poly_lut_invert — DRIVE tacscalib op

• Data kinds: rgb × table × image2d → normalmap

• Call: import fullseye as fs; fs.ledger.poly_lut_invert(rgb, poly: 'dict', mask=None, method: 'str' = 'coarse') -> 'np.ndarray' (to call the implementation directly, import tacscalib; tacscalib.poly_lut_invert(rgb, poly: 'dict', mask=None, method: 'str' = 'coarse') -> 'np.ndarray'; from the registry, opsdrive.get("poly_lut_invert"))

Usage

Adapter: passes an external position-dependent polynomial LUT (bins and grad_r / grad_g / grad_b = (bins, bins, 6); a format in which 6 coefficients per bin [x², y², xy, x, y, 1], with x = column and y = row in raw pixels, express each background-subtracted channel) through the same inverse lookup as gradient_lut_invert (same binning, same coarse → fine). rgb is the difference after the same background processing as in the external pipeline. Since every bin of the external table is filled, all bins are used. Raises ValueError: missing key / coefficients not of shape (bins, bins, 6) / non-finite.

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_tacscalib_sphere_lut — py -3.11 examples/poc_tacscalib_sphere_lut.py

Ops the type connects to (they accept normalmap as input)

spin_from_marker_sequence · carla_transform_matrix · carla_pose_to_world · carla_camera_pose · carla_xy_yaw · carla_scene_load · town_chain · osm_parse

Same category (tacscalib)

calib_pack_load · sphere_normals_known · lights_fit_from_sphere · membrane_predict_rgb · gradient_lut_build · gradient_lut_invert · normal_error_map · sphere_cap_height


*Provenance: tacscalib.py — DRIVE 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.