndexError:
pass
return b''.join(result) if sys.version[0] == '2' else bytes(result)
@_replace_by('_tifffile.decodelzw')
def decodelzw(encoded):
"""Decompress LZW (Lempel-Ziv-Welch) encoded TIFF strip (byte string).
The strip must begin with a CLEAR code and end with an EOI code.
This is an implementation of the LZW decoding algorithm described in (1).
It is not compatible with old style LZW compressed files like quad-lzw.tif.
"""
len_encoded = len(encoded)
bitcount_max = len_encoded * 8
unpack = struct.unpack
if sys.version[0] == '2':
newtable = [chr(i) for i in range(256)]
else:
newtable = [bytes([i]) for i in range(256)]
newtable.extend((0, 0))
def next_code():
"""Return integer of `bitw` bits at `bitcount` position in encoded."""
start = bitcount // 8
s = encoded[start:start+4]
try:
code = unpack('>I', s)[0]
except Exception:
code = unpack('>I', s + b'\x00'*(4-len(s)))[0]
code <<= bitcount % 8
code &= mask
return code >> shr
switchbitch = { # code: bit-width, shr-bits, bit-mask
255: (9, 23, int(9*'1'+'0'*23, 2)),
511: (10, 22, int(10*'1'+'0'*22, 2)),
1023: (11, 21, int(11*'1'+'0'*21, 2)),
2047: (12, 20, int(12*'1'+'0'*20, 2)), }
bitw, shr, mask = switchbitch[255]
bitcount = 0
if len_encoded < 4:
raise ValueError("strip must be at least 4 characters long")
if next_code() != 256:
raise ValueError("strip must begin with CLEAR code")
code = 0
oldcode = 0
result = []
result_append = result.append
while True:
code = next_code() # ~5% faster when inlining this function
bitcount += bitw
if code == 257 or bitcount >= bitcount_max: # EOI
break
if code == 256: # CLEAR
table = newtable[:]
table_append = table.append
lentable = 258
bitw, shr, mask = switchbitch[255]
code = next_code()
bitcount += bitw
if code == 257: # EOI
break
result_append(table[code])
else:
if code < lentable:
decoded = table[code]
newcode = table[oldcode] + decoded[:1]
else:
newcode = table[oldcode]
newcode += newcode[:1]
decoded = newcode
result_append(decoded)
table_append(newcode)
lentable += 1
oldcode = code
if lentable in switchbitch:
bitw, shr, mask = switchbitch[lentable]
if code != 257:
warnings.warn("unexpected end of lzw stream (code %i)" % code)
return b''.join(result)
@_replace_by('_tifffile.unpackints')
def unpackints(data, dtype, itemsize, runlen=0):
"""Decompress byte string to array of integers of any bit size <= 32.
Parameters
----------
data : byte str
Data to decompress.
dtype : numpy.dtype or str
A numpy boolean or integer type.
itemsize : int
Number of bits per integer.
runlen : int
Number of consecutive integers, after which to start at next byte.
"""
if itemsize == 1: # bitarray
data = numpy.fromstring(data, '|B')
data = numpy.unpackbits(data)
if runlen % 8:
data = data.reshape(-1, runlen + (8 - runlen % 8))
data = data[:, :runlen].reshape(-1)
return data.astype(dtype)
dtype = numpy.dtype(dtype)
if itemsize in (8, 16, 32, 64):
return numpy.fromstring(data, dtype)
if itemsize < 1 or itemsize > 32:
raise ValueError("itemsize out of range: %i" % itemsize)
if dtype.kind not in "biu":
raise ValueError("invalid dtype")
itembytes = next(i for i in (1, 2, 4, 8) if 8 * i >= itemsize)
if itembytes != dtype.itemsize:
raise ValueError("dtype.itemsize too small")
if runlen == 0:
runlen = len(data) // itembytes
skipbits = runlen*itemsize % 8
if skipbits:
skipbits = 8 - skipbits
shrbits = itembytes*8 - itemsize
bitmask = int(itemsize*'1'+'0'*shrbits, 2)
dtypestr = '>' + dtype.char # dtype always big endian?
unpack = struct.unpack
l = runlen * (len(data)*8 // (runlen*itemsize + skipbits))
result = numpy.empty((l, ), dtype)
bitcount = 0
for i in range(len(result)):
start = bitcount // 8
s = data[start:start+itembytes]
try:
code = unpack(dtypestr, s)[0]
except Exception:
code = unpack(dtypestr, s + b'\x00'*(itembytes-len(s)))[0]
code <<= bitcount % 8
code &= bitmask
result[i] = code >> shrbits
bitcount += itemsize
if (i+1) % runlen == 0:
bitcount += skipbits
return result
def unpackrgb(data, dtype='>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff)
>>> print(unpackrgb(data, '>> print(unpackrgb(data, '>> print(unpackrgb(data, '= bits)
data = numpy.fromstring(data, dtype.byteorder+dt)
result = numpy.empty((data.size, len(bitspersample)), dtype.char)
for i, bps in enumerate(bitspersample):
t = data >> int(numpy.sum(bitspersample[i+1:]))
t &= int('0b'+'1'*bps, 2)
if rescale:
o = ((dtype.itemsize * 8) // bps + 1) * bps
if o > data.dtype.itemsize * 8:
t = t.astype('I')
t *= (2**o - 1) // (2**bps - 1)
t //= 2**(o - (dtype.itemsize * 8))
result[:, i] = t
return result.reshape(-1)
def reorient(image, orientation):
"""Return reoriented view of image array.
Parameters
----------
image : numpy array
Non-squeezed output of asarray() functions.
Axes -3 and -2 must be image length and width respectively.
orientation : int or str
One of TIFF_ORIENTATIONS keys or values.
"""
o = TIFF_ORIENTATIONS.get(orientation, orientation)
if o == 'top_left':
return image
elif o == 'top_right':
return image[..., ::-1, :]
elif o == 'bottom_left':
return image[..., ::-1, :, :]
elif o == 'bottom_right':
return image[..., ::-1, ::-1, :]
elif o == 'left_top':
return numpy.swapaxes(image, -3, -2)
elif o == 'right_top':
return numpy.swapaxes(image, -3, -2)[..., ::-1, :]
elif o == 'left_bottom':
return numpy.swapaxes(image, -3, -2)[..., ::-1, :, :]
elif o == 'right_bottom':
return numpy.swapaxes(image, -3, -2)[..., ::-1, ::-1, :]
def squeeze_axes(shape, axes, skip='XY'):
"""Return shape and axes with single-dimensional entries removed.
Remove unused dimensions unless their axes are listed in 'skip'.
>>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
((5, 2, 1), 'TYX')
"""
if len(shape) != len(axes):
raise ValueError("dimensions of axes and shape don't match")
shape, axes = zip(*(i for i in zip(shape, axes)
if i[0] > 1 or i[1] in skip))
return shape, ''.join(axes)
def transpose_axes(data, axes, asaxes='CTZYX'):
"""Return data with its axes permuted to match specified axes.
A view is returned if possible.
>>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape
(5, 2, 1, 3, 4)
"""
for ax in axes:
if ax not in asaxes:
raise ValueError("unknown axis %s" % ax)
# add missing axes to data
shape = data.shape
for ax in reversed(asaxes):
if ax not in axes:
axes = ax + axes
shape = (1,) + shape
data = data.reshape(shape)
# transpose axes
data = data.transpose([axes.index(ax) for ax in asaxes])
return data
def stack_pages(pages, memmap=False, *args, **kwargs):
"""Read data from sequence of TiffPage and stack them vertically.
If memmap is True, return an array stored in a binary file on disk.
Additional parameters are passsed to the page asarray function.
"""
if len(pages) == 0:
raise ValueError("no pages")
if len(pages) == 1:
return pages[0].asarray(memmap=memmap, *args, **kwargs)
result = pages[0].asarray(*args, **kwargs)
shape = (len(pages),) + result.shape
if memmap:
with tempfile.NamedTemporaryFile() as fh:
result = numpy.memmap(fh, dtype=result.dtype, shape=shape)
else:
result = numpy.empty(shape, dtype=result.dtype)
for i, page in enumerate(pages):
result[i] = page.asarray(*args, **kwargs)
return result
def stripnull(string):
"""Return string truncated at first null character.
Clean NULL terminated C strings.
>>> stripnull(b'string\\x00')
b'string'
"""
i = string.find(b'\x00')
return string if (i < 0) else string[:i]
def stripascii(string):
"""Return string truncated at last byte that is 7bit ASCII.
Clean NULL separated and terminated TIFF strings.
>>> stripascii(b'string\\x00string\\n\\x01\\x00')
b'string\\x00string\\n'
>>> stripascii(b'\\x00')
b''
"""
# TODO: pythonize this
ord_ = ord if sys.version_info[0] < 3 else lambda x: x
i = len(string)
while i:
i -= 1
if 8 < ord_(string[i]) < 127:
break
else:
i = -1
return string[:i+1]
def format_size(size):
"""Return file size as string from byte size."""
for unit in ('B', 'KB', 'MB', 'GB', 'TB'):
if size < 2048:
return "%.f %s" % (size, unit)
size /= 1024.0
def sequence(value):
"""Return tuple containing value if value is not a sequence.
>>> sequence(1)
(1,)
>>> sequence([1])
[1]
"""
try:
len(value)
return value
except TypeError:
return (value, )
def product(iterable):
"""Return product of sequence of numbers.
Equivalent of functools.reduce(operator.mul, iterable, 1).
>>> product([2**8, 2**30])
274877906944
>>> product([])
1
"""
prod = 1
for i in iterable:
prod *= i
return prod
def natural_sorted(iterable):
"""Return human sorted list of strings.
E.g. for sorting file names.
>>> natural_sorted(['f1', 'f2', 'f10'])
['f1', 'f2', 'f10']
"""
def sortkey(x):
return [(int(c) if c.isdigit() else c) for c in re.split(numbers, x)]
numbers = re.compile(r'(\d+)')
return sorted(iterable, key=sortkey)
def excel_datetime(timestamp, epoch=datetime.datetime.fromordinal(693594)):
"""Return datetime object from timestamp in Excel serial format.
Convert LSM time stamps.
>>> excel_datetime(40237.029999999795)
datetime.datetime(2010, 2, 28, 0, 43, 11, 999982)
"""
return epoch + datetime.timedelta(timestamp)
def julian_datetime(julianday, milisecond=0):
"""Return datetime from days since 1/1/4713 BC and ms since midnight.
Convert Julian dates according to MetaMorph.
>>> julian_datetime(2451576, 54362783)
datetime.datetime(2000, 2, 2, 15, 6, 2, 783)
"""
if julianday <= 1721423:
# no datetime before year 1
return None
a = julianday + 1
if a > 2299160:
alpha = math.trunc((a - 1867216.25) / 36524.25)
a += 1 + alpha - alpha // 4
b = a + (1524 if a > 1721423 else 1158)
c = math.trunc((b - 122.1) / 365.25)
d = math.trunc(365.25 * c)
e = math.trunc((b - d) / 30.6001)
day = b - d - math.trunc(30.6001 * e)
month = e - (1 if e < 13.5 else 13)
year = c - (4716 if month > 2.5 else 4715)
hour, milisecond = divmod(milisecond, 1000 * 60 * 60)
minute, milisecond = divmod(milisecond, 1000 * 60)
second, milisecond = divmod(milisecond, 1000)
return datetime.datetime(year, month, day,
hour, minute, second, milisecond)
def test_tifffile(directory='testimages', verbose=True):
"""Read all images in directory.
Print error message on failure.
>>> test_tifffile(verbose=False)
"""
successful = 0
failed = 0
start = time.time()
for f in glob.glob(os.path.join(directory, '*.*')):
if verbose:
print("\n%s>\n" % f.lower(), end='')
t0 = time.time()
try:
tif = TiffFile(f, multifile=True)
except Exception as e:
if not verbose:
print(f, end=' ')
print("ERROR:", e)
failed += 1
continue
try:
img = tif.asarray()
except ValueError:
try:
img = tif[0].asarray()
except Exception as e:
if not verbose:
print(f, end=' ')
print("ERROR:", e)
failed += 1
continue
finally:
tif.close()
successful += 1
if verbose:
print("%s, %s %s, %s, %.0f ms" % (
str(tif), str(img.shape), img.dtype, tif[0].compression,
(time.time()-t0) * 1e3))
if verbose:
print("\nSuccessfully read %i of %i files in %.3f s\n" % (
successful, successful+failed, time.time()-start))
class TIFF_SUBFILE_TYPES(object):
def __getitem__(self, key):
result = []
if key & 1:
result.append('reduced_image')
if key & 2:
result.append('page')
if key & 4:
result.append('mask')
return tuple(result)
TIFF_PHOTOMETRICS = {
0: 'miniswhite',
1: 'minisblack',
2: 'rgb',
3: 'palette',
4: 'mask',
5: 'separated', # CMYK
6: 'ycbcr',
8: 'cielab',
9: 'icclab',
10: 'itulab',
32803: 'cfa', # Color Filter Array
32844: 'logl',
32845: 'logluv',
34892: 'linear_raw'
}
TIFF_COMPESSIONS = {
1: None,
2: 'ccittrle',
3: 'ccittfax3',
4: 'ccittfax4',
5: 'lzw',
6: 'ojpeg',
7: 'jpeg',
8: 'adobe_deflate',
9: 't85',
10: 't43',
32766: 'next',
32771: 'ccittrlew',
32773: 'packbits',
32809: 'thunderscan',
32895: 'it8ctpad',
32896: 'it8lw',
32897: 'it8mp',
32898: 'it8bl',
32908: 'pixarfilm',
32909: 'pixarlog',
32946: 'deflate',
32947: 'dcs',
34661: 'jbig',
34676: 'sgilog',
34677: 'sgilog24',
34712: 'jp2000',
34713: 'nef',
}
TIFF_DECOMPESSORS = {
None: lambda x: x,
'adobe_deflate': zlib.decompress,
'deflate': zlib.decompress,
'packbits': decodepackbits,
'lzw': decodelzw,
# 'jpeg': decodejpg
}
TIFF_DATA_TYPES = {
1: '1B', # BYTE 8-bit unsigned integer.
2: '1s', # ASCII 8-bit byte that contains a 7-bit ASCII code;
# the last byte must be NULL (binary zero).
3: '1H', # SHORT 16-bit (2-byte) unsigned integer
4: '1I', # LONG 32-bit (4-byte) unsigned integer.
5: '2I', # RATIONAL Two LONGs: the first represents the numerator of
# a fraction; the second, the denominator.
6: '1b', # SBYTE An 8-bit signed (twos-complement) integer.
7: '1s', # UNDEFINED An 8-bit byte that may contain anything,
# depending on the definition of the field.
8: '1h', # SSHORT A 16-bit (2-byte) signed (twos-complement) integer.
9: '1i', # SLONG A 32-bit (4-byte) signed (twos-complement) integer.
10: '2i', # SRATIONAL Two SLONGs: the first represents the numerator
# of a fraction, the second the denominator.
11: '1f', # FLOAT Single precision (4-byte) IEEE format.
12: '1d', # DOUBLE Double precision (8-byte) IEEE format.
13: '1I', # IFD unsigned 4 byte IFD offset.
#14: '', # UNICODE
#15: '', # COMPLEX
16: '1Q', # LONG8 unsigned 8 byte integer (BigTiff)
17: '1q', # SLONG8 signed 8 byte integer (BigTiff)
18: '1Q', # IFD8 unsigned 8 byte IFD offset (BigTiff)
}
TIFF_SAMPLE_FORMATS = {
1: 'uint',
2: 'int',
3: 'float',
#4: 'void',
#5: 'complex_int',
6: 'complex',
}
TIFF_SAMPLE_DTYPES = {
('uint', 1): '?', # bitmap
('uint', 2): 'B',
('uint', 3): 'B',
('uint', 4): 'B',
('uint', 5): 'B',
('uint', 6): 'B',
('uint', 7): 'B',
('uint', 8): 'B',
('uint', 9): 'H',
('uint', 10): 'H',
('uint', 11): 'H',
('uint', 12): 'H',
('uint', 13): 'H',
('uint', 14): 'H',
('uint', 15): 'H',
('uint', 16): 'H',
('uint', 17): 'I',
('uint', 18): 'I',
('uint', 19): 'I',
('uint', 20): 'I',
('uint', 21): 'I',
('uint', 22): 'I',
('uint', 23): 'I',
('uint', 24): 'I',
('uint', 25): 'I',
('uint', 26): 'I',
('uint', 27): 'I',
('uint', 28): 'I',
('uint', 29): 'I',
('uint', 30): 'I',
('uint', 31): 'I',
('uint', 32): 'I',
('uint', 64): 'Q',
('int', 8): 'b',
('int', 16): 'h',
('int', 32): 'i',
('int', 64): 'q',
('float', 16): 'e',
('float', 32): 'f',
('float', 64): 'd',
('complex', 64): 'F',
('complex', 128): 'D',
('uint', (5, 6, 5)): 'B',
}
TIFF_ORIENTATIONS = {
1: 'top_left',
2: 'top_right',
3: 'bottom_right',
4: 'bottom_left',
5: 'left_top',
6: 'right_top',
7: 'right_bottom',
8: 'left_bottom',
}
# TODO: is there a standard for character axes labels?
AXES_LABELS = {
'X': 'width',
'Y': 'height',
'Z': 'depth',
'S': 'sample', # rgb(a)
'I': 'series', # general sequence, plane, page, IFD
'T': 'time',
'C': 'channel', # color, emission wavelength
'A': 'angle',
'P': 'phase', # formerly F # P is Position in LSM!
'R': 'tile', # region, point, mosaic
'H': 'lifetime', # histogram
'E': 'lambda', # excitation wavelength
'L': 'exposure', # lux
'V': 'event',
'Q': 'other',
#'M': 'mosaic', # LSM 6
}
AXES_LABELS.update(dict((v, k) for k, v in AXES_LABELS.items()))
# Map OME pixel types to numpy dtype
OME_PIXEL_TYPES = {
'int8': 'i1',
'int16': 'i2',
'int32': 'i4',
'uint8': 'u1',
'uint16': 'u2',
'uint32': 'u4',
'float': 'f4',
# 'bit': 'bit',
'double': 'f8',
'complex': 'c8',
'double-complex': 'c16',
}
# NIH Image PicHeader v1.63
NIH_IMAGE_HEADER = [
('fileid', 'a8'),
('nlines', 'i2'),
('pixelsperline', 'i2'),
('version', 'i2'),
('oldlutmode', 'i2'),
('oldncolors', 'i2'),
('colors', 'u1', (3, 32)),
('oldcolorstart', 'i2'),
('colorwidth', 'i2'),
('extracolors', 'u2', (6, 3)),
('nextracolors', 'i2'),
('foregroundindex', 'i2'),
('backgroundindex', 'i2'),
('xscale', 'f8'),
('_x0', 'i2'),
('_x1', 'i2'),
('units_t', 'i2'), # NIH_UNITS_TYPE
('p1', [('x', 'i2'), ('y', 'i2')]),
('p2', [('x', 'i2'), ('y', 'i2')]),
('curvefit_t', 'i2'), # NIH_CURVEFIT_TYPE
('ncoefficients', 'i2'),
('coeff', 'f8', 6),
('_um_len', 'u1'),
('um', 'a15'),
('_x2', 'u1'),
('binarypic', 'b1'),
('slicestart', 'i2'),
('sliceend', 'i2'),
('scalemagnification', 'f4'),
('nslices', 'i2'),
('slicespacing', 'f4'),
('currentslice', 'i2'),
('frameinterval', 'f4'),
('pixelaspectratio', 'f4'),
('colorstart', 'i2'),
('colorend', 'i2'),
('ncolors', 'i2'),
('fill1', '3u2'),
('fill2', '3u2'),
('colortable_t', 'u1'), # NIH_COLORTABLE_TYPE
('lutmode_t', 'u1'), # NIH_LUTMODE_TYPE
('invertedtable', 'b1'),
('zeroclip', 'b1'),
('_xunit_len', 'u1'),
('xunit', 'a11'),
('stacktype_t', 'i2'), # NIH_STACKTYPE_TYPE
]
NIH_COLORTABLE_TYPE = (
'CustomTable', 'AppleDefault', 'Pseudo20', 'Pseudo32', 'Rainbow',
'Fire1', 'Fire2', 'Ice', 'Grays', 'Spectrum')
NIH_LUTMODE_TYPE = (
'PseudoColor', 'OldAppleDefault', 'OldSpectrum', 'GrayScale',
'ColorLut', 'CustomGrayscale')
NIH_CURVEFIT_TYPE = (
'StraightLine', 'Poly2', 'Poly3', 'Poly4', 'Poly5', 'ExpoFit',
'PowerFit', 'LogFit', 'RodbardFit', 'SpareFit1', 'Uncalibrated',
'UncalibratedOD')
NIH_UNITS_TYPE = (
'Nanometers', 'Micrometers', 'Millimeters', 'Centimeters', 'Meters',
'Kilometers', 'Inches', 'Feet', 'Miles', 'Pixels', 'OtherUnits')
NIH_STACKTYPE_TYPE = (
'VolumeStack', 'RGBStack', 'MovieStack', 'HSVStack')
# Map Universal Imaging Corporation MetaMorph internal tag ids to name and type
UIC_TAGS = {
0: ('auto_scale', int),
1: ('min_scale', int),
2: ('max_scale', int),
3: ('spatial_calibration', int),
4: ('x_calibration', Fraction),
5: ('y_calibration', Fraction),
6: ('calibration_units', str),
7: ('name', str),
8: ('thresh_state', int),
9: ('thresh_state_red', int),
10: ('tagid_10', None), # undefined
11: ('thresh_state_green', int),
12: ('thresh_state_blue', int),
13: ('thresh_state_lo', int),
14: ('thresh_state_hi', int),
15: ('zoom', int),
16: ('create_time', julian_datetime),
17: ('last_saved_time', julian_datetime),
18: ('current_buffer', int),
19: ('gray_fit', None),
20: ('gray_point_count', None),
21: ('gray_x', Fraction),
22: ('gray_y', Fraction),
23: ('gray_min', Fraction),
24: ('gray_max', Fraction),
25: ('gray_unit_name', str),
26: ('standard_lut', int),
27: ('wavelength', int),
28: ('stage_position', '(%i,2,2)u4'), # N xy positions as fractions
29: ('camera_chip_offset', '(%i,2,2)u4'), # N xy offsets as fractions
30: ('overlay_mask', None),
31: ('overlay_compress', None),
32: ('overlay', None),
33: ('special_overlay_mask', None),
34: ('special_overlay_compress', None),
35: ('special_overlay', None),
36: ('image_property', read_uic_image_property),
37: ('stage_label', '%ip'), # N str
38: ('autoscale_lo_info', Fraction),
39: ('autoscale_hi_info', Fraction),
40: ('absolute_z', '(%i,2)u4'), # N fractions
41: ('absolute_z_valid', '(%i,)u4'), # N long
42: ('gamma', int),
43: ('gamma_red', int),
44: ('gamma_green', int),
45: ('gamma_blue', int),
46: ('camera_bin', int),
47: ('new_lut', int),
48: ('image_property_ex', None),
49: ('plane_property', int),
50: ('user_lut_table', '(256,3)u1'),
51: ('red_autoscale_info', int),
52: ('red_autoscale_lo_info', Fraction),
53: ('red_autoscale_hi_info', Fraction),
54: ('red_minscale_info', int),
55: ('red_maxscale_info', int),
56: ('green_autoscale_info', int),
57: ('green_autoscale_lo_info', Fraction),
58: ('green_autoscale_hi_info', Fraction),
59: ('green_minscale_info', int),
60: ('green_maxscale_info', int),
61: ('blue_autoscale_info', int),
62: ('blue_autoscale_lo_info', Fraction),
63: ('blue_autoscale_hi_info', Fraction),
64: ('blue_min_scale_info', int),
65: ('blue_max_scale_info', int),
#66: ('overlay_plane_color', read_uic_overlay_plane_color),
}
# Olympus FluoView
MM_DIMENSION = [
('name', 'a16'),
('size', 'i4'),
('origin', 'f8'),
('resolution', 'f8'),
('unit', 'a64'),
]
MM_HEADER = [
('header_flag', 'i2'),
('image_type', 'u1'),
('image_name', 'a257'),
('offset_data', 'u4'),
('palette_size', 'i4'),
('offset_palette0', 'u4'),
('offset_palette1', 'u4'),
('comment_size', 'i4'),
('offset_comment', 'u4'),
('dimensions', MM_DIMENSION, 10),
('offset_position', 'u4'),
('map_type', 'i2'),
('map_min', 'f8'),
('map_max', 'f8'),
('min_value', 'f8'),
('max_value', 'f8'),
('offset_map', 'u4'),
('gamma', 'f8'),
('offset', 'f8'),
('gray_channel', MM_DIMENSION),
('offset_thumbnail', 'u4'),
('voice_field', 'i4'),
('offset_voice_field', 'u4'),
]
# Carl Zeiss LSM
CZ_LSM_INFO = [
('magic_number', 'u4'),
('structure_size', 'i4'),
('dimension_x', 'i4'),
('dimension_y', 'i4'),
('dimension_z', 'i4'),
('dimension_channels', 'i4'),
('dimension_time', 'i4'),
('data_type', 'i4'), # CZ_DATA_TYPES
('thumbnail_x', 'i4'),
('thumbnail_y', 'i4'),
('voxel_size_x', 'f8'),
('voxel_size_y', 'f8'),
('voxel_size_z', 'f8'),
('origin_x', 'f8'),
('origin_y', 'f8'),
('origin_z', 'f8'),
('scan_type', 'u2'),
('spectral_scan', 'u2'),
('type_of_data', 'u4'), # CZ_TYPE_OF_DATA
('offset_vector_overlay', 'u4'),
('offset_input_lut', 'u4'),
('offset_output_lut', 'u4'),
('offset_channel_colors', 'u4'),
('time_interval', 'f8'),
('offset_channel_data_types', 'u4'),
('offset_scan_info', 'u4'), # CZ_LSM_SCAN_INFO
('offset_ks_data', 'u4'),
('offset_time_stamps', 'u4'),
('offset_event_list', 'u4'),
('offset_roi', 'u4'),
('offset_bleach_roi', 'u4'),
('offset_next_recording', 'u4'),
# LSM 2.0 ends here
('display_aspect_x', 'f8'),
('display_aspect_y', 'f8'),
('display_aspect_z', 'f8'),
('display_aspect_time', 'f8'),
('offset_mean_of_roi_overlay', 'u4'),
('offset_topo_isoline_overlay', 'u4'),
('offset_topo_profile_overlay', 'u4'),
('offset_linescan_overlay', 'u4'),
('offset_toolbar_flags', 'u4'),
('offset_channel_wavelength', 'u4'),
('offset_channel_factors', 'u4'),
('objective_sphere_correction', 'f8'),
('offset_unmix_parameters', 'u4'),
# LSM 3.2, 4.0 end here
('offset_acquisition_parameters', 'u4'),
('offset_characteristics', 'u4'),
('offset_palette', 'u4'),
('time_difference_x', 'f8'),
('time_difference_y', 'f8'),
('time_difference_z', 'f8'),
('internal_use_1', 'u4'),
('dimension_p', 'i4'),
('dimension_m', 'i4'),
('dimensions_reserved', '16i4'),
('offset_tile_positions', 'u4'),
('reserved_1', '9u4'),
('offset_positions', 'u4'),
('reserved_2', '21u4'), # must be 0
]
# Import functions for LSM_INFO sub-records
CZ_LSM_INFO_READERS = {
'scan_info': read_cz_lsm_scan_info,
'time_stamps': read_cz_lsm_time_stamps,
'event_list': read_cz_lsm_event_list,
'channel_colors': read_cz_lsm_floatpairs,
'positions': read_cz_lsm_floatpairs,
'tile_positions': read_cz_lsm_floatpairs,
}
# Map cz_lsm_info.scan_type to dimension order
CZ_SCAN_TYPES = {
0: 'XYZCT', # x-y-z scan
1: 'XYZCT', # z scan (x-z plane)
2: 'XYZCT', # line scan
3: 'XYTCZ', # time series x-y
4: 'XYZTC', # time series x-z
5: 'XYTCZ', # time series 'Mean of ROIs'
6: 'XYZTC', # time series x-y-z
7: 'XYCTZ', # spline scan
8: 'XYCZT', # spline scan x-z
9: 'XYTCZ', # time series spline plane x-z
10: 'XYZCT', # point mode
}
# Map dimension codes to cz_lsm_info attribute
CZ_DIMENSIONS = {
'X': 'dimension_x',
'Y': 'dimension_y',
'Z': 'dimension_z',
'C': 'dimension_channels',
'T': 'dimension_time',
}
# Description of cz_lsm_info.data_type
CZ_DATA_TYPES = {
0: 'varying data types',
1: '8 bit unsigned integer',
2: '12 bit unsigned integer',
5: '32 bit float',
}
# Description of cz_lsm_info.type_of_data
CZ_TYPE_OF_DATA = {
0: 'Original scan data',
1: 'Calculated data',
2: '3D reconstruction',
3: 'Topography height map',
}
CZ_LSM_SCAN_INFO_ARRAYS = {
0x20000000: "tracks",
0x30000000: "lasers",
0x60000000: "detection_channels",
0x80000000: "illumination_channels",
0xa0000000: "beam_splitters",
0xc0000000: "data_channels",
0x11000000: "timers",
0x13000000: "markers",
}
CZ_LSM_SCAN_INFO_STRUCTS = {
# 0x10000000: "recording",
0x40000000: "track",
0x50000000: "laser",
0x70000000: "detection_channel",
0x90000000: "illumination_channel",
0xb0000000: "beam_splitter",
0xd0000000: "data_channel",
0x12000000: "timer",
0x14000000: "marker",
}
CZ_LSM_SCAN_INFO_ATTRIBUTES = {
# recording
0x10000001: "name",
0x10000002: "description",
0x10000003: "notes",
0x10000004: "objective",
0x10000005: "processing_summary",
0x10000006: "special_scan_mode",
0x10000007: "scan_type",
0x10000008: "scan_mode",
0x10000009: "number_of_stacks",
0x1000000a: "lines_per_plane",
0x1000000b: "samples_per_line",
0x1000000c: "planes_per_volume",
0x1000000d: "images_width",
0x1000000e: "images_height",
0x1000000f: "images_number_planes",
0x10000010: "images_number_stacks",
0x10000011: "images_number_channels",
0x10000012: "linscan_xy_size",
0x10000013: "scan_direction",
0x10000014: "time_series",
0x10000015: "original_scan_data",
0x10000016: "zoom_x",
0x10000017: "zoom_y",
0x10000018: "zoom_z",
0x10000019: "sample_0x",
0x1000001a: "sample_0y",
0x1000001b: "sample_0z",
0x1000001c: "sample_spacing",
0x1000001d: "line_spacing",
0x1000001e: "plane_spacing",
0x1000001f: "plane_width",
0x10000020: "plane_height",
0x10000021: "volume_depth",
0x10000023: "nutation",
0x10000034: "rotation",
0x10000035: "precession",
0x10000036: "sample_0time",
0x10000037: "start_scan_trigger_in",
0x10000038: "start_scan_trigger_out",
0x10000039: "start_scan_event",
0x10000040: "start_scan_time",
0x10000041: "stop_scan_trigger_in",
0x10000042: "stop_scan_trigger_out",
0x10000043: "stop_scan_event",
0x10000044: "stop_scan_time",
0x10000045: "use_rois",
0x10000046: "use_reduced_memory_rois",
0x10000047: "user",
0x10000048: "use_bc_correction",
0x10000049: "position_bc_correction1",
0x10000050: "position_bc_correction2",
0x10000051: "interpolation_y",
0x10000052: "camera_binning",
0x10000053: "camera_supersampling",
0x10000054: "camera_frame_width",
0x10000055: "camera_frame_height",
0x10000056: "camera_offset_x",
0x10000057: "camera_offset_y",
0x10000059: "rt_binning",
0x1000005a: "rt_frame_width",
0x1000005b: "rt_frame_height",
0x1000005c: "rt_region_width",
0x1000005d: "rt_region_height",
0x1000005e: "rt_offset_x",
0x1000005f: "rt_offset_y",
0x10000060: "rt_zoom",
0x10000061: "rt_line_period",
0x10000062: "prescan",
0x10000063: "scan_direction_z",
# track
0x40000001: "multiplex_type", # 0 after line; 1 after frame
0x40000002: "multiplex_order",
0x40000003: "sampling_mode", # 0 sample; 1 line average; 2 frame average
0x40000004: "sampling_method", # 1 mean; 2 sum
0x40000005: "sampling_number",
0x40000006: "acquire",
0x40000007: "sample_observation_time",
0x4000000b: "time_between_stacks",
0x4000000c: "name",
0x4000000d: "collimator1_name",
0x4000000e: "collimator1_position",
0x4000000f: "collimator2_name",
0x40000010: "collimator2_position",
0x40000011: "is_bleach_track",
0x40000012: "is_bleach_after_scan_number",
0x40000013: "bleach_scan_number",
0x40000014: "trigger_in",
0x40000015: "trigger_out",
0x40000016: "is_ratio_track",
0x40000017: "bleach_count",
0x40000018: "spi_center_wavelength",
0x40000019: "pixel_time",
0x40000021: "condensor_frontlens",
0x40000023: "field_stop_value",
0x40000024: "id_condensor_aperture",
0x40000025: "condensor_aperture",
0x40000026: "id_condensor_revolver",
0x40000027: "condensor_filter",
0x40000028: "id_transmission_filter1",
0x40000029: "id_transmission1",
0x40000030: "id_transmission_filter2",
0x40000031: "id_transmission2",
0x40000032: "repeat_bleach",
0x40000033: "enable_spot_bleach_pos",
0x40000034: "spot_bleach_posx",
0x40000035: "spot_bleach_posy",
0x40000036: "spot_bleach_posz",
0x40000037: "id_tubelens",
0x40000038: "id_tubelens_position",
0x40000039: "transmitted_light",
0x4000003a: "reflected_light",
0x4000003b: "simultan_grab_and_bleach",
0x4000003c: "bleach_pixel_time",
# laser
0x50000001: "name",
0x50000002: "acquire",
0x50000003: "power",
# detection_channel
0x70000001: "integration_mode",
0x70000002: "special_mode",
0x70000003: "detector_gain_first",
0x70000004: "detector_gain_last",
0x70000005: "amplifier_gain_first",
0x70000006: "amplifier_gain_last",
0x70000007: "amplifier_offs_first",
0x70000008: "amplifier_offs_last",
0x70000009: "pinhole_diameter",
0x7000000a: "counting_trigger",
0x7000000b: "acquire",
0x7000000c: "point_detector_name",
0x7000000d: "amplifier_name",
0x7000000e: "pinhole_name",
0x7000000f: "filter_set_name",
0x70000010: "filter_name",
0x70000013: "integrator_name",
0x70000014: "channel_name",
0x70000015: "detector_gain_bc1",
0x70000016: "detector_gain_bc2",
0x70000017: "amplifier_gain_bc1",
0x70000018: "amplifier_gain_bc2",
0x70000019: "amplifier_offset_bc1",
0x70000020: "amplifier_offset_bc2",
0x70000021: "spectral_scan_channels",
0x70000022: "spi_wavelength_start",
0x70000023: "spi_wavelength_stop",
0x70000026: "dye_name",
0x70000027: "dye_folder",
# illumination_channel
0x90000001: "name",
0x90000002: "power",
0x90000003: "wavelength",
0x90000004: "aquire",
0x90000005: "detchannel_name",
0x90000006: "power_bc1",
0x90000007: "power_bc2",
# beam_splitter
0xb0000001: "filter_set",
0xb0000002: "filter",
0xb0000003: "name",
# data_channel
0xd0000001: "name",
0xd0000003: "acquire",
0xd0000004: "color",
0xd0000005: "sample_type",
0xd0000006: "bits_per_sample",
0xd0000007: "ratio_type",
0xd0000008: "ratio_track1",
0xd0000009: "ratio_track2",
0xd000000a: "ratio_channel1",
0xd000000b: "ratio_channel2",
0xd000000c: "ratio_const1",
0xd000000d: "ratio_const2",
0xd000000e: "ratio_const3",
0xd000000f: "ratio_const4",
0xd0000010: "ratio_const5",
0xd0000011: "ratio_const6",
0xd0000012: "ratio_first_images1",
0xd0000013: "ratio_first_images2",
0xd0000014: "dye_name",
0xd0000015: "dye_folder",
0xd0000016: "spectrum",
0xd0000017: "acquire",
# timer
0x12000001: "name",
0x12000002: "description",
0x12000003: "interval",
0x12000004: "trigger_in",
0x12000005: "trigger_out",
0x12000006: "activation_time",
0x12000007: "activation_number",
# marker
0x14000001: "name",
0x14000002: "description",
0x14000003: "trigger_in",
0x14000004: "trigger_out",
}
# Map TIFF tag code to attribute name, default value, type, count, validator
TIFF_TAGS = {
254: ('new_subfile_type', 0, 4, 1, TIFF_SUBFILE_TYPES()),
255: ('subfile_type', None, 3, 1,
{0: 'undefined', 1: 'image', 2: 'reduced_image', 3: 'page'}),
256: ('image_width', None, 4, 1, None),
257: ('image_length', None, 4, 1, None),
258: ('bits_per_sample', 1, 3, 1, None),
259: ('compression', 1, 3, 1, TIFF_COMPESSIONS),
262: ('photometric', None, 3, 1, TIFF_PHOTOMETRICS),
266: ('fill_order', 1, 3, 1, {1: 'msb2lsb', 2: 'lsb2msb'}),
269: ('document_name', None, 2, None, None),
270: ('image_description', None, 2, None, None),
271: ('make', None, 2, None, None),
272: ('model', None, 2, None, None),
273: ('strip_offsets', None, 4, None, None),
274: ('orientation', 1, 3, 1, TIFF_ORIENTATIONS),
277: ('samples_per_pixel', 1, 3, 1, None),
278: ('rows_per_strip', 2**32-1, 4, 1, None),
279: ('strip_byte_counts', None, 4, None, None),
280: ('min_sample_value', None, 3, None, None),
281: ('max_sample_value', None, 3, None, None), # 2**bits_per_sample
282: ('x_resolution', None, 5, 1, None),
283: ('y_resolution', None, 5, 1, None),
284: ('planar_configuration', 1, 3, 1, {1: 'contig', 2: 'separate'}),
285: ('page_name', None, 2, None, None),
286: ('x_position', None, 5, 1, None),
287: ('y_position', None, 5, 1, None),
296: ('resolution_unit', 2, 4, 1, {1: 'none', 2: 'inch', 3: 'centimeter'}),
297: ('page_number', None, 3, 2, None),
305: ('software', None, 2, None, None),
306: ('datetime', None, 2, None, None),
315: ('artist', None, 2, None, None),
316: ('host_computer', None, 2, None, None),
317: ('predictor', 1, 3, 1, {1: None, 2: 'horizontal'}),
318: ('white_point', None, 5, 2, None),
319: ('primary_chromaticities', None, 5, 6, None),
320: ('color_map', None, 3, None, None),
322: ('tile_width', None, 4, 1, None),
323: ('tile_length', None, 4, 1, None),
324: ('tile_offsets', None, 4, None, None),
325: ('tile_byte_counts', None, 4, None, None),
338: ('extra_samples', None, 3, None,
{0: 'unspecified', 1: 'assocalpha', 2: 'unassalpha'}),
339: ('sample_format', 1, 3, 1, TIFF_SAMPLE_FORMATS),
340: ('smin_sample_value', None, None, None, None),
341: ('smax_sample_value', None, None, None, None),
347: ('jpeg_tables', None, 7, None, None),
530: ('ycbcr_subsampling', 1, 3, 2, None),
531: ('ycbcr_positioning', 1, 3, 1, None),
32996: ('sgi_matteing', None, None, 1, None), # use extra_samples
32996: ('sgi_datatype', None, None, 1, None), # use sample_format
32997: ('image_depth', None, 4, 1, None),
32998: ('tile_depth', None, 4, 1, None),
33432: ('copyright', None, 1, None, None),
33445: ('md_file_tag', None, 4, 1, None),
33446: ('md_scale_pixel', None, 5, 1, None),
33447: ('md_color_table', None, 3, None, None),
33448: ('md_lab_name', None, 2, None, None),
33449: ('md_sample_info', None, 2, None, None),
33450: ('md_prep_date', None, 2, None, None),
33451: ('md_prep_time', None, 2, None, None),
33452: ('md_file_units', None, 2, None, None),
33550: ('model_pixel_scale', None, 12, 3, None),
33922: ('model_tie_point', None, 12, None, None),
34665: ('exif_ifd', None, None, 1, None),
34735: ('geo_key_directory', None, 3, None, None),
34736: ('geo_double_params', None, 12, None, None),
34737: ('geo_ascii_params', None, 2, None, None),
34853: ('gps_ifd', None, None, 1, None),
37510: ('user_comment', None, None, None, None),
42112: ('gdal_metadata', None, 2, None, None),
42113: ('gdal_nodata', None, 2, None, None),
50289: ('mc_xy_position', None, 12, 2, None),
50290: ('mc_z_position', None, 12, 1, None),
50291: ('mc_xy_calibration', None, 12, 3, None),
50292: ('mc_lens_lem_na_n', None, 12, 3, None),
50293: ('mc_channel_name', None, 1, None, None),
50294: ('mc_ex_wavelength', None, 12, 1, None),
50295: ('mc_time_stamp', None, 12, 1, None),
50838: ('imagej_byte_counts', None, None, None, None),
65200: ('flex_xml', None, 2, None, None),
# code: (attribute name, default value, type, count, validator)
}
# Map custom TIFF tag codes to attribute names and import functions
CUSTOM_TAGS = {
700: ('xmp', read_bytes),
34377: ('photoshop', read_numpy),
33723: ('iptc', read_bytes),
34675: ('icc_profile', read_bytes),
33628: ('uic1tag', read_uic1tag), # Universal Imaging Corporation STK
33629: ('uic2tag', read_uic2tag),
33630: ('uic3tag', read_uic3tag),
33631: ('uic4tag', read_uic4tag),
34361: ('mm_header', read_mm_header), # Olympus FluoView
34362: ('mm_stamp', read_mm_stamp),
34386: ('mm_user_block', read_bytes),
34412: ('cz_lsm_info', read_cz_lsm_info), # Carl Zeiss LSM
43314: ('nih_image_header', read_nih_image_header),
# 40001: ('mc_ipwinscal', read_bytes),
40100: ('mc_id_old', read_bytes),
50288: ('mc_id', read_bytes),
50296: ('mc_frame_properties', read_bytes),
50839: ('imagej_metadata', read_bytes),
51123: ('micromanager_metadata', read_json),
}
# Max line length of printed output
PRINT_LINE_LEN = 79
def imshow(data, title=None, vmin=0, vmax=None, cmap=None,
bitspersample=None, photometric='rgb', interpolation='nearest',
dpi=96, figure=None, subplot=111, maxdim=8192, **kwargs):
"""Plot n-dimensional images using matplotlib.pyplot.
Return figure, subplot and plot axis.
Requires pyplot already imported ``from matplotlib import pyplot``.
Parameters
----------
bitspersample : int or None
Number of bits per channel in integer RGB images.
photometric : {'miniswhite', 'minisblack', 'rgb', or 'palette'}
The color space of the image data.
title : str
Window and subplot title.
figure : matplotlib.figure.Figure (optional).
Matplotlib to use for plotting.
subplot : int
A matplotlib.pyplot.subplot axis.
maxdim : int
maximum image size in any dimension.
kwargs : optional
Arguments for matplotlib.pyplot.imshow.
"""
#if photometric not in ('miniswhite', 'minisblack', 'rgb', 'palette'):
# raise ValueError("Can't handle %s photometrics" % photometric)
# TODO: handle photometric == 'separated' (CMYK)
isrgb = photometric in ('rgb', 'palette')
data = numpy.atleast_2d(data.squeeze())
data = data[(slice(0, maxdim), ) * len(data.shape)]
dims = data.ndim
if dims < 2:
raise ValueError("not an image")
elif dims == 2:
dims = 0
isrgb = False
else:
if isrgb and data.shape[-3] in (3, 4):
data = numpy.swapaxes(data, -3, -2)
data = numpy.swapaxes(data, -2, -1)
elif not isrgb and (data.shape[-1] < data.shape[-2] // 16 and
data.shape[-1] < data.shape[-3] // 16 and
data.shape[-1] < 5):
data = numpy.swapaxes(data, -3, -1)
data = numpy.swapaxes(data, -2, -1)
isrgb = isrgb and data.shape[-1] in (3, 4)
dims -= 3 if isrgb else 2
if photometric == 'palette' and isrgb:
datamax = data.max()
if datamax > 255:
data >>= 8 # possible precision loss
data = data.astype('B')
elif data.dtype.kind in 'ui':
if not (isrgb and data.dtype.itemsize <= 1) or bitspersample is None:
try:
bitspersample = int(math.ceil(math.log(data.max(), 2)))
except Exception:
bitspersample = data.dtype.itemsize * 8
elif not isinstance(bitspersample, int):
# bitspersample can be tuple, e.g. (5, 6, 5)
bitspersample = data.dtype.itemsize * 8
datamax = 2**bitspersample
if isrgb:
if bitspersample < 8:
data <<= 8 - bitspersample
elif bitspersample > 8:
data >>= bitspersample - 8 # precision loss
data = data.astype('B')
elif data.dtype.kind == 'f':
datamax = data.max()
if isrgb and datamax > 1.0:
if data.dtype.char == 'd':
data = data.astype('f')
data /= datamax
elif data.dtype.kind == 'b':
datamax = 1
elif data.dtype.kind == 'c':
raise NotImplementedError("complex type") # TODO: handle complex types
if not isrgb:
if vmax is None:
vmax = datamax
if vmin is None:
if data.dtype.kind == 'i':
dtmin = numpy.iinfo(data.dtype).min
vmin = numpy.min(data)
if vmin == dtmin:
vmin = numpy.min(data > dtmin)
if data.dtype.kind == 'f':
dtmin = numpy.finfo(data.dtype).min
vmin = numpy.min(data)
if vmin == dtmin:
vmin = numpy.min(data > dtmin)
else:
vmin = 0
pyplot = sys.modules['matplotlib.pyplot']
if figure is None:
pyplot.rc('font', family='sans-serif', weight='normal', size=8)
figure = pyplot.figure(dpi=dpi, figsize=(10.3, 6.3), frameon=True,
facecolor='1.0', edgecolor='w')
try:
figure.canvas.manager.window.title(title)
except Exception:
pass
pyplot.subplots_adjust(bottom=0.03*(dims+2), top=0.9,
left=0.1, right=0.95, hspace=0.05, wspace=0.0)
subplot = pyplot.subplot(subplot)
if title:
try:
title = unicode(title, 'Windows-1252')
except TypeError:
pass
pyplot.title(title, size=11)
if cmap is None:
if data.dtype.kind in 'ubf' or vmin == 0:
cmap = 'cubehelix'
else:
cmap = 'coolwarm'
if photometric == 'miniswhite':
cmap += '_r'
image = pyplot.imshow(data[(0, ) * dims].squeeze(), vmin=vmin, vmax=vmax,
cmap=cmap, interpolation=interpolation, **kwargs)
if not isrgb:
pyplot.colorbar() # panchor=(0.55, 0.5), fraction=0.05
def format_coord(x, y):
# callback function to format coordinate display in toolbar
x = int(x + 0.5)
y = int(y + 0.5)
try:
if dims:
return "%s @ %s [%4i, %4i]" % (cur_ax_dat[1][y, x],
current, x, y)
else:
return "%s @ [%4i, %4i]" % (data[y, x], x, y)
except IndexError:
return ""
pyplot.gca().format_coord = format_coord
if dims:
current = list((0, ) * dims)
cur_ax_dat = [0, data[tuple(current)].squeeze()]
sliders = [pyplot.Slider(
pyplot.axes([0.125, 0.03*(axis+1), 0.725, 0.025]),
'Dimension %i' % axis, 0, data.shape[axis]-1, 0, facecolor='0.5',
valfmt='%%.0f [%i]' % data.shape[axis]) for axis in range(dims)]
for slider in sliders:
slider.drawon = False
def set_image(current, sliders=sliders, data=data):
# change image and redraw canvas
cur_ax_dat[1] = data[tuple(current)].squeeze()
image.set_data(cur_ax_dat[1])
for ctrl, index in zip(sliders, current):
ctrl.eventson = False
ctrl.set_val(index)
ctrl.eventson = True
figure.canvas.draw()
def on_changed(index, axis, data=data, current=current):
# callback function for slider change event
index = int(round(index))
cur_ax_dat[0] = axis
if index == current[axis]:
return
if index >= data.shape[axis]:
index = 0
elif index < 0:
index = data.shape[axis] - 1
current[axis] = index
set_image(current)
def on_keypressed(event, data=data, current=current):
# callback function for key press event
key = event.key
axis = cur_ax_dat[0]
if str(key) in '0123456789':
on_changed(key, axis)
elif key == 'right':
on_changed(current[axis] + 1, axis)
elif key == 'left':
on_changed(current[axis] - 1, axis)
elif key == 'up':
cur_ax_dat[0] = 0 if axis == len(data.shape)-1 else axis + 1
elif key == 'down':
cur_ax_dat[0] = len(data.shape)-1 if axis == 0 else axis - 1
elif key == 'end':
on_changed(data.shape[axis] - 1, axis)
elif key == 'home':
on_changed(0, axis)
figure.canvas.mpl_connect('key_press_event', on_keypressed)
for axis, ctrl in enumerate(sliders):
ctrl.on_changed(lambda k, a=axis: on_changed(k, a))
return figure, subplot, image
def _app_show():
"""Block the GUI. For use as skimage plugin."""
pyplot = sys.modules['matplotlib.pyplot']
pyplot.show()
def main(argv=None):
"""Command line usage main function."""
if float(sys.version[0:3]) < 2.6:
print("This script requires Python version 2.6 or better.")
print("This is Python version %s" % sys.version)
return 0
if argv is None:
argv = sys.argv
import optparse
parser = optparse.OptionParser(
usage="usage: %prog [options] path",
description="Display image data in TIFF files.",
version="%%prog %s" % __version__)
opt = parser.add_option
opt('-p', '--page', dest='page', type='int', default=-1,
help="display single page")
opt('-s', '--series', dest='series', type='int', default=-1,
help="display series of pages of same shape")
opt('--nomultifile', dest='nomultifile', action='store_true',
default=False, help="don't read OME series from multiple files")
opt('--noplot', dest='noplot', action='store_true', default=False,
help="don't display images")
opt('--interpol', dest='interpol', metavar='INTERPOL', default='bilinear',
help="image interpolation method")
opt('--dpi', dest='dpi', type='int', default=96,
help="set plot resolution")
opt('--debug', dest='debug', action='store_true', default=False,
help="raise exception on failures")
opt('--test', dest='test', action='store_true', default=False,
help="try read all images in path")
opt('--doctest', dest='doctest', action='store_true', default=False,
help="runs the docstring examples")
opt('-v', '--verbose', dest='verbose', action='store_true', default=True)
opt('-q', '--quiet', dest='verbose', action='store_false')
settings, path = parser.parse_args()
path = ' '.join(path)
if settings.doctest:
import doctest
doctest.testmod()
return 0
if not path:
parser.error("No file specified")
if settings.test:
test_tifffile(path, settings.verbose)
return 0
if any(i in path for i in '?*'):
path = glob.glob(path)
if not path:
print('no files match the pattern')
return 0
# TODO: handle image sequences
#if len(path) == 1:
path = path[0]
print("Reading file structure...", end=' ')
start = time.time()
try:
tif = TiffFile(path, multifile=not settings.nomultifile)
except Exception as e:
if settings.debug:
raise
else:
print("\n", e)
sys.exit(0)
print("%.3f ms" % ((time.time()-start) * 1e3))
if tif.is_ome:
settings.norgb = True
images = [(None, tif[0 if settings.page < 0 else settings.page])]
if not settings.noplot:
print("Reading image data... ", end=' ')
def notnone(x):
return next(i for i in x if i is not None)
start = time.time()
try:
if settings.page >= 0:
images = [(tif.asarray(key=settings.page),
tif[settings.page])]
elif settings.series >= 0:
images = [(tif.asarray(series=settings.series),
notnone(tif.series[settings.series].pages))]
else:
images = []
for i, s in enumerate(tif.series):
try:
images.append(
(tif.asarray(series=i), notnone(s.pages)))
except ValueError as e:
images.append((None, notnone(s.pages)))
if settings.debug:
raise
else:
print("\n* series %i failed: %s... " % (i, e),
end='')
print("%.3f ms" % ((time.time()-start) * 1e3))
except Exception as e:
if settings.debug:
raise
else:
print(e)
tif.close()
print("\nTIFF file:", tif)
print()
for i, s in enumerate(tif.series):
print ("Series %i" % i)
print(s)
print()
for i, page in images:
print(page)
print(page.tags)
if page.is_palette:
print("\nColor Map:", page.color_map.shape, page.color_map.dtype)
for attr in ('cz_lsm_info', 'cz_lsm_scan_info', 'uic_tags',
'mm_header', 'imagej_tags', 'micromanager_metadata',
'nih_image_header'):
if hasattr(page, attr):
print("", attr.upper(), Record(getattr(page, attr)), sep="\n")
print()
if page.is_micromanager:
print('MICROMANAGER_FILE_METADATA')
print(Record(tif.micromanager_metadata))
if images and not settings.noplot:
try:
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot
except ImportError as e:
warnings.warn("failed to import matplotlib.\n%s" % e)
else:
for img, page in images:
if img is None:
continue
vmin, vmax = None, None
if 'gdal_nodata' in page.tags:
try:
vmin = numpy.min(img[img > float(page.gdal_nodata)])
except ValueError:
pass
if page.is_stk:
try:
vmin = page.uic_tags['min_scale']
vmax = page.uic_tags['max_scale']
except KeyError:
pass
else:
if vmax <= vmin:
vmin, vmax = None, None
title = "%s\n %s" % (str(tif), str(page))
imshow(img, title=title, vmin=vmin, vmax=vmax,
bitspersample=page.bits_per_sample,
photometric=page.photometric,
interpolation=settings.interpol,
dpi=settings.dpi)
pyplot.show()
TIFFfile = TiffFile # backwards compatibility
if sys.version_info[0] > 2:
basestring = str, bytes
unicode = str
if __name__ == "__main__":
sys.exit(main())