Use image scale for points annotations (#29)

This introduces a workaround for handling a pending change to the
physical scale and units of the OME-Zarr metadata:
chanzuckerberg/cryoet-data-portal-backend#24

The workaround is to use the highest-resolution image scale from the
tomogram image, and also use it for the annotation points layer because
its coordinates are given in the data space of the highest resolution
image.

This should not change the behavior with respect to the existing
OME-Zarrs (which have a high-res scale of 1), but should ensure that the
points remain aligned with the images when the scale change comes in.

An alternative fix would have been to multiply the points data with the
scale, but I avoided this because it mutates the primary data rather
than its visualization.

src/napari_cryoet_data_portal/_open_widget.py

@@ -146,8 +146,10 @@ def _loadTomogram(
         # Materialize low resolution immediately on this thread to prevent napari blocking.
         if resolution is LOW_RESOLUTION:
             image_data = np.asarray(image_data)
+        # Get scale before resolution scaling for annotations.
+        image_scale = image_attrs["scale"]
         image_attrs["scale"] = tuple(
-            resolution.scale * s for s in image_attrs["scale"]
+            resolution.scale * s for s in image_scale
         )
         image_translate = image_attrs.get("translate", (0,) * len(image_attrs["scale"]))
         image_attrs["translate"] = tuple(
@@ -171,7 +173,13 @@ def _loadTomogram(
                 if f.shape_type == "Point"
             )
             if len(point_paths) > 0:
-                yield read_annotation(annotation, tomogram=tomogram)
+                anno_data, anno_attrs, anno_type = read_annotation(annotation, tomogram=tomogram)
+                # Inherit scale from full resolution image so that we can pick up
+                # that scale when it changes.
+                anno_attrs["scale"] = image_scale
+                # Scaling points also changes the size, so adjust accordingly.
+                anno_attrs["size"] /= np.mean(image_scale)
+                yield anno_data, anno_attrs, anno_type
             else:
                 logger.warn("Found no points annotations. Skipping.")