typed op• Data kinds: points → signal
• Call: fullseye.apply(img, "tb_geodesic_distances", 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_geodesic_distances: 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_geodesic_distances: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_geodesic_distances: other inputs (docs site)
Geodesic distances from source to all points (Dijkstra on a kNN graph). → (N,) float (unreachable points are inf).
Makes the k-nearest-neighbour graph built by knn_graph(points, k) (edge weight = Euclidean distance between points = chord length) undirected with directed=False and solves the single-source shortest paths with scipy.sparse.csgraph.dijkstra. d[i] is the path length on the graph from source to point i, with d[source] = 0; points in connected components not connected to source are inf. Units are the coordinate units unchanged.
• points: (N,3) point cloud (converted to float64).
• source: index of the start point (an integer in 0..N-1; out of range raises an exception on the scipy side).
• k: number of neighbours (default 8). Small values fragment the graph and increase inf; large values create "shortcuts" directly joining distant surfaces, giving distances that do not follow the surface (front and back of a thin plate, folded surfaces, etc.).
Accuracy: since edges are chords, arcs are slightly underestimated, while zigzag in the path causes overestimation (see the sandwich bound of Bernstein et al. in the module docstring). If a triangle mesh is available, use geodesic_mesh, which does not depend on the number of neighbours. For even subsampling by this distance, use farthest_point_sampling.
The 3d op geodesic_distances 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 8). 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_geodesic_distances 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op geodesic_distances. 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).
• geodesic_distance — py -3.11 examples_3d/geodesic_distance.py
signal as input)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d
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.