Expose tensor slice selection to blueprint

[Wumpf]

What

Updated python example:

blueprint = rrb.Blueprint(
    rrb.TensorView(
        origin="tensor",
        name="Tensor",
        # Explicitly pick which dimensions to show.
        slice_selection=rrb.TensorSliceSelection(
            # Use the first dimension as width.
            width=0,
            # Use the second dimension as height and invert it.
            height=rr.TensorDimensionSelection(dimension=1, invert=True),
            # Set which indices to show for the other dimensions.
            indices=[
                rr.TensorDimensionIndexSelection(dimension=2, index=4),
                rr.TensorDimensionIndexSelection(dimension=3, index=5),
            ],
            # Show a slider for dimension 2 only. If not specified, all dimensions in `indices` will have sliders.
            slider=[2],
        ),
        # Set a scalar mapping with a custom colormap, gamma and magnification filter.
        scalar_mapping=rrb.TensorScalarMapping(colormap="turbo", gamma=1.5, mag_filter="linear"),
        # Change sizing mode to keep aspect ratio.
        view_fit="fill",
    ),
    collapse_panels=True,
)

Models out the tensor slice selection in store compatible data structures

Refrained from polishing up the ui all that much, it's pretty much the same but uses the new datastructure under the hood.
Minor differences:

• No longer show both arrows for 1D tensor slices.
• allow reset to default and reset to default blueprint
• change default for fill to keep aspect ratio

Had to do add a bit of codegen for python struct serialization, but ended up doing most of the new datatypes by hand after all because of hard to generalize conversions.

Tested manually against 1D tensors.

• Part of Blueprintify Space View properties #6083

Checklist

• I have read and agree to Contributor Guide and the Code of Conduct
• I've included a screenshot or gif (if applicable)
• I have tested the web demo (if applicable):
  • Using examples from latest main build: rerun.io/viewer
  • Using full set of examples from nightly build: rerun.io/viewer
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[github-actions]

Deployed docs

Commit	Link
523554b	https://landing-7zely1ym8-rerun.vercel.app/docs

[emilk]

(half-reviewed - I ran out of day)

[emilk]

nit: I think we should make our examples as realistic as possible to make it easier for users to copy-paste them

Suggested change

	tensor = np.random.randint(0, 256, (32, 20, 12, 14), dtype=np.uint8)
	rr.log("tensor", rr.Tensor(tensor, dim_names=("width", "height", "batch", "other")))
	tensor = np.random.randint(0, 256, (32, 768, 1024, 3), dtype=np.uint8)
	rr.log("tensor", rr.Tensor(tensor, dim_names=("batch", "height", "width", "channel")))

[Wumpf]

not going with an as-high resolution since it looks too ugly with this noise

[Wumpf]

hm unless I set the filter to "nearest" but that's already the default so not as interesting

[Wumpf]

thanks! some neat stuff in there. Plz have a quick look at the comments I replied to

[emilk]

The component defaults menu in the tensor selection is empty, which looks very buggy:

[emilk]

This could really use a unit-test or two.

For instance, what happens if your dimensions are named height, width and the input is height=1, width=null? From just reading the code, it looks like maybe the output will be height=1, width=1.

[Wumpf]

The component defaults menu in the tensor selection is empty, which looks very buggy:

I guess we should grey it out when there's nothing. But very unrelated to this PR

[emilk]

The component defaults menu in the tensor selection is empty, which looks very buggy:

I guess we should grey it out when there's nothing. But very unrelated to this PR

I'll make a new issue

[Wumpf]

@rerun-bot full-check

[github-actions]

Started a full build: https://github.com/rerun-io/rerun/actions/runs/9646353988