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load_tiffile_package.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py
# Copyright (c) 2008-2016, Christoph Gohlke
# Copyright (c) 2008-2016, 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 `python tifffile.py --help`
:Author:
`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
:Organization:
Laboratory for Fluorescence Dynamics, University of California, Irvine
:Version: 2016.06.21
Requirements
------------
* `CPython 2.7 or 3.5 <http://www.python.org>`_ (64 bit recommended)
* `Numpy 1.10 <http://www.numpy.org>`_
* `Matplotlib 1.5 <http://www.matplotlib.org>`_ (optional for plotting)
* `Tifffile.c 2016.04.13 <http://www.lfd.uci.edu/~gohlke/>`_
(recommended for faster decoding of PackBits and LZW encoded strings)
Revisions
---------
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
Fixed 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 TiffTags 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.
Simplified 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.
Other Python packages and modules for reading bio-scientific TIFF files:
* `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/>`_
* `CellProfiler.bioformats
<https://github.com/CellProfiler/python-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.
* 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) 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
import time
try:
import lzma
except ImportError:
try:
import backports.lzma as lzma
except ImportError:
lzma = None
try:
if __package__:
from . import _tifffile
else:
import _tifffile
except ImportError:
warnings.warn(
"ImportError: No module named '_tifffile'. "
"Loading of some compressed images will be very slow. "
"Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/")
__version__ = '2016.06.21'
__docformat__ = 'restructuredtext en'
__all__ = (
'imsave', 'imread', 'imshow', 'TiffFile', 'TiffWriter', 'TiffSequence',
# utility functions used in oiffile and czifile
'FileHandle', 'lazyattr', 'natural_sorted', 'decode_lzw', 'stripnull')
def imsave(file, data, **kwargs):
"""Write image data to TIFF 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.
kwargs : dict
Parameters 'byteorder', 'bigtiff', 'software', and 'imagej', are passed
to the TiffWriter class.
Parameters 'photometric', 'planarconfig', 'resolution', 'compress',
'colormap', 'tile', 'description', 'datetime', 'metadata', 'contiguous'
and 'extratags' are passed to the TiffWriter.save function.
Examples
--------
>>> data = numpy.random.rand(2, 5, 3, 301, 219)
>>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
"""
tifargs = parse_kwargs(kwargs, 'bigtiff', 'byteorder', 'software',
'imagej')
if 'bigtiff' not in tifargs and 'imagej' not in tifargs and (
data.size*data.dtype.itemsize > 2000*2**20):
tifargs['bigtiff'] = True
with TiffWriter(file, **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' 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)
"""
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, '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,
'smin_sample_value': 340, 'smax_sample_value': 341,
'image_depth': 32997, 'tile_depth': 32998}
def __init__(self, file, bigtiff=False, byteorder=None,
software='tifffile.py', imagej=False):
"""Open a TIFF file for writing.
Use bigtiff=True when creating files larger than 2 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.
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 byteorder not in (None, '<', '>'):
raise ValueError("invalid byteorder %s" % byteorder)
if byteorder is None:
byteorder = '<' if sys.byteorder == 'little' else '>'
if imagej and bigtiff:
warnings.warn("writing incompatible bigtiff ImageJ")
self._byteorder = byteorder
self._software = software
self._imagej = bool(imagej)
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
self._fh = FileHandle(file, mode='wb', size=0)
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._value_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._value_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, tile=None,
contiguous=True, compress=0, colormap=None,
description=None, datetime=None, resolution=None,
metadata={}, extratags=()):
"""Write image data and tags to TIFF file.
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
Input image. The 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.
photometric : {'minisblack', 'miniswhite', 'rgb', 'palette'}
The color space of the image data.
By default this setting is inferred from the data shape and the
value of colormap.
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. 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 data are stored contiguously after
the previous one. Parameters 'photometric' and 'planarconfig' are
ignored.
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, 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.
"""
# TODO: refactor this function
fh = self._fh
byteorder = self._byteorder
numtag_format = self._numtag_format
value_format = self._value_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')
if data.size == 0:
raise ValueError("can not save empty array")
# just append contiguous data if possible
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._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
fh.write_array(data)
return
if photometric not in (None, 'minisblack', 'miniswhite',
'rgb', 'palette'):
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 = 32946
# 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.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 = data.shape
if photometric == 'rgb':
data = reshape_nd(data, 3)
else:
data = reshape_nd(data, 2)
shape = data.shape
samplesperpixel = 1
extrasamples = 0
if volume and data.ndim < 3:
volume = False
if colormap is not None:
photometric = 'palette'
planarconfig = None
if photometric is None:
photometric = 'minisblack'
if planarconfig == 'contig':
if data.ndim > 2 and shape[-1] in (3, 4):
photometric = 'rgb'
elif planarconfig == 'planar':
if 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'
elif data.ndim > 2 and shape[-1] in (3, 4):
photometric = 'rgb'
elif self._imagej:
photometric = 'minisblack'
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'
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
data = data.reshape(
(-1, 1) + shape[(-3 if volume else -2):] + (1,))
# normalize shape to 6D
assert len(data.shape) in (5, 6)
if len(data.shape) == 5:
data = data.reshape(data.shape[:2] + (1,) + data.shape[2:])
shape = data.shape
if tile and not volume:
tile = (1, tile[-2], tile[-1])
if photometric == 'palette':
if (samplesperpixel != 1 or extrasamples or
shape[1] != 1 or shape[-1] != 1):
raise ValueError("invalid data shape for palette mode")
if photometric == 'rgb' and samplesperpixel == 2:
raise ValueError("not a RGB image (samplesperpixel=2)")
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)
strip_or_tile = 'tile' if tile else 'strip'
tag_byte_counts = TiffWriter.TAGS[strip_or_tile + '_byte_counts']
tag_offsets = TiffWriter.TAGS[strip_or_tile + '_offsets']
self._tag_offsets = tag_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)
rawcount = value.find(b'\0\0')
if rawcount < 0:
rawcount = count
else:
rawcount += 1 # length of string without buffer
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 struct.calcsize(dtype) * count <= offset_size:
# value(s) can be written directly
if count == 1:
if isinstance(value, (tuple, list, numpy.ndarray)):
value = value[0]
ifdentry.append(pack(value_format, pack(dtype, value)))
else:
ifdentry.append(pack(value_format,
pack(str(count)+dtype, *value)))
else:
# use offset to value(s)
ifdentry.append(pack(offset_format, 0))
if isinstance(value, numpy.ndarray):
assert value.size == count
assert value.dtype.char == dtype
ifdvalue = value.tobytes()
elif isinstance(value, (tuple, list)):
ifdvalue = pack(str(count)+dtype, *value)
else:
ifdvalue = pack(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 description:
# user provided description
addtag('image_description', 's', 0, description, writeonce=True)
# write shape and metadata to image_description
self._metadata = {} if not metadata else metadata
if self._imagej:
description = imagej_description(
data_shape, shape[-1] in (3, 4), self._colormap is not None,
**self._metadata)
elif metadata or metadata == {}:
description = image_description(
data_shape, self._colormap is not None, **self._metadata)
else:
description = None
if description:
# add 32 bytes buffer
# the image description might be updated later with the final shape
description += b'\0'*32
self._description_len = len(description)
addtag('image_description', 's', 0, description, writeonce=True)
if self._software:
addtag('software', 's', 0, self._software, writeonce=True)
self._software = None # only save to first page in file
if datetime is None:
datetime = self._now()
addtag('datetime', 's', 0, datetime.strftime("%Y:%m:%d %H:%M:%S"),
writeonce=True)
addtag('compression', 'H', 1, compress_tag)
addtag('image_width', 'I', 1, shape[-2])
addtag('image_length', 'I', 1, shape[-3])
if tile:
addtag('tile_width', 'I', 1, tile[-1])
addtag('tile_length', 'I', 1, tile[-2])
if tile[0] > 1:
addtag('image_depth', 'I', 1, shape[-4])
addtag('tile_depth', 'I', 1, tile[0])
addtag('new_subfile_type', 'I', 1, 0)
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, 'palette': 3}[photometric])
if colormap is not None:
addtag('color_map', 'H', colormap.size, colormap)
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]))
if len(resolution) > 2:
resolution_unit = {None: 1, 'inch': 2, 'cm': 3}[resolution[2]]
elif self._imagej:
resolution_unit = 1
else:
resolution_unit = 2
addtag('resolution_unit', 'H', 1, resolution_unit)
if not tile:
addtag('rows_per_strip', 'I', 1, shape[-3]) # * shape[-4]
if tile:
# use one chunk per tile per plane
tiles = ((shape[2] + tile[0] - 1) // tile[0],
(shape[3] + tile[1] - 1) // tile[1],
(shape[4] + tile[2] - 1) // tile[2])
numtiles = product(tiles) * shape[1]
strip_byte_counts = [
product(tile) * shape[-1] * data.dtype.itemsize] * numtiles
addtag(tag_byte_counts, offset_format, numtiles, strip_byte_counts)
addtag(tag_offsets, offset_format, numtiles, [0] * numtiles)
# allocate tile buffer
chunk = numpy.empty(tile + (shape[-1],), dtype=data.dtype)
else:
# use one strip 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 user
for t in extratags:
addtag(*t)
# TODO: check TIFFReadDirectoryCheckOrder warning in files containing
# multiple tags of same code
# 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 or self._imagej) and (
fh.tell() + data.size*data.dtype.itemsize > 2**31-1):
raise ValueError("data too large for standard TIFF file")
# if not compressed or tiled, write the first ifd and then all data
# contiguously; else, write all ifds and data interleaved
for pageindex in range(shape[0] if (compress or tile) else 1):
# 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
elif tag[0] == 270 and tag[2].endswith(b'\0\0\0\0'):
# image description buffer
self._description_offset = pos
self._description_len_offset = (
tag_offset + tagindex * tag_size + 4)
fh.write(tag[2])
# write image data
data_offset = fh.tell()
if compress:
strip_byte_counts = []
if tile:
for plane in data[pageindex]:
for tz in range(tiles[0]):
for ty in range(tiles[1]):
for tx in range(tiles[2]):
c0 = min(tile[0], shape[2] - tz*tile[0])
c1 = min(tile[1], shape[3] - ty*tile[1])
c2 = min(tile[2], shape[4] - tx*tile[2])
chunk[c0:, c1:, c2:] = 0
chunk[:c0, :c1, :c2] = plane[
tz*tile[0]:tz*tile[0]+c0,
ty*tile[1]:ty*tile[1]+c1,
tx*tile[2]:tx*tile[2]+c2]
if compress:
t = compress(chunk)
strip_byte_counts.append(len(t))
fh.write(t)
else:
fh.write_array(chunk)
fh.flush()
elif compress:
for plane in data[pageindex]:
plane = compress(plane)
strip_byte_counts.append(len(plane))
fh.write(plane)
else:
fh.write_array(data)
# update strip/tile offsets and byte_counts if necessary
pos = fh.tell()
for tagindex, tag in enumerate(tags):
if tag[0] == tag_offsets: # strip/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/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 = [tag for tag in tags if not tag[-1]]
# if uncompressed, write remaining ifds/tags later
if not (compress or tile):
self._tags = tags
self._shape = shape
self._data_shape = (1,) + data_shape
self._data_dtype = data.dtype
self._data_offset = data_offset
self._data_byte_counts = strip_byte_counts