xcv2_fast_count — 2D `features` op

• Data kinds: image → feature

• Call: fullseye.apply(img, "xcv2_fast_count", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

xcv2_fast_count: 入力 → 出力

*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).*

Usage

Number of keypoints detected by the FAST corner detector (`cv2.FastFeatureDetector`).

> The detailed description below is the original text — the summary and the headings are translated.

`a は検出しきい値を 5〜45 に振る（threshold = int(5 + 40*a)`、大きい

ほど検出されにくくなり数が減る）。`b` は未使用。テクスチャの豊富さ・

コーナーの多さを表す特徴量で、値が大きいほど局所的な輝度変化に富む画像

であることを示す。

Detailed usage guide

• gallery2d_features 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.

Try it in Studio

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.

xcv2_fast_count 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_features — py -3.11 examples/gallery2d_features.py

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

identity

Same category (`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).*

xcv2_fast_count — 2D features op