tb_pc_density_equalize — 2D `typed` op

• Data kinds: points → points

• Call: fullseye.apply(img, "tb_pc_density_equalize", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

*No figure: this op takes points as input. A Studio program starting from an image cannot reach that type — see the runnable examples below for how it is used.*

Usage

Fill the sparse regions, then thin the dense ones: uniform spacing (`points`).

:func:pc_fill_sparse followed by :func:pc_poisson_disk with radius

`0.8 × spacing` (the Poisson radius is a minimum, the k-NN spacing a

typical value; 0.8 keeps the median spacing at the target). Measured on a

cloud with a 6× density contrast: p95/p5 of spacing from 4.2 to ≤ 1.6.

Typed bridge of the 3d op `pc_density_equalize into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives k (default 8); b` is unused.

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept `points` as input)

identity · tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

tb_pc_density_equalize — 2D typed op