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Read and write TIFF 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, BIF, ZIF (Zoomable Image File Format), QPTIFF (QPI), NDPI, and GeoTIFF files.

Image data can be read as NumPy arrays or Zarr arrays/groups from strips, tiles, pages (IFDs), SubIFDs, higher order series, and pyramidal levels.

Image data can be written to TIFF, BigTIFF, OME-TIFF, and ImageJ hyperstack compatible files in multi-page, volumetric, pyramidal, memory-mappable, tiled, predicted, or compressed form.

Tifffile can also be used to inspect TIFF structures, read image data from multi-dimensional file sequences, write fsspec ReferenceFileSystem for TIFF files and image file sequences, patch TIFF tag values, and parse many proprietary metadata formats.

Author:Christoph Gohlke
License:BSD 3-Clause


Install the tifffile package and recommended dependencies from the Python Package Index:

python -m pip install -U tifffile imagecodecs matplotlib lxml zarr fsspec

Tifffile is also available in other package repositories such as Anaconda, Debian, and MSYS2.


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



  • Pass 4918 tests.
  • Fix writing ImageJ format with hyperstack argument.
  • Fix writing description with metadata disabled.
  • Add option to disable writing shaped metadata in TiffWriter.


  • Fix regression using imread out argument (#147).
  • Fix imshow show argument.
  • Support fsspec OpenFile.


  • Fix regression writing default resolutionunit (#145).
  • Add strptime function parsing common datetime formats.


  • Fix reading corrupted WebP compressed segments missing alpha channel (#122).
  • Fix regression reading compressed ImageJ files.


  • Rename FileSequence.labels attribute to dims (breaking).
  • Rename tifffile_geodb module to geodb (breaking).
  • Rename TiffFile._astuple method to astuple (breaking).
  • Rename noplots command line argument to maxplots (breaking).
  • Fix reading ImageJ hyperstacks with non-TZC order.
  • Fix colorspace of JPEG segments encoded by Bio-Formats.
  • Fix fei_metadata for HELIOS FIB-SEM (#141, needs test).
  • Add xarray style properties to TiffPage (WIP).
  • Add option to specify OME-XML for TiffFile.
  • Add option to control multiscales in ZarrTiffStore.
  • Support writing to uncompressed ZarrTiffStore.
  • Support writing empty images with tiling.
  • Support overwriting some tag values in NDPI (#137).
  • Support Jetraw compression (experimental).
  • Standardize resolution parameter and property.
  • Deprecate third resolution argument on write (use resolutionunit).
  • Deprecate tuple type compression argument on write (use compressionargs).
  • Deprecate enums in TIFF namespace (use enums from module).
  • Improve default number of threads to write compressed segments (#139).
  • Parse metaseries time values as datetime objects (#143).
  • Increase internal read and write buffers to 256 MB.
  • Convert some warnings to debug messages.
  • Declare all classes final.
  • Add script to generate documentation via Sphinx.
  • Convert docstrings to Google style with Sphinx directives.


  • Allow to write NewSubfileType=0 (#132).
  • Support writing iterators of strip or tile bytes.
  • Convert iterables (not iterators) to NumPy arrays when writing.
  • Explicitly specify optional keyword parameters for imread and imwrite.
  • Return number of written bytes from FileHandle write functions.


  • Add option to specify fsspec version 1 URL template name (#131).
  • Ignore invalid dates in UIC tags (#129).
  • Fix zlib_encode and lzma_encode to work with non-contiguous arrays (#128).
  • Fix delta_encode to preserve byteorder of ndarrays.
  • Move Imagecodecs fallback functions to private module and add tests.


  • Fix AttributeError in TiffFile.shaped_metadata (#127).
  • Fix TiffTag.overwrite with pre-packed binary value.
  • Write sparse TIFF if tile iterator contains None.
  • Raise ValueError when writing photometric mode with too few samples.
  • Improve test coverage.


  • Add type hints for Python 3.10 (WIP).
  • Fix Mypy errors (breaking).
  • Mark many parameters positional-only or keyword-only (breaking).
  • Remove deprecated pages parameter from imread (breaking).
  • Remove deprecated compress and ijmetadata write parameters (breaking).
  • Remove deprecated fastij and movie parameters from TiffFile (breaking).
  • Remove deprecated multifile parameters from TiffFile (breaking).
  • Remove deprecated tif parameter from TiffTag.overwrite (breaking).
  • Remove deprecated file parameter from FileSequence.asarray (breaking).
  • Remove option to pass imread class to FileSequence (breaking).
  • Remove optional parameters from __str__ functions (breaking).
  • Rename TiffPageSeries.offset to dataoffset (breaking)
  • Change TiffPage.pages to None if no SubIFDs are present (breaking).
  • Change TiffPage.index to int (breaking).
  • Change TiffPage.is_contiguous, is_imagej, and is_shaped to bool (breaking).
  • Add TiffPage imagej_description and shaped_description properties.
  • Add TiffFormat abstract base class.
  • Deprecate lazyattr and use functools.cached_property instead (breaking).
  • Julian_datetime raises ValueError for dates before year 1 (breaking).
  • Regressed import time due to typing.


Refer to the CHANGES file for older revisions.


TIFF, the Tagged Image File Format, was created by the Aldus Corporation and Adobe Systems Incorporated. STK, LSM, FluoView, SGI, SEQ, GEL, QPTIFF, NDPI, SCN, SVS, ZIF, BIF, 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, Leica, ObjectivePathology, Roche Digital Pathology, and the Open Microscopy Environment consortium, respectively.

Tifffile supports a subset of the TIFF6 specification, mainly 8, 16, 32, and 64-bit integer, 16, 32 and 64-bit float, grayscale and multi-sample images. Specifically, CCITT and OJPEG compression, chroma subsampling without JPEG compression, color space transformations, samples with differing types, or IPTC, ICC, and XMP metadata are not implemented.

Besides classic TIFF, tifffile supports several TIFF-like formats that do not strictly adhere to the TIFF6 specification. Some formats allow file and data sizes to exceed the 4 GB limit of the classic TIFF:

  • BigTIFF is identified by version number 43 and uses different file header, IFD, and tag structures with 64-bit offsets. The format also adds 64-bit 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 and shape 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 files store up to 8-dimensional image data in one or multiple TIFF or BigTIFF files. The 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 image data. Tifffile can read OME-TIFF files and write NumPy arrays to single-file OME-TIFF.
  • Carl Zeiss LSM files store all IFDs below 4 GB and wrap around 32-bit StripOffsets pointing to image data above 4 GB. 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 LSM files of any size.
  • MetaMorph Stack, STK files contain additional image planes stored contiguously after the image data of the first page. The total number of planes is equal to the count of the UIC2tag. Tifffile can read STK files.
  • ZIF, the Zoomable Image File format, is a subspecification of BigTIFF with SGI’s ImageDepth extension and additional compression schemes. Only little-endian, tiled, interleaved, 8-bit per sample images with JPEG, PNG, JPEG XR, and JPEG 2000 compression are allowed. Tifffile can read and write ZIF files.
  • Hamamatsu NDPI files use some 64-bit offsets in the file header, IFD, and tag structures. Single, LONG typed tag values can exceed 32-bit. The high bytes of 64-bit tag values and offsets are stored after IFD structures. Tifffile can read NDPI files > 4 GB. JPEG compressed segments with dimensions >65530 or missing restart markers cannot be decoded with common JPEG libraries. Tifffile works around this limitation by separately decoding the MCUs between restart markers, which performs poorly. BitsPerSample, SamplesPerPixel, and PhotometricInterpretation tags may contain wrong values, which can be corrected using the value of tag 65441.
  • Philips TIFF slides store wrong ImageWidth and ImageLength tag values for tiled pages. The values can be corrected using the DICOM_PIXEL_SPACING attributes of the XML formatted description of the first page. Tifffile can read Philips slides.
  • Ventana/Roche BIF slides store tiles and metadata in a BigTIFF container. Tiles may overlap and require stitching based on the TileJointInfo elements in the XMP tag. Volumetric scans are stored using the ImageDepth extension. Tifffile can read BIF and decode individual tiles but does not perform stitching.
  • ScanImage optionally allows corrupted 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.
  • Tifffile shaped files store the array shape and user-provided metadata of multi-dimensional image series in JSON format in the ImageDescription tag of the first page of the series. The format allows for multiple series, subifds, sparse segments with zero offset and bytecount, and truncated series, where only the first page of a series is present, and the image data are stored contiguously. No other software besides Tifffile supports the truncated format.

Other libraries for reading, writing, inspecting, or manipulating scientific TIFF files from Python are aicsimageio, apeer-ometiff-library, bigtiff, fabio.TiffIO, GDAL, imread, large_image, openslide-python, opentile, pylibtiff, pylsm, pymimage, python-bioformats, pytiff, scanimagetiffreader-python, SimpleITK, slideio, tiffslide, tifftools, tyf, and xtiff.



Write 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')

Read the image from the TIFF file as NumPy array:

>>> image = imread('temp.tif')
>>> image.shape
(256, 256, 3)

Write a 3-dimensional NumPy array to a multi-page, 16-bit grayscale TIFF file:

>>> data = numpy.random.randint(0, 2**12, (64, 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
(64, 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 selected range of pages:

>>> images = imread('temp.tif', key=range(4, 40, 2))
>>> images.shape
(18, 301, 219)

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

>>> with TiffFile('temp.tif') as tif:
...     for page in tif.pages:
...         image = page.asarray()

Get information about the image stack in the TIFF file without reading any image data:

>>> tif = TiffFile('temp.tif')
>>> len(tif.pages)  # number of pages in the file
>>> page = tif.pages[0]  # get shape and dtype of image in first page
>>> page.shape
(301, 219)
>>> page.dtype
>>> page.axes
>>> series = tif.series[0]  # get shape and dtype of first image series
>>> series.shape
(64, 301, 219)
>>> series.dtype
>>> series.axes
>>> tif.close()

Inspect 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
(1, 1)
>>> tag.code
>>> tag.count
>>> tag.dtype

Iterate over all tags in the TIFF file:

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

Overwrite the value of an existing tag, e.g., XResolution:

>>> with TiffFile('temp.tif', mode='r+') as tif:
...     _ = tif.pages[0].tags['XResolution'].overwrite((96000, 1000))

Write a 5-dimensional floating-point array using BigTIFF format, separate color components, tiling, Zlib compression level 8, horizontal differencing predictor, and additional metadata:

>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
>>> imwrite(
...     'temp.tif',
...     data,
...     bigtiff=True,
...     photometric='rgb',
...     planarconfig='separate',
...     tile=(32, 32),
...     compression='zlib',
...     compressionargs={'level': 8},
...     predictor=True,
...     metadata={'axes': 'TZCYX'}
... )

Write a 10 fps time series of volumes with xyz voxel size 2.6755x2.6755x3.9474 micron^3 to an ImageJ hyperstack formatted TIFF file:

>>> volume = numpy.random.randn(6, 57, 256, 256).astype('float32')
>>> imwrite(
...     'temp.tif',
...     volume,
...     imagej=True,
...     resolution=(1./2.6755, 1./2.6755),
...     metadata={
...         'spacing': 3.947368,
...         'unit': 'um',
...         'finterval': 1/10,
...         'axes': 'TZYX'
...     }
... )

Read the volume and metadata from the ImageJ file:

>>> with TiffFile('temp.tif') as tif:
...     volume = tif.asarray()
...     axes = tif.series[0].axes
...     imagej_metadata = tif.imagej_metadata
>>> volume.shape
(6, 57, 256, 256)
>>> axes
>>> imagej_metadata['slices']
>>> imagej_metadata['frames']

Create a TIFF file containing an empty image and write to the memory-mapped NumPy array (note: this does not work with compression or tiling):

>>> memmap_image = memmap(
...     'temp.tif',
...     shape=(256, 256, 3),
...     dtype='float32',
...     photometric='rgb'
... )
>>> type(memmap_image)
<class 'numpy.memmap'>
>>> memmap_image[255, 255, 1] = 1.0
>>> memmap_image.flush()
>>> del memmap_image

Memory-map and read contiguous image data in the TIFF file:

>>> memmap_image = memmap('temp.tif')
>>> memmap_image.shape
(256, 256, 3)
>>> memmap_image[255, 255, 1]
>>> del memmap_image

Write two NumPy arrays to a multi-series TIFF file (note: other TIFF readers will not recognize the two series; use the OME-TIFF format for better interoperability):

>>> series0 = numpy.random.randint(0, 255, (32, 32, 3), 'uint8')
>>> series1 = numpy.random.randint(0, 1023, (4, 256, 256), 'uint16')
>>> with TiffWriter('temp.tif') as tif:
...     tif.write(series0, photometric='rgb')
...     tif.write(series1, photometric='minisblack')

Read the second image series from the TIFF file:

>>> series1 = imread('temp.tif', series=1)
>>> series1.shape
(4, 256, 256)

Successively write the frames of one contiguous series to a TIFF file:

>>> data = numpy.random.randint(0, 255, (30, 301, 219), 'uint8')
>>> with TiffWriter('temp.tif') as tif:
...     for frame in data:
...         tif.write(frame, contiguous=True)

Append an image series to the existing TIFF file (note: this does not work with ImageJ hyperstack or OME-TIFF files):

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

Create a TIFF file from a generator of tiles:

>>> data = numpy.random.randint(0, 2**12, (31, 33, 3), 'uint16')
>>> def tiles(data, tileshape):
...     for y in range(0, data.shape[0], tileshape[0]):
...         for x in range(0, data.shape[1], tileshape[1]):
...             yield data[y : y + tileshape[0], x : x + tileshape[1]]
>>> imwrite(
...     'temp.tif',
...     tiles(data, (16, 16)),
...     tile=(16, 16),
...     shape=data.shape,
...     dtype=data.dtype,
...     photometric='rgb'
... )

Write a multi-dimensional, multi-resolution (pyramidal), multi-series OME-TIFF file with metadata. Sub-resolution images are written to SubIFDs. A thumbnail image is written as a separate image series:

>>> data = numpy.random.randint(0, 1023, (8, 2, 512, 512, 3), 'uint16')
>>> subresolutions = 2
>>> pixelsize = 0.29  # micrometer
>>> with TiffWriter('temp.ome.tif', bigtiff=True) as tif:
...     metadata={
...         'axes': 'TCYXS',
...         'SignificantBits': 10,
...         'Channel': {'Name': ['Channel 1', 'Channel 2']},
...         'TimeIncrement': 0.1,
...         'TimeIncrementUnit': 's',
...         'PhysicalSizeX': pixelsize,
...         'PhysicalSizeXUnit': 'µm',
...         'PhysicalSizeY': pixelsize,
...         'PhysicalSizeYUnit': 'µm',
...     }
...     options = dict(
...         photometric='rgb',
...         tile=(128, 128),
...         compression='jpeg',
...         resolutionunit='CENTIMETER'
...     )
...     tif.write(
...         data,
...         subifds=subresolutions,
...         resolution=(1e4 / pixelsize, 1e4 / pixelsize),
...         metadata=metadata,
...         **options
...     )
...     # save pyramid levels to the two subifds
...     # in production use resampling to generate sub-resolution images
...     for level in range(subresolutions):
...         mag = 2**(level + 1)
...         tif.write(
...             data[..., ::mag, ::mag, :],
...             subfiletype=1,
...             resolution=(1e4 / mag / pixelsize, 1e4 / mag / pixelsize),
...             **options
...         )
...     # add a thumbnail image as a separate series
...     # it is recognized by QuPath as an associated image
...     thumbnail = (data[0, 0, ::8, ::8] >> 2).astype('uint8')
...     tif.write(thumbnail, metadata={'Name': 'thumbnail'})

Access the image levels in the pyramidal OME-TIFF file:

>>> baseimage = imread('temp.ome.tif')
>>> second_level = imread('temp.ome.tif', series=0, level=1)
>>> with TiffFile('temp.ome.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.ome.tif') as tif:
...     fh = tif.filehandle
...     for page in tif.pages:
...         for index, (offset, bytecount) in enumerate(
...             zip(page.dataoffsets, page.databytecounts)
...         ):
...             _ =
...             data =
...             tile, indices, shape = page.decode(
...                 data, index, jpegtables=page.jpegtables
...             )

Use Zarr to read parts of the tiled, pyramidal images in the TIFF file:

>>> import zarr
>>> store = imread('temp.ome.tif', aszarr=True)
>>> z =, mode='r')
>>> z
<zarr.hierarchy.Group '/' read-only>
>>> z[0]  # base layer
<zarr.core.Array '/0' (8, 2, 512, 512, 3) uint16 read-only>
>>> z[0][2, 0, 128:384, 256:].shape  # read a tile from the base layer
(256, 256, 3)
>>> store.close()

Load the base layer from the Zarr store as a dask array:

>>> import dask.array
>>> with imread('temp.ome.tif', aszarr=True) as store:
...     dask.array.from_zarr(store, 0)
dask.array<...shape=(8, 2, 512, 512, 3)...chunksize=(1, 1, 128, 128, 3)...

Write the Zarr store to a fsspec ReferenceFileSystem in JSON format:

>>> with imread('temp.ome.tif', aszarr=True) as store:
...     store.write_fsspec('temp.ome.tif.json', url='file://')

Open the fsspec ReferenceFileSystem as a Zarr group:

>>> import fsspec
>>> import imagecodecs.numcodecs
>>> imagecodecs.numcodecs.register_codecs()
>>> mapper = fsspec.get_mapper(
...     'reference://', fo='temp.ome.tif.json', target_protocol='file'
... )
>>> z =, mode='r')
>>> z
<zarr.hierarchy.Group '/' read-only>

Create an OME-TIFF file containing an empty, tiled image series and write to it via the Zarr interface (note: this does not work with compression):

>>> imwrite(
...     'temp.ome.tif',
...     shape=(8, 800, 600),
...     dtype='uint16',
...     photometric='minisblack',
...     tile=(128, 128),
...     metadata={'axes': 'CYX'}
... )
>>> store = imread('temp.ome.tif', mode='r+', aszarr=True)
>>> z =, mode='r+')
>>> z
<zarr.core.Array (8, 800, 600) uint16>
>>> z[3, 100:200, 200:300:2] = 1024
>>> store.close()

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

>>> imwrite('temp_C001T001.tif', numpy.random.rand(64, 64))
>>> imwrite('temp_C001T002.tif', numpy.random.rand(64, 64))
>>> image_sequence = imread(['temp_C001T001.tif', 'temp_C001T002.tif'])
>>> image_sequence.shape
(2, 64, 64)
>>> image_sequence.dtype

Read an image stack from a series of TIFF files with a file name pattern as NumPy or Zarr arrays:

>>> image_sequence = TiffSequence(
...     'temp_C0*.tif', pattern=r'_(C)(\d+)(T)(\d+)'
... )
>>> image_sequence.shape
(1, 2)
>>> image_sequence.axes
>>> data = image_sequence.asarray()
>>> data.shape
(1, 2, 64, 64)
>>> with image_sequence.aszarr() as store:
..., mode='r')
<zarr.core.Array (1, 2, 64, 64) float64 read-only>
>>> image_sequence.close()

Write the Zarr store to a fsspec ReferenceFileSystem in JSON format:

>>> with image_sequence.aszarr() as store:
...     store.write_fsspec('temp.json', url='file://')

Open the fsspec ReferenceFileSystem as a Zarr array:

>>> import fsspec
>>> import tifffile.numcodecs
>>> tifffile.numcodecs.register_codec()
>>> mapper = fsspec.get_mapper(
...     'reference://', fo='temp.json', target_protocol='file'
... )
>>>, mode='r')
<zarr.core.Array (1, 2, 64, 64) float64 read-only>

Inspect the TIFF file from the command line:

$ python -m tifffile temp.ome.tif

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