lead_from_depth — DRIVE `carla` op

• Data kinds: image2d × image2d × matrix → table

• Call: import fullseye as fs; fs.ledger.lead_from_depth(depth, label, K, cam_height: 'float', *, pitch: 'float' = 0.0, band: 'float' = 0.5, car_label: 'int' = 2) -> 'dict' (to call the implementation directly, import carlabridge; carlabridge.lead_from_depth(depth, label, K, cam_height: 'float', *, pitch: 'float' = 0.0, band: 'float' = 0.5, car_label: 'int' = 2) -> 'dict'; from the registry, opsdrive.get("lead_from_depth"))

Usage

Rule-based distance to the lead vehicle (same formula for both worlds): depth_median = median depth of the car pixels in the central band of the image (width band × W) (constant, since the rear face is parallel to the image plane), row_distance = horizontal distance from the car's bottom row via the flat-road formula (driveenv.road_row_distance), bbox = (top, bottom, left, right), n_pixels, found. If there are no car pixels, found = False (distance inf).

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)

• (none yet)

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 (`carla`)

carla_labels · carla_label_map · carla_label_unmap · carla_depth_decode · carla_depth_encode · carla_intrinsics · intrinsics_to_fullseye · intrinsics_to_carla

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

lead_from_depth — DRIVE carla op