tf_log_polar — 2D `geometry` op

• Data kinds: image → image

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

Usage

log_polar: resample the image on a log-polar grid about its centre.

Output row = log-radius (log-spaced from ~1px to `rmax`), output column =

angle (0..2pi). A scaling of the source about the centre becomes a vertical

(row) shift and a rotation becomes a horizontal (column) shift -- the defining

property of the log-polar transform. `a` scales the maximum sampled radius;

`b` adds a rotation offset (an angular shift) to demonstrate rotation ->

column-shift.

Detailed usage guide

• gallery2d_geometry 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_geometry — py -3.11 examples/gallery2d_geometry.py

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

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

Same category (`geometry`)

rotate_img · rescale_img · affine_warp · sk_swirl · mirror_image · transpose_region · rotate_image · zoom_image_factor

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

tf_log_polar — 2D geometry op