tifffile 2020.7.4

Read and write TIFF(r) files

Tifffile is a Python library to

1. store numpy arrays in TIFF (Tagged Image File Format) files, and

2. read image and metadata from TIFF-like files used in bioimaging.

Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, SGI, NIHImage, ImageJ, MicroManager, FluoView, ScanImage, SEQ, GEL, SVS, SCN, SIS, ZIF (Zoomable Image File Format), QPTIFF (QPI), NDPI, and GeoTIFF files.

Numpy arrays can be written to TIFF, BigTIFF, and ImageJ hyperstack compatible files in multi-page, memory-mappable, tiled, predicted, or compressed form.

A subset of the TIFF specification is supported, mainly uncompressed and losslessly compressed 8, 16, 32 and 64-bit integer, 16, 32 and 64-bit float, grayscale and multi-sample images. Specifically, reading slices of image data, CCITT and OJPEG compression, chroma subsampling without JPEG compression, color space transformations, samples with differing types, or IPTC and XMP metadata are not implemented.

TIFF(r), the Tagged Image File Format, is a trademark and under control of Adobe Systems Incorporated. BigTIFF allows for files larger than 4 GB. STK, LSM, FluoView, SGI, SEQ, GEL, QPTIFF, NDPI, and OME-TIFF, are custom extensions defined by Molecular Devices (Universal Imaging Corporation), Carl Zeiss MicroImaging, Olympus, Silicon Graphics International, Media Cybernetics, Molecular Dynamics, PerkinElmer, Hamamatsu, and the Open Microscopy Environment consortium respectively.

For command line usage run python -m tifffile --help

Author:

Christoph Gohlke

Organization:

Laboratory for Fluorescence Dynamics, University of California, Irvine

License:

BSD 3-Clause

Version:

2020.7.4

Requirements

This release has been tested with the following requirements and dependencies (other versions may work):

• CPython 3.7.8, 3.8.3 64-bit

• Numpy 1.18.5

• Imagecodecs 2020.5.30 (required only for encoding or decoding LZW, JPEG, etc.)

• Matplotlib 3.2.2 (required only for plotting)

Revisions

2020.7.4

Pass 2932 tests. Deprecate support for Python 3.6 (NEP 29). Move pyramidal subresolution series to TiffPageSeries.levels (breaking). Add parser for SVS, SCN, NDPI, and QPI pyramidal series. Read single-file OME-TIFF pyramids. Read NDPI files > 4 GB (#15). Include SubIFDs in generic series. Preliminary support for writing packed integer arrays (#11, WIP). Read more LSM info subrecords. Fix missing ReferenceBlackWhite tag for YCbCr photometrics. Fix reading lossless JPEG compressed DNG files.

2020.6.3

Support os.PathLike file names (#9).

2020.5.30

Re-add pure Python PackBits decoder.

2020.5.25

Make imagecodecs an optional dependency again. Disable multi-threaded decoding of small LZW compressed segments. Fix caching of TiffPage.decode function. Fix xml.etree.cElementTree ImportError on Python 3.9. Fix tostring DeprecationWarning.

2020.5.11

Fix reading ImageJ grayscale mode RGB images (#6). Remove napari reader plugin.

2020.5.7

Add napari reader plugin (tentative). Fix writing single tiles larger than image data (#3). Always store ExtraSamples values in tuple (breaking).

2020.5.5

Allow to write tiled TIFF from iterator of tiles (WIP). Add function to iterate over decoded segments of TiffPage (WIP). Pass chunks of segments to ThreadPoolExecutor.map to reduce memory usage. Fix reading invalid files with too many strips. Fix writing over-aligned image data. Detect OME-XML without declaration (#2). Support LERC compression (WIP). Delay load imagecodecs functions. Remove maxsize parameter from asarray (breaking). Deprecate ijmetadata parameter from TiffWriter.save (use metadata).

2020.2.16

Add function to decode individual strips or tiles. Read strips and tiles in order of their offsets. Enable multi-threading when decompressing multiple strips. Replace TiffPage.tags dictionary with TiffTags (breaking). Replace TIFF.TAGS dictionary with TiffTagRegistry. Remove TIFF.TAG_NAMES (breaking). Improve handling of TiffSequence parameters in imread. Match last uncommon parts of file paths to FileSequence pattern (breaking). Allow letters in FileSequence pattern for indexing well plate rows. Allow to reorder axes in FileSequence. Allow to write > 4 GB arrays to plain TIFF when using compression. Allow to write zero size numpy arrays to nonconformant TIFF (tentative). Fix xml2dict. Require imagecodecs >= 2020.1.31. Remove support for imagecodecs-lite (breaking). Remove verify parameter to asarray function (breaking). Remove deprecated lzw_decode functions (breaking). Remove support for Python 2.7 and 3.5 (breaking).

2019.7.26

Fix infinite loop reading more than two tags of same code in IFD. Delay import of logging module.

2019.7.20

Fix OME-XML detection for files created by Imaris. Remove or replace assert statements.

2019.7.2

Do not write SampleFormat tag for unsigned data types. Write ByteCount tag values as SHORT or LONG if possible. Allow to specify axes in FileSequence pattern via group names. Add option to concurrently read FileSequence using threads. Derive TiffSequence from FileSequence. Use str(datetime.timedelta) to format Timer duration. Use perf_counter for Timer if possible.

2019.6.18

Fix reading planar RGB ImageJ files created by Bio-Formats. Fix reading single-file, multi-image OME-TIFF without UUID. Presume LSM stores uncompressed images contiguously per page. Reformat some complex expressions.

2019.5.30

Ignore invalid frames in OME-TIFF. Set default subsampling to (2, 2) for RGB JPEG compression. Fix reading and writing planar RGB JPEG compression. Replace buffered_read with FileHandle.read_segments. Include page or frame numbers in exceptions and warnings. Add Timer class.

2019.5.22

Add optional chroma subsampling for JPEG compression. Enable writing PNG, JPEG, JPEGXR, and JPEG2K compression (WIP). Fix writing tiled images with WebP compression. Improve handling GeoTIFF sparse files.

2019.3.18

Fix regression decoding JPEG with RGB photometrics. Fix reading OME-TIFF files with corrupted but unused pages. Allow to load TiffFrame without specifying keyframe. Calculate virtual TiffFrames for non-BigTIFF ScanImage files > 2GB. Rename property is_chroma_subsampled to is_subsampled (breaking). Make more attributes and methods private (WIP).

2019.3.8

Fix MemoryError when RowsPerStrip > ImageLength. Fix SyntaxWarning on Python 3.8. Fail to decode JPEG to planar RGB (tentative). Separate public from private test files (WIP). Allow testing without data files or imagecodecs.

2019.2.22

Use imagecodecs-lite as a fallback for imagecodecs. Simplify reading numpy arrays from file. Use TiffFrames when reading arrays from page sequences. Support slices and iterators in TiffPageSeries sequence interface. Auto-detect uniform series. Use page hash to determine generic series. Turn off TiffPages cache (tentative). Pass through more parameters in imread. Discontinue movie parameter in imread and TiffFile (breaking). Discontinue bigsize parameter in imwrite (breaking). Raise TiffFileError in case of issues with TIFF structure. Return TiffFile.ome_metadata as XML (breaking). Ignore OME series when last dimensions are not stored in TIFF pages.

2019.2.10

Assemble IFDs in memory to speed-up writing on some slow media. Handle discontinued arguments fastij, multifile_close, and pages.

2019.1.30

Use black background in imshow. Do not write datetime tag by default (breaking). Fix OME-TIFF with SamplesPerPixel > 1. Allow 64-bit IFD offsets for NDPI (files > 4GB still not supported).

2019.1.4

Fix decoding deflate without imagecodecs.

2019.1.1

Update copyright year. Require imagecodecs >= 2018.12.16. Do not use JPEG tables from keyframe. Enable decoding large JPEG in NDPI. Decode some old-style JPEG. Reorder OME channel axis to match PlanarConfiguration storage. Return tiled images as contiguous arrays. Add decode_lzw proxy function for compatibility with old czifile module. Use dedicated logger.

2018.11.28

Make SubIFDs accessible as TiffPage.pages. Make parsing of TiffSequence axes pattern optional (breaking). Limit parsing of TiffSequence axes pattern to file names, not path names. Do not interpolate in imshow if image dimensions <= 512, else use bilinear. Use logging.warning instead of warnings.warn in many cases. Fix numpy FutureWarning for out == ‘memmap’. Adjust ZSTD and WebP compression to libtiff-4.0.10 (WIP). Decode old-style LZW with imagecodecs >= 2018.11.8. Remove TiffFile.qptiff_metadata (QPI metadata are per page). Do not use keyword arguments before variable positional arguments. Make either all or none return statements in a function return expression. Use pytest parametrize to generate tests. Replace test classes with functions.

2018.11.6

Rename imsave function to imwrite. Readd Python implementations of packints, delta, and bitorder codecs. Fix TiffFrame.compression AttributeError.

2018.10.18

…

Refer to the CHANGES file for older revisions.

Notes

The API is not stable yet and might change between revisions.

Tested on little-endian platforms only.

Python 32-bit versions are deprecated. Python <= 3.6 are no longer supported.

Tifffile relies on the imagecodecs package for encoding and decoding LZW, JPEG, and other compressed images.

Several TIFF-like formats do not strictly adhere to the TIFF6 specification, some of which allow file or data sizes to exceed the 4 GB limit:

• BigTIFF is identified by version number 43 and uses different file header, IFD, and tag structures with 64-bit offsets. It adds more data types. Tifffile can read and write BigTIFF files.

• ImageJ hyperstacks store all image data, which may exceed 4 GB, contiguously after the first IFD. Files > 4 GB contain one IFD only. The size (shape and dtype) of the up to 6-dimensional image data can be determined from the ImageDescription tag of the first IFD, which is Latin-1 encoded. Tifffile can read and write ImageJ hyperstacks.

• OME-TIFF stores up to 8-dimensional data in one or multiple TIFF of BigTIFF files. The 8-bit UTF-8 encoded OME-XML metadata found in the ImageDescription tag of the first IFD defines the position of TIFF IFDs in the high dimensional data. Tifffile can read OME-TIFF files, except when the OME-XML metadata are stored in a separate file.

• LSM stores all IFDs below 4 GB but wraps around 32-bit StripOffsets. The StripOffsets of each series and position require separate unwrapping. The StripByteCounts tag contains the number of bytes for the uncompressed data. Tifffile can read large LSM files.

• NDPI uses some 64-bit offsets in the file header, IFD, and tag structures. Tag values/offsets can be corrected using high bits stored after IFD structures. JPEG compressed segments with dimensions >65536 are not readable with libjpeg. Tifffile can read NDPI files > 4 GB. JPEG segments up to 2 GB compressed size can be decoded with the imagecodecs library on Windows.

• ScanImage optionally allows corrupt non-BigTIFF files > 2 GB. The values of StripOffsets and StripByteCounts can be recovered using the constant differences of the offsets of IFD and tag values throughout the file. Tifffile can read such files if the image data are stored contiguously in each page.

• GeoTIFF sparse files allow strip or tile offsets and byte counts to be 0. Such segments are implicitly set to 0 or the NODATA value on reading. Tifffile can read GeoTIFF sparse files.

Other libraries for reading scientific TIFF files from Python:

• Python-bioformats

• Imread

• GDAL

• OpenSlide-python

• PyLibTiff

• SimpleITK

• PyLSM

• PyMca.TiffIO.py (same as fabio.TiffIO)

• BioImageXD.Readers

• CellCognition

• pymimage

• pytiff

• ScanImageTiffReaderPython

• bigtiff

Some libraries are using tifffile to write OME-TIFF files:

• Zeiss Apeer OME-TIFF library

• Allen Institute for Cell Science imageio

• xtiff

References

1. TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated. https://www.adobe.io/open/standards/TIFF.html

2. TIFF File Format FAQ. https://www.awaresystems.be/imaging/tiff/faq.html

3. MetaMorph Stack (STK) Image File Format. http://mdc.custhelp.com/app/answers/detail/a_id/18862

4. Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010). Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011

5. The OME-TIFF format. https://docs.openmicroscopy.org/ome-model/5.6.4/ome-tiff/

6. UltraQuant(r) Version 6.0 for Windows Start-Up Guide. http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf

7. Micro-Manager File Formats. https://micro-manager.org/wiki/Micro-Manager_File_Formats

8. Tags for TIFF and Related Specifications. Digital Preservation. https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml

9. ScanImage BigTiff Specification - ScanImage 2016. http://scanimage.vidriotechnologies.com/display/SI2016/ ScanImage+BigTiff+Specification

10. CIPA DC-008-2016: Exchangeable image file format for digital still cameras: Exif Version 2.31. http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf

11. ZIF, the Zoomable Image File format. http://zif.photo/

12. GeoTIFF File Format https://gdal.org/drivers/raster/gtiff.html

Examples

Save a 3D numpy array to a multi-page, 16-bit grayscale TIFF file:

>>> data = numpy.random.randint(0, 2**12, (4, 301, 219), 'uint16')
>>> imwrite('temp.tif', data, photometric='minisblack')

Read the whole image stack from the TIFF file as numpy array:

>>> image_stack = imread('temp.tif')
>>> image_stack.shape
(4, 301, 219)
>>> image_stack.dtype
dtype('uint16')

Read the image from the first page in the TIFF file as numpy array:

>>> image = imread('temp.tif', key=0)
>>> image.shape
(301, 219)

Read images from a sequence of TIFF files as numpy array:

>>> image_sequence = imread(['temp.tif', 'temp.tif'])
>>> image_sequence.shape
(2, 4, 301, 219)

Save a numpy array to a single-page RGB TIFF file:

>>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8')
>>> imwrite('temp.tif', data, photometric='rgb')

Save a floating-point array and metadata, using zlib compression:

>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
>>> imwrite('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})

Save a volume with xyz voxel size 2.6755x2.6755x3.9474 micron^3 to an ImageJ formatted TIFF file:

>>> volume = numpy.random.randn(57*256*256).astype('float32')
>>> volume.shape = 1, 57, 1, 256, 256, 1  # dimensions in TZCYXS order
>>> imwrite('temp.tif', volume, imagej=True, resolution=(1./2.6755, 1./2.6755),
...         metadata={'spacing': 3.947368, 'unit': 'um'})

Get the shape and dtype of the volume stored in the TIFF file:

>>> tif = TiffFile('temp.tif')
>>> len(tif.pages)  # number of pages in the file
57
>>> page = tif.pages[0]  # get shape and dtype of the image in the first page
>>> page.shape
(256, 256)
>>> page.dtype
dtype('float32')
>>> page.axes
'YX'
>>> series = tif.series[0]  # get shape and dtype of the first image series
>>> series.shape
(57, 256, 256)
>>> series.dtype
dtype('float32')
>>> series.axes
'ZYX'
>>> tif.close()

Read hyperstack and metadata from the ImageJ file:

>>> with TiffFile('temp.tif') as tif:
...     imagej_hyperstack = tif.asarray()
...     imagej_metadata = tif.imagej_metadata
>>> imagej_hyperstack.shape
(57, 256, 256)
>>> imagej_metadata['slices']
57

Read the “XResolution” tag from the first page in the TIFF file:

>>> with TiffFile('temp.tif') as tif:
...     tag = tif.pages[0].tags['XResolution']
>>> tag.value
(2000, 5351)
>>> tag.name
'XResolution'
>>> tag.code
282
>>> tag.count
1
>>> tag.dtype
'2I'

Read images from a selected range of pages:

>>> image = imread('temp.tif', key=range(4, 40, 2))
>>> image.shape
(18, 256, 256)

Create an empty TIFF file and write to the memory-mapped numpy array:

>>> memmap_image = memmap('temp.tif', shape=(256, 256), dtype='float32')
>>> memmap_image[255, 255] = 1.0
>>> memmap_image.flush()
>>> del memmap_image

Memory-map image data of the first page in the TIFF file:

>>> memmap_image = memmap('temp.tif', page=0)
>>> memmap_image[255, 255]
1.0
>>> del memmap_image

Successively append images to a BigTIFF file, which can exceed 4 GB:

>>> data = numpy.random.randint(0, 255, (5, 2, 3, 301, 219), 'uint8')
>>> with TiffWriter('temp.tif', bigtiff=True) as tif:
...     for i in range(data.shape[0]):
...         tif.save(data[i], compress=6, photometric='minisblack')

Append an image to the existing TIFF file:

>>> data = numpy.random.randint(0, 255, (301, 219, 3), 'uint8')
>>> imwrite('temp.tif', data, append=True)

Iterate over pages and tags in the TIFF file and successively read images:

>>> with TiffFile('temp.tif') as tif:
...     for page in tif.pages:
...         for tag in page.tags:
...             tag_name, tag_value = tag.name, tag.value
...         image = page.asarray()

Save two image series to a TIFF file:

>>> data0 = numpy.random.randint(0, 255, (301, 219, 3), 'uint8')
>>> data1 = numpy.random.randint(0, 255, (5, 301, 219), 'uint16')
>>> with TiffWriter('temp.tif') as tif:
...     tif.save(data0, compress=6, photometric='rgb')
...     tif.save(data1, compress=6, photometric='minisblack', contiguous=False)

Read the second image series from the TIFF file:

>>> series1 = imread('temp.tif', series=1)
>>> series1.shape
(5, 301, 219)

Read an image stack from a series of TIFF files with a file name pattern:

>>> imwrite('temp_C001T001.tif', numpy.random.rand(64, 64))
>>> imwrite('temp_C001T002.tif', numpy.random.rand(64, 64))
>>> image_sequence = TiffSequence('temp_C001*.tif', pattern='axes')
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
'CT'
>>> data = image_sequence.asarray()
>>> data.shape
(1, 2, 64, 64)

Create a TIFF file from a generator of tiles:

>>> def tiles():
...     data = numpy.arange(3*4*16*16, dtype='uint16').reshape((3*4, 16, 16))
...     for i in range(data.shape[0]): yield data[i]
>>> imwrite('temp.tif', tiles(), dtype='uint16', shape=(48, 64), tile=(16, 16))

Write a tiled, multi-resolution, pyramidal TIFF file using JPEG compression:

>>> data = numpy.arange(1024*1024*3, dtype='uint8').reshape((1024, 1024, 3))
>>> with TiffWriter('temp.tif') as tif:
...     options = dict(tile=(256, 256), compress='jpeg', metadata=None)
...     tif.save(data, **options)
...     # save pyramid levels. In production use resampling to generate levels!
...     tif.save(data[::2, ::2], subfiletype=1, **options)
...     tif.save(data[::4, ::4], subfiletype=1, **options)

Access the image levels in the pyramidal TIFF file:

>>> baseimage = imread('temp.tif')
>>> second_level = imread('temp.tif', series=0, level=1)
>>> with TiffFile('temp.tif') as tif:
...     baseimage = tif.series[0].asarray()
...     second_level = tif.series[0].levels[1].asarray()

Iterate over and decode single JPEG compressed tiles in the TIFF file:

>>> with TiffFile('temp.tif') as tif:
...     fh = tif.filehandle
...     for page in tif.pages:
...         jpegtables = page.tags.get('JPEGTables', None)
...         if jpegtables is not None:
...             jpegtables = jpegtables.value
...         for index, (offset, bytecount) in enumerate(
...             zip(page.dataoffsets, page.databytecounts)
...         ):
...             fh.seek(offset)
...             data = fh.read(bytecount)
...             tile, indices, shape = page.decode(data, index, jpegtables)