napari-aicsimageio 0.7.0

AICSImageIO for napari. Multiple file format reading directly into napari using pure Python.

napari-aicsimageio

AICSImageIO bindings for napari

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Features

• Supports reading metadata and imaging data for:
  • OME-TIFF
  • TIFF
  • CZI (Zeiss)
  • LIF (Leica)
  • ND2 (Nikon)
  • DV (DeltaVision)
  • Any formats supported by aicsimageio
  • Any formats supported by bioformats
    • SLD (Slidebook)
    • SVS (Aperio)
    • Full List
  • Any additional format supported by imageio
    • PNG
    • JPG
    • GIF
    • AVI
    • Full List

While upstream aicsimageio is released under BSD-3 license, this plugin is released under GPLv3 license because it installs all format reader dependencies.

Installation

Stable Release: pip install napari-aicsimageio or conda install napari-aicsimageio -c conda-forge
Development Head: pip install git+https://github.com/AllenCellModeling/napari-aicsimageio.git

Reading Mode Threshold

This image reading plugin will load the provided image directly into memory if it meets the following two conditions:

1. The filesize is less than 4GB.
2. 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 ome-types.

Multi-Scene Selection

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[0].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:

viewer.layers[0].metadata["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 an xml.etree.ElementTree.Element, for OME-TIFF the raw metadata is returned as an OME object from ome-types.

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 called "ome_types". For example, because the AICSImageIO CZIReader and 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[0].metadata["ome_types"]  # OME object from ome-types

Mosaic Reading

When reading CZI or LIF images, if the image is a mosaic tiled image, napari-aicsimageio will return the reconstructed image:

Development

See CONTRIBUTING.md for information related to developing the code.

For additional file format support, contributed directly to AICSImageIO. New file format support will become directly available in this plugin on new aicsimageio releases.

Citation

If you find aicsimageio (or 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