counting op• Datenarten: image2d → image2d
• Aufruf: import fullseye as fs; fs.ledger.photon_sample(image, photons_per_unit=100.0, dark_rate=0.0, seed=0) (die Implementierung direkt: import photoncount; photoncount.photon_sample(image, photons_per_unit=100.0, dark_rate=0.0, seed=0); aus dem Register: opsphoton.get("photon_sample"))
Zieht aus einem Bild erwarteter Photonenzahlen eine Poisson-Stichprobe: ein echtes Photonenzählbild.
> Die ausführliche Beschreibung unten ist der Originaltext — Zusammenfassung und Überschriften sind übersetzt.
*image* is a non-negative 2-D map of scene radiance in arbitrary units; lambda = image * photons_per_unit + dark_rate is the expected number of photons in each pixel over the exposure, and the result is one Poisson realisation of it. *dark_rate* is the dark-count contribution (a SPAD counts thermally generated carriers even in the dark) in the same photon units.
Returns the counts themselves as a float64 (H, W) image (integer valued). That is the deliberate difference from backends_aug.aug_shot_noise, which returns Poisson(v*K)/K clipped to [0, 1] for training-data augmentation: every operator downstream here (Fano factor, Anscombe, Coates, dToF) needs N, and the rescale-and-clip is not invertible.
seed is a required non-negative integer and the RNG is numpy.random.default_rng(seed) — same seed, same frame, on any machine.
Ground truth it reproduces (pinned in tests/test_photoncount.py): the sample mean and sample variance both converge to lambda. Measured on a flat lambda = 100 field of 512x512 pixels at seed 0 — mean 99.9796, Fano factor 1.001089, so the photon-limited SNR is sqrt(lambda): 9.9990 predicted from the mean, 9.9935 actually achieved.
Raises ValueError: negative or non-finite *image*, negative *photons_per_unit* / *dark_rate*, a non-integer or negative *seed*, an image
over MAX_IMAGE_ELEMENTS, and — instead of letting numpy fail deep inside the sampler — any lambda over MAX_LAMBDA.
• Leitfaden zur Familie photon_timeresolved
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_colocalization_crosstalk — py -3.11 examples/poc_colocalization_crosstalk.py
• poc_search_sweep_width — py -3.11 examples/poc_search_sweep_width.py
image2d als Eingabe)photon_statistics · photon_uncertainty · anscombe_transform · anscombe_inverse
counting)photon_statistics · photon_uncertainty
*Provenance: photoncount.py — PHOTON Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.