xcv_orb_count — 2D `features` op

• Data kinds: image → feature

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

xcv_orb_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 OpenCV's ORB (Oriented FAST and Rotated BRIEF).

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

`cv2.ORB_create` で検出のみ行い(記述子は計算しない)、検出できた

キーポイント数を返す(feature 出力)。`xsk_orb_count`(skimage 版)

と同じ発想の別実装で、検出器の実装が異なるため件数が一致するとは

限らない。

`a が要求する最大特徴点数 nfeatures` を 50〜500 の範囲で振る

(上限であり、実際の検出数は画像内容に依存してそれより少なくなる)。

`b` は未使用。

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.

xcv_orb_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).*

xcv_orb_count — 2D features op