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.
scipy.interpolate.splprep/splev. smooth=0 interpolates, >0 smooths. n = number of output points (default = number of inputs).
Fits a parametric B-spline with splprep([x, y, z], s=smooth, k=k) (parameter u∈[0,1], scipy's default chord-length parameterisation), evaluates it with splev at u = linspace(0, 1, n), and returns (n,3) float64. Since u is evenly spaced, the output points are not strictly evenly spaced in arc length (if you need even arc-length spacing, pass the result through resample_uniform).
• smooth: scipy's smoothing condition s. Fits with the fewest knots such that the residual sum of squares is at most s. 0 gives an interpolation through all points; the larger, the smoother (units are coordinates squared).
• k: spline degree (default 3). ValueError if the number of points N is k or less.
• n: number of output points. None means the number of input points.
• The input is assumed to be (N,3) ordered by index; there is no shape check other than the number of points.
Used as a pre-stage to smooth noisy edge point sequences or trajectories before passing them to curvature_torsion / frenet_frame. To carry tck around, use fit_bspline_curve / eval_bspline_curve.
The 3d op fit_spline_curve bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). a sweeps k (default 3). b is unused.
• 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.