dtof op• Data kinds: histcube → depth
• Call: import fullseye as fs; fs.ledger.dtof_cube_depth(cube, bin_ps=100.0, mode='peak', offset_ps=0.0, min_counts=1.0, empty_value=0.0, subtract_background=False) (to call the implementation directly, import photoncount; photoncount.dtof_cube_depth(cube, bin_ps=100.0, mode='peak', offset_ps=0.0, min_counts=1.0, empty_value=0.0, subtract_background=False); from the registry, opsphoton.get("dtof_cube_depth"))
Depth map from a (H, W, T) photon histogram cube — the dToF inversion.
The array version of dtof_depth, with the same four *mode* estimators and the same t_flight = t_measured - offset_ps sign convention. The time axis is last: a (D, H, W) voxel volume passed in here would be read as W time bins and return a plausible-wrong depth map, so the shape is checked and the error message says exactly that.
Pixels whose total counts are below *min_counts* — and, in the peak-based modes, pixels whose histogram is exactly flat (no peak to find, so argmax would report bin 0 for every one of them) — are set to *empty_value* (default 0.0, a value no real return can have since d > 0). Set empty_value=float('nan') if you would rather propagate a NaN — that is an opt-in, never the default, because a NaN depth map silently poisons every downstream reduction.
Where a sub-bin *mode* cannot be applied to a pixel — the peak is in the first or last bin, or the three samples are flat / non-positive for the log fit — that pixel falls back to the bin-centre ("peak") estimate. A per-pixel exception would be useless on a megapixel cube; the fallback is documented here and pinned in the tests, and it degrades to the coarser estimator rather than to a wrong one.
Ground truth: on a noiseless simulated cube of a tilted plane from 1.0 to 3.0 m (32x32 pixels, 256 bins x 100 ps, 500 ps IRF) the RMS depth error is 4.39 mm for "peak", 3.2e-16 m for "centroid", 0.114 mm for "parabolic" and 1.6e-8 m for "gaussian". With Poisson noise (20 signal + 5 ambient photons per pixel, seed 0) the same four give 19.9 mm, 164.8 mm (background subtracted), 18.7 mm and 19.2 mm — at 20 photons the estimator choice is worth about 6%, and the centroid is 8x worse than doing nothing clever at all.
Returns a float64 (H, W) depth map in metres.
Raises ValueError: a cube that is not 3-D / has fewer than 2 time bins / holds negative counts / exceeds MAX_CUBE_ELEMENTS, a non-positive *bin_ps*, an unknown *mode*, a negative *min_counts*, a non-finite *empty_value* other than NaN, and — instead of returning negative distances — an *offset_ps* that exceeds the measured arrival time of any valid pixel (a mis-signed or mis-scaled calibration delay).
• photon_timeresolved family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
depth as input)dtof)dtof_depth · dtof_cube_simulate
*Provenance: photoncount.py — PHOTON operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.