coat_image — DRIVE `polish` op

• Data kinds: image2d × scalar → image2d

• Call: import fullseye as fs; fs.ledger.coat_image(depth_map, coat: 'float', optical_depth: 'float' = 1.3862943611198906, bare: 'float' = 0.85, noise: 'float' = 0.0, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import polish; polish.coat_image(depth_map, coat: 'float', optical_depth: 'float' = 1.3862943611198906, bare: 'float' = 0.85, noise: 'float' = 0.0, seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsdrive.get("coat_image"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

除去の深さの地図 → 汚れ(膜)の残った板を真上から撮った画像 [0, 1]。残った厚さ t = clip(h0 − h, 0, h0)、明るさ

I = bare · exp(−τ t/h0)(Beer–Lambert、τ = `optical_depth は膜全体の光学的厚さ)+ 正規の雑音 noise`。

Raises ValueError: 地図が 2-D でない・非有限、coat ≤ 0、τ ≤ 0、bare が (0, 1] の外、noise < 0。

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_polish_wipe_measure — py -3.11 examples/poc_polish_wipe_measure.py

Ops the type connects to (they accept `image2d` as input)

flow_from_depth_motion · ttc_truth · ttc_from_flow · foe_from_flow · perlin2 · fbm_height · fbm_gradient · radial_periodogram

Same category (`polish`)

preston_pressure_kernel · preston_removal_map · preston_track_profile · wipe_band_width · raster_wipe_area · coat_thickness_from_image · wipe_coverage · band_width_profile

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

coat_image — DRIVE polish op