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ruv
22ca3da48c
- cog-person-count: no path deps, clean publish. - cog-pose-estimation: added explicit version="0.3.1" to the wifi-densepose-train path dep (crates.io rejects path-only deps). - cog-ha-matter: keeps publish=false; the published wifi-densepose-sensing-server@0.3.0 does not expose the `mqtt` feature this cog requires. Note added inline; republish sensing-server with the feature exposed before dropping the flag. Co-Authored-By: claude-flow <ruv@ruv.net>
54 lines
1.9 KiB
TOML
54 lines
1.9 KiB
TOML
[package]
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name = "cog-pose-estimation"
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version.workspace = true
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edition.workspace = true
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authors.workspace = true
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license.workspace = true
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repository.workspace = true
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description = "Cognitum Cog: 17-keypoint pose estimation from WiFi CSI. See ADR-100 (packaging) + ADR-101 (this Cog)."
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[[bin]]
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name = "cog-pose-estimation"
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path = "src/main.rs"
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[lib]
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name = "cog_pose_estimation"
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path = "src/lib.rs"
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[dependencies]
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clap = { version = "4", features = ["derive"] }
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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thiserror = "1"
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
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tokio = { version = "1", features = ["rt-multi-thread", "macros", "signal", "time"] }
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sha2 = "0.10"
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hex = "0.4"
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# Sensing-server subscriber over HTTP — kept minimal; no full reqwest dep
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ureq = { version = "2", default-features = false, features = ["tls"] }
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# Inference backend — Candle, CPU by default. The `cuda` feature gate
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# below pulls in CUDA support on hosts that have it. Pinned to 0.9 to
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# match the training script that produced pose_v1.safetensors.
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candle-core = { version = "0.9", default-features = false }
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candle-nn = { version = "0.9", default-features = false }
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safetensors = "0.4"
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# wifi-densepose-train re-exports the model types we need; depend by path
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# inside the workspace.
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wifi-densepose-train = { version = "0.3.1", path = "../wifi-densepose-train", default-features = false }
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[dev-dependencies]
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tempfile = "3"
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[features]
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default = []
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# Use CUDA for inference on hosts with a CUDA-capable GPU. Off by
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# default so CI on plain Linux/Windows boxes still builds; flip on for
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# the GPU-dev path on ruvultra.
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cuda = ["candle-core/cuda", "candle-nn/cuda"]
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# Stub for the future Hailo HEF runtime path. The actual Hailo
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# integration lives in the companion v0-appliance crate `cognitum-hailo`;
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# this crate keeps a feature flag so the binary can compile without the
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# Hailo SDK in CI.
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hailo = []
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