bridge op• Data kinds: image → keypoints
• Call: fullseye.apply(img, "img_to_keypoints", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*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):
▸ img_to_keypoints: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ img_to_keypoints: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ img_to_keypoints: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Turn the local maxima of the image into image-plane points (u, v) = (col, row), shape (N,2).
A pixel is a local maximum if it equals the window maximum from scipy.ndimage.maximum_filter and its value is at or above the threshold (on flat plateaus of equal value every pixel becomes a maximum —— putting gaussian in front reduces this).
• a → side length of the window k = 3 + 2 * int(a * 4) (7 at a=0.5. Larger means sparser).
• b → threshold thr = b (keeps only maxima with value ≥ thr. b=0 keeps every maximum).
• Returns: (N, 2) float64 (u, v) = (col, row). Sorted by descending value, ties in row-major order (deterministic). If not even 1 maximum exists, (0, 2) —— even empty it meets the shape contract, but some downstream ops reject empty input.
• The image border is handled with mode="nearest", so border pixels can also be maxima.
Where to use: entry to tb_keypoints_to_image2d (put the points back into an image) and tb_keypoints_uv_to_points (make a point cloud at z=0).
• gallery2d_bridge 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.
img_to_keypoints 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_bridge — py -3.11 examples/gallery2d_bridge.py
keypoints as input)identity · tb_keypoints_uv_to_points · tb_keypoints_to_image2d
bridge)img_to_points · img_to_signal · img_to_projection_profile · img_to_counts · img_to_matrix · img_to_video · img_to_volume · img_to_lightfield
*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.