dtof op• 数据种类:counts → measurement
• 调用:import photoncount; photoncount.dtof_depth(hist, bin_ps=100.0, mode='peak', offset_ps=0.0, subtract_background=False)(或 opsphoton.get("dtof_depth"))
由光子到达时间直方图求距离:`d = c*t/2`。
> 以下的详细说明为原文 —— 摘要与标题已翻译。
Direct time-of-flight. The light travels to the target and back, so the
one-way distance is half the round-trip time times the speed of light.
*bin_ps* is the width of one time bin (a 100 ps bin is 1.50 cm of depth).
Four estimators, from crudest to sharpest:
• `"peak"` — the centre of the fullest bin. Quantised to the bin grid;
the error is uniform in `+-half a bin (+-0.75 cm` at 100 ps).
• `"centroid"` — the first moment of the whole histogram. Exact for a
symmetric pulse *with no background*, and badly biased toward the middle
of the window with one — pass `subtract_background=True`.
• `"parabolic"` — a parabola through the peak bin and its two neighbours.
Sub-bin, cheap, and biased for a Gaussian pulse.
• `"gaussian"` — the same parabola fitted to the log of those three
samples, which is the exact vertex for a Gaussian pulse.
Measured on a noiseless simulated return at 2.4371 m (256 bins x 100 ps,
500 ps IRF), absolute error: `peak 1.29 mm, centroid` 4.4e-16 m,
`parabolic 0.067 mm, gaussian` 9.4e-9 m — three orders of magnitude
between the crudest and the sharpest.
With Poisson noise (200 signal + 200 ambient photons, seed 0) the same
four give 13.7 mm, 146.5 mm (with `subtract_background=True`), 8.5 mm and
8.0 mm. Two honest readings of that: once shot noise dominates the sub-bin
estimators buy about 1.6x, not three orders of magnitude, and the centroid
collapses because a median-subtracted ambient floor still leaves noise
across the whole window that drags the first moment toward the centre. Use
`"gaussian" or "parabolic" on noisy data; use "centroid"` only when
the background is genuinely gone.
*offset_ps* is a system delay to remove: ``t_flight = t_measured -
offset_ps``, so a positive offset makes the answer *closer*. Returns the
distance in metres as a float.
Raises `ValueError`: negative, non-finite, non-1-D or all-zero *hist*,
a non-positive *bin_ps*, an unknown *mode*, a non-finite *offset_ps*, a
flat histogram in a peak-based mode (`argmax` would silently pick bin 0
and report the first bin's depth), a peak in the first or last bin with a
sub-bin *mode* (there is no neighbour to fit to — use `"peak"`), a
degenerate three-sample fit, and — instead of returning a negative distance —
an *offset_ps* larger than the measured arrival time.
• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。
• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。
• 算法的正典(作者・年份)与用途见上面的族使用指南。
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
measurement 作为输入)—
dtof)dtof_cube_simulate · dtof_cube_depth
*Provenance: photoncount.py — PHOTON 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.