typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_random_scale", 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_random_scale: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_random_scale: other inputs (docs site)
Applies a uniform scale s ~ U(lo, hi) about the origin and returns (scaled, s).
scaled = points * s. The bbox diagonal becomes exactly s times longer (since s > 0, max/min both scale by s → the diagonal ‖max-min‖ also scales by s). Injects variation in object scale (distance / sensor magnification) into training. Requires 0 < lo <= hi (fail-closed).
lo <= 0 or hi < lo raises ValueError (lo == hi is allowed and always gives s = lo). Scaling is about the origin, so if the cloud is away from the origin its centroid also moves to s times its position (position and size change together). To change only the size, move the centroid to the origin first. s is a Python float; the returned point cloud is float64. Deterministic via seed. Empty input returns empty.
The 3d op random_scale 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 tunable parameters; 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_random_scale 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op random_scale. 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).
• augment_pointcloud — py -3.11 examples_3d/augment_pointcloud.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.