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tifffile.py
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tifffile.py
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#! /usr/bin/python3
# -*- coding: utf-8 -*-
# tifffile.py
# Copyright (c) 2008-2017, Christoph Gohlke
# Copyright (c) 2008-2017, 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
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Read image and meta data from (bio)TIFF files. Save numpy arrays as TIFF.
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, chroma subsampling,
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 aka Thousands of Incompatible File Formats,
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 C{python -m tifffile --help}
:Author:
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
:Organization:
Laboratory for Fluorescence Dynamics, University of California, Irvine
:Version: 2017.07.21.dev
Requirements
------------
* `CPython 3.6 64-bit <http://www.python.org>`_
* `Numpy 1.13 <http://www.numpy.org>`_
* `Matplotlib 2.0 <http://www.matplotlib.org>`_ (optional for plotting)
* `Tifffile.c 2017.01.10 <http://www.lfd.uci.edu/~gohlke/>`_
(recommended for faster decoding of PackBits and LZW encoded strings)
Revisions
---------
2017.07.21.dev (tentative)
Many backwards incompatible changes improving speed and resource usage:
Pass 2267 tests.
Replace nih_image_header tag and attribute with nih_metadata dict.
Rename imagej_tags attribute to imagej_metadata.
Remove cz_lsm attributes; use lsm_metadata instead.
Consolidate LSM lsm_scan_info header and subrecords in lsm_metadata dict.
Add function to convert LSM to tiled BIN files.
Align image data in file.
Make TiffPage.dtype a numpy.dtype.
Allow imsave to write non-BigTIFF files up to ~4 GB.
Only read one page for shaped series if possible.
Return offset and bytecounts of contiguous data from TiffWriter.save.
Add memmap function to create memory-mapped array stored in TIFF file.
Add option to save empty arrays to TIFF files.
Add option to save truncated TIFF files.
Allow single tile images to be saved contiguously.
Only return main image series for LSM files.
Add optional movie mode for files with uniform pages.
Lazy load pages.
Use lightweight TiffFrame for IFDs sharing properties with key TiffPage.
Move module constants to 'CONST' namespace (speed up module import).
Remove 'fastij' option from TiffFile.
Remove 'pages' parameter from TiffFile.
Remove TIFFfile alias.
Deprecate Python 2.
Remove Record and return all metadata as dict or numpy.record instead.
Add functions to parse STK, MetaSeries, ScanImage, SVS, Pilatus metadata.
Read tags from EXIF and GPS IFDs.
Use pprint for info strings.
Fix reading some UIC tags (bug fix).
Do not modify input array in imshow (bug fix).
Fix Python implementation of unpack_ints.
2017.05.23
Pass 1961 tests.
Write correct number of sample_format values (bug fix).
Use Adobe deflate code to write ZIP compressed files.
Add option to pass tag values as packed binary data for writing.
Defer tag validation to attribute access.
Use property instead of lazyattr decorator for simple expressions.
2017.03.17
Write IFDs and tag values on word boundaries.
Read ScanImage metadata.
Remove is_rgb and is_indexed attributes from TiffFile.
Create files used by doctests.
2017.01.12
Read Zeiss SEM metadata.
Read OME-TIFF with invalid references to external files.
Rewrite C LZW decoder (5x faster).
Read corrupted LSM files missing EOI code in LZW stream.
2017.01.01
Add option to append images to existing TIFF files.
Read files without pages.
Read S-FEG and Helios NanoLab tags created by FEI software.
Allow saving Color Filter Array (CFA) images.
Add info functions returning more information about TiffFile and TiffPage.
Add option to read specific pages only.
Remove maxpages argument (backwards incompatible).
Remove test_tifffile function.
2016.10.28
Pass 1944 tests.
Improve detection of ImageJ hyperstacks.
Read TVIPS metadata created by EM-MENU (by Marco Oster).
Add option to disable using OME-XML metadata.
Allow non-integer range attributes in modulo tags (by Stuart Berg).
2016.06.21
Do not always memmap contiguous data in page series.
2016.05.13
Add option to specify resolution unit.
Write grayscale images with extra samples when planarconfig is specified.
Do not write RGB color images with 2 samples.
Reorder TiffWriter.save keyword arguments (backwards incompatible).
2016.04.18
Pass 1932 tests.
TiffWriter, imread, and imsave accept open binary file streams.
2016.04.13
Correctly handle reversed fill order in 2 and 4 bps images (bug fix).
Implement reverse_bitorder in C.
2016.03.18
Fix saving additional ImageJ metadata.
2016.02.22
Pass 1920 tests.
Write 8 bytes double tag values using offset if necessary (bug fix).
Add option to disable writing second image description tag.
Detect tags with incorrect counts.
Disable color mapping for LSM.
2015.11.13
Read LSM 6 mosaics.
Add option to specify directory of memory-mapped files.
Add command line options to specify vmin and vmax values for colormapping.
2015.10.06
New helper function to apply colormaps.
Renamed is_palette attributes to is_indexed (backwards incompatible).
Color-mapped samples are now contiguous (backwards incompatible).
Do not color-map ImageJ hyperstacks (backwards incompatible).
Towards supporting Leica SCN.
2015.09.25
Read images with reversed bit order (fill_order is lsb2msb).
2015.09.21
Read RGB OME-TIFF.
Warn about malformed OME-XML.
2015.09.16
Detect some corrupted ImageJ metadata.
Better axes labels for 'shaped' files.
Do not create TiffTag for default values.
Chroma subsampling is not supported.
Memory-map data in TiffPageSeries if possible (optional).
2015.08.17
Pass 1906 tests.
Write ImageJ hyperstacks (optional).
Read and write LZMA compressed data.
Specify datetime when saving (optional).
Save tiled and color-mapped images (optional).
Ignore void byte_counts and offsets if possible.
Ignore bogus image_depth tag created by ISS Vista software.
Decode floating point horizontal differencing (not tiled).
Save image data contiguously if possible.
Only read first IFD from ImageJ files if possible.
Read ImageJ 'raw' format (files larger than 4 GB).
TiffPageSeries class for pages with compatible shape and data type.
Try to read incomplete tiles.
Open file dialog if no filename is passed on command line.
Ignore errors when decoding OME-XML.
Rename decoder functions (backwards incompatible)
2014.08.24
TiffWriter class for incremental writing images.
Simplify examples.
2014.08.19
Add memmap function to FileHandle.
Add function to determine if image data in TiffPage is memory-mappable.
Do not close files if multifile_close parameter is False.
2014.08.10
Pass 1730 tests.
Return all extrasamples by default (backwards incompatible).
Read data from series of pages into memory-mapped array (optional).
Squeeze OME dimensions (backwards incompatible).
Workaround missing EOI code in strips.
Support image and tile depth tags (SGI extension).
Better handling of STK/UIC tags (backwards incompatible).
Disable color mapping for STK.
Julian to datetime converter.
TIFF ASCII type may be NULL separated.
Unwrap strip offsets for LSM files greater than 4 GB.
Correct strip byte counts in compressed LSM files.
Skip missing files in OME series.
Read embedded TIFF files.
2014.02.05
Save rational numbers as type 5 (bug fix).
2013.12.20
Keep other files in OME multi-file series closed.
FileHandle class to abstract binary file handle.
Disable color mapping for bad OME-TIFF produced by bio-formats.
Read bad OME-XML produced by ImageJ when cropping.
2013.11.03
Allow zlib compress data in imsave function (optional).
Memory-map contiguous image data (optional).
2013.10.28
Read MicroManager metadata and little endian ImageJ tag.
Save extra tags in imsave function.
Save tags in ascending order by code (bug fix).
2012.10.18
Accept file like objects (read from OIB files).
2012.08.21
Rename TIFFfile to TiffFile and TIFFpage to TiffPage.
TiffSequence class for reading sequence of TIFF files.
Read UltraQuant tags.
Allow float numbers as resolution in imsave function.
2012.08.03
Read MD GEL tags and NIH Image header.
2012.07.25
Read ImageJ tags.
...
Notes
-----
The API is not stable yet and might change between revisions.
Tested on little-endian platforms only.
TIFF trees defined via sub_ifds tags are not supported.
Other Python packages and modules for reading bio-scientific TIFF files:
* `python-bioformats <https://github.com/CellProfiler/python-bioformats>`_
* `Imread <https://github.com/luispedro/imread>`_
* `PyLibTiff <https://github.com/pearu/pylibtiff>`_
* `SimpleITK <http://www.simpleitk.org>`_
* `PyLSM <https://launchpad.net/pylsm>`_
* `PyMca.TiffIO.py <https://github.com/vasole/pymca>`_ (same as fabio.TiffIO)
* `BioImageXD.Readers <http://www.bioimagexd.net/>`_
* `Cellcognition.io <http://cellcognition.org/>`_
* `pymimage <https://github.com/ardoi/pymimage>`_
Acknowledgements
----------------
* Egor Zindy, University of Manchester, for lsm_scan_info specifics.
* Wim Lewis for a bug fix and some read_lsm functions.
* Hadrien Mary for help on reading MicroManager files.
* Christian Kliche for help writing tiled and color-mapped 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) The OME-TIFF format.
http://www.openmicroscopy.org/site/support/file-formats/ome-tiff
6) UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf
7) Micro-Manager File Formats.
http://www.micro-manager.org/wiki/Micro-Manager_File_Formats
8) Tags for TIFF and Related Specifications. Digital Preservation.
http://www.digitalpreservation.gov/formats/content/tiff_tags.shtml
9) ScanImage BigTiff Specification - ScanImage 2016.
http://scanimage.vidriotechnologies.com/display/SI2016/
ScanImage+BigTiff+Specification
10) CIPA DC-008-2016: Exchangeable image file format for digital still cameras:
Exif Version 2.31.
http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf
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.pages:
... for tag in page.tags.values():
... _ = 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 # delay import
# from xml.etree import cElementTree as etree # delay import
import numpy
try:
import lzma
except ImportError:
try:
import backports.lzma as lzma
except ImportError:
lzma = None
__version__ = '2017.07.21.dev'
__docformat__ = 'restructuredtext en'
__all__ = (
'imsave', 'imread', 'imshow', 'memmap',
'TiffFile', 'TiffWriter', 'TiffSequence',
# utility functions used by oiffile or czifile
'FileHandle', 'lazyattr', 'natural_sorted', 'decode_lzw', 'stripnull',
'repeat_nd', 'format_size', 'product')
def imread(files, **kwargs):
"""Return image data from TIFF file(s) as numpy array.
Refer to the TiffFile class and member functions for documentation.
Parameters
----------
files : str, binary stream, or sequence
File name, seekable binary stream, glob pattern, or sequence of
file names.
kwargs : dict
Parameters 'multifile', 'multifile_close', and 'is_ome' are passed
to the TiffFile class.
The 'pattern' parameter is passed to the TiffSequence class.
Other parameters are passed to the asarray functions.
The first image series is returned if no arguments are provided.
Examples
--------
>>> imsave('temp.tif', numpy.random.rand(3, 4, 301, 219))
>>> im = imread('temp.tif', key=0)
>>> im.shape
(4, 301, 219)
>>> ims = imread(['temp.tif', 'temp.tif'])
>>> ims.shape
(2, 3, 4, 301, 219)
"""
kwargs_file = parse_kwargs(kwargs, 'multifile', 'multifile_close',
'is_ome')
kwargs_seq = parse_kwargs(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 not hasattr(files, 'seek') and len(files) == 1:
files = files[0]
if isinstance(files, basestring) or hasattr(files, 'seek'):
with TiffFile(files, **kwargs_file) as tif:
return tif.asarray(**kwargs)
else:
with TiffSequence(files, **kwargs_seq) as imseq:
return imseq.asarray(**kwargs)
def imsave(file, data=None, shape=None, dtype=None, bigsize=2**32-2**25,
**kwargs):
"""Write image data to TIFF file.
If data is None, an empty array of the specified shape and dtype is
saved to file.
If the image data are written contiguously, return offset and bytecount
of image data in the file.
Refer to the TiffWriter class and member functions for documentation.
Parameters
----------
file : str or binary stream
File name or writable binary stream, such as a open file or BytesIO.
data : array_like
Input image. The last dimensions are assumed to be image depth,
height, width, and samples.
shape : tuple
If data is None, shape of an empty array to save to the file.
dtype : numpy.dtype
If data is None, data-type of an empty array to save to the file.
bigsize : int
Create a BigTIFF file if the size of data in bytes is larger than
this threshold and 'imagej' or 'truncate' are not enabled.
By default, the threshold is 4 GB minus 32 MB reserved for metadata.
Use the 'bigtiff' parameter to explicitly specify the type of
file created.
kwargs : dict
Parameters 'append', 'byteorder', 'bigtiff', 'software', and 'imagej',
are passed to TiffWriter().
Other parameters are passed to TiffWriter.save().
Examples
--------
>>> data = numpy.random.rand(2, 5, 3, 301, 219)
>>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
"""
tifargs = parse_kwargs(kwargs, 'append', 'bigtiff', 'byteorder',
'software', 'imagej')
if data is None:
size = product(shape) * numpy.dtype(dtype).itemsize
else:
try:
size = data.size * data.dtype.itemsize
except:
size = 0
if size > bigsize and 'bigtiff' not in tifargs and not (
tifargs.get('imagej', False) or tifargs.get('truncate', False)):
tifargs['bigtiff'] = True
with TiffWriter(file, **tifargs) as tif:
return tif.save(data, shape, dtype, **kwargs)
def memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+',
**kwargs):
"""Return memory-mapped numpy array stored in TIFF file.
Memory-mapping requires data stored in native byte order, without tiling,
compression, predictors, etc.
If shape and dtype are provided, existing files will be overwritten or
appended to depending on the 'append' parameter.
Otherwise the image data of a specified page or series in an existing
file will be memory-mapped. By default, the image data of the first page
series is memory-mapped.
Call flush() to write any changes in the array to the file.
Raise ValueError if the image data in the file is not memory-mappable
Parameters
----------
filename : str
Name of the TIFF file which stores the array.
shape : tuple
Shape of the empty array.
dtype : numpy.dtype
Data-type of the empty array.
page : int
Index of the page which image data to memory-map.
series : int
Index of the page series which image data to memory-map.
mode : {'r+', 'r', 'c'}, optional
The file open mode. Default is to open existing file for reading and
writing ('r+').
kwargs : dict
Additional parameters passed to imsave() or TiffFile().
Examples
--------
>>> im = memmap('temp.tif', shape=(256, 256), dtype='float32')
>>> im[255, 255] = 1.0
>>> im.flush()
>>> im.shape, im.dtype
((256, 256), dtype('float32'))
>>> del im
>>> im = memmap('temp.tif', page=0)
>>> im[255, 255]
1.0
"""
if shape is not None and dtype is not None:
# create a new, empty array
kwargs.update(data=None, shape=shape, dtype=dtype, return_offset=True,
align=CONST.ALLOCATIONGRANULARITY)
result = imsave(filename, **kwargs)
if result is None:
# TODO: fail before creating file or writing data
raise ValueError("image data is not memory-mappable")
offset = result[0]
else:
# use existing file
with TiffFile(filename, **kwargs) as tif:
if page is not None:
page = tif.pages[page]
if not page.is_memmappable(False, False):
raise ValueError("image data is not memory-mappable")
offset, _ = page.is_contiguous
shape = page.shape
dtype = page.dtype
else:
series = tif.series[series]
if series.offset is None:
raise ValueError("image data is not memory-mappable")
shape = series.shape
dtype = series.dtype
offset = series.offset
return numpy.memmap(filename, dtype, mode, offset, shape, 'C')
class lazyattr(object):
"""Attribute whose value is computed on first access."""
# TODO: help() doesn't work
__slots__ = ('func',)
def __init__(self, func):
self.func = func
# self.__name__ = func.__name__
# self.__doc__ = func.__doc__
# self.lock = threading.RLock()
def __get__(self, instance, owner):
# with self.lock:
if instance is None:
return self
try:
value = self.func(instance)
except AttributeError as e:
raise RuntimeError(e)
if value is NotImplemented:
return getattr(super(owner, instance), self.func.__name__)
setattr(instance, self.func.__name__, value)
return value
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' context manager.
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)
"""
def __init__(self, file, bigtiff=False, byteorder=None,
software='tifffile.py', append=False, imagej=False):
"""Open a TIFF file for writing.
A empty TIFF file is created if the file does not exist, else the file
is overwritten unless append=True.
Use bigtiff=True when creating files larger than 4 GB.
Parameters
----------
file : str, binary stream, or FileHandle
File name or writable binary stream, such as a open file
or BytesIO.
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 file.
Saved with the first page in the file only.
append : bool
If True and 'file' is an existing standard TIFF file, image data
and tags are appended to the file.
Appending data may corrupt specifically formatted TIFF files
such as LSM, STK, ImageJ, NIH, or FluoView.
imagej : bool
If True, write an ImageJ hyperstack compatible file.
This format can handle data types uint8, uint16, or float32 and
data shapes up to 6 dimensions in TZCYXS order.
RGB images (S=3 or S=4) must be uint8.
ImageJ's default byte order is big endian but this implementation
uses the system's native byte order by default.
ImageJ does not support BigTIFF format or LZMA compression.
The ImageJ file format is undocumented.
"""
if append:
# determine if file is an existing TIFF file that can be extended
try:
with FileHandle(file, mode='rb', size=0) as fh:
pos = fh.tell()
try:
with TiffFile(fh) as tif:
if (append != 'force' and
any(getattr(tif, 'is_'+a) for a in
('lsm', 'stk', 'imagej', 'nih', 'fluoview',
'micromanager'))):
raise ValueError("file contains metadata")
byteorder = tif.byteorder
bigtiff = tif.is_bigtiff
self._ifd_offset = tif.pages.next_page_offset
if tif.pages:
software = None
except Exception as e:
raise ValueError("can not append to file: %s" % str(e))
finally:
fh.seek(pos)
except (IOError, FileNotFoundError):
append = False
if byteorder in (None, '='):
byteorder = '<' if sys.byteorder == 'little' else '>'
elif byteorder not in ('<', '>'):
raise ValueError("invalid byteorder %s" % byteorder)
if imagej and bigtiff:
warnings.warn("writing incompatible BigTIFF ImageJ")
self._byteorder = byteorder
self._software = software
self._imagej = bool(imagej)
self._truncate = False
self._metadata = None
self._colormap = None
self._description_offset = 0
self._description_len_offset = 0
self._description_len = 0
self._tags = None
self._shape = None # normalized shape of data in consecutive pages
self._data_shape = None # shape of data in consecutive pages
self._data_dtype = None # data type
self._data_offset = None # offset to data
self._data_byte_counts = None # byte counts per plane
self._tag_offsets = None # strip or tile offset tag code
if bigtiff:
self._bigtiff = True
self._offset_size = 8
self._tag_size = 20
self._tagno_format = 'Q'
self._offset_format = 'Q'
self._value_format = '8s'
else:
self._bigtiff = False
self._offset_size = 4
self._tag_size = 12
self._tagno_format = 'H'
self._offset_format = 'I'
self._value_format = '4s'
if append:
self._fh = FileHandle(file, mode='r+b', size=0)
self._fh.seek(0, 2)
else:
self._fh = FileHandle(file, mode='wb', size=0)
self._fh.write({'<': b'II', '>': b'MM'}[byteorder])
if bigtiff:
self._fh.write(struct.pack(byteorder+'HHH', 43, 8, 0))
else:
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=None, shape=None, dtype=None, return_offset=False,
photometric=None, planarconfig=None, tile=None,
contiguous=True, align=16, truncate=False, compress=0,
colormap=None, description=None, datetime=None, resolution=None,
metadata={}, extratags=()):
"""Write image data and tags to TIFF file.
The data shape's last dimensions are assumed to be image depth,
height (length), width, and samples.
If a colormap is provided, the dtype must be uint8 or uint16 and
the data values are indices into the last dimension of the
colormap.
If shape and dtype are specified, an empty array is saved.
This option can not be used with compression or multiple tiles.
Image data are written in one stripe per plane by default.
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' tags are derived from
the data type.
Parameters
----------
data : numpy.ndarray or None
Input image array.
shape : tuple or None
Shape of the empty array to save. Used only if data is None.
dtype : numpy.dtype or None
Data-type of the empty array to save. Used only if data is None.
return_offset : bool
If True and the image data in the file is memory-mappable, return
the offset and number of bytes of the image data in the file.
photometric : {'minisblack', 'miniswhite', 'rgb', 'palette', 'cfa'}
The color space of the image data.
By default, this setting is inferred from the data shape and the
value of colormap.
For CFA images, DNG tags must be specified in extratags.
planarconfig : {'contig', 'planar'}
Specifies if samples are stored contiguous or in separate planes.
By default, this setting is inferred from the data shape.
If this parameter is set, extra samples are used to store grayscale
images.
'contig': last dimension contains samples.
'planar': third last dimension contains samples.
tile : tuple of int
The shape (depth, length, width) of image tiles to write.
If None (default), image data are written in one stripe per plane.
The tile length and width must be a multiple of 16.
If the tile depth is provided, the SGI image_depth and tile_depth
tags are used to save volume data.
Unless a single tile is used, tiles cannot be used to write
contiguous files.
Few software can read the SGI format, e.g. MeVisLab.
contiguous : bool
If True (default) and the data and parameters are compatible with
previous ones, if any, the image data are stored contiguously after
the previous one. Parameters 'photometric' and 'planarconfig' are
ignored.
align : int
Byte boundary on which to align the image data in the file.
Default 16. Use mmap.ALLOCATIONGRANULARITY for memory-mapped data.
Following contiguous writes are not aligned.
truncate : bool
If True, only write the first page including shape metadata if
possible (uncompressed, contiguous, not tiled).
Other TIFF readers will only be able to read part of the data.
compress : int or 'lzma'
Values from 0 to 9 controlling the level of zlib compression.
If 0, data are written uncompressed (default).
Compression cannot be used to write contiguous files.
If 'lzma', LZMA compression is used, which is not available on
all platforms.
colormap : numpy.ndarray
RGB color values for the corresponding data value.
Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16.
description : str
The subject of the image. Saved with the first page only.
Cannot be used with the ImageJ format.
datetime : datetime
Date and time of image creation. Saved with the first page only.
If None (default), the current date and time is used.
resolution : (float, float[, str]) or ((int, int), (int, int)[, str])
X and Y resolutions in pixels per resolution unit as float or
rational numbers.
A third, optional parameter specifies the resolution unit,
which must be None (default for ImageJ), 'inch' (default), or 'cm'.
metadata : dict
Additional meta data to be saved along with shape information
in JSON or ImageJ formats in an image_description tag.
If None, do not write a second image_description tag.
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, 2i, f, d, Q, or q.
count : int
Number of data values. Not used for string or byte string
values.
value : sequence
'Count' values compatible with 'dtype'.
Byte strings must contain count values of dtype packed as
binary data.
writeonce : bool
If True, the tag is written to the first page only.
"""
# TODO: refactor this function
fh = self._fh
byteorder = self._byteorder
tagno_format = self._tagno_format
value_format = self._value_format
offset_format = self._offset_format
offset_size = self._offset_size
tag_size = self._tag_size
if data is None:
if compress:
raise ValueError("can not save compressed empty file")
data_shape = shape
data_dtype = numpy.dtype(dtype).newbyteorder(byteorder)
data_dtype_char = data_dtype.char
data = None
else:
data = numpy.asarray(data, byteorder+data.dtype.char, 'C')
if data.size == 0:
raise ValueError("can not save empty array")
data_shape = data.shape
data_dtype = data.dtype
data_dtype_char = data.dtype.char
return_offset = return_offset and data_dtype.isnative
_data_shape = data_shape # data shape of input array
data_size = product(data_shape) * data_dtype.itemsize
# just append contiguous data if possible
self._truncate = bool(truncate)
if self._data_shape:
if (not contiguous
or self._data_shape[1:] != data_shape
or self._data_dtype != data_dtype
or (compress and self._tags)
or tile
or not numpy.array_equal(colormap, self._colormap)):
# incompatible shape, dtype, compression mode, or colormap
self._write_remaining_pages()
self._write_image_description()
self._truncate = False
self._description_offset = 0
self._description_len_offset = 0
self._data_shape = None
self._colormap = None
if self._imagej:
raise ValueError(
"ImageJ does not support non-contiguous data")
else:
# consecutive mode
self._data_shape = (self._data_shape[0] + 1,) + data_shape
if not compress:
# write contiguous data, write ifds/tags later
offset = fh.tell()
if data is None:
fh.write_empty(data_size)
else:
fh.write_array(data)
if return_offset:
return offset, data_size
return
if photometric not in (None, 'minisblack', 'miniswhite',
'rgb', 'palette', 'cfa'):
raise ValueError("invalid photometric %s" % photometric)
if planarconfig not in (None, 'contig', 'planar'):
raise ValueError("invalid planarconfig %s" % planarconfig)
# prepare compression
if not compress:
compress = False
compress_tag = 1
elif compress == 'lzma':
compress = lzma.compress
compress_tag = 34925
if self._imagej:
raise ValueError("ImageJ can not handle LZMA compression")
elif not 0 <= compress <= 9:
raise ValueError("invalid compression level %s" % compress)
elif compress:
def compress(data, level=compress):
return zlib.compress(data, level)
compress_tag = 8
# prepare ImageJ format
if self._imagej:
if description:
warnings.warn("not writing description to ImageJ file")
description = None
volume = False
if data_dtype_char not in 'BHhf':
raise ValueError("ImageJ does not support data type '%s'"
% data_dtype_char)
ijrgb = photometric == 'rgb' if photometric else None
if data_dtype_char not in 'B':
ijrgb = False
ijshape = imagej_shape(data_shape, ijrgb)
if ijshape[-1] in (3, 4):
photometric = 'rgb'
if data_dtype_char not in 'B':
raise ValueError("ImageJ does not support data type '%s' "
"for RGB" % data_dtype_char)
elif photometric is None:
photometric = 'minisblack'
planarconfig = None
if planarconfig == 'planar':
raise ValueError("ImageJ does not support planar images")
else:
planarconfig = 'contig' if ijrgb else None
# verify colormap and indices
if colormap is not None:
if data_dtype_char not in 'BH':
raise ValueError("invalid data dtype for palette mode")
colormap = numpy.asarray(colormap, dtype=byteorder+'H')
if colormap.shape != (3, 2**(data_dtype.itemsize * 8)):
raise ValueError("invalid color map shape")
self._colormap = colormap
# verify tile shape
if tile:
tile = tuple(int(i) for i in tile[:3])
volume = len(tile) == 3
if (len(tile) < 2 or tile[-1] % 16 or tile[-2] % 16 or
any(i < 1 for i in tile)):
raise ValueError("invalid tile shape")
else:
tile = ()
volume = False
# normalize data shape to 5D or 6D, depending on volume:
# (pages, planar_samples, [depth,] height, width, contig_samples)
data_shape = reshape_nd(data_shape, 3 if photometric == 'rgb' else 2)
shape = data_shape
ndim = len(data_shape)
samplesperpixel = 1
extrasamples = 0
if volume and ndim < 3:
volume = False
if colormap is not None:
photometric = 'palette'
planarconfig = None
if photometric is None:
photometric = 'minisblack'
if planarconfig == 'contig':
if ndim > 2 and shape[-1] in (3, 4):
photometric = 'rgb'
elif planarconfig == 'planar':
if volume and ndim > 3 and shape[-4] in (3, 4):
photometric = 'rgb'
elif ndim > 2 and shape[-3] in (3, 4):
photometric = 'rgb'
elif ndim > 2 and shape[-1] in (3, 4):
photometric = 'rgb'
elif self._imagej:
photometric = 'minisblack'
elif volume and ndim > 3 and shape[-4] in (3, 4):
photometric = 'rgb'
elif ndim > 2 and shape[-3] in (3, 4):
photometric = 'rgb'
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_shape = (-1, 1) + shape[(-4 if volume else -3):]
samplesperpixel = data_shape[-1]
else:
data_shape = (-1,) + shape[(-4 if volume else -3):] + (1,)
samplesperpixel = data_shape[1]
if samplesperpixel > 3:
extrasamples = samplesperpixel - 3
elif photometric == 'cfa':
if len(shape) != 2:
raise ValueError("invalid CFA image")
volume = False
planarconfig = None
data_shape = (-1, 1) + shape[-2:] + (1,)
if 50706 not in (et[0] for et in extratags):
raise ValueError("must specify DNG tags for CFA image")
elif planarconfig and len(shape) > (3 if volume else 2):
if planarconfig == 'contig':
data_shape = (-1, 1) + shape[(-4 if volume else -3):]
samplesperpixel = data_shape[-1]
else:
data_shape = (-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