features op• Data kinds: image → feature
• Call: fullseye.apply(img, "xcv3_sift_count", 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 real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
SIFT keypoint count (OpenCV `cv2.SIFT_create().detect`). Returns as a feature the number of scale-invariant feature points (SIFT keypoints) detected in the image - an indicator of texture complexity / the richness of feature points usable for matching.
> The detailed description below is the original text — the summary and the headings are translated.
`a は検出上限数 nfeatures(int(50+450*a) で 50〜500)を振る —— 上限に達するまでは実際の検出数がそのまま返るので、上限に張り付いていないか確認が要る。b` は未使用。
• gallery2d_features 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.
xcv3_sift_count 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_features — py -3.11 examples/gallery2d_features.py
feature as input)features)blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat · sk_blur_effect
*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.