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name: "evaluation-geometry" description: "Route StarDist label validation, star-distance representations, polygon/polyhedron rendering, NMS, matching metrics, patch sampling, and visualization/error overlays." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Evaluation and geometry

Use this sub-skill for standalone label/data work: star distances, polygon or polyhedron conversion, dense/sparse non-maximum suppression, instance matching, temporal label grouping, patch centers, threshold concepts, and 2D diagnostic overlays. Keep neural model construction/training and model prediction in 2d-workflows or 3d-workflows. Keep file CLIs and optional BioImage.IO, ImageJ/ROI, OBJ, and QuPath integration in deployment-integration.

Route

1. Validate integer, non-negative labels; shape/rank; grid powers of two; and 3D Rays_* alignment before native calls.
2. Read api-reference.md for exact signatures, shapes, dtypes, tuples, and threshold inclusivity. Use representations.md to keep (Y,X) versus (Z,Y,X), grid, rays, and physical scales aligned.
3. Use CPU mode="cpp" and compiled C++ extensions for the required baseline; use mode="python" only as a bounded fallback. OpenCL is optional and must be explicitly probed, never inferred from CUDA visibility.
4. Evaluate with evaluation.md, visualize with visualization.md, and recover failures with troubleshooting.md.
5. Run scripts/smoke_geometry.py after the installed package and compiled CPU extensions are available. It is tiny, deterministic, CPU-only, and has no network or source-checkout dependency.

Core invariants

star_dist(a,n_rays,grid,mode) takes a 2D label image and returns float32 (ceil(Y/gy),ceil(X/gx),R) on the native path. star_dist3D(lbl,rays,grid,mode) takes a label image plus a Rays_* object and returns float32 (ceil(Z/gz),ceil(Y/gy),ceil(X/gx),len(rays)). dist_to_coord returns (N,2,R) and dist_to_coord3D returns (N,R,3), both in row/column or (z,y,x) coordinates respectively. Dense NMS returns (points,prob,dist); sparse NMS adds original_indices. matching returns a metrics named tuple and optionally match details.

polygons_to_label returns a 2D int32 image. In this baseline a non-empty native polyhedron_to_label result is int32, while its empty-point fast path is uint16; normalize explicitly if a downstream format requires one dtype.