napari-time-slicer 0.4.5

A meta plugin for processing timelapse data in napari timepoint by timepoint

napari-time-slicer

A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t data step by step when the user goes through the timelapse. Currently, these plugins are using napari-time-slicer:

• napari-segment-blobs-and-things-with-membranes
• napari-cupy-image-processing
• napari-pyclesperanto-assistant
• napari-accelerated-pixel-and-object-classification
• napari-simpleitk-image-processing

napari-time-slicer enables inter-plugin communication, e.g. allowing to combine the plugins listed above in one image processing workflow for segmenting a timelapse dataset:

The workflow can then also be exported as a script. The 'Generate Code' button can be found in the Workflow Inspector

If you want to convert a 3D dataset into a 2D + time dataset, use the menu Tools > Utilities > Convert 3D stack to 2D timelapse (time-slicer). It will turn the 3D dataset to a 4D datset where the Z-dimension (index 1) has only 1 element, which will in napari be displayed with a time-slider. Note: It is recommended to remove the original 3D dataset after this conversion.

Usage for plugin developers

Plugins which implement the napari_experimental_provide_function hook can make use of the @time_slicer. At the moment, only functions which take napari.types.ImageData, napari.types.LabelsData and basic python types such as int and float are supported. If you annotate such a function with @time_slicer it will internally convert any 4D dataset to a 3D dataset according to the timepoint currently selected in napari. Furthermore, when the napari user changes the current timepoint or the input data of the function changes, a re-computation is invoked. Thus, it is recommended to only use the time_slicer for functions which can provide [almost] real-time performance. Another constraint is that these annotated functions have to have a viewer parameter. This is necessary to read the current timepoint from the viewer when invoking the re-computions.

Example

import napari
from napari_time_slicer import time_slicer

@time_slicer
def threshold_otsu(image:napari.types.ImageData, viewer: napari.Viewer = None) -> napari.types.LabelsData:
    # ...

You can see a full implementations of this concept in the napari plugins listed above.

If you want to combine slicing in time and processing z-stack images slice-by-slice, you can use the @slice_by_slice annotation. Make sure, to insert it after @time_slicer as shown below and implemented in napari-pillow-image-processing

from napari_time_slicer import slice_by_slice

@time_slicer
@slice_by_slice
def blur_2d(image:napari.types.ImageData, sigma:float = 1, viewer: napari.Viewer = None) -> napari.types.LabelsData:
    # ...

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This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

You can install napari-time-slicer via pip:

pip install napari-time-slicer

To install latest development version :

pip install git+https://github.com/haesleinhuepf/napari-time-slicer.git

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-time-slicer" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.