#!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py
# Copyright (c) 2008-2012, Christoph Gohlke
# Copyright (c) 2008-2012, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the copyright holders nor the names of any
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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"""Read and write image data from and to TIFF files.
Image and meta-data can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH,
and FluoView 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/CCITT
compressed image data or EXIF/IPTC/GPS/XMP meta-data is not implemented.
Only primary info records are read for STK, FluoView, and NIH image formats.
TIFF, the Tagged Image File Format, is under the control of Adobe Systems.
BigTIFF allows for files greater than 4 GB. STK, LSM, FluoView, and OME-TIFF
are custom extensions defined by MetaMorph, Carl Zeiss MicroImaging,
Olympus, and the Open Microscopy Environment consortium respectively.
The API is not stable yet and might change between revisions.
Tested on little-endian platforms only.
For command line usage run ``python tifffile.py --help``
:Authors:
`Christoph Gohlke `__,
Laboratory for Fluorescence Dynamics, University of California, Irvine
:Version: 2012.07.05
Requirements
------------
* `CPython 2.7 or 3.2 `__
* `Numpy 1.6 `__
* `Matplotlib 1.1 `__
(optional for plotting)
* `tifffile.c 2012.01.01 `__
(optional for faster decoding of PackBits and LZW encoded strings)
Acknowledgements
----------------
* Egor Zindy, University of Manchester, for cz_lsm_scan_info specifics.
* Wim Lewis, for a bug fix and some read_cz_lsm functions.
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) File Format Description - LSM 5xx Release 2.0.
http://ibb.gsf.de/homepage/karsten.rodenacker/IDL/Lsmfile.doc
(5) BioFormats. http://www.loci.wisc.edu/ome/formats.html
(6) The OME-TIFF format.
http://www.openmicroscopy.org/site/support/file-formats/ome-tiff
Examples
--------
>>> data = numpy.random.rand(301, 219)
>>> imsave('temp.tif', data)
>>> image = imread('temp.tif')
>>> assert numpy.all(image == data)
>>> tif = TIFFfile('test.tif')
>>> images = tif.asarray()
>>> image0 = tif[0].asarray()
>>> for page in tif:
... for tag in page.tags.values():
... t = tag.name, tag.value
... image = page.asarray()
... if page.is_rgb: pass
... if page.is_palette:
... t = page.color_map
... if page.is_stk:
... t = page.mm_uic_tags.number_planes
... if page.is_lsm:
... t = page.cz_lsm_info
>>> tif.close()
"""
from __future__ import division, print_function
import sys
import os
import math
import zlib
import time
import struct
import warnings
import datetime
import collections
from xml.etree import cElementTree as ElementTree
import numpy
__all__ = ['imsave', 'imread', 'imshow', 'TIFFfile']
def imsave(filename, data, photometric=None, planarconfig=None,
resolution=None, description=None, software='tifffile.py',
byteorder=None, bigtiff=False):
"""Write image data to TIFF file.
Image data are written uncompressed in one stripe per plane.
Dimensions larger than 2 or 3 (depending on photometric mode and
planar configuration) are flattened and saved as separate pages.
Parameters
----------
filename : str
Name of file to write.
data : array_like
Input image. The last dimensions are assumed to be image height,
width, and samples.
photometric : {'minisblack', 'miniswhite', 'rgb'}
The color space of the image data.
By default this setting is inferred from the data shape.
planarconfig : {'contig', 'planar'}
Specifies if samples are stored contiguous or in separate planes.
By default this setting is inferred from the data shape.
'contig': last dimension contains samples.
'planar': third last dimension contains samples.
resolution : ((int, int), (int, int))
X and Y resolution in dots per inch as rational numbers.
description : str
The subject of the image. Saved with the first page only.
software : str
Name of the software used to create the image.
Saved with the first page only.
byteorder : {'<', '>'}
The endianness of the data in the file.
By default this is the system's native byte order.
bigtiff : bool
If True the BigTIFF format is used.
By default the standard TIFF format is used for data less than 2040 MB.
Examples
--------
>>> data = numpy.random.rand(10, 3, 301, 219)
>>> imsave('temp.tif', data)
"""
assert(photometric in (None, 'minisblack', 'miniswhite', 'rgb'))
assert(planarconfig in (None, 'contig', 'planar'))
assert(byteorder in (None, '<', '>'))
if byteorder is None:
byteorder = '<' if sys.byteorder == 'little' else '>'
data = numpy.asarray(data, dtype=byteorder+data.dtype.char, order='C')
data_shape = shape = data.shape
data = numpy.atleast_2d(data)
if not bigtiff and data.size * data.dtype.itemsize < 2040*2**20:
bigtiff = False
offset_size = 4
tag_size = 12
numtag_format = 'H'
offset_format = 'I'
val_format = '4s'
else:
bigtiff = True
offset_size = 8
tag_size = 20
numtag_format = 'Q'
offset_format = 'Q'
val_format = '8s'
# unify shape of data
samplesperpixel = 1
extrasamples = 0
if photometric is None:
if data.ndim > 2 and (shape[-3] in (3, 4) or shape[-1] in (3, 4)):
photometric = 'rgb'
else:
photometric = 'minisblack'
if photometric == 'rgb':
if len(shape) < 3:
raise ValueError("not a RGB(A) image")
if planarconfig is None:
planarconfig = 'planar' if shape[-3] in (3, 4) else 'contig'
if planarconfig == 'contig':
if shape[-1] not in (3, 4):
raise ValueError("not a contiguous RGB(A) image")
data = data.reshape((-1, 1) + shape[-3:])
samplesperpixel = shape[-1]
else:
if shape[-3] not in (3, 4):
raise ValueError("not a planar RGB(A) image")
data = data.reshape((-1, ) + shape[-3:] + (1, ))
samplesperpixel = shape[-3]
if samplesperpixel == 4:
extrasamples = 1
elif planarconfig and len(shape) > 2:
if planarconfig == 'contig':
data = data.reshape((-1, 1) + shape[-3:])
samplesperpixel = shape[-1]
else:
data = data.reshape((-1, ) + shape[-3:] + (1, ))
samplesperpixel = shape[-3]
extrasamples = samplesperpixel - 1
else:
planarconfig = None
data = data.reshape((-1, 1) + shape[-2:] + (1, ))
shape = data.shape # (pages, planes, height, width, contig samples)
bytestr = bytes if sys.version[0] == '2' else lambda x: bytes(x, 'ascii')
tifftypes = {'B': 1, 's': 2, 'H': 3, 'I': 4, '2I': 5, 'b': 6,
'h': 8, 'i': 9, 'f': 11, 'd': 12, 'Q': 16, 'q': 17}
tifftags = {'new_subfile_type': 254, 'subfile_type': 255,
'image_width': 256, 'image_length': 257, 'bits_per_sample': 258,
'compression': 259, 'photometric': 262, 'fill_order': 266,
'document_name': 269, 'image_description': 270, 'strip_offsets': 273,
'orientation': 274, 'samples_per_pixel': 277, 'rows_per_strip': 278,
'strip_byte_counts': 279, 'x_resolution': 282, 'y_resolution': 283,
'planar_configuration': 284, 'page_name': 285, 'resolution_unit': 296,
'software': 305, 'datetime': 306, 'predictor': 317, 'color_map': 320,
'extra_samples': 338, 'sample_format': 339}
tags = []
tag_data = []
def pack(fmt, *val):
return struct.pack(byteorder+fmt, *val)
def tag(name, dtype, number, value, offset=[0]):
# append tag binary string to tags list
# append (offset, value as binary string) to tag_data list
# increment offset by tag_size
if dtype == 's':
value = bytestr(value) + b'\0'
number = len(value)
value = (value, )
t = [pack('HH', tifftags[name], tifftypes[dtype]),
pack(offset_format, number)]
if len(dtype) > 1:
number *= int(dtype[:-1])
dtype = dtype[-1]
if number == 1:
if isinstance(value, (tuple, list)):
value = value[0]
t.append(pack(val_format, pack(dtype, value)))
elif struct.calcsize(dtype) * number <= offset_size:
t.append(pack(val_format, pack(str(number)+dtype, *value)))
else:
t.append(pack(offset_format, 0))
tag_data.append((offset[0] + offset_size + 4,
pack(str(number)+dtype, *value)))
tags.append(b''.join(t))
offset[0] += tag_size
if software:
tag('software', 's', 0, software)
if description:
tag('image_description', 's', 0, description)
elif shape != data_shape:
tag('image_description', 's', 0,
"shape=(%s)" % (",".join('%i' % i for i in data_shape)))
tag('datetime', 's', 0,
datetime.datetime.now().strftime("%Y:%m:%d %H:%M:%S"))
# write previous tags only once
writeonce = (len(tags), len(tag_data)) if shape[0] > 1 else None
tag('compression', 'H', 1, 1)
tag('orientation', 'H', 1, 1)
tag('image_width', 'I', 1, shape[-2])
tag('image_length', 'I', 1, shape[-3])
tag('new_subfile_type', 'I', 1, 0 if shape[0] == 1 else 2)
tag('sample_format', 'H', 1,
{'u': 1, 'i': 2, 'f': 3, 'c': 6}[data.dtype.kind])
tag('photometric', 'H', 1,
{'miniswhite': 0, 'minisblack': 1, 'rgb': 2}[photometric])
tag('samples_per_pixel', 'H', 1, samplesperpixel)
if planarconfig:
tag('planar_configuration', 'H', 1, 1 if planarconfig=='contig' else 2)
tag('bits_per_sample', 'H', samplesperpixel,
(data.dtype.itemsize * 8, ) * samplesperpixel)
else:
tag('bits_per_sample', 'H', 1, data.dtype.itemsize * 8)
if extrasamples:
if photometric == 'rgb':
tag('extra_samples', 'H', 1, 1) # alpha channel
else:
tag('extra_samples', 'H', extrasamples, (0, ) * extrasamples)
if resolution:
tag('x_resolution', '2I', 1, resolution[0])
tag('y_resolution', '2I', 1, resolution[1])
tag('resolution_unit', 'H', 1, 2)
tag('rows_per_strip', 'I', 1, shape[-3])
# use one strip per plane
strip_byte_counts = (data[0, 0].size * data.dtype.itemsize, ) * shape[1]
tag('strip_byte_counts', offset_format, shape[1], strip_byte_counts)
# strip_offsets must be the last tag; will be updated later
tag('strip_offsets', offset_format, shape[1], (0, ) * shape[1])
fd = open(filename, 'wb')
seek = fd.seek
tell = fd.tell
def write(arg, *args):
fd.write(pack(arg, *args) if args else arg)
write({'<': b'II', '>': b'MM'}[byteorder])
if bigtiff:
write('HHH', 43, 8, 0)
else:
write('H', 42)
ifd_offset = tell()
write(offset_format, 0) # first IFD
for i in range(shape[0]):
# update pointer at ifd_offset
pos = tell()
seek(ifd_offset)
write(offset_format, pos)
seek(pos)
# write tags
write(numtag_format, len(tags))
tag_offset = tell()
write(b''.join(tags))
ifd_offset = tell()
write(offset_format, 0) # offset to next ifd
# write extra tag data and update pointers
for off, dat in tag_data:
pos = tell()
seek(tag_offset + off)
write(offset_format, pos)
seek(pos)
write(dat)
# update strip_offsets
pos = tell()
if len(strip_byte_counts) == 1:
seek(ifd_offset - offset_size)
write(offset_format, pos)
else:
seek(pos - offset_size*shape[1])
strip_offset = pos
for size in strip_byte_counts:
write(offset_format, strip_offset)
strip_offset += size
seek(pos)
# write data
data[i].tofile(fd) # if this fails, try update Python and numpy
fd.flush()
# remove tags that should be written only once
if writeonce:
tags = tags[writeonce[0]:]
d = writeonce[0] * tag_size
tag_data = [(o-d, v) for (o, v) in tag_data[writeonce[1]:]]
writeonce = None
fd.close()
def imread(filename, *args, **kwargs):
"""Return image data from TIFF file as numpy array.
The first image series is returned if no arguments are provided.
Parameters
----------
key : int, slice, or sequence of page indices
Defines which pages to return as array.
series : int
Defines which series of pages to return as array.
Examples
--------
>>> image = imread('test.tif', 0)
"""
with TIFFfile(filename) as tif:
return tif.asarray(*args, **kwargs)
class lazyattr(object):
"""Lazy object attribute whose value is computed on first access."""
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
if instance is None:
return self
result = self.func(instance)
if result is NotImplemented:
return getattr(super(owner, instance), self.func.__name__)
setattr(instance, self.func.__name__, result)
return result
class TIFFfile(object):
"""Read image and meta-data from TIFF, STK, LSM, and FluoView files.
TIFFfile instances must be closed using the close method.
Attributes
----------
pages : list
All TIFFpages in file.
series : list of Records(shape, dtype, axes, TIFFpages)
TIFF pages with compatible shapes and types.
All attributes are read-only.
Examples
--------
>>> tif = TIFFfile('test.tif')
... try:
... images = tif.asarray()
... except Exception as e:
... print(e)
... finally:
... tif.close()
"""
def __init__(self, filename):
"""Initialize instance from file."""
filename = os.path.abspath(filename)
self._fd = open(filename, 'rb')
self.fname = os.path.basename(filename)
self.fpath = os.path.dirname(filename)
self._tiffs = {self.fname: self} # cache of TIFFfiles
self.offset_size = None
self.pages = []
try:
self._fromfile()
except Exception:
self._fd.close()
raise
def close(self):
"""Close open file handle(s)."""
if not hasattr(self, 'tiffs'):
return
for tif in self._tiffs.values():
if tif._fd:
tif._fd.close()
tif._fd = None
def _fromfile(self):
"""Read TIFF header and all page records from file."""
self._fd.seek(0)
try:
self.byte_order = {b'II': '<', b'MM': '>'}[self._fd.read(2)]
except KeyError:
raise ValueError("not a valid TIFF file")
version = struct.unpack(self.byte_order+'H', self._fd.read(2))[0]
if version == 43: # BigTiff
self.offset_size, zero = struct.unpack(self.byte_order+'HH',
self._fd.read(4))
if zero or self.offset_size != 8:
raise ValueError("not a valid BigTIFF file")
elif version == 42:
self.offset_size = 4
else:
raise ValueError("not a TIFF file")
self.pages = []
while True:
try:
page = TIFFpage(self)
self.pages.append(page)
except StopIteration:
break
if not self.pages:
raise ValueError("empty TIFF file")
@lazyattr
def series(self):
"""Return series of TIFFpage with compatible shape and properties."""
if self.is_ome:
series = self._omeseries()
elif self.is_fluoview:
dims = {b'X': 'X', b'Y': 'Y', b'Z': 'Z', b'T': 'T',
b'WAVELENGTH': 'C', b'TIME': 'T', b'XY': 'R',
b'EVENT': 'V', b'EXPOSURE': 'L'}
mmhd = list(reversed(self.pages[0].mm_header.dimensions))
series = [Record(
axes=''.join(dims.get(i[0].strip().upper(), 'O')
for i in mmhd if i[1] > 1),
shape=tuple(int(i[1]) for i in mmhd if i[1] > 1),
pages=self.pages, dtype=numpy.dtype(self.pages[0].dtype))]
elif self.is_lsm:
lsmi = self.pages[0].cz_lsm_info
axes = CZ_SCAN_TYPES[lsmi.scan_type]
if self.pages[0].is_rgb:
axes = axes.replace('C', '').replace('XY', 'XYC')
axes = axes[::-1]
shape = [getattr(lsmi, CZ_DIMENSIONS[i]) for i in axes]
pages = [p for p in self.pages if not p.is_reduced]
series = [Record(axes=axes, shape=shape, pages=pages,
dtype=numpy.dtype(pages[0].dtype))]
if len(pages) != len(self.pages): # reduced RGB pages
pages = [p for p in self.pages if p.is_reduced]
cp = 1
i = 0
while cp < len(pages) and i < len(shape)-2:
cp *= shape[i]
i += 1
shape = shape[:i] + list(pages[0].shape)
axes = axes[:i] + 'CYX'
series.append(Record(axes=axes, shape=shape, pages=pages,
dtype=numpy.dtype(pages[0].dtype)))
elif self.is_nih:
series = [Record(pages=self.pages,
shape=(len(self.pages),) + self.pages[0].shape,
axes='I' + self.pages[0].axes,
dtype=numpy.dtype(self.pages[0].dtype))]
elif self.pages[0].is_shaped:
shape = self.pages[0].tags['image_description'].value[7:-1]
shape = tuple(int(i) for i in shape.split(b','))
series = [Record(pages=self.pages, shape=shape,
axes='O' * len(shape),
dtype=numpy.dtype(self.pages[0].dtype))]
else:
shapes = []
pages = {}
for page in self.pages:
shape = page.shape + (page.axes,
page.compression in TIFF_DECOMPESSORS)
if not shape in pages:
shapes.append(shape)
pages[shape] = [page]
else:
pages[shape].append(page)
series = [Record(pages=pages[s],
axes=(('I' + s[-2])
if len(pages[s]) > 1 else s[-2]),
dtype=numpy.dtype(pages[s][0].dtype),
shape=((len(pages[s]), ) + s[:-2]
if len(pages[s]) > 1 else s[:-2]))
for s in shapes]
return series
def asarray(self, key=None, series=None):
"""Return image data of multiple TIFF pages as numpy array.
By default the first image series is returned.
Parameters
----------
key : int, slice, or sequence of page indices
Defines which pages to return as array.
series : int
Defines which series of pages to return as array.
"""
if key is None and series is None:
series = 0
if series is not None:
pages = self.series[series].pages
else:
pages = self.pages
if key is None:
pass
elif isinstance(key, int):
pages = [pages[key]]
elif isinstance(key, slice):
pages = pages[key]
elif isinstance(key, collections.Iterable):
pages = [pages[k] for k in key]
else:
raise TypeError('key must be an int, slice, or sequence')
if len(pages) == 1:
return pages[0].asarray()
elif self.is_nih:
result = numpy.vstack(p.asarray(colormapped=False,
squeeze=False) for p in pages)
if pages[0].is_palette:
result = numpy.take(pages[0].color_map, result, axis=1)
result = numpy.swapaxes(result, 0, 1)
else:
if self.is_ome and any(p is None for p in pages):
firstpage = next(p for p in pages if p)
nopage = numpy.zeros_like(
firstpage.asarray())
result = numpy.vstack((p.asarray() if p else nopage)
for p in pages)
if key is None:
try:
result.shape = self.series[series].shape
except ValueError:
warnings.warn("failed to reshape %s to %s" % (
result.shape, self.series[series].shape))
result.shape = (-1,) + pages[0].shape
else:
result.shape = (-1,) + pages[0].shape
return result
def _omeseries(self):
"""Return image series in OME-TIFF files."""
root = ElementTree.XML(self.pages[0].tags['image_description'].value)
uuid = root.attrib.get('UUID', None)
self._tiffs = {uuid: self}
modulo = {}
result = []
for element in root:
if element.tag.endswith('BinaryOnly'):
warnings.warn("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
axes = "".join(reversed(atr['DimensionOrder']))
shape = list(int(atr['Size'+ax]) for ax in axes)
size = numpy.prod(shape[:-2])
ifds = [None] * size
for data in pixels:
if not data.tag.endswith('TiffData'):
continue
atr = data.attrib
ifd = int(atr.get('IFD', 0))
num = int(atr.get('NumPlanes', 1 if 'IFD' in atr else 0))
num = int(atr.get('PlaneCount', num))
idx = [int(atr.get('First'+ax, 0)) for ax in axes[:-2]]
idx = numpy.ravel_multi_index(idx, shape[:-2])
for uuid in data:
if uuid.tag.endswith('UUID'):
if uuid.text not in self._tiffs:
fn = uuid.attrib['FileName']
try:
tf = TIFFfile(os.path.join(self.fpath, fn))
except (IOError, ValueError):
warnings.warn("failed to read %s" % fn)
break
self._tiffs[uuid.text] = tf
pages = self._tiffs[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")
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")
result.append(Record(axes=axes, shape=shape, pages=ifds,
dtype=numpy.dtype(ifds[0].dtype)))
for record in result:
for axis, (newaxis, labels) in modulo.items():
i = record.axes.index(axis)
size = len(labels)
if record.shape[i] == size:
record.axes = record.axes.replace(axis, newaxis, 1)
else:
record.shape[i] //= size
record.shape.insert(i+1, size)
record.axes = record.axes.replace(axis, axis+newaxis, 1)
return result
def __len__(self):
"""Return number of image pages in file."""
return len(self.pages)
def __getitem__(self, key):
"""Return specified page."""
return self.pages[key]
def __iter__(self):
"""Return iterator over pages."""
return iter(self.pages)
def __str__(self):
"""Return string containing information about file."""
result = [self.fname.capitalize(),
"%.2f MB" % (self.fstat[6] / 1048576),
{'<': 'little endian', '>': 'big endian'}[self.byte_order]]
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._tiffs) > 1:
result.append("%i files" % (len(self._tiffs)))
return ", ".join(result)
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
@lazyattr
def fstat(self):
return os.fstat(self._fd.fileno())
@lazyattr
def is_bigtiff(self):
return self.offset_size != 4
@lazyattr
def is_rgb(self):
return all(p.is_rgb for p in self.pages)
@lazyattr
def is_palette(self):
return all(p.is_palette for p in self.pages)
@lazyattr
def is_stk(self):
return all(p.is_stk for p in self.pages)
@lazyattr
def is_lsm(self):
return self.pages[0].is_lsm
@lazyattr
def is_nih(self):
return self.pages[0].is_nih
@lazyattr
def is_fluoview(self):
return self.pages[0].is_fluoview
@lazyattr
def is_ome(self):
return self.pages[0].is_ome
class TIFFpage(object):
"""A TIFF image file directory (IFD).
Attributes
----------
index : int
Index of page in file.
dtype : str {TIFF_SAMPLE_DTYPES}
Data type of image, colormapped if applicable.
shape : tuple
Dimensions of the image array in TIFF page,
colormapped and with one alpha channel if applicable.
axes : str
Axes label codes:
'X' width, 'Y' height, 'S' sample, 'P' plane, 'I' image series,
'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
'T' time, 'R' region|tile, 'A' angle, 'F' phase, 'H' lifetime,
'L' exposure, 'V' event, 'O' unknown, '_' missing
tags : TiffTags
Dictionary of tags in page.
Tag values are also directly accessible as attributes.
color_map : numpy array
Color look up table if exists.
mm_uic_tags: Record(dict)
Consolidated MetaMorph mm_uic# tags, if exists.
cz_lsm_scan_info: Record(dict)
LSM scan info attributes, if exists.
All attributes are read-only.
"""
def __init__(self, parent):
"""Initialize instance from file."""
self.parent = parent
self.index = len(parent.pages)
self.shape = self._shape = ()
self.dtype = self._dtype = None
self.axes = ""
self.tags = TiffTags()
self._fromfile()
self._process_tags()
def _fromfile(self):
"""Read TIFF IFD structure and its tags from file.
File cursor must be at storage position of IFD offset and is left at
offset to next IFD.
Raises StopIteration if offset (first bytes read) is 0.
"""
fd = self.parent._fd
byte_order = self.parent.byte_order
offset_size = self.parent.offset_size
fmt = {4: 'I', 8: 'Q'}[offset_size]
offset = struct.unpack(byte_order + fmt, fd.read(offset_size))[0]
if not offset:
raise StopIteration()
# read standard tags
tags = self.tags
fd.seek(offset)
fmt, size = {4: ('H', 2), 8: ('Q', 8)}[offset_size]
try:
numtags = struct.unpack(byte_order + fmt, fd.read(size))[0]
except Exception:
warnings.warn("corrupted page list")
raise StopIteration()
for _ in range(numtags):
tag = TIFFtag(self.parent)
tags[tag.name] = tag
# read LSM info subrecords
if self.is_lsm:
pos = fd.tell()
for name, reader in CZ_LSM_INFO_READERS.items():
try:
offset = self.cz_lsm_info["offset_"+name]
except KeyError:
continue
if not offset:
continue
fd.seek(offset)
try:
setattr(self, "cz_lsm_"+name, reader(fd, byte_order))
except ValueError:
pass
fd.seek(pos)
def _process_tags(self):
"""Validate standard tags and initialize attributes.
Raise ValueError if tag values are not supported.
"""
tags = self.tags
for code, (name, default, dtype, count, validate) in TIFF_TAGS.items():
if not (name in tags or default is None):
tags[name] = TIFFtag(code, dtype=dtype, count=count,
value=default, name=name)
if name in tags and validate:
try:
if tags[name].count == 1:
setattr(self, name, validate[tags[name].value])
else:
setattr(self, name, tuple(validate[value]
for value in tags[name].value))
except KeyError:
raise ValueError("%s.value (%s) not supported" %
(name, tags[name].value))
tag = tags['bits_per_sample']
if tag.count == 1:
self.bits_per_sample = tag.value
else:
value = tag.value[:self.samples_per_pixel]
if any((v-value[0] for v in value)):
self.bits_per_sample = value
else:
self.bits_per_sample = value[0]
tag = tags['sample_format']
if tag.count == 1:
self.sample_format = TIFF_SAMPLE_FORMATS[tag.value]
else:
value = tag.value[:self.samples_per_pixel]
if any((v-value[0] for v in value)):
self.sample_format = [TIFF_SAMPLE_FORMATS[v] for v in value]
else:
self.sample_format = TIFF_SAMPLE_FORMATS[value[0]]
self.strips_per_image = int(math.floor(float(self.image_length +
self.rows_per_strip - 1) / self.rows_per_strip))
key = (self.sample_format, self.bits_per_sample)
self.dtype = self._dtype = TIFF_SAMPLE_DTYPES.get(key, None)
if self.is_stk:
planes = tags['mm_uic2'].count
# consolidate mm_uci tags
self.mm_uic_tags = Record(tags['mm_uic2'].value)
for key in ('mm_uic3', 'mm_uic4', 'mm_uic1'):
if key in tags:
self.mm_uic_tags.update(tags[key].value)
if self.planar_configuration == 'contig':
self._shape = (planes, 1, self.image_length,
self.image_width, self.samples_per_pixel)
self.shape = tuple(self._shape[i] for i in (0, 2, 3, 4))
self.axes = "PYXS"
else:
self._shape = (planes, self.samples_per_pixel,
self.image_length, self.image_width, 1)
self.shape = self._shape[:4]
self.axes = "PSYX"
elif self.is_palette:
self.dtype = self.tags['color_map'].dtype[1]
self.color_map = numpy.array(self.color_map, self.dtype)
dmax = self.color_map.max()
if dmax < 256:
self.dtype = numpy.uint8
self.color_map = self.color_map.astype(self.dtype)
#else:
# self.dtype = numpy.uint8
# self.color_map >>= 8
# self.color_map = self.color_map.astype(self.dtype)
self.color_map.shape = (3, -1)
self._shape = (1, 1, self.image_length, self.image_width, 1)
if self.color_map.shape[1] >= 2**self.bits_per_sample:
self.shape = (3, self.image_length, self.image_width)
self.axes = "SYX"
else:
# LSM and FluoView
self.shape = (self.image_length, self.image_width)
self.axes = "YX"
elif self.is_rgb or self.samples_per_pixel > 1:
if self.planar_configuration == 'contig':
self._shape = (1, 1, self.image_length, self.image_width,
self.samples_per_pixel)
self.shape = (self.image_length, self.image_width,
self.samples_per_pixel)
self.axes = "YXS"
else:
self._shape = (1, self.samples_per_pixel, self.image_length,
self.image_width, 1)
self.shape = self._shape[1:-1]
self.axes = "SYX"
if self.is_rgb and 'extra_samples' in self.tags:
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'):
if self.planar_configuration == 'contig':
self.shape = self.shape[:2] + (4,)
else:
self.shape = (4,) + self.shape[1:]
break
else:
self._shape = (1, 1, self.image_length, self.image_width, 1)
self.shape = self._shape[2:4]
self.axes = "YX"
if not self.compression and not 'strip_byte_counts' in tags:
self.strip_byte_counts = numpy.prod(self.shape) * (
self.bits_per_sample // 8)
def asarray(self, squeeze=True, colormapped=True, rgbonly=True):
"""Read image data from file and return as numpy array.
Raise ValueError if format is unsupported.
If any argument is False, 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.
"""
fd = self.parent._fd
if not fd:
raise IOError("TIFF file is not open")
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 ('ycbcr_subsampling' in self.tags and
self.tags['ycbcr_subsampling'].value not in (1, (1, 1))):
raise ValueError("YCbCr subsampling not supported")
tag = self.tags['sample_format']
if tag.count != 1 and any((i-tag.value[0] for i in tag.value)):
raise ValueError("sample formats don't match %s" % str(tag.value))
dtype = self._dtype
shape = self._shape
image_width = self.image_width
image_length = self.image_length
typecode = self.parent.byte_order + dtype
bits_per_sample = self.bits_per_sample
if self.is_tiled:
if 'tile_offsets' in self.tags:
byte_counts = self.tile_byte_counts
offsets = self.tile_offsets
else:
byte_counts = self.strip_byte_counts
offsets = self.strip_offsets
tile_width = self.tile_width
tile_length = self.tile_length
tw = (image_width + tile_width - 1) // tile_width
tl = (image_length + tile_length - 1) // tile_length
shape = shape[:-3] + (tl*tile_length, tw*tile_width, shape[-1])
tile_shape = (tile_length, tile_width, shape[-1])
runlen = tile_width
else:
byte_counts = self.strip_byte_counts
offsets = self.strip_offsets
runlen = image_width
try:
offsets[0]
except TypeError:
offsets = (offsets, )
byte_counts = (byte_counts, )
if any(o < 2 for o in offsets):
raise ValueError("corrupted file")
if (not self.is_tiled and (self.is_stk or (not self.compression
and bits_per_sample in (8, 16, 32, 64)
and all(offsets[i] == offsets[i+1] - byte_counts[i]
for i in range(len(offsets)-1))))):
# contiguous data
fd.seek(offsets[0])
result = numpy.fromfile(fd, typecode, numpy.prod(shape))
result = result.astype('=' + dtype)
else:
if self.planar_configuration == '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")
unpack = lambda x: numpy.fromstring(x, typecode)
elif isinstance(bits_per_sample, tuple):
unpack = lambda x: unpackrgb(x, typecode, bits_per_sample)
else:
unpack = lambda x: unpackints(x, typecode, bits_per_sample,
runlen)
decompress = TIFF_DECOMPESSORS[self.compression]
if self.is_tiled:
result = numpy.empty(shape, dtype)
tw, tl, pl = 0, 0, 0
for offset, bytecount in zip(offsets, byte_counts):
fd.seek(offset)
tile = unpack(decompress(fd.read(bytecount)))
tile.shape = tile_shape
result[0, pl, tl:tl+tile_length,
tw:tw+tile_width, :] = tile
del tile
tw += tile_width
if tw >= shape[-2]:
tw, tl = 0, tl + tile_length
if tl >= shape[-3]:
tl, pl = 0, pl + 1
result = result[..., :image_length, :image_width, :]
else:
result = numpy.empty(shape, dtype).reshape(-1)
index = 0
for offset, bytecount in zip(offsets, byte_counts):
fd.seek(offset)
stripe = unpack(decompress(fd.read(bytecount)))
size = min(result.size, stripe.size)
result[index:index+size] = stripe[:size]
del stripe
index += size
result.shape = self._shape
if self.predictor == 'horizontal':
# workaround bug in LSM510 software
if not (self.parent.is_lsm and not self.compression):
numpy.cumsum(result, axis=3, dtype=dtype, out=result)
if colormapped and self.is_palette:
if self.color_map.shape[1] >= 2**bits_per_sample:
# FluoView and LSM might fail here
result = numpy.take(self.color_map, result, axis=1)
elif rgbonly and self.is_rgb and 'extra_samples' in self.tags:
# return only RGB and first alpha channel if exists
extra_samples = self.extra_samples
if self.tags['extra_samples'].count == 1:
extra_samples = (extra_samples, )
for i, exs in enumerate(extra_samples):
if exs in ('unassalpha', 'assocalpha'):
if self.planar_configuration == 'contig':
result = result[..., [0, 1, 2, 3+i]]
else:
result = result[:, [0, 1, 2, 3+i]]
break
else:
if self.planar_configuration == 'contig':
result = result[..., :3]
else:
result = result[:, :3]
if squeeze:
try:
result.shape = self.shape
except ValueError:
pass
return result
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,
self.compression if self.compression else 'raw',
','.join(t[3:] for t in ('is_stk', 'is_lsm', 'is_nih', 'is_ome',
'is_fluoview', 'is_reduced', 'is_tiled')
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 is_rgb(self):
"""True if page contains a RGB image."""
return self.tags['photometric'].value == 2
@lazyattr
def is_palette(self):
"""True if page contains a palette-colored image."""
return self.tags['photometric'].value == 3
@lazyattr
def is_tiled(self):
"""True if page contains tiled image."""
return 'tile_width' in self.tags
@lazyattr
def is_reduced(self):
"""True if page is a reduced image of another image."""
return bool(self.tags['new_subfile_type'].value & 1)
@lazyattr
def is_stk(self):
"""True if page contains MM_UIC2 tag."""
return 'mm_uic2' in self.tags
@lazyattr
def is_lsm(self):
"""True if page contains LSM CZ_LSM_INFO tag."""
return 'cz_lsm_info' in self.tags
@lazyattr
def is_fluoview(self):
"""True if page contains FluoView MM_STAMP tag."""
return 'mm_stamp' in self.tags
@lazyattr
def is_nih(self):
"""True if page contains NIH image header."""
return 'nih_image_header' in self.tags
@lazyattr
def is_ome(self):
"""True if page contains OME-XML in image_description tag."""
return ('image_description' in self.tags and self.tags[
'image_description'].value.startswith(b' parent.offset_size or code in CUSTOM_TAGS:
pos = fd.tell()
tof = {4: 'I', 8: 'Q'}[parent.offset_size]
self.value_offset = struct.unpack(byte_order+tof, value)[0]
fd.seek(self.value_offset)
if code in CUSTOM_TAGS:
readfunc = CUSTOM_TAGS[code][1]
value = readfunc(fd, byte_order, dtype, count)
fd.seek(0, 2) # bug in numpy/Python 3.x ?
if isinstance(value, dict): # numpy.core.records.record
value = Record(value)
elif code in TIFF_TAGS or dtype[-1] == 's':
value = struct.unpack(fmt, fd.read(size))
else:
value = read_numpy(fd, byte_order, dtype, count)
fd.seek(0, 2) # bug in numpy/Python 3.x ?
fd.seek(pos)
else:
value = struct.unpack(fmt, value[:size])
if not code in CUSTOM_TAGS:
if len(value) == 1:
value = value[0]
if dtype.endswith('s'):
value = stripnull(value)
self.code = code
self.name = name
self.dtype = dtype
self.count = count
self.value = value
def __str__(self):
"""Return string containing information about tag."""
return ' '.join(str(getattr(self, s)) for s in self.__slots__)
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:
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):
if k.startswith('_'):
continue
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])
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 TIFFtags with attribute access."""
def __str__(self):
"""Return string with information about all tags."""
s = []
#sortbycode = lambda a, b: cmp(a.code, b.code)
#for tag in sorted(self.values(), sortbycode):
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,
str(tag.value).split('\n', 1)[0])
s.append(line[:PRINT_LINE_LEN])
return '\n'.join(s)
def read_bytes(fd, byte_order, dtype, count):
"""Read tag data from file and return as byte string."""
return numpy.fromfile(fd, byte_order+dtype[-1], count).tostring()
def read_numpy(fd, byte_order, dtype, count):
"""Read tag data from file and return as numpy array."""
return numpy.fromfile(fd, byte_order+dtype[-1], count)
def read_nih_image_header(fd, byte_order, dtype, count):
"""Read NIH_IMAGE_HEADER tag from file and return as dictionary."""
fd.seek(12, 1)
return {'version': struct.unpack(byte_order+'H', fd.read(2))[0]}
def read_mm_header(fd, byte_order, dtype, count):
"""Read MM_HEADER tag from file and return as numpy.rec.array."""
return numpy.rec.fromfile(fd, MM_HEADER, 1, byteorder=byte_order)[0]
def read_mm_stamp(fd, byte_order, dtype, count):
"""Read MM_STAMP tag from file and return as numpy.array."""
return numpy.fromfile(fd, byte_order+'8f8', 1)[0]
def read_mm_uic1(fd, byte_order, dtype, count):
"""Read MM_UIC1 tag from file and return as dictionary."""
t = fd.read(8*count)
t = struct.unpack('%s%iI' % (byte_order, 2*count), t)
return dict((MM_TAG_IDS[k], v) for k, v in zip(t[::2], t[1::2])
if k in MM_TAG_IDS)
def read_mm_uic2(fd, byte_order, dtype, count):
"""Read MM_UIC2 tag from file and return as dictionary."""
result = {'number_planes': count}
values = numpy.fromfile(fd, byte_order+'I', 6*count)
result['z_distance'] = values[0::6] // values[1::6]
#result['date_created'] = tuple(values[2::6])
#result['time_created'] = tuple(values[3::6])
#result['date_modified'] = tuple(values[4::6])
#result['time_modified'] = tuple(values[5::6])
return result
def read_mm_uic3(fd, byte_order, dtype, count):
"""Read MM_UIC3 tag from file and return as dictionary."""
t = numpy.fromfile(fd, byte_order+'I', 2*count)
return {'wavelengths': t[0::2] // t[1::2]}
def read_mm_uic4(fd, byte_order, dtype, count):
"""Read MM_UIC4 tag from file and return as dictionary."""
t = struct.unpack(byte_order + 'hI'*count, fd.read(6*count))
return dict((MM_TAG_IDS[k], v) for k, v in zip(t[::2], t[1::2])
if k in MM_TAG_IDS)
def read_cz_lsm_info(fd, byte_order, dtype, count):
"""Read CS_LSM_INFO tag from file and return as numpy.rec.array."""
result = numpy.rec.fromfile(fd, CZ_LSM_INFO, 1,
byteorder=byte_order)[0]
{50350412: '1.3', 67127628: '2.0'}[result.magic_number] # validation
return result
def read_cz_lsm_time_stamps(fd, byte_order):
"""Read LSM time stamps from file and return as list."""
size, count = struct.unpack(byte_order+'II', fd.read(8))
if size != (8 + 8 * count):
raise ValueError("lsm_time_stamps block is too short")
return struct.unpack(('%s%dd' % (byte_order, count)),
fd.read(8*count))
def read_cz_lsm_event_list(fd, byte_order):
"""Read LSM events from file and return as list of (time, type, text)."""
count = struct.unpack(byte_order+'II', fd.read(8))[1]
events = []
while count > 0:
esize, etime, etype = struct.unpack(byte_order+'IdI', fd.read(16))
etext = stripnull(fd.read(esize - 16))
events.append((etime, etype, etext))
count -= 1
return events
def read_cz_lsm_scan_info(fd, byte_order):
"""Read LSM scan information from file and return as Record."""
block = Record()
blocks = [block]
unpack = struct.unpack
if 0x10000000 != struct.unpack(byte_order+"I", fd.read(4))[0]:
raise ValueError("not a lsm_scan_info structure")
fd.read(8)
while True:
entry, dtype, size = unpack(byte_order+"III", fd.read(12))
if dtype == 2:
value = stripnull(fd.read(size))
elif dtype == 4:
value = unpack(byte_order+"i", fd.read(4))[0]
elif dtype == 5:
value = unpack(byte_order+"d", fd.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:
block = blocks.pop()
else:
setattr(block, "unknown_%x" % entry, value)
if not blocks:
break
return block
def _replace_by(module_function, warn=False):
"""Try replace decorated function by module.function."""
def decorate(func, module_function=module_function, warn=warn):
sys.path.append(os.path.dirname(__file__))
try:
module, function = module_function.split('.')
func, oldfunc = getattr(__import__(module), function), func
globals()['__old_' + func.__name__] = oldfunc
except Exception:
if warn:
warnings.warn("failed to import %s" % module_function)
sys.path.pop()
return func
return decorate
@_replace_by('_tifffile.decodepackbits')
def decodepackbits(encoded):
"""Decompress PackBits encoded byte string.
PackBits is a simple byte-oriented run-length compression scheme.
"""
func = ord if sys.version[0] == '2' else lambda x: x
result = []
i = 0
try:
while True:
n = func(encoded[i]) + 1
i += 1
if n < 129:
result.extend(encoded[i:i+n])
i += n
elif n > 129:
result.extend(encoded[i:i+1] * (258-n))
i += 1
except IndexError:
pass
return b''.join(result) if sys.version[0] == '2' else bytes(result)
@_replace_by('_tifffile.decodelzw')
def decodelzw(encoded):
"""Decompress LZW (Lempel-Ziv-Welch) encoded TIFF strip (byte string).
The strip must begin with a CLEAR code and end with an EOI code.
This is an implementation of the LZW decoding algorithm described in (1).
It is not compatible with old style LZW compressed files like quad-lzw.tif.
"""
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 = code << (bitcount % 8)
code = 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 = oldcode = 0
result = []
while True:
code = next_code() # ~5% faster when inlining this function
bitcount += bitw
if code == 257: # EOI
break
if code == 256: # CLEAR
table = newtable[:]
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:
raise ValueError("unexpected end of stream (code %i)" % code)
return b''.join(result)
@_replace_by('_tifffile.unpackints')
def unpackints(data, dtype, itemsize, runlen=0):
"""Decompress byte string to array of integers of any bit size <= 32.
Parameters
----------
data : byte str
Data to decompress.
dtype : numpy.dtype or str
A numpy boolean or integer type.
itemsize : int
Number of bits per integer.
runlen : int
Number of consecutive integers, after which to start at next byte.
"""
if itemsize == 1: # bitarray
data = numpy.fromstring(data, '|B')
data = numpy.unpackbits(data)
if runlen % 8:
data = data.reshape(-1, runlen+(8-runlen%8))
data = data[:, :runlen].reshape(-1)
return data.astype(dtype)
dtype = numpy.dtype(dtype)
if itemsize in (8, 16, 32, 64):
return numpy.fromstring(data, dtype)
if itemsize < 1 or itemsize > 32:
raise ValueError("itemsize out of range: %i" % itemsize)
if dtype.kind not in "biu":
raise ValueError("invalid dtype")
itembytes = next(i for i in (1, 2, 4, 8) if 8 * i >= itemsize)
if itembytes != dtype.itemsize:
raise ValueError("dtype.itemsize too small")
if runlen == 0:
runlen = len(data) // itembytes
skipbits = runlen*itemsize % 8
if skipbits:
skipbits = 8 - skipbits
shrbits = itembytes*8 - itemsize
bitmask = int(itemsize*'1'+'0'*shrbits, 2)
dtypestr = '>' + dtype.char # dtype always big endian?
unpack = struct.unpack
l = runlen * (len(data)*8 // (runlen*itemsize + skipbits))
result = numpy.empty((l, ), dtype)
bitcount = 0
for i in range(len(result)):
start = bitcount // 8
s = data[start:start+itembytes]
try:
code = unpack(dtypestr, s)[0]
except Exception:
code = unpack(dtypestr, s + b'\x00'*(itembytes-len(s)))[0]
code = code << (bitcount % 8)
code = code & bitmask
result[i] = code >> shrbits
bitcount += itemsize
if (i+1) % runlen == 0:
bitcount += skipbits
return result
def unpackrgb(data, dtype='>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff)
>>> print(unpackrgb(data, '>> print(unpackrgb(data, '>> print(unpackrgb(data, '= bits)
data = numpy.fromstring(data, dtype.byteorder+dt)
result = numpy.empty((data.size, len(bitspersample)), dtype.char)
for i, bps in enumerate(bitspersample):
t = data >> int(numpy.sum(bitspersample[i+1:]))
t &= int('0b'+'1'*bps, 2)
if rescale:
o = ((dtype.itemsize * 8) // bps + 1) * bps
if o > data.dtype.itemsize * 8:
t = t.astype('I')
t *= (2**o - 1) // (2**bps - 1)
t //= 2**(o - (dtype.itemsize * 8))
result[:, i] = t
return result.reshape(-1)
def reorient(image, orientation):
"""Return reoriented view of image array.
Parameters
----------
image : numpy array
Non-squeezed output of asarray() functions.
Axes -3 and -2 must be image length and width respectively.
orientation : int or str
One of TIFF_ORIENTATIONS keys or values.
"""
o = TIFF_ORIENTATIONS.get(orientation, orientation)
if o == 'top_left':
return image
elif o == 'top_right':
return image[..., ::-1, :]
elif o == 'bottom_left':
return image[..., ::-1, :, :]
elif o == 'bottom_right':
return image[..., ::-1, ::-1, :]
elif o == 'left_top':
return numpy.swapaxes(image, -3, -2)
elif o == 'right_top':
return numpy.swapaxes(image, -3, -2)[..., ::-1, :]
elif o == 'left_bottom':
return numpy.swapaxes(image, -3, -2)[..., ::-1, :, :]
elif o == 'right_bottom':
return numpy.swapaxes(image, -3, -2)[..., ::-1, ::-1, :]
def stripnull(string):
"""Return string truncated at first null character."""
i = string.find(b'\x00')
return string if (i < 0) else string[:i]
def datetime_from_timestamp(n, epoch=datetime.datetime.fromordinal(693594)):
"""Return datetime object from timestamp in Excel serial format.
Examples
--------
>>> datetime_from_timestamp(40237.029999999795)
datetime.datetime(2010, 2, 28, 0, 43, 11, 999982)
"""
return epoch + datetime.timedelta(n)
def test_tifffile(directory='testimages', verbose=True):
"""Read all images in directory. Print error message on failure.
Examples
--------
>>> test_tifffile(verbose=False)
"""
import glob
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)
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',
6: 'cielab',
7: 'icclab',
8: 'itulab',
32844: 'logl',
32845: 'logluv'}
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'}
TIFF_DECOMPESSORS = {
None: lambda x: x,
'adobe_deflate': zlib.decompress,
'deflate': zlib.decompress,
'packbits': decodepackbits,
'lzw': decodelzw}
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: '1B', # 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'}
AXES_LABELS = {
'X': 'width',
'Y': 'height',
'Z': 'depth',
'S': 'sample',
'P': 'plane',
'T': 'time',
'C': 'channel', # color, emission wavelength
'A': 'angle',
'F': 'phase',
'R': 'tile', # region
'H': 'lifetime', # histogram
'E': 'lambda', # excitation wavelength
'L': 'exposure', # lux
'V': 'event',
'O': 'other'}
AXES_LABELS.update(dict((v, k) for k, v in AXES_LABELS.items()))
# MetaMorph STK tags
MM_TAG_IDS = {
0: 'auto_scale',
1: 'min_scale',
2: 'max_scale',
3: 'spatial_calibration',
#4: 'x_calibration',
#5: 'y_calibration',
#6: 'calibration_units',
#7: 'name',
8: 'thresh_state',
9: 'thresh_state_red',
11: 'thresh_state_green',
12: 'thresh_state_blue',
13: 'thresh_state_lo',
14: 'thresh_state_hi',
15: 'zoom',
#16: 'create_time',
#17: 'last_saved_time',
18: 'current_buffer',
19: 'gray_fit',
20: 'gray_point_count',
#21: 'gray_x',
#22: 'gray_y',
#23: 'gray_min',
#24: 'gray_max',
#25: 'gray_unit_name',
26: 'standard_lut',
27: 'wavelength',
#28: 'stage_position',
#29: 'camera_chip_offset',
#30: 'overlay_mask',
#31: 'overlay_compress',
#32: 'overlay',
#33: 'special_overlay_mask',
#34: 'special_overlay_compress',
#35: 'special_overlay',
36: 'image_property',
#37: 'stage_label',
#38: 'autoscale_lo_info',
#39: 'autoscale_hi_info',
#40: 'absolute_z',
#41: 'absolute_z_valid',
#42: 'gamma',
#43: 'gamma_red',
#44: 'gamma_green',
#45: 'gamma_blue',
#46: 'camera_bin',
47: 'new_lut',
#48: 'image_property_ex',
49: 'plane_property',
#50: 'user_lut_table',
51: 'red_autoscale_info',
#52: 'red_autoscale_lo_info',
#53: 'red_autoscale_hi_info',
54: 'red_minscale_info',
55: 'red_maxscale_info',
56: 'green_autoscale_info',
#57: 'green_autoscale_lo_info',
#58: 'green_autoscale_hi_info',
59: 'green_minscale_info',
60: 'green_maxscale_info',
61: 'blue_autoscale_info',
#62: 'blue_autoscale_lo_info',
#63: 'blue_autoscale_hi_info',
64: 'blue_min_scale_info',
65: 'blue_max_scale_info'}
#66: 'overlay_plane_color',
# Olymus 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', 'i4'),
('structure_size', 'i4'),
('dimension_x', 'i4'),
('dimension_y', 'i4'),
('dimension_z', 'i4'),
('dimension_channels', 'i4'),
('dimension_time', 'i4'),
('dimension_data_type', 'i4'),
('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'),
('data_type', 'u4'),
('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_information', 'u4'),
('offset_ks_data', 'u4'),
('offset_time_stamps', 'u4'),
('offset_event_list', 'u4'),
('offset_roi', 'u4'),
('offset_bleach_roi', 'u4'),
('offset_next_recording', 'u4'),
('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')]
# Import functions for LSM_INFO subrecords
CZ_LSM_INFO_READERS = {
'scan_information': read_cz_lsm_scan_info,
'time_stamps': read_cz_lsm_time_stamps,
'event_list': read_cz_lsm_event_list}
# 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'}
# Descriptions of cz_lsm_info.data_type
CZ_DATA_TYPES = {
0: 'varying data types',
2: '12 bit unsigned integer',
5: '32 bit float'}
CZ_LSM_SCAN_INFO_ARRAYS = {
0x20000000: "tracks",
0x30000000: "lasers",
0x60000000: "detectionchannels",
0x80000000: "illuminationchannels",
0xa0000000: "beamsplitters",
0xc0000000: "datachannels",
0x13000000: "markers",
0x11000000: "timers"}
CZ_LSM_SCAN_INFO_STRUCTS = {
0x40000000: "tracks",
0x50000000: "lasers",
0x70000000: "detectionchannels",
0x90000000: "illuminationchannels",
0xb0000000: "beamsplitters",
0xd0000000: "datachannels",
0x14000000: "markers",
0x12000000: "timers"}
CZ_LSM_SCAN_INFO_ATTRIBUTES = {
0x10000001: "name",
0x10000002: "description",
0x10000003: "notes",
0x10000004: "objective",
0x10000005: "processing_summary",
0x10000006: "special_scan_mode",
0x10000007: "oledb_recording_scan_type",
0x10000008: "oledb_recording_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_bccorrection",
0x10000049: "position_bccorrection1",
0x10000050: "position_bccorrection2",
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",
# lasers
0x50000001: "name",
0x50000002: "acquire",
0x50000003: "power",
# tracks
0x40000001: "multiplex_type",
0x40000002: "multiplex_order",
0x40000003: "sampling_mode",
0x40000004: "sampling_method",
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",
# detection_channels
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: "detection_channel_name",
0x70000015: "detection_detector_gain_bc1",
0x70000016: "detection_detector_gain_bc2",
0x70000017: "detection_amplifier_gain_bc1",
0x70000018: "detection_amplifier_gain_bc2",
0x70000019: "detection_amplifier_offset_bc1",
0x70000020: "detection_amplifier_offset_bc2",
0x70000021: "detection_spectral_scan_channels",
0x70000022: "detection_spi_wavelength_start",
0x70000023: "detection_spi_wavelength_stop",
0x70000026: "detection_dye_name",
0x70000027: "detection_dye_folder",
# illumination_channels
0x90000001: "name",
0x90000002: "power",
0x90000003: "wavelength",
0x90000004: "aquire",
0x90000005: "detchannel_name",
0x90000006: "power_bc1",
0x90000007: "power_bc2",
# beam_splitters
0xb0000001: "filter_set",
0xb0000002: "filter",
0xb0000003: "name",
# data_channels
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",
# markers
0x14000001: "name",
0x14000002: "description",
0x14000003: "trigger_in",
0x14000004: "trigger_out",
# timers
0x12000001: "name",
0x12000002: "description",
0x12000003: "interval",
0x12000004: "trigger_in",
0x12000005: "trigger_out",
0x12000006: "activation_time",
0x12000007: "activation_number"}
# Map TIFF tag code to attribute name, default value, type, count, validator
TIFF_TAGS = {
254: ('new_subfile_type', 0, 4, 1, TIFF_SUBFILE_TYPES()),
255: ('subfile_type', None, 3, 1,
{0: 'undefined', 1: 'image', 2: 'reduced_image', 3: 'page'}),
256: ('image_width', None, 4, 1, None),
257: ('image_length', None, 4, 1, None),
258: ('bits_per_sample', 1, 3, 1, None),
259: ('compression', 1, 3, 1, TIFF_COMPESSIONS),
262: ('photometric', None, 3, 1, TIFF_PHOTOMETRICS),
266: ('fill_order', 1, 3, 1, {1: 'msb2lsb', 2: 'lsb2msb'}),
269: ('document_name', None, 2, None, None),
270: ('image_description', None, 2, None, None),
271: ('make', None, 2, None, None),
272: ('model', None, 2, None, None),
273: ('strip_offsets', None, 4, None, None),
274: ('orientation', 1, 3, 1, TIFF_ORIENTATIONS),
277: ('samples_per_pixel', 1, 3, 1, None),
278: ('rows_per_strip', 2**32-1, 4, 1, None),
279: ('strip_byte_counts', None, 4, None, None),
#280: ('min_sample_value', 0, 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),
296: ('resolution_unit', 2, 4, 1, {1: 'none', 2: 'inch', 3: 'centimeter'}),
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'}),
320: ('color_map', None, 3, None, None),
322: ('tile_width', None, 4, 1, None),
323: ('tile_length', None, 4, 1, None),
324: ('tile_offsets', None, 4, None, None),
325: ('tile_byte_counts', None, 4, None, None),
338: ('extra_samples', None, 3, None,
{0: 'unspecified', 1: 'assocalpha', 2: 'unassalpha'}),
339: ('sample_format', 1, 3, 1, TIFF_SAMPLE_FORMATS),
530: ('ycbcr_subsampling', 1, 3, 2, None),
531: ('ycbcr_positioning', 1, 3, 1, None),
#37510: ('user_comment', None, None, None, None),
33432: ('copyright', None, 1, None, None),
32997: ('image_depth', None, 4, 1, None),
32998: ('tile_depth', None, 4, 1, None),
34665: ('exif_ifd', None, None, 1, None),
34853: ('gps_ifd', None, None, 1, None),
42112: ('gdal_metadata', None, 2, None, None)}
# 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_numpy),
33628: ('mm_uic1', read_mm_uic1),
33629: ('mm_uic2', read_mm_uic2),
33630: ('mm_uic3', read_mm_uic3),
33631: ('mm_uic4', read_mm_uic4),
34361: ('mm_header', read_mm_header),
34362: ('mm_stamp', read_mm_stamp),
34386: ('mm_user_block', read_bytes),
34412: ('cz_lsm_info', read_cz_lsm_info),
43314: ('nih_image_header', read_nih_image_header)}
# 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=4096, **kwargs):
"""Plot n-dimensional images using matplotlib.pyplot.
Return figure, subplot and plot axis.
Requires pyplot already imported ``from matplotlib import pyplot``.
Parameters
----------
bitspersample : int or None
Number of bits per channel in integer RGB images.
photometric : {'miniswhite', 'minisblack', 'rgb', or 'palette'}
The color space of the image data.
title : str
Window and subplot title.
figure : matplotlib.figure.Figure (optional).
Matplotlib to use for plotting.
subplot : int
A matplotlib.pyplot.subplot axis.
maxdim : int
maximum image size in any dimension.
kwargs : optional
Arguments for matplotlib.pyplot.imshow.
"""
#if photometric not in ('miniswhite', 'minisblack', 'rgb', 'palette'):
# raise ValueError("Can't handle %s photometrics" % photometric)
isrgb = photometric in ('rgb', 'palette')
data = numpy.atleast_2d(data.squeeze())
data = data[(slice(0, maxdim), ) * len(data.shape)]
dims = data.ndim
if dims < 2:
raise ValueError("not an image")
elif dims == 2:
dims = 0
isrgb = False
else:
if (isrgb and data.shape[-3] in (3, 4)):
data = numpy.swapaxes(data, -3, -2)
data = numpy.swapaxes(data, -2, -1)
elif (not isrgb and data.shape[-1] in (3, 4)):
data = numpy.swapaxes(data, -3, -1)
data = numpy.swapaxes(data, -2, -1)
isrgb = isrgb and data.shape[-1] in (3, 4)
dims -= 3 if isrgb else 2
if photometric == 'palette':
datamax = data.max()
if datamax > 255:
data >>= 8 # possible precision loss
data = data.astype('B')
elif data.dtype.kind in 'ui':
if not isrgb or bitspersample is None:
bitspersample = int(math.ceil(math.log(data.max(), 2)))
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
if vmax is None:
vmax = datamax
if vmin is None:
if data.dtype.kind != 'f':
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:
pyplot.title(title, size=11)
if cmap is None:
if photometric == 'miniswhite':
cmap = 'gray_r' if vmin == 0 else 'coolwarm_r'
else:
cmap = 'gray' if vmin == 0 else 'coolwarm'
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 re
import optparse
search_doc = lambda r, d: re.search(r, __doc__).group(1) if __doc__ else d
parser = optparse.OptionParser(
usage="usage: %prog [options] path",
description=search_doc("\n\n([^|]*?)\n\n", ''),
version="%%prog %s" % search_doc(":Version: (.*)", "Unknown"))
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('--noplot', dest='noplot', action='store_true', default=False,
help="don't display images")
opt('--interpol', dest='interpol', metavar='INTERPOL', default='bilinear',
help="image interpolation method")
opt('--dpi', dest='dpi', type='int', default=96,
help="set plot resolution")
opt('--debug', dest='debug', action='store_true', default=False,
help="raise exception on failures")
opt('--test', dest='test', action='store_true', default=False,
help="try read all images in path")
opt('--doctest', dest='doctest', action='store_true', default=False,
help="runs the internal tests")
opt('-v', '--verbose', dest='verbose', action='store_true', default=True)
opt('-q', '--quiet', dest='verbose', action='store_false')
settings, path = parser.parse_args()
path = ' '.join(path)
if settings.doctest:
import doctest
doctest.testmod()
return 0
if not path:
parser.error("No file specified")
if settings.test:
test_tifffile(path, settings.verbose)
return 0
print("Reading file structure...", end=' ')
start = time.time()
try:
tif = TIFFfile(path)
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=' ')
notnone = lambda x: next(i for i in x if i is not None)
start = time.time()
try:
if settings.page >= 0:
images = [(tif.asarray(key=settings.page),
tif[settings.page])]
elif settings.series >= 0:
images = [(tif.asarray(series=settings.series),
notnone(tif.series[settings.series].pages))]
else:
images = []
for i, s in enumerate(tif.series):
try:
images.append(
(tif.asarray(series=i), notnone(s.pages)))
except ValueError as e:
images.append((None, notnone(s.pages)))
if settings.debug:
raise
else:
print("\n* series %i failed: %s... " % (i, e),
end='')
print("%.3f ms" % ((time.time()-start) * 1e3))
except Exception as e:
if settings.debug:
raise
else:
print(e)
tif.close()
print("\nTIFF file:", tif)
print()
for i, s in enumerate(tif.series):
print ("Series %i" % i)
print(s)
print()
for i, page in images:
print(page)
print(page.tags)
if page.is_palette:
print("\nColor Map:", page.color_map.shape, page.color_map.dtype)
for attr in ('cz_lsm_info', 'cz_lsm_scan_information',
'mm_uic_tags', 'mm_header', 'nih_image_header'):
if hasattr(page, attr):
print("", attr.upper(), Record(getattr(page, attr)), sep="\n")
print()
if images and not settings.noplot:
try:
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot
except ImportError as e:
warnings.warn("failed to import matplotlib.\n%s" % e)
else:
for img, page in images:
if img is None:
continue
vmin, vmax = None, None
if page.is_stk:
try:
vmin = page.mm_uic_tags['min_scale']
vmax = page.mm_uic_tags['max_scale']
except KeyError:
pass
else:
if vmax <= vmin:
vmin, vmax = None, None
title = "%s\n %s" % (str(tif), str(page))
imshow(img, title=title, vmin=vmin, vmax=vmax,
bitspersample=page.bits_per_sample,
photometric=page.photometric,
interpolation=settings.interpol,
dpi=settings.dpi)
pyplot.show()
__version__ = '2012.07.05'
__docformat__ = 'restructuredtext en'
if __name__ == "__main__":
sys.exit(main())