Fix widget compatibility with multi-scale napari image layers#267
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Fix widget compatibility with multi-scale napari image layers#267
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Napari multi-scale Image layers store data as a MultiScaleData list rather than a single array. Passing this directly to processing functions caused a TypeError when the code tried to index or call .shape on it. Add get_layer_data() helper in utils.py that extracts the highest-resolution level (index 0) for multi-scale layers and returns .data unchanged for regular layers. Apply it in workflows.py at every point where user-supplied layer data is consumed: preprocessing (image/labels), segmentation (detection/contours), and optical flow. Add regression tests covering all three workflow modes (AUTO_DETECT, AUTO_FROM_LABELS, MANUAL) with two-level image pyramids.
The CLI already had the same multiscale-extraction pattern inline in flow.py, link.py, export.py, and as a private function in segment.py. Consolidate all five copies into a single get_layer_data() in ultrack/utils/napari.py and update every call site to import from there.
JoOkuma
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Problem
When a napari
Imagelayer is multi-scale (e.g. loaded from a zarr pyramid or created withviewer.add_image(..., multiscale=True)), its.dataattribute is aMultiScaleDataobject — a list of arrays at progressively lower resolutions — rather than a single array.The widget's workflow code passed
layer.datadirectly to processing functions and called.shapeon it, which raised:This affected all three workflow modes (AUTO_DETECT, AUTO_FROM_LABELS, MANUAL).
Fix
Added
get_layer_data(layer)helper inutils.pythat returnslayer.data[0](the highest-resolution level) when a layer is multi-scale, andlayer.dataotherwise.Applied it in
workflows.pyat every point where user-supplied layer data is consumed:Tests
Added
test_ultrack_widget_multiscaleparametrized over all three workflow choices, each using a two-level image pyramid built from the existing mock data fixtures. The tests confirmed the failure before the fix and pass after.