gauss_filter — 2D `smoothing` op

• Data kinds: image → image

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

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

gauss_filter: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

Usage

Smoothing via Gaussian blur (`scipy.ndimage.gaussian_filter, sigma 0.3+2.7*a). A stand-in for HALCON's gauss_filter` (Smooth using discrete Gauss functions.) -- HALCON uses a discrete Gaussian kernel (integer arithmetic), while this uses a continuous-Gaussian scipy implementation; it is an approximation, but the results are very close.

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

`a がシグマを 0.3〜3.0 の範囲で振る。b` は未使用。実装は

`gauss_image` と同一。

Detailed usage guide

• gallery2d_smoothing_rank 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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

gauss_filter 0.35 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

• poc_cell_counting — py -3.11 examples/poc_cell_counting.py

• poc_document_scan — py -3.11 examples/poc_document_scan.py

• poc_focus_stacking — py -3.11 examples/poc_focus_stacking.py

• poc_gear_tooth_metrology — py -3.11 examples/poc_gear_tooth_metrology.py

• poc_sea_ice_concentration — py -3.11 examples/poc_sea_ice_concentration.py

• poc_solar_limb_darkening — py -3.11 examples/poc_solar_limb_darkening.py

• poc_vessel_network — py -3.11 examples/poc_vessel_network.py

• poc_white_balance — py -3.11 examples/poc_white_balance.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`smoothing`)

gaussian · mean_box · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm

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

gauss_filter — 2D smoothing op