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https://github.com/storytold/effectcraft.git
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bflatasticandClaude Opus 5.5
e43bf66775
- effectcraft-segment: a FaceModel interface (find a face in a region, follow it; a Topology maps the model's points to the tracker's landmarks and outline), a TensorFlow Lite reader and interpreter (float ops, float16 weights folded; matches ai-edge-litert to ~1e-6), and MediaPipe Face Landmarker (Google, Apache-2.0: BlazeFace + Face Mesh V2, 3.8 MB, pinned SHA-256). The registry gains a task and authors per model; installed weights get a NOTICE. - effectcraft-track: FaceTracker::new_with uses the model, falls back to the classical fitter when it finds no face; chin and jaw come from the outline either way so measurements agree. - Engine: roto_models becomes models (both tasks); face.models / face.model.* commands, Settings ▸ Face Tracking, `wait` on select/install/download for scripts; track.mask reports faceModel. - Credits: ATTRIBUTION.md lists the optional models and the Apache-2.0 specs the code follows. - Docs: README, ROADMAP (G7), gaps, parity (TRK-3 score unchanged pending G1), agents, preferences, architecture, segment README. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
16 lines
563 B
TOML
16 lines
563 B
TOML
[package]
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name = "effectcraft-segment"
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description = "Swappable trained models for Roto Brush and face tracking: the MaskModel and FaceModel interfaces, a registry of open-licensed models, PyTorch checkpoint and TensorFlow Lite readers, a pure-Rust MobileSAM and MediaPipe Face Landmarker"
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version.workspace = true
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edition.workspace = true
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license.workspace = true
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rust-version.workspace = true
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[lints]
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workspace = true
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[dependencies]
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miniz_oxide = "0.8"
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ndarray = { version = "0.17", default-features = false, features = ["std"] }
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rayon = { workspace = true }
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