anomaly_patch op• 데이터 종류: video → table
• 호출: import fullseye as fs; fs.ledger.patchbank_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, coreset=0.1, proj_dim=32, position_weight=0.0, seed=0)(구현을 직접 호출하려면 import anomaly_patch; anomaly_patch.patchbank_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, coreset=0.1, proj_dim=32, position_weight=0.0, seed=0), 원장에서 가져오려면 opsspc.get("patchbank_fit"))
양품 패치 특징의 메모리 뱅크를 만들고 탐욕적 k-중심 커버링으로 솎아낸다 (table).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
stack is (n, H, W) or (n, H, W, 3) of good images (n >= 1). Features are standardised with the mean and the standard deviation of each dimension over all good patches. With position_weight > 0 the grid position (0..1 in each direction) times position_weight * sqrt(D) is appended, so a patch is compared with good patches from nearby positions (0 = position-free). coreset in (0, 1] is the fraction of patches kept: the greedy k-centre cover picks, from a seeded start, always the patch farthest from the ones already kept, measuring distances in a seeded random projection to proj_dim dimensions (None = exact). The covering radius in that space after the selection is returned: the largest distance from any good patch to the bank. coreset=1 keeps everything (radius 0, no selection, so seed and proj_dim have no effect).
Returns the model: bank (m, D'), mean / std (D), n_total patches, n_memory, fraction, covering_radius, the feature parameters and image_shape.
Raises ValueError for invalid parameters, a feature that is constant over all good patches, or a stack that would exceed the memory guard.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table 를 입력으로 받는 것)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
anomaly_patch)patch_features · patchdist_fit · patchdist_score · patchbank_score
*Provenance: anomaly_patch.py — SPC 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.