smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "mean_box", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: mean_image (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ mean_box: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ mean_box: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ mean_box: other inputs (docs site)
*No colour (H,W,3) input is shown: this op treats the colour channel as a third spatial axis (it crosses channels). Call it per channel.*
Simple averaging (box) filter over a rectangular window. Equivalent to HALCON's mean_image (Smooth by averaging.).
a switches the side length of the window among 4 steps 3,5,7,9 (_k(a): a is split into 4 bins and rounded). b selects the border handling: b <= 0.5 gives reflect (mirrors with the border pixel duplicated; scipy's default and the behaviour up to 2026-09-17), then nearest / constant (fills with 0) / wrap (wraps around from the opposite side). ★With a window of 3, reflect and nearest coincide in principle (the overhang is 1 pixel, so the duplicated source is the same) —— if changing the border handling does not move the result, suspect the window size first (with a window of 9 all 3 variants differ). Cheaper to compute than a Gaussian, but more prone to ringing, and edges get blurred.
Border handling: mirrored with the border pixel repeated (d c b a | a b c d, scipy's default reflect) (measured and recorded on 2026-09-16).
• gallery2d_smoothing_rank family guide
• 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.
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.
mean_box 0.35 0.50
▸ Load this pipeline · Load & run
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · bilateral · unsharp · median · min_filter · max_filter · percentile
smoothing)gaussian · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm · sk_tv_bregman
*Provenance: ops.py — 2D 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.