aug_barrel — 2D `augmentation` op

• Data kinds: image → image

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

Usage

Radial lens distortion using the standard polynomial model `r_src = r*(1 + k*r^2) on normalised radius r (1 = image corner): BARREL (straight lines bow outwards) when b < 0.5, PINCUSHION when b >= 0.5, with k = 0.6*a (a=0 -> undistorted). The destination grid is inverse-mapped and resampled bilinearly with ndimage.map_coordinates (mode="reflect"`), so no pixel is left undefined. Output keeps HxW.

Detailed usage guide

• gallery2d_color_artistic 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_color_artistic — py -3.11 examples/gallery2d_color_artistic.py

• sim2real_and_alife — py -3.11 examples/sim2real_and_alife.py

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

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

Same category (`augmentation`)

aug_shot_noise · aug_read_noise · aug_fixed_pattern · aug_motion_blur · aug_vignette · aug_chromatic · aug_rolling_shutter · aug_jpeg_blocks

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

aug_barrel — 2D augmentation op