typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_resample_uniform", 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.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
*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_resample_uniform: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_resample_uniform: other inputs (docs site)
Resamples to n points at equal intervals of arc length (linear interpolation). → (n,3).
Takes the positions np.linspace(0, total, n) with the cumulative arc length of arc_length as the parameter, and linearly interpolates x, y and z independently with np.interp. The output is float64 (n,3). The start and end points coincide with the first and last input points; intermediate points lie on the polyline (they do not necessarily pass through the original points).
• n: number of output points. 1 gives only the start point (1 point), 0 gives an empty (0,3). Making it larger than the input adds no information and merely chops the polyline finer.
• If the total length is below 1e-12 (all points coincide), the first point is duplicated n times and returned.
• The number of output columns is fixed at 3; if the input is not (N,3), it either fails for lack of columns or extra columns are discarded. There is no shape check.
• Closed curves are not closed (the end → start segment is not interpolated). Duplicate points still pass np.interp, but that segment is treated as having length 0.
curvature_torsion / frenet_frame use differences in the index parameter, so passing a point sequence with uneven spacing through this op first to make it evenly spaced stabilises the numerical differentiation. For smooth interpolation use fit_spline_curve.
The 3d op resample_uniform bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). This op has no tuning points; neither a nor b is used.
• 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_resample_uniform 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op resample_uniform. 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.