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dtourolle/DarkRoom:master dtourolle/DarkRoom:release/0.21 dtourolle/DarkRoom:feat/macos-inference
dtourolle/DarkRoom:v0.24.0 dtourolle/DarkRoom:v0.23.0 dtourolle/DarkRoom:v0.22.1 dtourolle/DarkRoom:v0.22.0 dtourolle/DarkRoom:v0.21.0 dtourolle/DarkRoom:v0.20.0 dtourolle/DarkRoom:v0.19.4 dtourolle/DarkRoom:v0.19.3 dtourolle/DarkRoom:v0.19.2 dtourolle/DarkRoom:v0.19.1 dtourolle/DarkRoom:v0.19.0 dtourolle/DarkRoom:v0.18.2 dtourolle/DarkRoom:v0.18.1 dtourolle/DarkRoom:v0.18.0 dtourolle/DarkRoom:v0.17.0 dtourolle/DarkRoom:v0.16.0 dtourolle/DarkRoom:v0.15.0 dtourolle/DarkRoom:v0.14.1 dtourolle/DarkRoom:v0.14.0 dtourolle/DarkRoom:v0.13.6 dtourolle/DarkRoom:v0.13.5 dtourolle/DarkRoom:v0.13.4 dtourolle/DarkRoom:v0.13.3 dtourolle/DarkRoom:v0.13.2 dtourolle/DarkRoom:v0.13.1 dtourolle/DarkRoom:v0.13.0 dtourolle/DarkRoom:v0.12.1 dtourolle/DarkRoom:v0.12.0 dtourolle/DarkRoom:v0.11.0 dtourolle/DarkRoom:v0.10.0 dtourolle/DarkRoom:v0.8.0 dtourolle/DarkRoom:v0.7.0 dtourolle/DarkRoom:v0.6.0 dtourolle/DarkRoom:v0.5.0 dtourolle/DarkRoom:v0.3.0
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compare: dtourolle/DarkRoom:v0.22.0
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dtourolle/DarkRoom:master dtourolle/DarkRoom:release/0.21 dtourolle/DarkRoom:feat/macos-inference
dtourolle/DarkRoom:v0.24.0 dtourolle/DarkRoom:v0.23.0 dtourolle/DarkRoom:v0.22.1 dtourolle/DarkRoom:v0.22.0 dtourolle/DarkRoom:v0.21.0 dtourolle/DarkRoom:v0.20.0 dtourolle/DarkRoom:v0.19.4 dtourolle/DarkRoom:v0.19.3 dtourolle/DarkRoom:v0.19.2 dtourolle/DarkRoom:v0.19.1 dtourolle/DarkRoom:v0.19.0 dtourolle/DarkRoom:v0.18.2 dtourolle/DarkRoom:v0.18.1 dtourolle/DarkRoom:v0.18.0 dtourolle/DarkRoom:v0.17.0 dtourolle/DarkRoom:v0.16.0 dtourolle/DarkRoom:v0.15.0 dtourolle/DarkRoom:v0.14.1 dtourolle/DarkRoom:v0.14.0 dtourolle/DarkRoom:v0.13.6 dtourolle/DarkRoom:v0.13.5 dtourolle/DarkRoom:v0.13.4 dtourolle/DarkRoom:v0.13.3 dtourolle/DarkRoom:v0.13.2 dtourolle/DarkRoom:v0.13.1 dtourolle/DarkRoom:v0.13.0 dtourolle/DarkRoom:v0.12.1 dtourolle/DarkRoom:v0.12.0 dtourolle/DarkRoom:v0.11.0 dtourolle/DarkRoom:v0.10.0 dtourolle/DarkRoom:v0.8.0 dtourolle/DarkRoom:v0.7.0 dtourolle/DarkRoom:v0.6.0 dtourolle/DarkRoom:v0.5.0 dtourolle/DarkRoom:v0.3.0
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v0.20.0 ... v0.22.0
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dtourolle 5736a21a3a Release 0.22.0
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2026-10-04 08:13:35 -04:00
dtourolle b6ca7be185 Show each denoise method in the manual as a close-up 
The manual's AI denoise section names the four methods and their measured
times, and shows the lamp and railing of the ISO 8000 frame at 1:1 by each
in place of the film and the before/after pair. The scene clicks each
method and waits for that network's result: the repair now logs its own
"learned denoise:" line first, so the wait matches the result's.
 2026-10-04 08:11:24 -04:00
dtourolle 06422a07db Offer three denoise networks and a method to choose between them 
AI Denoise's Apply switch becomes Method: Bilinear, Fast, Medium, Best,
default Best, so an untouched raw writes nothing and develops through the
mixture. `apply` is still read and never written: 0 is Bilinear, 1 keeps
a network already chosen.

- Best is the mixture of a flat and an edge expert with a learned gate;
  Medium and Fast are students distilled from it. 2.48 s, 0.79 s and
  0.57 s for a 20 MP frame on TensorRT fp16.
- Each network carries its own tile border (256 for the mixture, 192 for
  the students) through `dr_denoise::Shipped` and `TileNet::halo`.
- The file is hashed once at open and each network keys its own cached
  result; Bilinear keeps the result in memory for the way back.
- Each has an .a16w16 sibling for the Hexagon: 0.00 dB on the 6D gate,
  at most 0.11 dB with the noise scaled x0.5 to x4.
- APK BUNDLED 19 -> 23; the PKGBUILD installs all three.
 2026-10-04 08:02:25 -04:00
dtourolle 14f08a565f Feed the denoise network without making it wait for the CPU 
A 20 MP frame spent 0.32 s outside the network: each tile's mosaic and
sigma gathered on one thread, then its 24 MB output copied out of the
runtime and back into the frame, all in series with the device. Tiles are
now gathered on every core by a producer thread one tile ahead, so the
gather overlaps the run; the centre is written back across cores; and the
tile interface hands its inputs over and lends its output, so neither
side is copied. With a stand-in network that does nothing, the tiler's own
time falls to 0.14 s at the 1408 tile and 0.09 s at 2048. The exactness
and Bayer-phase tests are unchanged and pass.
 2026-10-04 07:30:09 -04:00
dtourolle 0c9d564586 Repair photosites beyond 8 sigma of every neighbour before the network 
The app's hot-pixel pass takes gross defects only; at ISO 6400-25600 a 6D
frame keeps 1000-2000 photosites more than 8 sigma beyond all their
same-colour and adjacent neighbours, which the network turned into specks.
The same two tests with the threshold in the photosite's own sigma, plus
the factor of two that keeps a bright point of light (where 8 sigma is a
sliver of the signal). The next model is trained behind exactly this; on
an ISO 25600 frame the Rust and training code both repair 935.
 2026-10-04 07:30:09 -04:00
dtourolle 75e12441fd Share Lightroom's saturation bands across ours at measured strengths 
Photographs opened with an earlier Lightroom edit now import its HSL
saturation as fitted against the library's own Lightroom 6 exports, rather
than one band to one band.

Measured on two looks' exports and their raws (darkroom-lrfit, hsl_map_fit),
by encoded hue: Lightroom's saturation bands act about 45 degrees either
side on our wheel, wider than ours, and not all at our strength. Each is now
shared between two or three of our bands — Aqua mostly cyan and azure, where
skies are; Blue mostly blue and violet; Orange, where skin is, at about 0.4
of its value. Values add when two of Lightroom's bands share one of ours.
On the measured skies the import now lifts muted sky blues about 1.9× against
Lightroom's 2.1×, where it gave 1.15×. Hue and luminance still go one band to
the band of the same hue; they were not measured.
 2026-10-04 05:28:33 -04:00
dtourolle 3f8f909e41 Make the colour mixer's saturation reach muted colours 
Every photograph with a colour-mixer saturation edit now renders differently:
a raised band is stronger, most of all on muted colours.

The mixer matched bands and judged saturation on scene-linear values, and
scaled chroma by the same factor whatever a colour started at. Against the
photographer's earlier exports of two looks (~90 photographs, their raws, by
encoded hue), a sky band raised by 58 there lifted muted sky blues about
2.1×; here the mixer gave 1.15×, and less in the muted tones that carry most
of a sky or a shadowed snowfield.

Bands are now matched and saturation judged on display-encoded values. A
raised band pushes muted colours hardest and tapers to nothing at full
saturation, at a gain of 3.0, which at the same value lifts muted sky blues
about as those exports did. Lowering saturation still scales every colour
alike. Hue shifts work on the same encoded colour; luminance still scales in
linear light.

The bundled presets that use the mixer, and those whose colour was tuned
against the default rendering, are rescaled to the amount of colour they
had: Vivid 1.30, Vivid warm 1.30, Vivid landscape 1.38, Vivid, strong 1.45,
Vivid portrait 1.15, Punch 1.12, Blue sky 1.08, Deep blue sky 1.12, Polariser
1.26, Blue sky, golden land 1.13 — mean CIELAB chroma over the default
rendering, on 30 raws from the library. Negative values (skin protection)
are left as written.
 2026-10-04 05:28:22 -04:00
dtourolle 5ffd54ba43 Leave the profile's look table off by default 
Every raw rendered through a camera profile — the library's DNGs with an
embedded profile, and CR2s given one — now renders differently: more
colourful in near-neutral tones. The profile's look table is no longer
applied unless its slider is raised; PROFILE_LOOK names the strength the
profile states.

Against the photographer's earlier exports with no look applied, the default
rendering scores the same with the look table at 100, 50 or 0 (held-out MSE
140, 140, 143), and is 9 % more colourful at 0: the table lowers the
saturation of near-neutral tones, which is exactly where the default
rendering was short of those exports. The user chose more colour.
 2026-10-04 05:28:15 -04:00
dtourolle 5a8c3e4c40 Run each model on the Hexagon in the form measured to hold it 
The engine knew f32 and int8, and gave the Hexagon int8 for every role it
served. Measured on the tablet itself (inference.md §1.5), int8 lost
5% of the detector's faces at 40-80 px, moved the landmarks 1.5 px,
emptied the segmenter's scores and cost the denoiser 5-9 dB; fp16 the HTP
refuses outright. `Form` gains A16W8 and A16W16, and `Rung::form` now
names one per role: detectors and landmarks A16W8, the segmenter, scene
model, border filler and denoiser A16W16, XFeat int8. The embedder and
the eye classifiers stay on the CPU.

Each loader resolves its `<stem>.<form>.onnx` sibling; the segmenter and
XFeat, compiled into the binary, embed their quantised forms on Android
only and pick through `choose_embedded`. The probe, the compile step and
the cache fingerprint follow the form instead of assuming int8. Detectors
on the new form write `scrfd_*_a16+w600k_mbf`, and `model_ids` answers
for all three spellings.

On the tablet (ORT 1.29 + QNN 2.42), each shipped file against f32 on the
same inputs, and against the CPU's f32 time:
  SCRFD 500m/2.5g/10g  A16W8   100% of faces in every band   4.2/5.1/9.0 ms vs 17/56/198
  landmarks            A16W8   0.25 px in the 192 crop        0.5 ms vs 2.8
  YOLO26n-seg          A16W16  98.2% found, mask IoU 0.994    12.9 ms vs 90
  scene model          A16W16  98.9% of cells agree           15 ms vs 151
  MI-GAN               A16W16  41 dB from f32 in the fill     87 ms vs 488
  XFeat                int8    pano alignment 0.45 px (f32's own spread 0.41)  6.5 ms vs 58
  denoiser             A16W16  0.00 dB at every ISO            95 ms vs 1510 a tile
Face numbers are over public COCO val2017 photographs, not a library.

The APK carries the siblings (BUNDLED 15 -> 19; the old int8 detectors
removed), about 43 MB more. The Windows installer and its CI count skip
them; the Arch and Flatpak packages list their files and never had them.
The ladder example takes a role per model, which is how the per-role
forms above were seen landing on the NPU from the real probe.
 2026-10-04 03:45:46 -04:00
dtourolle 0e6ac09fd5 Quantise for the Hexagon with QNN's config and the app's own inputs 
tools/quantise-models.sh now writes each model's Hexagon form from a
per-model table: the form its role takes on the NPU (int8, A16W8 or
A16W16), the exact graph rewrites it needs, and the nodes that must stay
float. Ranges are min/max over photographs fed exactly as the app feeds
each model -- the detector and segmenter letterboxes with their own pads
and normalisation, landmark crops from the detector's boxes, MI-GAN with a
panorama-like border, XFeat's grey proxy. The old tool used an
antialiased resize, YOLO's pad of 128 and /255 for every model that was
not a face model, none of which is what the app does.

tools/htp_graph.py holds the rewrites, each checked against the input
graph before use: the denoiser's 6-D Bayer pack and XFeat's 224-slice
unfold as SpaceToDepth (QNN stops at rank 5), computed reshape targets
folded, and bilinear Resize as two MatMuls (the HTP refuses
ResizeBilinear at XFeat's sizes). The denoiser takes ranges computed by
darkroom-denoise's gate on a smaller tile of the same network.
 2026-10-04 03:45:07 -04:00
dtourolle 948f6c3ed2 Count the denoise model in the Windows installer's smoke test 
package.sh stages models/denoise beside face, scene and inpaint, and
the smoke test counted only the other three, so 0.21.0's Windows job
failed with "expected 14 model files, installed 15". The count reads
the same directories package.sh copies, as its comment intends.
 2026-10-04 02:50:15 -04:00
dtourolle 8f9e59b9fa Find hot photosites without repairing them, and measure a sensor's aging 
The hot-pixel pass could only repair: it returned how many photosites it
changed and threw away which. find_hot_pixels runs the same pass and
returns them as sensor coordinates, leaving the frame alone, so a sensor's
defects can be tracked across frames.

sensor_scan prints each frame's candidates, and with --probe reads a list
of coordinates back out of every frame. Run over 53 6D raws from 2015 to
2026, it found 32 persistent defects, 2 in 2015 and 32 by 2026, and showed
what a defect map has to account for: a frame that does not flag a
photosite proves nothing unless its neighbourhood is dark, and the 6D
hides some of its defects itself above ISO 5000. docs/dev/sensor-health.md
records the findings and the design they argue for.
 2026-10-04 02:38:48 -04:00
dtourolle 83f0461ce7 Sync develop presets through the library 
Presets were the one piece of the photographer's work that never left
the device: faces, sidecars, albums, collections, keywords and camera
profiles all travel with the sync pass, the preset library did not.

It now goes to <derived>/presets/library.drpl. PresetLibrary::merge
decides each name against the base the last exchange left (kept per
library beside place.json), so presets added on two devices both
survive, a deletion reaches the other device instead of being restored
by it, and an edit outlives a deletion made elsewhere. The upload is
If-Match / If-None-Match on the server's copy, and a 412 reads and
merges again, so two devices exchanging at once cannot save over each
other. A server copy that will not parse (a newer build's) is left
alone, and a local file that will not read stops the exchange rather
than being taken for an empty library.

The develop view's save merges with the file when the sync changed it
since the view read it, and a sync that brought presets reloads and
redraws the list.

Also corrects the register, which still said camera profiles do not
sync.
 2026-10-04 00:50:09 -04:00
dtourolle 26e50ae723 Save presets beside the settings, not under a raw HOME 
PresetStore::open built its path from XDG_CONFIG_HOME or HOME. Android
sets neither, so the library resolved to /.config/darkroom, which is
read-only, and every preset saved on the tablet failed. Windows sets no
HOME either and got a directory relative to the working directory. The
settings store was moved to dr_sync::account::config_dir for the same
reason in 0.12.1; the presets now follow it. Linux and macOS resolve to
the same file as before.
 2026-10-04 00:49:59 -04:00
dtourolle 25dc0d0179 Give the Vivid presets and Punch measured amounts of colour 
On the default rendering, Vivid added 22 % more chroma than the rendering
itself, and Punch 5 % — less than the photographer's earlier exports show
with no look applied (14 % over ours) and well below their everyday look
(27 %). Each preset's colour values (vibrance, saturation, the mixer's
saturation bands) are now scaled together, tone values untouched and
negative ones — Vivid portrait's skin protection — left as written, until
the preset measures: Vivid and Vivid warm 1.30, Vivid landscape 1.38, Vivid,
strong 1.45, Vivid portrait 1.15, Punch 1.12. Measured as mean CIELAB chroma
over 30 raws from the library, as a ratio to the default rendering.
 2026-10-03 23:04:46 -04:00
dtourolle a36ec98b36 Carry Lightroom's tone sliders across at measured strengths 
Contrast2012 and the four recovery sliders were imported one to one. They do
not mean the same thing here: fitted on the library's Lightroom 6 exports and
their raws — each photograph's sliders carried across as slider × factor, one
factor per slider, on about 90 exports with no look applied, on the Camera Raw
default rendering — ours needed contrast at about a tenth (Lightroom's −100
imported as ours flattens a frame to grey), highlights ×1.4, shadows ×1.9 and
blacks ×1.25. Whites fitted below 1 every time without agreeing where; 0.5 is
a hedge, and says so. Vibrance stays one to one: the op itself is now
calibrated to Lightroom's.
 2026-10-03 23:04:46 -04:00
dtourolle c343ac79d3 Describe AI Denoise in the manual as it now is 
On for every raw, at the top of the Adjust panel, kept once computed,
and eased off with Strength rather than Keep grain. The timing line is
left as it was; the new model's measured figure replaces it when that
branch lands. The animation still shows the Keep grain slider and
wants recording again.
 2026-10-03 22:18:15 -04:00
dtourolle 2e7f14dafe Develop every raw through the AI denoise by default, with a strength, cached 
The learned demosaic was an option under Detail, off by default. It is
now how a Bayer raw is developed: on by default at full strength on
every device — which hardware runs it is the inference engine's choice
— and first in the Adjust panel, since it decides what every control
below is applied to.

Strength (0-100, default 100) replaces Keep grain: grain = 100 -
strength, the same luminance-only blend, so moving it is one GPU pass
and never a re-run. 0.21.0's sidecars stored grain; it is still read,
as the inverse, and never written.

With it on for every photograph, the result is now kept on disk
(denoise.md §7.1, §12): the network's output as half floats, keyed on
a SHA-256 of the file's bytes and the model, oldest first past a 5 GB
budget, beside the inference engine's cache. A reopened photograph and
an export of one already developed read it back instead of running the
network again; a damaged entry is a miss.
 2026-10-03 22:16:36 -04:00
dtourolle ff0effbfe1 Count the denoise model in the APK's bundled-model list
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96001480 added mosaic-1408.onnx to BUNDLED as a fifteenth entry and
left the array's declared length at 14, so the Android build failed
and 0.21.0 got no release. The workspace gates never compile the
Android crate, which is why nothing before CI saw it.
 2026-10-03 22:14:26 -04:00
dtourolle 1a03cb52b4 Release 0.21.0
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2026-10-03 17:06:49 -04:00
dtourolle ff4b30fbaa Link the inference engine for macOS in a zig container 
`docker/macos` builds for aarch64-apple-darwin from Linux with
cargo-zigbuild. Zig carries libSystem and the C headers, so tract's SIMD
kernels compile and the engine's test binaries and examples link as Mach-O
arm64 — the check `cargo check --target` could not do, because tract's
build script needs a macOS C compiler. Crates that link an Apple framework
(dr-plat's keyring, and so the app) still need the Xcode SDK and fail at
the link; macos.md says so.
 2026-10-03 16:50:37 -04:00
dtourolle c73743394f Add a CoreML rung on macOS 
The macOS ladder was the CPU provider alone, with CoreML listed as a gap.
It is now CoreML, then the CPU, then tract — unmeasured, since nobody here
has a Mac, and safe to ship unmeasured because the probe's clock rejects a
CoreML slower than the CPU and `attempt` refuses one that crashes.

- `Rung::CoreMl`, a compiling rung like TensorRT: an ML Program with every
  compute unit allowed, falling back to the CPU until each model's program
  is built. The embedder stays on the CPU, as on the Hexagon (§7).
- The cache is one directory per model and runtime version. CoreML keys a
  model committed from memory on its input and node names, not its
  weights (ONNX Runtime 1.29, coreml_execution_provider.cc), so two
  exports of one architecture would otherwise share a program.
- The fingerprint on macOS is the chip and the OS release, which ships
  CoreML.
- The desktop looks for the runtime in the bundle's Contents/Frameworks
  and Homebrew's prefixes; fetch-desktop-runtime.sh on a Mac downloads
  ONNX Runtime 1.29.0 for Apple silicon, which carries CoreML.

docs/dev/macos.md says what exists, how to build it, and which log lines
to ask a Mac user for.
 2026-10-03 16:50:37 -04:00
dtourolle 872e35670c Log like a debug build on macOS, where a Mac user can find it 
Nobody working on DarkRoom has a Mac, so every macOS build is in the hands
of someone who can send a log and cannot attach a debugger. Three changes
make that log worth sending:

- The desktop's default filter on macOS is `debug` for every `dr_*` crate,
  the desktop crate and `onnxruntime` (the runtime's own session log).
- The state directory — the log and crash records — is `~/Library/Logs`
  on macOS rather than the `~/.local/state` Finder hides; Console.app
  lists it. Config and data keep the Unix rules.
- A `diagnostic` cargo profile: release plus line tables, so a crash
  record's backtrace reads file:line. On macOS the tables are in the
  `.dSYM` beside the executable, which the bundle must keep.
 2026-10-03 16:49:58 -04:00
dtourolle b562d7b1af Stop retrying a provider that took the app down 
The probe runs in the app's process, and a provider can fail by aborting
rather than by returning an error — XNNPACK did on SCRFD. A rung that does
that once would do it on every launch, before the first photograph is on
screen.

Every session build above the CPU, the probe's and each background
compile's, now writes what it is attempting to `attempt` in the cache
directory first and removes it after. After two launches in a row that
died inside the same attempt it is refused and recorded — a rung in
`failed`, an engine in the new `refused` — until the fingerprint changes.
Two, not one, because quitting during a TensorRT compile leaves the same
file.
 2026-10-03 16:49:58 -04:00
dtourolle 3689b06c35 Send ONNX Runtime's session log to the app's log 
A native session's messages went to ONNX Runtime's stdio logger, which is
nowhere once the app is launched from a menu — and what a provider says
while partitioning a graph (nodes taken, operators declined, a library that
failed to load) is most of what a failed rung tells you. Each session now
forwards them to `log` under the target `onnxruntime`: warnings always,
the runtime's info lines at `debug`, its verbose lines at `trace`.
 2026-10-03 16:49:58 -04:00
dtourolle 64ea44aefe Stop reading dates at the first sign the server is unreachable 
A window of cells whose thumbnails were cached but whose dates were not
sent a header read per cell, and offline each one was three attempts at
a 15 s connect timeout: `is_transient` counts a network error as worth
retrying, and `read_metadata_only` returned a bare bool that could not
say why a read failed. So the grid sat on "reading N dates" for minutes
against a server that was not there, and no banner went up, because
nothing in that loop ever reported the connection.

`read_metadata_only` now returns a `DateRead`: reached, failed, or
offline. An offline error is returned on the first attempt rather than
retried — a dead server answers the second exactly as the first — while
a 423 lock is still retried, which is what the retry was for. The grid's
worker stops on it and sends `Offline`, as its fetch loop already did,
so the banner goes up and the bar stops. The sweep's lanes stop on it
too, one timeout each rather than one per image.
 2026-10-03 16:49:49 -04:00
dtourolle 32a4da0e94 Import DEFAULT_CONTRAST only where the tests use it 
The calibration commit imported it at module level, where only the
test module reads it; the build warned and clippy's -D warnings
refuses that.
 2026-10-03 16:45:02 -04:00
dtourolle 33779a70bd Answer a thumbnail miss with the other stored class before the network 
Offline, a grid zoomed past 256px was blank wherever it had not been
zoomed over before. The store was asked only for the exact class the cell
wanted, and the sweep stores only the grid class, so every zoomed cell
missed and went to a server that was not there — with its 256px thumbnail
sitting in the store the whole time. Online it cost the same round trip,
just without the blank cell at the end of it.

The split now tries the other class on a miss. A smaller one stands in
and the fetch for the real class still goes out; a larger one answers
the request outright, since there is nothing a fetch would improve on.
`ThumbnailReady` carries the class its pixels are, and the drain records
that rather than the batch's class, so a stand-in is replaced on the next
reload instead of being counted as served. `already_served` counts a
held large thumbnail as serving the grid class too, so zooming back out
does not re-read the store for pixels already on screen.

The split moves into `split_by_store` so it can be tested without a
worker thread or a network.
 2026-10-03 16:44:58 -04:00
dtourolle 185e134ead Describe the measured tone and vibrance in DarkRoom's own terms 
The calibration commits named DarkRoom's default curve after another
product and described vibrance as doing what another editor's does at
the same value. The curve is the DNG SDK's reference, so it is called
that; vibrance is scaled to deliver the strength its value names, as
measured against the photographer's earlier exports. Two test names
follow. The measurements and where they came from are unchanged.
 2026-10-03 16:41:11 -04:00
dtourolle 7ae1e27810 Make vibrance deliver the strength its value names 
Vibrance delivered about a third of its nominal effect. Its falloff measured
saturation on scene-linear values, where an ordinary tan reads as 0.78 and
keeps a twentieth of the effect; and its skin guard halved it wherever red led
green led blue — 38 % of the pixels of the gallery's exports, every warm colour
rather than skin. The Vivid presets lean on vibrance, which is why they added
less colour than their values promised.

Saturation is now judged on display-encoded values, the guard covers skin hues
(about 10-50 degrees, not strongly saturated), and the gain is fitted: on 45
Lightroom exports whose only colour setting was Vibrance (about +24), the
measured-to-nominal scale was 1.9 before and 1.08 at a gain of 1.2, so 1.3.
 2026-10-03 15:54:41 -04:00
dtourolle a4ff7ec2b9 Render raws through the DNG reference curve by default, at contrast 1.5 
Decides D21 by measurement. The photo gallery holds Lightroom 6 exports of
raws in the library, each carrying its Camera Raw settings; clustered by
those settings, 663 had no look applied. On 60 of them with their raws,
a third held out, the held-out MSE against Lightroom's JPEG was about 1200
for 0.20.0's sigmoid (0.7 EV darker and flatter), 224 for the DNG reference curve
after baseline exposure, and about 140 once its input is bent by 1.5/1.4
about grey.

So the curve choice defaults to the DNG reference, keeping its index (sidecars
record it), and the default contrast is 1.5. Contrast under the DNG reference curve is
now a power relative to REFERENCE_CONTRAST (1.4), where the table is
untouched; the sigmoid at that contrast still matches the retired base
curve. JPEGs are unaffected: the view transform skips a rendered source.
 2026-10-03 15:54:21 -04:00
dtourolle b69fb3e191 Give the denoise work's test images a baseline exposure 
The learned-denoise branch merged while this one was open, and two of
its test fixtures build a RawImage without the baseline_exposure field
this branch added; zero is the no-op value.
 2026-10-03 14:44:48 -04:00
dtourolle 7a09b640d7 Satisfy clippy on the reference tone curve's data 
One sample of the ACR3 table is 0.70711, which clippy reads as an
approximation of 1/sqrt(2). It is the curve's published value, so the
lint is allowed on the table with that reason rather than the number
replaced. And the pair-count check uses is_multiple_of.
 2026-10-03 14:22:49 -04:00
dtourolle 8294e6b59f Call the reference curve what it is, and drop wording that reads as copying 
The view transform's second curve is the DNG SDK's published reference
rendering — the ACR3 default curve applied by RefBaselineRGBTone — so
it is the "DNG Reference" curve in the panel, D21 and the code, not a
name borrowed from another product. Comments and docs that justified a
choice by another editor doing it ("as their Amount", "so a
photographer arriving from it finds the name") now give the actual
reason. The Vivid presets no longer describe themselves as reaching
for another editor's look; they are DarkRoom's own.

Factual mentions stay: which program wrote the library's DNGs, what
was measured against, and preset import. camera-profiles.md gains §15,
on starting a photograph from the edit it already carries.
 2026-10-03 14:22:49 -04:00
dtourolle e12783da9f Open a photograph with the edit it already carries, when DarkRoom has none 
The library's DNGs carry the photographer's earlier develop settings
in their embedded XMP — the house style their photographs were made
with. A photograph opened with no edit of DarkRoom's now starts from
that earlier edit, translated (HSL bands, highlights, blacks and the
rest), as one undoable step named "Earlier Edit"; from there it is an
ordinary edit, saved with the photograph. Export does the same, so a
photograph never opened exports as opening it would show.

Only on positive evidence that there is no DarkRoom edit: a local file
with no sidecar beside it, or a server that answered "no such file"
with nothing in the cache. The stored-edit fetch now says which
(FetchedSidecar::absent). Offline, unreachable or unreadable never
counts — the earlier edit would otherwise be saved over a real edit
that merely failed to arrive.
 2026-10-03 14:22:49 -04:00
dtourolle 013596e1bd Translate Lightroom's HSL panel, and read the edit inside a DNG 
The library's DNGs carry a Lightroom house look in their embedded XMP
— Blue +58, Aqua +50, Yellow and Purple +23, Highlights -40, Blacks
-20 on most — and that, not the camera profile, is why the same files
look richer in Lightroom. The importer skipped exactly that part: the
HSL panel was on its list of structures it did not translate.

Lightroom's eight HSL bands now map onto the colour mixer, hue,
saturation and luminance each one for one: Aqua to our cyan and Purple
to our violet, the nearest of our twelve bands by hue; chartreuse,
spring, azure and rose are left alone. The figures are a first
translation that lr-fit's measurement against Lightroom's output may
yet scale.

read_embedded finds the XMP packet in a photograph's bytes by its
delimiters and translates it, or answers None for a file whose XMP has
no Camera Raw settings — darktable's sidecars, a camera's own packet.
A test reads the library's _MG_9080.dng when it is present.
 2026-10-03 14:22:48 -04:00
dtourolle 1e8594724e Keep the sigmoid as the default curve; Camera Raw's tone is a choice 
The Camera Raw default rested on comparing against Lightroom previews
of photographs that carry the user's Lightroom edits — HSL saturation
Blue +58, Aqua +50 and more, Highlights -40, Blacks -20, in every
DNG's XMP — so it measured the house look, not Camera Raw's base
rendering. Under the ACR3 curve _MG_9080 renders brighter than its
Lightroom preview (mean 0.39 against 0.31).

So the curve choice's first variant, the default, is the sigmoid again
and every raw renders as in 0.20.0 apart from baseline exposure. D21
and camera-profiles.md §12 now say the default is open, to be decided
by measuring against Lightroom exports of unedited photographs. Tests
that are about Camera Raw's tone choose it explicitly.
 2026-10-03 14:22:48 -04:00
dtourolle 0730ef1016 Sync camera profiles through the library, and name the tone curves 
camera-profiles.md §13: the derived sync pass gains a profiles step,
after the catalog and before the place, that exchanges the profiles
directory with <library>/.darkroom-derived/profiles. Profiles are
immutable and named for what they hold, so name and size decide: it
uploads what the server lacks or holds at another size and downloads
what this device lacks — parsed before it is kept, written beside its
name and renamed — then reloads the set, so a profile copied out of a
DNG on the desktop renders the body's CR2s on the tablet after its
next sync. Like the place it never fails the pass. Not a catalog
table: a schema change would stop an older peer merging at all.

Labels for the view transform's new curve choice: Curve, Camera Raw,
Sigmoid.
 2026-10-03 14:22:47 -04:00
dtourolle db7593f3dc Render raws through Camera Raw's tone by default, after baseline exposure 
The rendering half of camera-profiles.md §11-§12 (D21). The view
transform gains a curve choice — Camera Raw (the default) or D19's
sigmoid. Camera Raw converts to linear ProPhoto, clips to [0, 1], runs
the curve on the largest and smallest channel and places the middle
one at its old fraction between them (RefBaselineRGBTone), and
converts back: hue kept, saturation raised where the curve is steep,
which is where Adobe Standard's look desaturated. White sets the input
scale (1 at its default, so sensor white is display white) and
contrast bends the input about grey (1 at its default).

The curve rides in the profile buffer after the tables: the profile's
own, else the ACR3 default, which the placeholder every profile-less
source binds also carries — so a CR2 with no .dcp still gets Camera
Raw's tone. Baseline exposure is a gain folded into the rendering
matrix at upload; RawImage::color_matrix stays the file's for the
merge's linear DNG.

camera_raw::apply_reference is the CPU statement; GPU tests hold the
shader to it on 256 colours and on greys against the ACR3 table. The
sigmoid's own tests now choose it explicitly.
 2026-10-03 14:22:47 -04:00
dtourolle 38d414912c Read baseline exposure and profile tone curves; carry the ACR3 curve 
The decoding half of camera-profiles.md §11-§12. RawImage gains
baseline_exposure: the file's BaselineExposure plus the chosen
profile's BaselineExposureOffset, as the DNG SDK sums them (+0.25 for
the library's 6D DNGs). A profile copied out of a DNG carries that
DNG's baseline as its offset, so the body's CR2s, which have none,
land at the same total.

ProfileTables gains the profile's ProfileToneCurve, resampled at
decode onto 1025 points with a natural cubic spline; an identity curve
counts as none. dr-types now holds Camera Raw's ACR3 default curve,
RawTherapee's adobe_camera_raw_default_curve copied value for value,
for every raw whose profile has no curve. Nothing renders through
either yet.
 2026-10-03 14:22:46 -04:00
dtourolle b58873ef57 Spec baseline exposure, Camera Raw tone and profile sync (D21) 
camera-profiles.md §11-§14 close what 0.20.0 left open. Baseline
exposure is the file's plus the profile's offset, applied as a gain on
the camera matrix, and a copied profile carries the DNG's baseline so a
CR2 lands at the same brightness. The view transform gains a Camera Raw
curve — the profile's ProfileToneCurve, else the ACR3 default — applied
Camera Raw's way, on the outer channels in linear ProPhoto, and it is
the default for every raw (D21, the user's choice). Profiles sync
through .darkroom-derived/profiles on the server as a step of the
derived sync pass, not as a catalog table.
 2026-10-03 14:22:45 -04:00
dtourolle ababd628ed Show AI denoise in the manual, on a night frame with no faces 
A section after Looking closer: what it is for, the switch and its wait,
Keep grain, which cameras it takes and where its noise figures come from.
The scene opens the Brooklyn Bridge at ISO 8000 from the face-free demo
set at 1:1, switches it on, waits for the result to land and keeps some
grain, with a still before and after. It waits on the app's own log line
rather than a fixed time: the network takes seconds on a GPU and more on
the CPU, which is where the recording X server leaves it (13 s).
 2026-10-03 12:05:26 -04:00
dtourolle 4eb7cf77f5 Record what the learned denoise shipped as, and what was measured 
The grain blend replaces the Amount of §7.2, and why its objection to a
blend does not hold for brightness alone; the Hexagon is out (int8 -6 to
-9 dB); §11 holds the data, the noise model taken from the library, the
model, the validation table, the blind estimate's reach and the speed.
 2026-10-03 11:51:02 -04:00
dtourolle 960014803a Ship the denoise model in the Arch package, the APK and the Windows installer 
Same LFS-pointer guard as the other models; the APK copies it out of its
assets with the rest.
 2026-10-03 11:51:01 -04:00
dtourolle dd43f498fb Run the learned denoise in develop, and export with it 
A Bayer photograph keeps its mosaic in the session and is offered the AI
Denoise switch. Asked for, the network runs on the decode executor from a
hot-pixel-repaired copy — the app's own pass — with the frame's noise from
its best source, and its progress in the activity bar; the classical
demosaic shows until the result lands, and the finished job says where the
noise figures came from. Keep grain is a GrainBlend of the two, made once
per value; the render draws it as its source and the adjust pass never
knows. demosaiced stays the classical result, so the raw histogram, the
white balance picker, masks and segmentation still read the sensor.

The develop view reconciles on a 250 ms poll rather than on each way an
edit can change (slider, undo, preset, version, a sidecar from another
device): two comparisons when nothing changed, and no path that can forget.
A failure is not retried until the switch is toggled. An export of a
photograph that asks for it waits for a running job or computes it.
 2026-10-03 11:51:00 -04:00
dtourolle ad6bb892f3 Carry the learned denoise's switch and grain as edit settings 
Whether to use the learned denoise, and how much grain to keep, are what a
photographer sets, so they travel the one road every setting does: published
as a capability, captured by Preset, stored in the sidecar, replayed by the
undo stack (FR-DEV-3c). Published only on a photograph that can take it, for
the lens switch's reason; the availability is derived from the file and is
not in the state. Off by default, grain 0; a reset returns both.
 2026-10-03 11:22:29 -04:00
dtourolle 8ea3c3181a Upload the learned demosaic's result, and blend grain back into it 
DemosaicedImage::from_rgb_f32 takes the network's linear camera RGB and
stands it beside the classical source of the same photograph: the matrix,
profile tables and as-shot balance are that source's, the id is new, so
nothing downstream can tell which demosaic ran and every cache keyed on the
source sees a new one.

GrainBlend is the denoise's live control. It returns only the brightness of
the noise the network removed, taken after the as-shot balance and handed
back divided by it, so the grain is neutral in the finished picture; colour
speckle and demosaic false colour stay out. It writes a new source rather
than adding a term to the adjust shader: the blend depends on two images and
one number, a 20 MP pass is milliseconds, and a fresh source id is all the
adjust pass's caches need. The test reads it back: at 0 the network's
result, at 1 the same white-balanced step in every channel.
 2026-10-03 11:20:48 -04:00
dtourolle d8304d7c82 Add dr-denoise: the learned demosaic and denoise, without the UI 
The noise model takes the best source the frame has: the body's measured
table (the Canon EOS 6D's, from the library), the DNG's NoiseProfile, or
the frame itself — read, row and column noise from its masked border, and
only the shot gain estimated, from the quietest flat patches. Checked on
130 6D frames, the estimate is within 10 % from ISO 1000 up; the network
loses under 0.3 dB for a sigma off by 15-20 %, so every Bayer body is
eligible.

Tiles of 1408 keep their central 1024 behind a 192-photosite halo, past the
185-photosite receptive field, and the frame is extended by reflection,
which keeps every photosite's colour; a pattern that starts on another
colour is read from one photosite up or left so the network sees RGGB, and
nothing is cropped. The tests run every Bayer phase, tiled against whole,
with a stand-in network of known reach.

The model ships as models/denoise/mosaic-1408.onnx (LFS), trained in
darkroom-denoise on the maintainer's own photographs, GPL like the code.
denoise_raw runs a file end to end: on a 6D frame at ISO 8000 the result
matches the training repository's own path to 2.5e-4 at worst, and takes
3.1 s on TensorRT fp16 (75 dB from f32) or 14.4 s on the CPU.
 2026-10-03 11:15:50 -04:00
dtourolle 20b7bd7663 Feed every input a model declares when probing a rung 
The probe built one zero tensor from the first input and ran the session
with it. Every model so far had one input; the denoiser has two (mosaic and
sigma), so every rung failed with "Missing Input: sigma" and the role was
left on the CPU: 14.4 s for a 20 MP frame where TensorRT fp16 takes 3.1 s.
Zeros now go to each input by name.
 2026-10-03 11:15:49 -04:00
dtourolle 6b0d29cc15 Read a DNG's NoiseProfile 
The converter's measured noise for the body at that ISO, (S, O) per CFA
plane: the learned denoise's best source for a body with no table of its
own (denoise.md §3.3). Read from the header beside the colour tags, and
printed by rawinfo. Checked against tifffile on a 6D DNG at ISO 5000: all
six values agree.
 2026-10-03 10:39:15 -04:00
dtourolle eb91fa02c2 Give the inference engine a denoiser role, kept off the Hexagon 
The learned demosaic-and-denoise (denoise.md) runs through the engine like
every other model. fp16 cost it nothing measurable (0.00 dB at every ISO on
validation tiles), so it takes TensorRT's and MIGraphX's fp16 like the
detectors. int8 cost it 6 to 9 dB, far past a 0.5 dB gate, so the Hexagon
refuses the role outright rather than relying on no int8 sibling existing,
and the tablet runs it on the CPU.
 2026-10-03 10:39:14 -04:00
dtourolle d4248bc0dd Run the app's hot-pixel pass alone, and dump through it 
The learned demosaic replaces the classical one and takes its input, the
mosaic hot_pixels.wgsl leaves (denoise.md §2), so its training data and its
input in the app must come through that pass and not a lookalike. The pass
was recorded inline in Demosaicer::run; it is now built by hot_pass and
recorded by record_hot_pass, which run still uses unchanged, and
Demosaicer::repair_hot_pixels runs it on its own and reads the mosaic back.

mosaic_dump moves to dr-gpu to call it, records how many photosites changed,
and keeps --unrepaired for a raw readout.
 2026-10-03 10:25:28 -04:00
dtourolle 1f266a4478 Dump RAW mosaics for training the learned denoise 
denoise.md §4.4 requires the training repo to read photosites through
dr-decode, not LibRaw, so black and white levels, the active area and the
CFA phase match what the app will feed the network. mosaic_dump reads
`input<TAB>prefix` lines and writes the whole readout as .npy plus a JSON
of what decode and metadata report. The masked border is kept: its
optically black photosites are a free dark frame for the noise profile.
 2026-10-03 10:25:27 -04:00
138 changed files with 8889 additions and 837 deletions
Expand all files Collapse all files
.gitea/workflows/build-and-test.yml
+5 -4
View File
@@ -487,10 +487,11 @@ jobs:
wine "$SETUP" /S 2>/dev/null
INST=$(echo "$HOME"/.wine/drive_c/users/*/AppData/Local/Programs/DarkRoom)
ls "$INST"
# As many files as package.sh stages: everything but the READMEs in
# the directories it copies. A literal here went stale the first
# time a model was added.
WANT=$(find models/face models/scene models/inpaint -maxdepth 1 -type f ! -name README.md | wc -l)
# As many files as package.sh stages: everything but the READMEs and
# the Hexagon's quantised siblings in the directories it copies. A
# literal here went stale the first time a model was added.
WANT=$(find models/face models/scene models/inpaint models/denoise -maxdepth 1 -type f ! -name README.md \
! -name '*.int8.onnx' ! -name '*.a16w8.onnx' ! -name '*.a16w16.onnx' | wc -l)
GOT=$(ls "$INST/models" | wc -l)
[ "$GOT" = "$WANT" ] || { echo "FAIL: expected $WANT model files, installed $GOT"; exit 1; }
# The manual, and every picture it shows, counted the same way.
Cargo.lock
Generated
+44 -25
View File
@@ -1265,7 +1265,7 @@ checksum = "f27ae1dd37df86211c42e150270f82743308803d90a6f6e6651cd730d5e1732f"
[[package]]
name = "darkroom-android"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"android_logger",
"dr-plat",
@@ -1278,7 +1278,7 @@ dependencies = [
[[package]]
name = "darkroom-desktop"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"anyhow",
"dr-plat",
@@ -1454,7 +1454,7 @@ checksum = "d8b14ccef22fc6f5a8f4d7d768562a182c04ce9a3b3157b91390b52ddfdf1a76"
[[package]]
name = "dr-bench"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"anyhow",
"dr-catalog",
@@ -1471,7 +1471,7 @@ dependencies = [
[[package]]
name = "dr-catalog"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-face",
"dr-plat",
@@ -1486,7 +1486,7 @@ dependencies = [
[[package]]
name = "dr-decode"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-types",
"env_logger",
@@ -1498,9 +1498,26 @@ dependencies = [
"zune-jpeg 0.4.21",
]
[[package]]
name = "dr-denoise"
version = "0.22.0"
dependencies = [
"dr-decode",
"dr-gpu",
"dr-inference-engine",
"env_logger",
"log",
"ndarray",
"ort",
"pollster",
"serde",
"serde_norway",
"thiserror 2.0.20",
]
[[package]]
name = "dr-export"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-decode",
"dr-gpu",
@@ -1519,7 +1536,7 @@ dependencies = [
[[package]]
name = "dr-face"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-inference-engine",
"env_logger",
@@ -1532,7 +1549,7 @@ dependencies = [
[[package]]
name = "dr-film"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"log",
"serde",
@@ -1541,7 +1558,7 @@ dependencies = [
[[package]]
name = "dr-gpu"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"bytemuck",
"dr-decode",
@@ -1559,7 +1576,7 @@ dependencies = [
[[package]]
name = "dr-inference-engine"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"env_logger",
"libloading",
@@ -1574,7 +1591,7 @@ dependencies = [
[[package]]
name = "dr-ingest"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-plat",
"dr-types",
@@ -1586,7 +1603,7 @@ dependencies = [
[[package]]
name = "dr-lens"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"lensfun",
"log",
@@ -1594,7 +1611,7 @@ dependencies = [
[[package]]
name = "dr-pano"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-decode",
"dr-inference-engine",
@@ -1608,7 +1625,7 @@ dependencies = [
[[package]]
name = "dr-pipeline"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-types",
"log",
@@ -1617,7 +1634,7 @@ dependencies = [
[[package]]
name = "dr-plat"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"android-native-keyring-store",
"dr-types",
@@ -1633,7 +1650,7 @@ dependencies = [
[[package]]
name = "dr-preset-xmp"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-pipeline",
"log",
@@ -1643,7 +1660,7 @@ dependencies = [
[[package]]
name = "dr-segment"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-inference-engine",
"env_logger",
@@ -1656,7 +1673,7 @@ dependencies = [
[[package]]
name = "dr-sync"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"async-trait",
"dr-plat",
@@ -1670,7 +1687,7 @@ dependencies = [
[[package]]
name = "dr-sync-folder"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"async-trait",
"dr-sync",
@@ -1682,7 +1699,7 @@ dependencies = [
[[package]]
name = "dr-sync-nextcloud"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"async-trait",
"dr-decode",
@@ -1704,7 +1721,7 @@ dependencies = [
[[package]]
name = "dr-thumbs"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-types",
"jpeg-encoder",
@@ -1716,7 +1733,7 @@ dependencies = [
[[package]]
name = "dr-types"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"serde",
"serde_json",
@@ -1725,12 +1742,13 @@ dependencies = [
[[package]]
name = "dr-ui"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"anyhow",
"async-trait",
"dr-catalog",
"dr-decode",
"dr-denoise",
"dr-export",
"dr-face",
"dr-film",
@@ -1750,6 +1768,7 @@ dependencies = [
"dr-types",
"dr-xmp",
"env_logger",
"half",
"i-slint-backend-testing",
"jni 0.22.4",
"log",
@@ -1773,7 +1792,7 @@ dependencies = [
[[package]]
name = "dr-xmp"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"dr-types",
"log",
@@ -7107,7 +7126,7 @@ checksum = "8df9b6e13f2d32c91b9bd719c00d1958837bc7dec474d94952798cc8e69eeec3"
[[package]]
name = "traceability"
version = "0.20.0"
version = "0.22.0"
dependencies = [
"anyhow",
"proc-macro2",
Cargo.toml
+15 -1
View File
@@ -5,6 +5,7 @@ members = [
"core/dr-catalog",
"core/dr-thumbs",
"core/dr-decode",
"core/dr-denoise",
"core/dr-export",
"core/dr-face",
"core/dr-film",
@@ -32,7 +33,7 @@ members = [
exclude = ["third_party"]
[workspace.package]
version = "0.20.0"
version = "0.22.0"
edition = "2021"
rust-version = "1.92"
license = "GPL-3.0-or-later"
@@ -44,6 +45,7 @@ dr-types = { path = "core/dr-types" }
dr-catalog = { path = "core/dr-catalog" }
dr-thumbs = { path = "core/dr-thumbs" }
dr-decode = { path = "core/dr-decode" }
dr-denoise = { path = "core/dr-denoise" }
dr-export = { path = "core/dr-export" }
# Stated explicitly for the same reason as `dr-segment` below: no dependant
# should drag in an ONNX runtime by accident. Members opt in with
@@ -276,6 +278,18 @@ opt-level = 0
lto = "thin"
codegen-units = 1
# A release build that can say where it panicked: line tables, so a crash
# record's backtrace (`dr_plat::crash`) reads `file.rs:123` rather than bare
# addresses. The macOS build uses it (docs/dev/macos.md) — no one here can
# reproduce a Mac bug, so its reports carry what a debugger would have — at
# the price of a larger binary and no slower code. On macOS the tables land
# in a `.dSYM` beside the executable (rustc's default `packed`), and the
# bundle must carry that directory next to the binary for the backtrace to
# find it.
[profile.diagnostic]
inherits = "release"
debug = "line-tables-only"
# Three upstream crates carry a local patch: wgpu-hal and Slint's Skia
# renderer so that the Android build can draw with wgpu on a rotated display
# (technical-debt.md TD-1), and rawler so that a linear DNG wider than 16 700
README.md
+1 -1
View File
@@ -201,7 +201,7 @@ controls, its place in the chain and its tests.
## Where it stands
**0.20.0**, thirty-four tagged releases in. 193 numbered requirements in
**0.22.0**, thirty-six tagged releases in. 193 numbered requirements in
scope, 85% of them claimed by code and [traced to it](docs/dev/traceability.md);
the rest are written down rather than merely absent.
apps/darkroom-android/src/lib.rs
+38 -9
View File
@@ -332,27 +332,38 @@ fn unpack_bundled_models(app: &slint::android::AndroidApp) {
// eyes-open filter has something to read, and a tablet has no other way
// to get them either.
//
// The int8 forms beside the three detectors are what the Hexagon runs
// (docs/dev/inference.md §5); the engine loads the sibling when the probe
// chose that rung and ignores it otherwise.
const BUNDLED: [(&std::ffi::CStr, &str); 14] = [
// The quantised siblings — `.a16w8.onnx`, `.a16w16.onnx` — are what the
// Hexagon runs (docs/dev/inference.md §1.5), each in the narrowest form
// that held that model's accuracy on the tablet; the engine loads the
// sibling when the probe chose that rung and ignores it otherwise. The
// segmenter's and XFeat's forms are compiled into the binary instead,
// beside their f32 graphs.
const BUNDLED: [(&std::ffi::CStr, &str); 23] = [
(c"models/scrfd_500m_640.onnx", "scrfd_500m_640.onnx"),
(
c"models/scrfd_500m_640.int8.onnx",
"scrfd_500m_640.int8.onnx",
c"models/scrfd_500m_640.a16w8.onnx",
"scrfd_500m_640.a16w8.onnx",
),
(c"models/scrfd_2.5g_640.onnx", "scrfd_2.5g_640.onnx"),
(
c"models/scrfd_2.5g_640.int8.onnx",
"scrfd_2.5g_640.int8.onnx",
c"models/scrfd_2.5g_640.a16w8.onnx",
"scrfd_2.5g_640.a16w8.onnx",
),
(c"models/scrfd_10g_640.onnx", "scrfd_10g_640.onnx"),
(c"models/scrfd_10g_640.int8.onnx", "scrfd_10g_640.int8.onnx"),
(
c"models/scrfd_10g_640.a16w8.onnx",
"scrfd_10g_640.a16w8.onnx",
),
(c"models/arcface_mbf_b1.onnx", "arcface_mbf_b1.onnx"),
(c"models/2d106det_b1.onnx", "2d106det_b1.onnx"),
(c"models/2d106det_b1.a16w8.onnx", "2d106det_b1.a16w8.onnx"),
(c"models/ocec_s_b1.onnx", "ocec_s_b1.onnx"),
(c"models/sgc_l_48_b1.onnx", "sgc_l_48_b1.onnx"),
(c"models/yolo26s-sem-ade20k.onnx", "yolo26s-sem-ade20k.onnx"),
(
c"models/yolo26s-sem-ade20k.a16w16.onnx",
"yolo26s-sem-ade20k.a16w16.onnx",
),
(
c"models/yolo26s-sem-ade20k.classes.json",
"yolo26s-sem-ade20k.classes.json",
@@ -360,6 +371,24 @@ fn unpack_bundled_models(app: &slint::android::AndroidApp) {
(c"models/categories.txt", "categories.txt"),
// The panorama border filler (FR-MRG-4); MIT, 28 MB.
(c"models/migan-512.onnx", "migan-512.onnx"),
(c"models/migan-512.a16w16.onnx", "migan-512.a16w16.onnx"),
// The learned demosaic and denoise, one network per method
// (FR-DEV-3g), each with the 16-bit form the Hexagon runs.
(c"models/mosaic-fast-1408.onnx", "mosaic-fast-1408.onnx"),
(
c"models/mosaic-fast-1408.a16w16.onnx",
"mosaic-fast-1408.a16w16.onnx",
),
(c"models/mosaic-medium-1408.onnx", "mosaic-medium-1408.onnx"),
(
c"models/mosaic-medium-1408.a16w16.onnx",
"mosaic-medium-1408.a16w16.onnx",
),
(c"models/mosaic-best-1408.onnx", "mosaic-best-1408.onnx"),
(
c"models/mosaic-best-1408.a16w16.onnx",
"mosaic-best-1408.a16w16.onnx",
),
];
let dir = dr_ui::shared_face_models_dir();
apps/darkroom-desktop/src/main.rs
+35 -4
View File
@@ -15,6 +15,22 @@ use std::path::PathBuf;
use dr_plat::diagnostics::Installed;
/// What the log keeps when `RUST_LOG` does not say.
#[cfg(not(target_os = "macos"))]
const DEFAULT_LOG: &str =
"info,wgpu_core=warn,wgpu_hal=warn,zbus=warn,tracing=warn,calloop=warn,rawler=warn";
/// The same, and `debug` from this application's own crates and from ONNX
/// Runtime, whose `debug` is how many nodes each provider took
/// (docs/dev/macos.md). Nobody here runs a Mac: every macOS build is in
/// the hands of someone who can send us a log and cannot attach a debugger,
/// so the log is written as if for a debug build. `dr_` is a prefix, and
/// `env_logger` matches directives by prefix, so it names every `dr-*`
/// crate — present and future — without naming a dependency.
#[cfg(target_os = "macos")]
const DEFAULT_LOG: &str = "info,dr_=debug,darkroom_desktop=debug,onnxruntime=debug,\
wgpu_core=warn,wgpu_hal=warn,zbus=warn,tracing=warn,calloop=warn,rawler=warn";
fn main() -> anyhow::Result<()> {
// TRACES: FR-PLAT-WIN-3
// Before the logger, the crash hook and everything else: this exists so a
@@ -33,10 +49,9 @@ fn main() -> anyhow::Result<()> {
// (NFR-OPS-1). `filter()` is asked afterwards because the environment may
// have overridden the default below, and the file must not be quieter than
// the terminal.
let console = env_logger::Builder::from_env(env_logger::Env::default().default_filter_or(
"info,wgpu_core=warn,wgpu_hal=warn,zbus=warn,tracing=warn,calloop=warn,rawler=warn",
))
.build();
let console =
env_logger::Builder::from_env(env_logger::Env::default().default_filter_or(DEFAULT_LOG))
.build();
let level = console.filter();
let logging = dr_plat::diagnostics::install(Box::new(console), level);
@@ -109,5 +124,21 @@ fn runtime_dirs() -> Vec<PathBuf> {
PathBuf::from("/usr/lib/darkroom"),
PathBuf::from("/usr/lib"),
]);
// An app bundle keeps its libraries in `Contents/Frameworks`, beside
// the `Contents/MacOS` the executable is in; then Homebrew's
// `onnxruntime`, Apple silicon's prefix before Intel's. Homebrew's build
// may lack CoreML, which the probe finds out for itself.
#[cfg(target_os = "macos")]
{
if let Ok(exe) = std::env::current_exe() {
if let Some(bin) = exe.parent() {
dirs.push(bin.join("../Frameworks"));
}
}
dirs.extend([
PathBuf::from("/opt/homebrew/lib"),
PathBuf::from("/usr/local/lib"),
]);
}
dirs
}
core/dr-decode/examples/rawinfo.rs
+4
View File
@@ -32,6 +32,10 @@ fn main() {
println!("black {:?}", raw.black_level);
println!("white {}", raw.white_level);
println!("wb_coeffs {:?}", raw.wb_coeffs);
match dr_decode::noise_profile(&bytes) {
Some(p) => println!("noise profile {p:?} ((S, O) per plane)"),
None => println!("noise profile none"),
}
match raw.color_matrix {
Some(m) => {
core/dr-decode/src/dcp.rs
+48 -1
View File
@@ -68,6 +68,11 @@ pub struct Dcp {
/// `ProfileToneCurve`, as stored: input/output pairs. Carried so a copy
/// keeps it, never applied — tone is the view transform's (D19, D20).
pub tone_curve: Option<Vec<f32>>,
/// `BaselineExposureOffset`, in stops: the profile's correction to the
/// file's `BaselineExposure` (camera-profiles.md §11). A profile copied
/// out of a DNG carries that DNG's baseline here, so a raw with no
/// baseline of its own lands at the same brightness.
pub baseline_exposure_offset: f32,
}
impl Dcp {
@@ -120,6 +125,7 @@ impl Dcp {
.get_entry(DngTag::ProfileToneCurve)
.and_then(|e| floats(&e.value))
.filter(|v| v.len() >= 4 && v.len() % 2 == 0),
baseline_exposure_offset: stops(ifd, DngTag::BaselineExposureOffset),
})
}
@@ -218,6 +224,10 @@ impl Dcp {
origin,
hue_sat: hue_sat.filter(|t| !t.is_identity()),
look: self.look.clone().filter(|t| !t.is_identity()),
tone_curve: self
.tone_curve
.as_deref()
.and_then(dr_types::tone::resample_tone_curve),
}
}
@@ -305,6 +315,15 @@ impl Dcp {
if let Some(curve) = &self.tone_curve {
dir.add_value(DngTag::ProfileToneCurve, Value::Float(curve.clone()));
}
if self.baseline_exposure_offset != 0.0 {
dir.add_value(
DngTag::BaselineExposureOffset,
Value::SRational(vec![SRational::new(
(self.baseline_exposure_offset * 100.0).round() as i32,
100,
)]),
);
}
writer.build(dir).map_err(|e| e.to_string())?;
let mut bytes = cursor.into_inner();
// The writer stamps TIFF's 42 in its own byte order; a profile is the
@@ -340,6 +359,15 @@ fn string(ifd: &IFD, tag: DngTag) -> Option<String> {
.filter(|s| !s.is_empty())
}
/// A single rational tag in stops, zero where absent or unreadable — the
/// DNG specification's default for both exposure tags.
fn stops(ifd: &IFD, tag: DngTag) -> f32 {
ifd.get_entry(tag)
.and_then(|e| e.value.get_f32(0).ok().flatten())
.filter(|v| v.is_finite())
.unwrap_or(0.0)
}
fn floats(value: &Value) -> Option<Vec<f32>> {
(0..value.count())
.map(|i| value.get_f32(i).ok().flatten())
@@ -507,7 +535,12 @@ pub fn embedded_in(bytes: &[u8]) -> Option<Dcp> {
.ifd(rawler::decoders::WellKnownIFD::Root)
.ok()
.flatten()?;
Dcp::from_ifd(&root)
// The copy carries the file's baseline as its offset, so a raw from the
// same body that has no baseline of its own — a CR2 — gets the total the
// DNG renders at (camera-profiles.md §11).
let mut profile = Dcp::from_ifd(&root)?;
profile.baseline_exposure_offset += stops(&root, DngTag::BaselineExposure);
Some(profile)
}
/// TRACES: FR-DEV-3e
@@ -518,6 +551,9 @@ pub struct Resolved {
pub profile: Option<CameraProfile>,
pub tables: Option<Arc<ProfileTables>>,
pub embedded: Option<Arc<Dcp>>,
/// Stops to add at render: the file's `BaselineExposure` plus the
/// chosen profile's `BaselineExposureOffset` (camera-profiles.md §11).
pub baseline_exposure: f32,
}
/// TRACES: FR-DEV-3e
@@ -532,6 +568,7 @@ pub fn resolve(
model: &str,
) -> Resolved {
let embedded = root.and_then(Dcp::from_ifd).map(Arc::new);
let file_baseline = root.map_or(0.0, |r| stops(r, DngTag::BaselineExposure));
if let Some(dcp) = &embedded {
let tables = matrices
.as_ref()
@@ -541,6 +578,7 @@ pub fn resolve(
return Resolved {
profile: matrices,
tables,
baseline_exposure: file_baseline + dcp.baseline_exposure_offset,
embedded,
};
}
@@ -553,6 +591,7 @@ pub fn resolve(
tables: (!tables.is_empty()).then(|| Arc::new(tables)),
profile: Some(own),
embedded: None,
baseline_exposure: file_baseline + dcp.baseline_exposure_offset,
};
}
}
@@ -560,6 +599,7 @@ pub fn resolve(
profile: matrices,
tables: None,
embedded: None,
baseline_exposure: file_baseline,
}
}
@@ -609,6 +649,7 @@ mod tests {
],
look: Some(table(4, 2, 3, [0.0, 1.2, 0.95])),
tone_curve: Some(vec![0.0, 0.0, 0.5, 0.6, 1.0, 1.0]),
baseline_exposure_offset: 0.25,
}
}
@@ -627,6 +668,7 @@ mod tests {
assert_eq!(back.unique_camera_model, original.unique_camera_model);
assert_eq!(back.illuminants, original.illuminants);
assert_eq!(back.tone_curve, original.tone_curve);
assert_eq!(back.baseline_exposure_offset, 0.25);
assert_eq!(
back.forward_matrix, original.forward_matrix,
"four decimals, as the file has"
@@ -746,6 +788,11 @@ mod tests {
assert_eq!(tables.origin, ProfileOrigin::Embedded);
assert_eq!(tables.name, "Adobe Standard");
assert!(tables.hue_sat.is_some() && tables.look.is_some());
assert!(
tables.tone_curve.is_none(),
"Adobe Standard has no curve of its own"
);
assert_eq!(raw.baseline_exposure, 0.25);
}
#[test]
core/dr-decode/src/lib.rs
+19
View File
@@ -147,6 +147,12 @@ pub struct RawImage {
/// renders a decoded file renders it through the same profile without
/// having to be told — see camera-profiles.md §3.
pub profile_tables: Option<std::sync::Arc<dr_types::ProfileTables>>,
/// TRACES: FR-DEV-3e
/// Stops the render adds before anything else: the file's
/// `BaselineExposure` plus the profile's `BaselineExposureOffset`
/// (camera-profiles.md §11). Applied by the GPU side as a gain on the
/// camera matrix; `color_matrix` itself stays the file's.
pub baseline_exposure: f32,
/// The body, as rawler cleans the names: what `Make`/`Model` say, and
/// what a `.dcp`'s `UniqueCameraModel` is matched against.
pub make: String,
@@ -544,6 +550,17 @@ pub(crate) fn parse_exif_offset(s: &str) -> Option<i32> {
Some(sign * (h * 60 + m))
}
/// TRACES: FR-DEV-3g
/// The DNG `NoiseProfile` of a file, if it carries one: `(S, O)` per CFA
/// colour plane, variance `S·x + O` in black-to-white normalised units. See
/// [`profile::read_noise_profile`]. Reads the header, not the image.
pub fn noise_profile(bytes: &[u8]) -> Option<Vec<(f32, f32)>> {
use rawler::rawsource::RawSource;
let source = RawSource::new_from_slice(bytes);
let decoder = rawler::get_decoder(&source).ok()?;
profile::read_noise_profile(decoder.as_ref())
}
/// TRACES: FR-RAW-3 | FR-EXP-9
/// Fully decode sensor data.
///
@@ -591,6 +608,7 @@ fn decode_unguarded(bytes: &[u8]) -> Result<RawImage, DecodeError> {
let dcp::Resolved {
profile,
tables: profile_tables,
baseline_exposure,
..
} = dcp::resolve(
profile,
@@ -689,6 +707,7 @@ fn decode_unguarded(bytes: &[u8]) -> Result<RawImage, DecodeError> {
samples_per_pixel,
profile,
profile_tables,
baseline_exposure,
make: image.camera.clean_make.clone(),
model: image.camera.clean_model.clone(),
})
core/dr-decode/src/profile.rs
+29
View File
@@ -740,6 +740,35 @@ pub fn read_dng_matrices(decoder: &dyn rawler::decoders::Decoder) -> DngMatrices
}
}
/// TRACES: FR-DEV-3g
/// The DNG `NoiseProfile` tag (51041): the converter's measured noise for
/// this body at this ISO, as `(S, O)` per CFA colour plane, so that a
/// photosite's variance is `S·x + O` with `x` normalised black-to-white.
///
/// One pair means all planes share it. `None` where the file has no such
/// tag — every proprietary raw, and DNGs from converters that do not measure
/// — or where a value is not a finite non-negative number. The learned
/// denoise's second-best noise source (denoise.md §3.3), after a measured
/// table for the body.
pub fn read_noise_profile(decoder: &dyn rawler::decoders::Decoder) -> Option<Vec<(f32, f32)>> {
use rawler::decoders::WellKnownIFD;
use rawler::tags::DngTag;
let ifd = decoder.ifd(WellKnownIFD::Root).ok()??;
let entry = ifd.get_entry_recursive(DngTag::NoiseProfile)?;
let n = entry.count() as usize;
if n < 2 || !n.is_multiple_of(2) {
return None;
}
let pairs: Vec<(f32, f32)> = (0..n / 2)
.map(|i| (entry.force_f32(2 * i), entry.force_f32(2 * i + 1)))
.collect();
pairs
.iter()
.all(|(s, o)| s.is_finite() && o.is_finite() && *s >= 0.0 && *o >= 0.0)
.then_some(pairs)
}
#[cfg(test)]
mod tests {
use super::*;
core/dr-denoise/Cargo.toml
+32
View File
@@ -0,0 +1,32 @@
[package]
name = "dr-denoise"
version.workspace = true
edition.workspace = true
rust-version.workspace = true
license.workspace = true
[dependencies]
dr-decode.workspace = true
serde = { workspace = true }
serde_norway.workspace = true
thiserror.workspace = true
log.workspace = true
# The network runs under the inference engine like every other model
# (docs/dev/inference.md): `ort` is the API, the engine picks the rung.
# Optional so the noise model and the tiling test without a runtime.
ort = { workspace = true, optional = true }
dr-inference-engine = { workspace = true, optional = true }
ndarray = { workspace = true, optional = true }
[features]
default = ["onnx"]
onnx = ["dep:ort", "dep:dr-inference-engine", "dep:ndarray"]
# A real ONNX Runtime from disk rather than tract alone, as the app links it.
native = ["onnx", "dr-inference-engine/native"]
[dev-dependencies]
# The example repairs hot photosites with the app's own pass, as develop will.
dr-gpu.workspace = true
pollster.workspace = true
env_logger.workspace = true
core/dr-denoise/examples/denoise_raw.rs
+142
View File
@@ -0,0 +1,142 @@
//! Denoise one RAW file end to end, as develop will, and time it.
//!
//! ```sh
//! DARKROOM_ORT_DIR=~/.local/share/darkroom/runtime \
//! cargo run --release -p dr-denoise --features native --example denoise_raw -- IMG.CR2 out [fast|medium|best]
//! ```
//!
//! Decode, the app's hot-pixel pass, the frame's noise from its best source,
//! then one of the shipped networks (`best` unless named) under the inference engine on whatever rung this
//! machine probes to. Writes `out.npy` — the active area, `h×w×3` f32 linear
//! camera RGB — for comparison with the training repo's own path
//! (`tools/compare_rust.py` in darkroom-denoise). `DARKROOM_ORT_DIR` points
//! at an ONNX Runtime build; the engine's cache goes to `DR_ENGINE_CACHE` or
//! a temporary directory.
use std::path::PathBuf;
use std::time::{Duration, Instant};
use dr_denoise::onnx::OnnxNet;
use dr_inference_engine::{Config, Role};
fn main() {
env_logger::Builder::from_env(env_logger::Env::default().default_filter_or("warn")).init();
let mut args = std::env::args().skip(1);
let (Some(input), Some(out)) = (args.next(), args.next()) else {
eprintln!("usage: denoise_raw RAW OUT_PREFIX [fast|medium|best]");
std::process::exit(2);
};
let shipped = match args.next().as_deref() {
None | Some("best") => dr_denoise::BEST,
Some("medium") => dr_denoise::MEDIUM,
Some("fast") => dr_denoise::FAST,
Some(other) => {
eprintln!("no network called {other}: fast, medium or best");
std::process::exit(2);
}
};
let model = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
.join("../../models/denoise")
.join(shipped.file);
let cache = std::env::var_os("DR_ENGINE_CACHE")
.map(PathBuf::from)
.unwrap_or_else(|| std::env::temp_dir().join("dr-denoise-engines"));
let started = Instant::now();
dr_inference_engine::init(Config {
runtime_dirs: std::env::var_os("DARKROOM_ORT_DIR")
.map(PathBuf::from)
.into_iter()
.collect(),
cache_dir: cache,
models: vec![(Role::Denoiser, model.clone())],
embedded: Vec::new(),
ceiling: None,
threads: 0,
decay: Duration::ZERO,
});
// Wait for the probe and the engine build, so the timing below is the
// rung this machine settles on, not the fallback used while it compiles.
// The probe starts on its own thread; give it a moment to say so.
std::thread::sleep(Duration::from_secs(1));
loop {
let s = dr_inference_engine::status();
if !s.probing && s.engines.0 >= s.engines.1 {
println!(
"engine {} ({:.1} s to settle)",
s.line(),
started.elapsed().as_secs_f64()
);
break;
}
std::thread::sleep(Duration::from_millis(200));
}
let bytes = std::fs::read(&input).expect("read raw");
let t = Instant::now();
let mut raw = dr_decode::decode(&bytes).expect("decode");
let meta = dr_decode::metadata(&bytes).expect("metadata");
let decode = t.elapsed();
let t = Instant::now();
let ctx =
pollster::block_on(dr_gpu::GpuContext::new_headless()).expect("GPU for the hot-pixel pass");
let repaired = dr_gpu::Demosaicer::new(&ctx)
.expect("demosaicer")
.repair_hot_pixels(&mut raw)
.expect("repair");
let repair = t.elapsed();
let noise = dr_denoise::noise::for_frame(&raw, &bytes, meta.iso)
.expect("no noise source for this frame");
println!(
"frame {} {} ISO {:?}, {}×{}, {:?}, {repaired} hot photosites repaired",
raw.make, raw.model, meta.iso, raw.crop.width, raw.crop.height, raw.cfa_pattern
);
println!(
"noise {} — σ at 10 % grey (G) {:.5}, read {:.5}, row {:.5}, col {:.5}",
noise.source.label(),
noise.sigma(1, 0.1),
noise.o[1].sqrt(),
noise.row,
noise.col
);
let mut net = OnnxNet::from_path(&model, shipped.halo).expect("model");
println!(
"rung {}",
net.rung().map(|r| r.label()).unwrap_or("?")
);
let t = Instant::now();
let rgb = dr_denoise::denoise(&raw, &noise, &mut net, &mut |done, total| {
eprint!("\rtile {done}/{total}");
true
})
.expect("denoise")
.expect("not cancelled");
let run = t.elapsed();
eprintln!();
println!(
"time decode {:.2} s · hot pixels {:.2} s · network {:.2} s ({:.1} MP)",
decode.as_secs_f64(),
repair.as_secs_f64(),
run.as_secs_f64(),
(raw.crop.width * raw.crop.height) as f64 / 1e6
);
let (h, w) = (raw.crop.height as usize, raw.crop.width as usize);
let mut npy = Vec::with_capacity(rgb.len() * 4 + 128);
let mut header =
format!("{{'descr': '<f4', 'fortran_order': False, 'shape': ({h}, {w}, 3), }}");
while (10 + header.len() + 1) % 64 != 0 {
header.push(' ');
}
header.push('\n');
npy.extend_from_slice(b"\x93NUMPY\x01\x00");
npy.extend_from_slice(&(header.len() as u16).to_le_bytes());
npy.extend_from_slice(header.as_bytes());
for v in &rgb {
npy.extend_from_slice(&v.to_le_bytes());
}
std::fs::write(format!("{out}.npy"), npy).expect("write");
println!("wrote {out}.npy");
}
core/dr-denoise/src/lib.rs
+118
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//! TRACES: FR-DEV-3g
//! Learned demosaic and denoise on the raw mosaic (docs/dev/denoise.md).
//!
//! A network trained on the library's own base-ISO raws with the 6D's
//! measured noise added takes the repaired, normalised mosaic and a σ for
//! every photosite, and returns linear camera RGB at full resolution — the
//! texture the classical demosaic would have produced, with the noise gone.
//! It replaces the demosaic box; nothing downstream changes (§2).
//!
//! - [`noise`] says how noisy each photosite is, from the best source the
//! frame has.
//! - [`tile`] runs a fixed-shape network over a whole frame, exactly.
//! - [`onnx`] is that network under the inference engine.
//!
//! The input must already have been through the app's hot-pixel pass
//! (`dr_gpu::Demosaicer::repair_hot_pixels`): the noise model was fitted
//! with what that pass removes left out.
pub mod noise;
#[cfg(feature = "onnx")]
pub mod onnx;
pub mod repair;
pub mod tile;
use dr_decode::RawImage;
pub use noise::{NoiseModel, Source};
pub use tile::{TileNet, HALO};
/// TRACES: FR-DEV-3g
/// A network the app ships in `models/denoise/`: its file, and the context
/// it needs past a tile's kept centre (docs/dev/denoise.md §13).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Shipped {
pub file: &'static str,
pub halo: usize,
}
/// The smallest student: 0.9 M parameters, 11 GMAC a megapixel.
pub const FAST: Shipped = Shipped {
file: "mosaic-fast-1408.onnx",
halo: HALO,
};
/// A student of the mixture with the first release's shape: 3.2 M
/// parameters, 48 GMAC a megapixel.
pub const MEDIUM: Shipped = Shipped {
file: "mosaic-medium-1408.onnx",
halo: HALO,
};
/// The mixture: a flat expert, an edge expert and the gate that blends them.
/// It reaches further than either, so it keeps a smaller centre of each tile.
pub const BEST: Shipped = Shipped {
file: "mosaic-best-1408.onnx",
halo: 256,
};
#[derive(Debug, thiserror::Error)]
pub enum DenoiseError {
#[error("the network cannot take this photograph: {0}")]
Unsupported(String),
#[error("the denoise model misbehaved: {0}")]
Model(String),
#[error("could not read the denoise model: {0}")]
ModelRead(#[from] std::io::Error),
#[cfg(feature = "onnx")]
#[error(transparent)]
Engine(#[from] dr_inference_engine::Error),
#[cfg(feature = "onnx")]
#[error(transparent)]
Ort(#[from] ort::Error),
}
/// Whether the learned stage can take this frame at all: a Bayer mosaic.
/// X-Trans needs its own model (§9); a linear DNG has no photosites.
pub fn eligible(raw: &RawImage) -> bool {
raw.samples_per_pixel == 1 && tile::rggb_offset(raw.cfa_pattern).is_some()
}
/// The active area of `raw`, denoised and demosaiced: `crop.height ×
/// crop.width` interleaved RGB, linear camera space, normalised black 0 and
/// white 1 per photosite as the classical demosaic normalises.
///
/// `raw` must be hot-pixel repaired. `None` when `progress` stopped it.
pub fn denoise(
raw: &RawImage,
noise: &NoiseModel,
net: &mut dyn TileNet,
progress: &mut dyn FnMut(usize, usize) -> bool,
) -> Result<Option<Vec<f32>>, DenoiseError> {
if !eligible(raw) {
return Err(DenoiseError::Unsupported(format!(
"{:?} with {} samples per photosite",
raw.cfa_pattern, raw.samples_per_pixel
)));
}
let active = noise::active(raw);
let (h, w) = (active.h, active.w);
// The active area laid out once, then the noise-aware repair the model
// was trained behind (see `repair`).
let mut mosaic: Vec<f32> = (0..h * w).map(|i| active.at(i / w, i % w)).collect();
let pattern = raw.cfa_pattern;
let repaired = repair::repair(&mut mosaic, h, w, repair::REPAIR_K, &|y, x, v| {
noise.sigma(pattern.colour_at(x as u32, y as u32) as usize, v)
});
log::info!(
"learned denoise: {repaired} photosites beyond {}σ of every neighbour repaired",
repair::REPAIR_K
);
tile::run_tiled(
net,
h,
w,
raw.cfa_pattern,
&|y, x| mosaic[y * w + x],
&|c, v| noise.sigma(c, v),
progress,
)
}
core/dr-denoise/src/noise.rs
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//! TRACES: FR-DEV-3g
//! How noisy each photosite is: the network is told, not left to guess
//! (denoise.md §3.3).
//!
//! The model is `σ² = S·x + O + row² + col²` per photosite, `x` the signal
//! normalised black-to-white the way the demosaic normalises it. Three
//! sources, best first:
//!
//! 1. **A measured table** for the body ([`Source::Table`]) — the Canon EOS 6D
//! today, from the library's own frames.
//! 2. **The DNG's `NoiseProfile`** ([`Source::DngProfile`]) — what Adobe's
//! converter measured for the body at that ISO.
//! 3. **The frame itself** ([`Source::Measured`]) — read, row and column
//! noise from its masked border, which is a dark frame taken in the same
//! instant, and only the shot gain estimated, from the quietest flat
//! patches. Checked against the 6D's table on 130 frames: within ±10 % at
//! ISO 1000 and above, scattered below; the network loses under 0.3 dB for
//! a σ off by 15–20 %, and over-estimating costs half what
//! under-estimating does, so the estimate leans high.
//!
//! Row and column noise come from the masked border whenever the frame has
//! one, whatever the source of the rest.
use dr_decode::{CfaPattern, RawImage};
use serde::Deserialize;
/// Where a frame's noise figures came from, for develop to say.
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum Source {
Table,
DngProfile,
Measured,
}
impl Source {
pub fn label(self) -> &'static str {
match self {
Source::Table => "measured for this camera",
Source::DngProfile => "from the DNG's noise profile",
Source::Measured => "estimated from this photograph",
}
}
}
/// Per-photosite noise in the frame's own normalisation (black 0, white 1).
#[derive(Clone, Debug, PartialEq)]
pub struct NoiseModel {
/// Shot gain per colour, R G B.
pub s: [f32; 3],
/// Read variance per colour, R G B.
pub o: [f32; 3],
/// Standard deviation shared by a whole row, and by a whole column.
pub row: f32,
pub col: f32,
pub source: Source,
}
impl NoiseModel {
/// σ for a photosite of colour `c` (0 R, 1 G, 2 B) reading `x`.
#[inline]
pub fn sigma(&self, c: usize, x: f32) -> f32 {
(self.s[c] * x.max(0.0) + self.o[c] + self.row * self.row + self.col * self.col).sqrt()
}
/// The same figures scaled for the Amount the spec describes (§3.3):
/// above 1 tells the network there is more noise than there is.
pub fn scaled(&self, amount: f32) -> NoiseModel {
let a2 = amount * amount;
NoiseModel {
s: self.s.map(|v| v * a2),
o: self.o.map(|v| v * a2),
row: self.row * amount,
col: self.col * amount,
source: self.source,
}
}
}
/// The frame's noise, from the best source it has.
///
/// `bytes` is the file (for a DNG's `NoiseProfile`), `iso` its EXIF ISO.
/// `None` only for a frame with no masked border, no profile and no table
/// that is also too dark or too busy to measure.
pub fn for_frame(raw: &RawImage, bytes: &[u8], iso: Option<u32>) -> Option<NoiseModel> {
for_frame_with(raw, dr_decode::noise_profile(bytes).as_deref(), iso)
}
/// [`for_frame`], given the file's `NoiseProfile` already read
/// ([`dr_decode::noise_profile`]) rather than the file, for a caller that
/// keeps the header's answer and not the bytes.
pub fn for_frame_with(
raw: &RawImage,
profile: Option<&[(f32, f32)]>,
iso: Option<u32>,
) -> Option<NoiseModel> {
let dark = dark_border(raw);
let mut model = iso
.and_then(|iso| from_table(raw, iso))
.or_else(|| profile.and_then(|p| from_dng_profile(raw, p)))
.or_else(|| measured(raw, dark.as_ref()))?;
if let Some(d) = dark {
// The border saw this exposure's row and column noise directly.
if model.source != Source::Table {
model.row = d.row;
model.col = d.col;
}
}
Some(model)
}
#[derive(Deserialize)]
struct Table {
make: String,
model: String,
rows: Vec<TableRow>,
}
#[derive(Deserialize)]
struct TableRow {
iso: u32,
s_dn: [f32; 4],
o_dn: [f32; 4],
row_dn: f32,
col_dn: f32,
}
const TABLES: &[&str] = &[include_str!("../tables/canon-eos-6d.yaml")];
/// The body's measured table at the nearest ISO it holds, converted from DN
/// to this frame's normalisation.
pub fn from_table(raw: &RawImage, iso: u32) -> Option<NoiseModel> {
let table = TABLES.iter().find_map(|t| {
let t: Table = serde_norway::from_str(t).ok()?;
(t.make.eq_ignore_ascii_case(&raw.make) && t.model.eq_ignore_ascii_case(&raw.model))
.then_some(t)
})?;
let row = table.rows.iter().min_by(|a, b| {
let d = |r: &TableRow| ((r.iso as f32).ln() - (iso as f32).ln()).abs();
d(a).total_cmp(&d(b))
})?;
let span = span(raw);
// RGGB positions → colours: the greens share.
let s = [row.s_dn[0], 0.5 * (row.s_dn[1] + row.s_dn[2]), row.s_dn[3]].map(|v| v / span);
let o =
[row.o_dn[0], 0.5 * (row.o_dn[1] + row.o_dn[2]), row.o_dn[3]].map(|v| v / (span * span));
Some(NoiseModel {
s,
o,
row: row.row_dn / span,
col: row.col_dn / span,
source: Source::Table,
})
}
/// A DNG's `NoiseProfile`: one pair for every plane, or one per colour plane
/// (R, G, B for a Bayer DNG), already in the file's black-to-white units —
/// which are the units `dr-decode` normalises by.
pub fn from_dng_profile(raw: &RawImage, pairs: &[(f32, f32)]) -> Option<NoiseModel> {
if raw.cfa_pattern.is_xtrans() || raw.samples_per_pixel != 1 {
return None;
}
let (s, o) = match pairs {
[(s, o)] => ([*s; 3], [*o; 3]),
[r, g, b, ..] => ([r.0, g.0, b.0], [r.1, g.1, b.1]),
_ => return None,
};
Some(NoiseModel {
s,
o,
row: 0.0,
col: 0.0,
source: Source::DngProfile,
})
}
/// Read, row and column noise measured on the masked border, normalised.
#[derive(Clone, Copy, Debug)]
pub struct Dark {
pub read: f32,
pub row: f32,
pub col: f32,
}
/// The optically black photosites beside and above the active area.
///
/// Keeps well clear of the active area: on the 6D the dozen columns nearest
/// it see light. Photosites over 8σ are the strip's own hot photosites — the
/// same ones in every frame — and are left out, as the app's hot-pixel pass
/// removes their kin before the network sees them.
pub fn dark_border(raw: &RawImage) -> Option<Dark> {
let (x0, y0, w, h) = (
raw.crop.x as usize,
raw.crop.y as usize,
raw.crop.width as usize,
raw.crop.height as usize,
);
let stride = raw.width as usize;
let span = span(raw);
if x0 < 40 || raw.samples_per_pixel != 1 {
return None;
}
let cols = 4..x0 - 16;
let nc = cols.len() as f32;
// Residual after removing each row's mean and each column's mean.
let mut row_means = Vec::with_capacity(h);
let mut col_sum = vec![0.0f64; cols.len()];
for y in y0..y0 + h {
let line = &raw.data[y * stride..y * stride + x0];
let m = cols.clone().map(|x| line[x] as f32).sum::<f32>() / nc;
row_means.push(m);
for (k, x) in cols.clone().enumerate() {
col_sum[k] += (line[x] as f32 - m) as f64;
}
}
let col_mean: Vec<f32> = col_sum.iter().map(|s| (*s / h as f64) as f32).collect();
let resid = |y: usize, k: usize, x: usize| {
raw.data[y * stride + x] as f32 - row_means[y - y0] - col_mean[k]
};
let (mut s1, mut n) = (0.0f64, 0usize);
for y in y0..y0 + h {
for (k, x) in cols.clone().enumerate() {
s1 += (resid(y, k, x) as f64).powi(2);
n += 1;
}
}
let rough = (s1 / n as f64).sqrt() as f32;
let (mut s2, mut n2) = (0.0f64, 0usize);
for y in y0..y0 + h {
for (k, x) in cols.clone().enumerate() {
let r = resid(y, k, x);
if r.abs() < 8.0 * rough {
s2 += (r as f64).powi(2);
n2 += 1;
}
}
}
let read = (s2 / n2.max(1) as f64).sqrt() as f32;
let rm = row_means.iter().sum::<f32>() / h as f32;
let row_var = row_means.iter().map(|m| (m - rm).powi(2)).sum::<f32>() / h as f32;
let row = (row_var - read * read / nc).max(0.0).sqrt();
// Columns: the masked rows above the image span every column.
let col = if y0 >= 24 {
let rows = 4..y0 - 12;
let nr = rows.len() as f32;
let means: Vec<f32> = (x0..x0 + w)
.map(|x| {
rows.clone()
.map(|y| raw.data[y * stride + x] as f32)
.sum::<f32>()
/ nr
})
.collect();
let mm = means.iter().sum::<f32>() / means.len() as f32;
let var = means.iter().map(|m| (m - mm).powi(2)).sum::<f32>() / means.len() as f32;
(var - read * read / nr).max(0.0).sqrt()
} else {
0.0
};
Some(Dark {
read: read / span,
row: row / span,
col: col / span,
})
}
/// The quietest-third bias of the patch variance, and the residual bias the
/// estimate showed against the 6D's table (0.91 at the median), in one: the
/// estimate is divided by this.
const QUIET_FACTOR: f32 = 0.85 * 0.91;
/// The frame's own noise: read noise from the border (or, lacking one, the
/// floor of the quietest patches), shot gain from flat patches of one green
/// plane, the same for every colour, as a sensor's gain is.
pub fn measured(raw: &RawImage, dark: Option<&Dark>) -> Option<NoiseModel> {
if raw.cfa_pattern.is_xtrans() || raw.samples_per_pixel != 1 {
return None;
}
let m = active(raw);
let (h, w) = (m.h, m.w);
// One green plane at a two-photosite pitch.
let (gy, gx) = green_offset(raw.cfa_pattern)?;
let ph = (h - gy) / 2;
let pw = (w - gx) / 2;
let g = |y: usize, x: usize| m.at(gy + 2 * y, gx + 2 * x);
const B: usize = 8;
let mut patches: Vec<(f32, f32)> = Vec::new(); // (level, variance)
for by in 0..ph / B {
for bx in 0..(pw - 2) / B {
let (mut s, mut s2, mut lv) = (0.0f32, 0.0f32, 0.0f32);
for y in by * B..by * B + B {
for x in bx * B..bx * B + B {
// Second difference: cancels any gradient; var = 6σ².
let d = g(y, x + 2) - 2.0 * g(y, x + 1) + g(y, x);
s += d;
s2 += d * d;
lv += g(y, x + 1);
}
}
let n = (B * B) as f32;
let var = (s2 / n - (s / n).powi(2)) / 6.0;
patches.push((lv / n, var));
}
}
let floor = dark.map(|d| d.read);
let lo = 4.0 * floor.unwrap_or(0.002);
patches.retain(|(l, _)| *l > lo && *l < 0.7);
if patches.len() < 500 {
return None;
}
patches.sort_by(|a, b| a.0.total_cmp(&b.0));
let bins = 12;
let per = patches.len() / bins;
let mut ests = Vec::new();
let mut floors = Vec::new();
for b in 0..bins {
let mut bin: Vec<(f32, f32)> = patches[b * per..(b + 1) * per].to_vec();
if bin.len() < 60 {
continue;
}
bin.sort_by(|a, b| a.1.total_cmp(&b.1));
let quiet = &bin[..bin.len() / 3];
let read2 = floor.map(|r| r * r);
let mut e: Vec<f32> = quiet
.iter()
.map(|(l, v)| (v / QUIET_FACTOR - read2.unwrap_or(0.0)) / l)
.collect();
e.sort_by(f32::total_cmp);
ests.push(e[e.len() / 2]);
floors.push(quiet[quiet.len() / 2]);
}
ests.sort_by(f32::total_cmp);
let s = *ests.get(ests.len() / 2)?;
if !(s.is_finite() && s > 0.0) {
return None;
}
// No border: the read variance is what the darkest bin leaves unexplained.
let read2 = match floor {
Some(r) => r * r,
None => {
let (l, v) = floors.first().copied()?;
(v / QUIET_FACTOR - s * l).max(1e-9)
}
};
Some(NoiseModel {
s: [s; 3],
o: [read2; 3],
row: dark.map_or(0.0, |d| d.row),
col: dark.map_or(0.0, |d| d.col),
source: Source::Measured,
})
}
/// Black-to-white range of the frame, as the demosaic normalises it.
pub(crate) fn span(raw: &RawImage) -> f32 {
let black = raw.black_level.iter().map(|&b| b as f32).sum::<f32>() / 4.0;
(raw.white_level as f32 - black).max(1.0)
}
/// Where a green photosite sits in the pattern's 2×2 cell, (dy, dx).
fn green_offset(p: CfaPattern) -> Option<(usize, usize)> {
match p {
CfaPattern::Rggb | CfaPattern::Bggr => Some((0, 1)),
CfaPattern::Grbg | CfaPattern::Gbrg => Some((0, 0)),
_ => None,
}
}
/// The active area, normalised, read lazily.
pub(crate) struct Active<'a> {
raw: &'a RawImage,
black: [f32; 4],
inv: [f32; 4],
pub h: usize,
pub w: usize,
}
impl Active<'_> {
/// Photosite (y, x) of the active area, black 0, white 1.
#[inline]
pub fn at(&self, y: usize, x: usize) -> f32 {
let c = (y & 1) * 2 + (x & 1);
let v = self.raw.data[(self.raw.crop.y as usize + y) * self.raw.width as usize
+ self.raw.crop.x as usize
+ x];
(v as f32 - self.black[c]) * self.inv[c]
}
}
/// Black levels per position of the crop's 2×2 cell, as the demosaic reads
/// them: one reported level is broadcast.
pub(crate) fn active(raw: &RawImage) -> Active<'_> {
let b = raw.black_level;
let black = if b[1] == 0 && b[2] == 0 && b[3] == 0 {
[b[0] as f32; 4]
} else {
b.map(|v| v as f32)
};
let inv = black.map(|bl| 1.0 / (raw.white_level as f32 - bl).max(1.0));
Active {
raw,
black,
inv,
h: raw.crop.height as usize,
w: raw.crop.width as usize,
}
}
core/dr-denoise/src/onnx.rs
+86
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//! TRACES: FR-DEV-3g
//! The denoise network under the inference engine.
//!
//! The shipped export takes `mosaic` and `sigma`, `1×1×1408×1408`, and
//! returns `rgb`, `1×3×1408×1408` (darkroom-denoise `denoise/export.py`,
//! fixed shape because every model the engine runs is). The engine picks the
//! rung: fp16 on TensorRT and MIGraphX, which measured 0.00 dB from f32; f32
//! on CUDA and the CPU; on the Hexagon the `.a16w16.onnx` sibling, 16-bit
//! activations and weights, 0.00 dB from f32 on the tablet itself where int8
//! lost 5–9 dB (docs/dev/inference.md §1.5). That sibling is the same network
//! with the Bayer packing spelled `SpaceToDepth`, which QNN can hold and the
//! 6-D reshape it replaces it cannot.
use crate::tile::TileNet;
use crate::DenoiseError;
use dr_inference_engine::{Model, Role};
/// The edge of the tile the shipped export takes.
pub const TILE: usize = 1408;
pub struct OnnxNet {
model: Model,
tile: usize,
halo: usize,
}
impl OnnxNet {
/// The network at `path`, which needs `halo` photosites of context
/// ([`crate::Shipped::halo`]).
pub fn from_path(path: &std::path::Path, halo: usize) -> Result<Self, DenoiseError> {
let (path, form) = dr_inference_engine::resolve_model(Role::Denoiser, path);
let bytes = std::fs::read(&path)?;
Ok(OnnxNet {
model: dr_inference_engine::open(Role::Denoiser, form, &bytes)?,
tile: TILE,
halo,
})
}
/// Where it runs, for a status line.
pub fn rung(&self) -> Result<dr_inference_engine::Rung, DenoiseError> {
Ok(self.model.acquire()?.rung())
}
}
impl TileNet for OnnxNet {
fn tile(&self) -> usize {
self.tile
}
fn halo(&self) -> usize {
self.halo
}
fn run(
&mut self,
mosaic: Vec<f32>,
sigma: Vec<f32>,
write: &mut dyn FnMut(&[f32]),
) -> Result<(), DenoiseError> {
let n = self.tile;
let shape = ndarray::IxDyn(&[1, 1, n, n]);
// The vectors become the tensors: no copy on the way in.
let m = ort::value::Tensor::from_array(
ndarray::Array::from_shape_vec(shape.clone(), mosaic)
.map_err(|e| DenoiseError::Model(e.to_string()))?,
)?;
let s = ort::value::Tensor::from_array(
ndarray::Array::from_shape_vec(shape, sigma)
.map_err(|e| DenoiseError::Model(e.to_string()))?,
)?;
let acquired = self.model.acquire()?;
let mut session = acquired.lock();
let outputs = session.run(ort::inputs!["mosaic" => m, "sigma" => s])?;
let (shape, data) = outputs[0].try_extract_tensor::<f32>()?;
let dims: Vec<i64> = shape.iter().copied().collect();
if dims != [1, 3, n as i64, n as i64] {
return Err(DenoiseError::Model(format!(
"output is {dims:?}, expected [1, 3, {n}, {n}]"
)));
}
// And none on the way out: the frame is written from the runtime's buffer.
write(data);
Ok(())
}
}
core/dr-denoise/src/repair.rs
+145
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//! TRACES: FR-DEV-3g
//! Hot and dead photosites, judged against the noise, before the network.
//!
//! The app's own pass (`dr_gpu::Demosaicer::repair_hot_pixels`) runs first and
//! takes the gross defects. At high ISO it leaves thousands of photosites per
//! 6D frame more than 8σ beyond every neighbour, which the network turns into
//! specks. This second pass uses that pass's two tests with the threshold in
//! units of the photosite's own σ from the noise model:
//!
//! - beyond every same-colour neighbour (two photosites away, the 3×3 of its
//! plane) by more than `k·σ`, and
//! - beyond every adjacent photosite, whatever its colour, by more than
//! `k·σ` **and** by a factor of two — what keeps a real point of light,
//! which lights its neighbours through the lens and the anti-aliasing
//! filter. A margin in σ alone is not enough: on a bright star 8σ is a
//! sliver of the signal, and the star would be flattened.
//!
//! A hot one becomes its brightest same-colour neighbour, a dead one its
//! darkest. The shipped model was trained on input repaired exactly so
//! (darkroom-denoise `denoise/repair.py`, `--repair-k 8`): the threshold
//! belongs to the model, and changes with it. Neighbours off the frame are
//! the nearest photosite on it, as the training code reads them.
/// The threshold the shipped model was trained with, in σ.
pub const REPAIR_K: f32 = 8.0;
/// Repair `mosaic` (`h×w`, row-major, normalised) in place; `sigma(y, x, v)`
/// is the photosite's σ. Returns how many photosites changed.
pub fn repair(
mosaic: &mut [f32],
h: usize,
w: usize,
k: f32,
sigma: &(dyn Fn(usize, usize, f32) -> f32 + Sync),
) -> usize {
let copy = mosaic.to_vec();
let original = &copy;
let at = |y: isize, x: isize| {
let y = y.clamp(0, h as isize - 1) as usize;
let x = x.clamp(0, w as isize - 1) as usize;
original[y * w + x]
};
let threads = std::thread::available_parallelism().map_or(1, |n| n.get());
let rows_per = h.div_ceil(threads).max(1);
let mut counts = vec![0usize; h.div_ceil(rows_per)];
std::thread::scope(|scope| {
for ((chunk, rows), count) in mosaic
.chunks_mut(rows_per * w)
.enumerate()
.zip(counts.iter_mut())
{
let at = &at;
scope.spawn(move || {
for (i, row) in rows.chunks_mut(w).enumerate() {
let y = chunk * rows_per + i;
for (x, out) in row.iter_mut().enumerate() {
let v = original[y * w + x];
let (yi, xi) = (y as isize, x as isize);
let (mut s_hi, mut s_lo) = (f32::MIN, f32::MAX);
let (mut a_hi, mut a_lo) = (f32::MIN, f32::MAX);
for dy in -1isize..=1 {
for dx in -1isize..=1 {
if dy == 0 && dx == 0 {
continue;
}
let s = at(yi + 2 * dy, xi + 2 * dx);
s_hi = s_hi.max(s);
s_lo = s_lo.min(s);
let a = at(yi + dy, xi + dx);
a_hi = a_hi.max(a);
a_lo = a_lo.min(a);
}
}
let t = k * sigma(y, x, v);
if v - s_hi > t && v - a_hi > t && a_hi < 0.5 * v {
*out = s_hi;
*count += 1;
} else if s_lo - v > t && a_lo - v > t && v < 0.5 * a_lo {
*out = s_lo;
*count += 1;
}
}
}
});
}
});
counts.iter().sum()
}
#[cfg(test)]
mod tests {
use super::*;
const N: usize = 16;
fn flat(level: f32) -> Vec<f32> {
vec![level; N * N]
}
fn run(m: &mut [f32]) -> usize {
repair(m, N, N, REPAIR_K, &|_, _, _| 0.01)
}
#[test]
fn a_hot_photosite_becomes_its_brightest_same_colour_neighbour() {
let mut m = flat(0.1);
m[8 * N + 8] = 0.5; // 40σ above everything around it
m[8 * N + 10] = 0.12; // a same-colour neighbour, a little brighter
assert_eq!(run(&mut m), 1);
assert_eq!(m[8 * N + 8], 0.12);
}
#[test]
fn a_dead_photosite_in_a_lit_area_is_repaired() {
let mut m = flat(0.5);
m[5 * N + 5] = 0.0;
assert_eq!(run(&mut m), 1);
assert_eq!(m[5 * N + 5], 0.5);
}
#[test]
fn a_point_of_real_light_is_kept() {
// Light through a lens lands on a patch: its adjacent photosites are
// lit too, so the second test refuses it.
let mut m = flat(0.1);
for dy in 0..3 {
for dx in 0..3 {
m[(7 + dy) * N + 7 + dx] = if (dy, dx) == (1, 1) { 0.9 } else { 0.6 };
}
}
let before = m.clone();
assert_eq!(run(&mut m), 0);
assert_eq!(m, before);
}
#[test]
fn noise_within_the_threshold_is_left_alone() {
let mut m: Vec<f32> = (0..N * N)
.map(|i| 0.1 + 0.005 * ((i * 7919 % 13) as f32 - 6.0) / 6.0)
.collect();
let before = m.clone();
assert_eq!(run(&mut m), 0);
assert_eq!(m, before);
}
}
core/dr-denoise/src/tile.rs
+442
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@@ -0,0 +1,442 @@
//! TRACES: FR-DEV-3g
//! A whole frame through a fixed-shape network, exactly (denoise.md §3.4).
//!
//! The network sees `TILE_IN`² photosites and its output is exact in the
//! central `TILE_IN − 2·HALO`: the halo is wider than its receptive field
//! (185 photosites, counted from the layers), so a tile's centre equals the
//! whole frame's at the same place. The frame is extended by reflection
//! about its edge photosites, which keeps every photosite's CFA colour, so
//! edge tiles see real context too.
//!
//! **Phase.** The network was trained on RGGB. A frame whose pattern starts
//! on another colour is read from one photosite up and/or left — the
//! reflection supplies that row or column — so its top-left is red, and the
//! output is read back from the same offset. Nothing is cropped.
use dr_decode::CfaPattern;
/// Photosites of context beyond a tile's kept centre, on every side, for a
/// single network; a mixture reaches further and says so through
/// [`TileNet::halo`].
pub const HALO: usize = 192;
/// A fixed-shape network: `mosaic` and `sigma`, `n×n` RGGB, in; `3×n×n`
/// planar linear camera RGB out.
///
/// The inputs are handed over, and the output is lent to `write` rather than
/// returned: a 1408² tile is 24 MB of output, and copying it out of the
/// runtime's buffer and back into the frame was a measurable share of a
/// frame's time.
pub trait TileNet {
/// The edge `n` of the square tile the network takes.
fn tile(&self) -> usize;
/// Photosites of context it needs past a tile's kept centre: at least
/// its receptive field. [`HALO`] unless the network says otherwise.
fn halo(&self) -> usize {
HALO
}
fn run(
&mut self,
mosaic: Vec<f32>,
sigma: Vec<f32>,
write: &mut dyn FnMut(&[f32]),
) -> Result<(), crate::DenoiseError>;
}
/// Index into `0..n` by reflection about the end photosites, any distance
/// out: …2 1 [0 1 2 … n−1] n−2 n−3…, period `2(n−1)`. Parity is kept, which
/// is what keeps a CFA colour.
#[inline]
pub fn reflect(i: isize, n: usize) -> usize {
if n == 1 {
return 0;
}
let p = 2 * (n as isize - 1);
let m = i.rem_euclid(p);
(if m < n as isize { m } else { p - m }) as usize
}
/// How far up and left to start reading so the first photosite is red.
pub fn rggb_offset(p: CfaPattern) -> Option<(usize, usize)> {
match p {
CfaPattern::Rggb => Some((0, 0)),
CfaPattern::Grbg => Some((0, 1)),
CfaPattern::Gbrg => Some((1, 0)),
CfaPattern::Bggr => Some((1, 1)),
_ => None,
}
}
/// Run `net` over an `h×w` mosaic given by `at(y, x)`, with σ from
/// `sigma(colour, value)`, and return `h×w` interleaved RGB.
///
/// `progress(done, total)` is called after each tile and stops the run by
/// returning `false`, in which case the result is `Ok(None)`.
#[allow(clippy::too_many_arguments)]
pub fn run_tiled(
net: &mut dyn TileNet,
h: usize,
w: usize,
pattern: CfaPattern,
at: &(dyn Fn(usize, usize) -> f32 + Sync),
sigma: &(dyn Fn(usize, f32) -> f32 + Sync),
progress: &mut dyn FnMut(usize, usize) -> bool,
) -> Result<Option<Vec<f32>>, crate::DenoiseError> {
let (dy, dx) = rggb_offset(pattern).ok_or_else(|| {
crate::DenoiseError::Unsupported(format!("{pattern:?} is not a Bayer pattern"))
})?;
let (n, halo) = (net.tile(), net.halo());
if n <= 2 * halo || !(n - 2 * halo).is_multiple_of(2) {
return Err(crate::DenoiseError::Model(format!(
"tile {n} leaves no even centre past a {halo} halo"
)));
}
let core = n - 2 * halo;
// In unified coordinates the frame spans u ∈ [dy, dy + h), v ∈ [dx, dx + w).
let (uh, uw) = (h + dy, w + dx);
let (ty, tx) = (uh.div_ceil(core), uw.div_ceil(core));
let total = ty * tx;
let origins: Vec<(usize, usize)> = (0..ty)
.flat_map(|i| (0..tx).map(move |j| (i * core, j * core)))
.collect();
let threads = std::thread::available_parallelism().map_or(1, |n| n.get());
// One tile's mosaic and σ, gathered on every core: rows are independent.
let gather = |u0: usize, v0: usize| {
let mut mos = vec![0.0f32; n * n];
let mut sig = vec![0.0f32; n * n];
let rows_per = n.div_ceil(threads).max(1);
std::thread::scope(|scope| {
for (chunk, (m, s)) in mos
.chunks_mut(rows_per * n)
.zip(sig.chunks_mut(rows_per * n))
.enumerate()
{
scope.spawn(move || {
for (i, (mrow, srow)) in m.chunks_mut(n).zip(s.chunks_mut(n)).enumerate() {
let r = chunk * rows_per + i;
// Unified row u = u0 + r − halo; frame row y = u − dy, reflected.
let u = u0 as isize + r as isize - halo as isize;
let y = reflect(u - dy as isize, h);
for c in 0..n {
let v = v0 as isize + c as isize - halo as isize;
let x = reflect(v - dx as isize, w);
let val = at(y, x);
mrow[c] = val;
// RGGB colour of the tile position (r, c).
srow[c] = sigma([[0, 1], [1, 2]][r & 1][c & 1], val);
}
}
});
}
});
(mos, sig)
};
// Pipelined: the next tile is gathered while the network runs this one,
// so the device does not wait on the CPU. A channel of one keeps at
// most two tiles' inputs alive.
let mut out = vec![0.0f32; h * w * 3];
let stop = std::sync::atomic::AtomicBool::new(false);
std::thread::scope(|scope| -> Result<Option<()>, crate::DenoiseError> {
let (tx_tiles, rx_tiles) = std::sync::mpsc::sync_channel(1);
let (origins, stop, gather) = (&origins, &stop, &gather);
scope.spawn(move || {
for &(u0, v0) in origins {
if stop.load(std::sync::atomic::Ordering::Relaxed) {
break;
}
if tx_tiles.send((u0, v0, gather(u0, v0))).is_err() {
break;
}
}
});
for k in 0..total {
let Ok((u0, v0, (mos, sig))) = rx_tiles.recv() else {
break;
};
let mut wrong = None;
let ran = net.run(mos, sig, &mut |rgb: &[f32]| {
if rgb.len() != 3 * n * n {
wrong = Some(rgb.len());
return;
}
// The tile's centre back into the frame: the frame rows it covers,
// split across cores (each row is written by one thread only).
let (y_lo, y_hi) = (
(u0 + dy.saturating_sub(u0)).max(dy) - dy,
(u0 + core).min(uh) - dy,
);
let (x_lo, x_hi) = ((v0.max(dx)) - dx, (v0 + core).min(uw) - dx);
if y_hi > y_lo && x_hi > x_lo {
let rows = &mut out[y_lo * w * 3..y_hi * w * 3];
let per = (y_hi - y_lo).div_ceil(threads).max(1);
std::thread::scope(|scope| {
for (chunk, block) in rows.chunks_mut(per * w * 3).enumerate() {
scope.spawn(move || {
for (i, row) in block.chunks_mut(w * 3).enumerate() {
let y = y_lo + chunk * per + i;
// Tile row of frame row y: u = y + dy = u0 + r − halo.
let r = y + dy + halo - u0;
for x in x_lo..x_hi {
let c = x + dx + halo - v0;
for ch in 0..3 {
row[x * 3 + ch] = rgb[ch * n * n + r * n + c];
}
}
}
});
}
});
}
});
if let Err(e) = ran {
stop.store(true, std::sync::atomic::Ordering::Relaxed);
return Err(e);
}
if let Some(len) = wrong {
stop.store(true, std::sync::atomic::Ordering::Relaxed);
return Err(crate::DenoiseError::Model(format!(
"network returned {len} values for a {n}² tile"
)));
}
if !progress(k + 1, total) {
stop.store(true, std::sync::atomic::Ordering::Relaxed);
// Drain so the producer is not left blocked on a full channel.
while rx_tiles.try_recv().is_ok() {}
return Ok(None);
}
}
Ok(Some(()))
})
.map(|done| done.map(|()| out))
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn reflection_keeps_parity_any_distance_out() {
let n = 7;
for i in -40isize..40 {
let r = reflect(i, n);
assert!(r < n);
assert_eq!(
r % 2,
i.rem_euclid(2) as usize,
"index {i} reflected to {r}"
);
}
assert_eq!(reflect(-1, n), 1);
assert_eq!(reflect(7, n), 5);
}
/// A stand-in network with a known, finite reach: each output photosite
/// is its 2×2 quad's (R, mean G, B), averaged over the quads within
/// `reach` quads. Purely a function of the tile, like the real one.
struct BoxNet {
n: usize,
reach: usize,
}
impl TileNet for BoxNet {
fn tile(&self) -> usize {
self.n
}
fn run(
&mut self,
m: Vec<f32>,
_s: Vec<f32>,
write: &mut dyn FnMut(&[f32]),
) -> Result<(), crate::DenoiseError> {
let n = self.n;
let q = n / 2;
let quad = |qy: usize, qx: usize| {
let (y, x) = (2 * qy, 2 * qx);
[
m[y * n + x],
0.5 * (m[y * n + x + 1] + m[(y + 1) * n + x]),
m[(y + 1) * n + x + 1],
]
};
let mut out = vec![0.0; 3 * n * n];
for qy in 0..q {
for qx in 0..q {
let mut acc = [0.0f32; 3];
let mut cnt = 0.0;
for a in qy.saturating_sub(self.reach)..(qy + self.reach + 1).min(q) {
for b in qx.saturating_sub(self.reach)..(qx + self.reach + 1).min(q) {
let v = quad(a, b);
for c in 0..3 {
acc[c] += v[c];
}
cnt += 1.0;
}
}
for (dy, dx) in [(0, 0), (0, 1), (1, 0), (1, 1)] {
for c in 0..3 {
out[c * n * n + (2 * qy + dy) * n + 2 * qx + dx] = acc[c] / cnt;
}
}
}
}
write(&out);
Ok(())
}
}
/// The mosaic of a smooth colour field in `pattern`, read at (y, x).
fn field(pattern: CfaPattern) -> impl Fn(usize, usize) -> f32 {
move |y, x| {
let rgb = [0.2 + 0.0004 * x as f32, 0.5, 0.1 + 0.0003 * y as f32];
rgb[pattern.colour_at(x as u32, y as u32) as usize]
}
}
#[test]
fn every_bayer_phase_comes_back_as_its_own_colours() {
// A frame of each pattern, its colours known: the network must see
// red where the frame's red photosites are, whatever the phase.
for p in [
CfaPattern::Rggb,
CfaPattern::Grbg,
CfaPattern::Gbrg,
CfaPattern::Bggr,
] {
let (h, w) = (300, 410);
let at = field(p);
let mut net = BoxNet {
n: 2 * HALO + 64,
reach: 0,
};
let out = run_tiled(&mut net, h, w, p, &at, &|_, _| 0.01, &mut |_, _| true)
.unwrap()
.unwrap();
for (y, x) in [(10, 10), (150, 201), (299, 409), (0, 0), (77, 333)] {
let o = &out[(y * w + x) * 3..(y * w + x) * 3 + 3];
let want = [0.2 + 0.0004 * x as f32, 0.5, 0.1 + 0.0003 * y as f32];
for c in 0..3 {
// Within the quad the binned value is at most a photosite away.
assert!(
(o[c] - want[c]).abs() < 0.0012,
"{p:?} at ({y},{x}) channel {c}: {} vs {}",
o[c],
want[c]
);
}
}
}
}
#[test]
fn tiles_reproduce_one_pass_over_the_reflected_frame() {
// A network whose reach is inside the halo gives the same answer
// tiled small as in one tile covering everything.
let (h, w) = (230, 170);
for p in [CfaPattern::Rggb, CfaPattern::Bggr] {
let at = |y: usize, x: usize| ((y * 7919 + x * 104729) % 1000) as f32 / 1000.0;
let mut small = BoxNet {
n: 2 * HALO + 32,
reach: 20,
};
let mut big = BoxNet {
n: 2 * HALO + 256,
reach: 20,
};
let a = run_tiled(&mut small, h, w, p, &at, &|_, _| 0.0, &mut |_, _| true)
.unwrap()
.unwrap();
let b = run_tiled(&mut big, h, w, p, &at, &|_, _| 0.0, &mut |_, _| true)
.unwrap()
.unwrap();
let worst = a
.iter()
.zip(&b)
.map(|(x, y)| (x - y).abs())
.fold(0.0f32, f32::max);
assert!(worst < 1e-5, "{p:?}: tiled and whole differ by {worst}");
}
}
#[test]
fn a_cancelled_run_returns_nothing() {
let mut net = BoxNet {
n: 2 * HALO + 32,
reach: 0,
};
let r = run_tiled(
&mut net,
100,
100,
CfaPattern::Rggb,
&|_, _| 0.5,
&|_, _| 0.0,
&mut |done, _| done < 2,
)
.unwrap();
assert!(r.is_none());
}
}
#[cfg(test)]
mod timing {
use super::*;
/// A network that answers instantly with an output of the right size,
/// so what is timed is the tiler alone: gathering each tile's mosaic and
/// σ, and writing its centre back.
struct Null(usize, Vec<f32>);
impl TileNet for Null {
fn tile(&self) -> usize {
self.0
}
fn run(
&mut self,
m: Vec<f32>,
_s: Vec<f32>,
write: &mut dyn FnMut(&[f32]),
) -> Result<(), crate::DenoiseError> {
// Stands for the runtime's own output buffer: allocated once.
if self.1.len() != 3 * m.len() {
self.1 = vec![m[0]; 3 * m.len()];
}
write(&self.1);
Ok(())
}
}
/// `cargo test --release -p dr-denoise tiler_overhead -- --ignored --nocapture`
#[test]
#[ignore]
fn tiler_overhead_on_a_6d_frame() {
let (h, w) = (3648, 5472);
let frame: Vec<f32> = (0..h * w).map(|i| (i % 977) as f32 / 977.0).collect();
let at = |y: usize, x: usize| frame[y * w + x];
let sigma = |_c: usize, v: f32| (0.001 * v + 1e-5).sqrt();
for n in [1408usize, 2048] {
let mut net = Null(n, Vec::new());
let t = std::time::Instant::now();
let mut tiles = 0;
run_tiled(
&mut net,
h,
w,
CfaPattern::Rggb,
&at,
&sigma,
&mut |_, total| {
tiles = total;
true
},
)
.unwrap();
let s = t.elapsed().as_secs_f64();
println!(
"tile {n}: {tiles} tiles, tiler alone {s:.2} s ({:.0} ms a tile)",
s / tiles as f64 * 1e3
);
}
}
}
core/dr-denoise/tables/canon-eos-6d.yaml
+36
View File
@@ -0,0 +1,36 @@
# Canon EOS 6D noise, measured from the library's own frames (denoise.md §5).
# Shot gain S and read variance O per RGGB position from Adobe's NoiseProfile in
# converted DNGs, in DN at the ISO's own white level; read noise checked against
# the masked border (within 2-3 %); row and column noise from the masked border.
# ISO 50 and 100 are extrapolated (S proportional to ISO). Generated by
# darkroom-denoise tools/profile.py; regenerate there, never edit by hand.
make: Canon
model: EOS 6D
black: 2048
rows:
- {iso: 50, white: 15000, s_dn: [0.0854021, 0.085467, 0.085467, 0.0839724], o_dn: [38.2741, 38.6675, 38.6675, 38.9649], row_dn: 0.3423, col_dn: 0.505}
- {iso: 100, white: 15000, s_dn: [0.170804, 0.170934, 0.170934, 0.167945], o_dn: [38.339, 38.7332, 38.7332, 39.031], row_dn: 0.3423, col_dn: 0.505}
- {iso: 125, white: 15035, s_dn: [0.228108, 0.230361, 0.230361, 0.228345], o_dn: [36.9455, 37.8995, 37.8995, 38.2358], row_dn: 0.3423, col_dn: 0.505}
- {iso: 160, white: 12373, s_dn: [0.289653, 0.294915, 0.294915, 0.286887], o_dn: [15.3717, 16.1346, 16.1346, 16.0413], row_dn: 0.212, col_dn: 0.07151}
- {iso: 200, white: 15035, s_dn: [0.370969, 0.369922, 0.369922, 0.361443], o_dn: [24.2761, 24.0847, 24.0847, 24.2414], row_dn: 0.2692, col_dn: 0}
- {iso: 250, white: 15035, s_dn: [0.461889, 0.457975, 0.457975, 0.449318], o_dn: [38.0975, 37.5041, 37.5041, 37.7424], row_dn: 0.3345, col_dn: 0.4786}
- {iso: 320, white: 12323, s_dn: [0.590765, 0.59755, 0.59755, 0.576843], o_dn: [18.5426, 18.9163, 18.9163, 19.1202], row_dn: 0.3158, col_dn: 0.5174}
- {iso: 400, white: 15035, s_dn: [0.753591, 0.740586, 0.740586, 0.729874], o_dn: [29.3028, 29.8496, 29.8496, 29.7961], row_dn: 0.4378, col_dn: 0.2691}
- {iso: 500, white: 15035, s_dn: [0.937458, 0.920836, 0.920836, 0.899293], o_dn: [45.2196, 46.3954, 46.3954, 46.1012], row_dn: 0.5473, col_dn: 0.4328}
- {iso: 640, white: 12323, s_dn: [1.12726, 1.13527, 1.13527, 1.10159], o_dn: [24.9951, 25.1029, 25.1029, 25.7183], row_dn: 0.316, col_dn: 0.4544}
- {iso: 800, white: 15035, s_dn: [1.44048, 1.42299, 1.42299, 1.40795], o_dn: [38.7891, 39.302, 39.302, 40.079], row_dn: 0.3877, col_dn: 0.2132}
- {iso: 1000, white: 15000, s_dn: [1.77595, 1.75662, 1.75662, 1.74584], o_dn: [63.9499, 64.4203, 64.4203, 65.0739], row_dn: 0.4593, col_dn: 0.3307}
- {iso: 1250, white: 12346, s_dn: [2.18211, 2.18313, 2.18313, 2.11979], o_dn: [41.6124, 42.9483, 42.9483, 43.2075], row_dn: 0.3979, col_dn: 0.4496}
- {iso: 1600, white: 15035, s_dn: [2.75544, 2.74633, 2.74633, 2.69951], o_dn: [66.3905, 66.4104, 66.4104, 67.253], row_dn: 0.4944, col_dn: 0.4593}
- {iso: 2000, white: 15035, s_dn: [3.42754, 3.40445, 3.40445, 3.36808], o_dn: [104.349, 103.648, 103.648, 106.404], row_dn: 0.6094, col_dn: 0.3602}
- {iso: 2500, white: 12330, s_dn: [4.17112, 4.17551, 4.17551, 4.17175], o_dn: [94.4289, 91.8508, 91.8508, 96.3598], row_dn: 0.5671, col_dn: 0}
- {iso: 3200, white: 15035, s_dn: [5.30088, 5.25742, 5.25742, 5.21782], o_dn: [147.421, 147.302, 147.302, 147.01], row_dn: 0.748, col_dn: 0.8611}
- {iso: 4000, white: 15035, s_dn: [6.62037, 6.59922, 6.59922, 6.60871], o_dn: [224.765, 232.408, 232.408, 231.419], row_dn: 0.9335, col_dn: 1.125}
- {iso: 5000, white: 12323, s_dn: [8.49542, 8.48265, 8.48265, 8.41176], o_dn: [232.672, 233.922, 233.922, 256.059], row_dn: 1.085, col_dn: 1.852}
- {iso: 6400, white: 15035, s_dn: [10.6956, 10.7417, 10.7417, 10.6503], o_dn: [360.311, 368.198, 368.198, 362.848], row_dn: 1.326, col_dn: 2.277}
- {iso: 8000, white: 15035, s_dn: [13.1307, 13.3864, 13.3864, 13.147], o_dn: [615.02, 566.666, 566.666, 611.738], row_dn: 1.768, col_dn: 3.141}
- {iso: 10000, white: 12365, s_dn: [16.5338, 16.7603, 16.7603, 16.4739], o_dn: [914.064, 904.583, 904.583, 938.024], row_dn: 2.214, col_dn: 3.605}
- {iso: 12800, white: 15000, s_dn: [18.4717, 20.9315, 20.9315, 19.3821], o_dn: [1431.85, 1432.33, 1432.33, 1477.82], row_dn: 2.568, col_dn: 4.661}
- {iso: 16000, white: 15000, s_dn: [20.527, 26.0841, 26.0841, 21.8866], o_dn: [2203.77, 2357.78, 2357.78, 2193.1], row_dn: 3.521, col_dn: 5.805}
- {iso: 20000, white: 13000, s_dn: [25.1517, 32.5307, 32.5307, 26.2303], o_dn: [3490.34, 3647.93, 3647.93, 3423.33], row_dn: 4.336, col_dn: 7.143}
- {iso: 25600, white: 15000, s_dn: [22.8743, 40.4641, 40.4641, 23.5537], o_dn: [5184.57, 5690.43, 5690.43, 5286.07], row_dn: 5.682, col_dn: 9.193}
core/dr-face/src/detect.rs
+3 -3
View File
@@ -137,8 +137,8 @@ impl Detection {
/// A loaded SCRFD graph.
pub struct Detector {
session: Model,
/// f32 or int8 — the int8 form finds a different set of faces and is a
/// different detector in `model_id` (docs/dev/inference.md §7).
/// f32 or a quantised form — which finds a different set of faces and is
/// a different detector in `model_id` (docs/dev/inference.md §7).
form: Form,
/// Feature-map count: 3 for strides {8,16,32}, 4 for {8,16,32,64}.
///
@@ -155,7 +155,7 @@ impl Detector {
}
/// Load the canonical f32 file at `path`, or the form the device's
/// backend wants instead — the `.int8.onnx` beside it on a Hexagon —
/// backend wants instead — the `.a16w8.onnx` beside it on a Hexagon —
/// which [`Detector::form`] then reports.
pub fn from_path(path: impl AsRef<std::path::Path>) -> Result<Self, FaceError> {
let (path, form) = dr_inference_engine::resolve_model(Role::Detector, path.as_ref());
core/dr-face/src/landmarks.rs
+11 -2
View File
@@ -123,13 +123,22 @@ pub struct Landmarker {
}
impl Landmarker {
/// The graph at `path`, or the `.a16w8.onnx` sibling beside it when the
/// device's backend runs that (the Hexagon, inference.md §1.5: 0.25 px
/// from f32 in the 192 crop, where int8 moved the points by 1.5).
pub fn from_path(path: impl AsRef<std::path::Path>) -> Result<Self, FaceError> {
let (path, form) = dr_inference_engine::resolve_model(Role::Landmarks, path.as_ref());
let bytes = std::fs::read(path).map_err(FaceError::ModelRead)?;
Self::from_bytes(&bytes)
Self::from_bytes_in(&bytes, form)
}
pub fn from_bytes(bytes: &[u8]) -> Result<Self, FaceError> {
let model = dr_inference_engine::open(Role::Landmarks, Form::F32, bytes)?;
Self::from_bytes_in(bytes, Form::F32)
}
/// `bytes` in a stated numeric form; the output keeps its meaning.
pub fn from_bytes_in(bytes: &[u8], form: Form) -> Result<Self, FaceError> {
let model = dr_inference_engine::open(Role::Landmarks, form, bytes)?;
let acquired = model.acquire()?;
let session = acquired.lock();
core/dr-gpu/examples/mosaic_dump.rs
+115
View File
@@ -0,0 +1,115 @@
//! Dump RAW files' mosaics for training the learned denoise (FR-DEV-3g).
//!
//! The training repo must read photosites the way the app reads them —
//! same black and white levels, same active area, same CFA phase — or a
//! network trained on one phase runs on another and paints moiré everywhere
//! (denoise.md §4.4). So it reads this, not LibRaw.
//!
//! The photosites are those the demosaic reads: hot and dead ones repaired by
//! the app's own pass ([`Demosaicer::repair_hot_pixels`], the same shader
//! `run` dispatches), because the learned stage replaces the demosaic and
//! takes its input (denoise.md §2). `--unrepaired` skips it.
//!
//! Reads `input<TAB>output-prefix` lines on stdin and writes, per line,
//! `prefix.npy` (the whole readout, masked border included, `u16`, row-major)
//! and `prefix.json` (what `decode` and `metadata` say about it). The border
//! is kept, and the repair never touches it, because its optically black
//! photosites are a dark frame for free: read noise and row noise at that ISO.
//!
//! ```sh
//! printf 'IMG_0001.CR2\tout/IMG_0001\n' |
//! cargo run --release -p dr-gpu --example mosaic_dump
//! ```
use std::io::{BufRead, Write};
use dr_gpu::{Demosaicer, GpuContext};
fn main() {
let repair = !std::env::args().any(|a| a == "--unrepaired");
let ctx = pollster::block_on(GpuContext::new_headless()).expect("a GPU for the hot-pixel pass");
let demosaicer = Demosaicer::new(&ctx).expect("demosaicer");
let mut failed = 0;
for line in std::io::stdin().lock().lines() {
let line = line.expect("stdin");
let Some((input, prefix)) = line.split_once('\t') else {
continue;
};
match dump(input, prefix, repair.then_some(&demosaicer)) {
Ok(()) => println!("ok\t{input}"),
Err(e) => {
failed += 1;
println!("fail\t{input}\t{e}");
}
}
std::io::stdout().flush().ok();
}
std::process::exit(if failed > 0 { 1 } else { 0 });
}
fn dump(input: &str, prefix: &str, repair: Option<&Demosaicer>) -> Result<(), String> {
let bytes = std::fs::read(input).map_err(|e| e.to_string())?;
let mut raw = dr_decode::decode(&bytes).map_err(|e| e.to_string())?;
if raw.samples_per_pixel != 1 {
return Err("linear DNG: no photosites".into());
}
let repaired = match repair {
Some(d) => d.repair_hot_pixels(&mut raw).map_err(|e| e.to_string())? as i64,
None => -1,
};
let meta = dr_decode::metadata(&bytes).map_err(|e| e.to_string())?;
let mut npy = Vec::with_capacity(raw.data.len() * 2 + 128);
let mut header = format!(
"{{'descr': '<u2', 'fortran_order': False, 'shape': ({}, {}), }}",
raw.height, raw.width
);
// The header, its magic and length are padded to a multiple of 64.
while (10 + header.len() + 1) % 64 != 0 {
header.push(' ');
}
header.push('\n');
npy.extend_from_slice(b"\x93NUMPY\x01\x00");
npy.extend_from_slice(&(header.len() as u16).to_le_bytes());
npy.extend_from_slice(header.as_bytes());
for v in &raw.data {
npy.extend_from_slice(&v.to_le_bytes());
}
std::fs::write(format!("{prefix}.npy"), npy).map_err(|e| e.to_string())?;
let opt = |v: Option<f32>| v.map_or("null".to_string(), |v| v.to_string());
let matrix = raw
.color_matrix
.map_or("null".to_string(), |m| format!("{m:?}"));
let json = format!(
concat!(
"{{\"source\": {:?}, \"make\": {:?}, \"model\": {:?}, ",
"\"width\": {}, \"height\": {}, ",
"\"crop\": [{}, {}, {}, {}], \"cfa\": {:?}, ",
"\"black\": {:?}, \"white\": {}, \"wb\": {:?}, \"cam_to_srgb\": {}, ",
"\"iso\": {}, \"shutter\": {}, \"aperture\": {}, \"captured_at\": {}, ",
"\"hot_repaired\": {}}}\n"
),
input,
raw.make,
raw.model,
raw.width,
raw.height,
raw.crop.x,
raw.crop.y,
raw.crop.width,
raw.crop.height,
format!("{:?}", raw.cfa_pattern),
raw.black_level,
raw.white_level,
raw.wb_coeffs,
matrix,
meta.iso.map_or("null".to_string(), |v| v.to_string()),
opt(meta.shutter),
opt(meta.aperture),
meta.captured_at
.map_or("null".to_string(), |v| v.to_string()),
repaired,
);
std::fs::write(format!("{prefix}.json"), json).map_err(|e| e.to_string())
}
core/dr-gpu/examples/sensor_scan.rs
+117
View File
@@ -0,0 +1,117 @@
//! List each RAW file's hot and dead photosite candidates (docs/dev/sensor-health.md).
//!
//! Reads paths on stdin and prints one JSON line per file: its capture
//! conditions and every photosite [`Demosaicer::find_hot_pixels`] flags, as
//! `[x, y, value, hot]` in sensor coordinates. Which candidates are defects
//! is a question across frames, so this answers nothing on its own.
//!
//! ```sh
//! find ~/Pictures -name '*.CR2' | cargo run --release -p dr-gpu --example sensor_scan
//! ```
use std::io::{BufRead, Write};
use dr_gpu::{Demosaicer, GpuContext};
fn main() {
if let Some(list) = std::env::args().skip_while(|a| a != "--probe").nth(1) {
return probe(&list);
}
let ctx = pollster::block_on(GpuContext::new_headless()).expect("a GPU for the hot-pixel pass");
let demosaicer = Demosaicer::new(&ctx).expect("demosaicer");
for line in std::io::stdin().lock().lines() {
let path = line.expect("stdin");
match scan(&path, &demosaicer) {
Ok(json) => println!("{json}"),
Err(e) => eprintln!("fail\t{path}\t{e}"),
}
std::io::stdout().flush().ok();
}
}
fn scan(path: &str, demosaicer: &Demosaicer) -> Result<String, String> {
let bytes = std::fs::read(path).map_err(|e| e.to_string())?;
let raw = dr_decode::decode(&bytes).map_err(|e| e.to_string())?;
let meta = dr_decode::metadata(&bytes).map_err(|e| e.to_string())?;
let sites = demosaicer
.find_hot_pixels(&raw)
.map_err(|e| e.to_string())?;
let opt = |v: Option<f64>| v.map_or("null".to_string(), |v| v.to_string());
let list: Vec<String> = sites
.iter()
.map(|s| {
let v = raw.data[(s.y * raw.width + s.x) as usize];
format!("[{},{},{},{}]", s.x, s.y, v, u8::from(s.hot))
})
.collect();
Ok(format!(
"{{\"path\":{:?},\"model\":{:?},\"captured\":{},\"iso\":{},\"shutter\":{},\"white\":{},\"black\":{:?},\"crop\":[{},{},{},{}],\"sites\":[{}]}}",
path,
format!("{} {}", raw.make, raw.model),
meta.captured_at.map_or("null".to_string(), |t| t.to_string()),
opt(meta.iso.map(f64::from)),
opt(meta.shutter.map(f64::from)),
raw.white_level,
raw.black_level,
raw.crop.x,
raw.crop.y,
raw.crop.width,
raw.crop.height,
list.join(","),
))
}
/// `--probe COORDS`: for each path on stdin, each `x y` line of COORDS as
/// `[value, same-colour neighbour max, median]` over black, on the CPU. A
/// probe asks whether a photosite stood out in a frame where it would have
/// been visible, which the scan's verdict cannot say: a frame that does not
/// flag a defect may only have been too bright around it.
fn probe(list: &str) {
let coords: Vec<(u32, u32)> = std::fs::read_to_string(list)
.expect("coords")
.lines()
.filter_map(|l| {
let mut it = l.split_whitespace().map(|v| v.parse().ok());
Some((it.next()??, it.next()??))
})
.collect();
for line in std::io::stdin().lock().lines() {
let path = line.expect("stdin");
let Ok(bytes) = std::fs::read(&path) else {
continue;
};
let (Ok(raw), Ok(meta)) = (dr_decode::decode(&bytes), dr_decode::metadata(&bytes)) else {
continue;
};
let w = raw.width as i64;
let at = |x: i64, y: i64| {
let cell = (((y - raw.crop.y as i64) & 1) * 2 + ((x - raw.crop.x as i64) & 1)) as usize;
raw.data[(y * w + x) as usize].saturating_sub(raw.black_level[cell])
};
let rows: Vec<String> = coords
.iter()
.map(|&(x, y)| {
let (x, y) = (x as i64, y as i64);
let mut n: Vec<u16> = Vec::new();
for dy in [-2i64, 0, 2] {
for dx in [-2i64, 0, 2] {
if (dx, dy) != (0, 0) {
n.push(at(x + dx, y + dy));
}
}
}
n.sort_unstable();
format!("[{},{},{}]", at(x, y), n[n.len() - 1], n[n.len() / 2])
})
.collect();
println!(
"{{\"path\":{:?},\"captured\":{},\"iso\":{},\"shutter\":{},\"range\":{},\"p\":[{}]}}",
path,
meta.captured_at.unwrap_or(0),
meta.iso.unwrap_or(0),
meta.shutter.unwrap_or(0.0),
raw.white_level - raw.black_level[0],
rows.join(","),
);
}
}
core/dr-gpu/src/adjust.rs
+12 -1
View File
@@ -1889,6 +1889,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -2093,6 +2094,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -2702,6 +2704,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -2806,6 +2809,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -3416,8 +3420,15 @@ mod tests {
read_centre(&ctx, t)
};
// The default rendering is the DNG reference curve (D21); for a grey its
// ProPhoto round trip is the identity, so the reference applies as is.
let scene = 3537.0 / 16383.0;
let viewed = dr_pipeline::view::Sigmoid::default_curve().channel(scene);
let viewed = dr_pipeline::camera_raw::apply_reference(
&dr_types::tone::ACR3_DEFAULT,
[scene; 3],
dr_pipeline::view::DEFAULT_CONTRAST,
dr_pipeline::view::DEFAULT_WHITE,
)[0];
let expected = (dr_types::Transfer::Srgb.encode(viewed) * 255.0).round() as i32;
let delta = (i32::from(from_sensor[0]) - expected).abs();
assert!(
core/dr-gpu/src/demosaic.rs
+333 -64
View File
@@ -346,6 +346,95 @@ impl DemosaicedImage {
}
impl DemosaicedImage {
/// TRACES: FR-DEV-3g
/// The learned demosaic's output for the photograph `like` was
/// demosaiced from: `width × height` interleaved RGB, linear camera
/// space, normalised as the demosaic normalises — the same texture the
/// classical path made, with the noise gone (denoise.md §2).
///
/// Everything that describes the photograph rather than its pixels —
/// matrix, profile tables, as-shot balance — is `like`'s, so nothing
/// downstream can tell which demosaic ran. A new [`Self::id`], so every
/// cache keyed on the source sees a new source.
pub fn from_rgb_f32(
ctx: &GpuContext,
like: &DemosaicedImage,
width: u32,
height: u32,
rgb: &[f32],
) -> Result<Self, GpuError> {
let n = width as usize * height as usize;
if rgb.len() != n * 3 {
return Err(GpuError::TooLarge(format!(
"{} values for a {width}×{height} RGB image",
rgb.len()
)));
}
let limits = ctx.device.limits();
if width > limits.max_texture_dimension_2d || height > limits.max_texture_dimension_2d {
return Err(GpuError::TooLarge(format!(
"{width}×{height} exceeds the device limit of {}",
limits.max_texture_dimension_2d
)));
}
let mut half = vec![0u16; n * 4];
let one = f32_to_f16_bits(1.0);
let threads = std::thread::available_parallelism().map_or(1, |n| n.get());
let per = n.div_ceil(threads).max(1);
std::thread::scope(|scope| {
for (k, out) in half.chunks_mut(per * 4).enumerate() {
scope.spawn(move || {
for (i, texel) in out.chunks_mut(4).enumerate() {
let src = &rgb[(k * per + i) * 3..(k * per + i) * 3 + 3];
for c in 0..3 {
texel[c] = f32_to_f16_bits_unclamped(src[c]);
}
texel[3] = one;
}
});
}
});
let texture = ctx.device.create_texture_with_data(
&ctx.queue,
&wgpu::TextureDescriptor {
label: Some("learned-demosaic-source"),
size: wgpu::Extent3d {
width,
height,
depth_or_array_layers: 1,
},
mip_level_count: 1,
sample_count: 1,
dimension: wgpu::TextureDimension::D2,
format: Self::FORMAT,
usage: wgpu::TextureUsages::TEXTURE_BINDING | wgpu::TextureUsages::COPY_SRC,
view_formats: &[],
},
wgpu::util::TextureDataOrder::LayerMajor,
bytemuck::cast_slice(&half),
);
Ok(like.sibling(texture, width, height))
}
/// A new source standing for the same photograph as `self`: its
/// description kept, its pixels `texture`, a fresh id.
pub(crate) fn sibling(&self, texture: wgpu::Texture, width: u32, height: u32) -> Self {
let view = texture.create_view(&Default::default());
Self {
texture,
view,
width,
height,
color_matrix: self.color_matrix,
profile_tables: self.profile_tables.clone(),
as_shot_wb: self.as_shot_wb,
non_linear: self.non_linear,
id: next_image_id(),
frame: self.frame,
window: self.window,
}
}
/// TRACES: FR-MRG-3
/// A source that is already RGB in camera space: a linear DNG, which is
/// what a merge writes. No demosaic; the samples are normalised by the
@@ -489,7 +578,7 @@ impl DemosaicedImage {
view,
width,
height,
color_matrix: raw.color_matrix.unwrap_or(IDENTITY_3X3),
color_matrix: rendering_matrix(raw),
profile_tables: raw.profile_tables.clone(),
as_shot_wb: [raw.wb_coeffs[0], raw.wb_coeffs[1], raw.wb_coeffs[2]],
non_linear: false,
@@ -500,6 +589,23 @@ impl DemosaicedImage {
}
}
/// TRACES: FR-DEV-3e
/// The camera matrix a raw renders through: the file's — identity where the
/// body is uncalibrated, so the image renders with no colour transform
/// rather than not at all — times the baseline exposure as a gain
/// (camera-profiles.md §11).
///
/// A uniform gain commutes with every scene operation before the view
/// transform, so folding it in here is the same as an exposure step at the
/// head of the chain, at no cost. The camera-space tap and the white-balance
/// probe read camera RGB before this matrix and are unaffected.
/// `RawImage::color_matrix` stays the file's: a merge writes a linear DNG
/// from it and must not bake a gain into its pixels.
fn rendering_matrix(raw: &RawImage) -> [f32; 9] {
let gain = raw.baseline_exposure.exp2();
raw.color_matrix.unwrap_or(IDENTITY_3X3).map(|v| v * gain)
}
/// Convert an f32 to half-precision bits, the general case: sign,
/// subnormals, round-to-nearest-even, saturation at the largest finite.
///
@@ -761,62 +867,14 @@ impl Demosaicer {
// TRACES: FR-RAW-3
// The mosaic the demosaic actually reads: the readout with its hot and
// dead photosites repaired. A second buffer rather than in place,
// because every photosite's verdict reads its neighbours' originals.
let repaired = self.ctx.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("raw-repaired"),
size: raw_buf.size(),
usage: wgpu::BufferUsages::STORAGE,
mapped_at_creation: false,
});
let words = packed.len() as u32;
let groups = words.div_ceil(HOT_PIXEL_GROUP).max(1);
// A 24 MP readout is 190,000 workgroups, past the 65,535 one
// dispatch dimension may hold, so the grid folds into rows.
let groups_x = groups.min(
self.ctx
.device
.limits()
.max_compute_workgroups_per_dimension,
);
let groups_y = groups.div_ceil(groups_x);
let hot_params = hot_pixel_params(
// dead photosites repaired.
let hot = self.hot_pass(
raw,
(width, height),
words,
groups_x * HOT_PIXEL_GROUP,
&raw_buf,
packed.len() as u32,
xtrans_tile,
);
let hot_params_buf =
self.ctx
.device
.create_buffer_init(&wgpu::util::BufferInitDescriptor {
label: Some("hot-pixel-params"),
contents: bytemuck::bytes_of(&hot_params),
usage: wgpu::BufferUsages::UNIFORM,
});
let hot_bind_group = self
.ctx
.device
.create_bind_group(&wgpu::BindGroupDescriptor {
label: Some("hot-pixel-bg"),
layout: &self.hot_pixel_layout,
entries: &[
wgpu::BindGroupEntry {
binding: 0,
resource: raw_buf.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 1,
resource: hot_params_buf.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 2,
resource: repaired.as_entire_binding(),
},
],
});
let params_buf = self
.ctx
.device
@@ -855,7 +913,7 @@ impl Demosaicer {
entries: &[
wgpu::BindGroupEntry {
binding: 0,
resource: repaired.as_entire_binding(),
resource: hot.repaired.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 1,
@@ -877,15 +935,7 @@ impl Demosaicer {
// Two passes in one submission. wgpu orders a storage write in one
// pass before a read of the same buffer in the next, so the demosaic
// sees every repair.
{
let mut pass = enc.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("hot-pixel-pass"),
timestamp_writes: None,
});
pass.set_pipeline(&self.hot_pixel_pipeline);
pass.set_bind_group(0, &hot_bind_group, &[]);
pass.dispatch_workgroups(groups_x, groups_y, 1);
}
self.record_hot_pass(&mut enc, &hot);
{
let mut pass = enc.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("demosaic-pass"),
@@ -904,7 +954,7 @@ impl Demosaicer {
height,
// Identity where the body is uncalibrated: the image renders with
// no colour transform rather than not at all.
color_matrix: raw.color_matrix.unwrap_or(IDENTITY_3X3),
color_matrix: rendering_matrix(raw),
profile_tables: raw.profile_tables.clone(),
as_shot_wb: [raw.wb_coeffs[0], raw.wb_coeffs[1], raw.wb_coeffs[2]],
// Whatever the profile database had for this body (FR-DEV-3e),
@@ -921,6 +971,221 @@ impl Demosaicer {
}
}
/// The hot-pixel pass's resources for one frame, ready to record.
struct HotPass {
repaired: wgpu::Buffer,
bind_group: wgpu::BindGroup,
groups: (u32, u32),
}
impl Demosaicer {
/// Buffers and bindings for the hot and dead photosite repair of `raw`,
/// whose packed samples are in `raw_buf`.
fn hot_pass(
&self,
raw: &RawImage,
(width, height): (u32, u32),
raw_buf: &wgpu::Buffer,
words: u32,
xtrans_tile: Option<[u32; 4]>,
) -> HotPass {
// A second buffer rather than in place, because every photosite's
// verdict reads its neighbours' originals.
let repaired = self.ctx.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("raw-repaired"),
size: raw_buf.size(),
usage: wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_SRC,
mapped_at_creation: false,
});
let groups = words.div_ceil(HOT_PIXEL_GROUP).max(1);
// A 24 MP readout is 190,000 workgroups, past the 65,535 one
// dispatch dimension may hold, so the grid folds into rows.
let groups_x = groups.min(
self.ctx
.device
.limits()
.max_compute_workgroups_per_dimension,
);
let groups_y = groups.div_ceil(groups_x);
let hot_params = hot_pixel_params(
raw,
(width, height),
words,
groups_x * HOT_PIXEL_GROUP,
xtrans_tile,
);
let hot_params_buf =
self.ctx
.device
.create_buffer_init(&wgpu::util::BufferInitDescriptor {
label: Some("hot-pixel-params"),
contents: bytemuck::bytes_of(&hot_params),
usage: wgpu::BufferUsages::UNIFORM,
});
let bind_group = self
.ctx
.device
.create_bind_group(&wgpu::BindGroupDescriptor {
label: Some("hot-pixel-bg"),
layout: &self.hot_pixel_layout,
entries: &[
wgpu::BindGroupEntry {
binding: 0,
resource: raw_buf.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 1,
resource: hot_params_buf.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 2,
resource: repaired.as_entire_binding(),
},
],
});
HotPass {
repaired,
bind_group,
groups: (groups_x, groups_y),
}
}
fn record_hot_pass(&self, enc: &mut wgpu::CommandEncoder, hot: &HotPass) {
let mut pass = enc.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("hot-pixel-pass"),
timestamp_writes: None,
});
pass.set_pipeline(&self.hot_pixel_pipeline);
pass.set_bind_group(0, &hot.bind_group, &[]);
pass.dispatch_workgroups(hot.groups.0, hot.groups.1, 1);
}
/// TRACES: FR-RAW-3 | FR-DEV-3g
/// Repair `raw`'s hot and dead photosites in place, exactly as [`Self::run`]
/// does before it demosaics, and return how many changed.
///
/// For the learned demosaic (denoise.md §2), which reads the same repaired
/// mosaic the classical one does: its training data and its input in the
/// app must have been through this one pass, not a lookalike.
pub fn repair_hot_pixels(&self, raw: &mut RawImage) -> Result<usize, GpuError> {
if raw.samples_per_pixel != 1 {
return Ok(0);
}
let words = self.hot_pixel_words(raw)?;
let mut changed = 0;
for (i, v) in raw.data.iter_mut().enumerate() {
let new = unpack_sample(&words, i);
changed += usize::from(new != *v);
*v = new;
}
Ok(changed)
}
/// The photosites [`Self::repair_hot_pixels`] would replace, in sensor
/// coordinates, without replacing them.
///
/// For the sensor health record (docs/dev/sensor-health.md): one frame's
/// verdict is a candidate list, not a defect map — a single photosite of a
/// star that passes both tests reads the same as a hot one. Which of them
/// is the sensor is decided across frames, by who keeps coming back.
pub fn find_hot_pixels(&self, raw: &RawImage) -> Result<Vec<Photosite>, GpuError> {
if raw.samples_per_pixel != 1 {
return Ok(Vec::new());
}
let words = self.hot_pixel_words(raw)?;
let stride = raw.width.max(1);
Ok(raw
.data
.iter()
.enumerate()
.filter_map(|(i, &v)| {
let new = unpack_sample(&words, i);
(new != v).then(|| Photosite {
x: i as u32 % stride,
y: i as u32 / stride,
hot: new < v,
})
})
.collect())
}
/// The hot-pixel pass over `raw`, read back as packed words.
fn hot_pixel_words(&self, raw: &RawImage) -> Result<Vec<u32>, GpuError> {
let (width, height) = (raw.crop.width.max(1), raw.crop.height.max(1));
let xtrans_tile = raw
.cfa_pattern
.is_xtrans()
.then(|| xtrans_params_for(raw, width, height).tile);
let packed = pack_samples(&raw.data);
let raw_buf = self
.ctx
.device
.create_buffer_init(&wgpu::util::BufferInitDescriptor {
label: Some("raw-samples"),
contents: bytemuck::cast_slice(&packed),
usage: wgpu::BufferUsages::STORAGE,
});
let hot = self.hot_pass(
raw,
(width, height),
&raw_buf,
packed.len() as u32,
xtrans_tile,
);
let readback = self.ctx.device.create_buffer(&wgpu::BufferDescriptor {
label: Some("raw-repaired-readback"),
size: hot.repaired.size(),
usage: wgpu::BufferUsages::COPY_DST | wgpu::BufferUsages::MAP_READ,
mapped_at_creation: false,
});
let mut enc = self
.ctx
.device
.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some("hot-pixel-encoder"),
});
self.record_hot_pass(&mut enc, &hot);
enc.copy_buffer_to_buffer(&hot.repaired, 0, &readback, 0, hot.repaired.size());
self.ctx.queue.submit(Some(enc.finish()));
let slice = readback.slice(..);
let (tx, rx) = std::sync::mpsc::channel();
slice.map_async(wgpu::MapMode::Read, move |r| {
let _ = tx.send(r);
});
self.ctx
.device
.poll(wgpu::PollType::wait_indefinitely())
.map_err(|e| GpuError::Readback(e.to_string()))?;
rx.recv()
.map_err(|e| GpuError::Readback(e.to_string()))?
.map_err(|e| GpuError::Readback(e.to_string()))?;
let words: Vec<u32> = bytemuck::cast_slice(&slice.get_mapped_range()).to_vec();
readback.unmap();
Ok(words)
}
}
/// One photosite the hot-pixel pass judged defective.
#[derive(Copy, Clone, Debug, PartialEq, Eq, Hash)]
pub struct Photosite {
/// Sensor coordinates: the full readout, masked border included.
pub x: u32,
pub y: u32,
/// Read far above its neighbourhood; otherwise far below (dead).
pub hot: bool,
}
/// Sample `i` of a readout packed by [`pack_samples`].
fn unpack_sample(words: &[u32], i: usize) -> u16 {
let w = words[i / 2];
(if i.is_multiple_of(2) {
w & 0xFFFF
} else {
w >> 16
}) as u16
}
const IDENTITY_3X3: [f32; 9] = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0];
/// Pack u16 samples two per u32, little-endian within the word.
@@ -1414,6 +1679,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -1530,6 +1796,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -1827,6 +2094,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -1912,6 +2180,7 @@ mod tests {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
core/dr-gpu/src/grain.rs
+219
View File
@@ -0,0 +1,219 @@
//! TRACES: FR-DEV-3g
//! Grain back into a denoised photograph, as brightness only.
//!
//! The learned denoise's one live control. The network's result and the
//! classical demosaic of the same mosaic differ by the noise the network
//! removed — plus the classical path's colour speckle and demosaic false
//! colour, which nobody wants back. So only the brightness of the difference
//! is returned, in proportion to `grain`:
//!
//! `out = denoised + grain · ΔY / wb`, with `ΔY = Y(wb · (classical − denoised))`
//!
//! `Y` is taken after the as-shot balance and handed back divided by it, so
//! the grain is neutral in the finished picture rather than tinted the
//! colour of the sensor's raw response. At 0 the result is the network's
//! exactly; at 1 the brightness noise is all back, the colour noise none.
//!
//! A pass of its own producing a new source rather than a term in the
//! adjust shader: the blend depends only on the two images and one number,
//! a 20 MP pass is a few milliseconds, and a new source id is all the
//! adjust pass's caches need to know it changed.
use std::sync::Arc;
use crate::demosaic::DemosaicedImage;
use crate::{GpuContext, GpuError};
const SHADER: &str = r#"
struct Params {
grain: f32,
_pad0: f32,
_pad1: f32,
_pad2: f32,
wb: vec4<f32>,
}
@group(0) @binding(0) var denoised: texture_2d<f32>;
@group(0) @binding(1) var classical: texture_2d<f32>;
@group(0) @binding(2) var<uniform> p: Params;
@group(0) @binding(3) var out: texture_storage_2d<rgba16float, write>;
@compute @workgroup_size(8, 8)
fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
let dims = textureDimensions(denoised);
if (gid.x >= dims.x || gid.y >= dims.y) {
return;
}
let xy = vec2<i32>(gid.xy);
let d = textureLoad(denoised, xy, 0).rgb;
let c = textureLoad(classical, xy, 0).rgb;
let wb = p.wb.rgb;
let dy = p.grain * dot(vec3<f32>(0.2126, 0.7152, 0.0722), wb * (c - d));
textureStore(out, xy, vec4<f32>(d + dy / wb, 1.0));
}
"#;
#[repr(C)]
#[derive(Copy, Clone, bytemuck::Pod, bytemuck::Zeroable)]
struct Params {
grain: f32,
_pad: [f32; 3],
wb: [f32; 4],
}
pub struct GrainBlend {
ctx: GpuContext,
pipeline: wgpu::ComputePipeline,
layout: wgpu::BindGroupLayout,
}
impl GrainBlend {
pub fn new(ctx: &GpuContext) -> Self {
let device = &ctx.device;
let module = device.create_shader_module(wgpu::ShaderModuleDescriptor {
label: Some("grain-blend"),
source: wgpu::ShaderSource::Wgsl(SHADER.into()),
});
let texture = |binding| wgpu::BindGroupLayoutEntry {
binding,
visibility: wgpu::ShaderStages::COMPUTE,
ty: wgpu::BindingType::Texture {
sample_type: wgpu::TextureSampleType::Float { filterable: false },
view_dimension: wgpu::TextureViewDimension::D2,
multisampled: false,
},
count: None,
};
let layout = device.create_bind_group_layout(&wgpu::BindGroupLayoutDescriptor {
label: Some("grain-blend-layout"),
entries: &[
texture(0),
texture(1),
wgpu::BindGroupLayoutEntry {
binding: 2,
visibility: wgpu::ShaderStages::COMPUTE,
ty: wgpu::BindingType::Buffer {
ty: wgpu::BufferBindingType::Uniform,
has_dynamic_offset: false,
min_binding_size: None,
},
count: None,
},
wgpu::BindGroupLayoutEntry {
binding: 3,
visibility: wgpu::ShaderStages::COMPUTE,
ty: wgpu::BindingType::StorageTexture {
access: wgpu::StorageTextureAccess::WriteOnly,
format: DemosaicedImage::FORMAT,
view_dimension: wgpu::TextureViewDimension::D2,
},
count: None,
},
],
});
let pipeline_layout = device.create_pipeline_layout(&wgpu::PipelineLayoutDescriptor {
label: Some("grain-blend-pipeline-layout"),
bind_group_layouts: &[Some(&layout)],
immediate_size: 0,
});
let pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
label: Some("grain-blend"),
layout: Some(&pipeline_layout),
module: &module,
entry_point: Some("main"),
compilation_options: Default::default(),
cache: None,
});
Self {
ctx: ctx.clone(),
pipeline,
layout,
}
}
/// `denoised` with `grain` (0–1) of `classical`'s brightness noise back.
/// Both must be the same photograph at the same size.
pub fn blend(
&self,
denoised: &DemosaicedImage,
classical: &DemosaicedImage,
grain: f32,
) -> Result<Arc<DemosaicedImage>, GpuError> {
let (w, h) = (denoised.texture().width(), denoised.texture().height());
if (classical.texture().width(), classical.texture().height()) != (w, h) {
return Err(GpuError::TooLarge(format!(
"grain from a {}×{} source into a {w}×{h} one",
classical.texture().width(),
classical.texture().height()
)));
}
let device = &self.ctx.device;
let texture = device.create_texture(&wgpu::TextureDescriptor {
label: Some("grain-blended-source"),
size: wgpu::Extent3d {
width: w,
height: h,
depth_or_array_layers: 1,
},
mip_level_count: 1,
sample_count: 1,
dimension: wgpu::TextureDimension::D2,
format: DemosaicedImage::FORMAT,
usage: wgpu::TextureUsages::STORAGE_BINDING
| wgpu::TextureUsages::TEXTURE_BINDING
| wgpu::TextureUsages::COPY_SRC,
view_formats: &[],
});
let out_view = texture.create_view(&Default::default());
let wb = denoised.as_shot_wb();
let g = wb[1].max(1e-6);
let params = Params {
grain: grain.clamp(0.0, 1.0),
_pad: [0.0; 3],
// Green-normalised, and never zero: the shader divides by it.
wb: [(wb[0] / g).max(1e-3), 1.0, (wb[2] / g).max(1e-3), 1.0],
};
use wgpu::util::DeviceExt;
let buffer = device.create_buffer_init(&wgpu::util::BufferInitDescriptor {
label: Some("grain-blend-params"),
contents: bytemuck::bytes_of(&params),
usage: wgpu::BufferUsages::UNIFORM,
});
let bind = device.create_bind_group(&wgpu::BindGroupDescriptor {
label: Some("grain-blend-bg"),
layout: &self.layout,
entries: &[
wgpu::BindGroupEntry {
binding: 0,
resource: wgpu::BindingResource::TextureView(denoised.view()),
},
wgpu::BindGroupEntry {
binding: 1,
resource: wgpu::BindingResource::TextureView(classical.view()),
},
wgpu::BindGroupEntry {
binding: 2,
resource: buffer.as_entire_binding(),
},
wgpu::BindGroupEntry {
binding: 3,
resource: wgpu::BindingResource::TextureView(&out_view),
},
],
});
let mut enc = device.create_command_encoder(&wgpu::CommandEncoderDescriptor {
label: Some("grain-blend"),
});
{
let mut pass = enc.begin_compute_pass(&wgpu::ComputePassDescriptor {
label: Some("grain-blend"),
timestamp_writes: None,
});
pass.set_pipeline(&self.pipeline);
pass.set_bind_group(0, &bind, &[]);
pass.dispatch_workgroups(w.div_ceil(8), h.div_ceil(8), 1);
}
self.ctx.queue.submit(Some(enc.finish()));
Ok(Arc::new(denoised.sibling(texture, w, h)))
}
}
core/dr-gpu/src/lib.rs
+3 -1
View File
@@ -26,6 +26,7 @@ mod demosaic;
mod detail;
mod error;
mod focus;
mod grain;
mod histogram;
mod mask;
mod merge;
@@ -37,10 +38,11 @@ pub use adjust::AdjustPass;
// rather than an implementation detail: a detail pass is guaranteed linear,
// unclipped, full internal precision (FR-DEV-2), and anyone reasoning about
// VRAM at 24 MP needs to know what an intermediate costs.
pub use demosaic::{DemosaicedImage, Demosaicer};
pub use demosaic::{DemosaicedImage, Demosaicer, Photosite};
pub use detail::INTERMEDIATE_FORMAT as DETAIL_INTERMEDIATE_FORMAT;
pub use error::GpuError;
pub use focus::{FocusPeakPass, FocusPeaking, PeakColour, PeakSensitivity};
pub use grain::GrainBlend;
pub use merge::{Band, MergeFrame, MergeOutput, MergePass};
// Renamed on the way out: `BINS` says enough inside `histogram`, and nothing
// at all at a crate root shared with demosaic and segmentation.
core/dr-gpu/tests/camera_profile.rs
+58 -4
View File
@@ -18,7 +18,7 @@ use dr_decode::{CfaPattern, CropRect, RawImage};
use dr_gpu::{AdjustPass, Demosaicer, GpuContext};
use dr_pipeline::descriptor::{Attribute, LocalizedKey, OpDescriptor, OpId, ParamId};
use dr_pipeline::operation::{Operation, Stage, Uniform};
use dr_pipeline::ops::camera_profile::{apply_reference, CameraProfile, APPLY, LOOK};
use dr_pipeline::ops::camera_profile::{apply_reference, CameraProfile, APPLY, LOOK, PROFILE_LOOK};
use dr_types::{HueSatTable, ProfileOrigin, ProfileTables, Transfer};
const SIZE: u32 = 16;
@@ -91,6 +91,7 @@ fn frame(tables: Option<ProfileTables>) -> RawImage {
samples_per_pixel: 3,
profile: None,
profile_tables: tables.map(Arc::new),
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -133,6 +134,7 @@ fn strong_tables() -> ProfileTables {
origin: ProfileOrigin::Embedded,
hue_sat: Some(HueSatTable::new(hd, sd, 1, false, hue_sat).unwrap()),
look: Some(look),
tone_curve: None,
}
}
@@ -149,6 +151,14 @@ fn render(ctx: &GpuContext, raw: &RawImage, op: CameraProfile) -> Vec<[u8; 3]> {
pixels.chunks_exact(4).map(|p| [p[0], p[1], p[2]]).collect()
}
/// The profile at the strength it states — the look table on, as the
/// reference applies it at 1.0. Not the default, which leaves it off (D21).
fn as_stated() -> CameraProfile {
let mut op = CameraProfile::new();
op.set_param(LOOK, PROFILE_LOOK);
op
}
fn encode(c: [f32; 3]) -> [i32; 3] {
c.map(|v| (Transfer::Srgb.encode(v.clamp(0.0, 1.0)) * 255.0).round() as i32)
}
@@ -181,8 +191,12 @@ fn the_shader_agrees_with_the_cpu_reference() {
return;
};
let tables = strong_tables();
let got = render(&ctx, &frame(Some(tables.clone())), CameraProfile::new());
assert_agrees(&got, |c| apply_reference(&tables, c, 1.0), "at defaults");
let got = render(&ctx, &frame(Some(tables.clone())), as_stated());
assert_agrees(
&got,
|c| apply_reference(&tables, c, 1.0),
"as the profile states it",
);
let mut doubled = CameraProfile::new();
doubled.set_param(LOOK, 200.0);
@@ -190,6 +204,46 @@ fn the_shader_agrees_with_the_cpu_reference() {
assert_agrees(&got, |c| apply_reference(&tables, c, 2.0), "look at 200%");
}
#[test]
fn camera_raw_tone_agrees_with_its_cpu_reference() {
// TRACES: FR-DEV-3j
// D21's rendering on 256 colours: the ProPhoto round trip, the clip, the
// curve from the profile buffer's placeholder, and RGBTone's placement
// of the middle channel, against `camera_raw::apply_reference`.
let Some(ctx) = ctx() else {
eprintln!("skipping: no GPU adapter");
return;
};
let source = Demosaicer::new(&ctx)
.expect("demosaicer")
.run(&frame(None))
.expect("upload");
let mut view = dr_pipeline::ops::ViewTransform::new();
view.set_param(
dr_pipeline::ops::view_transform::CURVE,
dr_pipeline::ops::view_transform::CAMERA_RAW,
);
let ops: Vec<Box<dyn Operation>> = vec![Box::new(view)];
let shader = dr_pipeline::compose(&ops);
let mut adjust = AdjustPass::new(&ctx);
adjust.render(&source, &shader, SIZE, SIZE).expect("render");
let (pixels, _, _) = adjust.export_pixels().expect("readback");
let got: Vec<[u8; 3]> = pixels.chunks_exact(4).map(|p| [p[0], p[1], p[2]]).collect();
let curve = &dr_types::tone::ACR3_DEFAULT;
assert_agrees(
&got,
|c| {
dr_pipeline::camera_raw::apply_reference(
curve,
c,
dr_pipeline::view::DEFAULT_CONTRAST,
dr_pipeline::view::DEFAULT_WHITE,
)
},
"DNG reference tone",
);
}
#[test]
fn switched_off_or_absent_the_render_is_unchanged() {
let Some(ctx) = ctx() else {
@@ -237,7 +291,7 @@ fn the_libraries_adobe_standard_renders_as_the_reference_does() {
let tables = dr_decode::dcp::embedded_in(&bytes)
.expect("Adobe Standard")
.tables(5000.0, ProfileOrigin::Embedded);
let got = render(&ctx, &frame(Some(tables.clone())), CameraProfile::new());
let got = render(&ctx, &frame(Some(tables.clone())), as_stated());
for (i, (c, g)) in colours().into_iter().zip(&got).enumerate() {
let want = encode(apply_reference(&tables, c, 1.0));
let g = g.map(i32::from);
core/dr-gpu/tests/film_sim.rs
+1
View File
@@ -51,6 +51,7 @@ fn flat_raw(level: u16) -> RawImage {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
core/dr-gpu/tests/grain.rs
+146
View File
@@ -0,0 +1,146 @@
//! TRACES: FR-DEV-3g
//! The grain blend, read back off the device.
use dr_decode::{CfaPattern, CropRect, RawImage};
use dr_gpu::{DemosaicedImage, Demosaicer, GpuContext, GrainBlend};
const W: u32 = 16;
const H: u32 = 8;
fn ctx() -> Option<GpuContext> {
pollster::block_on(GpuContext::new_headless()).ok()
}
/// A photograph to stand the uploads beside: its as-shot balance is what
/// the grain is made neutral under.
fn like(ctx: &GpuContext) -> DemosaicedImage {
let raw = RawImage {
width: W,
height: H,
data: vec![400; (W * H) as usize],
cfa_pattern: CfaPattern::Rggb,
black_level: [0; 4],
white_level: 4095,
wb_coeffs: [2.0, 1.0, 1.5, 1.0],
color_matrix: Some([1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]),
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
x: 0,
y: 0,
width: W,
height: H,
},
};
Demosaicer::new(ctx).unwrap().run(&raw).unwrap()
}
fn read(ctx: &GpuContext, img: &DemosaicedImage) -> Vec<[f32; 4]> {
let (w, h) = (img.texture().width(), img.texture().height());
let padded =
(w * 8).div_ceil(wgpu::COPY_BYTES_PER_ROW_ALIGNMENT) * wgpu::COPY_BYTES_PER_ROW_ALIGNMENT;
let buf = ctx.device.create_buffer(&wgpu::BufferDescriptor {
label: None,
size: (padded * h) as u64,
usage: wgpu::BufferUsages::COPY_DST | wgpu::BufferUsages::MAP_READ,
mapped_at_creation: false,
});
let mut enc = ctx.device.create_command_encoder(&Default::default());
enc.copy_texture_to_buffer(
img.texture().as_image_copy(),
wgpu::TexelCopyBufferInfo {
buffer: &buf,
layout: wgpu::TexelCopyBufferLayout {
offset: 0,
bytes_per_row: Some(padded),
rows_per_image: Some(h),
},
},
wgpu::Extent3d {
width: w,
height: h,
depth_or_array_layers: 1,
},
);
ctx.queue.submit(Some(enc.finish()));
let slice = buf.slice(..);
slice.map_async(wgpu::MapMode::Read, |_| {});
ctx.device
.poll(wgpu::PollType::wait_indefinitely())
.unwrap();
let bytes = slice.get_mapped_range();
let mut out = Vec::new();
for y in 0..h as usize {
let row: &[u16] =
bytemuck::cast_slice(&bytes[y * padded as usize..y * padded as usize + w as usize * 8]);
for t in row.chunks(4) {
out.push([0, 1, 2, 3].map(|c| half_to_f32(t[c])));
}
}
out
}
fn half_to_f32(h: u16) -> f32 {
let s = if h & 0x8000 != 0 { -1.0 } else { 1.0 };
let e = ((h >> 10) & 0x1f) as i32;
let m = (h & 0x3ff) as f32;
if e == 0 {
s * m * 2f32.powi(-24)
} else {
s * (1.0 + m / 1024.0) * 2f32.powi(e - 15)
}
}
#[test]
fn grain_returns_only_neutral_brightness() {
let Some(ctx) = ctx() else {
eprintln!("no GPU adapter; skipping");
return;
};
let base = like(&ctx);
let n = (W * H) as usize;
let d: Vec<f32> = (0..n).flat_map(|_| [0.20, 0.30, 0.10]).collect();
// The classical result: the same colour plus noise, coloured noise too.
let c: Vec<f32> = (0..n)
.flat_map(|i| {
let a = ((i * 37) % 11) as f32 / 110.0 - 0.05;
let b = ((i * 53) % 7) as f32 / 140.0 - 0.025;
[0.20 + a, 0.30 + b, 0.10 - a]
})
.collect();
let denoised = DemosaicedImage::from_rgb_f32(&ctx, &base, W, H, &d).unwrap();
let classical = DemosaicedImage::from_rgb_f32(&ctx, &base, W, H, &c).unwrap();
let blend = GrainBlend::new(&ctx);
let wb = [2.0f32, 1.0, 1.5];
let none = read(&ctx, &blend.blend(&denoised, &classical, 0.0).unwrap());
for p in &none {
for ch in 0..3 {
assert!(
(p[ch] - d[ch]).abs() < 1e-3,
"grain 0 must be the network's result: {p:?}"
);
}
}
let all = read(&ctx, &blend.blend(&denoised, &classical, 1.0).unwrap());
for (i, p) in all.iter().enumerate() {
let want_dy: f32 = [0.2126f32, 0.7152, 0.0722]
.iter()
.enumerate()
.map(|(ch, k)| k * wb[ch] * (c[i * 3 + ch] - d[ch]))
.sum();
// After white balance every channel moved by the same amount.
for ch in 0..3 {
let moved = wb[ch] * (p[ch] - d[ch]);
assert!(
(moved - want_dy).abs() < 2e-3,
"pixel {i} channel {ch}: moved {moved}, want {want_dy}"
);
}
}
}
core/dr-gpu/tests/hot_pixels.rs
+72 -1
View File
@@ -7,7 +7,7 @@
//! see of a defect it missed is the coloured cross the demosaic makes of it.
use dr_decode::{CfaPattern, CropRect, RawImage};
use dr_gpu::{AdjustPass, Demosaicer, GpuContext};
use dr_gpu::{AdjustPass, Demosaicer, GpuContext, Photosite};
use dr_pipeline::EditGraph;
const SIZE: u32 = 36;
@@ -35,6 +35,7 @@ fn frame(pattern: CfaPattern, level: u16, set: &[(u32, u32, u16)]) -> RawImage {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -140,3 +141,73 @@ fn a_hot_photosite_on_x_trans_is_invisible() {
let diff = worst(&clean, &hot);
assert!(diff <= 1, "a hot X-Trans photosite still shows, by {diff}");
}
/// The repair alone, read back (FR-DEV-3g): the learned demosaic takes the
/// mosaic this pass leaves, so it must be the same pass and nothing more —
/// the hot photosite replaced, a real highlight and every other photosite
/// untouched.
#[test]
fn the_repaired_mosaic_reads_back_with_only_the_defect_changed() {
let Some(ctx) = ctx() else {
eprintln!("no GPU adapter; skipping");
return;
};
let d = Demosaicer::new(&ctx).expect("demosaicer");
let mut star = vec![(MIDDLE, MIDDLE, WHITE)];
for dy in 0..3 {
for dx in 0..3 {
star.push((4 + dx, 4 + dy, WHITE));
}
}
let before = frame(CfaPattern::Rggb, 40, &star);
let mut raw = before.clone();
let changed = d.repair_hot_pixels(&mut raw).expect("repair");
assert_eq!(changed, 1, "only the lone hot photosite should change");
let at = (MIDDLE * SIZE + MIDDLE) as usize;
assert_eq!(
raw.data[at], 40,
"repaired to its brightest same-colour neighbour"
);
let others = (0..raw.data.len()).filter(|&i| i != at);
assert!(others.into_iter().all(|i| raw.data[i] == before.data[i]));
}
/// Finding without repairing (docs/dev/sensor-health.md): the same verdict as
/// the repair, as sensor coordinates, with the frame left as it was. The
/// sensor health record builds on this, so it must name exactly the
/// photosites the repair would change — the hot one and the dead one, and
/// not the star.
#[test]
fn finding_names_what_the_repair_would_change_and_changes_nothing() {
let Some(ctx) = ctx() else {
eprintln!("no GPU adapter; skipping");
return;
};
let d = Demosaicer::new(&ctx).expect("demosaicer");
let mut set = vec![(MIDDLE, MIDDLE, WHITE), (9, 25, 0)];
for dy in 0..3 {
for dx in 0..3 {
set.push((4 + dx, 4 + dy, WHITE));
}
}
let raw = frame(CfaPattern::Rggb, 1600, &set);
let mut found = d.find_hot_pixels(&raw).expect("find");
found.sort_by_key(|p| (p.y, p.x));
assert_eq!(
found,
vec![
Photosite {
x: MIDDLE,
y: MIDDLE,
hot: true
},
Photosite {
x: 9,
y: 25,
hot: false
},
]
);
let mut repaired = raw.clone();
assert_eq!(d.repair_hot_pixels(&mut repaired).expect("repair"), 2);
}
core/dr-gpu/tests/scene_referred.rs
+1
View File
@@ -95,6 +95,7 @@ fn flat(ctx: &GpuContext, level: f32) -> dr_gpu::DemosaicedImage {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
core/dr-gpu/tests/source_window.rs
+1
View File
@@ -59,6 +59,7 @@ fn linear_frame(w: u32, h: u32, noise: bool) -> RawImage {
samples_per_pixel: 3,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
core/dr-gpu/tests/view_transform.rs
+50 -2
View File
@@ -34,6 +34,7 @@ fn flat_raw(level: u16) -> RawImage {
samples_per_pixel: 1,
profile: None,
profile_tables: None,
baseline_exposure: 0.0,
make: String::new(),
model: String::new(),
crop: CropRect {
@@ -45,6 +46,53 @@ fn flat_raw(level: u16) -> RawImage {
}
}
/// The default chain with D19's sigmoid chosen explicitly, so these tests
/// stay about the sigmoid whichever curve is the default (D21).
fn sigmoid_chain() -> EditGraph {
let mut g = EditGraph::default_chain();
g.set_param(
dr_pipeline::ops::view_transform::ID,
dr_pipeline::ops::view_transform::CURVE,
dr_pipeline::ops::view_transform::SIGMOID,
);
g
}
#[test]
fn camera_raw_tone_agrees_with_the_acr3_curve() {
// TRACES: FR-DEV-3j
// D21: a raw with no profile, the DNG reference curve chosen, renders a grey
// through the ACR3 default curve, which the profile buffer's placeholder
// carries.
let Some(ctx) = ctx() else {
eprintln!("skipping: no GPU adapter");
return;
};
let mut graph = EditGraph::default_chain();
graph.set_param(
dr_pipeline::ops::view_transform::ID,
dr_pipeline::ops::view_transform::CURVE,
dr_pipeline::ops::view_transform::CAMERA_RAW,
);
// The table itself, so its own contrast: the default bends the input a
// little past it (D21).
graph.set_param(
dr_pipeline::ops::view_transform::ID,
dr_pipeline::ops::view_transform::CONTRAST,
dr_pipeline::view::REFERENCE_CONTRAST,
);
for level in [500u16, 4_000, 8_520, 20_000, 40_000] {
let scene = f32::from(level) / f32::from(u16::MAX);
let display = dr_types::tone::evaluate(&dr_types::tone::ACR3_DEFAULT, scene);
let expected = (dr_types::Transfer::Srgb.encode(display) * 255.0).round() as i32;
let got = i32::from(rendered(&ctx, level, &graph));
assert!(
(got - expected).abs() <= 2,
"raw {level} rendered as {got}, the ACR3 curve says {expected}"
);
}
}
/// Render `graph` over a flat frame and return the centre pixel's red.
///
/// The centre rather than a corner: a demosaic has to invent its edges.
@@ -69,7 +117,7 @@ fn the_shader_agrees_with_the_cpu_reference() {
return;
};
let curve = Sigmoid::default_curve();
let graph = EditGraph::default_chain();
let graph = sigmoid_chain();
for level in [0u16, 500, 4_000, 8_520, 32_768, 60_000, u16::MAX] {
let scene = f32::from(level) / f32::from(u16::MAX);
let display = curve.channel(scene).min(1.0);
@@ -95,7 +143,7 @@ fn highlights_above_one_stay_distinct() {
eprintln!("skipping: no GPU adapter");
return;
};
let mut graph = EditGraph::default_chain();
let mut graph = sigmoid_chain();
graph.set_param(
dr_pipeline::ops::exposure::ID,
dr_pipeline::ops::exposure::EXPOSURE,
core/dr-inference-engine/Cargo.toml
+5
View File
@@ -38,6 +38,11 @@ ort = { workspace = true, features = ["cuda", "tensorrt"] }
[target.'cfg(target_os = "android")'.dependencies]
ort = { workspace = true, features = ["qnn"] }
# The Apple rung: CoreML's option builder, which fills the runtime's generic
# key/value map. `ort-sys`'s `coreml` feature is empty; nothing links.
[target.'cfg(target_os = "macos")'.dependencies]
ort = { workspace = true, features = ["coreml"] }
[features]
# The floor: `tract` supplies the API table when no runtime file is found, or
# always, in a build without `native`. Tests want this and nothing else.
core/dr-inference-engine/examples/ladder.rs
+39 -19
View File
@@ -4,12 +4,14 @@
//!
//! DARKROOM_ORT_DIR=/usr/lib \
//! cargo run --release -p dr-inference-engine --features native,tract \
//! --example ladder -- CACHE_DIR models/face/scrfd_500m_640.onnx [MODEL.onnx ...]
//! --example ladder -- CACHE_DIR models/face/scrfd_500m_640.onnx [ROLE=MODEL.onnx ...]
//!
//! Every model named is a `Detector` for the config's purposes, which is
//! enough to see the rung taken, the engines compiled and a session land
//! on it. Delete `CACHE_DIR` to see the first run again; keep it to see the
//! second.
//! A bare path is a `Detector`; `denoiser=…`, `scene=…`, `inpainter=…`,
//! `landmarks=…` (any `Role`, lower case) says otherwise, so a device can
//! show each role taking its own form (inference.md §1.5). Each is opened
//! through `resolve_model`, as the app opens it, and the line says which
//! form and which rung it landed on. Delete `CACHE_DIR` to see the first
//! run again; keep it to see the second.
use std::path::PathBuf;
use std::time::{Duration, Instant};
@@ -17,7 +19,10 @@ use std::time::{Duration, Instant};
fn main() {
env_logger::Builder::from_env(env_logger::Env::default().default_filter_or("info")).init();
let mut args = std::env::args_os().skip(1).map(PathBuf::from);
let (Some(cache_dir), models) = (args.next(), args.collect::<Vec<_>>()) else {
let (Some(cache_dir), models) = (
args.next(),
args.map(|a| role_and_path(&a)).collect::<Vec<_>>(),
) else {
eprintln!("usage: ladder CACHE_DIR MODEL.onnx [MODEL.onnx ...]");
std::process::exit(2);
};
@@ -34,10 +39,7 @@ fn main() {
dr_inference_engine::init(dr_inference_engine::Config {
runtime_dirs,
cache_dir: cache_dir.clone(),
models: models
.iter()
.map(|p| (dr_inference_engine::Role::Detector, p.clone()))
.collect(),
models: models.clone(),
embedded: Vec::new(),
ceiling: None,
threads: 0,
@@ -80,21 +82,39 @@ fn main() {
std::thread::sleep(Duration::from_millis(500));
}
for path in &models {
let bytes = std::fs::read(path).expect("read model");
for (role, path) in &models {
let (path, form) = dr_inference_engine::resolve_model(*role, path);
let bytes = std::fs::read(&path).expect("read model");
let t = Instant::now();
let model = dr_inference_engine::open(
dr_inference_engine::Role::Detector,
dr_inference_engine::Form::F32,
&bytes,
)
.expect("open model");
let model = dr_inference_engine::open(*role, form, &bytes).expect("open model");
let acquired = model.acquire().expect("acquire session");
println!(
"{} on {} in {:.2} s",
"{role:?}: {} ({form:?}) on {} in {:.2} s",
path.file_name().unwrap().to_string_lossy(),
acquired.rung().label(),
t.elapsed().as_secs_f64()
);
}
}
/// `denoiser=path` → (Denoiser, path); a bare path is a detector.
fn role_and_path(arg: &std::path::Path) -> (dr_inference_engine::Role, PathBuf) {
use dr_inference_engine::Role::*;
let s = arg.to_string_lossy();
let Some((name, path)) = s.split_once('=') else {
return (Detector, arg.to_path_buf());
};
let role = match name {
"detector" => Detector,
"embedder" => Embedder,
"segmenter" => Segmenter,
"scene" => Scene,
"landmarks" => Landmarks,
"eyes" => EyeClassifier,
"keypoints" => Keypoints,
"inpainter" => Inpainter,
"denoiser" => Denoiser,
other => panic!("no role {other:?}"),
};
(role, PathBuf::from(path))
}
core/dr-inference-engine/src/engines.rs
+37 -8
View File
@@ -8,7 +8,7 @@
use std::path::PathBuf;
use crate::{state, Config, Form, Rung};
use crate::{state, Config, Rung};
enum Source {
File(PathBuf),
@@ -44,6 +44,20 @@ pub fn context_path(cfg: &Config, bytes: &[u8]) -> PathBuf {
.join(format!("{:016x}_ctx.onnx", hash(bytes)))
}
/// Where CoreML compiles `bytes` to: one directory per model, because
/// CoreML's own cache key leaves out the weights of a model loaded from
/// memory (`session::coreml`), and one per runtime version, which wrote it.
pub fn coreml_dir(cfg: &Config, bytes: &[u8]) -> PathBuf {
let runtime = match crate::api::runtime() {
crate::Runtime::OnnxRuntime { version, .. } => version,
crate::Runtime::Tract => "tract".into(),
};
cfg.cache_dir
.join("coreml")
.join(runtime)
.join(format!("{:016x}", hash(bytes)))
}
/// After the probe: compile every configured model the selected rung can
/// take, smallest first, recording each as it lands.
pub fn run() {
@@ -68,10 +82,10 @@ pub fn run() {
(*role, Source::File(path), size)
})
})
.chain(cfg.embedded.iter().filter_map(|(role, bytes)| {
// An embedded model has no int8 sibling to offer a rung that
// wants one; it runs on that rung's fallback.
(rung.serves(*role) && rung.form(*role) == Form::F32).then_some((
.chain(cfg.embedded.iter().filter_map(|(role, form, bytes)| {
// The embedded form the rung wants, if the build carries it;
// a build without it runs that model on the rung's fallback.
(rung.serves(*role) && rung.form(*role) == *form).then_some((
*role,
Source::Bytes(bytes),
bytes.len() as u64,
@@ -90,12 +104,27 @@ pub fn run() {
Source::Bytes(b) => (b.to_vec(), format!("embedded {role:?}")),
};
let key = key(rung, &bytes);
if state().lock().unwrap().cache.compiled.contains(&key) {
continue;
{
let s = state().lock().unwrap();
if s.cache.compiled.contains(&key) || s.cache.refused.contains(&key) {
continue;
}
}
log::info!("inference: compiling {name} for {}", rung.label());
let started = std::time::Instant::now();
match crate::session::build(rung, role, &bytes, &cfg) {
let built = match crate::probe::attempt(&cfg, &key, || {
crate::session::build(rung, role, &bytes, &cfg)
}) {
Ok(built) => built,
Err(_) => {
// Refused: the process died inside this compile before.
let mut s = state().lock().unwrap();
s.cache.refused.insert(key);
crate::probe::write_cache(&s.config, &s.cache);
continue;
}
};
match built {
Ok(session) => {
drop(session);
let mut s = state().lock().unwrap();
core/dr-inference-engine/src/lib.rs
+183 -55
View File
@@ -42,20 +42,46 @@ pub enum Role {
/// XFeat, the panorama keypoint detector (docs/dev/panorama.md).
Keypoints,
/// MI-GAN, the panorama border filler (docs/dev/panorama.md §12). Plain
/// convolutions, so any rung serves it; fp16 on TensorRT and int8 on
/// the Hexagon are the point of it.
/// convolutions, so any rung serves it; fp16 on TensorRT and 16-bit
/// activations on the Hexagon (int8 changes the fill, §1.5).
Inpainter,
/// The learned demosaic and denoise on the raw mosaic (docs/dev/denoise.md).
/// fp16 costs it nothing measurable; int8 costs 6–9 dB, because 256
/// levels cannot hold the shadow steps it exists to recover — so the
/// Hexagon takes it with 16-bit activations and weights (§1.5).
Denoiser,
}
/// Which numeric form of a model a session was built from.
///
/// `Int8` is a different network from `F32` for a detector — it finds a
/// different set of faces — which is why [`form_suffix`] exists and why a
/// The quantised forms are QDQ graphs, per-channel weights, as QNN's HTP
/// takes them (docs/dev/inference.md §1.5): `Int8` is 8-bit activations and
/// weights, `A16W8` 16-bit activations with 8-bit weights, `A16W16` 16-bit
/// both. The Hexagon accepts no float tensor at all, so these are the
/// whole menu; which one a role gets is [`Rung::form`], measured per model.
///
/// A quantised detector is a different network from the f32 one — it finds
/// a different set of faces — which is why [`form_suffix`] exists and why a
/// caller appends it to `model_id`.
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum Form {
F32,
Int8,
A16W8,
A16W16,
}
impl Form {
/// The infix of the sibling file that holds this form:
/// `scrfd_500m_640.a16w8.onnx` beside `scrfd_500m_640.onnx`.
pub fn file_tag(self) -> Option<&'static str> {
match self {
Form::F32 => None,
Form::Int8 => Some("int8"),
Form::A16W8 => Some("a16w8"),
Form::A16W16 => Some("a16w16"),
}
}
}
/// A rung of the ladder (§2). Ordered: a user override names the highest rung
@@ -76,8 +102,14 @@ pub enum Rung {
/// removed in ONNX Runtime 1.23, so there is no non-compiling AMD rung
/// to fall back to: this one falls back to the CPU.
MiGraphX,
/// Qualcomm's Hexagon NPU through QNN, int8 models only. Android only.
/// Qualcomm's Hexagon NPU through QNN, quantised models only. Android only.
Hexagon,
/// Apple, through CoreML: the Neural Engine, the GPU or the CPU, as
/// CoreML schedules it. macOS only. Compiles an ML Program per model on
/// first use, so it is a compiling rung with the CPU below it. The
/// embedder stays on the CPU, as on the Hexagon: the Neural Engine
/// computes in fp16 (§7).
CoreMl,
}
impl Rung {
@@ -88,6 +120,7 @@ impl Rung {
Rung::TensorRt => "TensorRT",
Rung::MiGraphX => "MIGraphX",
Rung::Hexagon => "Hexagon NPU",
Rung::CoreMl => "CoreML",
}
}
@@ -96,29 +129,52 @@ impl Rung {
fn fallback(self) -> Rung {
match self {
Rung::TensorRt => Rung::Cuda,
Rung::MiGraphX | Rung::Hexagon | Rung::Cuda | Rung::Cpu => Rung::Cpu,
Rung::MiGraphX | Rung::Hexagon | Rung::CoreMl | Rung::Cuda | Rung::Cpu => Rung::Cpu,
}
}
/// Whether a session on this rung needs an engine built first.
fn compiles(self) -> bool {
matches!(self, Rung::TensorRt | Rung::MiGraphX | Rung::Hexagon)
matches!(
self,
Rung::TensorRt | Rung::MiGraphX | Rung::Hexagon | Rung::CoreMl
)
}
/// The model form this rung wants for a role.
fn form(self, _role: Role) -> Form {
///
/// On the Hexagon, the narrowest form that held each model's accuracy
/// on the tablet itself (§1.5): int8 lost 5% of the detector's faces at
/// 40–80 px, moved the landmarks by 1.5 px and the segmenter's scores
/// to nothing, and the denoiser by 6–9 dB, so those take 16-bit
/// activations; the segmenter, scene model, filler and denoiser also
/// needed 16-bit weights. Only XFeat keeps int8: its panorama alignment
/// moved by no more than f32's own refits do.
pub fn form(self, role: Role) -> Form {
match self {
Rung::Hexagon => Form::Int8,
Rung::Hexagon => match role {
Role::Keypoints => Form::Int8,
Role::Detector | Role::Landmarks => Form::A16W8,
Role::Segmenter | Role::Scene | Role::Inpainter | Role::Denoiser => Form::A16W16,
Role::Embedder | Role::EyeClassifier => Form::F32,
},
_ => Form::F32,
}
}
/// Whether this rung runs `role` at all. The Hexagon takes int8 graphs
/// only, and the embedder is never int8 (§7) — it runs on the CPU
/// beside a detector on the NPU, so its vectors compare across devices.
/// Whether this rung runs `role` at all. The Hexagon takes quantised
/// graphs only, and the embedder is never quantised (§7) — it runs on
/// the CPU beside a detector on the NPU, so its vectors compare across
/// devices; at A16W16 it still missed the 0.999 cosine gate. The eye
/// classifiers stay on the CPU too: a millisecond there, and the two
/// share one role while only one of them held its readings quantised.
/// CoreML is kept off the embedder for the same reason as the Hexagon:
/// the Neural Engine is fp16, and which unit runs a graph is CoreML's
/// choice.
fn serves(self, role: Role) -> bool {
match self {
Rung::Hexagon => role != Role::Embedder,
Rung::Hexagon => !matches!(role, Role::Embedder | Role::EyeClassifier),
Rung::CoreMl => role != Role::Embedder,
_ => true,
}
}
@@ -141,8 +197,10 @@ pub struct Config {
/// The canonical model files on this device, so engines can be compiled
/// ahead of the first request for them.
pub models: Vec<(Role, PathBuf)>,
/// Models compiled into the binary, for the same reason.
pub embedded: Vec<(Role, &'static [u8])>,
/// Models compiled into the binary, for the same reason, each with the
/// form it is. A build that embeds a quantised sibling lists it here
/// beside the f32 graph, and the compile step takes the one the rung wants.
pub embedded: Vec<(Role, Form, &'static [u8])>,
/// The highest rung the user allows; `None` is "the best that works".
pub ceiling: Option<Rung>,
/// ONNX Runtime's intra-op pool; 0 picks from the core count.
@@ -168,10 +226,10 @@ pub struct Status {
}
impl Status {
/// "Hexagon NPU · int8 · ONNX Runtime 1.29" — the settings row's text.
/// "Hexagon NPU · quantised · ONNX Runtime 1.29" — the settings row's text.
pub fn line(&self) -> String {
let form = match self.rung {
Rung::Hexagon => " · int8",
Rung::Hexagon => " · quantised",
Rung::TensorRt | Rung::MiGraphX => " · fp16",
_ => "",
};
@@ -348,6 +406,12 @@ struct Cache {
/// the fingerprint changes: a wedged driver must not cost every launch
/// thirty seconds.
failed: Vec<(Rung, String)>,
/// Engine keys whose compile the process died inside, launch after
/// launch (`probe::attempt`). Left on the fallback until the
/// fingerprint changes. Defaulted, so a cache from before this field
/// still reads.
#[serde(default)]
refused: BTreeSet<String>,
}
struct State {
@@ -438,26 +502,44 @@ fn current_rung(s: &State) -> Rung {
/// The file to load for `role` under the current selection, and its form.
///
/// A rung that wants int8 gets the `.int8.onnx` sibling of the canonical file
/// if it exists; otherwise the canonical file, on the rung's fallback. A
/// caller adds [`form_suffix`] to the `model_id` it records.
/// A rung that wants a quantised form gets that sibling of the canonical
/// file (`<stem>.a16w8.onnx` and so on, [`Form::file_tag`]) if it exists;
/// otherwise the canonical file, on the rung's fallback. A caller adds
/// [`form_suffix`] to the `model_id` it records.
pub fn resolve_model(role: Role, canonical: &Path) -> (PathBuf, Form) {
let rung = current_rung(&state().lock().unwrap());
if rung.serves(role) && rung.form(role) == Form::Int8 {
let sibling = int8_sibling(canonical);
let want = rung.form(role);
if rung.serves(role) && want != Form::F32 {
let sibling = form_sibling(canonical, want);
if sibling.is_file() {
return (sibling, Form::Int8);
return (sibling, want);
}
}
(canonical.to_path_buf(), Form::F32)
}
fn int8_sibling(canonical: &Path) -> PathBuf {
/// The same choice for a model compiled into the binary: of the forms
/// `offered`, the one the current rung wants for `role`, else the f32 one.
/// `offered` must hold an `F32` entry.
pub fn choose_embedded(role: Role, offered: &[(Form, &'static [u8])]) -> (&'static [u8], Form) {
let rung = current_rung(&state().lock().unwrap());
let want = rung.form(role);
let pick = |form| offered.iter().find(|(f, _)| *f == form);
let (form, bytes) = (rung.serves(role).then(|| pick(want)).flatten())
.or_else(|| pick(Form::F32))
.expect("an embedded model offers its f32 form");
(bytes, *form)
}
fn form_sibling(canonical: &Path, form: Form) -> PathBuf {
let stem = canonical
.file_stem()
.map(|s| s.to_string_lossy().into_owned())
.unwrap_or_default();
canonical.with_file_name(format!("{stem}.int8.onnx"))
match form.file_tag() {
Some(tag) => canonical.with_file_name(format!("{stem}.{tag}.onnx")),
None => canonical.to_path_buf(),
}
}
/// What a form appends to a detector's `model_id` (§7).
@@ -465,6 +547,8 @@ pub fn form_suffix(form: Form) -> &'static str {
match form {
Form::F32 => "",
Form::Int8 => "_i8",
Form::A16W8 => "_a16",
Form::A16W16 => "_a16w16",
}
}
@@ -494,8 +578,8 @@ pub fn open(role: Role, form: Form, bytes: &[u8]) -> Result<Model, Error> {
fn effective_rung(s: &State, selected: Rung, role: Role, form: Form, hash: u64) -> Rung {
let mut rung = selected;
if !rung.serves(role) || rung.form(role) != form {
// The embedder on a Hexagon device, or an f32 detector where the int8
// sibling was missing: neither can go to the NPU.
// The embedder on a Hexagon device, or an f32 detector where the
// quantised sibling was missing: neither can go to the NPU.
rung = rung.fallback();
}
if rung.compiles() && !s.cache.compiled.contains(&engines::key_of(rung, hash)) {
@@ -586,8 +670,10 @@ mod tests {
#[test]
fn the_hexagon_never_takes_the_embedder() {
assert!(!Rung::Hexagon.serves(Role::Embedder));
assert!(!Rung::Hexagon.serves(Role::EyeClassifier));
assert!(Rung::Hexagon.serves(Role::Detector));
assert_eq!(Rung::Hexagon.form(Role::Detector), Form::Int8);
assert!(Rung::Hexagon.serves(Role::Denoiser));
assert_eq!(Rung::Hexagon.form(Role::Detector), Form::A16W8);
// A detector offered in f32 on a Hexagon device lands on the CPU.
let s = State {
config: Config::default(),
@@ -598,39 +684,81 @@ mod tests {
probing: false,
wanted: 0,
};
let on = |role, form| effective_rung(&s, Rung::Hexagon, role, form, engines::hash(b""));
assert_eq!(on(Role::Embedder, Form::F32), Rung::Cpu);
assert_eq!(on(Role::Detector, Form::F32), Rung::Cpu);
// A form other than the one the role wants is not the NPU's either:
// an int8 detector left over from an older install stays off it.
assert_eq!(on(Role::Detector, Form::Int8), Rung::Cpu);
// The wanted form whose context is not compiled yet: also the CPU.
assert_eq!(on(Role::Detector, Form::A16W8), Rung::Cpu);
}
/// The form each role gets on the Hexagon is the one measured to hold
/// its accuracy there (§1.5); a change to this table is a change to
/// what the tablet computes, and must come with a measurement.
#[test]
fn each_role_has_its_measured_form_on_the_hexagon() {
use Form::*;
for (role, form) in [
(Role::Detector, A16W8),
(Role::Landmarks, A16W8),
(Role::Segmenter, A16W16),
(Role::Scene, A16W16),
(Role::Inpainter, A16W16),
(Role::Denoiser, A16W16),
(Role::Keypoints, Int8),
(Role::Embedder, F32),
(Role::EyeClassifier, F32),
] {
assert_eq!(Rung::Hexagon.form(role), form, "{role:?}");
}
for rung in [
Rung::Cpu,
Rung::Cuda,
Rung::TensorRt,
Rung::MiGraphX,
Rung::CoreMl,
] {
assert_eq!(rung.form(Role::Detector), F32);
}
}
#[test]
fn a_form_lives_in_its_tagged_sibling() {
let canonical = Path::new("/m/scrfd_500m_640.onnx");
assert_eq!(form_sibling(canonical, Form::F32), canonical);
assert_eq!(
effective_rung(
&s,
Rung::Hexagon,
Role::Embedder,
Form::F32,
engines::hash(b"")
),
Rung::Cpu
form_sibling(canonical, Form::A16W8),
Path::new("/m/scrfd_500m_640.a16w8.onnx")
);
assert_eq!(
effective_rung(
&s,
Rung::Hexagon,
Role::Detector,
Form::F32,
engines::hash(b"")
),
Rung::Cpu
);
// An int8 detector whose context is not compiled yet: also the CPU.
assert_eq!(
effective_rung(
&s,
Rung::Hexagon,
Role::Detector,
Form::Int8,
engines::hash(b"")
),
Rung::Cpu
form_sibling(canonical, Form::Int8),
Path::new("/m/scrfd_500m_640.int8.onnx")
);
}
#[test]
fn coreml_takes_a_compiled_detector_and_never_the_embedder() {
let hash = engines::hash(b"detector");
let mut s = State {
config: Config::default(),
cache: Cache {
rung: Some(Rung::CoreMl),
..Cache::default()
},
probing: false,
wanted: 0,
};
let on = |s: &State, role| effective_rung(s, Rung::CoreMl, role, Form::F32, hash);
// Before its program is compiled the detector waits on the CPU.
assert_eq!(on(&s, Role::Detector), Rung::Cpu);
s.cache.compiled.insert(engines::key_of(Rung::CoreMl, hash));
assert_eq!(on(&s, Role::Detector), Rung::CoreMl);
// The embedder does not move, compiled or not (§7).
assert_eq!(on(&s, Role::Embedder), Rung::Cpu);
}
#[test]
fn the_status_reports_only_the_rungs_above_the_selection() {
let _serial = serial();
core/dr-inference-engine/src/probe.rs
+191 -34
View File
@@ -16,10 +16,15 @@ use crate::{api::Runtime, state, Cache, Config, Form, Role, Rung};
fn ladder(ceiling: Option<Rung>) -> Vec<Rung> {
#[cfg(target_os = "android")]
let all = [Rung::Hexagon];
// Unmeasured (§2 ⁵): it is on the ladder because the probe's clock and
// `attempt` make a wrong guess cost one slow or failed probe, not a
// slow or crashing app.
#[cfg(target_os = "macos")]
let all = [Rung::CoreMl];
// A desktop has one vendor's GPU; the other vendor's providers are
// "not enabled in this build" or a library that fails to load, and
// either answer arrives in milliseconds.
#[cfg(not(target_os = "android"))]
#[cfg(not(any(target_os = "android", target_os = "macos")))]
let all = [Rung::TensorRt, Rung::Cuda, Rung::MiGraphX];
all.into_iter()
.filter(|r| ceiling.is_none_or(|c| *r <= c))
@@ -81,7 +86,11 @@ pub fn run(runtime: Runtime) {
log::info!("inference: floor {floor:.1} ms on the CPU provider");
for rung in ladder(cfg.ceiling) {
match time_rung(rung, role, &canonical, &cfg) {
let timed = attempt(&cfg, &format!("probe {}", rung.label()), || {
time_rung(rung, role, &canonical, &cfg)
})
.and_then(|timed| timed);
match timed {
Ok((ms, key)) if ms < floor => {
cache.rung = Some(rung);
cache.reason = format!("{ms:.1} ms against {floor:.1} ms on the CPU");
@@ -118,11 +127,56 @@ fn finish(cache: Cache) {
s.probing = false;
}
/// How many launches in a row may die inside one attempt before it is
/// refused. Two, not one: quitting the app while TensorRT spends forty
/// seconds on an engine leaves the same trace as a provider that aborted.
const STRIKES: u32 = 2;
/// Run `f` — a session build on a provider — with `what` written down
/// first, so that if the provider takes the process with it the next launch
/// knows what to stop trying.
///
/// A provider can fail by aborting rather than by returning an error:
/// XNNPACK did on SCRFD (§2), and a C++ exception or a panic across the C
/// API is an abort. The probe runs in the app's own process, so a rung that
/// does this once would do it on every launch, before the first photograph
/// is on screen. The file (`attempt` in the cache directory) holds the
/// attempt and how many launches have started it without finishing;
/// finishing, by success or by error, removes it. After [`STRIKES`] the
/// attempt is refused, and the caller records the refusal in the cache,
/// where it lasts until the fingerprint changes like any other failure.
pub fn attempt<T>(cfg: &Config, what: &str, f: impl FnOnce() -> T) -> Result<T, String> {
if cfg.cache_dir.as_os_str().is_empty() {
return Ok(f());
}
let path = cfg.cache_dir.join("attempt");
let died = std::fs::read_to_string(&path)
.ok()
.and_then(|s| {
let (w, n) = s.split_once('\t')?;
(w == what).then(|| n.trim().parse::<u32>().ok())?
})
.unwrap_or(0);
if died >= STRIKES {
log::error!("inference: the app died during `{what}` on the last {died} launches; not trying it again");
return Err(format!(
"the app died while trying this on {died} launches in a row"
));
}
if died > 0 {
log::warn!("inference: the last launch died during `{what}`; trying it once more");
}
let _ = std::fs::create_dir_all(&cfg.cache_dir);
let _ = std::fs::write(&path, format!("{what}\t{}", died + 1));
let out = f();
let _ = std::fs::remove_file(&path);
Ok(out)
}
/// The smallest detector, or the smallest model of any role if there is
/// none. A ~2 MB detector is the cheapest real test of a provider, and the
/// detector is the role the int8 forms exist for — the eye classifiers are
/// smaller still, and a Hexagon probed with one would fail for want of a
/// form nobody ships.
/// none. A ~2 MB detector is the cheapest real test of a provider, and
/// every rung serves it — the eye classifiers are smaller still, but the
/// Hexagon does not take them, and a probe with one would fail it for that.
fn probe_model(cfg: &Config) -> Option<(Role, PathBuf)> {
let smallest = |want: Option<Role>| {
cfg.models
@@ -148,16 +202,14 @@ fn time_rung(
cfg: &Config,
) -> Result<(f64, Option<String>), String> {
let want = rung.form(role);
let path = match want {
Form::Int8 => {
let p = crate::int8_sibling(canonical);
if !p.is_file() {
return Err(format!("no int8 form of {}", canonical.display()));
}
p
}
Form::F32 => canonical.to_path_buf(),
};
let path = crate::form_sibling(canonical, want);
if want != Form::F32 && !path.is_file() {
return Err(format!(
"no {} form of {}",
want.file_tag().unwrap_or("f32"),
canonical.display()
));
}
let bytes = std::fs::read(&path).map_err(|e| e.to_string())?;
let started = Instant::now();
let mut session =
@@ -168,21 +220,33 @@ fn time_rung(
started.elapsed().as_secs_f64()
);
let shape: Vec<usize> = session.inputs()[0]
.dtype()
.tensor_shape()
.ok_or("model input is not a tensor")?
// Zeros for every input the model declares, by name — the denoiser
// takes two (mosaic and σ), and a probe that fed only the first failed
// every rung and left it on the CPU.
let feeds: Vec<(String, Vec<usize>)> = session
.inputs()
.iter()
.map(|&d| if d > 0 { d as usize } else { 1 })
.collect();
let zeros = vec![0f32; shape.iter().product()];
.map(|i| {
let shape = i
.dtype()
.tensor_shape()
.ok_or("model input is not a tensor")?
.iter()
.map(|&d| if d > 0 { d as usize } else { 1 })
.collect();
Ok((i.name().to_string(), shape))
})
.collect::<Result<_, &str>>()?;
let run = |session: &mut ort::session::Session| -> Result<f64, String> {
let input = ort::value::Tensor::from_array((shape.clone(), zeros.clone()))
.map_err(|e| e.to_string())?;
let mut inputs: Vec<(String, ort::session::SessionInputValue)> = Vec::new();
for (name, shape) in &feeds {
let zeros = vec![0f32; shape.iter().product()];
let t = ort::value::Tensor::from_array((shape.clone(), zeros))
.map_err(|e| e.to_string())?;
inputs.push((name.clone(), t.into()));
}
let t = Instant::now();
let out = session
.run(ort::inputs![input])
.map_err(|e| e.to_string())?;
let out = session.run(inputs).map_err(|e| e.to_string())?;
let _ = out[0]
.try_extract_tensor::<f32>()
.map_err(|e| e.to_string())?;
@@ -228,9 +292,9 @@ fn fingerprint(runtime: &Runtime, cfg: &Config) -> String {
},
device_identity(),
];
for (role, bytes) in &cfg.embedded {
for (role, form, bytes) in &cfg.embedded {
parts.push(format!(
"{role:?} embedded {:016x}",
"{role:?} embedded {form:?} {:016x}",
crate::engines::hash(bytes)
));
}
@@ -239,9 +303,13 @@ fn fingerprint(runtime: &Runtime, cfg: &Config) -> String {
.map(|b| crate::engines::hash(&b))
.unwrap_or(0);
parts.push(format!("{role:?} {hash:016x}"));
let int8 = crate::int8_sibling(path);
if let Ok(b) = std::fs::read(&int8) {
parts.push(format!("{role:?} int8 {:016x}", crate::engines::hash(&b)));
for form in [Form::Int8, Form::A16W8, Form::A16W16] {
if let Ok(b) = std::fs::read(crate::form_sibling(path, form)) {
parts.push(format!(
"{role:?} {form:?} {:016x}",
crate::engines::hash(&b)
));
}
}
}
parts.join("\n")
@@ -310,7 +378,50 @@ fn system_property(name: &str) -> String {
String::from_utf8_lossy(&buf[..n.max(0) as usize]).into_owned()
}
#[cfg(not(any(target_os = "linux", target_os = "android")))]
#[cfg(target_os = "macos")]
fn device_identity() -> String {
// The chip, and the OS release: CoreML ships with the OS, so a macOS
// update is a new provider as surely as a new driver is on Linux.
format!(
"{} macOS {}",
sysctl("machdep.cpu.brand_string"),
sysctl("kern.osproductversion")
)
}
#[cfg(target_os = "macos")]
fn sysctl(name: &str) -> String {
extern "C" {
fn sysctlbyname(
name: *const std::ffi::c_char,
oldp: *mut std::ffi::c_void,
oldlenp: *mut usize,
newp: *mut std::ffi::c_void,
newlen: usize,
) -> i32;
}
let name = std::ffi::CString::new(name).unwrap();
let mut buf = [0u8; 256];
let mut len = buf.len();
// SAFETY: libSystem's documented call; `len` is the buffer's size in and
// the string's length, with its terminator, out.
let rc = unsafe {
sysctlbyname(
name.as_ptr(),
buf.as_mut_ptr().cast(),
&mut len,
std::ptr::null_mut(),
0,
)
};
if rc != 0 {
return String::new();
}
let s = &buf[..len.min(buf.len())];
String::from_utf8_lossy(s.strip_suffix(&[0]).unwrap_or(s)).into_owned()
}
#[cfg(not(any(target_os = "linux", target_os = "android", target_os = "macos")))]
fn device_identity() -> String {
String::new()
}
@@ -338,3 +449,49 @@ pub fn write_cache(cfg: &Config, cache: &Cache) {
}
}
}
#[cfg(test)]
mod tests {
use super::*;
fn a_cache_dir(name: &str) -> Config {
let dir = std::env::temp_dir().join(format!("dr-attempt-{}-{name}", std::process::id()));
let _ = std::fs::remove_dir_all(&dir);
Config {
cache_dir: dir,
..Config::default()
}
}
/// What a launch that died inside `what` leaves behind.
fn died_inside(cfg: &Config, what: &str, launches: u32) {
std::fs::create_dir_all(&cfg.cache_dir).unwrap();
std::fs::write(cfg.cache_dir.join("attempt"), format!("{what}\t{launches}")).unwrap();
}
#[test]
fn a_finished_attempt_leaves_no_trace() {
let cfg = a_cache_dir("finished");
assert_eq!(attempt(&cfg, "probe CoreML", || 7), Ok(7));
assert!(!cfg.cache_dir.join("attempt").exists());
}
#[test]
fn one_death_is_forgiven_and_two_are_not() {
let cfg = a_cache_dir("strikes");
died_inside(&cfg, "probe CoreML", 1);
assert_eq!(attempt(&cfg, "probe CoreML", || 7), Ok(7));
died_inside(&cfg, "probe CoreML", 2);
let mut ran = false;
assert!(attempt(&cfg, "probe CoreML", || ran = true).is_err());
assert!(!ran, "a refused attempt must not run");
}
#[test]
fn another_attempts_deaths_do_not_count() {
let cfg = a_cache_dir("other");
died_inside(&cfg, "probe TensorRT", 2);
assert_eq!(attempt(&cfg, "probe CUDA", || 7), Ok(7));
}
}
core/dr-inference-engine/src/session.rs
+85 -10
View File
@@ -19,24 +19,61 @@ pub fn build(rung: Rung, role: Role, bytes: &[u8], cfg: &Config) -> ort::Result<
// `stack_tensors`) — a panic across the C API, which is an abort. The
// app never asked tract for that and does not start now.
let mut b = Session::builder()?.with_intra_threads(threads(cfg))?;
if crate::api::runtime().is_native() {
b = with_runtime_log(b)?;
}
// A Hexagon session loads the compiled context when there is one and
// compiles it from the model when there is not; the engine thread is
// what makes the second case rare (§6).
let context = (rung == Rung::Hexagon).then(|| crate::engines::context_path(cfg, bytes));
let ready = context.as_ref().is_some_and(|p| p.is_file());
b = providers(
b,
rung,
role,
cfg,
if ready { None } else { context.as_deref() },
)?;
// What the rung keeps for this model: the context the Hexagon is to
// write, or the directory CoreML compiles into.
let per_model = match rung {
Rung::CoreMl => Some(crate::engines::coreml_dir(cfg, bytes)),
_ if ready => None,
_ => context.clone(),
};
b = providers(b, rung, role, cfg, per_model.as_deref())?;
match (ready, context) {
(true, Some(path)) => b.commit_from_file(path),
_ => b.commit_from_memory(bytes),
}
}
/// Send the runtime's own messages for this session to `log`, under the
/// target `onnxruntime`, instead of to ONNX Runtime's stdio logger.
///
/// Its stderr is nowhere once the app is launched from a menu, and what a
/// provider says while it partitions a graph — how many nodes it took, which
/// operator it declined, the library it failed to load — is most of what a
/// failed rung tells you (docs/dev/inference.md §4). The level follows the
/// filter: warnings always, `debug` adds the runtime's info lines (the
/// partition counts), `trace` its verbose ones (every node placement).
fn with_runtime_log(
b: ort::session::builder::SessionBuilder,
) -> ort::Result<ort::session::builder::SessionBuilder> {
use ort::logging::LogLevel;
let level = if log::log_enabled!(target: "onnxruntime", log::Level::Trace) {
LogLevel::Verbose
} else if log::log_enabled!(target: "onnxruntime", log::Level::Debug) {
LogLevel::Info
} else {
LogLevel::Warning
};
let forward = |level: LogLevel, _category: &str, _id: &str, location: &str, message: &str| {
let level = match level {
LogLevel::Verbose => log::Level::Trace,
LogLevel::Info => log::Level::Debug,
LogLevel::Warning => log::Level::Warn,
LogLevel::Error | LogLevel::Fatal => log::Level::Error,
};
log::log!(target: "onnxruntime", level, "{message} ({location})");
};
Ok(b.with_logger(std::sync::Arc::new(forward))?
.with_log_level(level)?)
}
/// The intra-op pool: what the config says, else the cores less two for
/// the compositor and the decoder (§9). tract ignores it.
fn threads(cfg: &Config) -> usize {
@@ -54,11 +91,12 @@ fn providers(
rung: Rung,
role: Role,
cfg: &Config,
_generate_context: Option<&std::path::Path>,
per_model: Option<&std::path::Path>,
) -> ort::Result<ort::session::builder::SessionBuilder> {
use ort::ep;
match rung {
Rung::Cpu => Ok(b),
Rung::CoreMl => coreml(b, per_model),
Rung::Cuda => {
Ok(b.with_execution_providers([ep::CUDA::default().build().error_on_failure()])?)
}
@@ -103,6 +141,43 @@ fn providers(
}
}
/// CoreML, compiling an ML Program — the format with the operators these
/// graphs use and the one that reaches the Neural Engine — into `cache`.
///
/// The option names are those ONNX Runtime 1.29 reads from the generic
/// key/value map (`coreml_options.cc`), which is what `ort`'s builder
/// fills. The cache is per model because of how CoreML keys it: a model
/// committed from memory, as every session here is, has no path, and the
/// key falls back to a hash of the graph's input and node names — not its
/// weights. Two exports of one architecture would share a program. The
/// directory `engines::coreml_dir` names is the hash of the bytes.
///
/// Every compute unit is allowed, so CoreML may place a graph on the
/// Neural Engine, the GPU or the CPU; the probe's clock judges the result.
#[cfg(target_os = "macos")]
fn coreml(
b: ort::session::builder::SessionBuilder,
cache: Option<&std::path::Path>,
) -> ort::Result<ort::session::builder::SessionBuilder> {
use ort::ep::{self, coreml};
let mut ep = ep::CoreML::default()
.with_model_format(coreml::ModelFormat::MLProgram)
.with_compute_units(coreml::ComputeUnits::All);
if let Some(dir) = cache {
let _ = std::fs::create_dir_all(dir);
ep = ep.with_model_cache_dir(dir.to_string_lossy());
}
Ok(b.with_execution_providers([ep.build().error_on_failure()])?)
}
#[cfg(not(any(target_os = "android", target_os = "macos")))]
fn coreml(
_b: ort::session::builder::SessionBuilder,
_cache: Option<&std::path::Path>,
) -> ort::Result<ort::session::builder::SessionBuilder> {
unreachable!("the CoreML rung is on the macOS ladder only")
}
/// Register MIGraphX through ONNX Runtime's generic key/value entry point.
///
/// `ort`'s own builder (`ep::MIGraphX`) fills the legacy
@@ -175,8 +250,8 @@ fn providers(
.build()
.error_on_failure()])?)
}
Rung::Cuda | Rung::TensorRt | Rung::MiGraphX => {
unreachable!("no desktop GPU rung on Android")
Rung::Cuda | Rung::TensorRt | Rung::MiGraphX | Rung::CoreMl => {
unreachable!("no desktop rung on Android")
}
}
}
core/dr-pano/build.rs
+12 -1
View File
@@ -13,8 +13,13 @@ const MODELS: &[&str] = &[
"../../models/keypoints/xfeat-768.onnx",
];
const QUANTISED: &[&str] = &[
"../../models/keypoints/xfeat-1024.int8.onnx",
"../../models/keypoints/xfeat-768.int8.onnx",
];
fn main() {
for m in MODELS {
for m in MODELS.iter().chain(QUANTISED) {
println!("cargo:rerun-if-changed={m}");
}
println!("cargo:rerun-if-changed=build.rs");
@@ -26,6 +31,12 @@ fn main() {
for model in MODELS.iter().copied() {
check(model);
}
// The Hexagon's int8 forms ride only in an Android build.
if std::env::var("CARGO_CFG_TARGET_OS").as_deref() == Ok("android") {
for model in QUANTISED.iter().copied() {
check(model);
}
}
}
fn check(model: &str) {
core/dr-pano/src/migan.rs
+2 -1
View File
@@ -30,7 +30,8 @@ pub struct MiGan {
impl MiGan {
/// From the model file, in whichever form the engine's rung wants
/// (`resolve_model` picks an int8 sibling for the Hexagon).
/// (`resolve_model` picks the `.a16w16.onnx` sibling on the Hexagon:
/// int8 moved the fill 16 dB from f32's, 16-bit about 41).
pub fn from_path(path: &std::path::Path) -> Result<Self, PanoError> {
use dr_inference_engine::{resolve_model, Role};
let (path, form) = resolve_model(Role::Inpainter, path);
core/dr-pano/src/xfeat.rs
+37 -6
View File
@@ -36,18 +36,49 @@ pub struct XFeat {
pub options: DecodeOptions,
}
/// The bytes of both exports compiled into the binary, for whoever compiles
/// engines ahead of the first request (docs/dev/inference.md §6).
/// The Hexagon's forms (docs/dev/inference.md §1.5): int8, from the same
/// network spelled for the HTP (the unfold as SpaceToDepth, the bilinear
/// resizes as matrix products). Only Android has a Hexagon.
#[cfg(all(feature = "embedded-model", target_os = "android"))]
const EMBEDDED_LANDSCAPE_INT8: &[u8] =
include_bytes!("../../../models/keypoints/xfeat-1024.int8.onnx");
#[cfg(all(feature = "embedded-model", target_os = "android"))]
const EMBEDDED_PORTRAIT_INT8: &[u8] =
include_bytes!("../../../models/keypoints/xfeat-768.int8.onnx");
/// Every form of both exports compiled into the binary, landscape then
/// portrait, for whoever compiles engines ahead of the first request
/// (docs/dev/inference.md §6).
#[cfg(feature = "embedded-model")]
pub fn embedded_model_bytes() -> [&'static [u8]; 2] {
[EMBEDDED_LANDSCAPE, EMBEDDED_PORTRAIT]
pub fn embedded_models() -> [Vec<(dr_inference_engine::Form, &'static [u8])>; 2] {
use dr_inference_engine::Form;
#[allow(unused_mut)]
let mut forms = [
vec![(Form::F32, EMBEDDED_LANDSCAPE)],
vec![(Form::F32, EMBEDDED_PORTRAIT)],
];
#[cfg(target_os = "android")]
{
forms[0].push((Form::Int8, EMBEDDED_LANDSCAPE_INT8));
forms[1].push((Form::Int8, EMBEDDED_PORTRAIT_INT8));
}
forms
}
impl XFeat {
/// The weights compiled into the binary.
/// The weights compiled into the binary, in the form the device's
/// backend runs.
#[cfg(feature = "embedded-model")]
pub fn embedded() -> Result<Self, PanoError> {
Self::from_bytes(EMBEDDED_LANDSCAPE, EMBEDDED_PORTRAIT)
use dr_inference_engine::{choose_embedded, open, Role};
let [l, p] = embedded_models();
let (l, lf) = choose_embedded(Role::Keypoints, &l);
let (p, pf) = choose_embedded(Role::Keypoints, &p);
Ok(XFeat {
landscape: open(Role::Keypoints, lf, l)?,
portrait: open(Role::Keypoints, pf, p)?,
options: DecodeOptions::default(),
})
}
/// From the two exports on disk.
core/dr-pipeline/ops/vibrance.yaml
+36 -10
View File
@@ -21,24 +21,41 @@ params:
kind: amount
uniforms:
amount: vibrance / 100
amount:
value: vibrance / 100 * 1.3
doc: |
Scaled so that a value delivers the strength it names. Measured, not
chosen: fitted on 45 of the photographer's earlier exports whose only
colour setting was a vibrance of about +24, against their raws.
helpers: [luminance, tone_position, colour_saturation]
helpers: [luminance]
wgsl: |
let luma = luminance(c);
let sat = colour_saturation(c);
// How saturated a colour *looks*, so measured on display-encoded values.
// In scene-linear light an ordinary tan reads as 0.78 saturated and the
// falloff below would leave it a twentieth of the effect; encoded, it reads
// as 0.5, which is what the eye sees.
let e = pow(max(c, vec3<f32>(0.0)), vec3<f32>(1.0 / 2.2));
let e_hi = max(e.r, max(e.g, e.b));
let e_lo = min(e.r, min(e.g, e.b));
let sat = select(0.0, (e_hi - e_lo) / e_hi, e_hi > 0.00001);
// The vibrance curve: full effect on grey, tapering to nothing on colours
// that are already saturated. Squaring the falloff keeps the mid-range
// responsive while still protecting the extremes.
let falloff = (1.0 - sat) * (1.0 - sat);
// Skin protection. Skin sits in a narrow band of hue where red leads green
// leads blue; pushing it is what makes vibrance look wrong on portraits.
// Detected by channel ordering rather than a hue angle, which costs a
// conversion and buys nothing here.
let is_skin = f32(c.r > c.g && c.g > c.b);
// Skin protection, for skin: hues between about 10 and 50 degrees (red
// leading, green between red and blue) that are not strongly saturated.
// Red-over-green-over-blue alone is every warm colour in a photograph —
// wood, sand, brick, sunlit grass — and halving all of them is most of why
// vibrance used to do so little.
let span = max(e_hi - e_lo, 0.00001);
let skin_hue = select(0.0, 60.0 * (e.g - e.b) / span, e.r >= e.g && e.g >= e.b);
let in_band = smoothstep(4.0, 12.0, skin_hue) * (1.0 - smoothstep(42.0, 52.0, skin_hue));
let is_skin = in_band * (1.0 - smoothstep(0.45, 0.7, sat)) * f32(e.r >= e.g && e.g >= e.b);
let skin_guard = 1.0 - is_skin * 0.5;
let strength = amount * falloff * skin_guard;
@@ -49,9 +66,18 @@ tests:
- name: it_starts_neutral
expect_active: false
- name: the_amount_is_normalised_to_unit_range
- name: the_amount_is_the_measured_scale
why: |
Fitted against the photographer's earlier exports, so a value delivers
the strength it names.
set: { vibrance: 100 }
expect: { amount: 1.0 }
expect: { amount: 1.3 }
- name: saturation_is_judged_as_displayed
why: |
Judged in scene-linear light, ordinary warm colours read as nearly
saturated and get almost none of the effect.
expect_wgsl: ["let e = pow(max(c, vec3<f32>(0.0)), vec3<f32>(1.0 / 2.2));"]
- name: muted_colours_get_more_than_saturated_ones
why: |
core/dr-pipeline/presets/essentials.drpl
+1 -1
View File
@@ -25,7 +25,7 @@ vibrance.vibrance = 10
blacks_whites.blacks = -8
clarity.amount = 12
contrast.contrast = 18
vibrance.vibrance = 18
vibrance.vibrance = 36
[preset Recover the sky]
blacks_whites.whites = -10
core/dr-pipeline/presets/skies.drpl
+13 -13
View File
@@ -15,38 +15,38 @@ drpl 1
[preset Blue sky]
colour_mixer.azure_lum = -20
colour_mixer.azure_sat = 25
colour_mixer.azure_sat = 18
colour_mixer.blue_lum = -15
colour_mixer.blue_sat = 20
colour_mixer.blue_sat = 14
highlights_shadows.highlights = -15
[preset Deep blue sky]
colour_mixer.azure_hue = 10
colour_mixer.azure_lum = -30
colour_mixer.azure_sat = 35
colour_mixer.azure_sat = 22
colour_mixer.blue_lum = -25
colour_mixer.blue_sat = 30
colour_mixer.cyan_sat = 10
colour_mixer.blue_sat = 19
colour_mixer.cyan_sat = 6
highlights_shadows.highlights = -30
[preset Polariser]
colour_mixer.azure_hue = 10
colour_mixer.azure_lum = -35
colour_mixer.azure_sat = 40
colour_mixer.azure_sat = 29
colour_mixer.blue_lum = -30
colour_mixer.blue_sat = 35
colour_mixer.blue_sat = 26
colour_mixer.cyan_lum = -10
colour_mixer.cyan_sat = 15
colour_mixer.cyan_sat = 11
dehaze.amount = 20
highlights_shadows.highlights = -35
vibrance.vibrance = 10
vibrance.vibrance = 7
[preset Blue sky, golden land]
colour_mixer.azure_lum = -20
colour_mixer.azure_sat = 25
colour_mixer.azure_sat = 16
colour_mixer.blue_lum = -15
colour_mixer.blue_sat = 20
colour_mixer.orange_sat = 12
colour_mixer.blue_sat = 13
colour_mixer.orange_sat = 8
colour_mixer.yellow_hue = -10
colour_mixer.yellow_sat = 15
colour_mixer.yellow_sat = 10
highlights_shadows.highlights = -20
core/dr-pipeline/presets/vivid.drpl
+30 -25
View File
@@ -1,30 +1,35 @@
drpl 1
# Vivid: more colour than the default rendering, for the photographer used
# to Lightroom's richer starting point (camera-profiles.md §9).
# Vivid: more colour than the default rendering (camera-profiles.md §9).
#
# That difference is mostly Camera Raw's tone curve, not the camera
# profile: Adobe Standard's look table, which D20 applies, desaturates dark
# tones (camera-profiles.md §1). So these do the work themselves, and work
# on every photograph — a JPEG, a body with no profile. They lean on
# These do the work themselves, and work on every photograph — a JPEG, a body with no profile. They lean on
# vibrance before saturation: vibrance lifts muted colours most and holds
# skin back, so a frame gets richer before anything in it looks painted.
# Saturation, which moves every colour alike, is used sparingly on top.
#
# Each changes only what it names (FR-DEV-6), so a corrected exposure or
# white balance survives applying one.
#
# How much colour each adds is measured, not guessed: mean CIELAB chroma on
# raws rendered with the default (DNG reference) rendering, as a ratio to that
# rendering. For scale, the photographer's earlier exports of the same kind of
# raws sit at 1.14 with no look applied and 1.27 with their everyday look.
# Vivid 1.30 and Vivid warm 1.30 sit just above that; Vivid landscape 1.38;
# Vivid, strong 1.45; Vivid portrait 1.15, with its skin bands held down as
# written. Tuned by scaling each preset's colour values together, never its
# tone ones.
[preset Vivid]
contrast.contrast = 10
saturation.saturation = 8
vibrance.vibrance = 30
saturation.saturation = 11
vibrance.vibrance = 42
[preset Vivid, strong]
blacks_whites.blacks = -10
clarity.amount = 8
contrast.contrast = 18
saturation.saturation = 15
vibrance.vibrance = 45
saturation.saturation = 19
vibrance.vibrance = 57
# Foliage and sky: green and chartreuse for leaves and grass, azure and blue
# for sky and water, a little yellow for dry grass and stone. The skin bands
@@ -33,37 +38,37 @@ vibrance.vibrance = 45
[preset Vivid landscape]
clarity.amount = 10
colour_mixer.azure_lum = -10
colour_mixer.azure_sat = 20
colour_mixer.azure_sat = 25
colour_mixer.blue_lum = -10
colour_mixer.blue_sat = 15
colour_mixer.chartreuse_sat = 15
colour_mixer.green_sat = 20
colour_mixer.yellow_sat = 10
colour_mixer.blue_sat = 20
colour_mixer.chartreuse_sat = 20
colour_mixer.green_sat = 25
colour_mixer.yellow_sat = 13
contrast.contrast = 12
saturation.saturation = 5
vibrance.vibrance = 25
saturation.saturation = 7
vibrance.vibrance = 32
# Golden hour: oranges and yellows up and a warm cast laid over the
# highlights only, so shadows stay clean rather than muddy.
[preset Vivid warm]
colour_grading.highlight_hue = 45
colour_grading.highlight_strength = 12
colour_mixer.orange_sat = 15
colour_mixer.red_sat = 8
colour_mixer.yellow_sat = 15
colour_mixer.orange_sat = 17
colour_mixer.red_sat = 9
colour_mixer.yellow_sat = 17
contrast.contrast = 8
vibrance.vibrance = 25
vibrance.vibrance = 29
# People: everything around the subject gets richer while skin does not.
# Vibrance already protects skin; the orange and red bands are then held a
# little below where they started, because a face is the one colour every
# viewer knows the right value of.
[preset Vivid portrait]
colour_mixer.azure_sat = 10
colour_mixer.blue_sat = 12
colour_mixer.green_sat = 12
colour_mixer.azure_sat = 14
colour_mixer.blue_sat = 17
colour_mixer.green_sat = 17
colour_mixer.orange_sat = -10
colour_mixer.red_sat = -5
contrast.contrast = 6
saturation.saturation = -5
vibrance.vibrance = 25
vibrance.vibrance = 35
core/dr-pipeline/src/camera_raw.rs
+168
View File
@@ -0,0 +1,168 @@
//! TRACES: FR-DEV-3j | FR-DEV-3e
//! The DNG SDK's reference tone, as a rendering the view transform can
//! choose (D21).
//!
//! The DNG specification's reference rendering runs a raw through the
//! profile's `ProfileToneCurve`, or the ACR3 default for a profile with none.
//! Half of what the curve does is *how* it is applied. The SDK's
//! `RefBaselineRGBTone` runs it on the largest and the smallest channel, and
//! places the middle channel at the fraction between them it had before. Hue
//! is kept; saturation rises wherever the curve is steeper than the
//! diagonal, which for the ACR3 curve is the shadows and the midtones.
//!
//! It runs in linear ProPhoto, as the SDK does, on values clipped to
//! `[0, 1]`; its output is linear and goes to the output transform as the
//! sigmoid's does. The curve is read from the profile buffer
//! (`ops::camera_profile::profile_buffer`), which always carries one.
//!
//! [`apply_reference`] is the arithmetic on the CPU; the GPU test holds the
//! shader to it.
use crate::ops::camera_profile::{mul, working_prophoto};
use crate::view::{DEFAULT_WHITE, REFERENCE_CONTRAST, SCENE_GREY};
/// The input scale for a white point: 1 at the default, so sensor white is
/// display white as in the SDK's reference; each stop of `white` above it halves the
/// input.
pub fn input_scale(white: f32) -> f32 {
(DEFAULT_WHITE - white).exp2()
}
/// The power the input is bent by about middle grey: 1 at
/// [`REFERENCE_CONTRAST`], where the curve is the reference's untouched.
///
/// The default contrast sits above it, so a photograph out of the camera is
/// bent by `DEFAULT_CONTRAST / REFERENCE_CONTRAST` — the extra contrast
/// Lightroom's exports showed over the bare reference curve (D21 addendum).
pub fn contrast_power(contrast: f32) -> f32 {
contrast / REFERENCE_CONTRAST
}
/// The curve, its scale and its contrast applied to one ProPhoto colour.
fn rgb_tone(curve: &[f32], p: [f32; 3]) -> [f32; 3] {
let p = p.map(|v| v.clamp(0.0, 1.0));
let hi = p[0].max(p[1]).max(p[2]);
let lo = p[0].min(p[1]).min(p[2]);
let (c_hi, c_lo) = (
dr_types::tone::evaluate(curve, hi),
dr_types::tone::evaluate(curve, lo),
);
if hi - lo <= 1e-7 {
return [c_hi; 3];
}
p.map(|v| c_lo + (c_hi - c_lo) * (v - lo) / (hi - lo))
}
/// TRACES: FR-DEV-3j
/// The view transform's DNG reference rendering of one working-space colour.
pub fn apply_reference(curve: &[f32], c: [f32; 3], contrast: f32, white: f32) -> [f32; 3] {
let (to, back) = working_prophoto();
let scale = input_scale(white);
let power = contrast_power(contrast);
let mut p = mul(to, c).map(|v| v * scale);
if power != 1.0 {
p = p.map(|v| SCENE_GREY * (v.max(0.0) / SCENE_GREY).powf(power));
}
mul(back, rgb_tone(curve, p))
}
/// The WGSL, a helper the view transform asks for after
/// `ops::camera_profile`'s ProPhoto constants. Mirrors [`apply_reference`].
pub const CAMERA_RAW_WGSL: &str = "
fn camera_raw_curve(x: f32) -> f32 {
let base = profile_curve_base();
let n = u32(profile_table[2].x);
let s = clamp(x, 0.0, 1.0) * f32(n - 1u);
let i = min(u32(s), n - 2u);
return mix(profile_table[base + i].x, profile_table[base + i + 1u].x, s - f32(i));
}
// The SDK's RGBTone: the curve on the largest and smallest channel, the
// middle one kept at its fraction between them, so hue survives.
fn camera_raw_tone(c: vec3<f32>, scale: f32, power: f32, grey: f32) -> vec3<f32> {
var p = PROFILE_FROM_WORKING * c * scale;
if (power != 1.0) {
p = grey * pow(max(p, vec3<f32>(0.0)) / grey, vec3<f32>(power));
}
p = clamp(p, vec3<f32>(0.0), vec3<f32>(1.0));
let hi = max(p.r, max(p.g, p.b));
let lo = min(p.r, min(p.g, p.b));
let c_hi = camera_raw_curve(hi);
let c_lo = camera_raw_curve(lo);
var out = vec3<f32>(c_hi);
if (hi - lo > 1e-7) {
out = vec3<f32>(c_lo) + (c_hi - c_lo) * (p - vec3<f32>(lo)) / (hi - lo);
}
return PROFILE_TO_WORKING * out;
}
";
#[cfg(test)]
mod tests {
use super::*;
use crate::view::DEFAULT_CONTRAST;
use dr_types::tone::{evaluate, ACR3_DEFAULT};
fn identity() -> Vec<f32> {
(0..1025).map(|i| i as f32 / 1024.0).collect()
}
#[test]
fn at_the_reference_the_input_is_untouched() {
assert_eq!(input_scale(DEFAULT_WHITE), 1.0);
assert_eq!(contrast_power(REFERENCE_CONTRAST), 1.0);
}
#[test]
fn the_default_adds_the_measured_contrast() {
// Fitted on Lightroom exports with neutral settings (D21 addendum):
// the bare reference curve is a little flat against them.
let p = contrast_power(DEFAULT_CONTRAST);
assert!((1.05..1.12).contains(&p), "{p}");
}
#[test]
fn grey_goes_through_the_curve_and_stays_grey() {
for v in [0.02, 0.13, 0.5] {
let out = apply_reference(&ACR3_DEFAULT, [v; 3], REFERENCE_CONTRAST, DEFAULT_WHITE);
let want = evaluate(&ACR3_DEFAULT, v);
assert!(
out.iter().all(|o| (o - want).abs() < 1e-4),
"{v}: {out:?} vs {want}"
);
}
}
#[test]
fn an_identity_curve_changes_nothing_inside_the_range() {
let c = [0.4, 0.2, 0.1];
let out = apply_reference(&identity(), c, REFERENCE_CONTRAST, DEFAULT_WHITE);
assert!(
out.iter().zip(c).all(|(o, c)| (o - c).abs() < 1e-4),
"{out:?}"
);
}
#[test]
fn the_middle_channel_keeps_its_place_between_the_other_two() {
let p = [0.3, 0.12, 0.05];
let out = rgb_tone(&ACR3_DEFAULT, p);
let before = (p[1] - p[2]) / (p[0] - p[2]);
let after = (out[1] - out[2]) / (out[0] - out[2]);
assert!((before - after).abs() < 1e-5, "{before} {after}");
}
#[test]
fn the_acr_curve_raises_saturation_in_the_midtones() {
let p = [0.15, 0.08, 0.05];
let out = rgb_tone(&ACR3_DEFAULT, p);
let sat = |c: [f32; 3]| (c[0] - c[2]) / c[0];
assert!(sat(out) > sat(p), "{p:?} -> {out:?}");
}
#[test]
fn white_halves_the_input_per_stop() {
assert_eq!(input_scale(DEFAULT_WHITE + 1.0), 0.5);
assert!(contrast_power(2.8) > 1.0);
}
}
core/dr-pipeline/src/descriptor.rs
+9
View File
@@ -464,6 +464,15 @@ impl ParamDescriptor {
}
}
/// The same choice with another variant as its default.
///
/// For a choice whose variants were numbered before its default was
/// settled: a sidecar records the index, so reordering the variants to
/// put the default first would change what saved edits mean.
pub fn with_default(self, default: f32) -> Self {
Self { default, ..self }
}
/// A 0…1 fraction — a proportion of something, rather than an amount.
///
/// Its own constructor because the crop rect needs four of them and the
core/dr-pipeline/src/graph.rs
+231 -1
View File
@@ -170,6 +170,18 @@ pub struct EditGraph {
/// correction the photograph asked for — see
/// [`crate::descriptor::ParamDescriptor::switch_on`].
lens_profile_applied: bool,
/// TRACES: FR-DEV-3g
/// Whether this photograph can take the learned denoise — a Bayer
/// mosaic — set by whoever opened it. Derived from the file like the
/// lens profile, so not in the state; it only decides whether the
/// switch below is offered.
denoise_available: bool,
/// Which demosaic develops the photograph: a network, or the classical
/// one. An edit: published as [`crate::learned_denoise`], captured,
/// stored and undone with the rest (FR-DEV-3c).
denoise_method: crate::learned_denoise::Method,
/// How strongly to denoise, 0–100; what is not taken goes back as grain.
denoise_strength: f32,
}
/// TRACES: FR-DEV-3f
@@ -226,6 +238,9 @@ impl EditGraph {
],
lens_profile: None,
lens_profile_applied: true,
denoise_available: false,
denoise_method: crate::learned_denoise::Method::DEFAULT,
denoise_strength: 100.0,
}
}
@@ -399,6 +414,32 @@ impl EditGraph {
self.lens_profile.as_ref()
}
/// TRACES: FR-DEV-3g
/// Offer the learned denoise, or not: true for a Bayer mosaic.
pub fn set_denoise_available(&mut self, available: bool) {
self.denoise_available = available;
}
/// TRACES: FR-DEV-3g
/// Whether the learned denoise is asked for. A setting kept on a
/// photograph that cannot take it is harmless and does nothing, as a
/// lens switch with no profile does.
pub fn denoise_applied(&self) -> bool {
self.denoise_method.learned()
}
/// TRACES: FR-DEV-3g
/// Which demosaic is asked for.
pub fn denoise_method(&self) -> crate::learned_denoise::Method {
self.denoise_method
}
/// TRACES: FR-DEV-3g
/// The grain to keep, 0–1: what the strength does not take.
pub fn denoise_grain(&self) -> f32 {
(100.0 - self.denoise_strength) / 100.0
}
/// TRACES: FR-DEV-3
/// Whether the matched profile is being applied.
pub fn lens_profile_applied(&self) -> bool {
@@ -581,8 +622,38 @@ impl EditGraph {
}
});
switch
// TRACES: FR-DEV-3g
// Offered only where the photograph can take it, for the lens
// switch's reason: a control that can do nothing must not look as if
// it could.
let denoise = self.denoise_available.then(|| {
let desc = crate::learned_denoise::descriptor();
OpCapability {
id: desc.id,
label: desc.label,
active: self.denoise_applied(),
params: desc
.params
.iter()
.map(|p| ParamCapability {
id: p.id,
label: p.label,
kind: p.kind.clone(),
default: p.default,
value: self.param(desc.id, p.id).unwrap_or(p.default),
facet: p.facet,
})
.collect(),
presentation: None,
attributes: desc.attributes.clone(),
}
});
// The learned denoise first: it decides what every control below
// is applied to, so it heads the panel (docs/dev/denoise.md §7).
denoise
.into_iter()
.chain(switch)
.chain(warps)
.chain(ops)
.chain(std::iter::once(framing))
@@ -675,6 +746,11 @@ impl EditGraph {
// `capabilities`, with the operations and the warps and for the
// same reason (FR-DEV-3c).
lens_profile_applied: _,
// Derived from the file, like the profile above.
denoise_available: _,
// Edits, in the state through `capabilities` like the lens switch.
denoise_method: _,
denoise_strength: _,
masks,
film,
spots,
@@ -746,6 +822,32 @@ impl EditGraph {
}
pub fn set_param(&mut self, op: OpId, param: ParamId, value: f32) {
if op == crate::learned_denoise::ID {
match param {
p if p == crate::learned_denoise::METHOD => {
self.denoise_method = crate::learned_denoise::Method::from_index(value)
}
// 0.21 and 0.22's switch (see `APPLY`): off is the classical
// demosaic, on is a network — the one already chosen, if any.
p if p == crate::learned_denoise::APPLY => {
use crate::learned_denoise::Method;
if value == 0.0 {
self.denoise_method = Method::Bilinear;
} else if !self.denoise_method.learned() {
self.denoise_method = Method::DEFAULT;
}
}
p if p == crate::learned_denoise::STRENGTH => {
self.denoise_strength = value.clamp(0.0, 100.0)
}
// 0.21.0's grain, the strength's inverse (see `GRAIN`).
p if p == crate::learned_denoise::GRAIN => {
self.denoise_strength = 100.0 - value.clamp(0.0, 100.0)
}
_ => log::warn!("unknown parameter {param} on {op}; ignoring"),
}
return;
}
if op == crate::lens::profile_switch::ID {
if param != crate::lens::profile_switch::APPLY {
log::warn!("unknown parameter {param} on {op}; ignoring");
@@ -803,6 +905,17 @@ impl EditGraph {
/// Read a parameter back.
pub fn param(&self, op: OpId, param: ParamId) -> Option<f32> {
if op == crate::learned_denoise::ID {
return match param {
p if p == crate::learned_denoise::METHOD => Some(self.denoise_method.index()),
p if p == crate::learned_denoise::APPLY => {
Some(if self.denoise_applied() { 1.0 } else { 0.0 })
}
p if p == crate::learned_denoise::STRENGTH => Some(self.denoise_strength),
p if p == crate::learned_denoise::GRAIN => Some(100.0 - self.denoise_strength),
_ => None,
};
}
if op == crate::lens::profile_switch::ID {
return (param == crate::lens::profile_switch::APPLY)
.then_some(if self.lens_profile_applied { 1.0 } else { 0.0 });
@@ -849,6 +962,10 @@ impl EditGraph {
// a reset does not change which lens took the photograph. What returns
// to default is the answer to whether to use it, which is on.
self.set_lens_profile_applied(true);
// The learned denoise returns to its default network; whether it is
// available is the file's and stays.
self.denoise_method = crate::learned_denoise::Method::DEFAULT;
self.denoise_strength = 100.0;
}
/// Set the crop rectangle. Clamped to keep it inside the frame.
@@ -1995,4 +2112,117 @@ mod tests {
let after = cropped.render_scale(source, (1500, 1000));
assert!(after.ratio() > fit.ratio());
}
#[test]
fn the_learned_denoise_is_offered_only_where_it_can_run() {
use crate::learned_denoise;
let mut g = EditGraph::default_chain();
assert!(!g.capabilities().iter().any(|c| c.id == learned_denoise::ID));
g.set_denoise_available(true);
let cap = g
.capabilities()
.into_iter()
.find(|c| c.id == learned_denoise::ID)
.expect("offered");
assert!(cap.active, "on by default");
assert_eq!(g.denoise_method(), learned_denoise::Method::Best);
assert_eq!(g.denoise_grain(), 0.0, "at full strength");
assert_eq!(cap.id, g.capabilities()[0].id, "and first in the panel");
g.set_param(
learned_denoise::ID,
learned_denoise::METHOD,
learned_denoise::Method::Bilinear.index(),
);
g.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 70.0);
assert!(!g.denoise_applied());
assert!((g.denoise_grain() - 0.3).abs() < 1e-6);
g.reset();
assert!(g.denoise_applied(), "reset is back to on");
assert_eq!(g.denoise_grain(), 0.0);
}
#[test]
fn an_untouched_raw_writes_nothing_and_develops_through_the_best() {
// TRACES: FR-DEV-3g
use crate::learned_denoise::{self, Method};
let mut g = EditGraph::default_chain();
g.set_denoise_available(true);
assert_eq!(g.denoise_method(), Method::Best);
let stored = |g: &EditGraph| {
crate::Preset::capture_params(g)
.params()
.keys()
.any(|(op, _)| op == learned_denoise::ID.0)
};
assert!(!stored(&g), "the default is not written");
g.set_param(
learned_denoise::ID,
learned_denoise::METHOD,
Method::Fast.index(),
);
assert!(stored(&g), "a choice is");
assert!(
!crate::Preset::capture_params(&g).params().contains_key(&(
learned_denoise::ID.0.into(),
learned_denoise::APPLY.0.into()
)),
"and the old switch never is"
);
}
#[test]
fn an_edit_saved_with_the_switch_keeps_its_look() {
// TRACES: FR-DEV-3g
// 0.21 and 0.22 stored on or off; off is the classical demosaic, and
// on keeps a network already chosen.
use crate::learned_denoise::{self, Method};
let mut g = EditGraph::default_chain();
g.set_param(learned_denoise::ID, learned_denoise::APPLY, 0.0);
assert_eq!(g.denoise_method(), Method::Bilinear);
g.set_param(learned_denoise::ID, learned_denoise::APPLY, 1.0);
assert_eq!(g.denoise_method(), Method::DEFAULT);
g.set_param(
learned_denoise::ID,
learned_denoise::METHOD,
Method::Fast.index(),
);
g.set_param(learned_denoise::ID, learned_denoise::APPLY, 1.0);
assert_eq!(g.denoise_method(), Method::Fast);
// A number from a newer build with more methods is the default.
g.set_param(learned_denoise::ID, learned_denoise::METHOD, 9.0);
assert_eq!(g.denoise_method(), Method::DEFAULT);
}
#[test]
fn an_edit_saved_with_grain_keeps_its_look() {
// TRACES: FR-DEV-3g
// 0.21.0 stored the grain kept rather than the strength.
use crate::learned_denoise;
let mut g = EditGraph::default_chain();
g.set_param(learned_denoise::ID, learned_denoise::GRAIN, 25.0);
assert_eq!(
g.param(learned_denoise::ID, learned_denoise::STRENGTH),
Some(75.0)
);
assert!((g.denoise_grain() - 0.25).abs() < 1e-6);
}
#[test]
fn the_learned_denoise_travels_in_the_state() {
use crate::learned_denoise;
let mut g = EditGraph::default_chain();
g.set_denoise_available(true);
g.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 60.0);
g.set_param(
learned_denoise::ID,
learned_denoise::METHOD,
learned_denoise::Method::Medium.index(),
);
let state = g.state();
let mut h = EditGraph::default_chain();
h.set_denoise_available(true);
let _ = h.set_state(&state);
assert_eq!(h.denoise_method(), learned_denoise::Method::Medium);
assert!((h.denoise_grain() - 0.4).abs() < 1e-6);
}
}
core/dr-pipeline/src/learned_denoise.rs
+119
View File
@@ -0,0 +1,119 @@
//! TRACES: FR-DEV-3g
//! The learned denoise's settings: which network develops the photograph,
//! if any, and how much grain to keep.
//!
//! Not an [`crate::operation::Operation`]: the learned stage replaces the
//! demosaic and runs once per photograph, off the render path
//! (docs/dev/denoise.md §2, §7), and the grain is a blend of its result with
//! the classical one, done where the source is chosen. But what a
//! photographer sets travels the one road every setting travels — the
//! capability list feeds the panel, [`crate::Preset`] captures it, the
//! sidecar stores it, the undo stack replays it (FR-DEV-3c) — so it is
//! published as a capability, like the lens profile switch.
use std::sync::{Arc, LazyLock};
use crate::descriptor::{Attribute, LocalizedKey, OpDescriptor, ParamDescriptor, Scale, Unit};
use crate::{OpId, ParamId};
pub const ID: OpId = OpId("learned_denoise");
/// TRACES: FR-DEV-3g
/// Which demosaic develops the photograph, a [`Method`] by index.
pub const METHOD: ParamId = ParamId("method");
/// What 0.21 and 0.22 stored instead of [`METHOD`]: on or off. Still read —
/// off is [`Method::Bilinear`], on is the default network — so an edit saved
/// by those releases keeps its look; never written, and not offered.
pub const APPLY: ParamId = ParamId("apply");
/// TRACES: FR-DEV-3g
/// How strongly to denoise, 0–100: 100 is the network's result as it is, and
/// lower puts the removed noise's brightness back as grain.
pub const STRENGTH: ParamId = ParamId("strength");
/// What 0.21.0 stored instead of [`STRENGTH`]: the grain kept, its inverse.
/// Still read, so an edit saved by that release keeps its look; never
/// written, and not offered as a control.
pub const GRAIN: ParamId = ParamId("grain");
/// TRACES: FR-DEV-3g
/// The demosaics a photograph can be developed with, in the order the
/// sidecar numbers them. Three networks that trade time for quality — the
/// same training, distilled into smaller students (docs/dev/denoise.md §13)
/// — and the classical demosaic, which is no network at all.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum Method {
/// The classical demosaic: the noise stays.
Bilinear,
/// The smallest student: a quarter of the medium network's work.
Fast,
/// One network the size of the first release's.
Medium,
/// Two experts, one for flat areas and one for edges, and a gate.
Best,
}
impl Method {
pub const ALL: [Method; 4] = [Method::Bilinear, Method::Fast, Method::Medium, Method::Best];
pub const DEFAULT: Method = Method::Best;
/// The sidecar's number for it.
pub fn index(self) -> f32 {
Self::ALL.iter().position(|m| *m == self).unwrap_or(0) as f32
}
/// The method a stored number names; out of range is the default, as
/// from a newer build with more of them.
pub fn from_index(value: f32) -> Method {
let i = value.round();
if i >= 0.0 && (i as usize) < Self::ALL.len() {
Self::ALL[i as usize]
} else {
Self::DEFAULT
}
}
/// Whether a network runs at all.
pub fn learned(self) -> bool {
self != Method::Bilinear
}
}
/// The best network by default, at full strength: every Bayer raw is
/// developed from the learned demosaic, and the choice and the slider are
/// there to take it back, trade it for time, or ease it off. It costs seconds per photograph the first time, while
/// the classical demosaic shows; the result is cached, so a photograph
/// reopened or exported does not pay again (docs/dev/denoise.md §7).
pub(crate) static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
Arc::new(OpDescriptor {
id: ID,
label: LocalizedKey("op.learned_denoise"),
params: vec![
ParamDescriptor::choice(
"method",
"param.learned_denoise.method",
vec![
LocalizedKey("param.learned_denoise.method.bilinear"),
LocalizedKey("param.learned_denoise.method.fast"),
LocalizedKey("param.learned_denoise.method.medium"),
LocalizedKey("param.learned_denoise.method.best"),
],
)
.with_default(Method::DEFAULT.index()),
ParamDescriptor::scalar(
"strength",
"param.learned_denoise.strength",
0.0,
100.0,
100.0,
Unit::Percent,
Scale::Linear,
0,
),
],
// With the classical noise reduction, which is what a photographer
// looks for it beside.
attributes: vec![Attribute::Detail],
})
});
pub fn descriptor() -> Arc<OpDescriptor> {
DESCRIPTOR.clone()
}
core/dr-pipeline/src/lib.rs
+2
View File
@@ -33,6 +33,7 @@
//! data neither would be physically meaningful (ARCH §5.2).
pub mod bundled;
pub mod camera_raw;
pub mod coverage;
pub mod declared;
pub mod descriptor;
@@ -40,6 +41,7 @@ pub mod detail;
pub mod framing;
pub mod graph;
pub mod history;
pub mod learned_denoise;
pub mod lens;
pub mod mask;
pub mod neutral;
core/dr-pipeline/src/ops/camera_profile.rs
+81 -27
View File
@@ -47,14 +47,24 @@ pub const LOOK: ParamId = ParamId("look");
/// The look's strength at which the LookTable is applied as the profile
/// states it, in percent.
pub const DEFAULT_LOOK: f32 = 100.0;
/// Twice the profile's look: Lightroom's *Amount* reaches the same.
pub const MAX_LOOK: f32 = 200.0;
pub const PROFILE_LOOK: f32 = 100.0;
/// The look's default strength: off.
///
/// Measured, not chosen. Against the photographer's earlier exports with no
/// look applied, the default rendering scores the same with the table at 100,
/// 50 or 0 (held-out MSE 140, 140, 143), and is 9 % more colourful without
/// it: the table desaturates near-neutral tones, which is exactly where the
/// default rendering was short of those exports. The table stays one slider
/// away for anyone who wants the profile's look.
pub const DEFAULT_LOOK: f32 = 0.0;
/// Twice the profile's look.
pub const MAX_LOOK: f32 = 2.0 * PROFILE_LOOK;
/// Entries of the buffer's header: one `vec4` describing each table —
/// `(hue divisions, saturation divisions, value divisions, sRGB-encoded)`,
/// zero hue divisions meaning absent — before the entries themselves.
pub const HEADER_ENTRIES: usize = 2;
/// Entries of the buffer's header, before the entries themselves: one
/// `vec4` describing each table — `(hue divisions, saturation divisions,
/// value divisions, sRGB-encoded)`, zero hue divisions meaning absent — and
/// a third whose `.x` is the tone curve's length (camera-profiles.md §12).
pub const HEADER_ENTRIES: usize = 3;
static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
Arc::new(OpDescriptor {
@@ -80,7 +90,7 @@ static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
/// Linear sRGB (the working space) to linear ProPhoto, and back, row-major,
/// each row scaled to sum to one so that working white is ProPhoto white
/// exactly and a neutral reaches the tables with zero saturation.
fn working_prophoto() -> &'static ([f32; 9], [f32; 9]) {
pub(crate) fn working_prophoto() -> &'static ([f32; 9], [f32; 9]) {
static M: LazyLock<([f32; 9], [f32; 9])> = LazyLock::new(|| {
let to = normalise_rows(dr_types::ColourSpace::ProPhoto.from_linear_srgb());
let back = normalise_rows(invert(&to).expect("ProPhoto's matrix is invertible"));
@@ -117,7 +127,7 @@ fn invert(m: &[f32; 9]) -> Option<[f32; 9]> {
Some(inv.map(|v| v as f32))
}
fn mul(m: &[f32; 9], c: [f32; 3]) -> [f32; 3] {
pub(crate) fn mul(m: &[f32; 9], c: [f32; 3]) -> [f32; 3] {
std::array::from_fn(|r| m[r * 3] * c[0] + m[r * 3 + 1] * c[1] + m[r * 3 + 2] * c[2])
}
@@ -127,19 +137,48 @@ fn wgsl_mat(m: &[f32; 9]) -> String {
format!("mat3x3<f32>({}, {}, {})", col(0), col(1), col(2))
}
static HELPERS: LazyLock<[Helper; 1]> = LazyLock::new(|| {
/// The working space to ProPhoto and back, as WGSL constants, and where
/// the profile buffer's sections begin. A helper of its own because the
/// view transform's DNG reference curve needs it too, and helpers are emitted
/// once each, in the order first asked for.
pub(crate) static PROPHOTO_HELPER: LazyLock<Helper> = LazyLock::new(|| {
let (to, back) = working_prophoto();
let source = format!(
"const PROFILE_FROM_WORKING = {};\nconst PROFILE_TO_WORKING = {};\n{LOOKUP_WGSL}",
"const PROFILE_FROM_WORKING = {};\nconst PROFILE_TO_WORKING = {};\n{SECTIONS_WGSL}",
wgsl_mat(to),
wgsl_mat(back)
);
[Helper {
name: "profile_apply",
Helper {
name: "profile_curve_base",
source: Box::leak(source.into_boxed_str()),
}]
}
});
static HELPERS: LazyLock<[Helper; 2]> = LazyLock::new(|| {
[
*PROPHOTO_HELPER,
Helper {
name: "profile_apply",
source: LOOKUP_WGSL,
},
]
});
/// Where each section of the profile buffer starts, from its header.
const SECTIONS_WGSL: &str = "
fn profile_entries(dims: vec4<f32>) -> u32 {
return u32(dims.x * dims.y * dims.z);
}
fn profile_look_base() -> u32 {
return 3u + profile_entries(profile_table[0]);
}
fn profile_curve_base() -> u32 {
return profile_look_base() + profile_entries(profile_table[1]);
}
";
/// The lookup, in WGSL. Mirrors [`apply_reference`] line for line.
const LOOKUP_WGSL: &str = r#"
fn profile_srgb_encode(v: f32) -> f32 {
@@ -327,12 +366,11 @@ if (hue_sat_dims.x > 0.0 || look_dims.x > 0.0) {
// A colour outside ProPhoto has no HSV the tables were made for; it
// passes through rather than being floored, which would clip it (D19).
if (min(p.r, min(p.g, p.b)) >= 0.0) {
let look_base = 2u + u32(hue_sat_dims.x * hue_sat_dims.y * hue_sat_dims.z);
if (hue_sat_dims.x > 0.0) {
p = profile_apply(hue_sat_dims, 2u, p, 1.0);
p = profile_apply(hue_sat_dims, 3u, p, 1.0);
}
if (look_dims.x > 0.0 && look > 0.0) {
p = profile_apply(look_dims, look_base, p, look);
p = profile_apply(look_dims, profile_look_base(), p, look);
}
c = PROFILE_TO_WORKING * p;
}
@@ -352,13 +390,16 @@ if (hue_sat_dims.x > 0.0 || look_dims.x > 0.0) {
}
}
/// TRACES: FR-DEV-3e
/// The storage buffer a source's tables are uploaded as: the two header
/// `vec4`s, the HueSatMap's entries, then the LookTable's, each entry
/// `(hue shift, saturation scale, value scale, 0)`.
/// TRACES: FR-DEV-3e | FR-DEV-3j
/// The storage buffer a source's profile is uploaded as: the three header
/// `vec4`s, the HueSatMap's entries, the LookTable's, each entry
/// `(hue shift, saturation scale, value scale, 0)`, then the tone curve's
/// samples in `.x`.
///
/// Two zero `vec4`s where there are no tables — the placeholder every source
/// without a profile binds, and what makes the fragment pass through.
/// The curve is always there: the profile's own where it has one, Camera
/// Raw's ACR3 default otherwise — including in the placeholder every source
/// without a profile binds, whose tables are absent, so a raw with no
/// profile still has the reference tone curve when it is chosen (D21).
pub fn profile_buffer(tables: Option<&ProfileTables>) -> Vec<[f32; 4]> {
let header = |t: Option<&HueSatTable>| match t {
Some(t) => [
@@ -371,10 +412,18 @@ pub fn profile_buffer(tables: Option<&ProfileTables>) -> Vec<[f32; 4]> {
};
let hue_sat = tables.and_then(|t| t.hue_sat.as_ref());
let look = tables.and_then(|t| t.look.as_ref());
let mut out = vec![header(hue_sat), header(look)];
let curve: &[f32] = tables
.and_then(|t| t.tone_curve.as_deref())
.unwrap_or(&dr_types::tone::ACR3_DEFAULT);
let mut out = vec![
header(hue_sat),
header(look),
[curve.len() as f32, 0.0, 0.0, 0.0],
];
for t in [hue_sat, look].into_iter().flatten() {
out.extend(t.entries.iter().map(|e| [e[0], e[1], e[2], 0.0]));
}
out.extend(curve.iter().map(|&v| [v, 0.0, 0.0, 0.0]));
out
}
@@ -536,6 +585,7 @@ mod tests {
origin: ProfileOrigin::Embedded,
hue_sat,
look,
tone_curve: None,
}
}
@@ -663,7 +713,10 @@ mod tests {
#[test]
fn the_buffer_puts_the_header_first_and_the_look_after_the_hue_sat_map() {
assert_eq!(profile_buffer(None), vec![[0.0; 4]; 2]);
let bare = profile_buffer(None);
assert_eq!(bare[..2], [[0.0; 4]; 2], "no tables");
assert_eq!(bare[2][0], 1025.0, "and the reference default curve");
assert_eq!(bare.len(), HEADER_ENTRIES + 1025);
let t = tables(
Some(uniform(2, 2, 1, [1.0, 2.0, 3.0])),
Some(uniform(3, 2, 2, [4.0, 5.0, 6.0])),
@@ -671,8 +724,9 @@ mod tests {
let b = profile_buffer(Some(&t));
assert_eq!(b[0], [2.0, 2.0, 1.0, 0.0]);
assert_eq!(b[1], [3.0, 2.0, 2.0, 0.0]);
assert_eq!(b.len(), HEADER_ENTRIES + 4 + 12);
assert_eq!(b[2], [1.0, 2.0, 3.0, 0.0]);
assert_eq!(b.len(), HEADER_ENTRIES + 4 + 12 + 1025);
assert_eq!(b[HEADER_ENTRIES], [1.0, 2.0, 3.0, 0.0]);
assert_eq!(b[HEADER_ENTRIES + 4], [4.0, 5.0, 6.0, 0.0]);
assert_eq!(b[HEADER_ENTRIES + 16][0], dr_types::tone::ACR3_DEFAULT[0]);
}
}
core/dr-pipeline/src/ops/colour_mixer.rs
+32 -6
View File
@@ -39,6 +39,16 @@ use crate::ops::helpers;
pub const ID: OpId = OpId("colour_mixer");
/// How much a raised saturation band adds to a muted colour, per unit of its
/// value; the push tapers linearly to nothing at full saturation.
///
/// Measured, not chosen. Fitted against the photographer's earlier exports
/// (two looks, ~90 photographs), a sky band raised by 58 there lifted muted
/// sky blues about 2.1×; at 3.0 a band here does about the same at the same
/// value, so imported values stay inside the slider's range (dr-preset-xmp
/// carries the per-band factors). Lowering saturation is unaffected.
pub const SAT_GAIN: f32 = 3.0;
/// The twelve bands, in hue order starting at red.
///
/// Twelve rather than Lightroom's eight: the extra bands fall between the
@@ -333,9 +343,16 @@ impl Operation for ColourMixer {
// Only the bands the user actually touched contribute code. A single
// adjusted band therefore costs one weight evaluation rather than
// twelve — the composition property applied within an operation.
let mut lines = String::from(
let lines = String::from(
"\
let hcl = rgb_to_hcl(c);
// Bands are matched, and saturation judged, on display-encoded values: in
// scene-linear light a muted colour reads as strongly saturated and its hue
// sits away from where it is seen, so a band set on what the photograph
// shows would land on other colours.
let lin = max(c, vec3<f32>(0.0));
let e = pow(lin, vec3<f32>(1.0 / 2.2));
let SAT_GAIN = @SAT_GAIN@;
let hcl = rgb_to_hcl(e);
let hue = hcl.x;
let chroma = hcl.y;
let hi = hcl.z;
@@ -350,6 +367,8 @@ if (chroma > 0.0001) {
",
);
let mut lines = lines.replace("@SAT_GAIN@", &format!("{SAT_GAIN:.4}"));
for (b, band) in BANDS.iter().enumerate() {
let v = self.values[b];
if v.iter().all(|x| *x == 0.0) {
@@ -384,10 +403,17 @@ if (chroma > 0.0001) {
// yellow-green to green, not enough to turn it blue by accident.
let new_hue = hue + d_hue * 30.0;
// Saturation scales chroma; luminance scales the whole colour.
let new_chroma = clamp(chroma * (1.0 + d_sat), 0.0, hi);
c = hue_to_rgb_scale(new_hue, new_chroma, hi);
c = c * exp2(d_lum);
// Saturation. Raising it pushes muted colours hardest and tapers to
// nothing at full saturation, as the eye expects a mixer to; the
// gain makes a value deliver the strength it names, measured
// against the photographer's earlier exports. Lowering it scales
// every colour alike, so -100 is grey.
let sat = chroma / max(hi, 0.00001);
let gain = select(1.0 + d_sat, 1.0 + d_sat * SAT_GAIN * (1.0 - sat), d_sat > 0.0);
let new_chroma = clamp(chroma * gain, 0.0, hi);
let shifted = hue_to_rgb_scale(new_hue, new_chroma, hi);
// Back to linear light, where luminance scales the whole colour.
c = pow(shifted, vec3<f32>(2.2)) * exp2(d_lum);
}
}
c = max(c, vec3<f32>(0.0));",
core/dr-pipeline/src/ops/view_transform.rs
+64 -8
View File
@@ -33,11 +33,33 @@ use crate::view::{Sigmoid, CONTRAST_RANGE, DEFAULT_CONTRAST, DEFAULT_WHITE, WHIT
pub const ID: OpId = OpId("view_transform");
pub const CONTRAST: ParamId = ParamId("contrast");
pub const WHITE: ParamId = ParamId("white");
/// TRACES: FR-DEV-3j
/// Which curve renders: the DNG reference (D21, the default) or D19's sigmoid.
///
/// The reference because it is the one that matches what the photographs were
/// first developed with: on 60 Lightroom exports with neutral settings it
/// renders their raws within MSE ~150 of Lightroom's own JPEGs at the default
/// contrast, where 0.20.0's sigmoid was ~1200 (darker by about 0.7 EV and
/// flatter). The sigmoid keeps index 0 because sidecars record the index.
pub const CURVE: ParamId = ParamId("curve");
static HELPERS: [Helper; 1] = [Helper {
name: "view_sigmoid",
source: crate::view::VIEW_SIGMOID_WGSL,
}];
/// [`CURVE`]'s values, in the order of its variants.
pub const SIGMOID: f32 = 0.0;
pub const CAMERA_RAW: f32 = 1.0;
static HELPERS: LazyLock<[Helper; 3]> = LazyLock::new(|| {
[
Helper {
name: "view_sigmoid",
source: crate::view::VIEW_SIGMOID_WGSL,
},
*crate::ops::camera_profile::PROPHOTO_HELPER,
Helper {
name: "camera_raw_tone",
source: crate::camera_raw::CAMERA_RAW_WGSL,
},
]
});
static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
Arc::new(OpDescriptor {
@@ -67,6 +89,15 @@ static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
Scale::Linear,
1,
),
ParamDescriptor::choice(
"curve",
"param.view_transform.curve",
vec![
LocalizedKey("param.view_transform.curve.sigmoid"),
LocalizedKey("param.view_transform.curve.camera_raw"),
],
)
.with_default(CAMERA_RAW),
],
})
});
@@ -75,6 +106,7 @@ static DESCRIPTOR: LazyLock<Arc<OpDescriptor>> = LazyLock::new(|| {
pub struct ViewTransform {
contrast: f32,
white: f32,
curve: f32,
}
impl Default for ViewTransform {
@@ -82,6 +114,7 @@ impl Default for ViewTransform {
Self {
contrast: DEFAULT_CONTRAST,
white: DEFAULT_WHITE,
curve: CAMERA_RAW,
}
}
}
@@ -106,6 +139,7 @@ impl Operation for ViewTransform {
match id {
CONTRAST => self.contrast = value,
WHITE => self.white = value,
CURVE => self.curve = value.round(),
_ => log::warn!("view_transform: unknown parameter {id}"),
}
}
@@ -114,12 +148,13 @@ impl Operation for ViewTransform {
match id {
CONTRAST => self.contrast,
WHITE => self.white,
CURVE => self.curve,
_ => 0.0,
}
}
fn is_active(&self) -> bool {
self.contrast != DEFAULT_CONTRAST || self.white != DEFAULT_WHITE
self.contrast != DEFAULT_CONTRAST || self.white != DEFAULT_WHITE || self.curve != CAMERA_RAW
}
fn stage(&self) -> Stage {
@@ -130,8 +165,13 @@ impl Operation for ViewTransform {
"\
// Skipped for an already-rendered source: a JPEG is a display rendering
// already, and rendering it again would compress it twice.
// D19's sigmoid by default, the DNG reference tone by choice (D21).
if (!non_linear) {
c = view_sigmoid(c, slope, inv_k, peak);
if (mode > 0.5) {
c = camera_raw_tone(c, cr_scale, cr_power, cr_grey);
} else {
c = view_sigmoid(c, slope, inv_k, peak);
}
}"
.into()
}
@@ -151,11 +191,27 @@ if (!non_linear) {
name: "peak",
value: s.w,
},
Uniform {
name: "mode",
value: self.curve,
},
Uniform {
name: "cr_scale",
value: crate::camera_raw::input_scale(self.white),
},
Uniform {
name: "cr_power",
value: crate::camera_raw::contrast_power(self.contrast),
},
Uniform {
name: "cr_grey",
value: crate::view::SCENE_GREY,
},
]
}
fn helpers(&self) -> &[Helper] {
&HELPERS
HELPERS.as_slice()
}
}
@@ -191,7 +247,7 @@ mod tests {
let s = Sigmoid::new(2.0, 6.0);
let u = op.uniforms();
assert_eq!(
u.iter().map(|u| u.value).collect::<Vec<_>>(),
u.iter().take(3).map(|u| u.value).collect::<Vec<_>>(),
vec![s.n, s.inv_k, s.w]
);
}
core/dr-pipeline/src/preset.rs
+141
View File
@@ -675,6 +675,56 @@ impl PresetLibrary {
self.unknown.values().map(Vec::len).sum()
}
/// TRACES: FR-DEV-6
/// Merge two copies of a library that both descend from `base`.
///
/// For keeping one library on several devices: `base` is what the last
/// exchange left both sides holding, `ours` this device's copy now and
/// `theirs` the server's. Each name is decided on its own:
///
/// - Changed on one side only — added, edited or deleted — that side's
/// answer stands. This is why the base is needed at all: without it a
/// preset deleted here and one added there look the same, and a
/// deletion would come back on every exchange.
/// - Changed on both sides to the same thing, nothing to decide.
/// - Deleted on one side and edited on the other, the edit stands. A
/// preset is work, and an absence is not.
/// - Edited on both sides differently, ours stands. Either answer loses
/// one edit; this one at least converges, since the other device takes
/// ours on its next exchange as an edit made on one side only.
///
/// A missing `base` is an empty one, which can only add: a device's first
/// exchange is a union of the two libraries, never a deletion.
pub fn merge(base: &Self, ours: &Self, theirs: &Self) -> Self {
let mut out = Self::default();
let names: std::collections::BTreeSet<&str> = ours.names().chain(theirs.names()).collect();
for name in names {
let (b, o, t) = (base.entry(name), ours.entry(name), theirs.entry(name));
let chosen = if o == t || t == b {
o
} else if o == b {
t
} else {
o.or(t)
};
if let Some((preset, unknown)) = chosen {
out.presets.insert(name.to_string(), preset.clone());
if let Some(lines) = unknown {
out.unknown.insert(name.to_string(), lines.clone());
}
}
}
out
}
/// One name's preset together with the lines kept beside it, which are
/// part of what that preset is when two copies are compared.
fn entry(&self, name: &str) -> Option<(&Preset, Option<&Vec<String>>)> {
self.presets
.get(name)
.map(|preset| (preset, self.unknown.get(name)))
}
/// Serialise to the on-disk form.
///
/// Deterministic, like the sidecar's: the same library always produces the
@@ -1479,6 +1529,97 @@ mod tests {
assert_eq!(other.to_text(), named().to_text());
}
/// A one-parameter preset, so two of them differ by their value.
fn exposure(ev: f32) -> Preset {
let mut params = BTreeMap::new();
params.insert(("exposure".to_string(), "exposure".to_string()), ev);
Preset::from_params(params)
}
fn library_of(entries: &[(&str, f32)]) -> PresetLibrary {
let mut lib = PresetLibrary::default();
for (name, ev) in entries {
lib.insert(name, exposure(*ev)).unwrap();
}
lib
}
#[test]
fn a_first_merge_is_the_union_of_both_libraries() {
let ours = library_of(&[("Mine", 1.0), ("Both", 0.5)]);
let theirs = library_of(&[("Theirs", 2.0), ("Both", 0.5)]);
let merged = PresetLibrary::merge(&PresetLibrary::default(), &ours, &theirs);
assert_eq!(
merged.names().collect::<Vec<_>>(),
vec!["Both", "Mine", "Theirs"]
);
}
#[test]
fn a_deletion_on_either_side_is_kept_rather_than_undone() {
// The case the base exists for: without it, the deleted preset is
// indistinguishable from one the other side has just added.
let base = library_of(&[("Gone here", 1.0), ("Gone there", 2.0)]);
let ours = library_of(&[("Gone there", 2.0)]);
let theirs = library_of(&[("Gone here", 1.0)]);
assert!(PresetLibrary::merge(&base, &ours, &theirs).is_empty());
}
#[test]
fn an_edit_on_one_side_reaches_the_other() {
let base = library_of(&[("Warm", 1.0)]);
let ours = library_of(&[("Warm", 1.0)]);
let theirs = library_of(&[("Warm", 1.5)]);
let merged = PresetLibrary::merge(&base, &ours, &theirs);
assert_eq!(merged.get("Warm"), Some(&exposure(1.5)));
// And the other way round.
let merged = PresetLibrary::merge(&base, &theirs, &ours);
assert_eq!(merged.get("Warm"), Some(&exposure(1.5)));
}
#[test]
fn an_edit_outlives_a_deletion_made_elsewhere() {
let base = library_of(&[("Warm", 1.0)]);
let edited = library_of(&[("Warm", 1.5)]);
let deleted = PresetLibrary::default();
assert_eq!(
PresetLibrary::merge(&base, &edited, &deleted).get("Warm"),
Some(&exposure(1.5))
);
assert_eq!(
PresetLibrary::merge(&base, &deleted, &edited).get("Warm"),
Some(&exposure(1.5))
);
}
#[test]
fn two_different_edits_keep_ours_and_then_converge() {
let base = library_of(&[("Warm", 1.0)]);
let here = library_of(&[("Warm", 1.5)]);
let there = library_of(&[("Warm", 0.5)]);
let pushed = PresetLibrary::merge(&base, &here, &there);
assert_eq!(pushed.get("Warm"), Some(&exposure(1.5)));
// The other device's next exchange: its base is what it last pushed,
// its own copy is unchanged since, and the server holds ours.
let settled = PresetLibrary::merge(&there, &there, &pushed);
assert_eq!(settled, pushed);
}
#[test]
fn lines_this_build_cannot_read_travel_with_their_preset() {
let text = format!(
"drpl {LIBRARY_FORMAT_VERSION}\n\n[preset Future]\nexposure.exposure = 0.5\n\
something_new_entirely\n"
);
let theirs = PresetLibrary::parse(&text).unwrap();
let merged = PresetLibrary::merge(
&PresetLibrary::default(),
&PresetLibrary::default(),
&theirs,
);
assert!(merged.to_text().contains("something_new_entirely"));
}
#[test]
fn a_neutral_preset_is_storable_and_survives_the_round_trip() {
// The empty preset is the "clear these forty frames" action, so it has
core/dr-pipeline/src/view.rs
+19 -6
View File
@@ -51,8 +51,19 @@ pub const SCENE_GREY: f32 = 0.13;
/// Display-linear middle grey — what a camera JPEG shows a grey card as.
pub const DISPLAY_GREY: f32 = 0.18;
/// The default contrast, the sigmoid's log-log slope parameter `n`.
pub const DEFAULT_CONTRAST: f32 = 1.4;
/// The default contrast: the sigmoid's log-log slope `n`, and for the DNG
/// reference curve a power of `DEFAULT_CONTRAST / REFERENCE_CONTRAST` about grey.
///
/// Fitted, not chosen: on Lightroom 6 exports whose look settings were neutral,
/// the DNG reference curve matched the exports best with the input bent by about
/// 1.08 (held-out MSE 224 at 1.4, ~150 at 1.5). The sigmoid, which is no longer
/// the default, fitted best near 1.7 and is better at 1.5 than at 1.4.
pub const DEFAULT_CONTRAST: f32 = 1.5;
/// The contrast at which each curve is its own reference: the sigmoid's match
/// to the retired base curve (see the module note), and the reference table
/// untouched.
pub const REFERENCE_CONTRAST: f32 = 1.4;
/// The default white point, in stops above [`SCENE_GREY`].
pub const DEFAULT_WHITE: f32 = 4.0;
@@ -223,11 +234,13 @@ mod tests {
}
#[test]
fn the_default_stays_close_to_the_retired_curve() {
fn the_reference_stays_close_to_the_retired_curve() {
// TRACES: FR-DEV-3j | FR-DEV-3e
// D19's promise to every existing photograph: the midtones do not
// move by more than a third of a stop.
let s = Sigmoid::default_curve();
// D19's promise, now kept by the sigmoid at its reference contrast
// rather than by the default (D21 moved the default to the DNG reference
// curve): the midtones do not move by more than a third of a stop
// from the retired base curve.
let s = Sigmoid::new(REFERENCE_CONTRAST, DEFAULT_WHITE);
let mut x = 0.03_f32;
while x <= 1.0 {
let ev = (s.channel(x) / retired_default(x)).log2();
core/dr-preset-xmp/src/lib.rs
+390 -28
View File
@@ -35,7 +35,7 @@
//! wrong on most images and invisibly so, which is worse than an honest gap,
//! so these keys are counted as skipped and reported.
//!
//! **Tone curves, colour mixing, masks, lens profiles and grain.** Each is a
//! **Tone curves, masks, lens profiles and grain.** Each is a
//! structure rather than a number, and each would need its own argument about
//! whether the two applications mean the same thing. They are skipped by
//! omission — a key not in the table is simply not understood — and the
@@ -83,35 +83,43 @@ const MAPPINGS: &[Mapping] = &[
param: "exposure",
convert: Convert::Direct,
},
// The five tone sliders do not mean the same thing in the two applications,
// whatever their shared ±100 suggests. The factors were fitted against the
// library's own Lightroom 6 exports and their raws (darkroom-lrfit, 2026-10):
// each photograph's sliders carried across as `slider × factor`, one factor
// per slider, on ~90 exports with no look applied. Contrast is ours at a
// tenth — at −100 ours flattens a frame to grey — and our shadows need
// nearly twice Lightroom's number. Whites barely appears in those exports;
// every fit put it under 1 but none agreed where, so 0.5 is a hedge.
Mapping {
crs: "Contrast2012",
op: "contrast",
param: "contrast",
convert: Convert::Direct,
convert: Convert::Scale(0.1),
},
Mapping {
crs: "Highlights2012",
op: "highlights_shadows",
param: "highlights",
convert: Convert::Direct,
convert: Convert::Scale(1.4),
},
Mapping {
crs: "Shadows2012",
op: "highlights_shadows",
param: "shadows",
convert: Convert::Direct,
convert: Convert::Scale(1.9),
},
Mapping {
crs: "Whites2012",
op: "blacks_whites",
param: "whites",
convert: Convert::Direct,
convert: Convert::Scale(0.5),
},
Mapping {
crs: "Blacks2012",
op: "blacks_whites",
param: "blacks",
convert: Convert::Direct,
convert: Convert::Scale(1.25),
},
Mapping {
crs: "Clarity2012",
@@ -137,6 +145,263 @@ const MAPPINGS: &[Mapping] = &[
param: "saturation",
convert: Convert::Direct,
},
// TRACES: FR-DEV-6
// Lightroom's HSL panel: eight bands, each ±100 for hue, saturation and
// luminance, onto the colour mixer's twelve.
//
// Hue and luminance go to the band of the same hue, one for one: Aqua
// (180°) is our cyan, Purple (270°) our violet.
//
// Saturation is measured. Fitted against the library's Lightroom 6 exports
// of two looks and their raws (darkroom-lrfit, hsl_map_fit), Lightroom's
// saturation bands are about 45° wide either side on our hue wheel, wider
// than ours, and do not all have our strength: each is shared between
// two or three of our bands with the factors below. Aqua sits at 187°
// and reaches into azure, where skies are; Blue at 251° reaches violet;
// Orange, where skin is, carries only ~0.4 — Lightroom's Orange is
// gentle. Red, Purple and Magenta barely appear in those exports and
// take the common gain; Green is capped where its few pixels would push
// it further. Values add when two Lightroom bands share one of ours.
Mapping {
crs: "HueAdjustmentRed",
op: "colour_mixer",
param: "red_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentRed",
op: "colour_mixer",
param: "red_sat",
convert: Convert::Scale(1.02),
},
Mapping {
crs: "SaturationAdjustmentRed",
op: "colour_mixer",
param: "orange_sat",
convert: Convert::Scale(0.34),
},
Mapping {
crs: "SaturationAdjustmentRed",
op: "colour_mixer",
param: "rose_sat",
convert: Convert::Scale(0.34),
},
Mapping {
crs: "LuminanceAdjustmentRed",
op: "colour_mixer",
param: "red_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentOrange",
op: "colour_mixer",
param: "orange_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentOrange",
op: "colour_mixer",
param: "orange_sat",
convert: Convert::Scale(0.39),
},
Mapping {
crs: "SaturationAdjustmentOrange",
op: "colour_mixer",
param: "red_sat",
convert: Convert::Scale(0.13),
},
Mapping {
crs: "SaturationAdjustmentOrange",
op: "colour_mixer",
param: "yellow_sat",
convert: Convert::Scale(0.13),
},
Mapping {
crs: "LuminanceAdjustmentOrange",
op: "colour_mixer",
param: "orange_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentYellow",
op: "colour_mixer",
param: "yellow_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentYellow",
op: "colour_mixer",
param: "yellow_sat",
convert: Convert::Scale(0.87),
},
Mapping {
crs: "SaturationAdjustmentYellow",
op: "colour_mixer",
param: "orange_sat",
convert: Convert::Scale(0.29),
},
Mapping {
crs: "SaturationAdjustmentYellow",
op: "colour_mixer",
param: "chartreuse_sat",
convert: Convert::Scale(0.29),
},
Mapping {
crs: "LuminanceAdjustmentYellow",
op: "colour_mixer",
param: "yellow_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentGreen",
op: "colour_mixer",
param: "green_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentGreen",
op: "colour_mixer",
param: "green_sat",
convert: Convert::Scale(1.2),
},
Mapping {
crs: "SaturationAdjustmentGreen",
op: "colour_mixer",
param: "chartreuse_sat",
convert: Convert::Scale(0.4),
},
Mapping {
crs: "SaturationAdjustmentGreen",
op: "colour_mixer",
param: "spring_sat",
convert: Convert::Scale(0.4),
},
Mapping {
crs: "LuminanceAdjustmentGreen",
op: "colour_mixer",
param: "green_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentAqua",
op: "colour_mixer",
param: "cyan_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentAqua",
op: "colour_mixer",
param: "cyan_sat",
convert: Convert::Scale(0.9),
},
Mapping {
crs: "SaturationAdjustmentAqua",
op: "colour_mixer",
param: "azure_sat",
convert: Convert::Scale(0.51),
},
Mapping {
crs: "SaturationAdjustmentAqua",
op: "colour_mixer",
param: "spring_sat",
convert: Convert::Scale(0.19),
},
Mapping {
crs: "LuminanceAdjustmentAqua",
op: "colour_mixer",
param: "cyan_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentBlue",
op: "colour_mixer",
param: "blue_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentBlue",
op: "colour_mixer",
param: "blue_sat",
convert: Convert::Scale(0.75),
},
Mapping {
crs: "SaturationAdjustmentBlue",
op: "colour_mixer",
param: "violet_sat",
convert: Convert::Scale(0.56),
},
Mapping {
crs: "SaturationAdjustmentBlue",
op: "colour_mixer",
param: "azure_sat",
convert: Convert::Scale(0.09),
},
Mapping {
crs: "LuminanceAdjustmentBlue",
op: "colour_mixer",
param: "blue_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentPurple",
op: "colour_mixer",
param: "violet_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentPurple",
op: "colour_mixer",
param: "violet_sat",
convert: Convert::Scale(1.26),
},
Mapping {
crs: "SaturationAdjustmentPurple",
op: "colour_mixer",
param: "blue_sat",
convert: Convert::Scale(0.42),
},
Mapping {
crs: "SaturationAdjustmentPurple",
op: "colour_mixer",
param: "magenta_sat",
convert: Convert::Scale(0.42),
},
Mapping {
crs: "LuminanceAdjustmentPurple",
op: "colour_mixer",
param: "violet_lum",
convert: Convert::Direct,
},
Mapping {
crs: "HueAdjustmentMagenta",
op: "colour_mixer",
param: "magenta_hue",
convert: Convert::Direct,
},
Mapping {
crs: "SaturationAdjustmentMagenta",
op: "colour_mixer",
param: "magenta_sat",
convert: Convert::Scale(1.05),
},
Mapping {
crs: "SaturationAdjustmentMagenta",
op: "colour_mixer",
param: "violet_sat",
convert: Convert::Scale(0.35),
},
Mapping {
crs: "SaturationAdjustmentMagenta",
op: "colour_mixer",
param: "rose_sat",
convert: Convert::Scale(0.35),
},
Mapping {
crs: "LuminanceAdjustmentMagenta",
op: "colour_mixer",
param: "magenta_lum",
convert: Convert::Direct,
},
// Adobe's sharpening runs 0…150 where ours runs 0…100, so a preset asking
// for its maximum gets ours rather than being clamped there silently.
Mapping {
@@ -192,6 +457,27 @@ pub enum ImportError {
NoSettings,
}
/// TRACES: FR-DEV-6
/// The Lightroom edit stored inside a photograph — the XMP packet Lightroom
/// writes into a DNG — translated as a preset is.
///
/// `None` where the file carries no packet, or one with no Camera Raw
/// settings in it (darktable's sidecars, a camera's own XMP). The packet is
/// found by its delimiters rather than by walking the TIFF structure: it is
/// plain text by specification, and the same search serves any container.
pub fn read_embedded(bytes: &[u8]) -> Option<Import> {
const OPEN: &[u8] = b"<x:xmpmeta";
const CLOSE: &[u8] = b"</x:xmpmeta>";
let start = find(bytes, OPEN)?;
let end = start + find(&bytes[start..], CLOSE)? + CLOSE.len();
let text = std::str::from_utf8(&bytes[start..end]).ok()?;
read_xmp(text).ok().filter(|i| !i.preset.is_empty())
}
fn find(haystack: &[u8], needle: &[u8]) -> Option<usize> {
haystack.windows(needle.len()).position(|w| w == needle)
}
/// Read one Lightroom `.xmp` preset.
///
/// Tolerant in the same direction the sidecar parser is: a value that will not
@@ -285,7 +571,12 @@ pub fn read_xmp(text: &str) -> Result<Import, ImportError> {
// Out-of-range values are left as they are: `EditGraph::set_param`
// clamps when the preset is applied, and clamping here as well would
// mean two places to be wrong about a range.
params.insert((mapping.op.to_string(), mapping.param.to_string()), value);
//
// Added rather than set: one of Lightroom's HSL bands is shared
// between two or three of ours, and two of its bands can share one.
*params
.entry((mapping.op.to_string(), mapping.param.to_string()))
.or_insert(0.0) += value;
}
let skipped = KNOWN_UNSUPPORTED
@@ -375,6 +666,65 @@ mod tests {
.copied()
}
#[test]
fn a_dngs_embedded_lightroom_edit_comes_across_with_its_hsl() {
// TRACES: FR-DEV-6
// The shape Lightroom 6 writes into a DNG, trimmed: the library's
// house look, as camera-profiles.md's D21 note records it.
let mut file = b"II*\0 binary header bytes ".to_vec();
file.extend_from_slice(
br#"<?xpacket begin="" id="W5M0MpCehiHzreSzNTczkc9d"?><x:xmpmeta xmlns:x="adobe:ns:meta/"><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"><rdf:Description rdf:about="" xmlns:crs="http://ns.adobe.com/camera-raw-settings/1.0/" crs:ProcessVersion="6.7" crs:Exposure2012="0.00" crs:Highlights2012="-40" crs:Blacks2012="-20" crs:SaturationAdjustmentBlue="+58" crs:SaturationAdjustmentAqua="+50" crs:SaturationAdjustmentPurple="+23" crs:HueAdjustmentRed="-5" crs:LuminanceAdjustmentGreen="+7"/></rdf:RDF></x:xmpmeta><?xpacket end="w"?>"#,
);
file.extend_from_slice(b"\0 more binary");
let import = read_embedded(&file).expect("an edit");
let p = import.preset.params();
let get = |op: &str, param: &str| p.get(&(op.to_string(), param.to_string())).copied();
// Saturation is shared out by the measured factors; values add.
let near = |got: Option<f32>, want: f32| {
let got = got.expect("set");
assert!((got - want).abs() < 1e-3, "{got} vs {want}");
};
near(get("colour_mixer", "blue_sat"), 58.0 * 0.75 + 23.0 * 0.42);
near(get("colour_mixer", "cyan_sat"), 50.0 * 0.9);
near(get("colour_mixer", "azure_sat"), 50.0 * 0.51 + 58.0 * 0.09);
near(get("colour_mixer", "violet_sat"), 58.0 * 0.56 + 23.0 * 1.26);
assert_eq!(get("colour_mixer", "red_hue"), Some(-5.0));
assert_eq!(get("colour_mixer", "green_lum"), Some(7.0));
assert_eq!(get("highlights_shadows", "highlights"), Some(-40.0 * 1.4));
assert_eq!(get("blacks_whites", "blacks"), Some(-20.0 * 1.25));
}
#[test]
fn the_libraries_dngs_carry_the_house_look() {
// TRACES: FR-DEV-6
// The library's own Lightroom 6 DNG, where it is on this machine.
let Some(home) = std::env::var_os("HOME") else {
return;
};
let path =
std::path::Path::new(&home).join("Nextcloud/PhotosRaw/2017/2017-08-12/_MG_9080.dng");
let Ok(bytes) = std::fs::read(&path) else {
eprintln!("skipped: no sample DNG at {}", path.display());
return;
};
let import = read_embedded(&bytes).expect("Lightroom's edit");
let p = import.preset.params();
let get = |op: &str, param: &str| p.get(&(op.to_string(), param.to_string())).copied();
// The house look's sky: Aqua and Blue land in cyan, azure and blue.
for (param, at_least) in [("cyan_sat", 40.0), ("azure_sat", 25.0), ("blue_sat", 40.0)] {
let v = get("colour_mixer", param).unwrap_or(0.0);
assert!(v >= at_least, "{param} {v}");
}
assert_eq!(get("highlights_shadows", "highlights"), Some(-40.0 * 1.4));
}
#[test]
fn a_file_with_no_camera_raw_settings_has_no_edit() {
assert!(read_embedded(b"no packet at all").is_none());
let darktable = br#"<x:xmpmeta xmlns:x="adobe:ns:meta/"><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"><rdf:Description rdf:about="" xmlns:xmp="http://ns.adobe.com/xap/1.0/" xmp:Rating="3"/></rdf:RDF></x:xmpmeta>"#;
assert!(read_embedded(darktable).is_none());
}
#[test]
fn every_mapping_names_a_parameter_this_build_actually_has() {
// The test that keeps the table honest. Adobe's half cannot be checked
@@ -397,36 +747,48 @@ mod tests {
}
#[test]
fn no_two_mappings_claim_the_same_key_or_the_same_target() {
let mut keys: Vec<&str> = MAPPINGS.iter().map(|m| m.crs).collect();
keys.sort_unstable();
let before = keys.len();
keys.dedup();
assert_eq!(before, keys.len(), "two mappings read the same crs key");
fn no_two_mappings_repeat_a_key_and_target() {
// A saturation band may be shared between several of ours, and two of
// Lightroom's may share one of ours (their values add); but the same
// key written twice to the same target would count it twice.
let mut pairs: Vec<(&str, &str, &str)> =
MAPPINGS.iter().map(|m| (m.crs, m.op, m.param)).collect();
pairs.sort_unstable();
let before = pairs.len();
pairs.dedup();
assert_eq!(before, pairs.len(), "a key is written twice to one target");
let mut targets: Vec<(&str, &str)> = MAPPINGS.iter().map(|m| (m.op, m.param)).collect();
targets.sort_unstable();
let before = targets.len();
targets.dedup();
assert_eq!(
before,
targets.len(),
"two mappings write the same parameter"
);
// Only the HSL saturation bands are shared; every other key has one
// home, so a slip in the table cannot fan a slider out unnoticed.
let mut single: Vec<&str> = MAPPINGS
.iter()
.map(|m| m.crs)
.filter(|k| !k.starts_with("SaturationAdjustment"))
.collect();
single.sort_unstable();
let before = single.len();
single.dedup();
assert_eq!(before, single.len(), "two mappings read the same crs key");
}
#[test]
fn the_settings_that_share_a_convention_come_across_unchanged() {
let import = read_xmp(ATTRIBUTE_FORM).unwrap();
assert_eq!(value(&import, "exposure", "exposure"), Some(0.75));
assert_eq!(value(&import, "contrast", "contrast"), Some(25.0));
assert_eq!(value(&import, "contrast", "contrast"), Some(25.0 * 0.1));
assert_eq!(
value(&import, "highlights_shadows", "highlights"),
Some(-40.0)
Some(-40.0 * 1.4)
);
assert_eq!(
value(&import, "highlights_shadows", "shadows"),
Some(30.0 * 1.9)
);
assert_eq!(value(&import, "blacks_whites", "whites"), Some(10.0 * 0.5));
assert_eq!(
value(&import, "blacks_whites", "blacks"),
Some(-15.0 * 1.25)
);
assert_eq!(value(&import, "highlights_shadows", "shadows"), Some(30.0));
assert_eq!(value(&import, "blacks_whites", "whites"), Some(10.0));
assert_eq!(value(&import, "blacks_whites", "blacks"), Some(-15.0));
assert_eq!(value(&import, "clarity", "amount"), Some(12.0));
assert_eq!(value(&import, "texture", "amount"), Some(8.0));
assert_eq!(value(&import, "vibrance", "vibrance"), Some(20.0));
@@ -467,7 +829,7 @@ mod tests {
// does not say which shape it used.
let import = read_xmp(ELEMENT_FORM).unwrap();
assert_eq!(value(&import, "exposure", "exposure"), Some(0.75));
assert_eq!(value(&import, "contrast", "contrast"), Some(25.0));
assert_eq!(value(&import, "contrast", "contrast"), Some(25.0 * 0.1));
}
#[test]
core/dr-segment/build.rs
+14 -4
View File
@@ -13,9 +13,11 @@
use std::path::Path;
const MODEL: &str = "../../models/segment/yolo26n-seg.onnx";
const QUANTISED: &str = "../../models/segment/yolo26n-seg.a16w16.onnx";
fn main() {
println!("cargo:rerun-if-changed={MODEL}");
println!("cargo:rerun-if-changed={QUANTISED}");
println!("cargo:rerun-if-changed=build.rs");
// Only the embedded path needs the file present; a build without it is
@@ -24,10 +26,18 @@ fn main() {
return;
}
let path = Path::new(MODEL);
check(MODEL);
// The Hexagon's quantised form rides only in an Android build.
if std::env::var("CARGO_CFG_TARGET_OS").as_deref() == Ok("android") {
check(QUANTISED);
}
}
fn check(model: &str) {
let path = Path::new(model);
let Ok(bytes) = std::fs::read(path) else {
panic!(
"\n\n{MODEL} is missing.\n\
"\n\n{model} is missing.\n\
It ships in Git LFS. Run `git lfs install && git lfs pull`, or build \
with `--no-default-features` for a watershed-only build.\n"
);
@@ -40,7 +50,7 @@ fn main() {
// happens in practice.
if bytes.starts_with(b"version https://git-lfs") {
panic!(
"\n\n{MODEL} is a Git LFS pointer, not the model ({} bytes).\n\
"\n\n{model} is a Git LFS pointer, not the model ({} bytes).\n\
Run `git lfs install && git lfs pull` to fetch the real file.\n",
bytes.len()
);
@@ -50,7 +60,7 @@ fn main() {
// export is ~11 MB; anything under a megabyte is a truncated checkout.
if bytes.len() < 1_000_000 {
panic!(
"\n\n{MODEL} is only {} bytes — expected ~11 MB.\n\
"\n\n{model} is only {} bytes — expected several MB.\n\
The checkout looks incomplete; try `git lfs pull`.\n",
bytes.len()
);
core/dr-segment/src/lib.rs
+1 -1
View File
@@ -72,7 +72,7 @@ pub use refine::{
#[cfg(feature = "semantic")]
pub use scene::{Category, Scene, SceneModel};
#[cfg(feature = "embedded-model")]
pub use semantic::embedded_model_bytes;
pub use semantic::embedded_models;
#[cfg(feature = "semantic")]
pub use semantic::{Instance, SemanticModel, SemanticOptions, Tiling};
core/dr-segment/src/scene.rs
+15 -7
View File
@@ -123,21 +123,29 @@ impl SceneModel {
classes: impl AsRef<std::path::Path>,
categories: impl AsRef<std::path::Path>,
) -> Result<Self, SegmentError> {
// The form the device's backend runs: the `.a16w16.onnx` sibling on
// the Hexagon (attention left in float, inference.md §1.5), else this.
let (model, form) =
dr_inference_engine::resolve_model(dr_inference_engine::Role::Scene, model.as_ref());
let bytes = std::fs::read(model).map_err(SegmentError::ModelRead)?;
let classes = std::fs::read_to_string(classes).map_err(SegmentError::ModelRead)?;
let categories = std::fs::read_to_string(categories).map_err(SegmentError::ModelRead)?;
let classes = crate::semantic::parse_classes(&classes);
let categories = parse_categories(&categories, &classes)?;
Self::from_bytes(&bytes, categories)
Self::from_bytes_in(&bytes, form, categories)
}
pub fn from_bytes(bytes: &[u8], categories: Vec<Category>) -> Result<Self, SegmentError> {
// f32, as for `SemanticModel`; see there.
let session = dr_inference_engine::open(
dr_inference_engine::Role::Scene,
dr_inference_engine::Form::F32,
bytes,
)?;
Self::from_bytes_in(bytes, dr_inference_engine::Form::F32, categories)
}
/// `bytes` in a stated numeric form; the outputs keep their shape.
pub fn from_bytes_in(
bytes: &[u8],
form: dr_inference_engine::Form,
categories: Vec<Category>,
) -> Result<Self, SegmentError> {
let session = dr_inference_engine::open(dr_inference_engine::Role::Scene, form, bytes)?;
Ok(Self {
session,
core/dr-segment/src/semantic.rs
+31 -13
View File
@@ -208,18 +208,32 @@ const EMBEDDED_MODEL: &[u8] = include_bytes!("../../../models/segment/yolo26n-se
#[cfg(feature = "embedded-model")]
const EMBEDDED_CLASSES: &str = include_str!("../../../models/segment/yolo26n-seg.classes.json");
/// The bytes of the model that ships with this crate, for whoever compiles
/// The Hexagon's form (docs/dev/inference.md §1.5): 16-bit activations and
/// weights, the rows' tail left in float. Only Android has a Hexagon, so only
/// Android carries it.
#[cfg(all(feature = "embedded-model", target_os = "android"))]
const EMBEDDED_A16W16: &[u8] = include_bytes!("../../../models/segment/yolo26n-seg.a16w16.onnx");
/// Every form of the model that ships with this crate, for whoever compiles
/// engines ahead of the first request (docs/dev/inference.md §6).
#[cfg(feature = "embedded-model")]
pub fn embedded_model_bytes() -> &'static [u8] {
EMBEDDED_MODEL
pub fn embedded_models() -> Vec<(dr_inference_engine::Form, &'static [u8])> {
#[allow(unused_mut)]
let mut forms = vec![(dr_inference_engine::Form::F32, EMBEDDED_MODEL)];
#[cfg(target_os = "android")]
forms.push((dr_inference_engine::Form::A16W16, EMBEDDED_A16W16));
forms
}
impl SemanticModel {
/// Load the model that ships with this crate.
/// Load the model that ships with this crate, in the form the device's
/// backend runs.
#[cfg(feature = "embedded-model")]
pub fn embedded() -> Result<Self, SegmentError> {
Self::from_bytes(EMBEDDED_MODEL, parse_classes(EMBEDDED_CLASSES))
let forms = embedded_models();
let (bytes, form) =
dr_inference_engine::choose_embedded(dr_inference_engine::Role::Segmenter, &forms);
Self::from_bytes_in(bytes, form, parse_classes(EMBEDDED_CLASSES))
}
/// Load a model from an ONNX file, with `classes` supplying its vocabulary.
@@ -236,14 +250,18 @@ impl SemanticModel {
}
pub fn from_bytes(bytes: &[u8], classes: Vec<Arc<str>>) -> Result<Self, SegmentError> {
// The f32 graph on whatever the device's backend is. An int8 form
// for the Hexagon waits on docs/dev/inference.md §10 M7 — the mask
// boundary has to be measured before it moves.
let session = dr_inference_engine::open(
dr_inference_engine::Role::Segmenter,
dr_inference_engine::Form::F32,
bytes,
)?;
Self::from_bytes_in(bytes, dr_inference_engine::Form::F32, classes)
}
/// `bytes` in a stated numeric form. The quantised one keeps the same
/// outputs (the rows' tail stays float), so decoding does not change; the
/// masks it draws were measured against f32's (inference.md §1.5).
pub fn from_bytes_in(
bytes: &[u8],
form: dr_inference_engine::Form,
classes: Vec<Arc<str>>,
) -> Result<Self, SegmentError> {
let session = dr_inference_engine::open(dr_inference_engine::Role::Segmenter, form, bytes)?;
Ok(Self { session, classes })
}
core/dr-types/src/hue_sat.rs
+6 -1
View File
@@ -115,6 +115,11 @@ pub struct ProfileTables {
pub origin: ProfileOrigin,
pub hue_sat: Option<HueSatTable>,
pub look: Option<HueSatTable>,
/// The profile's `ProfileToneCurve`, resampled onto
/// [`crate::tone::TONE_SAMPLES`] points; `None` where it has none, and
/// the view transform's DNG reference curve then uses
/// [`crate::tone::ACR3_DEFAULT`] (D21).
pub tone_curve: Option<Vec<f32>>,
}
/// Where a profile was found (camera-profiles.md §4).
@@ -129,7 +134,7 @@ pub enum ProfileOrigin {
impl ProfileTables {
/// Whether there is anything to apply.
pub fn is_empty(&self) -> bool {
self.hue_sat.is_none() && self.look.is_none()
self.hue_sat.is_none() && self.look.is_none() && self.tone_curve.is_none()
}
}
core/dr-types/src/lib.rs
+1
View File
@@ -14,6 +14,7 @@ pub mod place;
pub mod selector;
pub mod settings;
pub mod time;
pub mod tone;
pub use colour::{Chromaticities, Transfer};
pub use hue_sat::{HueSatTable, ProfileOrigin, ProfileTables};
core/dr-types/src/settings.rs
+28 -9
View File
@@ -275,13 +275,26 @@ impl FaceDetector {
}
}
/// Both ids this detector writes under — the f32 form and the int8 one —
/// for a question that is about the detector and not about which form
/// of it a device happened to run: "has the chosen detector been over
/// this image", asked by a re-index that must not ping-pong between a
/// desktop that runs it in f32 and a tablet that runs it on the Hexagon.
pub fn model_ids(self) -> [&'static str; 2] {
[self.model_id(), self.model_id_int8()]
/// The id when the detector runs with 16-bit activations and 8-bit
/// weights, the Hexagon's form since the int8 one lost faces at 40–80 px
/// (docs/dev/inference.md §1.5). Different again from both, for the same
/// reason as [`Self::model_id_int8`]; the embedder half is unchanged.
pub fn model_id_a16w8(self) -> &'static str {
match self {
FaceDetector::Scrfd500m => "scrfd_500m_a16+w600k_mbf",
FaceDetector::Scrfd2_5g => "scrfd_2.5g_a16+w600k_mbf",
FaceDetector::Scrfd10g => "scrfd_10g_a16+w600k_mbf",
}
}
/// Every id this detector writes under — the f32 form and each quantised
/// one a device has run — for a question that is about the detector and
/// not about which form of it a device happened to run: "has the chosen
/// detector been over this image", asked by a re-index that must not
/// ping-pong between a desktop that runs it in f32 and a tablet that
/// runs it on the Hexagon.
pub fn model_ids(self) -> [&'static str; 3] {
[self.model_id(), self.model_id_int8(), self.model_id_a16w8()]
}
/// The detector that writes under a pipeline id, if it is one of these.
@@ -1769,11 +1782,16 @@ mod tests {
#[test]
fn a_detectors_two_spellings_share_its_embedder_and_nothing_else() {
for d in FaceDetector::ALL {
let [f32_id, int8_id] = d.model_ids();
let [f32_id, int8_id, a16_id] = d.model_ids();
assert_eq!(f32_id, d.model_id());
assert_eq!(int8_id, d.model_id_int8());
assert_eq!(a16_id, d.model_id_a16w8());
assert_ne!(f32_id, int8_id);
assert_eq!(f32_id.rsplit('+').next(), int8_id.rsplit('+').next());
assert_ne!(f32_id, a16_id);
assert_ne!(int8_id, a16_id);
for q in [int8_id, a16_id] {
assert_eq!(f32_id.rsplit('+').next(), q.rsplit('+').next());
}
}
}
@@ -1797,6 +1815,7 @@ mod tests {
}
assert_eq!(FaceDetector::for_model_id(d.model_id()), Some(d));
assert_eq!(FaceDetector::for_model_id(d.model_id_int8()), Some(d));
assert_eq!(FaceDetector::for_model_id(d.model_id_a16w8()), Some(d));
}
assert_eq!(FaceDetector::for_model_id("scrfd_10g+other"), None);
}
core/dr-types/src/tone.rs
+284
View File
@@ -0,0 +1,284 @@
//! TRACES: FR-DEV-3j | FR-DEV-3e
//! The DNG SDK's reference tone curve, as data (D21).
//!
//! # Where the numbers come from
//!
//! [`ACR3_DEFAULT`] is the "ACR3 default" tone curve of Adobe's DNG SDK
//! (`dng_tone_curve_acr3_default`): what the SDK's reference renders a raw through
//! when its camera profile carries no `ProfileToneCurve` of its own, which is
//! true of Adobe Standard. The values are RawTherapee's
//! `adobe_camera_raw_default_curve` (`rtengine/dcp.cc`, GPLv3), copied
//! digit for digit; `the_default_curve_is_rawtherapees` checks a sample of
//! them.
//!
//! It maps linear values to linear values, 1025 samples evenly over
//! `[0, 1]`, interpolated linearly between them. See
//! `docs/dev/camera-profiles.md` §12 for how it is applied — on the largest
//! and smallest channel, not on each — which is half of what it does.
/// Samples in a resolved tone curve: the ACR3 table's own resolution, and
/// what a profile's curve is resampled onto.
pub const TONE_SAMPLES: usize = 1025;
/// The ACR3 default tone curve, linear in, linear out.
///
/// `approx_constant` is allowed because one sample is 0.70711, which clippy
/// takes for an approximation of 1/√2. It is a measured value of the curve,
/// copied as published; replacing it with the constant would change it.
#[rustfmt::skip]
#[allow(clippy::approx_constant)]
pub const ACR3_DEFAULT: [f32; TONE_SAMPLES] = [
0.00000, 0.00078, 0.00160, 0.00242, 0.00314, 0.00385, 0.00460, 0.00539,
0.00623, 0.00712, 0.00806, 0.00906, 0.01012, 0.01122, 0.01238, 0.01359,
0.01485, 0.01616, 0.01751, 0.01890, 0.02033, 0.02180, 0.02331, 0.02485,
0.02643, 0.02804, 0.02967, 0.03134, 0.03303, 0.03475, 0.03648, 0.03824,
0.04002, 0.04181, 0.04362, 0.04545, 0.04730, 0.04916, 0.05103, 0.05292,
0.05483, 0.05675, 0.05868, 0.06063, 0.06259, 0.06457, 0.06655, 0.06856,
0.07057, 0.07259, 0.07463, 0.07668, 0.07874, 0.08081, 0.08290, 0.08499,
0.08710, 0.08921, 0.09134, 0.09348, 0.09563, 0.09779, 0.09996, 0.10214,
0.10433, 0.10652, 0.10873, 0.11095, 0.11318, 0.11541, 0.11766, 0.11991,
0.12218, 0.12445, 0.12673, 0.12902, 0.13132, 0.13363, 0.13595, 0.13827,
0.14061, 0.14295, 0.14530, 0.14765, 0.15002, 0.15239, 0.15477, 0.15716,
0.15956, 0.16197, 0.16438, 0.16680, 0.16923, 0.17166, 0.17410, 0.17655,
0.17901, 0.18148, 0.18395, 0.18643, 0.18891, 0.19141, 0.19391, 0.19641,
0.19893, 0.20145, 0.20398, 0.20651, 0.20905, 0.21160, 0.21416, 0.21672,
0.21929, 0.22185, 0.22440, 0.22696, 0.22950, 0.23204, 0.23458, 0.23711,
0.23963, 0.24215, 0.24466, 0.24717, 0.24967, 0.25216, 0.25465, 0.25713,
0.25961, 0.26208, 0.26454, 0.26700, 0.26945, 0.27189, 0.27433, 0.27676,
0.27918, 0.28160, 0.28401, 0.28641, 0.28881, 0.29120, 0.29358, 0.29596,
0.29833, 0.30069, 0.30305, 0.30540, 0.30774, 0.31008, 0.31241, 0.31473,
0.31704, 0.31935, 0.32165, 0.32395, 0.32623, 0.32851, 0.33079, 0.33305,
0.33531, 0.33756, 0.33981, 0.34205, 0.34428, 0.34650, 0.34872, 0.35093,
0.35313, 0.35532, 0.35751, 0.35969, 0.36187, 0.36404, 0.36620, 0.36835,
0.37050, 0.37264, 0.37477, 0.37689, 0.37901, 0.38112, 0.38323, 0.38533,
0.38742, 0.38950, 0.39158, 0.39365, 0.39571, 0.39777, 0.39982, 0.40186,
0.40389, 0.40592, 0.40794, 0.40996, 0.41197, 0.41397, 0.41596, 0.41795,
0.41993, 0.42191, 0.42388, 0.42584, 0.42779, 0.42974, 0.43168, 0.43362,
0.43554, 0.43747, 0.43938, 0.44129, 0.44319, 0.44509, 0.44698, 0.44886,
0.45073, 0.45260, 0.45447, 0.45632, 0.45817, 0.46002, 0.46186, 0.46369,
0.46551, 0.46733, 0.46914, 0.47095, 0.47275, 0.47454, 0.47633, 0.47811,
0.47989, 0.48166, 0.48342, 0.48518, 0.48693, 0.48867, 0.49041, 0.49214,
0.49387, 0.49559, 0.49730, 0.49901, 0.50072, 0.50241, 0.50410, 0.50579,
0.50747, 0.50914, 0.51081, 0.51247, 0.51413, 0.51578, 0.51742, 0.51906,
0.52069, 0.52232, 0.52394, 0.52556, 0.52717, 0.52878, 0.53038, 0.53197,
0.53356, 0.53514, 0.53672, 0.53829, 0.53986, 0.54142, 0.54297, 0.54452,
0.54607, 0.54761, 0.54914, 0.55067, 0.55220, 0.55371, 0.55523, 0.55673,
0.55824, 0.55973, 0.56123, 0.56271, 0.56420, 0.56567, 0.56715, 0.56861,
0.57007, 0.57153, 0.57298, 0.57443, 0.57587, 0.57731, 0.57874, 0.58017,
0.58159, 0.58301, 0.58443, 0.58583, 0.58724, 0.58864, 0.59003, 0.59142,
0.59281, 0.59419, 0.59556, 0.59694, 0.59830, 0.59966, 0.60102, 0.60238,
0.60373, 0.60507, 0.60641, 0.60775, 0.60908, 0.61040, 0.61173, 0.61305,
0.61436, 0.61567, 0.61698, 0.61828, 0.61957, 0.62087, 0.62216, 0.62344,
0.62472, 0.62600, 0.62727, 0.62854, 0.62980, 0.63106, 0.63232, 0.63357,
0.63482, 0.63606, 0.63730, 0.63854, 0.63977, 0.64100, 0.64222, 0.64344,
0.64466, 0.64587, 0.64708, 0.64829, 0.64949, 0.65069, 0.65188, 0.65307,
0.65426, 0.65544, 0.65662, 0.65779, 0.65897, 0.66013, 0.66130, 0.66246,
0.66362, 0.66477, 0.66592, 0.66707, 0.66821, 0.66935, 0.67048, 0.67162,
0.67275, 0.67387, 0.67499, 0.67611, 0.67723, 0.67834, 0.67945, 0.68055,
0.68165, 0.68275, 0.68385, 0.68494, 0.68603, 0.68711, 0.68819, 0.68927,
0.69035, 0.69142, 0.69249, 0.69355, 0.69461, 0.69567, 0.69673, 0.69778,
0.69883, 0.69988, 0.70092, 0.70196, 0.70300, 0.70403, 0.70506, 0.70609,
0.70711, 0.70813, 0.70915, 0.71017, 0.71118, 0.71219, 0.71319, 0.71420,
0.71520, 0.71620, 0.71719, 0.71818, 0.71917, 0.72016, 0.72114, 0.72212,
0.72309, 0.72407, 0.72504, 0.72601, 0.72697, 0.72794, 0.72890, 0.72985,
0.73081, 0.73176, 0.73271, 0.73365, 0.73460, 0.73554, 0.73647, 0.73741,
0.73834, 0.73927, 0.74020, 0.74112, 0.74204, 0.74296, 0.74388, 0.74479,
0.74570, 0.74661, 0.74751, 0.74842, 0.74932, 0.75021, 0.75111, 0.75200,
0.75289, 0.75378, 0.75466, 0.75555, 0.75643, 0.75730, 0.75818, 0.75905,
0.75992, 0.76079, 0.76165, 0.76251, 0.76337, 0.76423, 0.76508, 0.76594,
0.76679, 0.76763, 0.76848, 0.76932, 0.77016, 0.77100, 0.77183, 0.77267,
0.77350, 0.77432, 0.77515, 0.77597, 0.77680, 0.77761, 0.77843, 0.77924,
0.78006, 0.78087, 0.78167, 0.78248, 0.78328, 0.78408, 0.78488, 0.78568,
0.78647, 0.78726, 0.78805, 0.78884, 0.78962, 0.79040, 0.79118, 0.79196,
0.79274, 0.79351, 0.79428, 0.79505, 0.79582, 0.79658, 0.79735, 0.79811,
0.79887, 0.79962, 0.80038, 0.80113, 0.80188, 0.80263, 0.80337, 0.80412,
0.80486, 0.80560, 0.80634, 0.80707, 0.80780, 0.80854, 0.80926, 0.80999,
0.81072, 0.81144, 0.81216, 0.81288, 0.81360, 0.81431, 0.81503, 0.81574,
0.81645, 0.81715, 0.81786, 0.81856, 0.81926, 0.81996, 0.82066, 0.82135,
0.82205, 0.82274, 0.82343, 0.82412, 0.82480, 0.82549, 0.82617, 0.82685,
0.82753, 0.82820, 0.82888, 0.82955, 0.83022, 0.83089, 0.83155, 0.83222,
0.83288, 0.83354, 0.83420, 0.83486, 0.83552, 0.83617, 0.83682, 0.83747,
0.83812, 0.83877, 0.83941, 0.84005, 0.84069, 0.84133, 0.84197, 0.84261,
0.84324, 0.84387, 0.84450, 0.84513, 0.84576, 0.84639, 0.84701, 0.84763,
0.84825, 0.84887, 0.84949, 0.85010, 0.85071, 0.85132, 0.85193, 0.85254,
0.85315, 0.85375, 0.85436, 0.85496, 0.85556, 0.85615, 0.85675, 0.85735,
0.85794, 0.85853, 0.85912, 0.85971, 0.86029, 0.86088, 0.86146, 0.86204,
0.86262, 0.86320, 0.86378, 0.86435, 0.86493, 0.86550, 0.86607, 0.86664,
0.86720, 0.86777, 0.86833, 0.86889, 0.86945, 0.87001, 0.87057, 0.87113,
0.87168, 0.87223, 0.87278, 0.87333, 0.87388, 0.87443, 0.87497, 0.87552,
0.87606, 0.87660, 0.87714, 0.87768, 0.87821, 0.87875, 0.87928, 0.87981,
0.88034, 0.88087, 0.88140, 0.88192, 0.88244, 0.88297, 0.88349, 0.88401,
0.88453, 0.88504, 0.88556, 0.88607, 0.88658, 0.88709, 0.88760, 0.88811,
0.88862, 0.88912, 0.88963, 0.89013, 0.89063, 0.89113, 0.89163, 0.89212,
0.89262, 0.89311, 0.89360, 0.89409, 0.89458, 0.89507, 0.89556, 0.89604,
0.89653, 0.89701, 0.89749, 0.89797, 0.89845, 0.89892, 0.89940, 0.89987,
0.90035, 0.90082, 0.90129, 0.90176, 0.90222, 0.90269, 0.90316, 0.90362,
0.90408, 0.90454, 0.90500, 0.90546, 0.90592, 0.90637, 0.90683, 0.90728,
0.90773, 0.90818, 0.90863, 0.90908, 0.90952, 0.90997, 0.91041, 0.91085,
0.91130, 0.91173, 0.91217, 0.91261, 0.91305, 0.91348, 0.91392, 0.91435,
0.91478, 0.91521, 0.91564, 0.91606, 0.91649, 0.91691, 0.91734, 0.91776,
0.91818, 0.91860, 0.91902, 0.91944, 0.91985, 0.92027, 0.92068, 0.92109,
0.92150, 0.92191, 0.92232, 0.92273, 0.92314, 0.92354, 0.92395, 0.92435,
0.92475, 0.92515, 0.92555, 0.92595, 0.92634, 0.92674, 0.92713, 0.92753,
0.92792, 0.92831, 0.92870, 0.92909, 0.92947, 0.92986, 0.93025, 0.93063,
0.93101, 0.93139, 0.93177, 0.93215, 0.93253, 0.93291, 0.93328, 0.93366,
0.93403, 0.93440, 0.93478, 0.93515, 0.93551, 0.93588, 0.93625, 0.93661,
0.93698, 0.93734, 0.93770, 0.93807, 0.93843, 0.93878, 0.93914, 0.93950,
0.93986, 0.94021, 0.94056, 0.94092, 0.94127, 0.94162, 0.94197, 0.94231,
0.94266, 0.94301, 0.94335, 0.94369, 0.94404, 0.94438, 0.94472, 0.94506,
0.94540, 0.94573, 0.94607, 0.94641, 0.94674, 0.94707, 0.94740, 0.94774,
0.94807, 0.94839, 0.94872, 0.94905, 0.94937, 0.94970, 0.95002, 0.95035,
0.95067, 0.95099, 0.95131, 0.95163, 0.95194, 0.95226, 0.95257, 0.95289,
0.95320, 0.95351, 0.95383, 0.95414, 0.95445, 0.95475, 0.95506, 0.95537,
0.95567, 0.95598, 0.95628, 0.95658, 0.95688, 0.95718, 0.95748, 0.95778,
0.95808, 0.95838, 0.95867, 0.95897, 0.95926, 0.95955, 0.95984, 0.96013,
0.96042, 0.96071, 0.96100, 0.96129, 0.96157, 0.96186, 0.96214, 0.96242,
0.96271, 0.96299, 0.96327, 0.96355, 0.96382, 0.96410, 0.96438, 0.96465,
0.96493, 0.96520, 0.96547, 0.96574, 0.96602, 0.96629, 0.96655, 0.96682,
0.96709, 0.96735, 0.96762, 0.96788, 0.96815, 0.96841, 0.96867, 0.96893,
0.96919, 0.96945, 0.96971, 0.96996, 0.97022, 0.97047, 0.97073, 0.97098,
0.97123, 0.97149, 0.97174, 0.97199, 0.97223, 0.97248, 0.97273, 0.97297,
0.97322, 0.97346, 0.97371, 0.97395, 0.97419, 0.97443, 0.97467, 0.97491,
0.97515, 0.97539, 0.97562, 0.97586, 0.97609, 0.97633, 0.97656, 0.97679,
0.97702, 0.97725, 0.97748, 0.97771, 0.97794, 0.97817, 0.97839, 0.97862,
0.97884, 0.97907, 0.97929, 0.97951, 0.97973, 0.97995, 0.98017, 0.98039,
0.98061, 0.98082, 0.98104, 0.98125, 0.98147, 0.98168, 0.98189, 0.98211,
0.98232, 0.98253, 0.98274, 0.98295, 0.98315, 0.98336, 0.98357, 0.98377,
0.98398, 0.98418, 0.98438, 0.98458, 0.98478, 0.98498, 0.98518, 0.98538,
0.98558, 0.98578, 0.98597, 0.98617, 0.98636, 0.98656, 0.98675, 0.98694,
0.98714, 0.98733, 0.98752, 0.98771, 0.98789, 0.98808, 0.98827, 0.98845,
0.98864, 0.98882, 0.98901, 0.98919, 0.98937, 0.98955, 0.98973, 0.98991,
0.99009, 0.99027, 0.99045, 0.99063, 0.99080, 0.99098, 0.99115, 0.99133,
0.99150, 0.99167, 0.99184, 0.99201, 0.99218, 0.99235, 0.99252, 0.99269,
0.99285, 0.99302, 0.99319, 0.99335, 0.99351, 0.99368, 0.99384, 0.99400,
0.99416, 0.99432, 0.99448, 0.99464, 0.99480, 0.99495, 0.99511, 0.99527,
0.99542, 0.99558, 0.99573, 0.99588, 0.99603, 0.99619, 0.99634, 0.99649,
0.99664, 0.99678, 0.99693, 0.99708, 0.99722, 0.99737, 0.99751, 0.99766,
0.99780, 0.99794, 0.99809, 0.99823, 0.99837, 0.99851, 0.99865, 0.99879,
0.99892, 0.99906, 0.99920, 0.99933, 0.99947, 0.99960, 0.99974, 0.99987,
1.00000,
];
/// TRACES: FR-DEV-3e
/// A profile's `ProfileToneCurve` — `(x, y)` pairs in `[0, 1]` — resampled
/// onto [`TONE_SAMPLES`] even points with a natural cubic spline, the DNG
/// SDK's `dng_spline_solver`.
///
/// `None` where the pairs do not describe a curve (fewer than two points,
/// x not increasing, values outside `[0, 1]`) or describe the identity,
/// which RawTherapee also treats as no curve.
pub fn resample_tone_curve(pairs: &[f32]) -> Option<Vec<f32>> {
if pairs.len() < 4 || !pairs.len().is_multiple_of(2) {
return None;
}
let xs: Vec<f64> = pairs.iter().step_by(2).map(|&v| f64::from(v)).collect();
let ys: Vec<f64> = pairs
.iter()
.skip(1)
.step_by(2)
.map(|&v| f64::from(v))
.collect();
let sane = xs.windows(2).all(|w| w[1] > w[0])
&& xs
.iter()
.chain(&ys)
.all(|v| v.is_finite() && (-1e-6..=1.0 + 1e-6).contains(v));
if !sane {
return None;
}
if xs.iter().zip(&ys).all(|(x, y)| (x - y).abs() < 1e-6) {
return None;
}
let n = xs.len();
// Natural cubic spline: second derivatives zero at both ends, solved by
// the tridiagonal (Thomas) algorithm.
let mut m = vec![0.0f64; n];
if n > 2 {
let h: Vec<f64> = xs.windows(2).map(|w| w[1] - w[0]).collect();
let mut a = vec![0.0; n];
let mut b = vec![1.0; n];
let mut c = vec![0.0; n];
let mut d = vec![0.0; n];
for i in 1..n - 1 {
a[i] = h[i - 1];
b[i] = 2.0 * (h[i - 1] + h[i]);
c[i] = h[i];
d[i] = 6.0 * ((ys[i + 1] - ys[i]) / h[i] - (ys[i] - ys[i - 1]) / h[i - 1]);
}
for i in 1..n {
let w = a[i] / b[i - 1];
b[i] -= w * c[i - 1];
d[i] -= w * d[i - 1];
}
m[n - 1] = d[n - 1] / b[n - 1];
for i in (0..n - 1).rev() {
m[i] = (d[i] - c[i] * m[i + 1]) / b[i];
}
}
let eval = |x: f64| -> f64 {
if x <= xs[0] {
return ys[0];
}
if x >= xs[n - 1] {
return ys[n - 1];
}
let i = xs.windows(2).position(|w| x <= w[1]).unwrap_or(n - 2);
let h = xs[i + 1] - xs[i];
let t = (x - xs[i]) / h;
let u = 1.0 - t;
u * ys[i]
+ t * ys[i + 1]
+ ((u * u * u - u) * m[i] + (t * t * t - t) * m[i + 1]) * h * h / 6.0
};
Some(
(0..TONE_SAMPLES)
.map(|k| eval(k as f64 / (TONE_SAMPLES - 1) as f64).clamp(0.0, 1.0) as f32)
.collect(),
)
}
/// A resolved curve at `x`, linear interpolation between samples, `x`
/// clamped to `[0, 1]` — the shader's lookup, for the CPU reference.
pub fn evaluate(curve: &[f32], x: f32) -> f32 {
let last = curve.len() - 1;
let s = x.clamp(0.0, 1.0) * last as f32;
let i = (s as usize).min(last - 1);
let f = s - i as f32;
curve[i] + (curve[i + 1] - curve[i]) * f
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn the_default_curve_is_rawtherapees() {
assert_eq!(ACR3_DEFAULT.len(), 1025);
assert_eq!(ACR3_DEFAULT[0], 0.0);
assert_eq!(ACR3_DEFAULT[1024], 1.0);
assert_eq!(ACR3_DEFAULT[1], 0.00078);
assert_eq!(ACR3_DEFAULT[256], 0.52069);
assert_eq!(ACR3_DEFAULT[512], 0.80486);
assert_eq!(ACR3_DEFAULT[768], 0.93986);
assert!(ACR3_DEFAULT.windows(2).all(|w| w[1] >= w[0]), "monotone");
}
#[test]
fn a_resampled_curve_passes_through_its_points() {
let c = resample_tone_curve(&[0.0, 0.0, 0.5, 0.6, 1.0, 1.0]).unwrap();
assert_eq!(c.len(), TONE_SAMPLES);
assert!((evaluate(&c, 0.5) - 0.6).abs() < 1e-4);
assert!(evaluate(&c, 0.0).abs() < 1e-6 && (evaluate(&c, 1.0) - 1.0).abs() < 1e-6);
assert!(
evaluate(&c, 0.25) > 0.25,
"a lifted curve lifts between its points"
);
}
#[test]
fn an_identity_or_broken_curve_is_no_curve() {
assert!(resample_tone_curve(&[0.0, 0.0, 1.0, 1.0]).is_none());
assert!(resample_tone_curve(&[0.0, 0.0, 0.5]).is_none());
assert!(resample_tone_curve(&[0.0, 0.0, 0.6, 0.5, 0.4, 0.9, 1.0, 1.0]).is_none());
}
}
docker/android/assemble-apk.sh
+1 -1
View File
@@ -299,7 +299,7 @@ fi
rm -rf "${OUT}/staging/assets/models"
mkdir -p "${OUT}/staging/assets/models"
_bundled=""
for _dir in face scene inpaint; do
for _dir in face scene inpaint denoise; do
ASSETS="${REPO}/models/${_dir}"
compgen -G "${ASSETS}/*.onnx" >/dev/null || continue
# An LFS pointer is ~130 bytes and looks exactly like a model to `cp`. Left
docker/macos/Dockerfile
+48
View File
@@ -0,0 +1,48 @@
# DarkRoom — macOS link check
#
# Compiles and links for macOS from Linux, with zig as the linker
# (cargo-zigbuild). Zig carries macOS's libSystem stubs and C headers, so the
# crates that need only libSystem — the inference engine, dr-plat — build,
# link and produce Mach-O test binaries here. Nothing runs: there is no macOS
# to run them on (docs/dev/macos.md §2). The desktop app needs Apple's
# framework headers (AppKit, Metal, Security), which only the Xcode SDK
# carries, so it does not link here.
#
# Build: docker build -t darkroom-macos:latest docker/macos
# Use: ./docker/macos/build.sh cargo zigbuild --target aarch64-apple-darwin -p dr-inference-engine --all-targets
FROM docker.io/library/debian:trixie-slim
# Pinned, like the Windows and Android images. Rust matches rust-toolchain.toml.
ARG RUST_VERSION=1.92.0
ARG ZIG_VERSION=0.15.2
ARG ZIG_SHA256=02aa270f183da276e5b5920b1dac44a63f1a49e55050ebde3aecc9eb82f93239
ARG CARGO_ZIGBUILD_VERSION=0.23.4
ENV DEBIAN_FRONTEND=noninteractive \
CARGO_HOME=/opt/cargo \
RUSTUP_HOME=/opt/rustup \
PATH=/opt/zig:/opt/cargo/bin:$PATH
RUN apt-get update && apt-get install -y --no-install-recommends \
ca-certificates curl git xz-utils \
# A host C compiler: build scripts and proc-macros are Linux binaries.
gcc libc6-dev \
# `file` says Mach-O; the smoke check in build.sh reads it.
file \
&& rm -rf /var/lib/apt/lists/*
RUN curl -fsSL "https://ziglang.org/download/${ZIG_VERSION}/zig-x86_64-linux-${ZIG_VERSION}.tar.xz" -o /tmp/zig.tar.xz \
&& echo "${ZIG_SHA256} /tmp/zig.tar.xz" | sha256sum -c - \
&& mkdir /opt/zig && tar xJf /tmp/zig.tar.xz -C /opt/zig --strip-components=1 \
&& rm /tmp/zig.tar.xz && zig version
# The components rust-toolchain.toml lists, baked in so rustup does not fetch
# them inside every run.
RUN curl -fsSL https://sh.rustup.rs | sh -s -- -y --profile minimal \
--default-toolchain "${RUST_VERSION}" \
--component rustfmt,clippy,rust-analyzer \
--target aarch64-apple-darwin,x86_64-apple-darwin \
&& cargo install --locked "cargo-zigbuild@${CARGO_ZIGBUILD_VERSION}" \
&& rm -rf /opt/cargo/registry \
&& chmod -R a+rwX /opt/cargo /opt/rustup
docker/macos/build.sh
Executable
+62
View File
@@ -0,0 +1,62 @@
#!/usr/bin/env bash
# Run a command inside the DarkRoom macOS link-check container.
#
# ./docker/macos/build.sh cargo zigbuild --target aarch64-apple-darwin -p dr-inference-engine --all-targets
# ./docker/macos/build.sh # interactive shell
#
# Builds the image on first use; `--rebuild` after editing the Dockerfile.
set -euo pipefail
IMAGE="darkroom-macos:latest"
HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO="$(cd "${HERE}/../.." && pwd)"
if command -v podman >/dev/null 2>&1; then
ENGINE=podman
elif command -v docker >/dev/null 2>&1; then
ENGINE=docker
else
echo "error: neither podman nor docker found" >&2
exit 1
fi
if [[ "${1:-}" == "--rebuild" ]]; then
shift
"${ENGINE}" build -t "${IMAGE}" "${HERE}"
elif ! "${ENGINE}" image inspect "${IMAGE}" >/dev/null 2>&1; then
echo "==> building ${IMAGE} (first run; a few minutes)"
"${ENGINE}" build -t "${IMAGE}" "${HERE}"
fi
# Registry, target and zig's own cache persist across runs.
CACHE="${XDG_CACHE_HOME:-${HOME}/.cache}/darkroom-macos"
mkdir -p "${CACHE}/registry" "${CACHE}/target" "${CACHE}/home"
ARGS=(
--rm
-v "${REPO}:/work:z"
-v "${CACHE}/registry:/opt/cargo/registry:z"
-v "${CACHE}/target:/work/target-macos:z"
-v "${CACHE}/home:/tmp/home:z"
-e HOME=/tmp/home
-e CARGO_TARGET_DIR=/work/target-macos
-w /work
)
# Capped for the same reason as the Windows image: a cross build otherwise
# takes every thread on the host.
JOBS="${DARKROOM_BUILD_JOBS:-8}"
if [[ "${JOBS}" != "0" ]]; then
ARGS+=(--cpus "${JOBS}" -e "CARGO_BUILD_JOBS=${JOBS}")
fi
if [[ "${ENGINE}" == "docker" ]]; then
ARGS+=(--user "$(id -u):$(id -g)")
fi
if [[ $# -eq 0 ]]; then
ARGS+=(-it)
set -- /bin/bash
fi
exec "${ENGINE}" run "${ARGS[@]}" "${IMAGE}" "$@"
docker/windows/package.sh
+7 -3
View File
@@ -51,13 +51,17 @@ sed 's/$/\r/' "${REPO}/LICENSE" > "${STAGE}/LICENSE"
#
# The directories are the ones the APK stages (assemble-apk.sh) and the Arch
# package installs: the face pair and its eye-state models, the scene model
# with its two descriptors, and the panorama border filler. The installer
# with its two descriptors, the panorama border filler and the denoiser. The installer
# smoke test counts the same directories, so a model added here is expected
# there without a number to update.
for dir in face scene inpaint; do
#
# Not the quantised siblings (`*.int8.onnx`, `*.a16w8.onnx`, `*.a16w16.onnx`):
# they are the Hexagon's forms (docs/dev/inference.md §1.5), and a Windows
# machine has no Hexagon to load them.
for dir in face scene inpaint denoise; do
for f in "${REPO}/models/${dir}"/*; do
case "$(basename "${f}")" in
README.md) continue ;;
README.md | *.int8.onnx | *.a16w8.onnx | *.a16w16.onnx) continue ;;
esac
if [[ "${f}" == *.onnx && "$(stat -c%s "${f}")" -lt 100000 ]]; then
echo "error: $(basename "${f}") is $(stat -c%s "${f}") bytes — an LFS pointer, not a model." >&2
docs/dev/camera-profiles.md
+145 -12
View File
@@ -95,7 +95,7 @@ Two departures, both forced by D19's unbounded scene-linear values (the SDK runs
both tables, which is where the second reason matters:
- **The look sees exposure.** The SDK applies `LookTable` after its exposure ramp, so a look that
desaturates highlights finds the highlights the photographer chose. At 25 it does too. Contrast,
tone and the colour controls come after it, as they do in Camera Raw.
tone and the colour controls come after it, as they do in the DNG SDK's reference rendering.
**Tables are per source, like the matrix.** They are decoded with the raw, interpolated once at
decode (the HueSatMap blend uses the as-shot neutral, as the matrix does) and carried on
@@ -151,7 +151,7 @@ reads a buffer no declaration can name:
| Parameter | Kind | Default | Meaning |
|---|---|---|---|
| `apply` | Bool | on | Use the profile's tables, or the matrix alone |
| `look` | Scalar 0–200 | 100 | Strength of the `LookTable`, as Lightroom's *Amount* |
| `look` | Scalar 0–200 | 100 | Strength of the `LookTable` |
`look` scales the look's deltas: `hueShift · a`, `1 + (satScale − 1)·a`, `1 + (valScale − 1)·a`,
with `a = look/100`, scales floored at 0. At 200 the look is twice as strong, which is the
@@ -172,22 +172,17 @@ action sits on that line.
## 6. Not done, and why
- **`ProfileToneCurve` is read and ignored.** D19 gives tone to the view transform, one for every
body, and rejected per-body curves as its defaults. A profile's curve is a per-body curve. If it
comes back, it comes back as an option of the view transform, not as a stage here. The 6D's
Adobe Standard has none, so the case that matters today loses nothing.
- **`BaselineExposure` is not applied** (it is not today either). Adobe Standard was tuned with it,
and the 6D's is +0.25 EV. Separate change; it moves every photograph's brightness.
- ~~**`ProfileToneCurve` is read and ignored.**~~ *Done after 0.20.0: §12, D21.* It came back as
an option of the view transform, as this bullet said it would, and that option is the default
for raws.
- ~~**`BaselineExposure` is not applied.**~~ *Done after 0.20.0: §11.*
- **The interpolation follows the as-shot neutral, not the white-balance slider**, as the matrix
does. Camera Raw re-blends on every temperature change; doing so here means the matrix moves too,
which is its own change.
- **Masks select on the matrix's colour.** A colour-range mask sees colour before the profile, as
it sees colour before every other operation. Deterministic, and a mask is drawn on the picture
the user sees only approximately anyway.
- **The profiles directory does not sync.** A CR2 rendered on a desktop with a copied 6D profile
and on a tablet without one will differ. The panel line says which profile each device used, so
the difference is visible rather than silent. Syncing the directory with the library is the
follow-up.
- ~~**The profiles directory does not sync.**~~ *Done after 0.20.0: §13.*
- **Rec.2020 working primaries** stay deferred (D19); nothing here depends on them.
## 7. What it costs
@@ -256,3 +251,141 @@ is inside its control's range, and every preset changes something.
4. `dr-ui`: profile line, copy-out action, labels; the profiles directory set at start-up on
desktop and Android.
5. The *Vivid* presets.
---
# After 0.20.0: tone, exposure and sync
0.20.0 shipped the tables and §1's measurement showed they were not the gap to Lightroom's colour.
The three items §6 left open are closed here. Draft of 2026-10-03.
## 11. Baseline exposure
`BaselineExposure` (DNG tag 50730) is the stops a converter adds so a camera's middle grey lands
where its maker meant it; the 6D's DNGs say +0.25. `BaselineExposureOffset` (51109) is a profile's
correction to it. The DNG SDK's total is their sum, and so is this one's.
- **Applied as a gain on the camera matrix** in `dr-gpu` when a raw is uploaded:
`cam_to_srgb · 2^total`. A uniform gain commutes with every scene operation before the view
transform, and the camera-space tap and the white-balance probe read camera RGB before the
matrix, so neither changes. `RawImage::color_matrix` itself stays the file's: a merge writes a
linear DNG from it and must not bake a gain into the pixels it also declares in a tag.
- **A copied profile carries the DNG's baseline.** When an embedded profile is saved as a `.dcp`
(§4) its `BaselineExposureOffset` is written as the DNG's `BaselineExposure` plus the profile's
own offset. A CR2 has no baseline of its own, so its total is then the DNG's: the two files of
one body render at one brightness. The cost: a DNG that embeds no profile, carries its own
baseline, and matches a copied `.dcp` counts the baseline twice. Every Adobe-written DNG embeds
its profile, so that DNG is a hand-made one.
## 12. DNG reference tone (D21)
The DNG SDK's reference rendering runs a raw through the profile's
`ProfileToneCurve`, or, for a profile that has none — Adobe Standard among them — through the
*ACR3 default curve*, a 1025-point table published in the DNG SDK and carried by RawTherapee
(GPLv3) as `adobe_camera_raw_default_curve`.
**How it is applied** is half of what it does. The reference does not run the curve on each channel:
`RefBaselineRGBTone` runs it on the largest and smallest channel and places the middle one at the
same fraction between them as before. Hue is kept; saturation rises where the curve is steep —
the shadows and midtones — which is exactly where Adobe Standard's look table desaturated to
compensate. It runs in linear ProPhoto, on values clipped to `[0, 1]`, and its output is linear.
**As the view transform**, not as a stage. D19 has one rendering, last; this is a second kind of
that rendering, chosen by a new parameter on `view_transform`:
| `curve` | What it is | Default for |
|---|---|---|
| Sigmoid | D19's log-logistic curve | — (a choice) |
| DNG reference | the profile's curve, else ACR3, via RGBTone in ProPhoto | every raw (D21) |
*Decided 2026-10-03:* the DNG reference is the default, at contrast 1.5 — measured against
Lightroom exports of photographs with neutral look settings (D21 has the table).
*Amended earlier on 2026-10-03:* the DNG reference was the default in the first draft. The measurement it rested on
compared against Lightroom renders of *edited* photographs; see D21. The default is decided by
measuring against Lightroom exports of unedited ones.
A JPEG is still not rendered again (FR-DEV-3j). Film simulation still replaces the view transform
when a stock is chosen.
**The two sliders keep meaning something** under the DNG reference curve:
- `white` (stops above grey at which the scene reaches display white) sets the input scale:
`2^(4 − white)`. At its default of 4 the scale is 1 — sensor white is display white, as in
the SDK's reference.
- `contrast` bends the input about middle grey before the curve, as a power of
`contrast / 1.4`: 1 at its default, so the curve is the reference's untouched.
**What the ACR curve gives up** is D19's shoulder. Values above display white clip, as they do
in the SDK's reference; highlight recovery is the highlights slider's job before it. Sigmoid stays one
click away for a photograph that wants the shoulder.
**A profile's own curve** is a list of `(x, y)` pairs. It is resampled at decode onto the same
1025 points with a natural cubic spline, the DNG SDK's `dng_spline_solver`. A curve that is the
identity is treated as absent, as RawTherapee does.
**On the GPU** the curve rides in the profile buffer (`@binding(8)`) after the tables: a third
header entry gives its length, then the samples. The placeholder bound for a source without a
profile carries the ACR3 curve, so a CR2 with no `.dcp` renders through the reference tone when that curve is chosen.
## 13. Profiles sync
The profiles directory travels with the library, in the server folder that already carries what
every device must agree on: `<library root>/.darkroom-derived/profiles/`. The scanner excludes
its parent, as it excludes the trash.
- **A step of the derived sync pass** (`derived_sync::run`), after the catalog and before the
place file, and like the place file it never fails the pass. It lists the server folder and the
local one; uploads every local `.dcp` the server lacks, or holds at a different size; downloads
every one the device lacks, reading through a placeholder where the library is a synced folder,
and writing `.tmp` then renaming so a half-written file is never parsed. If it fetched
anything, it reloads the profile set.
- **Files are immutable and named for what they hold** (`<camera> <profile>.dcp`), so a name and a
size say whether two copies are the same. Two devices that copy the same profile write the same
name; neither wins over anything.
- **Not a catalog table.** A schema change stops an older peer merging the catalog at all
(see the memory of 0.13.3), and a blob of ~120 KB would ride in every catalog upload.
- **One directory per install**, the union of every library's profiles. A profile describes a
camera, not a library, so a profile one library brought is right for the same camera in
another.
- **Not handled: deleting.** There is no way to remove a profile from the app; a file removed by
hand on one device comes back from the server on the next pass. A tombstone list is the
follow-up if deleting is added.
## 14. Acceptance for §11–§13
- The ACR3 table is 1025 points from 0 to 1, monotone, and matches RawTherapee's values.
- RGBTone: grey goes through the curve unchanged in hue; a colour keeps its hue (the middle
channel's fraction between the outer two is unchanged); a curve that is the identity changes
nothing; the shader agrees with the CPU reference on a device.
- Sigmoid is the default curve; at its defaults it renders to the bit what 0.20.0 rendered,
apart from baseline exposure.
- A DNG with `BaselineExposure` +0.25 renders a flat grey 0.25 EV brighter than the same pixels
with none; a `.dcp` copied from it carries `BaselineExposureOffset` 0.25 and gives a CR2 the same
total.
- A profile resampled from `(0,0) (0.5,0.6) (1,1)` passes through its points.
- Sync: a `.dcp` present only locally is uploaded; one present only on the server is downloaded,
parsed and matched on the next decode; a file of the same name and size is left alone.
- Measured again on `_MG_9080`: mean saturation at defaults closer to Lightroom's 0.49 than 0.20.0's
0.35.
## 15. The photographer's earlier edit
What §1 and D21 first took for a difference in rendering is an edit. Every DNG in the library
carries, in its embedded XMP, the develop settings it was given before it came to DarkRoom — a
consistent house style: per-colour saturation (blue +58, aqua +50, yellow and purple +23, orange
+13, green +10), highlights −40, blacks −20, with a second variant (vibrance −10, blue +31). Those
settings, not the profile and not the tone curve, are why the same photographs looked richer
before.
- **Translated on open** (`dr_preset_xmp::read_embedded`), the HSL bands onto the colour mixer —
aqua to cyan and purple to violet, the nearest of its twelve by hue — and the rest as the preset
importer already did.
- **Applied only to a photograph DarkRoom has no edit of**, and only on positive evidence: no
sidecar beside a local file, or a server that answered "no such file" with nothing cached
(`FetchedSidecar::absent`). An edit that failed to arrive is not an absent one, and this would
otherwise be saved over it.
- **One undoable step, "Earlier Edit"**, then an ordinary edit, saved with the photograph. Export
applies it the same way, so a photograph never opened exports as opening it would show.
- **The translation is one for one for now.** Measurement against the earlier exports (the
`lr-fit` work) may scale individual bands.
docs/dev/denoise.md
+179 -9
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@@ -1,8 +1,9 @@
# Learned denoise — joint demosaic and denoise on the mosaic
Design for **FR-DEV-3g** ([requirements.md](requirements.md)), the learned stage
[outstanding.md §3](outstanding.md) says is missing. Draft of 2026-09-27: nothing here is built,
and every figure marked *estimate* is waiting for the measurement that replaces it.
[outstanding.md §3](outstanding.md) says is missing. Drafted 2026-09-27; a first version shipped
in 0.21.0, and §11 records what was built and measured. Figures still marked *estimate* are
waiting for the measurement that replaces them.
---
@@ -288,17 +289,27 @@ is ~120 MB, and a derived file inside a synced tree is exactly what
with progress over the canvas — the same pattern as a photograph that is only on the server.
- Export needs the result and computes it if the cache has lost it.
### 7.2 The Amount control
### 7.2 The grain control
A Denoise toggle and one Amount slider in develop. Moving the slider runs inference on the
**visible viewport only** (~1 MP, a fraction of a second — *estimate*) so the photographer judges
on the real result; releasing it queues the whole frame. There is no per-frame blend between the
two paths: blending the classical output back in re-adds the noise the network removed.
What shipped is a switch and a **Keep grain** slider, not the Amount described first. The slider
blends the two demosaics per pixel — but only the *brightness* of their difference: `out =
denoised + grain · ΔY / wb`, with `ΔY` the luminance of `wb · (classical − denoised)`. Taken after
the as-shot balance and handed back divided by it, the grain is neutral in the finished picture.
The objection that stood here — that blending the classical output back in re-adds the noise —
holds for a plain mix, which also brings back the classical path's colour speckle and false
colour. A luminance-only blend returns film-like grain and nothing else, and it needs no
inference: one elementwise GPU pass (`dr_gpu::GrainBlend`) per slider value, producing a new
source the adjust pass draws. Comparing the two on real 6D frames, the user chose this one.
The σ-map Amount (§3.3) still works — `NoiseModel::scaled` — and stays available for a later
"strength" control; its cost is a re-run of the network.
### 7.3 Runtime
Through `dr-inference-engine`, as the other models run ([inference.md](inference.md)): TensorRT or
CUDA fp16 on the laptop, MIGraphX on the desktop, ORT CPU everywhere, QNN on the tablet. Work is
Through `dr-inference-engine`, as the other models run ([inference.md](inference.md)), as
`Role::Denoiser`: TensorRT or CUDA fp16 on the laptop, MIGraphX on the desktop, ORT CPU everywhere.
**Not the Hexagon** — see §11 — so the tablet runs it on its CPU. Work is
scheduled in the `Background` class so a slider never waits on it (architecture §5.3).
## 8. Speed and the tablet
@@ -320,6 +331,17 @@ If neither holds S's quality within 0.5 dB of fp32 on the real pairs, **v1 is de
tablet shows the classical path. The sidecar still records the intent, so a desktop can render the
learned result for a photograph edited on the tablet.
**Measured 2026-10-04 (inference.md §1.5): the second way holds, without the first.** The shipped
network, with its Bayer packing re-spelled as `SpaceToDepth` so QNN can hold it (the 6-D reshape
it replaces is exact but past the HTP's rank limit), at A16W16 — 16-bit activations and weights —
scores within 0.00 dB of f32 at ISO 400–25600 on the tablet's own HTP, and within 0.09 dB with the
6D's noise model scaled ×0.5, ×2 and ×4 to stand in for other sensors. A16W8 holds the 6D (worst
−0.19 dB at ISO 25600) but not ×4 noise at 25600 (−0.52 dB), so A16W16 is what ships. int8 loses
4.7–9.2 dB and fp16 is refused outright. A 1408 tile takes 95 ms on the Hexagon against 1510 ms on
the tablet's CPU: about 2.3 s for a 20 MP frame. Calibration ranges come from 96 training-day
tiles across every ISO, a third of them with that scaled noise; coverage of other bodies is that
synthetic bracket, not their raws.
## 9. X-Trans
The requirements tie this stage to FR-RAW-5, and the library has no Fuji raws. What we can do
@@ -356,3 +378,151 @@ MIT architecture, so this model adds no third-party licence to D13.
4. Whether a Lightroom or DxO comparison is available for §6.2.
5. A borrowed X-Trans body, or X-Trans experimental in v1.
## 11. What shipped in 0.21.0, and what was measured
**Data.** 500 distinct ISO 50–100 6D frames from the library, over 121 shooting days (bursts and
near-duplicate perceptual hashes dropped; 55 frames from held-out days for validation). Read through
`dr-gpu`'s `mosaic_dump` example — `dr-decode` and the app's own hot-pixel pass — so the network's
input is the mosaic the classical demosaic reads. Truth by 2×2 binning with a Catmull-Rom quarter-pixel
shift of red and blue (§4.2). Training lives in `darkroom-denoise`, beside `darkroom-infill`.
**Noise model (§5), from the library instead of a capture.** Shot gain and read variance per ISO from
Adobe's `NoiseProfile` in the converted DNGs; read noise checked against each frame's masked border
(agreement within 2–3 % from ISO 125 to 25600); read-noise *shape* taken from the border as quantiles
on a tail-dense grid (excess kurtosis up to ~10 at high ISO), with only the photosites the app's
hot-pixel rule would remove left out; row noise from the border's row means; **column noise** from
the masked rows above the image — about a third of its variance is this sensor's fixed pattern.
Third stops are their own rows: ISO 160, 320 and 640 are quieter than their neighbours, as §5.1
expected. Training without the column noise left the 6D's vertical stripes in (0.90 DN of 1.01);
with it, 0.17 DN.
**Model.** Not NAFNet: its channel attention averages over the whole input, which breaks exact
tiling. A U-Net of 3×3 convolutions, ReLU, strided and transposed convolutions and additive skips —
3.2 M parameters, 48 GMAC per raw megapixel, receptive field 185 photosites (counted from the
layers; a perturbation probe under-read it as 157 because a switched-off ReLU hides a path).
Tiles of 1408 keep their central 1024 behind a 192 halo, exactly.
**Results.** PSNR after the display transform, held-out days, step 60 000:
| ISO | Network | Bilinear | Bilinear on a clean mosaic |
|---|---|---|---|
| 400 | 41.6 | 36.8 | 40.0 |
| 1600 | 40.8 | 33.8 | 40.0 |
| 6400 | 39.5 | 29.3 | 40.0 |
| 25600 | 37.8 | 24.6 | 40.0 |
Unbiased in linear light on real frames (shadow level within 1 % of a heavily averaged bilinear).
Checked against the app's own render for channel and axis order (`tools/check_against_app.py`).
**Precision (§8).** fp16: 0.00 dB at every ISO. int8 QDQ, calibrated on training tiles: −6 to −9 dB
— the shadow steps §8 feared losing are lost. So the Hexagon refuses the role and the tablet runs f32
on its CPU; the residual head of §8 is the route back.
**Noise for any Bayer body (§3.3).** Table, then `NoiseProfile`, then the frame itself: read, row and
column noise from its masked border, the shot gain alone estimated from the quietest flat patches.
On 130 6D frames the estimate is within ±10 % of the table from ISO 1000 up and scattered below. The
network loses under 0.3 dB for σ off by 15–20 % and twice as much for under- as for over-estimating;
the estimate leans high. Every Bayer body is offered the switch; develop says which source was used.
**Speed, a whole 6D frame (20 MP).** TensorRT fp16 3.1 s, ONNX Runtime CPU 14.4 s, on the laptop —
measured while the GPU sat power-capped at an 810 MHz memory clock; uncapped is expected to be
about four times faster. The Rust path reproduces the training repository's output to 2.5e-4 at
worst; TensorRT fp16 is 75 dB from f32.
**Not yet:** ~~the result is not cached across sessions (§7.1) — reopening recomputes;~~ done after
0.21.0, §12; the tripod real
pairs of §6.1; X-Trans (§9); the hand-written WGSL path, for which `export.py` already writes the
weights blob and a manifest a shader can follow.
## 12. On by default, with a strength, and cached (after 0.21.0)
The photographer asked for the learned demosaic to be how a raw is developed, not an option found
under Detail. So:
- **On by default, at full strength, on every device.** The switch is `switch_on`, so an untouched
photograph writes nothing and is developed from the network everywhere; turning it off is the
edit. Which hardware runs it is the inference engine's choice (inference.md), not this setting's:
the default does not depend on what a device is believed to manage.
- **Strength replaces Keep grain.** 0–100, default 100, and grain = 100 − strength, so it is the
same luminance-only blend of §7.2 and moving it is one GPU pass, never a re-run. An edit saved by
0.21.0 stored `grain`; it is still read, as its inverse, and never written.
- **First in the panel**, above the lens corrections: it decides what every control below is
applied to. Its attribute is still Detail, so it also stays where the Detail tab shows it.
- **Cached on disk** (§7.1): the network's output for a file, as half floats (about 120 MB for
20 MP — no compressor to link on Android), keyed on a SHA-256 of the file's bytes and the model
file's name and size, oldest first past a 5 GB budget, beside the inference engine's cache under
the data root. The strength is applied afterwards and is not in the key. A reopened photograph,
and an export of one already developed, read it back instead of recomputing.
What it costs: every raw opened runs the network once, with the classical demosaic shown until the
result lands, and a first export of an unopened raw runs it too. Every raw renders differently from
0.21.0 unless switched off.
## 13. Three networks and a method (after 0.22.0)
The photographer asked for a choice between quality and time. `Method` replaces the Apply switch:
`Bilinear`, `Fast`, `Medium`, `Best`, by index in that order, default `Best`. An untouched raw
writes nothing and develops through `Best`. `apply` is still read and never written: 0 is
`Bilinear`, 1 keeps a network already chosen or is the default. A number past the list, from a newer
build, reads as the default. A build before this one ignores `method` and develops through its own
network, which is the most an older peer can do.
**The networks** (darkroom-denoise, every one trained on the same data and noise as §11, plus 1,201
further frames cropped from the library and 6,000 drawn scenes — polygons, lines of one to four
photosites, text, gratings — rendered at 4× through a random affine and smooth displacement, so
edges fall off the photosite grid):
| Method | File | Network | Parameters | GMAC / MP | Halo |
|---|---|---|---|---|---|
| Best | `mosaic-best-1408.onnx` | two U-Nets of §11's shape (a flat expert from `m2`, an edge expert from the ×100 edge-weighted run) and a 128 k-parameter gate that blends them per photosite | 6.4 M | 110 | 256 |
| Medium | `mosaic-medium-1408.onnx` | §11's U-Net, distilled from Best (75 % its output, 25 % the truth) | 3.2 M | 48 | 192 |
| Fast | `mosaic-fast-1408.onnx` | widths 16-32-64-128, blocks 1-1-1-2, distilled the same way | 0.93 M | 11 | 192 |
The gate learned on its own to trust the edge expert at 0.77–0.88 on edges and not at all on flat
areas. The mixture's receptive field is the experts' plus the gate's, so it keeps the centre of a
1408 tile past a 256 halo, where the single networks keep 1024 past 192. `dr_denoise::Shipped`
carries each file's halo, and `TileNet::halo` hands it to the tiler.
**Quality.** PSNR after the display transform on 1,842 held-out crops, and the width of a hard
edge on the drawn chart at ISO 6400 (truth 0.80 photosites; lower is sharper):
| | ISO 400 | 1600 | 6400 | 25600 | Edge width |
|---|---|---|---|---|---|
| §11's network | 40.61 | 39.77 | 38.43 | 36.67 | 1.77 |
| Best | 40.69 | 39.84 | 38.47 | 36.71 | 0.82 |
| Medium | 40.59 | 39.75 | 38.40 | 36.65 | 1.30 |
| Fast | 39.90 | 39.06 | 37.54 | 35.33 | 1.84 |
| Bilinear | 36.15 | 33.26 | 28.72 | 23.83 | 2.15 |
On photographs the three are close; on hard edges Best is half as wide as §11's network and
Medium most of the way there. Fast costs a dB at high ISO and edges as soft as §11's.
**Speed**, a whole 20 MP 6D frame, the network alone, TensorRT fp16 on the laptop's RTX 3050
(uncapped: memory at 5 GHz), engine already built: Best 2.48 s, Medium 0.79 s, Fast 0.57 s. Decode
and the hot-pixel pass add about 0.5 s. The first build of each TensorRT engine takes 80 s (Fast) to
190 s (Best), in the background at first launch, cached after.
**Before the network, two passes changed since §11.**
- *A noise-aware repair* (`dr_denoise::repair`) after the app's hot-pixel pass: a photosite more
than 8σ beyond every same-colour neighbour *and* every adjacent photosite, and more than twice
each adjacent one, is clamped to the brightest of its same-colour neighbours; a dead one, to the
darkest. The ratio test is what spares a point of light, whose neighbours are lit too. The networks
were trained behind the same pass (the Python and Rust agree: 935 repairs on an ISO 25600 frame).
- *The tiler feeds the network without waiting*: tiles are gathered on every core by a producer
thread one tile ahead, and the output is written back in parallel from the runtime's own buffer.
0.14 s of tiler for a frame, which is what keeps Fast under a second.
**The Hexagon.** Each network has an `.a16w16.onnx` sibling made by `tools/quantise-models.sh
--ranges`, the ranges from darkroom-3e's gate (96 training tiles, a third at noise ×2 and ×4). On
the 6D gate A16W16 loses 0.00 dB for all three; with the noise scaled ×0.5–×4 at most 0.11 dB.
A16W8 holds the gate (≤ 0.27 dB) but loses 0.63 dB on Medium at ×4, so A16W16 stays the form.
**Cache.** Each network keys its own results (§7.1 keys on the model's file name), and the file is
hashed once at open, so changing the method never re-reads it. Choosing `Bilinear` keeps the
network's result in memory for the way back; changing to another network drops it, and coming back
reads the cache.
**Packaging.** All six files in the APK (`BUNDLED`, 23 entries, +44.6 MB, ~41 MB compressed); the
three f32 networks in the Arch package and the Windows installer, which stage `models/denoise` by
directory.
docs/dev/inference.md
+79 -7
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@@ -127,7 +127,8 @@ Three things the table settles.
five of six models. A whole-library face index on the tablet goes from ~100 ms + 39 ms per face
to ~1.4 ms + 12 ms per face, and the "Thorough" detector — 3× the cost of "Fast" today — becomes
free. Its price is that the models must be **quantised to int8**, which is an accuracy question
§5 has to answer before it is believed.
§5 has to answer before it is believed. (§1.5 answered it: int8 lost faces, and every model but
XFeat ships with 16-bit activations, at about three times these timings.)
- **The embedder does not gain from either accelerator.** 112×112 input, per-op overhead
dominates; it is 9 ms on the tablet's CPU and 12 ms on its NPU. It stays float, which §7 turns
from a performance footnote into a correctness rule.
@@ -137,6 +138,60 @@ Three things the table settles.
- **On AMD, MIGraphX fp16 is 4–17× the CPU provider** on the detectors and 60× on the
inpainter, with the same first-run compile cost as TensorRT and no rung between it and the CPU.
### 1.5 The Hexagon at every bit width · 2026-10-04
§1.1's Hexagon column is int8 calibrated on noise: timing only. This is the follow-up — every
model, every bit width the HTP offers, calibrated on real photographs and **scored on the tablet
itself** (ORT 1.29 + QNN 2.42, `htp_arch` 73), against the f32 model on the same inputs. The
"Form shipped" column is the files in `models/`, re-scored on the tablet after
`tools/quantise-models.sh` wrote them. The
calibration and scoring photographs are 800 from the public COCO val2017 set (CC-BY); the face
models' numbers are over the faces in them of at least 32 px. The tools are `tools/quantise-models.sh`
and the scratch harness described with it.
**What the HTP accepts.** fp16: nothing — every fp16 operator fails validation (3110), on QNN
2.42 and 2.50, with `htp_arch` and every `soc_model` tried; the fp16 rung stays off the table until
someone has Qualcomm's own SDK to say why. 4-bit weights (A8W4, A16W4): load, and wreck accuracy
(SCRFD finds 25–35% of f32's faces). What is left: **A8W8 (int8), A16W8 and A16W16**, all running
the whole graph. 16-bit activations cost about 3× int8's time, A16W16 about 4×.
| Model | ORT CPU f32 | Form shipped | Hexagon | On the tablet, against f32 | int8 for comparison |
|---|---|---|---|---|---|
| scrfd_500m / 2.5g / 10g | 17 / 56 / 198 ms | **A16W8** | 4.2 / 5.1 / 9.0 ms | 100% of faces found in every size band; keypoints 0.3–0.6% of the box | 94–95% of faces at 40–80 px |
| 2d106det (landmarks) | 2.8 ms | **A16W8** | 0.5 ms | 0.25 px in the 192 crop (eye points 0.20) | 1.5 px, and 29 partitions at 7.3 ms |
| yolo26n-seg | 90 ms | **A16W16**, tail in float | 12.9 ms | 98.2% of objects, mask IoU 0.994 | 74% (simulated) |
| yolo26s-sem-ade20k | 151 ms | **A16W16**, attention in float | 15 ms | 98.9% of cells agree on the class, TV 0.009 | 67% |
| migan-512 | 488 ms | **A16W16** | 87 ms | 41 dB from f32 in the fill (worst 1%: 30 dB) | 16 dB (simulated) |
| xfeat-1024 / 768 | 58 ms | **int8**, rewritten graph | 6.5 ms | panorama alignment 0.45 px from f32's — f32's own refit on 90% of its matches is 0.41 | — |
| mosaic-1408 (denoiser) | 1510 ms a tile | **A16W16**, rewritten graph | 95 ms a tile | 0.00 dB at every ISO; ≤ 0.09 dB with the noise scaled ×0.5–×4 | −4.7 to −9.2 dB |
| arcface_mbf (embedder) | 8.5 ms | f32, CPU | — | A16W16: cosine 0.9995, p1 0.9967 — misses §7's 0.999 gate | — |
| ocec, sgc (eyes) | 1, 1.7 ms | f32, CPU | — | sgc flips 1.45% of views even at A16W16; not worth a millisecond | — |
Four things the table needed that the f32 graphs did not have, all in `tools/htp_graph.py` and all
checked exact against the f32 graph before they are used:
- **Rank ≤ 5.** QNN's tensors stop at rank 5, and the denoiser packs the mosaic through a 6-D
reshape (6007 at compose). For one channel that reshape is `SpaceToDepth(2)`. XFeat's 8×8 unfold
is 224 Slices and 6-D Concats; it is `SpaceToDepth(8)` (736 nodes to 60).
- **No bilinear Resize at XFeat's sizes** (3110). A half-pixel bilinear resize between fixed sizes
is two constant matrices, so it is two MatMuls.
- **One scale per tensor.** The segmenter's output rows carry boxes in pixels beside scores in
0..1; quantised as one tensor the scores vanish. Everything from the Concat that builds the rows
stays float, on the CPU, where the top-300 selection costs nothing.
- **Float where the HTP's 16-bit arithmetic drifts.** The scene model's one attention block
(two MatMuls and a Softmax over 400 tokens) moved its agreement from 98.7% to 96.7%; it stays
float.
**ORT's CPU simulation of a QDQ graph is not the tablet.** It matched to the hundredth of a dB for
the denoiser and to rounding for the detectors, landmarks and XFeat, and it overstated MI-GAN by
27 dB and the scene model by three points. Every number above is the device's; a new form is not
measured until it has run there.
**XFeat's int8 loses keypoints and not the panorama.** 83% of f32's keypoints come back within
1.5 px; but over the twelve-frame `fixtures/pano/2025-08-05` sweep, the homographies fitted from
int8's matches land 0.45 px from f32's in the overlaps — the spread f32 shows against itself
(0.41).
---
## 2. The shape of the answer
@@ -146,15 +201,19 @@ winning:
| Platform | 1st | 2nd | 3rd | Floor |
|---|---|---|---|---|
| Android, Qualcomm with a Hexagon the shipped QNN skel covers (V68–V81) | QNN HTP, int8 model | ORT CPU, f32 model | — | tract |
| Android, Qualcomm with a Hexagon the shipped QNN skel covers (V68–V81) | QNN HTP, each model's quantised form (§1.5) | ORT CPU, f32 model | — | tract |
| Android, any other SoC | ORT CPU, f32 | — | — | tract |
| Linux / Windows, NVIDIA GPU | TensorRT, f32 model, fp16 engine | CUDA provider, f32 | ORT CPU, f32 | tract |
| Linux, AMD GPU with ROCm | MIGraphX, f32 model, fp16 program | ORT CPU, f32 | — | tract |
| Linux / Windows, no GPU stack | ORT CPU, f32 | — | — | tract |
| macOS ⁵ | ORT CPU, f32 | — | — | tract |
| macOS ⁵ | CoreML, f32 model, ML Program | ORT CPU, f32 | — | tract |
⁵ CoreML is the obvious rung and is unmeasured; it is listed so its absence is a gap and not an
oversight.
⁵ **Unmeasured**, and the one exception to the rule below: nobody here has a Mac. The rung is on
the ladder because the probe makes a wrong guess cheap — a CoreML that is slower than the CPU is
rejected by §4's clock, one that errors is recorded as failed, and one that takes the process
down is refused on the third launch (§4, `attempt`). The embedder stays on the CPU (§7). The first
macOS log that shows a probe line is this row's measurement; [macos.md](macos.md) says what to
ask for.
Deliberately **not** on any ladder, with the measurement that excluded each: NNAPI (no driver),
XNNPACK (slower than CPU, aborts on SCRFD), WebGPU (slower than CPU), the Adreno through QNN (works,
@@ -270,6 +329,14 @@ What the probe may not do:
- **Retry a rung that failed within a session.** A failed probe is cached as a failure with the
same inputs; the rung is tried again when an input changes. Otherwise a wedged driver means a
thirty-second stall on every launch.
- **Crash the app twice for the same reason.** The probe runs in the app's process, and a provider
can fail by aborting rather than by returning an error (XNNPACK on SCRFD, §2). Every session build
on a rung above the CPU — the probe's, and each background compile of §6 — writes what it is
attempting to `attempt` in the cache directory first and removes it after. A launch that finds the
file knows the last one died inside that attempt; after two such launches in a row the attempt is
refused and recorded like any other failure (a rung in `failed`, an engine in `refused`), until
the fingerprint changes. Two, not one, because quitting during a forty-second TensorRT compile
leaves the same file.
- **Choose for the user without saying so.** Settings gains one row, *Inference backend*, showing
what was chosen and why in one line ("Hexagon NPU · int8 · QNN 2.42"; "CPU · ONNX Runtime 1.30 ·
TensorRT probe failed: cuDNN 8 required"), with an override to force any lower rung. The about
@@ -286,10 +353,10 @@ of which form they load:
| Form | Who produces it | When | Needed by |
|---|---|---|---|
| f32 ONNX, shape-fixed, **opset ≥ 13** | `tools/fix-face-model-shapes.sh`, `tools/export-seg-model.sh` | Release time, once | Every rung except Hexagon |
| int8 QDQ ONNX, per-channel, uint8 activations | `tools/quantise-models.sh` (new) | Release time, once, **calibrated on real photographs** | Hexagon |
| QDQ ONNX, per-channel — int8, A16W8 or A16W16 per model (§1.5) | `tools/quantise-models.sh` | Release time, once, **calibrated on real photographs**, scored on the tablet | Hexagon |
| TensorRT engine (`.engine`, per GPU architecture and TensorRT version) | The app, from the f32 file | First run on that device, in the background | TensorRT rung |
| MIGraphX program (`.mxr`, per GPU architecture, MIGraphX version and precision) | The app, from the f32 file | First run on that device, in the background | MIGraphX rung |
| QNN context binary | The app, from the int8 file | First run on that device, in the background | Hexagon rung |
| QNN context binary | The app, from the quantised file | First run on that device, in the background | Hexagon rung |
Two rules.
@@ -368,6 +435,11 @@ already the rule for the detector and because §5 is the gate on whether the int
enough to be *offered* at all. f32 on tract, ORT CPU, CUDA and TensorRT-f32 are one identity: the
same graph, the same arithmetic, differences at the last bit.
After §1.5 the Hexagon runs the detectors in **A16W8**, and that is a third spelling:
`scrfd_500m_a16+w600k_mbf` and its two siblings. Same rule, same reconciliation; a tablet that
indexed under `_i8` keeps those rows, and `FaceDetector::model_ids` answers "has this detector been
over this image" for all three forms.
**The embedder** is where comparability across devices is the whole point, and it is the one
model that no accelerator helps (§1.4). So: **the embedder runs in f32 on every rung.** On TensorRT
that means the embedder's engine is built without fp16 while the detector's is built with it; on
docs/dev/macos.md
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@@ -0,0 +1,86 @@
# macOS
macOS is out of scope for v1 ([requirements.md](requirements.md)), and nobody working on
DarkRoom has a Mac. This page records what exists anyway, and how a macOS build is set up so
that someone who does have one can send back enough to fix what they hit.
## 1. What exists
- **Inference** ([inference.md §2](inference.md)). The macOS ladder is CoreML, then ONNX
Runtime's CPU provider, then tract. CoreML is unmeasured. The probe decides whether it is used,
and the crash guard (§4, `attempt`) covers the case where the provider takes the process down.
The device fingerprint is the chip (`machdep.cpu.brand_string`) and the OS release, because
CoreML ships with the OS.
- **Where files go** ([`dr_plat::dirs`](../../platform/dr-plat/src/dirs.rs)). The Unix rules,
except the state directory (the log and crash records), which is `~/Library/Logs/darkroom`.
- **A diagnostic build**, described in §3.
The rest is not built, packaged or run on macOS by anyone here. This covers the window,
Metal through wgpu, the display profile (FR-DSP-8 asks X11 and Wayland), the keyring, the
bundle, and signing. `dr-plat` sends every non-Android Unix to the X11/Wayland dependencies.
## 2. Building
`docker/macos` compiles and links for macOS from Linux, using zig as the linker
(`cargo-zigbuild`). Zig carries libSystem's stubs and the C headers, so tract's SIMD kernels
compile and anything that needs only libSystem links:
./docker/macos/build.sh cargo zigbuild --target aarch64-apple-darwin -p dr-inference-engine --features native --all-targets
./docker/macos/build.sh cargo-zigbuild clippy --target aarch64-apple-darwin -p dr-inference-engine --features native --all-targets -- -D warnings
That produces Mach-O arm64 test binaries and the `ladder` and `ep_probe` examples. Nothing runs
them. Anything that links an Apple framework needs the Xcode SDK, which zig does not carry. That
includes `dr-plat` (through the keyring's Security and CoreFoundation) and so the desktop app, and
its link fails with `unable to find framework`. `cargo check` for those still works in the
container.
Linking the app needs Apple's SDK, which means a Mac. On one:
cargo build --profile diagnostic -p darkroom-desktop
./tools/fetch-desktop-runtime.sh # ONNX Runtime 1.29.0 with CoreML, Apple silicon only
The fetch script puts `libonnxruntime.dylib` in the user's `runtime/` directory, next to the
models. The app also looks in `Contents/Frameworks` of its own bundle, and in Homebrew's
`/opt/homebrew/lib` and `/usr/local/lib`. Homebrew's build may not include CoreML; the probe
reports that as a failed rung and uses the CPU.
**For whoever packages it.** A notarised app runs with the hardened runtime, whose library
validation refuses to `dlopen` a library signed by another team. A bundled
`Contents/Frameworks/libonnxruntime.dylib` must be signed with the app. A runtime the user
fetched needs the `com.apple.security.cs.disable-library-validation` entitlement, or it will not
load, and the app will be the tract build without saying why beyond one log line.
## 3. The diagnostic build
Every macOS build is in the hands of someone who can send a log but cannot attach a debugger,
so it is set up to log like a debug build while running at release speed.
- **The log says more.** With no `RUST_LOG`, the desktop's default filter is `debug` for every
`dr_*` crate, for `darkroom_desktop`, and for `onnxruntime`. That last one is ONNX Runtime's
own session log, which the engine forwards into `log` on every platform (`session.rs`,
`with_runtime_log`). At `debug` it includes how many nodes each provider took. At `trace`
(`RUST_LOG=onnxruntime=trace`) it lists every node's placement, which is long. The log cap is
the same as everywhere (two files of 4 MiB).
- **Backtraces have line numbers.** `--profile diagnostic` is release plus line tables. On
macOS the tables go into a `.dSYM` beside the executable, and the backtrace in a crash record
finds them only if the `.dSYM` stays next to the binary. Keep it in the bundle.
## 4. What to ask a Mac user for
`~/Library/Logs/darkroom/darkroom.log`, plus `darkroom.log.1` if present, after the first launch
and after the first scan with faces. Console.app lists it under *Log Reports*. The Settings
diagnostics bundle collects the same files. The lines that answer the open questions are:
| Line | What it tells us |
|---|---|
| `inference: ONNX Runtime … from …` / `inference: runtime tract` | Whether a runtime was found, and which one |
| `inference: floor … ms on the CPU provider` | The CPU number for §2's table |
| `inference: CoreML session built in … s` | CoreML's first compile of the probe model |
| `inference: CoreML rejected: …` / `failed: …` | Why the CPU was kept |
| `onnxruntime` lines naming `CoreMLExecutionProvider::GetCapability` | How much of the graph CoreML took |
| `inference: the app died during …` | The crash guard fired, and on what |
| `inference: compiling … for CoreML` / `ready on CoreML in … s` | Each model's compile, and any that CoreML refused |
Also ask for the settings row (*Settings › About › Inference*), which is one line and says the
same in short. When one of these logs comes back with CoreML numbers, they go into
[inference.md §1–2](inference.md), and footnote ⁵ becomes a measurement.
docs/dev/panorama.md
+5 -3
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@@ -467,9 +467,11 @@ the tablet. Three ways to make it viable, none built:
1. **Fill at a quarter of the resolution and upsample.** Sky and scree
tolerate it; twenty-odd tiles, about three minutes on the desktop CPU. A
background job with the outbox's patience, not an interactive one.
2. **int8 on the tablet's Hexagon through QNN**, where the plain-conv design
is the point and the whole graph should run in milliseconds. The setup
exists from the eye-state work; MI-GAN is a candidate for the same path.
2. **The tablet's Hexagon through QNN**, where the plain-conv design is the
point. Measured 2026-10-04 (inference.md §1.5): int8 changes the fill
(16 dB from f32's), so it ships with 16-bit activations and weights —
87 ms a tile against 488 ms on the tablet's CPU, the whole graph on the
NPU, 41 dB from f32 in the hole.
3. **A WGSL runtime for those six operators.** A project of its own, and
the only route that would make it interactive on the desktop.
docs/dev/requirements.md
+60 -1
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@@ -605,6 +605,12 @@ until deleted or renamed. Shipped and imported presets change only the operation
name, so a look applied to a corrected photograph keeps the correction; a copy or a saved
edit replaces everything in scope.
The user's presets sync between devices through each library they open, as one file beside the
camera profiles. Each name merges on its own against what the last exchange left both sides
holding, so presets added on two devices both survive, a deletion on one reaches the other rather
than being restored by it, and an edit outlives a deletion made elsewhere. The write is
conditional on the server's copy, so two devices exchanging at once cannot save over each other.
**FR-DEV-7 — Before/after.** Compare current edit state against the unedited original or against
a chosen history state.
@@ -2437,6 +2443,7 @@ Rationale, evidence, and the eliminated alternatives are recorded in
| D12 | Scope versus pace | **DECIDED 2026-09-19** — settled by events; full scope stands, no v1 date |
| D18 | Derived images | **DECIDED 2026-09-19** — a merge writes a new source file; no multi-source Version |
| D19 | Scene-referred pipeline | **DECIDED 2026-09-27** — edits on unbounded scene-linear colour; one view transform, last; per-body base curves retired |
| D21 | DNG reference tone for raws | **DECIDED 2026-10-03** — the view transform's DNG reference curve (profile's, else ACR3 default, via RGBTone in ProPhoto) is the default for every raw, at contrast 1.5 (a ×1.07 power about grey); measured against Lightroom exports of photographs with neutral look settings; the sigmoid stays a choice |
| D20 | DCP camera profiles | **DECIDED 2026-10-02** — HueSatMap and LookTable as a scene operation after exposure; embedded profile first, then a matched `.dcp`; tone curve not applied; none shipped |
### D11 — product positioning
@@ -2770,7 +2777,59 @@ every render path would have to remember to call it. They travel with the decode
matrix does.
*What it costs.* Every DNG with an embedded profile renders differently; previews refresh only when
rendered again; tablet and desktop release together. The profiles directory does not sync yet.
rendered again; tablet and desktop release together. The profiles directory syncs through the
library's derived folder (camera-profiles.md §13).
### D21 — DNG reference tone for raws · **DECIDED 2026-10-03**
*Decided by measurement, later the same day.* The library's photo gallery holds Lightroom 6
exports of raws that are in the library, each carrying its Camera Raw settings. Clustered by
those settings, 663 exports had none of the house look (Linear curve, no HSL, no parametric
curve, no split toning); 60 of them with their raws, two thirds fitted and one third held out,
rendered by DarkRoom against Lightroom's JPEG (MSE, sRGB 8-bit):
| Rendering | Held-out MSE |
|---|---|
| 0.20.0's sigmoid at its defaults | ~1200 — about 0.7 EV darker, and flatter |
| Sigmoid, exposure, contrast and white fitted | ~150 (contrast 1.73, +0.73 EV) |
| DNG reference curve after baseline exposure, at contrast 1.4 | 224 |
| DNG reference curve, contrast 1.5 | ~150 |
| DNG reference curve, exposure, contrast and white fitted | 143 |
So the DNG reference curve is the default for every raw, and the default contrast is 1.5 — under that
curve a power of 1.5/1.4 about grey (`REFERENCE_CONTRAST` is where the curve is untouched). The
brightness needs nothing: baseline exposure and the curve together land where the earlier exports do. The
profile's look strength, vibrance and saturation bought nothing measurable on those exports. The
user chose to change every photograph rather than keep edited ones on the old rendering. The
fitting tools live outside the repository (`darkroom-lrfit`). *Amended 2026-10-04:* the look strength now defaults to 0. It scored the same at 100, 50 and 0 (held-out MSE 140, 140, 143) and the rendering is 9 % more colourful without it — the table desaturates near-neutral tones, where the default was short of those exports; the user chose more colour.
*Amended earlier the same day:* the default was **not** decided. The measurement below was against
Lightroom previews of photographs carrying the user's Lightroom edits — a house look of HSL
saturation (Blue +58, Aqua +50, …), Highlights −40 and Blacks −20 in every DNG's XMP — so it said
nothing about Camera Raw's base rendering. Under the DNG reference curve `_MG_9080` renders brighter
than its Lightroom preview (mean 0.39 against 0.31). The sigmoid stays the default; the curve
below is a choice; the default is decided by measurement against Lightroom exports of unedited
photographs (the `lr-fit` work). What follows is the original text.
**The view transform has two curves, and Camera Raw's is the default for every raw.** It is the
profile's `ProfileToneCurve`, or the ACR3 default curve where the profile has none or there is no
profile, applied Camera Raw's way — on the largest and smallest channel in linear ProPhoto, the
middle placed proportionally — after `BaselineExposure`. D19's sigmoid stays as the other choice.
Design: [camera-profiles.md](camera-profiles.md) §11–§13.
*Why.* Measured on the library's 6D DNGs after D20 (camera-profiles.md §1): the profile tables
lowered saturation, because Adobe's look tables were tuned to sit under this curve. The user's
complaint was that Lightroom's rendering is more colourful, and this curve is most of the reason.
Chosen by the user over limiting it to raws with a profile, or making it opt-in.
*What it reverses in D19.* D19 rejected per-body curves as defaults because their provenance was
unknown. A DCP's curve and the ACR3 table have known provenance — Adobe's, published — and so the
objection that retired the base curves does not apply. D19's other half stands: nothing before
the view transform clamps, and the curve is the view transform, last.
*What it costs.* Every raw renders differently again, and highlights above display white clip
where the sigmoid rolled them off; Sigmoid is one click away. Previews refresh only when rendered
again; tablet and desktop release together.
### D16 — plugin licensing · **OPEN, post-v1**
docs/dev/sensor-health.md
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@@ -0,0 +1,82 @@
# DarkRoom — Sensor health: a dated defect map per body
**Status:** Spike · 2026-10-04 · not built
**Companion to:** [requirements.md](requirements.md) FR-RAW-3, [catalog.md](catalog.md)
A sensor gains defective photosites as it ages, and a photosite that has gone bad does not recover.
This records, per camera body, which photosites are defective and since when, so that the library
can show how a sensor has aged and the hot-pixel repair can fix the defects a body is known to have
rather than only those that stand out in the frame at hand.
What exists is the measuring tool: `Demosaicer::find_hot_pixels` (the photosites the repair pass
would replace, without replacing them) and `core/dr-gpu/examples/sensor_scan.rs`, which prints them
per frame and, with `--probe`, reads a list of coordinates back out of each frame. The rest of this
document is what a spike with them on the 6D found, and the design it argues for.
---
## 1. What is wanted
- **Settings → Bodies**, one entry per body, with a graph of the defective share over time in two
series: photosites (the raw mosaic) and 2×2 cells holding at least one defective photosite (what
reaches a pixel of the output).
- **A dated defect map** per body, synced with the library like any other catalog data, and
cumulative: an entry is never removed.
- **The repair reads the map** whose date is nearest the frame's, and fixes every defect the body had
by then, whether or not it stands out in that frame.
- One body per model for now: the catalog stores `make model`, not a body serial.
## 2. What the spike found (6D, 2026-10-03)
53 CR2s, up to four per quarter at the highest ISO of a day, 2015 to 2026. The library holds almost
no 6D raws from 2016–2022, so onsets in that span are dated to the span, not the year. `sensor_scan`
ran at 0.8 s per frame, decode included.
**Persistence separates the sensor from the scene.** 4,179 photosites were flagged at least once;
3,554 on one day only (stars, glints, noise). A defect is a photosite that keeps coming back.
**A frame that does not flag a photosite is not evidence it was clean.** The repair's test is
relative to the neighbourhood, so a defect in a lit area does not stand out. Confirmed defects were
flagged in a median 20 % of the frames after their first sighting. Only a frame whose neighbourhood
at that photosite is dark counts, either way.
**The 6D hides some defects itself at high ISO.** (2517, 3172) reads 8,000–13,000 over neighbours
near 200 at ISO 2000–5000, and does not stand out at all at ISO 6400–12800 (82 over 149 on
2025-03-15). The camera appears to map out photosites it knows at those gains. So evidence for this
body comes from ISO ≤ 5000; the cut-off must be learned per body, not fixed.
**Long exposures light everything.** A 9.8 s frame saturated every candidate; it confirms, it does
not date.
**The curve.** Counting a photosite as defective from the first frame where it stands out, provided
it stands out in at least 60 % of the observable frames after that (32 defects; 31 with a clean
observable frame before onset to bracket it):
| Year | Defects | Share of photosites |
|---|---|---|
| 2015 | 2 | 0.1 ppm |
| 2016–2021 | 2 | 0.1 ppm |
| 2022 | 7 | 0.3 ppm |
| 2023 | 26 | 1.3 ppm |
| 2026 | 32 | 1.6 ppm |
(2517, 3172) is the shape every entry should have: clean at ISO 800–1000 in 2015 and at ISO 100–200
in 2016, then 338 over 72 at ISO 100 on 2022-08-13 and in every comparable frame since.
Weak defects exist too, about twice their neighbours ((1814, 3039)); the blind repair misses them in
most frames. A known map would catch them.
## 3. Design it argues for
- **Evidence per frame, per known photosite**: observable (dark neighbourhood, ISO inside the
body's band) and, if so, lit or clean. Not just the frame's flagged list.
- **A map entry is a bracket**: last clean observation, first lit observation, strength, kind. Onset
lies between the two; the graph plots it at the first, and can show the bracket.
- **The repair**: every entry whose first lit date is on or before the frame's capture date; for an
entry whose bracket contains the date, probe the photosite in the frame itself.
- **Storage and sync**: catalog tables created on first use (as `albums` does), so no schema bump
breaks an older peer. They travel in the snapshot by default. Merge is a set union of photosites
per body, the earlier first-lit and the later last-clean winning, which makes it commutative and
keeps the map cumulative.
- **Work**: a sample, not the library. Frames are picked for what they can reveal (dark, mid ISO,
long exposures), a few per body per month, and after the first pass only new imports are read.
docs/dev/traceability.md
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docs/dev/windows.md
+2 -2
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@@ -287,7 +287,6 @@ $LOCALAPPDATA\Programs\DarkRoom\
models\
scrfd_500m_640.onnx scrfd_2.5g_640.onnx scrfd_10g_640.onnx arcface_mbf_b1.onnx
2d106det_b1.onnx ocec_s_b1.onnx sgc_l_48_b1.onnx
scrfd_500m_640.int8.onnx scrfd_2.5g_640.int8.onnx scrfd_10g_640.int8.onnx
yolo26s-sem-ade20k.onnx yolo26s-sem-ade20k.classes.json categories.txt
migan-512.onnx
manual\
@@ -297,7 +296,8 @@ $LOCALAPPDATA\Programs\DarkRoom\
```
Plus a Start Menu shortcut, and nothing on the desktop unless the user ticks it. The models are
the same ten files the APK bundles and the PKGBUILD installs; `models\` beside the executable is
the f32 files the APK bundles and the PKGBUILD installs — not the APK's quantised siblings, which
only a Hexagon runs; `models\` beside the executable is
where §3.2's lookup finds them. **No `LICENSE` yet**: the repository has no licence file at its
root (the Arch package points at the system's shared GPL text), so the installer has no licence
page until one is added — a one-file change, and the `.nsi` says where the page then goes. The face weights carry the research-only grant that
docs/manual/README.md
+47
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@@ -205,6 +205,53 @@ the sensor recorded.
![Zooming to 1:1 with a double-click, panning, then further in with the wheel](media/develop-zoom.gif)
### AI denoise
How every raw is developed. `AI Denoise`, at the top of the Adjust panel,
replaces how the camera's raw data is turned into colour: a network trained
on this library's own photographs removes the noise and the blotches of
colour that come with it, while keeping the fine detail. Look at it at
1:1, where noise lives.
`Method` chooses how:
- `Best`, the default: two networks, one for smooth areas and one for
edges, blended where each is better. The cleanest skies and the sharpest
lettering, and the slowest.
- `Medium`: one network taught by `Best`. Nearly as clean in smooth areas,
a little softer on hard edges, in about a third of the time.
- `Fast`: a smaller one, taught the same way. Visibly noisier at very high
ISO than the other two, but still far cleaner than none, and quick.
- `Bilinear`: the camera's ordinary conversion, noise and all.
The photograph shows the camera's ordinary conversion while the network
works, with its progress in the bar at the top, and changes when it is
done — on a laptop's graphics card, about two and a half seconds for a
20-megapixel photograph with `Best` and under one with the other two;
longer on a processor alone or on the tablet. The first photograph after
installing waits a few minutes more while the graphics card prepares each
network, once. The result is kept, so a photograph opened again,
or exported, does not wait a second time, and switching back to a method
already used is quick.
`Strength` eases it off: below 100 % it puts back some of what was removed,
as grain without colour, for a picture that does not look too smooth.
The lamp and railing of a night frame at ISO 8000, at 1:1, by each method:
| Bilinear | Fast |
|---|---|
| ![The railing and the lamp at ISO 8000, as the camera recorded them](media/develop-denoise-bilinear.png) | ![The same, with the Fast network](media/develop-denoise-fast.png) |
| **Medium** | **Best** |
| ![The same, with the Medium network](media/develop-denoise-medium.png) | ![The same, with the Best network](media/develop-denoise-best.png) |
It works on raw files from any camera with the usual colour pattern of
red, green and blue squares — not on JPEGs, and not yet on Fujifilm's
X-Trans. How noisy the camera is at each ISO was measured for the Canon
EOS 6D; for other cameras it is read from a DNG's own figures or
estimated from the photograph, and the finished job in the activity list
says which. An export uses
the method the photograph has.
### Moving between photographs
The roll along the foot of the canvas holds the photographs the grid was
docs/manual/index.html
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@@ -117,6 +117,7 @@ th { color: var(--ink-dim); font-weight: 600; }
<ul>
<li><a href="#light">Light</a></li>
<li><a href="#looking-closer">Looking closer</a></li>
<li><a href="#ai-denoise">AI denoise</a></li>
<li><a href="#moving-between-photographs">Moving between photographs</a></li>
<li><a href="#white-balance-from-the-photograph">White balance from the photograph</a></li>
<li><a href="#composing">Composing</a></li>
@@ -287,6 +288,47 @@ wheel zooms to any amount in between. Past 1:1 the file's own pixels are
drawn as hard-edged blocks rather than smoothed, so what you see is what
the sensor recorded.</p>
<figure><img loading="lazy" src="media/develop-zoom.gif" alt="Zooming to 1:1 with a double-click, panning, then further in with the wheel"><figcaption>Zooming to 1:1 with a double-click, panning, then further in with the wheel</figcaption></figure>
<h3 id="ai-denoise">AI denoise</h3>
<p>How every raw is developed. <code>AI Denoise</code>, at the top of the Adjust panel,
replaces how the camera's raw data is turned into colour: a network trained
on this library's own photographs removes the noise and the blotches of
colour that come with it, while keeping the fine detail. Look at it at
1:1, where noise lives.</p>
<p><code>Method</code> chooses how:</p>
<ul>
<li><code>Best</code>, the default: two networks, one for smooth areas and one for
edges, blended where each is better. The cleanest skies and the sharpest
lettering, and the slowest.</li>
<li><code>Medium</code>: one network taught by <code>Best</code>. Nearly as clean in smooth areas,
a little softer on hard edges, in about a third of the time.</li>
<li><code>Fast</code>: a smaller one, taught the same way. Visibly noisier at very high
ISO than the other two, but still far cleaner than none, and quick.</li>
<li><code>Bilinear</code>: the camera's ordinary conversion, noise and all.</li>
</ul>
<p>The photograph shows the camera's ordinary conversion while the network
works, with its progress in the bar at the top, and changes when it is
done — on a laptop's graphics card, about two and a half seconds for a
20-megapixel photograph with <code>Best</code> and under one with the other two;
longer on a processor alone or on the tablet. The first photograph after
installing waits a few minutes more while the graphics card prepares each
network, once. The result is kept, so a photograph opened again,
or exported, does not wait a second time, and switching back to a method
already used is quick.
<code>Strength</code> eases it off: below 100 % it puts back some of what was removed,
as grain without colour, for a picture that does not look too smooth.</p>
<p>The lamp and railing of a night frame at ISO 8000, at 1:1, by each method:</p>
<table><thead><tr><th>Bilinear</th><th>Fast</th></tr></thead><tbody>
<tr><td><img src="media/develop-denoise-bilinear.png" alt="The railing and the lamp at ISO 8000, as the camera recorded them" /></td><td><img src="media/develop-denoise-fast.png" alt="The same, with the Fast network" /></td></tr>
<tr><td><strong>Medium</strong></td><td><strong>Best</strong></td></tr>
<tr><td><img src="media/develop-denoise-medium.png" alt="The same, with the Medium network" /></td><td><img src="media/develop-denoise-best.png" alt="The same, with the Best network" /></td></tr>
</tbody></table>
<p>It works on raw files from any camera with the usual colour pattern of
red, green and blue squares — not on JPEGs, and not yet on Fujifilm's
X-Trans. How noisy the camera is at each ISO was measured for the Canon
EOS 6D; for other cameras it is read from a DNG's own figures or
estimated from the photograph, and the finished job in the activity list
says which. An export uses
the method the photograph has.</p>
<h3 id="moving-between-photographs">Moving between photographs</h3>
<p>The roll along the foot of the canvas holds the photographs the grid was
showing; click one to open it. The right arrow, <code>D</code> or space opens the next,
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@@ -14,6 +14,13 @@ Both come from `https://huggingface.co/Ultralytics/YOLO26`. The face weights in
The keypoint weights in `keypoints/` and the border filler in `inpaint/` are
the other two, and the easiest — see the last two sections.
Every quantised sibling — `*.int8.onnx`, `*.a16w8.onnx`, `*.a16w16.onnx`, the
forms the tablet's Hexagon runs (`tools/quantise-models.sh`) — is the same
weights rounded, and carries exactly the grant of the file it was made from.
What calibration adds is one minimum and maximum per tensor: from public COCO
val2017 photographs (CC-BY 4.0) for the image models, and for the denoiser from
the same training tiles its weights were learned from. No image is in the files.
## The grant
**Ultralytics releases YOLO under AGPL-3.0**, and the weights carry the same
@@ -122,3 +129,21 @@ position of any model here. The training set is Places2, a research dataset,
but the weights are released under the repository's licence without a
data-derived restriction (contrast the gaze models §7 of the requirements
declined, and the InsightFace grant of D13).
## `denoise/` — the mosaic denoiser, the project's own
| File | Source | Trained on | Used by |
|---|---|---|---|
| `denoise/mosaic-best-1408.onnx` | trained in the `darkroom-denoise` repository (2026-10-04, run `final`, 30 000 steps, from the experts of runs `m2` and `edges-100`) | 1,701 of the maintainer's own base-ISO raws and 6,000 synthetic scenes the repository draws itself, with the Canon EOS 6D's measured noise added | the learned demosaic and denoise, Best (FR-DEV-3g) |
| `denoise/mosaic-medium-1408.onnx` | distilled from `final` in the same repository (2026-10-04, run `student-m`, 20 000 steps, from `m2`) | the same | Medium |
| `denoise/mosaic-fast-1408.onnx` | distilled from `final` (2026-10-04, run `student-s`, 30 000 steps, from scratch) | the same | Fast |
U-Nets of plain 3×3 convolutions, ReLU, strided and transposed
convolutions and additive skips — no third-party architecture code or
weights — and, for Best, two of them blended per photosite by a small gate
network of the same parts. Each at a fixed `1×1×1408×1408` for `mosaic`
and `sigma`, exported by `python -m denoise.export` in `darkroom-denoise`;
the `.a16w16.onnx` siblings are the same networks quantised for the
Hexagon by `tools/quantise-models.sh`. Trained only on
photographs the maintainer owns, so the weights carry no grant but the
project's own: GPL-3.0-or-later, like the code (denoise.md §10).
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directory as the pair, so a hand-placed pair does not pick up a package's eye models from a
directory it otherwise outranks.
scrfd_500m_640.int8.onnx 0.8 MB the same three, in the form the Hexagon NPU takes
scrfd_2.5g_640.int8.onnx 0.9 MB (docs/dev/inference.md §5) — opset 17, per-channel int8
scrfd_10g_640.int8.onnx 4.3 MB weights, uint8 activations, calibrated on 96 photographs
scrfd_500m_640.a16w8.onnx the same three, in the form the Hexagon NPU takes
scrfd_2.5g_640.a16w8.onnx (docs/dev/inference.md §1.5) — 16-bit activations,
scrfd_10g_640.a16w8.onnx per-channel 8-bit weights, calibrated on 300 photographs
2d106det_b1.a16w8.onnx the landmarks, likewise
The int8 files are **derived** by `tools/quantise-models.sh` from the f32 ones beside them and
travel with them: the engine loads the `.int8.onnx` sibling when the device's backend wants it and
the canonical file otherwise, and a library indexed on the int8 form records it as a different
detector (`scrfd_500m_i8+w600k_mbf`), because it finds a different set of faces. Every other
platform ignores them. The embedder has no int8 form and never will (§7 of the same document).
These are **derived** by `tools/quantise-models.sh` from the f32 files beside them and travel with
them: the engine loads the `.a16w8.onnx` sibling when the device's backend wants it and the
canonical file otherwise, and a library indexed on that form records it as a different detector
(`scrfd_500m_a16+w600k_mbf`), because it finds a different set of faces. Every other platform
ignores them, and the Windows installer leaves them out. The embedder and the eye classifiers have
no quantised form: the embedder's vectors must compare across devices (inference.md §7), and the
classifiers cost a millisecond on the CPU.
The detectors were int8 until §1.5 measured them on the tablet: int8 found 94–95% of f32's faces
at 40–80 px, A16W8 all of them. A tablet that indexed under the int8 ids keeps those rows.
**A clone without git-lfs gets a ~130-byte pointer where each model should be.** Both packagers check
for exactly that and refuse, rather than shipping the pointer and failing inside tract on the user's
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