magnify op• 데이터 종류: video → table
• 호출: import fullseye as fs; fs.ledger.motion_magnify(video, alpha, f_lo, f_hi, fps, scales: 'int' = 4, orientations: 'int' = 4) -> 'dict'(구현을 직접 호출하려면 import motionmag; motionmag.motion_magnify(video, alpha, f_lo, f_hi, fps, scales: 'int' = 4, orientations: 'int' = 4) -> 'dict', 원장에서 가져오려면 opsmotionmag.get("motion_magnify"))
클립의 대역 내 움직임을 *alpha* 배로 -> dict.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
For every oriented sub-band of every frame the local phase is taken relative to that band's temporal mean as the wrapped deviation angle(z * conj(z_mean)) in (-pi, pi] — it is deliberately not unwrapped along time (see the design note above _AMP_FLOOR: unwrapping a noise band is a random walk that manufactured a 12.27 rad step where 0.039 rad was intended) — band-passed to [f_lo, f_hi], multiplied by alpha - 1 and added back. Because a translation by d shifts a band's phase by -k·d, the phase of the result is -alpha * k·d for *any* k — the output displacement is alpha * d without the local spatial frequency ever being estimated. Low-pass, high-pass and completion residuals are reconstructed untouched (they have no single k to be consistent about).
alpha is the displacement gain: 1 is the identity, 0 removes the in-band motion, 2 doubles it, -1 reverses it. (The literature writes the magnified motion as (1 + alpha_paper) d; this alpha is 1 + alpha_paper.)
Returns a dict::
{"video": (T, H, W) magnified frames,
"alpha": ..., "band_hz": (f_lo, f_hi), "fps": ...,
"snr_in": {...}, "snr_out": {...}, # raw band_snr of in / out
"image_snr_change_db": ..., # <= 0 once |alpha| > 1
"motion_snr_out_db": ..., # gain-corrected; never rises
"motion_snr_change_db": ...,
"band_power_ratio": ..., # 1.0 = perfectly linear
"phase_shift_max_rad": ..., "phase_shift_rms_rad": ...,
"linear_regime": bool, "reference_coherence": ...}
The SNR block is part of the contract, not decoration. Amplifying the in-band phase amplifies the in-band noise by exactly the same factor, so the *motion* SNR cannot rise — magnification reveals motion, it never measures it better than the recording allowed. What degrades is the image:
the output's temporal fluctuation grows like alpha^2 against an unchanged static scene. Measured on a 64x64, 64-frame, 32 fps clip carrying 0.2 px of 4 Hz motion under sigma = 0.01 sensor noise, band 3-5 Hz:
====== =============== ================== ============== =============
alpha image_snr (dB) image change (dB) motion_snr_out band_power
(dB) ratio
====== =============== ================== ============== =============
1 29.2574 -0.0000 11.9404 1.000000
2 24.4304 -4.8270 11.6285 0.934861
4 18.9039 -10.3535 11.2270 0.857626
8 13.7428 -15.5146 9.7565 0.628551
====== =============== ================== ============== =============
Roughly 5 dB of image SNR per doubling (the algebra's asymptote is 20*log10(2) = 6.02 once the amplified band dominates the noise budget), while the motion SNR only ever falls. band_power_ratio is the measured band_power_out / (alpha^2 * band_power_in): 1.0 means the magnification stayed linear, and the shortfall is the energy the phase modulation threw into harmonics.
phase_shift_max_rad is the largest increment applied anywhere, including in contrast-free bands that hold only noise, so it is routinely large and is reported for completeness rather than as a verdict. phase_shift_rms_rad is the contrast-weighted RMS — the number that describes the structure a viewer actually sees — and linear_regime is phase_shift_rms_rad < pi.
★ linear_regime is wrong in both directions. Read reference_coherence instead. Measured 2026-09-06 on a 64x64 / 100 frame / 37 fps clip, 3.7 Hz on-bin, band 3.0-4.5 Hz:
========== ======= ============== ============== ==================
amplitude alpha rms [rad] linear_regime reference_coherence
========== ======= ============== ============== ==================
0.10 px 200 7.809 False 0.9992
2.50 px 3 0.661 True 0.6225
3.05 px 3 0.017 True 0.5024
3.10 px 3 0.032 True 0.5077
========== ======= ============== ============== ==================
Row 1: alpha = 200 on a 0.1 px motion is reproduced to a fidelity error of 2e-14 against a clip that really was moved 200x — perfectly linear, and the flag says False. The magnification factor does not break linearity; the input amplitude does. Rows 3 and 4 straddle the actual breakdown, at the first zero of J0(k A) (2.4048 / k = 3.0619 px here), where the fidelity error jumps from machine precision to 1.6e+01 — and the flag says True. Worse, phase_shift_rms_rad collapses across that boundary (0.661 -> 0.017) rather than growing, because the temporal-mean phase reference the RMS is measured against is itself dying. A statistic that points the wrong way cannot be repaired by moving its threshold, so the flag is left as it is (it does report what it says: whether the phase increment stayed under pi) and reference_coherence — which falls monotonically 1.00 -> 0.50 as the amplitude approaches that zero — is the number to act on. Pinned by tests/test_motionmag.py::test_linear_regime_flag_is_wrong_in_both_directions. reference_coherence is |mean_t z| / mean_t |z|, weighted by band
energy: it is 1 for small motion and collapses towards 0 when the motion is large enough that the temporal-mean phase reference stops being meaningful (see phase_displacement for the closed form).
Narrow-band condition, measured. The relation is exact when each sub-band carries a single moving component. On broadband texture (isotropic noise smoothed by a Gaussian, 0.2 px of motion, alpha = 3) the recovered magnified amplitude is 4.8 % low at sigma = 1.0, 5.5 % at 1.5 and 9.1 % at 3.0 px of smoothing — the more spatial frequencies share a band, the more the phase of their sum departs from linearity in the displacement. That is inherent to phase-based processing, not a tuning fault.
• motion_magnification 패밀리 가이드
• 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.
• motion_magnification — py -3.11 examples/motion_magnification.py
• poc_motion_magnification — py -3.11 examples/poc_motion_magnification.py
• quaternion_monogenic — py -3.11 examples/quaternion_monogenic.py
table 를 입력으로 받는 것)magnify)—
*Provenance: motionmag.py — MOTIONMAG 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.