lines_facet — 2D `contour` op

• Data kinds: image → contour

• Call: fullseye.apply(img, "lines_facet", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: lines_facet (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Detects line-like structures (ridges) and returns them as a contour (XLD contour). The implementation computes 'line-ness' using a Frangi filter (`skimage.filters.frangi, multiscale sigma=1..3 ridge enhancement), and outputs the pixel coordinates of connected components exceeding the threshold 0.1+0.4a` directly as a contour point sequence. b is unused.

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

HALCON の `lines_facet`（局所多項式近似=ファセットモデルで線を検出する演算）とは検出原理が異なる近似 —— facet モデルによるサブピクセル位置推定は行わず、Frangi 応答の二値化領域を輪郭化しているだけなので、座標はサブピクセル精度を持たない。

Detailed usage guide

• gallery2d_contour_measure family guide

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)

• gallery2d_contour_measure — py -3.11 examples/gallery2d_contour_measure.py

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

identity · select_contours · smooth_contours · fit_line_contours · contours_to_region · count_contours · total_length · select_contours_xld

Same category (`contour`)

select_contours · smooth_contours · fit_line_contours · contours_to_region · sk_find_contours · edges_sub_pix · lines_gauss · select_contours_xld

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

lines_facet — 2D contour op