AICSImageIO for napari. Multiple file format reading directly into napari using pure Python.
- Supports reading metadata and imaging data for:
aicsimageio is released under BSD-3 license, this plugin is released under GPLv3 license because it installs all format reader dependencies.
Reading Mode Threshold¶
This image reading plugin will load the provided image directly into memory if it meets the following two conditions:
- The filesize is less than 4GB.
- The filesize is less than 30% of machine memory available.
If either of these conditions isn't met, the image is loaded in chunks only as needed.
Examples of Features¶
General Image Reading¶
All image file formats supported by aicsimageio will be read and all raw data will be available in the napari viewer.
In addition, when reading an OME-TIFF, you can view all OME metadata directly in the
napari viewer thanks to
When reading a multi-scene file, a widget will be added to the napari viewer to manage scene selection (clearing the viewer each time you change scene or adding the scene content to the viewer) and a list of all scenes in the file.
Access to the AICSImage Object and Metadata¶
You can access the
AICSImage object used to load the image pixel data and
image metadata using the built-in napari console:
img = viewer.layers.metadata["aicsimage"] img.dims.order # TCZYX img.channel_names # ["Bright", "Struct", "Nuc", "Memb"] img.get_image_dask_data("ZYX") # dask.array.Array
The napari layer metadata dictionary also stores a shorthand for the raw image metadata:
The metadata is returned in whichever format is used by the underlying
file format reader, i.e. for CZI the raw metadata is returned as
xml.etree.ElementTree.Element, for OME-TIFF the raw metadata is returned
OME object from
Lastly, if the underlying file format reader has an OME metadata conversion function,
you may additionally see a key in the napari layer metadata dictionary
"ome_types". For example, because the AICSImageIO
BioformatsReader both support converting raw image metadata
to OME metadata, you will see an
"ome_types" key that stores the metadata transformed
into the OME metadata model.
viewer.layers.metadata["ome_types"] # OME object from ome-types
When reading CZI or LIF images, if the image is a mosaic tiled image,
will return the reconstructed image:
If you find
napari-aicsimageio) useful, please cite as:
AICSImageIO Contributors (2021). AICSImageIO: Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure Python [Computer software]. GitHub. https://github.com/AllenCellModeling/aicsimageio
Free software: GPLv3
- 21 July 2022
- 26 March 2020
- Information not submitted
- Stars: 20
- Forks: 6
- Issues + PRs: 8