typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_fit_spline_curve", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_fit_spline_curve: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Stages (the ops that come before → this op, left to right):
▸ tb_fit_spline_curve: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_fit_spline_curve: other inputs (docs site)
Smooths an ordered sequence of 3D points with a B-spline and resamples it. → (M,3). For smoothing noisy trajectories/edges.
> The detailed description below is the original text — the summary and the headings are translated.
scipy.interpolate.splprep/splev。smooth=0 は補間、>0 で平滑。n=出力点数(既定=入力数)。
`splprep([x, y, z], s=smooth, k=k)` でパラメトリック B スプライン(パラメータ u∈[0,1]、
scipy 既定の弦長パラメータ化)を当て、`u = linspace(0, 1, n) で splev` 評価した
(n,3) float64 を返す。u 等間隔なので出力点は弧長で厳密に等間隔ではない(弧長等間隔が
要るなら結果を `resample_uniform` に通す)。
• `smooth`: scipy の平滑化条件 s。残差二乗和が s 以下になる最少ノットで当てる。
0 なら全点を通る補間、大きいほど滑らか(単位は座標の二乗)。
• `k: スプライン次数(既定 3)。点数 N が k 以下だと ValueError`。
• `n`: 出力点数。None で入力点数。
• 入力は index 順に並んだ (N,3) を前提とし、形状検証は点数以外に無い。
ノイズのあるエッジ点列や軌跡を平滑してから `curvature_torsion / frenet_frame` に
渡す前段として使う。tck を持ち回りたい場合は `fit_bspline_curve / eval_bspline_curve`。
2-D 進化レジストリへ橋渡しした 3d の op `fit_spline_curve。実装は同じで、呼び出し規約だけ op(v, a, b) に合わせてある。a が k(既定 3)を振る。b` は未使用。
• 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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_points 0.50 0.50 tb_fit_spline_curve 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op fit_spline_curve. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• bspline_freeform — py -3.11 examples_3d/bspline_freeform.py
points as input)identity · tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.