osm_road_loops — DRIVE `japan` op

• Data kinds: table → table

• Call: import fullseye as fs; fs.ledger.osm_road_loops(roadmask: 'dict', *, tolerance_px: 'float' = 0.75) -> 'Dict[str, object]' (to call the implementation directly, import drivejapan; drivejapan.osm_road_loops(roadmask: 'dict', *, tolerance_px: 'float' = 0.75) -> 'Dict[str, object]'; from the registry, opsdrive.get("osm_road_loops"))

Usage

Boundary loops of the union of roads. Splits them into outer loops (road edges) and holes (city blocks) and simplifies them with Douglas–Peucker.

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

追跡は画素の辺に沿う(contours_xld._trace_mask_boundaries)ので、間引く前のループの面積の和 = 画素数 × step² (外側 − 穴)が厳密に成り立つ(area_check)。穴か外側かは、ループの最も左の縦の辺のすぐ右の画素が道路かどうかで決める。返り値 {"outer": [(K,2) xy], "holes": [(K,2) xy], "outer_raw", "holes_raw", "area_check": {"loops_m2", "pixels_m2", "abs_err"}}。

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_driving_japan_town — py -3.11 examples/poc_driving_japan_town.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 (`japan`)

osm_synthetic · osm_parse · osm_road_graph · osm_road_mask · osm_route · japan_world · japan_stats · latlon_to_local

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

osm_road_loops — DRIVE japan op