temporal op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.band_snr(video, f_lo, f_hi, fps) -> 'dict' (implementation को सीधे call करना हो तो import motionmag; motionmag.band_snr(video, f_lo, f_hi, fps) -> 'dict', registry से लेना हो तो opsmotionmag.get("band_snr"))
मापता है कि clip के temporal band में क्या है, और उसकी क्या कीमत है -> dict।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Every quantity is a measured mean-square power obtained from the per-pixel temporal DFT (Parseval-normalised so that the bins of one pixel sum to that pixel's mean square), averaged over pixels:
• static_power — the DC bin. The scene that is simply *there*.
• band_power — the bins inside [f_lo, f_hi]. Coherent motion plus whatever noise happens to fall in the band.
• out_of_band_power / out_of_band_bins — everything else except DC. With broadband sensor noise this is the noise floor, and noise_power_per_bin is its per-bin density.
• noise_in_band = noise_power_per_bin * band_bins — how much of band_power is expected to be noise.
• motion_power = max(band_power - noise_in_band, 0) and motion_snr_db = 10*log10(motion_power / noise_in_band).
• image_snr_db = 10*log10(static_power / (band_power + out_of_band_power)) — the static scene against everything that flickers.
The two SNRs answer different questions and magnification moves only one of them. Scaling the in-band phase by alpha scales the in-band motion *and* the in-band noise by the same factor, so the true motion SNR cannot
improve: magnification never makes a measurement more certain than the recording was. What does change is image_snr_db, because the temporal fluctuation of the output frames grows like alpha^2 while the static scene does not.
A caveat that matters when this is run on an already-magnified clip. motion_snr_db here divides the in-band signal by a noise floor estimated from the *out-of-band* bins, and magnification does not touch those. Applied to a magnified video it therefore credits alpha^2 more in-band power against an unchanged noise estimate and reports an improvement that did not occur — measured, +6.86 dB at alpha = 2 on a clip whose true motion SNR cannot have moved. motion_magnify knows the gain and returns the corrected figure as motion_snr_out_db; use that one, not result["snr_out"]["motion_snr_db"].
snr_clamped is True when a reported dB hit the [-100, +100] window (a noiseless synthetic has zero out-of-band power, which is a division by zero rather than an infinite SNR).
• motion_magnification family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• motion_magnification — py -3.11 examples/motion_magnification.py
• poc_motion_magnification — py -3.11 examples/poc_motion_magnification.py
table को input के रूप में लेते हैं)temporal)temporal_bandpass · temporal_band_power
*Provenance: motionmag.py — MOTIONMAG operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.