ttc_truth — DRIVE `ttc` op

• Data kinds: image2d × matrix × matrix → table

• Call: import fullseye as fs; fs.ledger.ttc_truth(depth, K, T_rel, dt: 'float') -> 'dict' (to call the implementation directly, import drivettc; drivettc.ttc_truth(depth, K, T_rel, dt: 'float') -> 'dict'; from the registry, opsdrive.get("ttc_truth"))

Usage

Per-pixel ground-truth τ (seconds, at the time of the first frame) and closing speed: {"tau", "closing_speed", "depth1", "valid"}.

> The detailed description below is the original text — the summary and the headings are translated.

τ₀ = Z₀ · Δt / (Z₀ − Z₁)(Lee 1976 の Z / (−Ż) を 2 コマの差分で。等速なら厳密)。近づかない画素(Z₁ ≥ Z₀)は inf。closing_speed = (Z₀ − Z₁) / Δt(m/s、正 = 近づく)。

References (sample data, literature)

• 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 canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_ttc_rss — py -3.11 examples/poc_ttc_rss.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`ttc`)

relative_motion · foe_from_motion · flow_from_depth_motion · ttc_from_flow · ttc_from_scale · ttc_from_range · label_extent · foe_from_flow

*Provenance: drivettc.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

ttc_truth — DRIVE ttc op