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Read and write TIFF(r) files

Project description

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, QPTIFF, 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.

Only 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


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


Pass 2899 tests. 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).
Fix infinite loop reading more than two tags of same code in IFD. Delay import of logging module.
Fix OME-XML detection for files created by Imaris. Remove or replace assert statements.
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.
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.
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.
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.
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).
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.
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.
Assemble IFDs in memory to speed-up writing on some slow media. Handle discontinued arguments fastij, multifile_close, and pages.
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).
Fix decoding deflate without imagecodecs.
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.
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.
Rename imsave function to imwrite. Readd Python implementations of packints, delta, and bitorder codecs. Fix TiffFrame.compression AttributeError.

Refer to the CHANGES file for older revisions.


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

Tested on little-endian platforms only.

Python 32-bit versions are deprecated.

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 is 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 tiles with dimensions > 65536 are not readable with libjpeg. Tifffile can read NDPI files < 4 GB and decompress large JPEG tiles using 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 on Python 3 if the image data is 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:

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


  1. TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated.
  2. TIFF File Format FAQ.
  3. MetaMorph Stack (STK) Image File Format.
  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.
  6. UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
  7. Micro-Manager File Formats.
  8. Tags for TIFF and Related Specifications. Digital Preservation.
  9. ScanImage BigTiff Specification - ScanImage 2016. ScanImage+BigTiff+Specification
  10. CIPA DC-008-2016: Exchangeable image file format for digital still cameras: Exif Version 2.31.
  11. ZIF, the Zoomable Image File format.
  12. GeoTIFF File Format


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

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
>>> page = tif.pages[0]  # get shape and dtype of the image in the first page
>>> page.shape
(256, 256)
>>> page.dtype
>>> page.axes
>>> series = tif.series[0]  # get shape and dtype of the first image series
>>> series.shape
(57, 256, 256)
>>> series.dtype
>>> series.axes
>>> 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']

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.code
>>> tag.count
>>> tag.dtype

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]
>>> 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]):
...[i], compress=6, photometric='minisblack')

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

>>> with TiffFile('temp.tif') as tif:
...     image_stack = tif.asarray()
...     for page in tif.pages:
...         for tag in page.tags:
...             tag_name, tag_value =, 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:
..., compress=6, photometric='rgb')
..., 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
>>> data = image_sequence.asarray()
>>> data.shape
(1, 2, 64, 64)

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