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scikit-image/skimage/external/tifffile/tifffile.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: 2016.02.22
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 2015.08.17 <http://www.lfd.uci.edu/~gohlke/>`_
(recommended for faster decoding of PackBits and LZW encoded strings)
Revisions
---------
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 <http://luispedro.org/software/imread>`_
* `PyLibTiff <http://code.google.com/p/pylibtiff>`_
* `SimpleITK <http://www.simpleitk.org>`_
* `PyLSM <https://launchpad.net/pylsm>`_
* `PyMca.TiffIO.py <http://pymca.sourceforge.net/>`_ (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
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(
"failed to import the optional _tifffile C extension module.\n"
"Loading of some compressed images will be very slow.\n"
"Tifffile.c can be obtained at http://www.lfd.uci.edu/~gohlke/")
__version__ = '2016.02.22'
__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(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', '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)
>>> metadata = {'axes': 'TZCYX'}
>>> imsave('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
"""
tifargs = {}
for key in ('byteorder', 'bigtiff', 'software', 'imagej'):
if key in kwargs:
tifargs[key] = kwargs[key]
del kwargs[key]
if 'bigtiff' not in tifargs and 'imagej' 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, '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, filename, bigtiff=False, byteorder=None,
software='tifffile.py', imagej=False):
"""Create a new TIFF file for writing.
Use bigtiff=True when creating files larger 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 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 = 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._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, resolution=None,
compress=0, colormap=None, tile=None, datetime=None,
description=None, metadata={}, contiguous=True, 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.
'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.
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.
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.
datetime : datetime
Date and time of image creation. Saved with the first page only.
If None (default), the current date and time is used.
description : str
The subject of the image. Saved with the first page only.
Cannot be used with the ImageJ format.
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 second image_description tag.
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.
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')
# 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
data.tofile(fh)
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 = shape = data.shape
data = numpy.atleast_2d(data)
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:
if planarconfig:
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'
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 (
photometric != 'palette' 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,))
# 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 samplesperpixel == 2:
warnings.warn("writing non-standard TIFF (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]))
addtag('resolution_unit', 'H', 1, 2)
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:
chunk.tofile(fh)
fh.flush()
elif compress:
for plane in data[pageindex]:
plane = compress(plane)
strip_byte_counts.append(len(plane))
fh.write(plane)
else:
data.tofile(fh) # if this fails try update Python and numpy
# 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
def _write_remaining_pages(self):
"""Write outstanding IFDs and tags to file."""
if not self._tags:
return
fh = self._fh
byteorder = self._byteorder
numtag_format = self._numtag_format
offset_format = self._offset_format
offset_size = self._offset_size
tag_size = self._tag_size
data_offset = self._data_offset
page_data_size = sum(self._data_byte_counts)
tag_bytes = b''.join(t[1] for t in self._tags)
numpages = self._shape[0] * self._data_shape[0] - 1
pos = fh.tell()
if not self._bigtiff and pos + len(tag_bytes) * numpages > 2**32 - 256:
if self._imagej:
warnings.warn("truncating ImageJ file")
return
raise ValueError("data too large for non-bigtiff file")
def pack(fmt, *val):
return struct.pack(byteorder+fmt, *val)
for _ in range(numpages):
# update pointer at ifd_offset
pos = fh.tell()
fh.seek(self._ifd_offset)
fh.write(pack(offset_format, pos))
fh.seek(pos)
# write ifd entries
fh.write(pack(numtag_format, len(self._tags)))
tag_offset = fh.tell()
fh.write(tag_bytes)
self._ifd_offset = fh.tell()
fh.write(pack(offset_format, 0)) # offset to next IFD
# offset to image data
data_offset += page_data_size
# write tag values and patch offsets in ifdentries, if necessary
for tagindex, tag in enumerate(self._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] == self._tag_offsets:
strip_offsets_offset = pos
fh.write(tag[2])
# update strip/tile offsets if necessary
pos = fh.tell()
for tagindex, tag in enumerate(self._tags):
if tag[0] == self._tag_offsets: # strip/tile offsets
if tag[2]:
fh.seek(strip_offsets_offset)
strip_offset = data_offset
for size in self._data_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))
break
fh.seek(pos)
self._tags = None
self._data_dtype = None
self._data_offset = None
self._data_byte_counts = None
# do not reset _shape or _data_shape
def _write_image_description(self):
"""Write meta data to image_description tag."""
if (not self._data_shape or self._data_shape[0] == 1 or
self._description_offset <= 0):
return
colormapped = self._colormap is not None
if self._imagej:
isrgb = self._shape[-1] in (3, 4)
description = imagej_description(
self._data_shape, isrgb, colormapped, **self._metadata)
else:
description = image_description(
self._data_shape, colormapped, **self._metadata)
# rewrite description and its length to file
description = description[:self._description_len-1]
pos = self._fh.tell()
self._fh.seek(self._description_offset)
self._fh.write(description)
self._fh.seek(self._description_len_offset)
self._fh.write(struct.pack(self._byteorder+self._offset_format,
len(description)+1))
self._fh.seek(pos)
self._description_offset = 0
self._description_len_offset = 0
self._description_len = 0
def _now(self):
"""Return current date and time."""
return datetime.datetime.now()
def close(self, truncate=False):
"""Write remaining pages (if not truncate) and close file handle."""
if not truncate:
self._write_remaining_pages()
self._write_image_description()
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
--------
>>> 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 = {}
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 of TiffPage
All TIFF pages in file.
series : list of TiffPageSeries
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('temp.tif') as tif:
... data = tif.asarray()
... data.shape
(5, 301, 219)
"""
def __init__(self, arg, name=None, offset=None, size=None,
multifile=True, multifile_close=True, maxpages=None,
fastij=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.
maxpages : int
Number of pages to read (default: no limit).
fastij : bool
If True (default), try to use only the metadata from the first page
of ImageJ files. Significantly speeds up loading movies with
thousands of pages.
"""
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(maxpages, fastij)
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, maxpages=None, fastij=True):
"""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")
self._is_native = self.byteorder == {'big': '>',
'little': '<'}[sys.byteorder]
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 maxpages and len(self.pages) > maxpages:
break
if fastij and page.is_imagej:
if page._patch_imagej():
break # only read the first page of ImageJ files
fastij = False
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."""
# each series and position require separate unwrappig (undocumented)
for series in self.series:
positions = 1
for i in 0, 1:
if series.axes[i] in 'PM':
positions *= series.shape[i]
positions = len(series.pages) // positions
for i, page in enumerate(series.pages):
if not i % positions:
wrap = 0
previous_offset = 0
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)
def asarray(self, key=None, series=None, memmap=False, tempdir=None):
"""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 or TiffPageSeries
Defines which series of pages to return as array.
memmap : bool
If True, return an read-only array stored in a binary file on disk
if possible. The TIFF file is used if possible, else a temporary
file is created.
tempdir : str
The directory where the memory-mapped file will be created.
"""
if key is None and series is None:
series = 0
if series is not None:
try:
series = self.series[series]
except (KeyError, TypeError):
pass
pages = 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_indexed:
result = stack_pages(pages, colormapped=False, squeeze=False)
result = apply_colormap(result, pages[0].color_map)
else:
result = stack_pages(pages, memmap=memmap, tempdir=tempdir,
colormapped=False, squeeze=False)
elif len(pages) == 1:
result = pages[0].asarray(memmap=memmap)
elif self.is_ome:
assert not self.is_indexed, "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))
if memmap:
with tempfile.NamedTemporaryFile() as fh:
result = numpy.memmap(fh, series.dtype, shape=series.shape)
result = result.reshape(-1)
else:
result = numpy.empty(series.shape, series.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()
elif key is None and series and series.offset:
result = self.filehandle.memmap_array(series.dtype, series.shape,
series.offset)
else:
result = stack_pages(pages, memmap=memmap, tempdir=tempdir)
if key is None:
try:
result.shape = series.shape
except ValueError:
try:
warnings.warn("failed to reshape %s to %s" % (
result.shape, series.shape))
# try series of expected shapes
result.shape = (-1,) + series.shape
except ValueError:
# revert to generic shape
result.shape = (-1,) + pages[0].shape
elif len(pages) == 1:
result.shape = pages[0].shape
else:
result.shape = (-1,) + pages[0].shape
return result
@lazyattr
def series(self):
"""Return pages with compatible properties as TiffPageSeries."""
if not self.pages:
return []
series = []
if self.is_ome:
series = self._ome_series()
elif self.is_fluoview:
series = self._fluoview_series()
elif self.is_lsm:
series = self._lsm_series()
elif self.is_imagej:
series = self._imagej_series()
elif self.is_nih:
series = self._nih_series()
if not series:
# generic detection of series
shapes = []
pages = {}
index = 0
for page in self.pages:
if not page.shape:
continue
if page.is_shaped:
index += 1 # shape starts a new series
shape = page.shape + (index, page.axes,
page.compression in TIFF_DECOMPESSORS)
if shape in pages:
pages[shape].append(page)
else:
shapes.append(shape)
pages[shape] = [page]
series = []
for s in shapes:
shape = ((len(pages[s]),) + s[:-3] if len(pages[s]) > 1
else s[:-3])
axes = (('I' + s[-2]) if len(pages[s]) > 1 else s[-2])
page0 = pages[s][0]
if page0.is_shaped:
metadata = image_description_dict(page0.is_shaped)
reshape = metadata['shape']
if 'axes' in metadata:
reaxes = metadata['axes']
if len(reaxes) == len(reshape):
axes = reaxes
shape = reshape
else:
warnings.warn("axes do not match shape")
try:
axes = reshape_axes(axes, shape, reshape)
shape = reshape
except ValueError as e:
warnings.warn(e.message)
series.append(
TiffPageSeries(pages[s], shape, page0.dtype, axes))
# remove empty series, e.g. in MD Gel files
series = [s for s in series if sum(s.shape) > 0]
return series
def _fluoview_series(self):
"""Return image series in FluoView file."""
page0 = self.pages[0]
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))
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)
return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
def _lsm_series(self):
"""Return image series in LSM file."""
page0 = self.pages[0]
lsmi = page0.cz_lsm_info
axes = CZ_SCAN_TYPES[lsmi.scan_type]
if page0.is_rgb:
axes = axes.replace('C', '').replace('XY', 'XYC')
if hasattr(lsmi, 'dimension_p') and lsmi.dimension_p > 1:
axes += 'P'
if hasattr(lsmi, 'dimension_m') and lsmi.dimension_m > 1:
axes += 'M'
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]
dtype = pages[0].dtype
series = [TiffPageSeries(pages, shape, dtype, axes)]
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'
dtype = pages[0].dtype
series.append(TiffPageSeries(pages, shape, dtype, axes))
return series
def _imagej_series(self):
"""Return image series in ImageJ file."""
# ImageJ's dimension order is always TZCYXS
# TODO: fix loading of color, composite or palette images
shape = []
axes = []
page0 = self.pages[0]
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 and not
ij.get('hyperstack', False)):
shape.append(ij['channels'])
axes.append('C')
remain = ij.get('images', len(self.pages)) // (product(shape)
if shape else 1)
if remain > 1:
shape.append(remain)
axes.append('I')
if page0.axes[0] == 'I':
# contiguous multiple images
shape.extend(page0.shape[1:])
axes.extend(page0.axes[1:])
elif page0.axes[:2] == 'SI':
# color-mapped contiguous multiple images
shape = page0.shape[0:1] + tuple(shape) + page0.shape[2:]
axes = list(page0.axes[0]) + axes + list(page0.axes[2:])
else:
shape.extend(page0.shape)
axes.extend(page0.axes)
return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
def _nih_series(self):
"""Return image series in NIH file."""
page0 = self.pages[0]
if len(self.pages) == 1:
shape = page0.shape
axes = page0.axes
else:
shape = (len(self.pages),) + page0.shape
axes = 'I' + page0.axes
return [TiffPageSeries(self.pages, shape, page0.dtype, axes)]
def _ome_series(self):
"""Return image series in OME-TIFF file(s)."""
omexml = self.pages[0].tags['image_description'].value
try:
root = etree.fromstring(omexml)
except etree.ParseError as e:
# TODO: test this
warnings.warn("ome-xml: %s" % e)
omexml = omexml.decode('utf-8', 'ignore').encode('utf-8')
root = etree.fromstring(omexml)
uuid = root.attrib.get('UUID', None)
self._files = {uuid: self}
dirname = self._fh.dirname
modulo = {}
series = []
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 // self.pages[0].samples_per_pixel)
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
series.append(TiffPageSeries(ifds, shape, dtype, axes, self))
for serie in series:
shape = list(serie.shape)
for axis, (newaxis, labels) in modulo.items():
i = serie.axes.index(axis)
size = len(labels)
if shape[i] == size:
serie.axes = serie.axes.replace(axis, newaxis, 1)
else:
shape[i] //= size
shape.insert(i+1, size)
serie.axes = serie.axes.replace(axis, axis+newaxis, 1)
serie.shape = tuple(shape)
# squeeze dimensions
for serie in series:
serie.shape, serie.axes = squeeze_axes(serie.shape, serie.axes)
return series
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):
"""File has BigTIFF format."""
return self.offset_size != 4
@lazyattr
def is_rgb(self):
"""File contains only RGB images."""
return all(p.is_rgb for p in self.pages)
@lazyattr
def is_indexed(self):
"""File contains only indexed images."""
return all(p.is_indexed for p in self.pages)
@lazyattr
def is_mdgel(self):
"""File has MD Gel format."""
return any(p.is_mdgel for p in self.pages)
@lazyattr
def is_mediacy(self):
"""File was created by Media Cybernetics software."""
return any(p.is_mediacy for p in self.pages)
@lazyattr
def is_stk(self):
"""File has MetaMorph STK format."""
return all(p.is_stk for p in self.pages)
@lazyattr
def is_lsm(self):
"""File was created by Carl Zeiss software."""
return len(self.pages) and self.pages[0].is_lsm
@lazyattr
def is_vista(self):
"""File was created by ISS Vista."""
return len(self.pages) and self.pages[0].is_vista
@lazyattr
def is_imagej(self):
"""File has ImageJ format."""
return len(self.pages) and self.pages[0].is_imagej
@lazyattr
def is_micromanager(self):
"""File was created by MicroManager."""
return len(self.pages) and self.pages[0].is_micromanager
@lazyattr
def is_nih(self):
"""File has NIH Image format."""
return len(self.pages) and self.pages[0].is_nih
@lazyattr
def is_fluoview(self):
"""File was created by Olympus FluoView."""
return len(self.pages) and self.pages[0].is_fluoview
@lazyattr
def is_ome(self):
"""File has OME-TIFF format."""
return len(self.pages) and self.pages[0].is_ome
@lazyattr
def is_scn(self):
"""File has Leica SCN format."""
return len(self.pages) and self.pages[0].is_scn
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, color-mapped if applicable.
shape : tuple
Dimensions of the image array in TIFF page,
color-mapped and with extra samples 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.ndarray
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/images (stk, ij).
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._offset = 0
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
or a corrupted page list is encountered.
"""
fh = self.parent.filehandle
byteorder = self.parent.byteorder
offset_size = self.parent.offset_size
# read offset to this IFD
fmt = {4: 'I', 8: 'Q'}[offset_size]
offset = struct.unpack(byteorder + fmt, fh.read(offset_size))[0]
if not offset:
raise StopIteration()
if offset >= fh.size:
warnings.warn("invalid page offset > file size")
raise StopIteration()
self._offset = offset
# 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]
if numtags > 4096:
raise ValueError("suspicious number of tags")
except Exception:
warnings.warn("corrupted page list at offset %i" % offset)
raise StopIteration()
tagcode = 0
for _ in range(numtags):
try:
tag = TiffTag(self.parent)
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() # where offset to next IFD can be found
if self.is_lsm or (self.index and self.parent.is_lsm):
# correct non standard LSM bitspersample tags
self.tags['bits_per_sample']._fix_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 name in tags:
#tags[name] = TiffTag(code, dtype=dtype, count=count,
# value=default, name=name)
if 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))
elif default is not None:
setattr(self, name, validate[default] if validate else default)
if 'bits_per_sample' in tags:
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]
if 'sample_format' in tags:
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_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_vista or self.parent.is_vista:
# ISS Vista writes wrong image_depth tag
self.image_depth = 1
if self.is_indexed:
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)
# TODO: support other photometric modes than RGB
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_indexed:
assert False, "color mapping disabled for stk"
if self.color_map.shape[1] >= 2**self.bits_per_sample:
if image_depth == 1:
self.shape = (planes, image_length, image_width,
self.color_map.shape[0])
else:
self.shape = (planes, image_depth, image_length,
image_width, self.color_map.shape[0])
self.axes = self.axes + 'S'
else:
warnings.warn("palette cannot be applied")
self.is_indexed = False
elif self.is_indexed:
samples = 1
if 'extra_samples' in self.tags:
samples += self.tags['extra_samples'].count
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 = (image_length, image_width,
self.color_map.shape[0])
self.axes = 'YXS'
else:
self.shape = (image_depth, image_length, image_width,
self.color_map.shape[0])
self.axes = 'ZYXS'
else:
warnings.warn("palette cannot be applied")
self.is_indexed = 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 _patch_imagej(self):
"""Return if ImageJ data are contiguous and adjust page attributes.
Patch 'strip_offsets' and 'strip_byte_counts' tags to span the
complete contiguous data.
ImageJ stores all image metadata in the first page and image data is
stored contiguously before the second page, if any. No need to
read other pages.
"""
if not self.is_imagej or not self.is_contiguous or self.parent.is_ome:
return
images = self.imagej_tags.get('images', 0)
if images <= 1:
return
offset, count = self.is_contiguous
shape = self.shape
if self.is_indexed:
shape = shape[:-1]
if (count != product(shape) * self.bits_per_sample // 8 or
offset + count*images > self.parent.filehandle.size):
self.is_imagej = False
warnings.warn("corrupted ImageJ metadata or file")
return
pre = 'tile' if self.is_tiled else 'strip'
self.tags[pre+'_offsets'].value = (offset,)
self.tags[pre+'_byte_counts'].value = (count * images,)
self.shape = (images,) + self.shape
self._shape = (images,) + self._shape[1:]
self.axes = 'I' + self.axes
return True
def asarray(self, squeeze=True, colormapped=True, rgbonly=False,
scale_mdgel=False, memmap=False, reopen=True,
maxsize=64*1024*1024*1024):
"""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.
maxsize: int or None
Maximum size of data before a ValueError is raised.
Can be used to catch DOS. Default: 64 GB.
"""
if not self._shape:
return
if maxsize and product(self._shape) > maxsize:
raise ValueError("data is too large %s" % str(self._shape))
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)
if 'sample_format' in self.tags:
tag = self.tags['sample_format']
if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
raise ValueError("sample formats do not match %s" % tag.value)
if self.is_chroma_subsampled:
# TODO: implement chroma subsampling
raise NotImplementedError("chroma subsampling not supported")
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
byte_counts, offsets = self._byte_counts_offsets
if self.is_tiled:
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:
runlen = image_width
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, typecode=typecode):
if self.predictor == 'float':
# the floating point horizontal differencing decoder
# needs the raw byte order
typecode = dtype
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 unpack_rgb(x, typecode, bits_per_sample)
else:
def unpack(x):
return unpack_ints(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''
def decompress(x):
return decode_jpeg(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)))
try:
tile.shape = tile_shape
except ValueError:
# incomplete tiles; see gdal issue #1179
warnings.warn("invalid tile data")
t = numpy.zeros(tile_shape, dtype).reshape(-1)
s = min(tile.size, t.size)
t[:s] = tile[:s]
tile = t.reshape(tile_shape)
if self.predictor == 'horizontal':
numpy.cumsum(tile, axis=-2, dtype=dtype, out=tile)
elif self.predictor == 'float':
raise NotImplementedError()
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 and not (self.is_tiled and not self.is_contiguous):
if self.parent.is_lsm and not self.compression:
pass # work around bug in LSM510 software
elif self.predictor == 'horizontal':
numpy.cumsum(result, axis=-2, dtype=dtype, out=result)
elif self.predictor == 'float':
result = decode_floats(result)
if self.fill_order == 'lsb2msb':
reverse_bitorder(result)
if colormapped and self.is_indexed:
if self.color_map.shape[1] >= 2**bits_per_sample:
# FluoView and LSM might fail here
result = apply_colormap(result[:, 0:1, :, :, :, 0:1],
self.color_map)
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 should remain open if an exception occurred above
fh.close()
return result
@lazyattr
def _byte_counts_offsets(self):
"""Return simplified byte_counts and offsets."""
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
j = 0
for i, (b, o) in enumerate(zip(byte_counts, offsets)):
if b > 0 and o > 0:
if i > j:
byte_counts[j] = b
offsets[j] = o
j += 1
elif b > 0 and o <= 0:
raise ValueError("invalid offset")
else:
warnings.warn("empty byte count")
if j == 0:
j = 1
return byte_counts[:j], offsets[:j]
def _is_memmappable(self, rgbonly, colormapped):
"""Return if page's image data in file can be memory-mapped."""
return (self.parent.filehandle.is_file and
self.is_contiguous and
(self.bits_per_sample == 8 or self.parent._is_native) and
self.fill_order == 'msb2lsb' and
not self.predictor and
not self.is_chroma_subsampled and
not (rgbonly and 'extra_samples' in self.tags) and
not (colormapped and self.is_indexed))
@lazyattr
def is_contiguous(self):
"""Return offset and size of contiguous data, else None.
Excludes prediction, fill_order, 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_scn', '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")
result = imagej_description_dict(self.is_imagej)
if 'imagej_metadata' in self.tags:
try:
result.update(imagej_metadata(
self.tags['imagej_metadata'].value,
self.tags['imagej_byte_counts'].value,
self.parent.byteorder))
except Exception as e:
warnings.warn(str(e))
return Record(result)
@lazyattr
def is_rgb(self):
"""Page contains a RGB image."""
return ('photometric' in self.tags and
self.tags['photometric'].value == 2)
@lazyattr
def is_contig(self):
"""Page contains contiguous image."""
if 'planar_configuration' in self.tags:
return self.tags['planar_configuration'].value == 1
return True
@lazyattr
def is_indexed(self):
"""Page contains indexed, palette-colored image.
Disable color-mapping for OME, LSM, STK, and ImageJ hyperstacks.
"""
if (self.is_stk or self.is_lsm or self.parent.is_lsm or
self.is_ome or self.parent.is_ome):
return False
if self.is_imagej:
if b'mode' in self.is_imagej:
return False
elif self.parent.is_imagej:
return self.parent.is_indexed
return ('photometric' in self.tags and
self.tags['photometric'].value == 3)
@lazyattr
def is_tiled(self):
"""Page contains tiled image."""
return 'tile_width' in self.tags
@lazyattr
def is_reduced(self):
"""Page is reduced image of another image."""
return ('new_subfile_type' in self.tags and
self.tags['new_subfile_type'].value & 1)
@lazyattr
def is_chroma_subsampled(self):
"""Page contains chroma subsampled image."""
return ('ycbcr_subsampling' in self.tags and
self.tags['ycbcr_subsampling'].value != (1, 1))
@lazyattr
def is_mdgel(self):
"""Page contains md_file_tag tag."""
return 'md_file_tag' in self.tags
@lazyattr
def is_mediacy(self):
"""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):
"""Page contains UIC2Tag tag."""
return 'uic2tag' in self.tags
@lazyattr
def is_lsm(self):
"""Page contains LSM CZ_LSM_INFO tag."""
return 'cz_lsm_info' in self.tags
@lazyattr
def is_fluoview(self):
"""Page contains FluoView MM_STAMP tag."""
return 'mm_stamp' in self.tags
@lazyattr
def is_nih(self):
"""Page contains NIH image header."""
return 'nih_image_header' in self.tags
@lazyattr
def is_sgi(self):
"""Page contains SGI image and tile depth tags."""
return 'image_depth' in self.tags and 'tile_depth' in self.tags
@lazyattr
def is_vista(self):
"""Software tag is 'ISS Vista'."""
return ('software' in self.tags and
self.tags['software'].value == b'ISS Vista')
@lazyattr
def is_ome(self):
"""Page contains OME-XML in image_description tag."""
if 'image_description' not in self.tags:
return False
d = self.tags['image_description'].value.strip()
return d.startswith(b'<?xml version=') and d.endswith(b'</OME>')
@lazyattr
def is_scn(self):
"""Page contains Leica SCN XML in image_description tag."""
if 'image_description' not in self.tags:
return False
d = self.tags['image_description'].value.strip()
return d.startswith(b'<?xml version=') and d.endswith(b'</scn>')
@lazyattr
def is_shaped(self):
"""Return description containing shape if exists, else None."""
if 'image_description' in self.tags:
description = self.tags['image_description'].value
if b'"shape":' in description or b'shape=(' in description:
return description
if 'image_description_1' in self.tags:
description = self.tags['image_description_1'].value
if b'"shape":' in description or b'shape=(' in description:
return description
@lazyattr
def is_imagej(self):
"""Return ImageJ description if exists, else None."""
if 'image_description' in self.tags:
description = self.tags['image_description'].value
if description.startswith(b'ImageJ='):
return description
if 'image_description_1' in self.tags:
# Micromanager
description = self.tags['image_description_1'].value
if description.startswith(b'ImageJ='):
return description
@lazyattr
def is_micromanager(self):
"""Page contains Micro-Manager metadata."""
return 'micromanager_metadata' in self.tags
class TiffTag(object):
"""A TIFF tag structure.
Attributes
----------
name : string
Attribute name of tag.
code : int
Decimal code of tag.
dtype : str
Datatype of tag data. One of TIFF_DATA_TYPES.
count : int
Number of values.
value : various types
Tag data as Python object.
value_offset : int
Location of value in file, if any.
All attributes are read-only.
"""
__slots__ = ('code', 'name', 'count', 'dtype', 'value', 'value_offset',
'_offset', '_value', '_type')
class Error(Exception):
pass
def __init__(self, arg, **kwargs):
"""Initialize instance from file or arguments."""
self._offset = None
if hasattr(arg, '_fh'):
self._fromfile(arg, **kwargs)
else:
self._fromdata(arg, **kwargs)
def _fromdata(self, code, dtype, count, value, name=None):
"""Initialize instance from arguments."""
self.code = int(code)
self.name = name if name else str(code)
self.dtype = TIFF_DATA_TYPES[dtype]
self.count = int(count)
self.value = value
self._value = value
self._type = dtype
def _fromfile(self, parent):
"""Read tag structure from open file. Advance file cursor."""
fh = parent.filehandle
byteorder = parent.byteorder
self._offset = fh.tell()
self.value_offset = self._offset + parent.offset_size + 4
fmt, size = {4: ('HHI4s', 12), 8: ('HHQ8s', 20)}[parent.offset_size]
data = fh.read(size)
code, dtype = struct.unpack(byteorder + fmt[:2], data[:4])
count, value = struct.unpack(byteorder + fmt[2:], data[4:])
self._value = value
self._type = dtype
if code in TIFF_TAGS:
name, _, _, cout_, _ = TIFF_TAGS[code]
if cout_ and cout_ != count:
count = cout_
warnings.warn("incorrect count for tag '%s'" % name)
elif code in CUSTOM_TAGS:
name = CUSTOM_TAGS[code][0]
else:
name = str(code)
try:
dtype = TIFF_DATA_TYPES[self._type]
except KeyError:
raise TiffTag.Error("unknown tag data type %i" % self._type)
fmt = '%s%i%s' % (byteorder, count*int(dtype[0]), dtype[1])
size = struct.calcsize(fmt)
if size > 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, 530, 531):
# scalar value if not strip/tile offsets/byte_counts or subsampling
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 _fix_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 case; need example file
warnings.warn("correcting LSM bitspersample tag")
fh = parent.filehandle
tof = {4: '<I', 8: '<Q'}[parent.offset_size]
self.value_offset = struct.unpack(tof, self._value)[0]
fh.seek(self.value_offset)
self.value = struct.unpack("<HH", fh.read(4))
def as_str(self):
"""Return value as human readable string."""
return ((str(self.value).split('\n', 1)[0]) if (self._type != 7)
else '<undefined>')
def __str__(self):
"""Return string containing information about tag."""
return ' '.join(str(getattr(self, s)) for s in self.__slots__)
class TiffPageSeries(object):
"""Series of TIFF pages with compatible shape and data type.
Attributes
----------
pages : list of TiffPage
Sequence of TiffPages in series.
dtype : numpy.dtype or str
Data type of the image array in series.
shape : tuple
Dimensions of the image array in series.
axes : str
Labels of axes in shape. See TiffPage.axes.
offset : int or None
Position of image data in file if memory-mappable, else None.
"""
#__slots__ = 'pages', 'shape', 'dtype', 'axes', 'parent'
def __init__(self, pages, shape, dtype, axes, parent=None):
# TODO? sort pages by page number?
self.pages = pages
self.shape = tuple(shape)
self.axes = ''.join(axes)
self.dtype = numpy.dtype(dtype)
if parent:
self.parent = parent
elif len(pages):
self.parent = pages[0].parent
else:
self.parent = None
def asarray(self, memmap=False):
"""Return image data from series of TIFF pages as numpy array.
Parameters
----------
memmap : bool
If True, return an array stored in a binary file on disk
if possible.
"""
if self.parent:
return self.parent.asarray(series=self, memmap=memmap)
@lazyattr
def offset(self):
"""Return offset to memory-mappable data in page series."""
if len(self.pages) == 0:
return
rgbonly = False
colormapped = self.pages[0].is_indexed
pos = 0
for page in self.pages:
if page is None:
return
if not page._is_memmappable(rgbonly, colormapped):
return
if not pos:
pos = page.is_contiguous[0] + page.is_contiguous[1]
continue
if pos != page.is_contiguous[0]:
return
pos += page.is_contiguous[1]
offset = self.pages[0].is_contiguous[0]
if (pos != offset + product(self.shape) * self.dtype.itemsize and
not self.pages[0].is_imagej):
return
return offset
def __len__(self):
"""Return number of TiffPages in series."""
return len(self.pages)
def __getitem__(self, key):
"""Return specified TiffPage."""
return self.pages[key]
def __iter__(self):
"""Return iterator over TiffPages in series."""
return iter(self.pages)
def __str__(self):
"""Return string with information about series."""
return "\n".join("* %s: %s" % kv for kv in (
("pages", len(self.pages)),
("dtype", str(self.dtype)),
("shape", str(self.shape)),
("axes", self.axes),
("offset", self.offset)
))
class TiffSequence(object):
"""Sequence of image files.
The data shape and dtype of all files must match.
Attributes
----------
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, tempdir=None, *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 do not match.
"""
im = self.imread(self.files[0], *args, **kwargs)
shape = self.shape + im.shape
if memmap:
with tempfile.NamedTemporaryFile(dir=tempdir) 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 does not match file names")
matches = matches[-1]
if len(matches) % 2:
raise self.ParseError("pattern does not match axis name and index")
axes = ''.join(m for m in matches[::2] if m)
if not axes:
raise self.ParseError("pattern does not 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 do not 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
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.ndarray."""
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('<u4', 2*count).reshape(count, 2)
result = {'z_distance': values[:, 0] / values[:, 1]}
elif plane_count:
for _ in range(count):
tagid = struct.unpack('<I', fh.read(4))[0]
if tagid in (28, 29, 37, 40, 41):
# silently skip unexpected tags
fh.read(4)
continue
name, value = read_uic_tag(fh, tagid, plane_count, offset=True)
result[name] = value
return result
def read_uic2tag(fh, byteorder, dtype, plane_count):
"""Read MetaMorph STK UIC2Tag from file and return as dictionary."""
assert dtype == '2I' and byteorder == '<'
values = fh.read_array('<u4', 6*plane_count).reshape(plane_count, 6)
return {
'z_distance': values[:, 0] / values[:, 1],
'date_created': values[:, 2], # julian days
'time_created': values[:, 3], # milliseconds
'date_modified': values[:, 4], # julian days
'time_modified': values[:, 5], # milliseconds
}
def read_uic3tag(fh, byteorder, dtype, plane_count):
"""Read MetaMorph STK UIC3Tag from file and return as dictionary."""
assert dtype == '2I' and byteorder == '<'
values = fh.read_array('<u4', 2*plane_count).reshape(plane_count, 2)
return {'wavelengths': values[:, 0] / values[:, 1]}
def read_uic4tag(fh, byteorder, dtype, plane_count):
"""Read MetaMorph STK UIC4Tag from file and return as dictionary."""
assert dtype == '1I' and byteorder == '<'
result = {}
while True:
tagid = struct.unpack('<H', fh.read(2))[0]
if tagid == 0:
break
name, value = read_uic_tag(fh, tagid, plane_count, offset=False)
result[name] = value
return result
def read_uic_tag(fh, tagid, plane_count, offset):
"""Read a single UIC tag value from file and return tag name and value.
UIC1Tags use an offset.
"""
def read_int(count=1):
value = struct.unpack('<%iI' % count, fh.read(4*count))
return value[0] if count == 1 else value
try:
name, dtype = UIC_TAGS[tagid]
except KeyError:
# unknown tag
return '_tagid_%i' % tagid, read_int()
if offset:
pos = fh.tell()
if dtype not in (int, None):
off = read_int()
if off < 8:
warnings.warn("invalid offset for uic tag '%s': %i"
% (name, off))
return name, off
fh.seek(off)
if dtype is None:
# skip
name = '_' + name
value = read_int()
elif dtype is int:
# int
value = read_int()
elif dtype is Fraction:
# fraction
value = read_int(2)
value = value[0] / value[1]
elif dtype is julian_datetime:
# datetime
value = julian_datetime(*read_int(2))
elif dtype is read_uic_image_property:
# ImagePropertyEx
value = read_uic_image_property(fh)
elif dtype is str:
# pascal string
size = read_int()
if 0 <= size < 2**10:
value = struct.unpack('%is' % size, fh.read(size))[0][:-1]
value = stripnull(value)
elif offset:
value = ''
warnings.warn("corrupt string in uic tag '%s'" % name)
else:
raise ValueError("invalid string size %i" % size)
elif dtype == '%ip':
# sequence of pascal strings
value = []
for _ in range(plane_count):
size = read_int()
if 0 <= size < 2**10:
string = struct.unpack('%is' % size, fh.read(size))[0][:-1]
string = stripnull(string)
value.append(string)
elif offset:
warnings.warn("corrupt string in uic tag '%s'" % name)
else:
raise ValueError("invalid string size %i" % size)
else:
# struct or numpy type
dtype = '<' + dtype
if '%i' in dtype:
dtype = dtype % plane_count
if '(' in dtype:
# numpy type
value = fh.read_array(dtype, 1)[0]
if value.shape[-1] == 2:
# assume fractions
value = value[..., 0] / value[..., 1]
else:
# struct format
value = struct.unpack(dtype, fh.read(struct.calcsize(dtype)))
if len(value) == 1:
value = value[0]
if offset:
fh.seek(pos + 4)
return name, value
def read_uic_image_property(fh):
"""Read UIC ImagePropertyEx tag from file and return as dict."""
# TODO: test this
size = struct.unpack('B', fh.read(1))[0]
name = struct.unpack('%is' % size, fh.read(size))[0][:-1]
flags, prop = struct.unpack('<IB', fh.read(5))
if prop == 1:
value = struct.unpack('II', fh.read(8))
value = value[0] / value[1]
else:
size = struct.unpack('B', fh.read(1))[0]
value = struct.unpack('%is' % size, fh.read(size))[0]
return dict(name=name, flags=flags, value=value)
def read_cz_lsm_info(fh, byteorder, dtype, count):
"""Read CS_LSM_INFO tag from file and return as numpy.rec.array."""
assert byteorder == '<'
magic_number, structure_size = struct.unpack('<II', fh.read(8))
if magic_number not in (50350412, 67127628):
raise ValueError("not a valid CS_LSM_INFO structure")
fh.seek(-8, 1)
if structure_size < numpy.dtype(CZ_LSM_INFO).itemsize:
# adjust structure according to structure_size
cz_lsm_info = []
size = 0
for name, dtype in CZ_LSM_INFO:
size += numpy.dtype(dtype).itemsize
if size > 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('<i', fh.read(4))[0]
return fh.read_array('<2f8', count=size)
def read_cz_lsm_positions(fh):
"""Read LSM positions from file and return as list."""
size = struct.unpack('<I', fh.read(4))[0]
return fh.read_array('<2f8', count=size)
def read_cz_lsm_time_stamps(fh):
"""Read LSM time stamps from file and return as list."""
size, count = struct.unpack('<ii', fh.read(8))
if size != (8 + 8 * count):
raise ValueError("lsm_time_stamps block is too short")
# return struct.unpack('<%dd' % count, fh.read(8*count))
return fh.read_array('<f8', count=count)
def read_cz_lsm_event_list(fh):
"""Read LSM events from file and return as list of (time, type, text)."""
count = struct.unpack('<II', fh.read(8))[1]
events = []
while count > 0:
esize, etime, etype = struct.unpack('<IdI', fh.read(16))
etext = stripnull(fh.read(esize - 16))
events.append((etime, etype, etext))
count -= 1
return events
def read_cz_lsm_scan_info(fh):
"""Read LSM scan information from file and return as Record."""
block = Record()
blocks = [block]
unpack = struct.unpack
if 0x10000000 != struct.unpack('<I', fh.read(4))[0]:
# not a Recording sub block
raise ValueError("not a lsm_scan_info structure")
fh.read(8)
while True:
entry, dtype, size = unpack('<III', fh.read(12))
if dtype == 2:
# ascii
value = stripnull(fh.read(size))
elif dtype == 4:
# long
value = unpack('<i', fh.read(4))[0]
elif dtype == 5:
# rational
value = unpack('<d', fh.read(8))[0]
else:
value = 0
if entry in CZ_LSM_SCAN_INFO_ARRAYS:
blocks.append(block)
name = CZ_LSM_SCAN_INFO_ARRAYS[entry]
newobj = []
setattr(block, name, newobj)
block = newobj
elif entry in CZ_LSM_SCAN_INFO_STRUCTS:
blocks.append(block)
newobj = Record()
block.append(newobj)
block = newobj
elif entry in CZ_LSM_SCAN_INFO_ATTRIBUTES:
name = CZ_LSM_SCAN_INFO_ATTRIBUTES[entry]
setattr(block, name, value)
elif entry == 0xffffffff:
# end sub block
block = blocks.pop()
else:
# unknown entry
setattr(block, "entry_0x%x" % entry, value)
if not blocks:
break
return block
def read_nih_image_header(fh, byteorder, dtype, count):
"""Read NIH_IMAGE_HEADER tag from file and return as numpy.rec.array."""
a = fh.read_record(NIH_IMAGE_HEADER, byteorder=byteorder)
a = a.newbyteorder(byteorder)
a.xunit = a.xunit[:a._xunit_len]
a.um = a.um[:a._um_len]
return a
def read_micromanager_metadata(fh):
"""Read MicroManager non-TIFF settings from open file and return as dict.
The settings can be used to read image data without parsing the TIFF file.
Raise ValueError if file does not contain valid MicroManager metadata.
"""
fh.seek(0)
try:
byteorder = {b'II': '<', b'MM': '>'}[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 dictionary 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_dict(description):
"""Return dictionary from ImageJ image description byte string.
Raise ValueError if not a valid ImageJ description.
>>> description = b'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
>>> imagej_description_dict(description) # doctest: +SKIP
{'ImageJ': '1.11a', 'images': 510, 'hyperstack': True}
"""
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
if 'ImageJ' not in result:
raise ValueError("not a ImageJ image description")
return result
def imagej_description(shape, rgb=None, colormaped=False, version='1.11a',
hyperstack=None, mode=None, loop=None, kwargs={}):
"""Return ImageJ image decription from data shape as byte string.
ImageJ can handle up to 6 dimensions in order TZCYXS.
>>> imagej_description((51, 5, 2, 196, 171)) # doctest: +SKIP
ImageJ=1.11a
images=510
channels=2
slices=5
frames=51
hyperstack=true
mode=grayscale
loop=false
"""
if colormaped:
raise NotImplementedError("ImageJ colormapping not supported")
shape = imagej_shape(shape, rgb=rgb)
rgb = shape[-1] in (3, 4)
result = ['ImageJ=%s' % version]
append = []
result.append('images=%i' % product(shape[:-3]))
if hyperstack is None:
#if product(shape[:-3]) > 1:
hyperstack = True
append.append('hyperstack=true')
else:
append.append('hyperstack=%s' % bool(hyperstack))
if shape[2] > 1:
result.append('channels=%i' % shape[2])
if mode is None and not rgb:
mode = 'grayscale'
if hyperstack and mode:
append.append('mode=%s' % mode)
if shape[1] > 1:
result.append('slices=%i' % shape[1])
if shape[0] > 1:
result.append("frames=%i" % shape[0])
if loop is None:
append.append('loop=false')
if loop is not None:
append.append('loop=%s' % bool(loop))
for key, value in kwargs.items():
append.append('%s=%s' % (key.lower(), value))
return str2bytes('\n'.join(result + append + ['']))
def imagej_shape(shape, rgb=None):
"""Return shape normalized to 6D ImageJ hyperstack TZCYXS.
Raise ValueError if not a valid ImageJ hyperstack shape.
>>> imagej_shape((2, 3, 4, 5, 3), False)
(2, 3, 4, 5, 3, 1)
"""
shape = tuple(int(i) for i in shape)
ndim = len(shape)
if 1 > ndim > 6:
raise ValueError("invalid ImageJ hyperstack: not 2 to 6 dimensional")
if rgb is None:
rgb = shape[-1] in (3, 4) and ndim > 2
if rgb and shape[-1] not in (3, 4):
raise ValueError("invalid ImageJ hyperstack: not a RGB image")
if not rgb and ndim == 6 and shape[-1] != 1:
raise ValueError("invalid ImageJ hyperstack: not a non-RGB image")
if rgb or shape[-1] == 1:
return (1, ) * (6 - ndim) + shape
else:
return (1, ) * (5 - ndim) + shape + (1,)
def image_description_dict(description):
"""Return dictionary from image description byte string.
Raise ValuError if description is of unknown format.
>>> image_description_dict(b'shape=(256, 256, 3)')
{'shape': (256, 256, 3)}
>>> description = b'{"shape": [256, 256, 3], "axes": "YXS"}'
>>> image_description_dict(description) # doctest: +SKIP
{'shape': [256, 256, 3], 'axes': 'YXS'}
"""
if description.startswith(b'shape='):
# old style 'shaped' description
shape = tuple(int(i) for i in description[7:-1].split(b','))
return dict(shape=shape)
if description.startswith(b'{') and description.endswith(b'}'):
# JSON description
return json.loads(description.decode('utf-8'))
raise ValueError("unknown image description")
def image_description(shape, colormaped=False, **metadata):
"""Return image description from data shape and meta data.
Return UTF-8 encoded JSON.
>>> image_description((256, 256, 3), axes='YXS') # doctest: +SKIP
b'{"shape": [256, 256, 3], "axes": "YXS"}'
"""
if colormaped:
shape = shape + (3,)
metadata.update({'shape': shape})
return json.dumps(metadata).encode('utf-8')
def _replace_by(module_function, package=__package__, 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 package:
module = import_module('.' + module, package=package)
else:
module = import_module(module)
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 decode_floats(data):
"""Decode floating point horizontal differencing.
The TIFF predictor type 3 reorders the bytes of the image values and
applies horizontal byte differencing to improve compression of floating
point images. The ordering of interleaved color channels is preserved.
Parameters
----------
data : numpy.ndarray
The image to be decoded. The dtype must be a floating point.
The shape must include the number of contiguous samples per pixel
even if 1.
"""
shape = data.shape
dtype = data.dtype
if len(shape) < 3:
raise ValueError('invalid data shape')
if dtype.char not in 'dfe':
raise ValueError('not a floating point image')
littleendian = data.dtype.byteorder == '<' or (
sys.byteorder == 'little' and data.dtype.byteorder == '=')
# undo horizontal byte differencing
data = data.view('uint8')
data.shape = shape[:-2] + (-1,) + shape[-1:]
numpy.cumsum(data, axis=-2, dtype='uint8', out=data)
# reorder bytes
if littleendian:
data.shape = shape[:-2] + (-1,) + shape[-2:]
data = numpy.swapaxes(data, -3, -2)
data = numpy.swapaxes(data, -2, -1)
data = data[..., ::-1]
# back to float
data = numpy.ascontiguousarray(data)
data = data.view(dtype)
data.shape = shape
return data
def decode_jpeg(encoded, tables=b'', photometric=None,
ycbcr_subsampling=None, ycbcr_positioning=None):
"""Decode JPEG encoded byte string (using _czifile extension module)."""
from czifile import _czifile
image = _czifile.decode_jpeg(encoded, tables)
if photometric == 'rgb' and ycbcr_subsampling and ycbcr_positioning:
# TODO: convert YCbCr to RGB
pass
return image.tostring()
@_replace_by('_tifffile.decode_packbits')
def decode_packbits(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.decode_lzw')
def decode_lzw(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.unpack_ints')
def unpack_ints(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 unpack_rgb(data, dtype='<B', bitspersample=(5, 6, 5), rescale=True):
"""Return array from byte string containing packed samples.
Use to unpack RGB565 or RGB555 to RGB888 format.
Parameters
----------
data : byte str
The data to be decoded. Samples in each pixel are stored consecutively.
Pixels are aligned to 8, 16, or 32 bit boundaries.
dtype : numpy.dtype
The sample data type. The byteorder applies also to the data stream.
bitspersample : tuple
Number of bits for each sample in a pixel.
rescale : bool
Upscale samples to the number of bits in dtype.
Returns
-------
result : ndarray
Flattened array of unpacked samples of native dtype.
Examples
--------
>>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff)
>>> print(unpack_rgb(data, '<B', (5, 6, 5), False))
[ 1 1 1 31 63 31]
>>> print(unpack_rgb(data, '<B', (5, 6, 5)))
[ 8 4 8 255 255 255]
>>> print(unpack_rgb(data, '<B', (5, 5, 5)))
[ 16 8 8 255 255 255]
"""
dtype = numpy.dtype(dtype)
bits = int(numpy.sum(bitspersample))
if not (bits <= 32 and all(i <= dtype.itemsize*8 for i in bitspersample)):
raise ValueError("sample size not supported %s" % str(bitspersample))
dt = next(i for i in 'BHI' if numpy.dtype(i).itemsize*8 >= 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 reverse_bitorder(data):
"""In-place reverse bits in each byte.
Parameters
----------
data : ndarray
The data to be bit reversed in-place.
Pixels with lower column values stored in the lower-order bits
of the bytes (fill_order == 'lsb2msb').
Examples
--------
>>> data = numpy.array([1, 666], dtype='uint16')
>>> reverse_bitorder(data)
>>> data
array([ 128, 16473], dtype=uint16)
"""
reverse = numpy.array([
0x00, 0x80, 0x40, 0xC0, 0x20, 0xA0, 0x60, 0xE0,
0x10, 0x90, 0x50, 0xD0, 0x30, 0xB0, 0x70, 0xF0,
0x08, 0x88, 0x48, 0xC8, 0x28, 0xA8, 0x68, 0xE8,
0x18, 0x98, 0x58, 0xD8, 0x38, 0xB8, 0x78, 0xF8,
0x04, 0x84, 0x44, 0xC4, 0x24, 0xA4, 0x64, 0xE4,
0x14, 0x94, 0x54, 0xD4, 0x34, 0xB4, 0x74, 0xF4,
0x0C, 0x8C, 0x4C, 0xCC, 0x2C, 0xAC, 0x6C, 0xEC,
0x1C, 0x9C, 0x5C, 0xDC, 0x3C, 0xBC, 0x7C, 0xFC,
0x02, 0x82, 0x42, 0xC2, 0x22, 0xA2, 0x62, 0xE2,
0x12, 0x92, 0x52, 0xD2, 0x32, 0xB2, 0x72, 0xF2,
0x0A, 0x8A, 0x4A, 0xCA, 0x2A, 0xAA, 0x6A, 0xEA,
0x1A, 0x9A, 0x5A, 0xDA, 0x3A, 0xBA, 0x7A, 0xFA,
0x06, 0x86, 0x46, 0xC6, 0x26, 0xA6, 0x66, 0xE6,
0x16, 0x96, 0x56, 0xD6, 0x36, 0xB6, 0x76, 0xF6,
0x0E, 0x8E, 0x4E, 0xCE, 0x2E, 0xAE, 0x6E, 0xEE,
0x1E, 0x9E, 0x5E, 0xDE, 0x3E, 0xBE, 0x7E, 0xFE,
0x01, 0x81, 0x41, 0xC1, 0x21, 0xA1, 0x61, 0xE1,
0x11, 0x91, 0x51, 0xD1, 0x31, 0xB1, 0x71, 0xF1,
0x09, 0x89, 0x49, 0xC9, 0x29, 0xA9, 0x69, 0xE9,
0x19, 0x99, 0x59, 0xD9, 0x39, 0xB9, 0x79, 0xF9,
0x05, 0x85, 0x45, 0xC5, 0x25, 0xA5, 0x65, 0xE5,
0x15, 0x95, 0x55, 0xD5, 0x35, 0xB5, 0x75, 0xF5,
0x0D, 0x8D, 0x4D, 0xCD, 0x2D, 0xAD, 0x6D, 0xED,
0x1D, 0x9D, 0x5D, 0xDD, 0x3D, 0xBD, 0x7D, 0xFD,
0x03, 0x83, 0x43, 0xC3, 0x23, 0xA3, 0x63, 0xE3,
0x13, 0x93, 0x53, 0xD3, 0x33, 0xB3, 0x73, 0xF3,
0x0B, 0x8B, 0x4B, 0xCB, 0x2B, 0xAB, 0x6B, 0xEB,
0x1B, 0x9B, 0x5B, 0xDB, 0x3B, 0xBB, 0x7B, 0xFB,
0x07, 0x87, 0x47, 0xC7, 0x27, 0xA7, 0x67, 0xE7,
0x17, 0x97, 0x57, 0xD7, 0x37, 0xB7, 0x77, 0xF7,
0x0F, 0x8F, 0x4F, 0xCF, 0x2F, 0xAF, 0x6F, 0xEF,
0x1F, 0x9F, 0x5F, 0xDF, 0x3F, 0xBF, 0x7F, 0xFF], dtype='uint8')
view = data.view('uint8')
numpy.take(reverse, view, out=view)
def apply_colormap(image, colormap, contig=True):
"""Return palette-colored image.
The image values are used to index the colormap on axis 1. The returned
image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype.
Parameters
----------
image : numpy.ndarray
Indexes into the colormap.
colormap : numpy.ndarray
RGB lookup table aka palette of shape (3, 2**bits_per_sample).
contig : bool
If True, return a contiguous array.
Examples
--------
>>> image = numpy.arange(256, dtype='uint8')
>>> colormap = numpy.vstack([image, image, image]).astype('uint16') * 256
>>> apply_colormap(image, colormap)[-1]
array([65280, 65280, 65280], dtype=uint16)
"""
image = numpy.take(colormap, image, axis=1)
image = numpy.rollaxis(image, 0, image.ndim)
if contig:
image = numpy.ascontiguousarray(image)
return image
def reorient(image, orientation):
"""Return reoriented view of image array.
Parameters
----------
image : numpy.ndarray
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 do not match")
shape, axes = zip(*(i for i in zip(shape, axes)
if i[0] > 1 or i[1] in skip))
return tuple(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 reshape_axes(axes, shape, newshape):
"""Return axes matching new shape.
Unknown dimensions are labelled 'Q'.
>>> reshape_axes('YXS', (219, 301, 1), (219, 301))
'YX'
>>> reshape_axes('IYX', (12, 219, 301), (3, 4, 219, 1, 301, 1))
'QQYQXQ'
"""
if len(axes) != len(shape):
raise ValueError("axes do not match shape")
if product(shape) != product(newshape):
raise ValueError("can not reshape %s to %s" % (shape, newshape))
if not axes or not newshape:
return ''
lendiff = max(0, len(shape) - len(newshape))
if lendiff:
newshape = newshape + (1,) * lendiff
i = len(shape)-1
prodns = 1
prods = 1
result = []
for ns in newshape[::-1]:
prodns *= ns
while i > 0 and shape[i] == 1 and ns != 1:
i -= 1
if ns == shape[i] and prodns == prods*shape[i]:
prods *= shape[i]
result.append(axes[i])
i -= 1
else:
result.append('Q')
return ''.join(reversed(result[lendiff:]))
def stack_pages(pages, memmap=False, tempdir=None, *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)
data0 = pages[0].asarray(*args, **kwargs)
shape = (len(pages),) + data0.shape
if memmap:
with tempfile.NamedTemporaryFile(dir=tempdir) as fh:
data = numpy.memmap(fh, dtype=data0.dtype, shape=shape)
else:
data = numpy.empty(shape, dtype=data0.dtype)
data[0] = data0
if memmap:
data.flush()
del data0
for i, page in enumerate(pages[1:]):
data[i+1] = page.asarray(*args, **kwargs)
if memmap:
data.flush()
return data
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',
34925: 'lzma',
}
TIFF_DECOMPESSORS = {
None: lambda x: x,
'adobe_deflate': zlib.decompress,
'deflate': zlib.decompress,
'packbits': decode_packbits,
'lzw': decode_lzw,
# 'jpeg': decode_jpeg
}
if lzma:
TIFF_DECOMPESSORS['lzma'] = lzma.decompress
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',
'P': 'dimension_p',
'M': 'dimension_m',
}
# 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, None, 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', 3: 'float'}),
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, None, TIFF_SAMPLE_FORMATS),
340: ('smin_sample_value', None, None, None, None),
341: ('smax_sample_value', None, None, None, None),
346: ('indexed', 0, 3, 1, None),
347: ('jpeg_tables', None, 7, None, None),
530: ('ycbcr_subsampling', (1, 1), 3, 2, None),
531: ('ycbcr_positioning', (1, 1), 3, 1, None),
532: ('reference_black_white', None, 5, 1, None),
32996: ('sgi_matteing', None, None, 1, None), # use extra_samples
32996: ('sgi_datatype', None, None, None, None), # use sample_format
32997: ('image_depth', 1, 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),
51023: ('fibics_xml', None, 2, 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 width and length.
kwargs : optional
Arguments for matplotlib.pyplot.imshow.
"""
#if photometric not in ('miniswhite', 'minisblack', 'rgb', 'palette'):
# raise ValueError("Can not handle %s photometrics" % photometric)
# TODO: handle photometric == 'separated' (CMYK)
isrgb = photometric in ('rgb', 'palette')
data = numpy.atleast_2d(data.squeeze())
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] // 8 and
data.shape[-1] < data.shape[-3] // 8 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 isrgb:
data = data[..., :maxdim, :maxdim, :maxdim]
else:
data = data[..., :maxdim, :maxdim]
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':
# TODO: handle complex types
raise NotImplementedError("complex type")
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="do not read OME series from multiple files")
opt('--noplot', dest='noplot', action='store_true', default=False,
help="do not 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('--vmin', dest='vmin', type='int', default=None,
help="set minimum value for colormapping")
opt('--vmax', dest='vmax', type='int', default=None,
help="set maximum value for colormapping")
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:
try:
import tkFileDialog as filedialog
except ImportError:
from tkinter import filedialog
path = filedialog.askopenfilename(filetypes=[
("TIF files", "*.tif"), ("LSM files", "*.lsm"),
("STK files", "*.stk"), ("allfiles", "*")])
#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_indexed:
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 = settings.vmin, settings.vmax
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 = settings.vmin, settings.vmax
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
def str2bytes(s, encoding="latin-1"):
return s.encode(encoding)
else:
def str2bytes(s):
return s
if __name__ == "__main__":
sys.exit(main())
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