dtourolle
6b51726322
Two MIT classifiers from the same author as the reference pipeline's whole-body detector: OCEC answers P(open) for one 40×24 eye, SGC P(sunglasses) for a 48×48 head. Both load in tract once their batch dimension is pinned by tools/fix-face-model-shapes.sh, like the embedder. The crops come through the same fitted similarity the aligned face does, so an eye window is a constant in template units rather than a second warp, and a tilted head yields an upright eye. Measured on 60 proxies from the reference library: the eye window plateaus at 22×11, the S variant beats M and L (which overfit their own domain), and for sunglasses the aligned face beats a head framing but the higher of the two catches 11 of 12 pairs against 9 for either alone. The reading keeps both eyes and the sunglasses number apart, because a wink averages to the least informative value and a lens of dark glass draws a confident answer from the eye classifier — over a woman in sunglasses it read the right eye 0.97 open. Sunglasses take precedence, and a face behind them is neither open nor a blink.
52 lines
1.6 KiB
TOML
52 lines
1.6 KiB
TOML
[package]
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name = "dr-face"
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version.workspace = true
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edition.workspace = true
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rust-version.workspace = true
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license.workspace = true
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[dependencies]
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thiserror.workspace = true
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log.workspace = true
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# Inference. `ort` is the API; **tract is the engine** — see the workspace
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# manifest, and docs/faces.md §3, for why the C++ ONNX Runtime is not linked.
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ort = { workspace = true, optional = true }
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ort-tract = { workspace = true, optional = true }
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ndarray = { workspace = true, optional = true }
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[dev-dependencies]
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zune-jpeg.workspace = true
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env_logger.workspace = true
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# The M1 probe drives `ort` directly so it can print the raw load error.
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ort = { workspace = true }
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ort-tract = { workspace = true }
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[[example]]
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name = "probe"
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required-features = ["inference"]
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[[example]]
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name = "faces"
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required-features = ["inference"]
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[[example]]
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name = "eyes"
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required-features = ["inference"]
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[features]
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# Nothing on by default, and in particular **no `embedded-model`**: the weights
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# are not a build input and never become one (docs/faces.md §2.2). A feature
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# flag that *could* embed them is a flag someone eventually sets in a packaging
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# script, and the InsightFace grant does not survive that.
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default = []
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# The ONNX runtime, and the two stages that need it.
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#
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# Separable because the accuracy of this subsystem lives in `calibrate` and
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# `cluster`, which are arithmetic over embeddings with no model in them. They
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# must be testable against synthetic embeddings on a machine with no weights on
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# it — a test suite that needs a research-licensed download is a test suite
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# that does not run in CI.
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inference = ["dep:ort", "dep:ort-tract", "dep:ndarray"]
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