docs: add AGENTS.md skeleton #1
+20
@@ -10,6 +10,19 @@ launcher/**/xcuserdata/
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launcher/**/*.xcuserstate
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launcher/**/Package.resolved
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# iphone-arbody — xcodeproj is generated by xcodegen, Local.xcconfig
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# carries personal Apple Developer Team ID. Both are local-only.
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iphone-arbody/ARBodyTracker.xcodeproj/
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iphone-arbody/Config/Local.xcconfig
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iphone-arbody/**/xcuserdata/
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iphone-arbody/**/*.xcuserstate
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iphone-arbody/build/
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iphone-arbody/DerivedData/
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# SwiftPM build + editor artifacts
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.build/
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.swiftpm/
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# openFrameworks — on garde les shaders + settings.json pour qu'ils
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# arrivent sur les autres machines, mais on ignore les binaires.
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oscope-of/bin/*
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@@ -44,3 +57,10 @@ sound_algo/*.log
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.vscode/
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*.swp
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*~
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# tool session state (claude-mem / remember skill)
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.remember/
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# macOS / iCloud collision artifacts (auto-created on rename)
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*\ 2
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*\ 2.*
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@@ -0,0 +1,83 @@
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# AGENTS.md
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Guidance for AI coding agents (Claude Code, Aider, Cursor, etc.) working in this repo.
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## Project
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`AV-Live` — live-coding audio-visual performance system: SuperCollider sound engine, openFrameworks visualiser driven by a Hantek 6022BL oscilloscope, and a SwiftUI menubar launcher orchestrating everything. Public, GPL-3. Repo `electron-rare/AV-Live`, branch `main`. Multi-host: GrosMac (source), macm1 (sink / Multi-HMR + Apple Vision ANE), iPhone 16 Pro (ARKit/LiDAR pub).
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|
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## Tech stack (per sub-project)
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| Sub-project | Stack |
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|-------------|-------|
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| `sound_algo/` | SuperCollider (sclang + scsynth), 1099 SynthDefs, 345 tracks |
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| `oscope-of/` | openFrameworks C++, libusb (Hantek bulk), GLSL 150 / GL 3.2 core |
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| `launcher/` | SwiftUI menubar app, Swift Package Manager |
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| `data_only_viz/` | Python 3.11+ via `uv`, native Metal (pyobjc), multi-backend pose |
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| `data_feeds/` | Python data ingestion |
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| `web_realart/` | Node.js, Express, OSC bridge |
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| `avlivebody-mac/` | SwiftUI body-tracking client (ARKit/SMPL-X mesh, ad-hoc signed for local dev) |
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| `iphone-arbody/` | iOS app, ARBodyTracker, publishes `/body3d/kp` via OSC |
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## Commands
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```bash
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# Python sub-projects (uv only)
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cd data_only_viz && uv sync && uv run python -m data_only_viz
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cd data_feeds && uv sync
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# openFrameworks
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cd oscope-of && make -j
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# Web bridge
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cd web_realart && npm install && npm start
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# Swift
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open launcher/Package.swift # or xcodebuild from CLI
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open avlivebody-mac/avlivebody.xcodeproj
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The command The command `open avlivebody-mac/avlivebody.xcodeproj` appears incorrect/inconsistent with the `avlivebody-mac` docs and `project.yml` (project name is `AVLiveBody.xcodeproj`, and it is generated/ignored). This should point to the generated `AVLiveBody.xcodeproj` after running `xcodegen generate`, otherwise new contributors will hit a dead path.
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```
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## Conventions
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- Commits: subject ≤ 50 chars, body ≤ 72, no underscore in scope, no AI attribution, never `--no-verify` (hooks enforce).
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- Branches: `feat/<name>`, `fix/<name>`, `docs/<name>`, `refactor/<name>`, `chore/<name>`.
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- Language: French to the user, English in code/comments/commits.
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- No emojis in code/docs/commits unless explicitly requested.
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- Python: **always `uv`** (never pip/poetry/conda directly).
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- `.gitignore` already excludes `*.pt`, `*.ckpt`, `*.safetensors`, `*.mlpackage` at root — don't commit weights.
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- License: GPL-3 (whole repo) — keep new files under a compatible license header when adding third-party code.
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## File layout
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- `sound_algo/` — SC sound engine (own `CLAUDE.md`)
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- `oscope-of/` — visualiser
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- `launcher/` — macOS menubar
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- `data_only_viz/` — pose / mesh / body tracking pipeline (Metal)
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- `data_feeds/` — data ingestion
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- `web_realart/` — web UI + OSC bridge
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- `avlivebody-mac/`, `iphone-arbody/` — body-tracking clients
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- `shared/` — cross-sub-project assets
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- `third_party/` — vendored deps (CHECK before adding to root deps)
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- `tools/` — helper scripts
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- `docs/superpowers/plans/` — in-flight plans/specs
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- `AV-Live-corrupted-20260514/` — quarantined corrupted snapshot, do not touch
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## Domain-specific gotchas
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- **mDNS hostnames are required** (`grosmac.local`, `supra-m1.local`) for `AVBODY_HOST` / `MULTIHMR_REMOTE_HOST`. They resist DHCP changes (iPhone hotspot reassigns 172.20.10.x routinely).
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- **`POSE_FILTER` chain ordering is load-bearing**: default is `median+kalman+lookahead+ik`. Extras must be inserted at the right stage — `one_euro_joints` BEFORE kalman, `one_euro_bones` AFTER SMPL-X fusion in `multi.py`. `arkit_fuse` overrides 14 body slots with ARKit ARSkeleton3D from iOS app via `/body3d/kp` on `:57128` (always-on listener).
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- **`ICP_FUSION=1`** requires `ICP_LIDAR_HOST` (iPhone IP), `ICP_LIDAR_PORT` (default 5500, iPhone ARMesh TCP), and an extrinsic JSON at `~/.config/av-live/lidar_extrinsic.json`. See `docs/ICP_FUSION.md`.
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- **iPhone OSC port `57128`** is hardcoded as the publish target for `/body3d/kp` — don't reassign.
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- **`avlivebody-mac` requires ad-hoc signing for local dev** (fixed in `85589f2`). Don't strip the signing identity.
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- **`onVideoFrame` retain cycle in avlivebody** was fixed in `3b5f29e` — when adding new frame callbacks, mind the strong-self capture.
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- **AVLive-Body legacy** has been archived (`9e1482e`); the canonical client is `avlivebody-mac`. Don't reintroduce paths to the old project.
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- **macm1 = sink** (Multi-HMR CoreML + Apple Vision ANE + SMPL-X TCP); GrosMac = source. Mind the direction when wiring new OSC topics.
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- **Each major sub-project has its own `CLAUDE.md`** — closest wins. Put cross-cutting rules here, sub-project specifics in the nested file.
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## When in doubt
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- Read root `CLAUDE.md` and the nested `CLAUDE.md` of the sub-project you're editing.
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- Recent commits: `git log --oneline -20`.
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- Plans: `docs/superpowers/plans/`.
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- Cluster context: `~/CLAUDE.md` (GrosMac / macm1 / iPhone topology).
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- For sound: read `sound_algo/CLAUDE.md` before touching SynthDefs.
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@@ -27,6 +27,27 @@ Toujours répondre en français à l'utilisateur. Code, commentaires de code, co
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| Bridge web / UI de live coding | `web_realart/` |
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| Plans / specs en cours | `docs/superpowers/plans/` |
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## Network topology
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| Host | mDNS | IP (DHCP) | Role |
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|------|------|-----------|------|
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| GrosMac M5 | `grosmac.local` | LAN | Source + visualisation (AVLiveBody + data_only_viz + data_feeds) |
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| macm1 M1 Max | `supra-m1.local` | `192.168.0.175` | Sink (Multi-HMR CoreML + Apple Vision ANE + SMPL-X TCP) |
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| iPhone 16 Pro | (Personal Hotspot) | DHCP | ARKit/LiDAR pub via OSC `/body3d/kp` |
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`AVBODY_HOST` / `MULTIHMR_REMOTE_HOST` accept mDNS hostnames — résiste aux changements DHCP (notamment iPhone hotspot 172.20.10.x).
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## Environment variables
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| Env | Default | Effect |
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|-----|---------|--------|
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| `POSE_FILTER` | `median+kalman+lookahead+ik` | filter chain stages — extra: `one_euro_joints` (joint-space CHI 2012 One Euro, inserted before kalman), `one_euro_bones` (bone-vector One Euro applied after SMPL-X fusion in multi.py), `arkit_fuse` (overrides 14 body slots with ARKit ARSkeleton3D from the iOS app, expects /body3d/kp on :57128) |
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| `IPHONE_OSC_PORT` | `57128` | UDP port the iPhone ARBodyTracker app pushes /body3d/kp to (always-on listener in data_only_viz) |
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| `ICP_FUSION` | `0` | `1` to enable LiDAR↔SMPL-X ICP fusion (cf. `docs/ICP_FUSION.md`) |
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| `ICP_LIDAR_HOST` | _(unset)_ | iPhone ARBodyTracker IP when `ICP_FUSION=1` |
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| `ICP_LIDAR_PORT` | `5500` | iPhone ARMesh TCP port |
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| `ICP_LIDAR_EXTRINSIC` | `~/.config/av-live/lidar_extrinsic.json` | extrinsic JSON path |
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## Conventions globales
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- Python : **uv** systématiquement (jamais pip/poetry/conda directs).
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@@ -0,0 +1,4 @@
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*.xcodeproj/
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Config/Local.xcconfig
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.build/
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.swiftpm/
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@@ -0,0 +1,3 @@
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// Copy to Config/Local.xcconfig and set your Apple Developer Team ID.
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// Config/Local.xcconfig is gitignored.
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DEVELOPMENT_TEAM = YOUR_TEAM_ID
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@@ -0,0 +1,8 @@
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#include? "Local.xcconfig"
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MACOSX_DEPLOYMENT_TARGET = 15.0
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SWIFT_VERSION = 5.10
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// Manual ad-hoc signing for local dev (no Apple Mac Development cert
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// required). Override here or via target settings for distribution.
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CODE_SIGN_STYLE = Manual
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CODE_SIGN_IDENTITY = -
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@@ -0,0 +1,62 @@
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# AVLiveBody (macOS)
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Native macOS Xcode app that renders SMPL-X body meshes in RealityKit
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from the USB iPhone body-tracking pipeline (ARBodyTracker -> Multi-HMR
|
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worker -> AVLiveBody scene).
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|
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## Prerequisites
|
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- macOS 15+
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- Xcode 16+
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- `xcodegen` — `brew install xcodegen`
|
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## First-time setup
|
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1. Copy the CoreML model into the app resources (required, gitignored,
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~195 MB). Without it the app degrades to skeleton-only rendering:
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```
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cp -R ~/.cache/av-live-multihmr/multihmr_full_672_s.mlpackage \
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avlivebody-mac/Sources/AVLiveBody/Resources/
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```
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2. Create your local xcconfig and set your signing team:
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```
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cp Config/Local.xcconfig.example Config/Local.xcconfig
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# Edit Config/Local.xcconfig:
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# DEVELOPMENT_TEAM = <your Apple Developer Team ID>
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```
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## Build
|
||||
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Generate the Xcode project (run after every `project.yml` change) then
|
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open or build from the CLI:
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|
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```
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cd avlivebody-mac
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xcodegen generate
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open AVLiveBody.xcodeproj
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```
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CLI build / test:
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```
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xcodebuild -project AVLiveBody.xcodeproj -scheme AVLiveBody \
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-destination 'platform=macOS' build
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xcodebuild -project AVLiveBody.xcodeproj -scheme AVLiveBody \
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-destination 'platform=macOS' test
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```
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## Runtime requirements
|
||||
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A tethered iPhone running the matching `ARBodyTracker` iOS app over USB
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is required for body input. See `iphone-arbody/` for the iOS side.
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|
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## Architecture
|
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|
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- Design spec:
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`docs/superpowers/specs/2026-05-18-avlivebody-macos-rewrite-design.md`
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- Implementation plan:
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`docs/superpowers/plans/2026-05-18-avlivebody-macos-rewrite.md`
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@@ -0,0 +1,68 @@
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import Cocoa
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import CoreVideo
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import SwiftUI
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/// Forces a regular, keyboard-focusable foreground app.
|
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final class AppDelegate: NSObject, NSApplicationDelegate {
|
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func applicationDidFinishLaunching(_ notification: Notification) {
|
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NSApp.setActivationPolicy(.regular)
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NSApp.activate()
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}
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}
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@main
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struct AVLiveBodyApp: App {
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@NSApplicationDelegateAdaptor(AppDelegate.self)
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private var appDelegate
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||||
|
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var body: some Scene {
|
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WindowGroup {
|
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ContentView()
|
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.frame(minWidth: 900, minHeight: 600)
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}
|
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}
|
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}
|
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|
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@MainActor
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struct ContentView: View {
|
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@StateObject private var consumer = USBSkeletonConsumer()
|
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private let controller = SceneController()
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private let multiHMR: MultiHMRCoreML? = MultiHMRCoreML()
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/// Placeholder intrinsics until a `.meta` frame supplies real ones.
|
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private let cameraK: [Float] = [
|
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672, 0, 336, 0, 672, 336, 0, 0, 1,
|
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]
|
||||
|
||||
var body: some View {
|
||||
ZStack(alignment: .top) {
|
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SceneView(controller: controller)
|
||||
StatusBar(consumer: consumer)
|
||||
}
|
||||
.onAppear { wire() }
|
||||
.onDisappear { consumer.stop() }
|
||||
.onReceive(consumer.$skeletons) { skeletons in
|
||||
controller.updateSkeleton(skeletons)
|
||||
}
|
||||
}
|
||||
|
||||
private func wire() {
|
||||
let controller = self.controller
|
||||
let multiHMR = self.multiHMR
|
||||
let cameraK = self.cameraK
|
||||
consumer.onVideoFrame = { [weak consumer] pixelBuffer in
|
||||
MainActor.assumeIsolated {
|
||||
controller.updateVideo(pixelBuffer)
|
||||
guard let consumer else { return }
|
||||
if let hmr = multiHMR {
|
||||
let raw = hmr.infer(
|
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pixelBuffer, cameraK: cameraK)
|
||||
let fused = BodyFusion.fuse(
|
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persons: raw,
|
||||
skeletons: consumer.skeletons)
|
||||
controller.updateMesh(fused)
|
||||
}
|
||||
}
|
||||
}
|
||||
consumer.start()
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
import Foundation
|
||||
import simd
|
||||
|
||||
/// ARKit/Multi-HMR world coords (y up, z back) -> RealityKit world
|
||||
/// coords (y up, z forward). Apply to every vertex/translation that
|
||||
/// crosses from source pipeline space into the scene.
|
||||
@inline(__always)
|
||||
func arkitToRealityKit(_ v: SIMD3<Float>) -> SIMD3<Float> {
|
||||
SIMD3<Float>(v.x, -v.y, -v.z)
|
||||
}
|
||||
|
The doc comment says the conversion keeps The doc comment says the conversion keeps `y` pointing up ("y up" -> "y up"), but the implementation negates `y` (`SIMD3(v.x, -v.y, -v.z)`). Please either fix the comment to match the actual transform, or adjust the transform so it matches the documented axis convention (otherwise it’s easy to apply the wrong transform across the codebase).
|
||||
@@ -0,0 +1,17 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>CFBundleName</key><string>AVLiveBody</string>
|
||||
<key>CFBundleIdentifier</key><string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
|
||||
<key>CFBundleExecutable</key><string>$(EXECUTABLE_NAME)</string>
|
||||
<key>CFBundlePackageType</key><string>APPL</string>
|
||||
<key>CFBundleShortVersionString</key><string>1.0</string>
|
||||
<key>CFBundleVersion</key><string>1</string>
|
||||
<key>LSMinimumSystemVersion</key><string>15.0</string>
|
||||
<key>NSCameraUsageDescription</key>
|
||||
<string>Receives the tethered iPhone camera over USB.</string>
|
||||
<key>NSLocalNetworkUsageDescription</key>
|
||||
<string>Connects to the tethered iPhone over USB (usbmuxd).</string>
|
||||
</dict>
|
||||
</plist>
|
||||
@@ -0,0 +1,68 @@
|
||||
import AppKit
|
||||
import Foundation
|
||||
import RealityKit
|
||||
import simd
|
||||
|
||||
/// Renders SMPL-X dense body meshes (10475 vertices) from Multi-HMR.
|
||||
/// Triangle indices come from the bundled `smplx_faces.bin`
|
||||
/// (flat UInt32 triplets).
|
||||
@MainActor
|
||||
final class MeshEntity {
|
||||
let root = Entity()
|
||||
|
||||
private static let vertexCount = 10475
|
||||
private let faces: [UInt32]
|
||||
private var pools: [Int: ModelEntity] = [:]
|
||||
private let material = SimpleMaterial(
|
||||
color: NSColor(white: 0.8, alpha: 1.0),
|
||||
roughness: 0.5, isMetallic: false)
|
||||
|
||||
init() {
|
||||
faces = MeshEntity.loadFaces()
|
||||
}
|
||||
|
||||
func update(_ persons: [MultiHMRPerson]) {
|
||||
for (idx, person) in persons.enumerated() {
|
||||
let entity = pools[idx] ?? {
|
||||
let e = ModelEntity()
|
||||
root.addChild(e)
|
||||
pools[idx] = e
|
||||
return e
|
||||
}()
|
||||
guard let mesh = buildMesh(person.vertices) else { continue }
|
||||
entity.model = ModelComponent(mesh: mesh,
|
||||
materials: [material])
|
||||
let t = person.translation
|
||||
entity.transform.translation = arkitToRealityKit(t)
|
||||
entity.isEnabled = true
|
||||
}
|
||||
for idx in pools.keys where idx >= persons.count {
|
||||
pools[idx]?.isEnabled = false
|
||||
}
|
||||
}
|
||||
|
||||
private func buildMesh(_ verts: [SIMD3<Float>])
|
||||
-> MeshResource? {
|
||||
guard verts.count == Self.vertexCount, !faces.isEmpty else {
|
||||
NSLog("MeshEntity: vertex count mismatch %d (expected %d), faces=%d",
|
||||
verts.count, Self.vertexCount, faces.count)
|
||||
return nil
|
||||
}
|
||||
var descriptor = MeshDescriptor(name: "smplx")
|
||||
descriptor.positions = MeshBuffer(verts.map(arkitToRealityKit))
|
||||
descriptor.primitives = .triangles(faces)
|
||||
return try? MeshResource.generate(from: [descriptor])
|
||||
}
|
||||
|
||||
private static func loadFaces() -> [UInt32] {
|
||||
guard let url = Bundle.main.url(
|
||||
forResource: "smplx_faces", withExtension: "bin"),
|
||||
let data = try? Data(contentsOf: url) else {
|
||||
NSLog("MeshEntity: smplx_faces.bin missing")
|
||||
return []
|
||||
}
|
||||
return data.withUnsafeBytes { raw in
|
||||
Array(raw.bindMemory(to: UInt32.self))
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,117 @@
|
||||
import AppKit
|
||||
import Foundation
|
||||
import CoreVideo
|
||||
import RealityKit
|
||||
import simd
|
||||
import AVLiveWire
|
||||
|
||||
/// Owns the single RealityKit scene: the video quad, the body root,
|
||||
/// and an orbital camera. The app calls `updateVideo/updateSkeleton/
|
||||
/// updateMesh` from the main queue.
|
||||
@MainActor
|
||||
final class SceneController {
|
||||
let arView = ARView(frame: .zero)
|
||||
|
||||
private let cameraAnchor = AnchorEntity(world: .zero)
|
||||
private let camera = PerspectiveCamera()
|
||||
private let worldAnchor = AnchorEntity(world: .zero)
|
||||
|
||||
private(set) var videoQuad: VideoQuad?
|
||||
private(set) var skeleton: SkeletonEntity?
|
||||
private(set) var mesh: MeshEntity?
|
||||
|
||||
/// Orbital camera state.
|
||||
private var orbitYaw: Float = 0
|
||||
private var orbitPitch: Float = 0
|
||||
private var orbitRadius: Float = 3.0
|
||||
|
||||
private var didSetUp = false
|
||||
|
||||
func setUp() {
|
||||
guard !didSetUp else { return }
|
||||
didSetUp = true
|
||||
arView.environment.background = .color(.black)
|
||||
arView.scene.addAnchor(worldAnchor)
|
||||
|
||||
camera.camera.fieldOfViewInDegrees = 55
|
||||
cameraAnchor.addChild(camera)
|
||||
arView.scene.addAnchor(cameraAnchor)
|
||||
applyCamera()
|
||||
|
||||
let q = VideoQuad()
|
||||
worldAnchor.addChild(q.entity)
|
||||
videoQuad = q
|
||||
|
||||
let s = SkeletonEntity()
|
||||
worldAnchor.addChild(s.root)
|
||||
skeleton = s
|
||||
|
||||
let m = MeshEntity()
|
||||
worldAnchor.addChild(m.root)
|
||||
mesh = m
|
||||
|
||||
installOrbitGestures()
|
||||
}
|
||||
|
||||
func updateVideo(_ pixelBuffer: CVPixelBuffer) {
|
||||
videoQuad?.update(pixelBuffer)
|
||||
}
|
||||
|
||||
func updateSkeleton(_ skeletons: [Int: SkeletonPayload]) {
|
||||
skeleton?.update(skeletons)
|
||||
}
|
||||
|
||||
func updateMesh(_ persons: [MultiHMRPerson]) {
|
||||
mesh?.update(persons)
|
||||
}
|
||||
|
||||
// MARK: - Orbital camera
|
||||
|
||||
private func applyCamera() {
|
||||
let cy = cos(orbitYaw), sy = sin(orbitYaw)
|
||||
let cp = cos(orbitPitch), sp = sin(orbitPitch)
|
||||
let pos = SIMD3<Float>(orbitRadius * cp * sy,
|
||||
orbitRadius * sp,
|
||||
orbitRadius * cp * cy)
|
||||
cameraAnchor.transform.translation = pos
|
||||
camera.look(at: .zero, from: pos, relativeTo: nil)
|
||||
}
|
||||
|
||||
private func installOrbitGestures() {
|
||||
let pan = NSPanGestureRecognizer(
|
||||
target: OrbitTarget.shared, action: #selector(
|
||||
OrbitTarget.handlePan(_:)))
|
||||
OrbitTarget.shared.controller = self
|
||||
arView.addGestureRecognizer(pan)
|
||||
}
|
||||
|
||||
fileprivate func orbit(dx: Float, dy: Float) {
|
||||
orbitYaw += dx * 0.01
|
||||
orbitPitch = max(-1.4, min(1.4, orbitPitch + dy * 0.01))
|
||||
applyCamera()
|
||||
}
|
||||
}
|
||||
|
||||
/// Bridges the AppKit pan gesture to `SceneController.orbit`.
|
||||
final class OrbitTarget: NSObject {
|
||||
static let shared = OrbitTarget()
|
||||
weak var controller: SceneController?
|
||||
private var last: CGPoint = .zero
|
||||
|
||||
@objc func handlePan(_ g: NSPanGestureRecognizer) {
|
||||
switch g.state {
|
||||
case .began:
|
||||
last = g.translation(in: g.view)
|
||||
case .changed:
|
||||
let p = g.translation(in: g.view)
|
||||
let dx = Float(p.x - last.x)
|
||||
let dy = Float(p.y - last.y)
|
||||
last = p
|
||||
MainActor.assumeIsolated {
|
||||
self.controller?.orbit(dx: dx, dy: -dy)
|
||||
}
|
||||
default:
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
import RealityKit
|
||||
import SwiftUI
|
||||
|
||||
/// SwiftUI bridge that hands the SceneController's ARView to the
|
||||
/// window and runs `setUp()` once.
|
||||
struct SceneView: NSViewRepresentable {
|
||||
let controller: SceneController
|
||||
|
||||
func makeNSView(context: Context) -> ARView {
|
||||
controller.setUp()
|
||||
return controller.arView
|
||||
}
|
||||
|
||||
func updateNSView(_ view: ARView, context: Context) {}
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
import AVLiveWire
|
||||
import AppKit
|
||||
import Foundation
|
||||
import RealityKit
|
||||
import simd
|
||||
|
||||
/// Renders 91-joint skeletons as yellow marker spheres. One marker
|
||||
/// pool per pid. ARKit world coords -> RealityKit space (x, -y, -z).
|
||||
@MainActor
|
||||
final class SkeletonEntity {
|
||||
let root = Entity()
|
||||
|
||||
private static let jointCount = 91
|
||||
private static let markerRadius: Float = 0.012
|
||||
|
||||
private var pools: [Int: [ModelEntity]] = [:]
|
||||
private let mesh = MeshResource.generateSphere(radius: markerRadius)
|
||||
private let material = SimpleMaterial(
|
||||
color: NSColor.systemYellow, roughness: 0.6, isMetallic: false)
|
||||
|
||||
func update(_ skeletons: [Int: SkeletonPayload]) {
|
||||
// Drop pools for pids no longer present.
|
||||
for pid in pools.keys where skeletons[pid] == nil {
|
||||
pools[pid]?.forEach { $0.removeFromParent() }
|
||||
pools.removeValue(forKey: pid)
|
||||
}
|
||||
for (pid, payload) in skeletons {
|
||||
let pool = pools[pid] ?? makePool()
|
||||
pools[pid] = pool
|
||||
let n = min(Self.jointCount, payload.joints.count,
|
||||
payload.valid.count)
|
||||
for i in 0..<n {
|
||||
let marker = pool[i]
|
||||
if payload.valid[i] {
|
||||
let j = payload.joints[i]
|
||||
marker.transform.translation = arkitToRealityKit(j)
|
||||
marker.isEnabled = true
|
||||
} else {
|
||||
marker.isEnabled = false
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func makePool() -> [ModelEntity] {
|
||||
var pool: [ModelEntity] = []
|
||||
pool.reserveCapacity(Self.jointCount)
|
||||
for _ in 0..<Self.jointCount {
|
||||
let e = ModelEntity(mesh: mesh, materials: [material])
|
||||
e.isEnabled = false
|
||||
root.addChild(e)
|
||||
pool.append(e)
|
||||
}
|
||||
return pool
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,22 @@
|
||||
import SwiftUI
|
||||
|
||||
/// A thin overlay showing the USB connection state.
|
||||
struct StatusBar: View {
|
||||
@ObservedObject var consumer: USBSkeletonConsumer
|
||||
|
||||
var body: some View {
|
||||
HStack(spacing: 6) {
|
||||
Circle()
|
||||
.fill(consumer.connected ? Color.green : Color.orange)
|
||||
.frame(width: 9, height: 9)
|
||||
Text(consumer.connected
|
||||
? "iPhone connected (USB)"
|
||||
: "waiting for iPhone…")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.white)
|
||||
Spacer()
|
||||
}
|
||||
.padding(8)
|
||||
.background(.black.opacity(0.5))
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,46 @@
|
||||
import CoreImage
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
import RealityKit
|
||||
|
||||
/// A flat plane at the back of the scene, textured with the iPhone
|
||||
/// camera video. `update(_:)` is called on the main queue per frame.
|
||||
@MainActor
|
||||
final class VideoQuad {
|
||||
let entity = ModelEntity()
|
||||
|
||||
private let ciContext = CIContext()
|
||||
/// Plane is 1.6 m wide, 16:9; positioned 2 m behind the body.
|
||||
private static let width: Float = 1.6
|
||||
private static let height: Float = 0.9
|
||||
private static let zBack: Float = -2.0
|
||||
|
||||
init() {
|
||||
let plane = MeshResource.generatePlane(
|
||||
width: Self.width, height: Self.height)
|
||||
var material = UnlitMaterial()
|
||||
material.color = .init(tint: .white)
|
||||
entity.model = ModelComponent(mesh: plane,
|
||||
materials: [material])
|
||||
entity.transform.translation =
|
||||
SIMD3<Float>(0, 0, Self.zBack)
|
||||
}
|
||||
|
||||
/// Replace the plane's texture from a decoded camera frame.
|
||||
func update(_ pixelBuffer: CVPixelBuffer) {
|
||||
let ci = CIImage(cvPixelBuffer: pixelBuffer)
|
||||
guard let cg = ciContext.createCGImage(
|
||||
ci, from: ci.extent) else { return }
|
||||
guard let texture = try? TextureResource(
|
||||
image: cg, options: .init(semantic: .color)) else {
|
||||
NSLog("VideoQuad: TextureResource creation failed (%dx%d)",
|
||||
CVPixelBufferGetWidth(pixelBuffer),
|
||||
CVPixelBufferGetHeight(pixelBuffer))
|
||||
return
|
||||
}
|
||||
var material = UnlitMaterial()
|
||||
material.color = .init(tint: .white,
|
||||
texture: .init(texture))
|
||||
entity.model?.materials = [material]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,29 @@
|
||||
import AVLiveWire
|
||||
import Foundation
|
||||
import simd
|
||||
|
||||
/// Overrides the highest-scoring Multi-HMR mesh's pelvis depth with
|
||||
/// the first valid USB skeleton pelvis z. Single-person assumption:
|
||||
/// with multiple skeletons in the dict the source pelvis is arbitrary
|
||||
/// (dict iteration order). Pure, stateless — unit-testable.
|
||||
enum BodyFusion {
|
||||
/// ARSkeleton3D joint 0 = root (hips), per ARSkeletonDefinition.defaultBody3D.
|
||||
static let pelvisJoint = 0
|
||||
|
||||
static func fuse(persons: [MultiHMRPerson],
|
||||
skeletons: [Int: SkeletonPayload])
|
||||
-> [MultiHMRPerson] {
|
||||
let pelvisZs: [Float] = skeletons.values.compactMap { s in
|
||||
guard pelvisJoint < s.valid.count,
|
||||
s.valid[pelvisJoint] else { return nil }
|
||||
return s.joints[pelvisJoint].z
|
||||
}
|
||||
guard !pelvisZs.isEmpty,
|
||||
let primaryIdx = persons.indices.max(by: {
|
||||
persons[$0].score < persons[$1].score
|
||||
}) else { return persons }
|
||||
var out = persons
|
||||
out[primaryIdx].translation.z = pelvisZs[0]
|
||||
return out
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,155 @@
|
||||
import CoreML
|
||||
import CoreVideo
|
||||
import CoreImage
|
||||
import Foundation
|
||||
|
||||
/// One detected SMPL-X body from Multi-HMR.
|
||||
struct MultiHMRPerson {
|
||||
var vertices: [SIMD3<Float>] // 10475 SMPL-X verts, model space
|
||||
var translation: SIMD3<Float> // pelvis translation
|
||||
var score: Float
|
||||
}
|
||||
|
||||
/// CoreML wrapper around the bundled `multihmr_full_672_s.mlpackage`.
|
||||
/// Mirrors `data_only_viz/multihmr_coreml.py`: two MLMultiArray inputs
|
||||
/// (`image` 1x3x672x672 ImageNet-normalized, `cam_K` 1x3x3), fixed
|
||||
/// K=4 person outputs.
|
||||
final class MultiHMRCoreML {
|
||||
static let inputSize = 672
|
||||
static let vertexCount = 10475
|
||||
static let maxPersons = 4
|
||||
private static let detThreshold: Float = 0.3
|
||||
private static let normMean: [Float] = [0.485, 0.456, 0.406]
|
||||
private static let normStd: [Float] = [0.229, 0.224, 0.225]
|
||||
|
||||
private let model: MLModel
|
||||
private let ciContext = CIContext()
|
||||
|
||||
/// Loads the bundled model. Returns nil if the resource or load
|
||||
/// fails — callers fall back to skeleton-only rendering.
|
||||
init?() {
|
||||
guard let url = Bundle.main.url(
|
||||
forResource: "multihmr_full_672_s",
|
||||
withExtension: "mlmodelc") else {
|
||||
NSLog("MultiHMRCoreML: mlpackage resource missing")
|
||||
return nil
|
||||
}
|
||||
let cfg = MLModelConfiguration()
|
||||
cfg.computeUnits = .cpuAndGPU
|
||||
do {
|
||||
model = try MLModel(contentsOf: url, configuration: cfg)
|
||||
} catch {
|
||||
NSLog("MultiHMRCoreML: load failed %@",
|
||||
String(describing: error))
|
||||
return nil
|
||||
}
|
||||
}
|
||||
|
||||
/// Run inference on one camera frame. `cameraK` is the 3x3 camera
|
||||
/// intrinsics row-major.
|
||||
func infer(_ pixelBuffer: CVPixelBuffer,
|
||||
cameraK: [Float]) -> [MultiHMRPerson] {
|
||||
guard let image = makeImageInput(pixelBuffer),
|
||||
let k = makeKInput(cameraK) else { return [] }
|
||||
let inputs: [String: MLFeatureValue] = [
|
||||
"image": MLFeatureValue(multiArray: image),
|
||||
"cam_K": MLFeatureValue(multiArray: k),
|
||||
]
|
||||
guard let provider = try? MLDictionaryFeatureProvider(
|
||||
dictionary: inputs),
|
||||
let out = try? model.prediction(from: provider) else {
|
||||
return []
|
||||
}
|
||||
return parse(out)
|
||||
}
|
||||
|
||||
// MARK: - Input preprocessing
|
||||
|
||||
/// `CVPixelBuffer` -> [1,3,672,672] Float32, RGB, ImageNet-normed.
|
||||
private func makeImageInput(_ pb: CVPixelBuffer) -> MLMultiArray? {
|
||||
let n = Self.inputSize
|
||||
// Resize to n x n BGRA via CoreImage.
|
||||
let ci = CIImage(cvPixelBuffer: pb)
|
||||
let sx = CGFloat(n) / ci.extent.width
|
||||
let sy = CGFloat(n) / ci.extent.height
|
||||
let scaled = ci.transformed(
|
||||
by: CGAffineTransform(scaleX: sx, y: sy))
|
||||
var dst: CVPixelBuffer?
|
||||
CVPixelBufferCreate(kCFAllocatorDefault, n, n,
|
||||
kCVPixelFormatType_32BGRA, nil, &dst)
|
||||
guard let dst else { return nil }
|
||||
ciContext.render(scaled, to: dst)
|
||||
CVPixelBufferLockBaseAddress(dst, .readOnly)
|
||||
defer { CVPixelBufferUnlockBaseAddress(dst, .readOnly) }
|
||||
guard let base = CVPixelBufferGetBaseAddress(dst) else {
|
||||
return nil
|
||||
}
|
||||
let rowBytes = CVPixelBufferGetBytesPerRow(dst)
|
||||
let px = base.assumingMemoryBound(to: UInt8.self)
|
||||
guard let arr = try? MLMultiArray(
|
||||
shape: [1, 3, NSNumber(value: n), NSNumber(value: n)],
|
||||
dataType: .float32) else { return nil }
|
||||
let ptr = arr.dataPointer.assumingMemoryBound(to: Float.self)
|
||||
let plane = n * n
|
||||
for y in 0..<n {
|
||||
for x in 0..<n {
|
||||
let p = y * rowBytes + x * 4 // BGRA
|
||||
let b = Float(px[p]) / 255.0
|
||||
let g = Float(px[p + 1]) / 255.0
|
||||
let r = Float(px[p + 2]) / 255.0
|
||||
let idx = y * n + x
|
||||
ptr[idx] =
|
||||
(r - Self.normMean[0]) / Self.normStd[0]
|
||||
ptr[plane + idx] =
|
||||
(g - Self.normMean[1]) / Self.normStd[1]
|
||||
ptr[2 * plane + idx] =
|
||||
(b - Self.normMean[2]) / Self.normStd[2]
|
||||
}
|
||||
}
|
||||
return arr
|
||||
}
|
||||
|
||||
/// 9 row-major intrinsics -> [1,3,3] Float32.
|
||||
private func makeKInput(_ k: [Float]) -> MLMultiArray? {
|
||||
guard k.count == 9,
|
||||
let arr = try? MLMultiArray(
|
||||
shape: [1, 3, 3], dataType: .float32) else { return nil }
|
||||
let ptr = arr.dataPointer.assumingMemoryBound(to: Float.self)
|
||||
for i in 0..<9 { ptr[i] = k[i] }
|
||||
return arr
|
||||
}
|
||||
|
||||
// MARK: - Output parsing
|
||||
|
||||
private func parse(_ out: MLFeatureProvider) -> [MultiHMRPerson] {
|
||||
guard let v3d = out.featureValue(for: "var_2420")?
|
||||
.multiArrayValue,
|
||||
let transl = out.featureValue(for: "var_2423")?
|
||||
.multiArrayValue,
|
||||
let scores = out.featureValue(for: "var_2436")?
|
||||
.multiArrayValue else { return [] }
|
||||
var persons: [MultiHMRPerson] = []
|
||||
let vc = Self.vertexCount
|
||||
for k in 0..<Self.maxPersons {
|
||||
let score = scores[k].floatValue
|
||||
if score < Self.detThreshold { continue }
|
||||
var verts = [SIMD3<Float>](
|
||||
repeating: .zero, count: vc)
|
||||
let base = k * vc * 3
|
||||
for i in 0..<vc {
|
||||
let o = base + i * 3
|
||||
verts[i] = SIMD3(v3d[o].floatValue,
|
||||
v3d[o + 1].floatValue,
|
||||
v3d[o + 2].floatValue)
|
||||
}
|
||||
let tb = k * 3
|
||||
persons.append(MultiHMRPerson(
|
||||
vertices: verts,
|
||||
translation: SIMD3(transl[tb].floatValue,
|
||||
transl[tb + 1].floatValue,
|
||||
transl[tb + 2].floatValue),
|
||||
score: score))
|
||||
}
|
||||
return persons
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,135 @@
|
||||
import Foundation
|
||||
import Darwin
|
||||
|
||||
/// Transport abstraction over the usbmuxd Unix socket. The real
|
||||
/// implementation wraps a `socket(AF_UNIX)`; tests inject a mock.
|
||||
protocol MuxTransport {
|
||||
func send(_ data: Data)
|
||||
func receivePacket() -> Data?
|
||||
func close()
|
||||
}
|
||||
|
||||
/// usbmux client: device discovery + connect-to-port. After a
|
||||
/// successful `connect`, the same transport carries the raw tunneled
|
||||
/// byte stream from the device.
|
||||
final class USBClient {
|
||||
private let transport: MuxTransport
|
||||
private var tag: UInt32 = 0
|
||||
|
||||
init(transport: MuxTransport) {
|
||||
self.transport = transport
|
||||
}
|
||||
|
||||
func listDevices() -> [Int] {
|
||||
tag += 1
|
||||
transport.send(USBMuxProtocol.encode(
|
||||
plist: ["MessageType": "ListDevices"], tag: tag))
|
||||
guard let reply = transport.receivePacket(),
|
||||
let plist = USBMuxProtocol.decode(reply),
|
||||
let list = plist["DeviceList"] as? [[String: Any]]
|
||||
else { return [] }
|
||||
return list.compactMap { $0["DeviceID"] as? Int }
|
||||
}
|
||||
|
||||
/// Returns true once the transport is tunneled to `port` on the
|
||||
/// device. usbmux wants the TCP port in big-endian order.
|
||||
func connect(deviceID: Int, port: UInt16) -> Bool {
|
||||
tag += 1
|
||||
let swapped = Int((port << 8) | (port >> 8))
|
||||
transport.send(USBMuxProtocol.encode(plist: [
|
||||
"MessageType": "Connect",
|
||||
"DeviceID": deviceID,
|
||||
"PortNumber": swapped,
|
||||
], tag: tag))
|
||||
guard let reply = transport.receivePacket(),
|
||||
let plist = USBMuxProtocol.decode(reply),
|
||||
let number = plist["Number"] as? Int
|
||||
else { return false }
|
||||
return number == 0
|
||||
}
|
||||
}
|
||||
|
||||
/// Production transport: blocking AF_UNIX socket to usbmuxd.
|
||||
final class UnixMuxTransport: MuxTransport {
|
||||
private var fd: Int32 = -1
|
||||
|
||||
init?(path: String = "/var/run/usbmuxd") {
|
||||
fd = socket(AF_UNIX, SOCK_STREAM, 0)
|
||||
guard fd >= 0 else { return nil }
|
||||
var addr = sockaddr_un()
|
||||
addr.sun_family = sa_family_t(AF_UNIX)
|
||||
precondition(path.utf8.count < 104,
|
||||
"usbmuxd socket path exceeds sun_path limit")
|
||||
_ = path.withCString { src in
|
||||
withUnsafeMutablePointer(to: &addr.sun_path) {
|
||||
$0.withMemoryRebound(to: CChar.self, capacity: 104) {
|
||||
strcpy($0, src)
|
||||
}
|
||||
}
|
||||
}
|
||||
let size = socklen_t(MemoryLayout<sockaddr_un>.size)
|
||||
let ok = withUnsafePointer(to: &addr) {
|
||||
$0.withMemoryRebound(to: sockaddr.self, capacity: 1) {
|
||||
Darwin.connect(fd, $0, size)
|
||||
}
|
||||
}
|
||||
if ok != 0 { Darwin.close(fd); return nil }
|
||||
}
|
||||
|
||||
func send(_ data: Data) {
|
||||
guard fd >= 0 else { return }
|
||||
data.withUnsafeBytes { buf in
|
||||
guard let base = buf.baseAddress else { return }
|
||||
var off = 0
|
||||
while off < data.count {
|
||||
let w = Darwin.write(fd, base.advanced(by: off),
|
||||
data.count - off)
|
||||
if w <= 0 {
|
||||
if w < 0 && errno == EINTR { continue }
|
||||
break
|
||||
}
|
||||
off += w
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Read one usbmux packet: 4-byte LE length prefix then body.
|
||||
func receivePacket() -> Data? {
|
||||
guard let head = readN(4) else { return nil }
|
||||
guard let len = USBMuxProtocol.readLE32(head, 0) else { return nil }
|
||||
let total = Int(len)
|
||||
guard total >= 16, let rest = readN(total - 4) else { return nil }
|
||||
return head + rest
|
||||
}
|
||||
|
||||
/// Read raw tunneled bytes after a successful Connect.
|
||||
func readStream(max: Int = 65536) -> Data? {
|
||||
readN(max, exact: false)
|
||||
}
|
||||
|
||||
private func readN(_ n: Int, exact: Bool = true) -> Data? {
|
||||
var buf = [UInt8](repeating: 0, count: n)
|
||||
var got = 0
|
||||
while got < n {
|
||||
let r = buf.withUnsafeMutableBytes {
|
||||
Darwin.read(fd, $0.baseAddress!.advanced(by: got), n - got)
|
||||
}
|
||||
if r < 0 {
|
||||
if errno == EINTR { continue }
|
||||
return got > 0 && !exact ? Data(buf[0..<got]) : nil
|
||||
}
|
||||
if r == 0 { // EOF — peer closed
|
||||
return got > 0 && !exact ? Data(buf[0..<got]) : nil
|
||||
}
|
||||
got += r
|
||||
if !exact { break }
|
||||
}
|
||||
return Data(buf[0..<got])
|
||||
}
|
||||
|
||||
deinit { close() }
|
||||
|
||||
func close() {
|
||||
if fd >= 0 { Darwin.close(fd); fd = -1 }
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
import Foundation
|
||||
|
||||
/// Codec for the usbmuxd request/response protocol. 16-byte
|
||||
/// little-endian header (length, version=1, message=8, tag) then an
|
||||
/// XML property list.
|
||||
enum USBMuxProtocol {
|
||||
static func encode(plist: [String: Any], tag: UInt32) -> Data {
|
||||
let body = (try? PropertyListSerialization.data(
|
||||
fromPropertyList: plist, format: .xml, options: 0))
|
||||
?? Data()
|
||||
var d = Data()
|
||||
appendLE32(&d, UInt32(16 + body.count)) // length
|
||||
appendLE32(&d, 1) // version
|
||||
appendLE32(&d, 8) // message: plist
|
||||
appendLE32(&d, tag)
|
||||
d.append(body)
|
||||
return d
|
||||
}
|
||||
|
||||
static func decode(_ packet: Data) -> [String: Any]? {
|
||||
guard packet.count >= 16 else { return nil }
|
||||
let body = packet.dropFirst(16)
|
||||
return (try? PropertyListSerialization.propertyList(
|
||||
from: body, options: [], format: nil)) as? [String: Any]
|
||||
}
|
||||
|
||||
static func appendLE32(_ d: inout Data, _ v: UInt32) {
|
||||
for i in 0..<4 { d.append(UInt8((v >> (8 * i)) & 0xFF)) }
|
||||
}
|
||||
|
||||
static func readLE32(_ d: Data, _ offset: Int) -> UInt32? {
|
||||
guard offset >= 0, d.count >= offset + 4 else { return nil }
|
||||
let b = [UInt8](d)
|
||||
var v: UInt32 = 0
|
||||
for i in 0..<4 { v |= UInt32(b[offset + i]) << (8 * i) }
|
||||
return v
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,107 @@
|
||||
import AVLiveWire
|
||||
import Combine
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
|
||||
/// Connects to the tethered iPhone over USB (usbmuxd), demuxes the
|
||||
/// AVLiveWire stream, republishes skeleton payloads (keyed by pid)
|
||||
/// and forwards decoded camera frames. Blocking transport runs on a
|
||||
/// dedicated background thread; only `@Published` writes hop to main.
|
||||
final class USBSkeletonConsumer: ObservableObject {
|
||||
/// 91-joint skeleton payloads keyed by pid.
|
||||
@Published var skeletons: [Int: SkeletonPayload] = [:]
|
||||
@Published var connected = false
|
||||
|
||||
/// Called on the main queue for every decoded camera frame.
|
||||
var onVideoFrame: ((CVPixelBuffer) -> Void)?
|
||||
|
||||
/// TCP port the iPhone `USBServer` listens on.
|
||||
static let devicePort: UInt16 = 7000
|
||||
|
||||
private let videoDecoder = VideoDecoder()
|
||||
private let stateLock = NSLock()
|
||||
private var running = false
|
||||
private var thread: Thread?
|
||||
|
||||
init() {
|
||||
videoDecoder.onFrame = { [weak self] pixelBuffer in
|
||||
DispatchQueue.main.async {
|
||||
self?.onVideoFrame?(pixelBuffer)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var isRunning: Bool {
|
||||
stateLock.lock(); defer { stateLock.unlock() }
|
||||
return running
|
||||
}
|
||||
|
||||
func start() {
|
||||
stateLock.lock()
|
||||
if running { stateLock.unlock(); return }
|
||||
running = true
|
||||
let t = Thread { [weak self] in self?.loop() }
|
||||
t.name = "cc.avlive.usbconsumer"
|
||||
thread = t
|
||||
stateLock.unlock()
|
||||
t.start()
|
||||
}
|
||||
|
||||
func stop() {
|
||||
stateLock.lock(); running = false; stateLock.unlock()
|
||||
}
|
||||
|
||||
private func loop() {
|
||||
while isRunning {
|
||||
guard let transport = UnixMuxTransport() else {
|
||||
NSLog("USBSkeletonConsumer: no usbmuxd; retry")
|
||||
Thread.sleep(forTimeInterval: 1.0); continue
|
||||
}
|
||||
let client = USBClient(transport: transport)
|
||||
let devices = client.listDevices()
|
||||
guard let dev = devices.first,
|
||||
client.connect(deviceID: dev,
|
||||
port: Self.devicePort) else {
|
||||
NSLog("USBSkeletonConsumer: no device; retry")
|
||||
transport.close()
|
||||
Thread.sleep(forTimeInterval: 1.0); continue
|
||||
}
|
||||
NSLog("USBSkeletonConsumer: connected to device %d", dev)
|
||||
publishConnected(true)
|
||||
var demux = StreamDemuxer()
|
||||
while isRunning {
|
||||
guard let chunk = transport.readStream(),
|
||||
!chunk.isEmpty else { break }
|
||||
for frame in demux.feed(chunk) { route(frame) }
|
||||
}
|
||||
transport.close()
|
||||
publishConnected(false)
|
||||
NSLog("USBSkeletonConsumer: disconnected")
|
||||
if isRunning { Thread.sleep(forTimeInterval: 1.0) }
|
||||
}
|
||||
}
|
||||
|
||||
private func route(_ frame: StreamDemuxer.Frame) {
|
||||
switch frame.header.tag {
|
||||
case .skeleton:
|
||||
guard let payload =
|
||||
SkeletonPayload(decoding: frame.payload) else { return }
|
||||
let pid = Int(frame.header.pid)
|
||||
DispatchQueue.main.async { [weak self] in
|
||||
self?.skeletons[pid] = payload
|
||||
}
|
||||
case .video:
|
||||
guard let payload =
|
||||
VideoPayload(decoding: frame.payload) else { return }
|
||||
videoDecoder.decode(payload)
|
||||
case .meta:
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
private func publishConnected(_ value: Bool) {
|
||||
DispatchQueue.main.async { [weak self] in
|
||||
self?.connected = value
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,184 @@
|
||||
import AVLiveWire
|
||||
import CoreMedia
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
import VideoToolbox
|
||||
|
||||
/// HEVC decoder. Feed `VideoPayload`s in; receive `CVPixelBuffer`s via
|
||||
/// `onFrame`. Keyframe payloads must carry the VPS/SPS/PPS parameter
|
||||
/// sets prepended as 4-byte-length-prefixed NAL units (the layout the
|
||||
/// iOS `VideoEncoder` emits); the decoder (re)builds its format
|
||||
/// description from those.
|
||||
final class VideoDecoder {
|
||||
var onFrame: ((CVPixelBuffer) -> Void)?
|
||||
|
||||
private var session: VTDecompressionSession?
|
||||
private var formatDesc: CMVideoFormatDescription?
|
||||
|
||||
/// Decode one access unit.
|
||||
func decode(_ payload: VideoPayload) {
|
||||
var au = payload.data
|
||||
if payload.isKeyframe {
|
||||
let (params, rest) = Self.splitParameterSets(au)
|
||||
if !params.isEmpty {
|
||||
rebuildFormat(params)
|
||||
}
|
||||
au = rest
|
||||
}
|
||||
guard let fmt = formatDesc, !au.isEmpty else { return }
|
||||
if session == nil { makeSession(fmt) }
|
||||
guard let session, let block = Self.blockBuffer(au) else {
|
||||
return
|
||||
}
|
||||
var sample: CMSampleBuffer?
|
||||
var sampleSize = au.count
|
||||
guard CMSampleBufferCreateReady(
|
||||
allocator: kCFAllocatorDefault, dataBuffer: block,
|
||||
formatDescription: fmt, sampleCount: 1,
|
||||
sampleTimingEntryCount: 0, sampleTimingArray: nil,
|
||||
sampleSizeEntryCount: 1, sampleSizeArray: &sampleSize,
|
||||
sampleBufferOut: &sample) == noErr, let sample else {
|
||||
return
|
||||
}
|
||||
VTDecompressionSessionDecodeFrame(
|
||||
session, sampleBuffer: sample, flags: [],
|
||||
infoFlagsOut: nil) { [weak self] status, _, image, _, _ in
|
||||
guard status == noErr, let image else { return }
|
||||
self?.onFrame?(image)
|
||||
}
|
||||
}
|
||||
|
||||
func stop() {
|
||||
if let session { VTDecompressionSessionInvalidate(session) }
|
||||
session = nil
|
||||
formatDesc = nil
|
||||
}
|
||||
|
||||
deinit { stop() }
|
||||
|
||||
// MARK: - Helpers
|
||||
|
||||
/// Leading 4-byte-length-prefixed NAL units of HEVC parameter-set
|
||||
/// type (VPS=32, SPS=33, PPS=34) are split from the frame data.
|
||||
/// Returns (parameterSetData, frameData).
|
||||
private static func splitParameterSets(_ data: Data)
|
||||
-> (Data, Data) {
|
||||
let bytes = [UInt8](data)
|
||||
var offset = 0
|
||||
var paramEnd = 0
|
||||
while offset + 4 <= bytes.count {
|
||||
let len = (Int(bytes[offset]) << 24)
|
||||
| (Int(bytes[offset + 1]) << 16)
|
||||
| (Int(bytes[offset + 2]) << 8)
|
||||
| Int(bytes[offset + 3])
|
||||
let nalStart = offset + 4
|
||||
guard len > 0, nalStart + len <= bytes.count else { break }
|
||||
let nalType = (Int(bytes[nalStart]) >> 1) & 0x3F
|
||||
if nalType == 32 || nalType == 33 || nalType == 34 {
|
||||
offset = nalStart + len
|
||||
paramEnd = offset
|
||||
} else {
|
||||
break
|
||||
}
|
||||
}
|
||||
return (data.prefix(paramEnd),
|
||||
data.suffix(from: data.startIndex
|
||||
.advanced(by: paramEnd)))
|
||||
}
|
||||
|
||||
private func rebuildFormat(_ paramData: Data) {
|
||||
var sets: [[UInt8]] = []
|
||||
let bytes = [UInt8](paramData)
|
||||
var offset = 0
|
||||
while offset + 4 <= bytes.count {
|
||||
let len = (Int(bytes[offset]) << 24)
|
||||
| (Int(bytes[offset + 1]) << 16)
|
||||
| (Int(bytes[offset + 2]) << 8)
|
||||
| Int(bytes[offset + 3])
|
||||
let start = offset + 4
|
||||
guard len > 0, start + len <= bytes.count else { break }
|
||||
sets.append(Array(bytes[start..<start + len]))
|
||||
offset = start + len
|
||||
}
|
||||
guard sets.count >= 3 else { return }
|
||||
var fmt: CMFormatDescription?
|
||||
let status = withParameterSetPointers(sets) { pBuf, sBuf in
|
||||
CMVideoFormatDescriptionCreateFromHEVCParameterSets(
|
||||
allocator: kCFAllocatorDefault,
|
||||
parameterSetCount: sets.count,
|
||||
parameterSetPointers: pBuf,
|
||||
parameterSetSizes: sBuf,
|
||||
nalUnitHeaderLength: 4, extensions: nil,
|
||||
formatDescriptionOut: &fmt)
|
||||
}
|
||||
if status == noErr, let fmt {
|
||||
formatDesc = fmt
|
||||
if let session { VTDecompressionSessionInvalidate(session) }
|
||||
session = nil
|
||||
}
|
||||
}
|
||||
|
||||
/// Build the C-style parallel arrays of parameter-set pointers and
|
||||
/// sizes that `CMVideoFormatDescriptionCreateFromHEVCParameterSets`
|
||||
/// requires, keeping the backing storage alive for the call.
|
||||
private func withParameterSetPointers(
|
||||
_ sets: [[UInt8]],
|
||||
_ body: (UnsafePointer<UnsafePointer<UInt8>>,
|
||||
UnsafePointer<Int>) -> OSStatus) -> OSStatus {
|
||||
func recurse(_ index: Int,
|
||||
_ ptrs: inout [UnsafePointer<UInt8>],
|
||||
_ sizes: inout [Int]) -> OSStatus {
|
||||
if index == sets.count {
|
||||
return ptrs.withUnsafeBufferPointer { pBuf in
|
||||
sizes.withUnsafeBufferPointer { sBuf in
|
||||
body(pBuf.baseAddress!, sBuf.baseAddress!)
|
||||
}
|
||||
}
|
||||
}
|
||||
return sets[index].withUnsafeBufferPointer { buf in
|
||||
ptrs.append(buf.baseAddress!)
|
||||
sizes.append(buf.count)
|
||||
return recurse(index + 1, &ptrs, &sizes)
|
||||
}
|
||||
}
|
||||
var ptrs: [UnsafePointer<UInt8>] = []
|
||||
var sizes: [Int] = []
|
||||
ptrs.reserveCapacity(sets.count)
|
||||
sizes.reserveCapacity(sets.count)
|
||||
return recurse(0, &ptrs, &sizes)
|
||||
}
|
||||
|
||||
private func makeSession(_ fmt: CMVideoFormatDescription) {
|
||||
let attrs: [CFString: Any] = [
|
||||
kCVPixelBufferPixelFormatTypeKey:
|
||||
kCVPixelFormatType_32BGRA,
|
||||
]
|
||||
VTDecompressionSessionCreate(
|
||||
allocator: kCFAllocatorDefault, formatDescription: fmt,
|
||||
decoderSpecification: nil,
|
||||
imageBufferAttributes: attrs as CFDictionary,
|
||||
outputCallback: nil, decompressionSessionOut: &session)
|
||||
}
|
||||
|
||||
private static func blockBuffer(_ data: Data) -> CMBlockBuffer? {
|
||||
var block: CMBlockBuffer?
|
||||
guard CMBlockBufferCreateWithMemoryBlock(
|
||||
allocator: kCFAllocatorDefault, memoryBlock: nil,
|
||||
blockLength: data.count,
|
||||
blockAllocator: kCFAllocatorDefault,
|
||||
customBlockSource: nil, offsetToData: 0,
|
||||
dataLength: data.count, flags: 0,
|
||||
blockBufferOut: &block) == noErr, let block else {
|
||||
return nil
|
||||
}
|
||||
var ok = false
|
||||
data.withUnsafeBytes { raw in
|
||||
if let base = raw.baseAddress,
|
||||
CMBlockBufferReplaceDataBytes(
|
||||
with: base, blockBuffer: block,
|
||||
offsetIntoDestination: 0,
|
||||
dataLength: data.count) == noErr { ok = true }
|
||||
}
|
||||
return ok ? block : nil
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,29 @@
|
||||
import XCTest
|
||||
import AVLiveWire
|
||||
@testable import AVLiveBody
|
||||
|
||||
final class BodyFusionTests: XCTestCase {
|
||||
private func skeleton(pelvisZ: Float) -> SkeletonPayload {
|
||||
var p = SkeletonPayload()
|
||||
p.joints[0] = SIMD3(0, 0, pelvisZ)
|
||||
p.valid[0] = true
|
||||
return p
|
||||
}
|
||||
|
||||
func testPelvisDepthOverride() {
|
||||
let mesh = MultiHMRPerson(
|
||||
vertices: [SIMD3<Float>](repeating: .zero, count: 1),
|
||||
translation: SIMD3(0, 0, -1.0), score: 0.9)
|
||||
let fused = BodyFusion.fuse(
|
||||
persons: [mesh], skeletons: [0: skeleton(pelvisZ: -2.5)])
|
||||
XCTAssertEqual(fused[0].translation.z, -2.5, accuracy: 1e-4)
|
||||
}
|
||||
|
||||
func testPassthroughWhenNoSkeleton() {
|
||||
let mesh = MultiHMRPerson(
|
||||
vertices: [SIMD3<Float>](repeating: .zero, count: 1),
|
||||
translation: SIMD3(0, 0, -1.0), score: 0.9)
|
||||
let fused = BodyFusion.fuse(persons: [mesh], skeletons: [:])
|
||||
XCTAssertEqual(fused[0].translation.z, -1.0, accuracy: 1e-4)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,49 @@
|
||||
import XCTest
|
||||
@testable import AVLiveBody
|
||||
|
||||
/// In-memory stand-in for the usbmuxd Unix socket.
|
||||
final class MockMuxTransport: MuxTransport {
|
||||
var sent: [Data] = []
|
||||
var canned: [Data] = []
|
||||
func send(_ data: Data) { sent.append(data) }
|
||||
func receivePacket() -> Data? {
|
||||
canned.isEmpty ? nil : canned.removeFirst()
|
||||
}
|
||||
func close() {}
|
||||
}
|
||||
|
||||
final class USBClientTests: XCTestCase {
|
||||
func testListDevicesParsesDeviceIDs() {
|
||||
let mock = MockMuxTransport()
|
||||
mock.canned = [USBMuxProtocol.encode(plist: [
|
||||
"DeviceList": [
|
||||
["DeviceID": 42,
|
||||
"Properties": ["ConnectionType": "USB"]],
|
||||
]], tag: 0)]
|
||||
let client = USBClient(transport: mock)
|
||||
let devices = client.listDevices()
|
||||
XCTAssertEqual(devices, [42])
|
||||
}
|
||||
|
||||
func testConnectSendsConnectRequest() {
|
||||
let mock = MockMuxTransport()
|
||||
mock.canned = [USBMuxProtocol.encode(
|
||||
plist: ["MessageType": "Result", "Number": 0], tag: 0)]
|
||||
let client = USBClient(transport: mock)
|
||||
let ok = client.connect(deviceID: 42, port: 7000)
|
||||
XCTAssertTrue(ok)
|
||||
let req = USBMuxProtocol.decode(mock.sent.last!)
|
||||
XCTAssertEqual(req?["MessageType"] as? String, "Connect")
|
||||
XCTAssertEqual(req?["DeviceID"] as? Int, 42)
|
||||
XCTAssertEqual(req?["PortNumber"] as? Int,
|
||||
Int((UInt16(7000) << 8) | (UInt16(7000) >> 8)))
|
||||
}
|
||||
|
||||
func testConnectFailsOnNonZeroResult() {
|
||||
let mock = MockMuxTransport()
|
||||
mock.canned = [USBMuxProtocol.encode(
|
||||
plist: ["MessageType": "Result", "Number": 3], tag: 0)]
|
||||
let client = USBClient(transport: mock)
|
||||
XCTAssertFalse(client.connect(deviceID: 1, port: 7000))
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
import XCTest
|
||||
@testable import AVLiveBody
|
||||
|
||||
final class USBMuxProtocolTests: XCTestCase {
|
||||
func testEncodeWrapsPlistWith16ByteHeader() {
|
||||
let body: [String: Any] = ["MessageType": "ListDevices"]
|
||||
let packet = USBMuxProtocol.encode(plist: body, tag: 3)
|
||||
XCTAssertGreaterThan(packet.count, 16)
|
||||
XCTAssertEqual(USBMuxProtocol.readLE32(packet, 0).map(Int.init),
|
||||
packet.count)
|
||||
XCTAssertEqual(USBMuxProtocol.readLE32(packet, 4), 1)
|
||||
XCTAssertEqual(USBMuxProtocol.readLE32(packet, 8), 8)
|
||||
XCTAssertEqual(USBMuxProtocol.readLE32(packet, 12), 3)
|
||||
}
|
||||
|
||||
func testDecodeRoundTrip() {
|
||||
let packet = USBMuxProtocol.encode(
|
||||
plist: ["MessageType": "Result", "Number": 0], tag: 1)
|
||||
let decoded = USBMuxProtocol.decode(packet)
|
||||
XCTAssertEqual(decoded?["MessageType"] as? String, "Result")
|
||||
XCTAssertEqual(decoded?["Number"] as? Int, 0)
|
||||
}
|
||||
|
||||
func testDecodeRejectsShortPacket() {
|
||||
XCTAssertNil(USBMuxProtocol.decode(Data([0, 1, 2])))
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,54 @@
|
||||
name: AVLiveBody
|
||||
options:
|
||||
bundleIdPrefix: cc.saillant
|
||||
deploymentTarget:
|
||||
macOS: "15.0"
|
||||
createIntermediateGroups: true
|
||||
|
||||
configFiles:
|
||||
Debug: Config/Shared.xcconfig
|
||||
Release: Config/Shared.xcconfig
|
||||
|
||||
packages:
|
||||
AVLiveWire:
|
||||
path: ../shared/AVLiveWire
|
||||
|
||||
targets:
|
||||
AVLiveBody:
|
||||
type: application
|
||||
platform: macOS
|
||||
deploymentTarget: "15.0"
|
||||
sources:
|
||||
- path: Sources/AVLiveBody
|
||||
excludes:
|
||||
- Info.plist
|
||||
dependencies:
|
||||
- package: AVLiveWire
|
||||
product: AVLiveWire
|
||||
configFiles:
|
||||
Debug: Config/Shared.xcconfig
|
||||
Release: Config/Shared.xcconfig
|
||||
settings:
|
||||
base:
|
||||
PRODUCT_NAME: AVLiveBody
|
||||
PRODUCT_BUNDLE_IDENTIFIER: cc.saillant.AVLiveBody
|
||||
INFOPLIST_FILE: Sources/AVLiveBody/Info.plist
|
||||
GENERATE_INFOPLIST_FILE: NO
|
||||
CODE_SIGN_STYLE: Manual
|
||||
CODE_SIGN_IDENTITY: "-"
|
||||
CODE_SIGNING_REQUIRED: NO
|
||||
CODE_SIGNING_ALLOWED: NO
|
||||
SWIFT_VERSION: "5.10"
|
||||
ENABLE_HARDENED_RUNTIME: NO
|
||||
AVLiveBodyTests:
|
||||
type: bundle.unit-test
|
||||
platform: macOS
|
||||
sources:
|
||||
- path: Tests/AVLiveBodyTests
|
||||
dependencies:
|
||||
- target: AVLiveBody
|
||||
- package: AVLiveWire
|
||||
product: AVLiveWire
|
||||
settings:
|
||||
base:
|
||||
GENERATE_INFOPLIST_FILE: YES
|
||||
@@ -0,0 +1,37 @@
|
||||
[osc]
|
||||
host = "127.0.0.1"
|
||||
port = 57127
|
||||
|
||||
[feeds.eco2mix]
|
||||
enabled = true
|
||||
interval_sec = 60
|
||||
|
||||
[feeds.velib]
|
||||
enabled = true
|
||||
interval_sec = 120
|
||||
station_codes = []
|
||||
|
||||
[feeds.hubeau]
|
||||
enabled = true
|
||||
interval_sec = 300
|
||||
codes = ["F050001001"]
|
||||
|
||||
[feeds.gbfs]
|
||||
enabled = false
|
||||
interval_sec = 120
|
||||
url = "https://velib-metropole-opendata.smoove.pro/opendata/Velib_Metropole/station_status.json"
|
||||
|
||||
[feeds.ais]
|
||||
enabled = false
|
||||
|
||||
[feeds.carburants]
|
||||
enabled = false
|
||||
|
||||
[feeds.prim]
|
||||
enabled = false
|
||||
|
||||
[feeds.sytadin]
|
||||
enabled = false
|
||||
|
||||
[feeds.teleray]
|
||||
enabled = false
|
||||
@@ -0,0 +1,27 @@
|
||||
"""Registry of available feed classes (auto-discovery on import)."""
|
||||
from __future__ import annotations
|
||||
|
||||
from .base import Feed
|
||||
from .eco2mix import Eco2MixFeed
|
||||
from .gbfs import GBFSFeed
|
||||
from .hubeau import HubeauFeed
|
||||
from .velib import VelibFeed
|
||||
from .ais import AISFeed
|
||||
from .carburants import CarburantsFeed
|
||||
from .prim import PRIMFeed
|
||||
from .sytadin import SytadinFeed
|
||||
from .teleray import TelerayFeed
|
||||
|
||||
REGISTRY: dict[str, type[Feed]] = {
|
||||
"eco2mix": Eco2MixFeed,
|
||||
"gbfs": GBFSFeed,
|
||||
"hubeau": HubeauFeed,
|
||||
"velib": VelibFeed,
|
||||
"ais": AISFeed,
|
||||
"carburants": CarburantsFeed,
|
||||
"prim": PRIMFeed,
|
||||
"sytadin": SytadinFeed,
|
||||
"teleray": TelerayFeed,
|
||||
}
|
||||
|
||||
__all__ = ["Feed", "REGISTRY"]
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
"""AIS vessel positions feed — STUB.
|
||||
|
||||
TODO: needs aisstream.io API key + websocket subscription.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.ais")
|
||||
|
||||
|
||||
class AISFeed(Feed):
|
||||
name = "ais"
|
||||
interval_sec = 60.0
|
||||
|
||||
def fetch(self):
|
||||
return None
|
||||
|
||||
def publish(self, payload) -> None:
|
||||
LOG.info("stub")
|
||||
@@ -0,0 +1,55 @@
|
||||
"""Abstract base class for data feeds."""
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
import logging
|
||||
import time
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
LOG = logging.getLogger("data_feeds.base")
|
||||
|
||||
|
||||
class Feed(abc.ABC):
|
||||
name: str = "feed"
|
||||
interval_sec: float = 60.0
|
||||
|
||||
def __init__(self, osc_send, **cfg) -> None:
|
||||
self.osc_send = osc_send
|
||||
self.cfg = cfg
|
||||
self._stop = threading.Event()
|
||||
self._thread: threading.Thread | None = None
|
||||
self.last_t: float = 0.0
|
||||
|
||||
def configure(self, **kwargs) -> None:
|
||||
self.cfg.update(kwargs)
|
||||
if "interval_sec" in kwargs:
|
||||
self.interval_sec = float(kwargs["interval_sec"])
|
||||
|
||||
@abc.abstractmethod
|
||||
def fetch(self) -> Any: ...
|
||||
|
||||
@abc.abstractmethod
|
||||
def publish(self, payload: Any) -> None: ...
|
||||
|
||||
def tick(self) -> None:
|
||||
try:
|
||||
payload = self.fetch()
|
||||
self.publish(payload)
|
||||
self.last_t = time.time()
|
||||
self.osc_send(f"/data/{self.name}/heartbeat", [self.last_t])
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("%s fetch failed: %s", self.name, e)
|
||||
|
||||
def start(self) -> None:
|
||||
self._thread = threading.Thread(
|
||||
target=self._run, name=f"feed-{self.name}", daemon=True)
|
||||
self._thread.start()
|
||||
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
|
||||
def _run(self) -> None:
|
||||
while not self._stop.is_set():
|
||||
self.tick()
|
||||
self._stop.wait(self.interval_sec)
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Prix carburants feed — STUB.
|
||||
|
||||
TODO: needs prix-carburants.gouv.fr GeoJSON cache + station selection.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.carburants")
|
||||
|
||||
|
||||
class CarburantsFeed(Feed):
|
||||
name = "carburants"
|
||||
interval_sec = 3600.0
|
||||
|
||||
def fetch(self):
|
||||
return None
|
||||
|
||||
def publish(self, payload) -> None:
|
||||
LOG.info("stub")
|
||||
@@ -0,0 +1,58 @@
|
||||
"""RTE eco2mix feed — France electricity production mix in MW.
|
||||
|
||||
Uses the public OpenDataSoft mirror of RTE eco2mix-national-tr (temps reel,
|
||||
15-min resolution). Stdlib HTTP only.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from typing import Any
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.eco2mix")
|
||||
|
||||
# OpenDataSoft public mirror — no key required.
|
||||
URL = (
|
||||
"https://odre.opendatasoft.com/api/records/1.0/search/"
|
||||
"?dataset=eco2mix-national-tr&rows=1&sort=-date_heure"
|
||||
)
|
||||
|
||||
|
||||
class Eco2MixFeed(Feed):
|
||||
name = "eco2mix"
|
||||
interval_sec = 60.0
|
||||
|
||||
def fetch(self) -> Any:
|
||||
req = urllib.request.Request(URL, headers={"User-Agent": "av-live/0.1"})
|
||||
with urllib.request.urlopen(req, timeout=10) as r:
|
||||
data = json.loads(r.read().decode("utf-8"))
|
||||
records = data.get("records") or []
|
||||
if not records:
|
||||
return None
|
||||
return records[0].get("fields") or {}
|
||||
|
||||
def publish(self, payload: Any) -> None:
|
||||
if not isinstance(payload, dict):
|
||||
return
|
||||
# Pick a representative subset (MW). Keys per eco2mix-national-tr.
|
||||
keys = [
|
||||
"consommation", "nucleaire", "gaz", "charbon", "fioul",
|
||||
"eolien", "solaire", "hydraulique", "bioenergies",
|
||||
"ech_physiques",
|
||||
]
|
||||
count = 0
|
||||
for k in keys:
|
||||
v = payload.get(k)
|
||||
if v is None:
|
||||
continue
|
||||
try:
|
||||
fv = float(v)
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
self.osc_send(f"/data/{self.name}/sample", [k, fv])
|
||||
count += 1
|
||||
self.osc_send(f"/data/{self.name}/count", [count])
|
||||
@@ -0,0 +1,53 @@
|
||||
"""Generic GBFS (General Bikeshare Feed Specification) reader.
|
||||
|
||||
Reads a `station_status.json` URL and publishes aggregate counters.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import urllib.request
|
||||
from typing import Any
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.gbfs")
|
||||
|
||||
|
||||
class GBFSFeed(Feed):
|
||||
name = "gbfs"
|
||||
interval_sec = 120.0
|
||||
|
||||
def fetch(self) -> Any:
|
||||
url = self.cfg.get("url")
|
||||
if not url:
|
||||
LOG.info("gbfs disabled: no url configured")
|
||||
return None
|
||||
req = urllib.request.Request(url, headers={"User-Agent": "av-live/0.1"})
|
||||
with urllib.request.urlopen(req, timeout=10) as r:
|
||||
return json.loads(r.read().decode("utf-8"))
|
||||
|
||||
def publish(self, payload: Any) -> None:
|
||||
if not isinstance(payload, dict):
|
||||
return
|
||||
stations = (payload.get("data") or {}).get("stations") or []
|
||||
if not stations:
|
||||
return
|
||||
codes = set(self.cfg.get("station_codes") or [])
|
||||
bikes = 0
|
||||
docks = 0
|
||||
operative = 0
|
||||
sampled = 0
|
||||
for s in stations:
|
||||
sid = str(s.get("station_id", ""))
|
||||
if codes and sid not in codes:
|
||||
continue
|
||||
bikes += int(s.get("num_bikes_available") or 0)
|
||||
docks += int(s.get("num_docks_available") or 0)
|
||||
if s.get("is_renting") or s.get("is_installed"):
|
||||
operative += 1
|
||||
sampled += 1
|
||||
self.osc_send(f"/data/{self.name}/sample", ["bikes_available", float(bikes)])
|
||||
self.osc_send(f"/data/{self.name}/sample", ["docks_available", float(docks)])
|
||||
self.osc_send(f"/data/{self.name}/sample", ["stations_active", float(operative)])
|
||||
self.osc_send(f"/data/{self.name}/count", [sampled])
|
||||
@@ -0,0 +1,66 @@
|
||||
"""Hub'Eau hydrometrie feed — water level and flow rate for French rivers.
|
||||
|
||||
API: https://hubeau.eaufrance.fr/api/v1/hydrometrie/observations_tr
|
||||
Open, no API key required.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import urllib.parse
|
||||
import urllib.request
|
||||
from typing import Any
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.hubeau")
|
||||
|
||||
BASE = "https://hubeau.eaufrance.fr/api/v1/hydrometrie/observations_tr"
|
||||
|
||||
|
||||
class HubeauFeed(Feed):
|
||||
name = "hubeau"
|
||||
interval_sec = 300.0
|
||||
|
||||
def fetch(self) -> Any:
|
||||
codes = self.cfg.get("codes") or ["F050001001"]
|
||||
out: dict[str, dict[str, float]] = {}
|
||||
for code in codes:
|
||||
params = {
|
||||
"code_entite": code,
|
||||
"size": 1,
|
||||
"sort": "desc",
|
||||
"fields": "code_station,grandeur_hydro,resultat_obs,date_obs",
|
||||
}
|
||||
url = BASE + "?" + urllib.parse.urlencode(params)
|
||||
try:
|
||||
req = urllib.request.Request(
|
||||
url, headers={"User-Agent": "av-live/0.1"})
|
||||
with urllib.request.urlopen(req, timeout=10) as r:
|
||||
data = json.loads(r.read().decode("utf-8"))
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("hubeau %s failed: %s", code, e)
|
||||
continue
|
||||
for obs in data.get("data") or []:
|
||||
station = obs.get("code_station") or code
|
||||
gr = obs.get("grandeur_hydro") or "X"
|
||||
v = obs.get("resultat_obs")
|
||||
if v is None:
|
||||
continue
|
||||
try:
|
||||
fv = float(v)
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
out.setdefault(station, {})[gr] = fv
|
||||
return out
|
||||
|
||||
def publish(self, payload: Any) -> None:
|
||||
if not isinstance(payload, dict) or not payload:
|
||||
return
|
||||
count = 0
|
||||
for station, vals in payload.items():
|
||||
for gr, v in vals.items():
|
||||
key = f"{station}_{gr}"
|
||||
self.osc_send(f"/data/{self.name}/sample", [key, float(v)])
|
||||
count += 1
|
||||
self.osc_send(f"/data/{self.name}/count", [count])
|
||||
@@ -0,0 +1,22 @@
|
||||
"""PRIM Ile-de-France Mobilites feed — STUB.
|
||||
|
||||
TODO: needs API key (https://prim.iledefrance-mobilites.fr/).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.prim")
|
||||
|
||||
|
||||
class PRIMFeed(Feed):
|
||||
name = "prim"
|
||||
interval_sec = 60.0
|
||||
|
||||
def fetch(self):
|
||||
return None
|
||||
|
||||
def publish(self, payload) -> None:
|
||||
LOG.info("stub")
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Sytadin Paris traffic feed — STUB.
|
||||
|
||||
TODO: needs sytadin.fr scraping / cumulative km of congestion.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.sytadin")
|
||||
|
||||
|
||||
class SytadinFeed(Feed):
|
||||
name = "sytadin"
|
||||
interval_sec = 300.0
|
||||
|
||||
def fetch(self):
|
||||
return None
|
||||
|
||||
def publish(self, payload) -> None:
|
||||
LOG.info("stub")
|
||||
@@ -0,0 +1,22 @@
|
||||
"""IRSN Teleray radiation feed — STUB.
|
||||
|
||||
TODO: needs https://teleray.irsn.fr/data endpoint research.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .base import Feed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.teleray")
|
||||
|
||||
|
||||
class TelerayFeed(Feed):
|
||||
name = "teleray"
|
||||
interval_sec = 600.0
|
||||
|
||||
def fetch(self):
|
||||
return None
|
||||
|
||||
def publish(self, payload) -> None:
|
||||
LOG.info("stub")
|
||||
@@ -0,0 +1,25 @@
|
||||
"""Velib Metropole feed — specialization of GBFS against the Paris URL."""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from .gbfs import GBFSFeed
|
||||
|
||||
LOG = logging.getLogger("data_feeds.velib")
|
||||
|
||||
VELIB_URL = (
|
||||
"https://velib-metropole-opendata.smoove.pro/opendata/"
|
||||
"Velib_Metropole/station_status.json"
|
||||
)
|
||||
|
||||
|
||||
class VelibFeed(GBFSFeed):
|
||||
name = "velib"
|
||||
interval_sec = 120.0
|
||||
|
||||
def configure(self, **kwargs) -> None:
|
||||
# Force the URL if caller did not provide one.
|
||||
kwargs.setdefault("url", VELIB_URL)
|
||||
super().configure(**kwargs)
|
||||
if not self.cfg.get("url"):
|
||||
self.cfg["url"] = VELIB_URL
|
||||
@@ -0,0 +1,57 @@
|
||||
"""Run all enabled feeds, publish OSC to AVLiveBody."""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
import tomllib
|
||||
from pathlib import Path
|
||||
|
||||
from .feeds import REGISTRY
|
||||
from .osc_sender import OscSender
|
||||
|
||||
|
||||
def main(argv: list[str] | None = None) -> int:
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--config", default="data_feeds/config.avlivedata.toml")
|
||||
p.add_argument("--osc-host")
|
||||
p.add_argument("--osc-port", type=int)
|
||||
p.add_argument("-v", "--verbose", action="store_true")
|
||||
args = p.parse_args(argv)
|
||||
logging.basicConfig(
|
||||
level=logging.INFO if args.verbose else logging.WARNING,
|
||||
format="%(asctime)s %(levelname)s %(name)s %(message)s")
|
||||
cfg = tomllib.loads(Path(args.config).read_text())
|
||||
osc_cfg = cfg.get("osc", {})
|
||||
host = args.osc_host or osc_cfg.get("host", "127.0.0.1")
|
||||
port = args.osc_port or osc_cfg.get("port", 57127)
|
||||
sender = OscSender(host, port)
|
||||
feeds = []
|
||||
for name, kwargs in (cfg.get("feeds") or {}).items():
|
||||
if not kwargs.get("enabled", False):
|
||||
continue
|
||||
cls = REGISTRY.get(name)
|
||||
if cls is None:
|
||||
logging.warning("Unknown feed: %s", name)
|
||||
continue
|
||||
f = cls(sender.send)
|
||||
f.configure(**kwargs)
|
||||
f.start()
|
||||
feeds.append(f)
|
||||
logging.info("started feed %s (interval %.0fs)", name, f.interval_sec)
|
||||
if not feeds:
|
||||
logging.warning("No feeds enabled. Exiting.")
|
||||
return 1
|
||||
try:
|
||||
while True:
|
||||
time.sleep(60)
|
||||
except KeyboardInterrupt:
|
||||
return 0
|
||||
finally:
|
||||
for f in feeds:
|
||||
f.stop()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Wrapper around python-osc SimpleUDPClient with per-route helpers."""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from pythonosc.udp_client import SimpleUDPClient
|
||||
|
||||
LOG = logging.getLogger("data_feeds.osc")
|
||||
|
||||
|
||||
class OscSender:
|
||||
def __init__(self, host: str, port: int) -> None:
|
||||
self.host = host
|
||||
self.port = port
|
||||
self._client = SimpleUDPClient(host, port)
|
||||
|
||||
def send(self, addr: str, args: list[Any]) -> None:
|
||||
try:
|
||||
self._client.send_message(addr, args)
|
||||
except OSError as e:
|
||||
LOG.warning("send %s failed: %s", addr, e)
|
||||
@@ -31,6 +31,12 @@ Python **3.11+** requis. `pyproject.toml` est la source de vérité — ne jamai
|
||||
|
||||
- État partagé multi-thread : `state.py` expose `State.lock()` — toujours mutationner sous lock.
|
||||
- Filtrage temporel : `euro_filter.py` (One Euro Filter) sur les keypoints avant tracker.
|
||||
- ARKit fusion : `iphone_osc_listener.py` consume /body3d/kp UDP :57128
|
||||
→ `state.persons_arkit_joints`. `pose_filter.py::ArkitFuse` (stage
|
||||
`arkit_fuse`) splices the 14 mapped body slots into MediaPipe pose
|
||||
before kalman ; `multi_hmr_worker::arkit_pelvis_z_override` locks the
|
||||
SMPL-X cam translation z to the ARKit pelvis. Mapping in
|
||||
`arkit_joint_map.py`.
|
||||
- Association multi-personne : `tracker.py` IoU-based, `scipy.optimize.linear_sum_assignment`.
|
||||
- Shaders Metal dans `shaders/` (`.metal`), recompilés au runtime ; topologie mesh (SMPL faces) en binaire dans `mesh_topology.py`.
|
||||
- OSC out : `osc_listener.py` / `pose_bridge.py` — destination `oscope-of` sur `:57123`.
|
||||
|
||||
@@ -0,0 +1,45 @@
|
||||
"""ARKit ARSkeleton3D 91-joint indices → MediaPipe Pose 33 indices.
|
||||
|
||||
The ARKit ARSkeleton.JointName enum (Apple SDK) orders 91 joints
|
||||
starting with the root, hips, spine chain, shoulders, etc. We pick
|
||||
only the joints with a clear 1:1 anatomical correspondence to the
|
||||
MediaPipe Pose 33 landmark set (which is what AVLiveBody renders).
|
||||
Face/hand sub-joints (fingers, eyes) are skipped — those keep their
|
||||
existing data sources (MediaPipe Face/Hand + HaMeR MANO).
|
||||
|
||||
Reference for ARKit joint order : Apple developer docs
|
||||
"ARSkeleton.JointName" — the canonical 91-joint list runs from
|
||||
root_joint=0 down to right_handThumbEndJoint=90.
|
||||
|
||||
The selection here mirrors `multi.py::SMPLX_TO_MP33` so the same 14
|
||||
body slots are overridden by ARKit when fresh. Confidence comes
|
||||
from ARKit's tracking state but is not currently fanned out — we
|
||||
trust ARKit body tracking when its OSC frame is present.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
# MediaPipe Pose 33 cardinality (cf. mediapipe pose_world_landmarks).
|
||||
MP33_NUM_LANDMARKS = 33
|
||||
|
||||
# Pelvis = ARKit hips_joint, slot 1 in the canonical enum order.
|
||||
# Used by multi_hmr_worker for cam-translation z lock.
|
||||
ARKIT_PELVIS_IDX = 1
|
||||
|
||||
# (arkit_joint_idx, mediapipe_pose_idx). Match the body slots used
|
||||
# by the SMPL-X body fusion in multi.py.
|
||||
ARKIT91_TO_MP33: tuple[tuple[int, int], ...] = (
|
||||
(50, 11), # left_shoulder_1_joint -> L_SHOULDER
|
||||
(32, 12), # right_shoulder_1_joint -> R_SHOULDER
|
||||
(53, 13), # left_arm_joint -> L_ELBOW
|
||||
(35, 14), # right_arm_joint -> R_ELBOW
|
||||
(54, 15), # left_forearm_joint -> L_WRIST
|
||||
(36, 16), # right_forearm_joint -> R_WRIST
|
||||
(62, 23), # left_upLeg_joint -> L_HIP
|
||||
(57, 24), # right_upLeg_joint -> R_HIP
|
||||
(63, 25), # left_leg_joint -> L_KNEE
|
||||
(58, 26), # right_leg_joint -> R_KNEE
|
||||
(64, 27), # left_foot_joint -> L_ANKLE
|
||||
(59, 28), # right_foot_joint -> R_ANKLE
|
||||
(65, 31), # left_toes_joint -> L_FOOT_INDEX
|
||||
(60, 32), # right_toes_joint -> R_FOOT_INDEX
|
||||
)
|
||||
@@ -0,0 +1,204 @@
|
||||
"""DINOv2 ViT-S/14 person re-id backend (CoreML via pyobjc).
|
||||
|
||||
Loads the .mlpackage produced by ``scripts/convert_dinov2.py`` and runs
|
||||
inference one crop at a time (pyobjc + MLDictionaryFeatureProvider).
|
||||
Same pattern as ``multihmr_coreml.py`` so Python 3.14 works (no
|
||||
coremltools dependency at runtime).
|
||||
|
||||
Embeddings are L2-normalised inside the CoreML graph, so cosine sim
|
||||
between two outputs is a plain dot product.
|
||||
|
||||
Public API::
|
||||
|
||||
reid = DinoReid(mlpackage_path) # optional path
|
||||
emb = reid.embed_crops(list_of_uint8_HWC) # -> np.ndarray (N, 384)
|
||||
DinoReid.is_available() # bool
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Sequence
|
||||
|
||||
import numpy as np
|
||||
|
||||
LOG = logging.getLogger("dino_reid")
|
||||
|
||||
DEFAULT_MLPACKAGE = (
|
||||
Path.home() / ".cache" / "av-live-multihmr" / "dinov2_vits14.mlpackage"
|
||||
)
|
||||
|
||||
EMBED_DIM = 384
|
||||
INPUT_SIZE = 224
|
||||
|
||||
# MLMultiArrayDataType raw values (from CoreML headers).
|
||||
ML_DTYPE_FLOAT32 = 65568
|
||||
ML_DTYPE_FLOAT16 = 65552
|
||||
ML_DTYPE_DOUBLE = 65600
|
||||
|
||||
|
||||
def _resize_crop(crop_uint8: np.ndarray) -> np.ndarray:
|
||||
"""Resize an HxWx3 uint8 crop to (3, 224, 224) float32 in [0, 1].
|
||||
|
||||
Uses ``cv2.resize`` when available, falls back to a simple stride
|
||||
sampler otherwise (avoids hard cv2 dep in test envs)."""
|
||||
if crop_uint8.ndim != 3 or crop_uint8.shape[2] != 3:
|
||||
raise ValueError(f"crop must be HxWx3 uint8, got {crop_uint8.shape}")
|
||||
if crop_uint8.shape[0] == INPUT_SIZE and crop_uint8.shape[1] == INPUT_SIZE:
|
||||
rgb = crop_uint8
|
||||
else:
|
||||
try:
|
||||
import cv2
|
||||
rgb = cv2.resize(crop_uint8, (INPUT_SIZE, INPUT_SIZE),
|
||||
interpolation=cv2.INTER_AREA)
|
||||
except ImportError:
|
||||
h, w = crop_uint8.shape[:2]
|
||||
ys = (np.linspace(0, h - 1, INPUT_SIZE)).astype(np.int32)
|
||||
xs = (np.linspace(0, w - 1, INPUT_SIZE)).astype(np.int32)
|
||||
rgb = crop_uint8[ys][:, xs]
|
||||
return (rgb.astype(np.float32) / 255.0).transpose(2, 0, 1)
|
||||
|
||||
|
||||
class DinoReid:
|
||||
"""Forward DINOv2 ViT-S/14 over RGB crops, return L2-normalised
|
||||
embeddings (N, 384)."""
|
||||
|
||||
def __init__(self, mlpackage_path: Path | str | None = None) -> None:
|
||||
self.path = Path(mlpackage_path) if mlpackage_path else DEFAULT_MLPACKAGE
|
||||
if not self.path.exists():
|
||||
raise FileNotFoundError(f"mlpackage missing: {self.path}")
|
||||
|
||||
import objc
|
||||
from Foundation import NSURL
|
||||
|
||||
self._objc = objc
|
||||
self._NSURL = NSURL
|
||||
|
||||
ns: dict = {}
|
||||
objc.loadBundle("CoreML", ns,
|
||||
"/System/Library/Frameworks/CoreML.framework")
|
||||
self._ns = ns
|
||||
|
||||
MLModel = ns["MLModel"]
|
||||
MLModelConfiguration = ns["MLModelConfiguration"]
|
||||
cfg = MLModelConfiguration.alloc().init()
|
||||
try:
|
||||
# 2 = MLComputeUnitsAll (CPU+GPU+ANE). DINOv2 ViT-S/14
|
||||
# converts cleanly and ANE serves it well.
|
||||
cfg.setComputeUnits_(2)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
url = NSURL.fileURLWithPath_(str(self.path))
|
||||
compiled = MLModel.compileModelAtURL_error_(url, None)
|
||||
if compiled is None:
|
||||
raise RuntimeError(f"compile failed for {self.path}")
|
||||
model = MLModel.modelWithContentsOfURL_configuration_error_(
|
||||
compiled, cfg, None)
|
||||
if model is None:
|
||||
raise RuntimeError(f"load failed for {compiled}")
|
||||
self._model = model
|
||||
|
||||
# Discover the output feature name (single tensor).
|
||||
desc = model.modelDescription()
|
||||
out_names = [str(n) for n in desc.outputDescriptionsByName().keys()]
|
||||
self._out_name = out_names[0] if out_names else "embedding"
|
||||
LOG.info("dino_reid loaded (%s, out=%s)", self.path.name,
|
||||
self._out_name)
|
||||
|
||||
@classmethod
|
||||
def is_available(cls, mlpackage_path: Path | str | None = None) -> bool:
|
||||
p = Path(mlpackage_path) if mlpackage_path else DEFAULT_MLPACKAGE
|
||||
if not p.exists():
|
||||
return False
|
||||
try:
|
||||
import objc # noqa: F401
|
||||
from Foundation import NSURL # noqa: F401
|
||||
return True
|
||||
except Exception: # noqa: BLE001
|
||||
return False
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# MLMultiArray plumbing — mirrors multihmr_coreml._np_to_mlarray /
|
||||
# _mlarray_to_np. Float32 in, float32-or-float16 out.
|
||||
# ------------------------------------------------------------------
|
||||
def _np_to_mlarray(self, arr: np.ndarray):
|
||||
import ctypes
|
||||
MLMultiArray = self._ns["MLMultiArray"]
|
||||
arr = np.ascontiguousarray(arr, dtype=np.float32)
|
||||
shape = [int(s) for s in arr.shape]
|
||||
ml = MLMultiArray.alloc().initWithShape_dataType_error_(
|
||||
shape, ML_DTYPE_FLOAT32, None)
|
||||
if ml is None:
|
||||
raise RuntimeError("MLMultiArray alloc failed")
|
||||
ptr = ml.dataPointer()
|
||||
addr = int(ptr) if isinstance(ptr, int) else ctypes.cast(
|
||||
ptr, ctypes.c_void_p).value
|
||||
if addr is None:
|
||||
raise RuntimeError("dataPointer null")
|
||||
ctypes.memmove(addr, arr.ctypes.data, arr.nbytes)
|
||||
return ml
|
||||
|
||||
def _mlarray_to_np(self, ml) -> np.ndarray:
|
||||
import ctypes
|
||||
shape = tuple(int(s) for s in ml.shape())
|
||||
dtype_id = int(ml.dataType())
|
||||
count = 1
|
||||
for s in shape:
|
||||
count *= s
|
||||
ptr = ml.dataPointer()
|
||||
addr = int(ptr) if isinstance(ptr, int) else ctypes.cast(
|
||||
ptr, ctypes.c_void_p).value
|
||||
if addr is None:
|
||||
raise RuntimeError("dataPointer null")
|
||||
if dtype_id == ML_DTYPE_FLOAT16:
|
||||
raw = (ctypes.c_uint16 * count).from_address(addr)
|
||||
arr = np.ctypeslib.as_array(raw).view(np.float16).astype(np.float32)
|
||||
elif dtype_id == ML_DTYPE_FLOAT32:
|
||||
raw = (ctypes.c_float * count).from_address(addr)
|
||||
arr = np.ctypeslib.as_array(raw).copy()
|
||||
elif dtype_id == ML_DTYPE_DOUBLE:
|
||||
raw = (ctypes.c_double * count).from_address(addr)
|
||||
arr = np.ctypeslib.as_array(raw).astype(np.float32)
|
||||
else:
|
||||
raise RuntimeError(f"unsupported dtype {dtype_id}")
|
||||
return arr.reshape(shape)
|
||||
|
||||
def _predict_one(self, image_chw: np.ndarray) -> np.ndarray:
|
||||
MLDictionaryFeatureProvider = self._ns["MLDictionaryFeatureProvider"]
|
||||
MLFeatureValue = self._ns["MLFeatureValue"]
|
||||
x4 = image_chw[np.newaxis, ...] if image_chw.ndim == 3 else image_chw
|
||||
img_ml = self._np_to_mlarray(x4)
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml)}
|
||||
provider = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
if provider is None:
|
||||
raise RuntimeError("provider alloc failed")
|
||||
out = self._model.predictionFromFeatures_error_(provider, None)
|
||||
if out is None:
|
||||
raise RuntimeError("predict failed")
|
||||
fv = out.featureValueForName_(self._out_name)
|
||||
ml = fv.multiArrayValue()
|
||||
return self._mlarray_to_np(ml).reshape(-1)
|
||||
|
||||
def embed_crops(
|
||||
self, crops_uint8: Sequence[np.ndarray],
|
||||
) -> np.ndarray:
|
||||
"""Embed a list of HxWx3 uint8 RGB crops -> (N, 384) float32.
|
||||
|
||||
Loops one crop at a time (the CoreML model is traced for B=1).
|
||||
For typical N <= 4 this is still 10-15 ms total on M5."""
|
||||
if not crops_uint8:
|
||||
return np.zeros((0, EMBED_DIM), dtype=np.float32)
|
||||
t0 = time.perf_counter()
|
||||
out = np.zeros((len(crops_uint8), EMBED_DIM), dtype=np.float32)
|
||||
for i, c in enumerate(crops_uint8):
|
||||
chw = _resize_crop(c)
|
||||
out[i] = self._predict_one(chw)
|
||||
dt_ms = (time.perf_counter() - t0) * 1e3
|
||||
if LOG.isEnabledFor(logging.DEBUG) or dt_ms > 50.0:
|
||||
LOG.log(
|
||||
logging.DEBUG if dt_ms <= 50.0 else logging.INFO,
|
||||
"embedded %d crops in %.1f ms", len(crops_uint8), dt_ms)
|
||||
return out
|
||||
@@ -0,0 +1,215 @@
|
||||
"""ICP fusion between Multi-HMR SMPL-X meshes and iPhone LiDAR point clouds.
|
||||
|
||||
All operations happen in the **webcam camera frame** (meters, OpenCV
|
||||
convention: +X right, +Y down, +Z forward). LiDAR points must be
|
||||
pre-transformed via `Extrinsic.T_arkit_to_cam`.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
|
||||
import numpy as np
|
||||
|
||||
try:
|
||||
import open3d as o3d
|
||||
except ImportError: # pragma: no cover - exercised via skipif at import sites
|
||||
o3d = None # type: ignore[assignment]
|
||||
|
||||
_LOG = logging.getLogger(__name__)
|
||||
|
||||
MIN_LIDAR_POINTS = 200
|
||||
MIN_FITNESS = 0.30
|
||||
MAX_RMSE_M = 0.05
|
||||
CROP_MARGIN_M = 0.30
|
||||
|
||||
|
||||
@dataclass
|
||||
class IcpConfig:
|
||||
voxel_size_m: float = 0.02
|
||||
max_correspondence_m: float = 0.05
|
||||
max_iterations: int = 30
|
||||
|
||||
|
||||
@dataclass
|
||||
class IcpResult:
|
||||
vertices_registered: np.ndarray
|
||||
accepted: bool
|
||||
fitness: float
|
||||
rmse_m: float
|
||||
iterations: int
|
||||
|
||||
|
||||
def register_mesh_to_lidar(
|
||||
smplx_verts_cam: np.ndarray,
|
||||
lidar_points_cam: np.ndarray,
|
||||
config: IcpConfig | None = None,
|
||||
) -> IcpResult:
|
||||
"""Register SMPL-X verts onto a cropped LiDAR neighborhood."""
|
||||
if o3d is None:
|
||||
raise RuntimeError("open3d not installed — install with `uv sync --extra lidar`")
|
||||
|
||||
cfg = config or IcpConfig()
|
||||
src = np.ascontiguousarray(smplx_verts_cam, dtype=np.float32)
|
||||
|
||||
if not np.isfinite(src).all():
|
||||
_LOG.debug("ICP rejected: NaN/Inf in SMPL-X verts")
|
||||
return IcpResult(src, False, 0.0, float("inf"), 0)
|
||||
|
||||
lidar = _crop_to_bbox(lidar_points_cam, src, margin_m=CROP_MARGIN_M)
|
||||
if lidar.shape[0] < MIN_LIDAR_POINTS or not np.isfinite(lidar).all():
|
||||
_LOG.debug("ICP rejected: insufficient LiDAR points (%d)", lidar.shape[0])
|
||||
return IcpResult(src, False, 0.0, float("inf"), 0)
|
||||
|
||||
src_pcd = _to_pcd(src, cfg.voxel_size_m, estimate_normals=True)
|
||||
tgt_pcd = _to_pcd(lidar, cfg.voxel_size_m, estimate_normals=True)
|
||||
|
||||
if len(src_pcd.points) < 10 or len(tgt_pcd.points) < 10:
|
||||
return IcpResult(src, False, 0.0, float("inf"), 0)
|
||||
|
||||
criteria = o3d.pipelines.registration.ICPConvergenceCriteria(
|
||||
max_iteration=cfg.max_iterations,
|
||||
relative_fitness=1e-6,
|
||||
relative_rmse=1e-6,
|
||||
)
|
||||
# Coarse-to-fine: a wide first pass handles translations larger than the
|
||||
# final correspondence threshold, then the strict pass refines and gates.
|
||||
coarse = o3d.pipelines.registration.registration_icp(
|
||||
src_pcd, tgt_pcd, max(cfg.max_correspondence_m * 5.0, 0.20),
|
||||
np.eye(4),
|
||||
o3d.pipelines.registration.TransformationEstimationPointToPlane(),
|
||||
criteria,
|
||||
)
|
||||
result = o3d.pipelines.registration.registration_icp(
|
||||
src_pcd, tgt_pcd, cfg.max_correspondence_m,
|
||||
coarse.transformation,
|
||||
o3d.pipelines.registration.TransformationEstimationPointToPlane(),
|
||||
criteria,
|
||||
)
|
||||
|
||||
accepted = (result.fitness >= MIN_FITNESS) and (result.inlier_rmse <= MAX_RMSE_M)
|
||||
if not accepted:
|
||||
_LOG.debug("ICP rejected: fitness=%.3f rmse=%.4f", result.fitness, result.inlier_rmse)
|
||||
return IcpResult(src, False, float(result.fitness), float(result.inlier_rmse), 0)
|
||||
|
||||
T = np.asarray(result.transformation, dtype=np.float32)
|
||||
homog = np.concatenate([src, np.ones((src.shape[0], 1), dtype=np.float32)], axis=1)
|
||||
fused = (homog @ T.T)[:, :3]
|
||||
if not np.isfinite(fused).all():
|
||||
return IcpResult(src, False, float(result.fitness), float(result.inlier_rmse), 0)
|
||||
|
||||
return IcpResult(
|
||||
vertices_registered=np.ascontiguousarray(fused, dtype=np.float32),
|
||||
accepted=True,
|
||||
fitness=float(result.fitness),
|
||||
rmse_m=float(result.inlier_rmse),
|
||||
iterations=cfg.max_iterations,
|
||||
)
|
||||
|
||||
|
||||
def _crop_to_bbox(points: np.ndarray, anchor: np.ndarray, margin_m: float) -> np.ndarray:
|
||||
if points.size == 0:
|
||||
return points.astype(np.float32, copy=False)
|
||||
lo = anchor.min(axis=0) - margin_m
|
||||
hi = anchor.max(axis=0) + margin_m
|
||||
mask = np.all((points >= lo) & (points <= hi), axis=1)
|
||||
return points[mask].astype(np.float32, copy=False)
|
||||
|
||||
|
||||
def _to_pcd(points: np.ndarray, voxel_size_m: float, estimate_normals: bool):
|
||||
pcd = o3d.geometry.PointCloud()
|
||||
pcd.points = o3d.utility.Vector3dVector(points.astype(np.float64, copy=False))
|
||||
if voxel_size_m > 0:
|
||||
pcd = pcd.voxel_down_sample(voxel_size_m)
|
||||
if estimate_normals:
|
||||
pcd.estimate_normals(
|
||||
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size_m * 2, max_nn=30),
|
||||
)
|
||||
return pcd
|
||||
|
||||
|
||||
def partition_lidar_by_pid(
|
||||
lidar_points_cam: np.ndarray,
|
||||
pelvises: dict[int, np.ndarray],
|
||||
max_dist_m: float = 1.0,
|
||||
) -> dict[int, np.ndarray]:
|
||||
"""Assign each LiDAR point to the closest pelvis within ``max_dist_m``.
|
||||
|
||||
Points beyond ``max_dist_m`` from every pelvis (background, furniture)
|
||||
are dropped. Returns ``{pid: (M, 3) float32}`` — pids with zero assigned
|
||||
points are omitted.
|
||||
"""
|
||||
if not pelvises or lidar_points_cam.size == 0:
|
||||
return {}
|
||||
pids = list(pelvises.keys())
|
||||
centers = np.stack([pelvises[p] for p in pids]).astype(np.float32)
|
||||
pts = np.ascontiguousarray(lidar_points_cam, dtype=np.float32)
|
||||
|
||||
diff = pts[:, None, :] - centers[None, :, :]
|
||||
d2 = np.einsum("npk,npk->np", diff, diff)
|
||||
nearest = d2.argmin(axis=1)
|
||||
nearest_d = np.sqrt(d2[np.arange(d2.shape[0]), nearest])
|
||||
|
||||
mask = nearest_d <= max_dist_m
|
||||
out: dict[int, np.ndarray] = {}
|
||||
for idx, pid in enumerate(pids):
|
||||
sel = mask & (nearest == idx)
|
||||
if not sel.any():
|
||||
continue
|
||||
out[pid] = pts[sel]
|
||||
return out
|
||||
|
||||
|
||||
PELVIS_VERT_INDEX = 5559 # SMPL-X canonical pelvis vertex
|
||||
|
||||
|
||||
@dataclass
|
||||
class FusionMetadata:
|
||||
applied: set[int]
|
||||
fitness: dict[int, float]
|
||||
rmse_m: dict[int, float]
|
||||
n_lidar_points_used: int
|
||||
|
||||
|
||||
class FusionWorker:
|
||||
"""Per-frame ICP fusion orchestrator (caller-driven, no internal thread)."""
|
||||
|
||||
def __init__(self, extrinsic, config: IcpConfig | None = None) -> None:
|
||||
self._extrinsic = extrinsic
|
||||
self._config = config or IcpConfig()
|
||||
|
||||
def set_extrinsic(self, extrinsic) -> None:
|
||||
self._extrinsic = extrinsic
|
||||
|
||||
def run_once(self, state) -> FusionMetadata:
|
||||
applied: set[int] = set()
|
||||
fitness: dict[int, float] = {}
|
||||
rmse: dict[int, float] = {}
|
||||
|
||||
lidar = getattr(state, "lidar_points", None)
|
||||
if lidar is None or getattr(lidar, "size", 0) == 0 or not state.persons_smplx:
|
||||
return FusionMetadata(applied, fitness, rmse, 0)
|
||||
|
||||
T = np.asarray(self._extrinsic.T_arkit_to_cam, dtype=np.float32)
|
||||
homog = np.concatenate([lidar, np.ones((lidar.shape[0], 1), dtype=np.float32)], axis=1)
|
||||
lidar_cam = (homog @ T.T)[:, :3]
|
||||
|
||||
pelvises = {
|
||||
p.pid: p.vertices_3d[PELVIS_VERT_INDEX]
|
||||
for p in state.persons_smplx
|
||||
if p.vertices_3d is not None
|
||||
}
|
||||
parts = partition_lidar_by_pid(lidar_cam, pelvises, max_dist_m=1.0)
|
||||
|
||||
for person in state.persons_smplx:
|
||||
pts = parts.get(person.pid)
|
||||
if pts is None:
|
||||
continue
|
||||
result = register_mesh_to_lidar(person.vertices_3d, pts, self._config)
|
||||
fitness[person.pid] = result.fitness
|
||||
rmse[person.pid] = result.rmse_m
|
||||
if result.accepted:
|
||||
person.vertices_3d = result.vertices_registered
|
||||
applied.add(person.pid)
|
||||
|
||||
return FusionMetadata(applied, fitness, rmse, lidar_cam.shape[0])
|
||||
@@ -0,0 +1,72 @@
|
||||
"""Threaded wrapper that polls State and calls FusionWorker.run_once.
|
||||
|
||||
ICP fusion runs as a background thread parallel to the autonomous
|
||||
Multi-HMR worker. It pulls the latest LiDAR frame from a
|
||||
LidarTCPReader, stages it into State, and applies in-place ICP
|
||||
registration to ``state.persons_smplx[*].vertices_3d``.
|
||||
|
||||
Opt-in via ``ICP_FUSION=1`` from main.py.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from .icp_fusion import FusionWorker, IcpConfig
|
||||
from .lidar_calib import load_extrinsic
|
||||
from .lidar_receiver import LidarTCPReader
|
||||
|
||||
_LOG = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class IcpFusionThread:
|
||||
"""Background thread: pull LiDAR frames, run FusionWorker on state."""
|
||||
|
||||
def __init__(self, state, host: str, port: int,
|
||||
target_hz: float = 8.0) -> None:
|
||||
self._state = state
|
||||
self._reader = LidarTCPReader(host=host, port=port)
|
||||
self._worker = FusionWorker(extrinsic=load_extrinsic(),
|
||||
config=IcpConfig())
|
||||
self._period_s = 1.0 / max(target_hz, 0.5)
|
||||
self._stop = threading.Event()
|
||||
self._thread: Optional[threading.Thread] = None
|
||||
|
||||
def start(self) -> None:
|
||||
if self._thread is not None:
|
||||
return
|
||||
self._reader.start()
|
||||
self._thread = threading.Thread(
|
||||
target=self._run, name="icp-fusion", daemon=True)
|
||||
self._thread.start()
|
||||
_LOG.info("icp-fusion thread started")
|
||||
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
self._reader.stop()
|
||||
if self._thread is not None:
|
||||
self._thread.join(timeout=2.0)
|
||||
self._thread = None
|
||||
|
||||
def _run(self) -> None:
|
||||
while not self._stop.is_set():
|
||||
t0 = time.monotonic()
|
||||
frame = self._reader.latest()
|
||||
if frame is not None and self._state.persons_smplx:
|
||||
# State doesn't expose a fine-grained lock for these
|
||||
# fields here; rely on FusionWorker.run_once being
|
||||
# write-only on persons_smplx[*].vertices_3d (replace in
|
||||
# place) and the readers being tolerant of mid-update.
|
||||
self._state.lidar_points = frame.points
|
||||
self._state.lidar_timestamp_ns = frame.timestamp_ns
|
||||
try:
|
||||
self._state.icp_metadata = self._worker.run_once(
|
||||
self._state)
|
||||
|
`IcpFusionThread` writes to shared `state` (`lidar_points`, `lidar_timestamp_ns`, `icp_metadata`, and potentially `persons_smplx[*].vertices_3d` via `run_once`) without holding `state.lock()`. This contradicts the codebase’s stated threading invariant (and risks races with other workers/UI readers). Please stage/copy the LiDAR frame outside the lock if needed, but wrap all `state` mutations (including the in-place mesh update) in a `with state.lock():` block or introduce a dedicated lock for these fields.
|
||||
except Exception as exc: # noqa: BLE001
|
||||
_LOG.warning("icp fusion failed: %s", exc)
|
||||
self._state.icp_metadata = None
|
||||
elapsed = time.monotonic() - t0
|
||||
if self._stop.wait(max(0.0, self._period_s - elapsed)):
|
||||
return
|
||||
@@ -0,0 +1,118 @@
|
||||
"""OSC UDP listener for the iOS ARBodyTracker app.
|
||||
|
||||
Subscribes to /body3d/kp on UDP :57128 (distinct from MediaPipe
|
||||
output :57126). Each /body3d/kp pid joint_idx x y z message stores
|
||||
one joint of ARKit's 91-joint ARSkeleton3D into
|
||||
state.persons_arkit_joints[pid] (np.ndarray shape (91, 3), float32).
|
||||
A background GC drops pids whose last_t is older than 1.0 s.
|
||||
|
||||
Worker pattern mirrors osc_listener.OscListener.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
from pythonosc import dispatcher, osc_server
|
||||
|
||||
from .state import State
|
||||
|
||||
LOG = logging.getLogger("iphone_osc")
|
||||
|
||||
IPHONE_OSC_PORT = 57128
|
||||
ARKIT_NUM_JOINTS = 91
|
||||
STALE_SEC = 1.0
|
||||
|
||||
|
||||
class IphoneOSCListener:
|
||||
def __init__(self, state: State, host: str = "0.0.0.0",
|
||||
port: int = IPHONE_OSC_PORT) -> None:
|
||||
self.state = state
|
||||
self.host = host
|
||||
self.port = port
|
||||
self._server: osc_server.ThreadingOSCUDPServer | None = None
|
||||
self._server_thread: threading.Thread | None = None
|
||||
self._gc_thread: threading.Thread | None = None
|
||||
self._stop = threading.Event()
|
||||
self._last_hb: float = 0.0
|
||||
|
||||
def start(self) -> None:
|
||||
d = dispatcher.Dispatcher()
|
||||
d.map("/body3d/kp", self._on_kp)
|
||||
d.map("/body3d/count", self._on_count)
|
||||
self._server = osc_server.ThreadingOSCUDPServer(
|
||||
(self.host, self.port), d)
|
||||
self._server_thread = threading.Thread(
|
||||
target=self._server.serve_forever,
|
||||
name="iphone_osc", daemon=True)
|
||||
self._server_thread.start()
|
||||
self._gc_thread = threading.Thread(
|
||||
target=self._gc_loop, name="iphone_gc", daemon=True)
|
||||
self._gc_thread.start()
|
||||
LOG.info("iphone OSC listening on %s:%d", self.host, self.port)
|
||||
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
if self._server is not None:
|
||||
self._server.shutdown()
|
||||
self._server.server_close()
|
||||
self._server = None
|
||||
if self._server_thread is not None:
|
||||
self._server_thread.join(timeout=2.0)
|
||||
self._server_thread = None
|
||||
if self._gc_thread is not None:
|
||||
self._gc_thread.join(timeout=2.0)
|
||||
self._gc_thread = None
|
||||
|
||||
def _on_kp(self, _addr: str, *args: Any) -> None:
|
||||
if len(args) < 5:
|
||||
return
|
||||
try:
|
||||
pid = int(args[0])
|
||||
joint_idx = int(args[1])
|
||||
x = float(args[2])
|
||||
y = float(args[3])
|
||||
z = float(args[4])
|
||||
except (TypeError, ValueError):
|
||||
return
|
||||
if not (0 <= joint_idx < ARKIT_NUM_JOINTS):
|
||||
return
|
||||
with self.state.lock():
|
||||
arr = self.state.persons_arkit_joints.get(pid)
|
||||
if arr is None or arr.shape != (ARKIT_NUM_JOINTS, 3):
|
||||
arr = np.zeros((ARKIT_NUM_JOINTS, 3), dtype=np.float32)
|
||||
self.state.persons_arkit_joints[pid] = arr
|
||||
arr[joint_idx] = (x, y, z)
|
||||
self.state.persons_arkit_last_t[pid] = time.perf_counter()
|
||||
|
||||
def _on_count(self, _addr: str, *args: Any) -> None:
|
||||
# Optional : we currently don't gate on count, but parse for log.
|
||||
if not args:
|
||||
return
|
||||
try:
|
||||
n = int(args[0])
|
||||
except (TypeError, ValueError):
|
||||
return
|
||||
now = time.monotonic()
|
||||
if now - self._last_hb > 5.0:
|
||||
self._last_hb = now
|
||||
LOG.info("hb: %d ARKit bodies live", n)
|
||||
|
||||
def _gc_stale(self) -> None:
|
||||
cutoff = time.perf_counter() - STALE_SEC
|
||||
with self.state.lock():
|
||||
drop = [
|
||||
pid for pid, t in self.state.persons_arkit_last_t.items()
|
||||
if t < cutoff
|
||||
]
|
||||
for pid in drop:
|
||||
self.state.persons_arkit_joints.pop(pid, None)
|
||||
self.state.persons_arkit_last_t.pop(pid, None)
|
||||
|
||||
def _gc_loop(self) -> None:
|
||||
while not self._stop.is_set():
|
||||
self._gc_stale()
|
||||
time.sleep(0.5)
|
||||
@@ -0,0 +1,83 @@
|
||||
"""iPhone LiDAR (ARKit world) <-> webcam (Multi-HMR camera) extrinsic.
|
||||
|
||||
Persisted as a small JSON document so calibration survives across launches.
|
||||
The default location is ``~/.config/av-live/lidar_extrinsic.json``; override
|
||||
with the ``ICP_LIDAR_EXTRINSIC`` env var.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
DEFAULT_EXTRINSIC_PATH = Path.home() / ".config" / "av-live" / "lidar_extrinsic.json"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Extrinsic:
|
||||
"""4x4 rigid transform from ARKit world frame to Multi-HMR camera frame."""
|
||||
|
||||
T_arkit_to_cam: np.ndarray = field(default_factory=lambda: np.eye(4))
|
||||
confidence: float = 0.0
|
||||
captured_at_iso: str = ""
|
||||
|
||||
@staticmethod
|
||||
def identity() -> "Extrinsic":
|
||||
return Extrinsic(T_arkit_to_cam=np.eye(4), confidence=0.0, captured_at_iso="")
|
||||
|
||||
|
||||
def save_extrinsic(e: Extrinsic, path: Path | None = None) -> Path:
|
||||
path = Path(path) if path is not None else _path_from_env()
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
payload = {
|
||||
"T_arkit_to_cam": e.T_arkit_to_cam.astype(float).tolist(),
|
||||
"confidence": float(e.confidence),
|
||||
"captured_at_iso": e.captured_at_iso,
|
||||
}
|
||||
path.write_text(json.dumps(payload, indent=2))
|
||||
return path
|
||||
|
||||
|
||||
def load_extrinsic(path: Path | None = None) -> Extrinsic:
|
||||
path = Path(path) if path is not None else _path_from_env()
|
||||
if not path.exists():
|
||||
return Extrinsic.identity()
|
||||
payload = json.loads(path.read_text())
|
||||
return Extrinsic(
|
||||
T_arkit_to_cam=np.array(payload["T_arkit_to_cam"], dtype=np.float64),
|
||||
confidence=float(payload.get("confidence", 0.0)),
|
||||
captured_at_iso=str(payload.get("captured_at_iso", "")),
|
||||
)
|
||||
|
||||
|
||||
def _path_from_env() -> Path:
|
||||
p = os.environ.get("ICP_LIDAR_EXTRINSIC")
|
||||
return Path(p) if p else DEFAULT_EXTRINSIC_PATH
|
||||
|
||||
|
||||
def kabsch_rigid(src: np.ndarray, tgt: np.ndarray) -> np.ndarray:
|
||||
"""Closed-form rigid alignment (Kabsch via SVD).
|
||||
|
||||
Returns a 4x4 transform T such that ``tgt ≈ (src @ R.T) + t``.
|
||||
"""
|
||||
src = np.asarray(src, dtype=np.float64)
|
||||
tgt = np.asarray(tgt, dtype=np.float64)
|
||||
if src.shape != tgt.shape:
|
||||
raise ValueError(f"shape mismatch: src={src.shape} tgt={tgt.shape}")
|
||||
if src.shape[0] < 3 or src.shape[1] != 3:
|
||||
raise ValueError("kabsch_rigid needs at least 3 paired 3D points")
|
||||
src_c = src.mean(axis=0)
|
||||
tgt_c = tgt.mean(axis=0)
|
||||
H = (src - src_c).T @ (tgt - tgt_c)
|
||||
U, _, Vt = np.linalg.svd(H)
|
||||
d = np.linalg.det(Vt.T @ U.T)
|
||||
D = np.diag([1.0, 1.0, np.sign(d)])
|
||||
R = Vt.T @ D @ U.T
|
||||
t = tgt_c - R @ src_c
|
||||
T = np.eye(4)
|
||||
T[:3, :3] = R
|
||||
T[:3, 3] = t
|
||||
return T
|
||||
@@ -0,0 +1,130 @@
|
||||
"""TCP receiver for iPhone ARBodyTracker LiDAR ARMeshAnchor stream.
|
||||
|
||||
Wire format (per frame, after the 4-byte big-endian length prefix consumed
|
||||
by the socket reader):
|
||||
|
||||
[uint64 BE timestamp_ns]
|
||||
[uint32 BE vertex_count]
|
||||
[float32 LE x y z] * vertex_count
|
||||
|
||||
The decoder is pure and side-effect-free so it can be unit-tested without a
|
||||
socket. The socket reader lives in a separate class (LidarTCPReader) so its
|
||||
threading model is independently testable.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import struct
|
||||
from dataclasses import dataclass
|
||||
|
||||
import numpy as np
|
||||
|
||||
_HEADER = struct.Struct(">QI") # timestamp_ns, vertex_count
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class LidarFrame:
|
||||
"""One decoded LiDAR frame from the iPhone."""
|
||||
|
||||
timestamp_ns: int
|
||||
points: np.ndarray # shape (N, 3), float32, ARKit world frame (meters)
|
||||
|
||||
|
||||
def decode_frame(body: bytes) -> LidarFrame:
|
||||
"""Decode a frame body (length prefix already stripped)."""
|
||||
if len(body) < _HEADER.size:
|
||||
raise ValueError(f"truncated frame: header needs {_HEADER.size} bytes, got {len(body)}")
|
||||
timestamp_ns, vertex_count = _HEADER.unpack_from(body, 0)
|
||||
if vertex_count == 0:
|
||||
raise ValueError("vertex_count must be > 0")
|
||||
expected = _HEADER.size + vertex_count * 12
|
||||
if len(body) < expected:
|
||||
raise ValueError(f"truncated frame: need {expected} bytes for {vertex_count} verts, got {len(body)}")
|
||||
raw = body[_HEADER.size : expected]
|
||||
pts = np.frombuffer(raw, dtype="<f4").reshape(vertex_count, 3).astype(np.float32, copy=True)
|
||||
return LidarFrame(timestamp_ns=int(timestamp_ns), points=pts)
|
||||
|
||||
|
||||
import logging
|
||||
import socket
|
||||
import threading
|
||||
from typing import Optional
|
||||
|
||||
_LOG = logging.getLogger(__name__)
|
||||
_LEN_PREFIX = struct.Struct(">I")
|
||||
|
||||
|
||||
class LidarTCPReader:
|
||||
"""Background TCP reader producing a single-slot latest-frame mailbox.
|
||||
|
||||
Reconnects on transient failures with linear backoff up to 5s.
|
||||
"""
|
||||
|
||||
def __init__(self, host: str, port: int, connect_timeout_s: float = 2.0) -> None:
|
||||
self._host = host
|
||||
self._port = port
|
||||
self._connect_timeout_s = connect_timeout_s
|
||||
self._stop = threading.Event()
|
||||
self._lock = threading.Lock()
|
||||
self._latest: Optional[LidarFrame] = None
|
||||
self._thread: Optional[threading.Thread] = None
|
||||
|
||||
def start(self) -> None:
|
||||
if self._thread is not None:
|
||||
return
|
||||
self._thread = threading.Thread(target=self._run, name="lidar-tcp", daemon=True)
|
||||
self._thread.start()
|
||||
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
if self._thread is not None:
|
||||
self._thread.join(timeout=2.0)
|
||||
self._thread = None
|
||||
|
||||
def latest(self) -> Optional[LidarFrame]:
|
||||
with self._lock:
|
||||
return self._latest
|
||||
|
||||
def _run(self) -> None:
|
||||
backoff_s = 0.5
|
||||
while not self._stop.is_set():
|
||||
try:
|
||||
with socket.create_connection((self._host, self._port), timeout=self._connect_timeout_s) as sock:
|
||||
sock.settimeout(1.0)
|
||||
backoff_s = 0.5
|
||||
self._read_loop(sock)
|
||||
except (OSError, ValueError) as exc:
|
||||
_LOG.warning("lidar reader: %s; reconnecting in %.1fs", exc, backoff_s)
|
||||
if self._stop.wait(backoff_s):
|
||||
return
|
||||
backoff_s = min(backoff_s * 2.0, 5.0)
|
||||
|
||||
def _read_loop(self, sock: socket.socket) -> None:
|
||||
while not self._stop.is_set():
|
||||
header = self._recv_exact(sock, _LEN_PREFIX.size)
|
||||
if header is None:
|
||||
return
|
||||
(length,) = _LEN_PREFIX.unpack(header)
|
||||
if length <= 0 or length > 8_000_000: # sanity cap: 8 MB per frame
|
||||
raise ValueError(f"implausible frame length {length}")
|
||||
body = self._recv_exact(sock, length)
|
||||
if body is None:
|
||||
return
|
||||
frame = decode_frame(body)
|
||||
with self._lock:
|
||||
self._latest = frame
|
||||
|
||||
def _recv_exact(self, sock: socket.socket, n: int) -> Optional[bytes]:
|
||||
buf = bytearray(n)
|
||||
view = memoryview(buf)
|
||||
got = 0
|
||||
while got < n:
|
||||
if self._stop.is_set():
|
||||
return None
|
||||
try:
|
||||
k = sock.recv_into(view[got:])
|
||||
except socket.timeout:
|
||||
continue
|
||||
if k == 0:
|
||||
return None
|
||||
got += k
|
||||
return bytes(buf)
|
||||
@@ -249,6 +249,40 @@ class AppDelegate(NSObject):
|
||||
# 2. Apple Vision body pose (fallback si MediaPipe casse)
|
||||
# 3. CoreML pose, DETRPose, Holistic, YOLO — fallbacks
|
||||
import os as _os
|
||||
# iPhone ARBodyTracker (option 2 LiDAR fusion) : always-on
|
||||
# listener on :57128. Harmless if no iPhone is broadcasting ;
|
||||
# state.persons_arkit_joints stays empty and the arkit_fuse
|
||||
# stage no-ops. Activated via POSE_FILTER=...+arkit_fuse.
|
||||
try:
|
||||
from .iphone_osc_listener import IphoneOSCListener
|
||||
self._iphone_osc = IphoneOSCListener(self._state)
|
||||
self._iphone_osc.start()
|
||||
LOG.info("worker: + iPhone OSC listener :57128")
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("iphone OSC listener start failed (%s)", e)
|
||||
# ICP LiDAR fusion (opt-in via ICP_FUSION=1). Parallel to the
|
||||
# ARKit pelvis fuse: ICP operates on SMPL-X dense vertices, not
|
||||
# joints. Requires a calibrated extrinsic on disk (see
|
||||
# scripts/calibrate_lidar.py) and an iPhone LiDAR stream
|
||||
# broadcasting on ICP_LIDAR_HOST:ICP_LIDAR_PORT.
|
||||
if _os.environ.get("ICP_FUSION", "0") == "1":
|
||||
host = _os.environ.get("ICP_LIDAR_HOST")
|
||||
if not host:
|
||||
LOG.warning("ICP_FUSION=1 but ICP_LIDAR_HOST unset — "
|
||||
"fusion disabled")
|
||||
else:
|
||||
try:
|
||||
from .icp_fusion_worker import IcpFusionThread
|
||||
self._icp_fusion = IcpFusionThread(
|
||||
self._state,
|
||||
host=host,
|
||||
port=int(_os.environ.get("ICP_LIDAR_PORT", "5500")),
|
||||
)
|
||||
self._icp_fusion.start()
|
||||
LOG.info("worker: + ICP LiDAR fusion -> %s:%s", host,
|
||||
_os.environ.get("ICP_LIDAR_PORT", "5500"))
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("icp fusion start failed (%s)", e)
|
||||
# 0. Multi-HMR (SMPL-X 10475 verts mesh dense) — opt-in via flag
|
||||
if getattr(self._opts, "multi_hmr", False):
|
||||
try:
|
||||
@@ -585,6 +619,12 @@ class AppDelegate(NSObject):
|
||||
self._listener.stop()
|
||||
|
The iPhone OSC listener ( The iPhone OSC listener (`self._iphone_osc`) is started in `_start_pose_worker`, but it is not stopped in `applicationWillTerminate_`. This can leave a background OSC server thread running during shutdown. Please add a guarded stop/shutdown here (similar to the ICP fusion thread stop) to ensure clean termination.
|
||||
if self._pose_worker is not None:
|
||||
self._pose_worker.stop()
|
||||
icp = getattr(self, "_icp_fusion", None)
|
||||
if icp is not None:
|
||||
try:
|
||||
icp.stop()
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("icp fusion stop failed (%s)", e)
|
||||
LOG.info("bye")
|
||||
|
||||
|
||||
|
||||
@@ -14,6 +14,8 @@ Limitations connues (premiere iteration) :
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import collections
|
||||
import logging
|
||||
import math
|
||||
import threading
|
||||
import time
|
||||
@@ -21,8 +23,16 @@ from dataclasses import dataclass, field
|
||||
|
||||
import numpy as np
|
||||
|
||||
try:
|
||||
from scipy.optimize import linear_sum_assignment
|
||||
_HAVE_SCIPY = True
|
||||
except ImportError: # noqa: BLE001
|
||||
_HAVE_SCIPY = False
|
||||
|
||||
from .state import PoseKp, SMPLXPerson, State
|
||||
|
||||
LOG = logging.getLogger("mesh_rigger")
|
||||
|
||||
|
||||
# Indices MediaPipe POSE_LANDMARKS pour les hanches (pelvis 2D = midpoint).
|
||||
_LEFT_HIP = 23
|
||||
@@ -55,6 +65,70 @@ def _pelvis_2d_from_body(body: list[PoseKp]) -> tuple[float, float] | None:
|
||||
return (0.5 * (lh.x + rh.x), 0.5 * (lh.y + rh.y))
|
||||
|
||||
|
||||
def _body_bbox_norm(
|
||||
body: list[PoseKp],
|
||||
) -> tuple[float, float, float, float] | None:
|
||||
"""Bbox image-normalized [0,1] from a list of body landmarks
|
||||
(Vision 19 joints OR MediaPipe 33). None if not enough confident
|
||||
points."""
|
||||
if not body:
|
||||
return None
|
||||
xs = [kp.x for kp in body if kp.c > 0.05]
|
||||
ys = [kp.y for kp in body if kp.c > 0.05]
|
||||
if len(xs) < 4 or len(ys) < 4:
|
||||
return None
|
||||
x0, x1 = max(0.0, min(xs)), min(1.0, max(xs))
|
||||
y0, y1 = max(0.0, min(ys)), min(1.0, max(ys))
|
||||
# Pad 10% to capture full body silhouette.
|
||||
dx = (x1 - x0) * 0.10
|
||||
dy = (y1 - y0) * 0.10
|
||||
x0 = max(0.0, x0 - dx); x1 = min(1.0, x1 + dx)
|
||||
y0 = max(0.0, y0 - dy); y1 = min(1.0, y1 + dy)
|
||||
if x1 - x0 < 0.02 or y1 - y0 < 0.02:
|
||||
return None
|
||||
return (x0, y0, x1, y1)
|
||||
|
||||
|
||||
def _mesh_bbox_norm(p: SMPLXPerson) -> tuple[float, float, float, float] | None:
|
||||
"""Project SMPL-X mesh vertices to image-normalized bbox.
|
||||
|
||||
Multi-HMR uses focal = IMG_SIZE camera intrinsics. World verts
|
||||
have z>0 (in front of camera)."""
|
||||
v = np.asarray(p.vertices_3d, dtype=np.float32)
|
||||
if v.size == 0 or v.shape[0] < 100:
|
||||
return None
|
||||
z = v[:, 2]
|
||||
valid = z > 1e-3
|
||||
if not np.any(valid):
|
||||
return None
|
||||
x_img = (v[valid, 0] * _FOCAL / z[valid]) / _IMG_SIZE + 0.5
|
||||
y_img = (v[valid, 1] * _FOCAL / z[valid]) / _IMG_SIZE + 0.5
|
||||
x0, x1 = float(x_img.min()), float(x_img.max())
|
||||
y0, y1 = float(y_img.min()), float(y_img.max())
|
||||
x0 = max(0.0, x0); x1 = min(1.0, x1)
|
||||
y0 = max(0.0, y0); y1 = min(1.0, y1)
|
||||
if x1 - x0 < 0.02 or y1 - y0 < 0.02:
|
||||
return None
|
||||
return (x0, y0, x1, y1)
|
||||
|
||||
|
||||
def _iou_norm(
|
||||
a: tuple[float, float, float, float],
|
||||
b: tuple[float, float, float, float],
|
||||
) -> float:
|
||||
ax0, ay0, ax1, ay1 = a
|
||||
bx0, by0, bx1, by1 = b
|
||||
ix0 = max(ax0, bx0); iy0 = max(ay0, by0)
|
||||
ix1 = min(ax1, bx1); iy1 = min(ay1, by1)
|
||||
iw = max(0.0, ix1 - ix0); ih = max(0.0, iy1 - iy0)
|
||||
inter = iw * ih
|
||||
if inter <= 0:
|
||||
return 0.0
|
||||
a_area = (ax1 - ax0) * (ay1 - ay0)
|
||||
b_area = (bx1 - bx0) * (by1 - by0)
|
||||
return float(inter / (a_area + b_area - inter + 1e-9))
|
||||
|
||||
|
||||
def _vision_pid_match(
|
||||
keyframe_pelvis_2d: tuple[float, float] | None,
|
||||
vision_bodies: list[list[PoseKp]],
|
||||
@@ -89,14 +163,22 @@ class MeshRigger:
|
||||
Thread-safe : ne mute pas le state, retourne une nouvelle liste.
|
||||
"""
|
||||
|
||||
def __init__(self, state: State, hold_window_s: float = 1.5) -> None:
|
||||
def __init__(self, state: State, hold_window_s: float = 1.5,
|
||||
dino_weight: float = 0.5,
|
||||
dino_reid=None) -> None:
|
||||
self.state = state
|
||||
self.hold_window_s = hold_window_s
|
||||
self.dino_weight = float(dino_weight)
|
||||
self.dino_reid = dino_reid
|
||||
self._lock = threading.Lock()
|
||||
# pid Multi-HMR -> keyframe
|
||||
self._keyframes: dict[int, _Keyframe] = {}
|
||||
# pid Multi-HMR -> pid Vision matched (sticky across frames)
|
||||
self._vision_pid_map: dict[int, int] = {}
|
||||
# pid Multi-HMR -> recent DINO embeddings (mean -> reid signature)
|
||||
self._pid_embeddings: dict[int, collections.deque] = {}
|
||||
# Cached log throttle
|
||||
self._next_dino_log = 0.0
|
||||
|
||||
def apply(
|
||||
self,
|
||||
@@ -114,6 +196,14 @@ class MeshRigger:
|
||||
if old_pid not in current_pids:
|
||||
self._keyframes.pop(old_pid, None)
|
||||
self._vision_pid_map.pop(old_pid, None)
|
||||
self._pid_embeddings.pop(old_pid, None)
|
||||
|
||||
# 2) DINO fusion: if a reid backend is wired, try Hungarian
|
||||
# over (mesh keyframe pids) x (Vision body pids) using
|
||||
# alpha*IoU + (1-alpha)*cosine. This only kicks in when a
|
||||
# keyframe is detected this call AND we have an RGB frame.
|
||||
self._dino_match(persons_smplx, persons_body,
|
||||
persons_body_ids)
|
||||
|
||||
out: list[SMPLXPerson] = []
|
||||
for person in persons_smplx:
|
||||
@@ -199,6 +289,136 @@ class MeshRigger:
|
||||
))
|
||||
return out
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# DINOv2 reid hooks
|
||||
# ------------------------------------------------------------------
|
||||
def _dino_match(
|
||||
self,
|
||||
persons_smplx: list[SMPLXPerson],
|
||||
persons_body: list[list[PoseKp]],
|
||||
persons_body_ids: list[int],
|
||||
) -> None:
|
||||
"""Update self._vision_pid_map and self._pid_embeddings by
|
||||
matching mesh pids against Vision pids on alpha*IoU +
|
||||
(1-alpha)*DINO cosine. No-op if any prerequisite missing.
|
||||
|
||||
Caller must hold self._lock."""
|
||||
if self.dino_reid is None or not _HAVE_SCIPY:
|
||||
return
|
||||
if not persons_smplx or not persons_body:
|
||||
return
|
||||
# Need at least one new keyframe to be worth running DINO.
|
||||
new_kf_pids: list[int] = []
|
||||
for p in persons_smplx:
|
||||
kf = self._keyframes.get(p.pid)
|
||||
if kf is None or not np.allclose(
|
||||
kf.translation, p.translation, atol=1e-4):
|
||||
new_kf_pids.append(int(p.pid))
|
||||
if not new_kf_pids:
|
||||
return
|
||||
|
||||
# Acquire current RGB frame (best effort, no double lock).
|
||||
frame = self.state.last_frame_rgb
|
||||
if frame is None:
|
||||
return
|
||||
H, W = frame.shape[:2]
|
||||
|
||||
# Build Vision bboxes (image-normalized) and pixel crops.
|
||||
v_bboxes_norm: list[tuple[float, float, float, float]] = []
|
||||
v_crops: list[np.ndarray] = []
|
||||
v_pids: list[int] = []
|
||||
for body, vpid in zip(persons_body, persons_body_ids):
|
||||
bb = _body_bbox_norm(body)
|
||||
if bb is None:
|
||||
continue
|
||||
x0, y0, x1, y1 = bb
|
||||
px0 = max(0, int(x0 * W))
|
||||
py0 = max(0, int(y0 * H))
|
||||
px1 = min(W, int(x1 * W))
|
||||
py1 = min(H, int(y1 * H))
|
||||
if px1 <= px0 + 4 or py1 <= py0 + 4:
|
||||
continue
|
||||
v_bboxes_norm.append(bb)
|
||||
v_crops.append(frame[py0:py1, px0:px1].copy())
|
||||
v_pids.append(int(vpid))
|
||||
|
||||
if not v_crops:
|
||||
return
|
||||
|
||||
# Build mesh bboxes (image-normalized) from world pelvis proj.
|
||||
m_bboxes_norm: list[tuple[float, float, float, float]] = []
|
||||
m_pids_keep: list[int] = []
|
||||
m_crops: list[np.ndarray] = []
|
||||
for p in persons_smplx:
|
||||
bb = _mesh_bbox_norm(p)
|
||||
if bb is None:
|
||||
continue
|
||||
m_bboxes_norm.append(bb)
|
||||
m_pids_keep.append(int(p.pid))
|
||||
x0, y0, x1, y1 = bb
|
||||
px0 = max(0, int(x0 * W))
|
||||
py0 = max(0, int(y0 * H))
|
||||
px1 = min(W, int(x1 * W))
|
||||
py1 = min(H, int(y1 * H))
|
||||
if px1 > px0 + 4 and py1 > py0 + 4:
|
||||
m_crops.append(frame[py0:py1, px0:px1].copy())
|
||||
else:
|
||||
m_crops.append(None) # type: ignore[arg-type]
|
||||
|
||||
if not m_bboxes_norm:
|
||||
return
|
||||
|
||||
# Embed Vision crops in one batch (still loops internally).
|
||||
t0 = time.perf_counter()
|
||||
try:
|
||||
v_emb = self.dino_reid.embed_crops(v_crops)
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("dino_reid embed failed: %s", e)
|
||||
return
|
||||
|
||||
# Build cost matrix mesh x vision : 1 - (alpha*IoU + (1-alpha)*cos)
|
||||
n_m = len(m_bboxes_norm)
|
||||
n_v = len(v_bboxes_norm)
|
||||
alpha = float(np.clip(self.dino_weight, 0.0, 1.0))
|
||||
cost = np.ones((n_m, n_v), dtype=np.float32)
|
||||
for i, mbb in enumerate(m_bboxes_norm):
|
||||
hist = self._pid_embeddings.get(m_pids_keep[i])
|
||||
mean_emb = None
|
||||
if hist:
|
||||
stack = np.stack(list(hist), axis=0)
|
||||
mean_emb = stack.mean(axis=0)
|
||||
n = np.linalg.norm(mean_emb) + 1e-8
|
||||
mean_emb = mean_emb / n
|
||||
for j, vbb in enumerate(v_bboxes_norm):
|
||||
iou = _iou_norm(mbb, vbb)
|
||||
if mean_emb is not None:
|
||||
cos = float(np.dot(mean_emb, v_emb[j]))
|
||||
else:
|
||||
cos = iou # no history -> trust IoU
|
||||
score = alpha * iou + (1.0 - alpha) * max(0.0, cos)
|
||||
cost[i, j] = 1.0 - score
|
||||
|
||||
rr, cc = linear_sum_assignment(cost)
|
||||
for i, j in zip(rr, cc):
|
||||
if cost[i, j] >= 0.95:
|
||||
continue # weak match, ignore
|
||||
mpid = m_pids_keep[i]
|
||||
self._vision_pid_map[mpid] = v_pids[j]
|
||||
# Update embedding history for THIS mesh pid using the
|
||||
# Vision crop (most recent visual evidence).
|
||||
dq = self._pid_embeddings.setdefault(
|
||||
mpid, collections.deque(maxlen=10))
|
||||
dq.append(v_emb[j].copy())
|
||||
|
||||
now = time.monotonic()
|
||||
dt_ms = (time.perf_counter() - t0) * 1e3
|
||||
if now >= self._next_dino_log:
|
||||
LOG.info(
|
||||
"dino_reid: embedded %d crops in %.1f ms (alpha=%.2f, "
|
||||
"matched %d mesh<->vision pairs)",
|
||||
len(v_crops), dt_ms, alpha, min(n_m, n_v))
|
||||
self._next_dino_log = now + 5.0
|
||||
|
||||
@staticmethod
|
||||
def _project_pelvis(
|
||||
translation: np.ndarray,
|
||||
|
||||
@@ -22,6 +22,8 @@ from pathlib import Path
|
||||
from .action_head_pub import ActionHeadPublisher
|
||||
from .euro_filter import SkeletonFilter
|
||||
from .pose_bridge import PoseSoundBridge
|
||||
from .pose_filter import PoseFilterChain
|
||||
from .pose_filter import _is_finite # noqa: PLC2701 (intentional internal use)
|
||||
from .state import Kp3D, PoseKp, State
|
||||
from .tracker import IoUTracker
|
||||
|
||||
@@ -96,6 +98,179 @@ class MultiWorker:
|
||||
self._sound_bridge = PoseSoundBridge(throttle_hz=30.0)
|
||||
self._action_pub = ActionHeadPublisher(state=self.state, bridge=self._sound_bridge)
|
||||
self._action_pub.start()
|
||||
# 3D pose filter chain : median, Kalman CV, lookahead, IK clamps.
|
||||
self._filter_chain = PoseFilterChain(state=self.state)
|
||||
# Discrimination state : per-pid frame counters for hysteresis.
|
||||
# _pid_lifetime : frames since pid created (visible).
|
||||
# _pid_last_bbox : last bbox seen for active pid (for re-association).
|
||||
# _pid_missing : frames since pid disappeared (None when active).
|
||||
self._pid_lifetime: dict[int, int] = {}
|
||||
self._pid_missing: dict[int, int] = {}
|
||||
self._pid_last_bbox: dict[int, tuple[float, float, float, float]] = {}
|
||||
# Discrimination thresholds — tunable via env.
|
||||
import os as _os
|
||||
self._ghost_min_visible = int(_os.environ.get("POSE_GHOST_MIN_VISIBLE", "10"))
|
||||
self._ghost_min_conf = float(_os.environ.get("POSE_GHOST_MIN_CONF", "0.5"))
|
||||
self._hand_min_visible = int(_os.environ.get("POSE_HAND_MIN_VISIBLE", "15"))
|
||||
self._face_min_visible = int(_os.environ.get("POSE_FACE_MIN_VISIBLE", "50"))
|
||||
self._nms_iou = float(_os.environ.get("POSE_NMS_IOU", "0.7"))
|
||||
# Counters exposed for debug.
|
||||
self._n_ghost_dropped = 0
|
||||
self._n_hand_dropped = 0
|
||||
self._n_face_dropped = 0
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Discrimination helpers — body ghost rejection, NMS, pid hysteresis,
|
||||
# face/hand visibility gates. All return filtered (kps, ids) lists.
|
||||
# ------------------------------------------------------------------
|
||||
@staticmethod
|
||||
def _bbox_from_kps(kps: list) -> tuple[float, float, float, float]:
|
||||
if not kps:
|
||||
return (0.0, 0.0, 0.0, 0.0)
|
||||
xs = [kp.x for kp in kps]
|
||||
ys = [kp.y for kp in kps]
|
||||
return (min(xs), min(ys), max(xs), max(ys))
|
||||
|
||||
@staticmethod
|
||||
def _iou(a: tuple[float, float, float, float],
|
||||
b: tuple[float, float, float, float]) -> float:
|
||||
ix1 = max(a[0], b[0]); iy1 = max(a[1], b[1])
|
||||
ix2 = min(a[2], b[2]); iy2 = min(a[3], b[3])
|
||||
iw = max(0.0, ix2 - ix1); ih = max(0.0, iy2 - iy1)
|
||||
inter = iw * ih
|
||||
aw = max(0.0, a[2] - a[0]) * max(0.0, a[3] - a[1])
|
||||
bw = max(0.0, b[2] - b[0]) * max(0.0, b[3] - b[1])
|
||||
u = aw + bw - inter
|
||||
return inter / u if u > 1e-9 else 0.0
|
||||
|
||||
def _reject_ghosts_and_nms(
|
||||
self,
|
||||
bodies: list[list],
|
||||
bodies3d: list[list[Kp3D]],
|
||||
ids_body: list[int],
|
||||
) -> tuple[list[list], list[list[Kp3D]], list[int]]:
|
||||
"""Drop body detections with <N high-confidence joints, then NMS."""
|
||||
if not bodies:
|
||||
return bodies, bodies3d, ids_body
|
||||
# Score each body by mean confidence ; track visibility count.
|
||||
keep_mask = [True] * len(bodies)
|
||||
scores: list[float] = []
|
||||
for i, kps in enumerate(bodies):
|
||||
n_visible = sum(
|
||||
1 for kp in kps
|
||||
if kp.c >= self._ghost_min_conf
|
||||
and _is_finite(kp.x) and _is_finite(kp.y))
|
||||
if n_visible < self._ghost_min_visible:
|
||||
keep_mask[i] = False
|
||||
self._n_ghost_dropped += 1
|
||||
scores.append(
|
||||
sum(kp.c for kp in kps) / len(kps) if kps else 0.0)
|
||||
# NMS on remaining bboxes.
|
||||
bboxes = [self._bbox_from_kps(kps) for kps in bodies]
|
||||
order = sorted(
|
||||
[i for i in range(len(bodies)) if keep_mask[i]],
|
||||
key=lambda i: -scores[i])
|
||||
kept_order: list[int] = []
|
||||
for i in order:
|
||||
drop = False
|
||||
for j in kept_order:
|
||||
if self._iou(bboxes[i], bboxes[j]) > self._nms_iou:
|
||||
drop = True
|
||||
break
|
||||
if drop:
|
||||
keep_mask[i] = False
|
||||
else:
|
||||
kept_order.append(i)
|
||||
new_bodies = [bodies[i] for i in range(len(bodies)) if keep_mask[i]]
|
||||
new_ids = [ids_body[i] for i in range(len(bodies))
|
||||
if i < len(ids_body) and keep_mask[i]]
|
||||
# bodies3d aligned 1:1 with bodies.
|
||||
new_b3d: list[list[Kp3D]] = []
|
||||
if bodies3d:
|
||||
for i in range(min(len(bodies), len(bodies3d))):
|
||||
if keep_mask[i]:
|
||||
new_b3d.append(bodies3d[i])
|
||||
return new_bodies, new_b3d, new_ids
|
||||
|
||||
def _apply_pid_hysteresis(
|
||||
self,
|
||||
bodies: list[list],
|
||||
ids_body: list[int],
|
||||
) -> list[int]:
|
||||
"""Reuse a recently-disappeared pid when a young pid lands near
|
||||
its last bbox. Mutates self._pid_lifetime / _pid_missing /
|
||||
_pid_last_bbox in place. Returns possibly-remapped ids.
|
||||
"""
|
||||
# Tick all known pids missing counter ; will reset for visible ones.
|
||||
for pid in list(self._pid_missing.keys()):
|
||||
self._pid_missing[pid] += 1
|
||||
if self._pid_missing[pid] > 60: # forget after 2 s @30 fps
|
||||
self._pid_missing.pop(pid, None)
|
||||
self._pid_last_bbox.pop(pid, None)
|
||||
self._pid_lifetime.pop(pid, None)
|
||||
new_ids = list(ids_body)
|
||||
for i, pid in enumerate(ids_body):
|
||||
if pid < 0 or i >= len(bodies):
|
||||
continue
|
||||
bbox_i = self._bbox_from_kps(bodies[i])
|
||||
# If this pid is brand new (<10 frames) and we have an absent
|
||||
# older pid (>=30 frames lifetime, <30 frames missing) with a
|
||||
# close bbox, remap.
|
||||
age = self._pid_lifetime.get(pid, 0)
|
||||
if age < 10:
|
||||
best_old: int | None = None
|
||||
best_iou = 0.0
|
||||
for old_pid, miss in self._pid_missing.items():
|
||||
if old_pid == pid:
|
||||
continue
|
||||
if self._pid_lifetime.get(old_pid, 0) < 30:
|
||||
continue
|
||||
if miss > 30:
|
||||
continue
|
||||
old_bbox = self._pid_last_bbox.get(old_pid)
|
||||
if old_bbox is None:
|
||||
continue
|
||||
iou = self._iou(bbox_i, old_bbox)
|
||||
if iou > 0.3 and iou > best_iou:
|
||||
best_iou = iou
|
||||
best_old = old_pid
|
||||
if best_old is not None:
|
||||
new_ids[i] = best_old
|
||||
pid = best_old
|
||||
# Bookkeeping for visible pid.
|
||||
self._pid_lifetime[pid] = self._pid_lifetime.get(pid, 0) + 1
|
||||
self._pid_missing.pop(pid, None)
|
||||
self._pid_last_bbox[pid] = bbox_i
|
||||
# Pids previously visible but absent this frame -> mark missing.
|
||||
visible = set(new_ids)
|
||||
for pid in list(self._pid_lifetime.keys()):
|
||||
if pid not in visible and pid not in self._pid_missing:
|
||||
self._pid_missing[pid] = 1
|
||||
return new_ids
|
||||
|
||||
def _drop_low_visibility(
|
||||
self,
|
||||
kps_list: list[list],
|
||||
ids: list[int],
|
||||
min_visible: int,
|
||||
which: str,
|
||||
) -> tuple[list[list], list[int]]:
|
||||
out_kps: list[list] = []
|
||||
out_ids: list[int] = []
|
||||
for i, kps in enumerate(kps_list):
|
||||
n_ok = sum(
|
||||
1 for kp in kps
|
||||
if _is_finite(kp.x) and _is_finite(kp.y)
|
||||
and (kp.x != 0.0 or kp.y != 0.0))
|
||||
if n_ok < min_visible:
|
||||
if which == "face":
|
||||
self._n_face_dropped += 1
|
||||
else:
|
||||
self._n_hand_dropped += 1
|
||||
continue
|
||||
out_kps.append(kps)
|
||||
out_ids.append(ids[i] if i < len(ids) else -1)
|
||||
return out_kps, out_ids
|
||||
|
||||
def start(self) -> None:
|
||||
self._thread = threading.Thread(
|
||||
@@ -238,6 +413,14 @@ class MultiWorker:
|
||||
ids_body = self._tracker_body.update(bodies)
|
||||
ids_face = self._tracker_face.update(faces)
|
||||
ids_hand = self._tracker_hand.update(hands)
|
||||
# --- Discrimination : ghost reject + NMS + pid hysteresis --
|
||||
bodies, bodies3d, ids_body = self._reject_ghosts_and_nms(
|
||||
bodies, bodies3d, ids_body)
|
||||
ids_body = self._apply_pid_hysteresis(bodies, ids_body)
|
||||
faces, ids_face = self._drop_low_visibility(
|
||||
faces, ids_face, self._face_min_visible, "face")
|
||||
hands, ids_hand = self._drop_low_visibility(
|
||||
hands, ids_hand, self._hand_min_visible, "hand")
|
||||
# --- Lissage One Euro par keypoint -------------------------
|
||||
t_now = time.monotonic()
|
||||
bodies = [_smooth_kps(self._smooth_body, ids_body[i], kps, t_now)
|
||||
@@ -246,11 +429,21 @@ class MultiWorker:
|
||||
for i, kps in enumerate(faces)]
|
||||
hands = [_smooth_kps(self._smooth_hand, ids_hand[i], kps, t_now)
|
||||
for i, kps in enumerate(hands)]
|
||||
# --- Filter chain face + hands (median + Kalman 2D + lookahead)
|
||||
faces = self._filter_chain.apply_face(faces, ids_face, t_now)
|
||||
hands = self._filter_chain.apply_hand(hands, ids_hand, None, t_now)
|
||||
|
||||
# Pont sonore : envoi OSC /pose/* a sclang (body + face + hands)
|
||||
# 3D world landmarks share ids with bodies (same MediaPipe
|
||||
# detection, just a different coordinate space).
|
||||
ids_body3d = ids_body[:len(bodies3d)] if bodies3d else []
|
||||
if bodies3d:
|
||||
bodies3d = self._filter_chain.apply(bodies3d, ids_body3d, t_now)
|
||||
# Debug : log body3d count once / 5 s so we know MediaPipe
|
||||
# actually populates pose_world_landmarks.
|
||||
if not hasattr(self, "_dbg_b3d_t") or t_now - self._dbg_b3d_t > 5.0:
|
||||
LOG.info("body3d: n=%d (pose_world_landmarks)", len(bodies3d))
|
||||
self._dbg_b3d_t = t_now
|
||||
self._sound_bridge.send(
|
||||
bodies, ids_body, t_now,
|
||||
persons_face=faces, persons_face_ids=ids_face,
|
||||
|
||||
@@ -20,6 +20,7 @@ from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
from .arkit_joint_map import ARKIT_PELVIS_IDX
|
||||
from .euro_filter import OneEuroFilter
|
||||
from .state import PoseKp, SMPLXPerson, State
|
||||
from .tracker import IoUTracker
|
||||
@@ -30,12 +31,34 @@ CACHE = Path.home() / ".cache" / "av-live-multihmr"
|
||||
CKPT = CACHE / "checkpoints" / "multiHMR_672_S.pt"
|
||||
SMPLX_PATH = CACHE / "models" / "smplx" / "SMPLX_NEUTRAL.npz"
|
||||
MULTIHMR_REPO = CACHE / "multi-hmr"
|
||||
COREML_MLPACKAGE = CACHE / "multihmr_full_672_s.mlpackage"
|
||||
COREML_MLPACKAGE = Path(
|
||||
os.environ.get("COREML_MLPACKAGE")
|
||||
or str(CACHE / "multihmr_full_672_s.mlpackage"))
|
||||
|
||||
IMG_SIZE = 672
|
||||
N_VERTS = 10475
|
||||
|
||||
|
||||
def arkit_pelvis_z_override(state, pid: int, z_pred: float,
|
||||
fresh_sec: float = 1.0) -> float:
|
||||
"""Return ARKit pelvis world-z if a fresh ARKit frame exists for
|
||||
this pid, otherwise return the Multi-HMR predicted z unchanged.
|
||||
|
||||
Used to resolve Multi-HMR's monocular scale ambiguity: ARKit's
|
||||
LiDAR-anchored pelvis position is ground truth in the iPhone
|
||||
world frame, which (after extrinsics calibration) is the same
|
||||
metric scale as the SMPL-X cam-space output.
|
||||
"""
|
||||
with state.lock():
|
||||
arr = state.persons_arkit_joints.get(pid)
|
||||
last_t = state.persons_arkit_last_t.get(pid, 0.0)
|
||||
if arr is None:
|
||||
return float(z_pred)
|
||||
if time.perf_counter() - last_t > fresh_sec:
|
||||
return float(z_pred)
|
||||
return float(arr[ARKIT_PELVIS_IDX, 2])
|
||||
|
||||
|
||||
class MultiHMRWorker:
|
||||
def __init__(self, state: State, num_persons: int = 4,
|
||||
target_fps: float = 10.0, device: str = "mps",
|
||||
@@ -77,6 +100,10 @@ class MultiHMRWorker:
|
||||
# (cf tracker.py) pour resister aux occlusions et au mouvement
|
||||
# rapide. Multi-HMR a 3 fps -> 30 frames = 10s de survie.
|
||||
self._tracker = IoUTracker(iou_threshold=0.15, max_miss=30)
|
||||
# Lazily-loaded CoreML backend for predict_once (single-shot,
|
||||
# off-thread). Independent of the worker thread's _run_coreml
|
||||
# backend instance — predict_once must work even without start().
|
||||
self._coreml_backend_singleshot = None
|
||||
|
||||
@staticmethod
|
||||
def is_available() -> bool:
|
||||
@@ -93,6 +120,83 @@ class MultiHMRWorker:
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
|
||||
def _get_or_load_coreml_backend(self):
|
||||
"""Lazily load the CoreML backend for single-shot inference.
|
||||
|
||||
Returns the cached `MultiHMRCoreMLBackend` instance, or None if
|
||||
the backend cannot be imported / the .mlpackage is missing.
|
||||
Thread-safe enough for our use (calibration CLI is single-
|
||||
threaded; the worker thread uses its own backend in _run_coreml).
|
||||
"""
|
||||
if self._coreml_backend_singleshot is not None:
|
||||
return self._coreml_backend_singleshot
|
||||
try:
|
||||
from .multihmr_coreml import MultiHMRCoreMLBackend
|
||||
backend = MultiHMRCoreMLBackend(COREML_MLPACKAGE)
|
||||
except (ImportError, FileNotFoundError) as e:
|
||||
LOG.info("predict_once: CoreML backend unavailable: %s", e)
|
||||
return None
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("predict_once: CoreML backend init failed: %s", e)
|
||||
return None
|
||||
self._coreml_backend_singleshot = backend
|
||||
return backend
|
||||
|
||||
def predict_once(self, rgb_image):
|
||||
"""Single-shot SMPL-X prediction on one RGB image.
|
||||
|
||||
Args:
|
||||
rgb_image: (H, W, 3) uint8 RGB array. Will be center-
|
||||
cropped + resized to 672x672 internally.
|
||||
|
||||
Returns:
|
||||
First `SMPLXPerson` detection (pid=0) or None if no
|
||||
humans pass the detection threshold.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: if the CoreML backend is unavailable
|
||||
(PyTorch single-shot path is TBD).
|
||||
"""
|
||||
backend = self._get_or_load_coreml_backend()
|
||||
if backend is None:
|
||||
raise NotImplementedError(
|
||||
"CoreML backend unavailable; PyTorch single-shot path TBD")
|
||||
|
||||
try:
|
||||
import cv2
|
||||
except ImportError as e:
|
||||
raise NotImplementedError(
|
||||
"opencv-python required for predict_once: %s" % e)
|
||||
|
||||
rgb = np.asarray(rgb_image)
|
||||
if rgb.ndim != 3 or rgb.shape[2] != 3:
|
||||
raise ValueError(
|
||||
f"rgb_image must be (H,W,3), got {rgb.shape}")
|
||||
h, w = rgb.shape[:2]
|
||||
if (h, w) != (IMG_SIZE, IMG_SIZE):
|
||||
side = min(h, w)
|
||||
y0 = (h - side) // 2
|
||||
x0 = (w - side) // 2
|
||||
rgb = rgb[y0:y0 + side, x0:x0 + side]
|
||||
rgb = cv2.resize(rgb, (IMG_SIZE, IMG_SIZE))
|
||||
|
||||
img = rgb.transpose(2, 0, 1).astype(np.float32) / 255.0
|
||||
focal = float(IMG_SIZE)
|
||||
K_np = np.array([[focal, 0.0, IMG_SIZE / 2.0],
|
||||
[0.0, focal, IMG_SIZE / 2.0],
|
||||
[0.0, 0.0, 1.0]], dtype=np.float32)
|
||||
|
||||
humans = backend.infer(img, K_np, det_thresh=self.det_thresh)
|
||||
if not humans:
|
||||
return None
|
||||
|
||||
hh = humans[0]
|
||||
v3d = hh["v3d"].detach().cpu().numpy()
|
||||
return SMPLXPerson(
|
||||
pid=0,
|
||||
vertices_3d=np.ascontiguousarray(v3d, dtype=np.float32),
|
||||
)
|
||||
|
||||
def _run(self) -> None:
|
||||
if self.backend == "coreml":
|
||||
self._run_coreml()
|
||||
@@ -238,6 +342,10 @@ class MultiHMRWorker:
|
||||
prev_thumb = thumb
|
||||
|
||||
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
|
||||
# Publish to state for DINOv2 reid in MeshRigger.
|
||||
with self.state.lock():
|
||||
self.state.last_frame_rgb = frame_rgb
|
||||
self.state.last_frame_rgb_t = time.monotonic()
|
||||
tensor = torch.from_numpy(frame_rgb).permute(2, 0, 1).float()
|
||||
tensor = (tensor / 255.0).unsqueeze(0).to(device)
|
||||
|
||||
@@ -365,6 +473,10 @@ class MultiHMRWorker:
|
||||
v3d = hh["v3d"].detach().cpu().numpy()
|
||||
transl = hh.get("transl_pelvis", hh.get("transl"))
|
||||
transl_np = transl.detach().cpu().numpy().flatten()
|
||||
if transl_np.size >= 3:
|
||||
transl_np = transl_np.copy()
|
||||
transl_np[2] = arkit_pelvis_z_override(
|
||||
self.state, pid, float(transl_np[2]))
|
||||
|
||||
shape_raw = hh["shape"].detach().cpu().numpy().flatten()
|
||||
expr_raw = hh["expression"].detach().cpu().numpy().flatten()
|
||||
@@ -517,6 +629,9 @@ class MultiHMRWorker:
|
||||
prev_thumb = thumb
|
||||
|
||||
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
|
||||
with self.state.lock():
|
||||
self.state.last_frame_rgb = frame_rgb
|
||||
self.state.last_frame_rgb_t = time.monotonic()
|
||||
img = frame_rgb.transpose(2, 0, 1).astype(np.float32) / 255.0
|
||||
|
||||
t_inf_start = time.monotonic()
|
||||
@@ -603,6 +718,10 @@ class MultiHMRWorker:
|
||||
continue
|
||||
v3d = hh["v3d"].detach().cpu().numpy()
|
||||
transl_np = hh["transl_pelvis"].detach().cpu().numpy().flatten()
|
||||
if transl_np.size >= 3:
|
||||
transl_np = transl_np.copy()
|
||||
transl_np[2] = arkit_pelvis_z_override(
|
||||
self.state, pid, float(transl_np[2]))
|
||||
shape_raw = hh["shape"].detach().cpu().numpy().flatten()
|
||||
expr_raw = hh["expression"].detach().cpu().numpy().flatten()
|
||||
|
||||
|
||||
@@ -20,6 +20,7 @@ Public API:
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
@@ -38,12 +39,24 @@ DEFAULT_MLPACKAGE = (
|
||||
N_PERSONS_FIXED = 4
|
||||
N_VERTS = 10475
|
||||
|
||||
# CoreML output names from the exported .mlpackage.
|
||||
OUT_V3D = "var_2412" # (4, 10475, 3)
|
||||
OUT_TRANSL = "var_2415" # (4, 1, 3)
|
||||
OUT_SCORES = "var_2428" # (4,)
|
||||
OUT_BETAS = "var_2431" # (4, 10)
|
||||
OUT_EXPR = "var_2434" # (4, 10)
|
||||
# CoreML output names from the exported .mlpackage. The exported
|
||||
# `multihmr_full_672_s.mlpackage` (2026-05-14 re-convert) renumbered
|
||||
# the MIL vars; verified against the on-disk artifact's spec.
|
||||
OUT_V3D = "var_2420" # (4, 10475, 3)
|
||||
OUT_TRANSL = "var_2423" # (4, 1, 3)
|
||||
OUT_SCORES = "var_2436" # (4,)
|
||||
OUT_BETAS = "var_2439" # (4, 10)
|
||||
OUT_EXPR = "var_2442" # (4, 10)
|
||||
# var_2445 (4, 127, 3) = j3d joints — present but unused here.
|
||||
|
||||
# DINOv2 backbone was trained on ImageNet-normalized RGB; the public
|
||||
# `infer()` contract takes [0,1] CHW input and applies this here so
|
||||
# every caller stays normalization-agnostic. Feeding raw [0,1] to the
|
||||
# model collapses all detection scores to ~0.01 ("0 detections" bug).
|
||||
_IMG_NORM_MEAN = np.array([0.485, 0.456, 0.406],
|
||||
dtype=np.float32).reshape(1, 3, 1, 1)
|
||||
_IMG_NORM_STD = np.array([0.229, 0.224, 0.225],
|
||||
dtype=np.float32).reshape(1, 3, 1, 1)
|
||||
|
||||
# MLMultiArrayDataType raw values (from CoreML headers).
|
||||
ML_DTYPE_FLOAT32 = 65568
|
||||
@@ -161,12 +174,22 @@ class MultiHMRCoreMLBackend:
|
||||
MLModel = ns["MLModel"]
|
||||
MLModelConfiguration = ns["MLModelConfiguration"]
|
||||
cfg = MLModelConfiguration.alloc().init()
|
||||
# MLComputeUnits: 0=CPUOnly, 1=CPUAndGPU, 2=All (ANE+GPU+CPU),
|
||||
# 3=CPUAndNeuralEngine. Bench M5 2026-05-14 (under live-worker
|
||||
# contention, 30 iter median, full Multi-HMR predict+copy):
|
||||
# CPU_AND_GPU = 252 ms (baseline)
|
||||
# ALL = 246 ms (within noise, ANE doesn't help)
|
||||
# CPU_AND_NE = 1301 ms (ANE solo catastrophic)
|
||||
# CPU_ONLY = 1152 ms
|
||||
# Standalone (no contention) FP32 = 139 ms = 7.2 fps. Default
|
||||
# stays CPU+GPU. Override with COREML_COMPUTE_UNITS env var
|
||||
# (`all`, `cpu_and_gpu`, `cpu_and_ne`, `cpu_only`) for A/B testing.
|
||||
cu_env = os.environ.get("COREML_COMPUTE_UNITS", "").strip().lower()
|
||||
cu_map = {"cpu_only": 0, "cpu_and_gpu": 1, "all": 2,
|
||||
"cpu_and_ne": 3}
|
||||
cu = cu_map.get(cu_env, 1)
|
||||
try:
|
||||
# MLComputeUnits: 0=CPUOnly, 1=CPUAndGPU, 2=All (ANE+GPU+CPU),
|
||||
# 3=CPUAndNeuralEngine. Multi-HMR's ANEF compile fails
|
||||
# (validated 2026-05-13 on M5), and 'All' falls back to a
|
||||
# slow path (~146ms). CPU+GPU = 28ms = ~35fps on M5.
|
||||
cfg.setComputeUnits_(1)
|
||||
cfg.setComputeUnits_(cu)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
url = NSURL.fileURLWithPath_(str(self.path))
|
||||
@@ -182,8 +205,10 @@ class MultiHMRCoreMLBackend:
|
||||
raise RuntimeError(f"MLModel load failed for {compiled_url}")
|
||||
self._model = model
|
||||
self._ns = ns
|
||||
LOG.info("Multi-HMR CoreML model loaded (%s, computeUnits=CPU+GPU)",
|
||||
self.path.name)
|
||||
cu_name = {0: "CPU_ONLY", 1: "CPU+GPU", 2: "ALL", 3: "CPU+NE"}.get(
|
||||
cu, str(cu))
|
||||
LOG.info("Multi-HMR CoreML model loaded (%s, computeUnits=%s)",
|
||||
self.path.name, cu_name)
|
||||
|
||||
@staticmethod
|
||||
def is_available(mlpackage_path: Path | None = None) -> bool:
|
||||
@@ -232,7 +257,8 @@ class MultiHMRCoreMLBackend:
|
||||
"""Run a forward pass and return list of humans dicts.
|
||||
|
||||
Args:
|
||||
image_chw_float32: (3, 672, 672) or (1, 3, 672, 672) in [0,1].
|
||||
image_chw_float32: (3, 672, 672) or (1, 3, 672, 672), RGB in
|
||||
[0,1]. ImageNet normalization is applied internally.
|
||||
K_33: (3, 3) or (1, 3, 3) camera intrinsics.
|
||||
det_thresh: scores threshold; CoreML forwards K=4 always.
|
||||
|
||||
@@ -251,6 +277,7 @@ class MultiHMRCoreMLBackend:
|
||||
if K.shape != (1, 3, 3):
|
||||
raise ValueError(f"K shape {K.shape}, expected (1,3,3)")
|
||||
|
||||
img = (img - _IMG_NORM_MEAN) / _IMG_NORM_STD
|
||||
raw = self._predict(img, K)
|
||||
v3d = raw.get(OUT_V3D)
|
||||
transl = raw.get(OUT_TRANSL)
|
||||
|
||||
@@ -0,0 +1,896 @@
|
||||
"""3D pose filtering chain : median spike removal, Kalman CV smoothing,
|
||||
spring-damper organic inertia, lookahead extrapolation, IK angular clamps.
|
||||
|
||||
Operates on lists of Kp3D (metric, hip-centered) keyed by track id.
|
||||
|
||||
Stages are toggleable via the POSE_FILTER env var :
|
||||
POSE_FILTER=median+kalman+lookahead+ik (default)
|
||||
POSE_FILTER=median
|
||||
POSE_FILTER=off
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import time
|
||||
from collections import deque
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Iterable
|
||||
|
||||
from .arkit_joint_map import ARKIT91_TO_MP33
|
||||
from .euro_filter import OneEuroFilter, SkeletonFilter
|
||||
from .state import Kp3D, State
|
||||
|
||||
LOG = logging.getLogger("pose_filter")
|
||||
|
||||
NUM_JOINTS = 33
|
||||
DEFAULT_STAGES = ("median", "kalman", "lookahead", "ik")
|
||||
ALL_STAGES = (
|
||||
"median", "kalman", "spring", "lookahead", "ik",
|
||||
"one_euro_joints", "one_euro_bones", "arkit_fuse",
|
||||
)
|
||||
|
||||
# MediaPipe POSE_LANDMARKS indices used by IK constraints.
|
||||
L_SHOULDER, R_SHOULDER = 11, 12
|
||||
L_ELBOW, R_ELBOW = 13, 14
|
||||
L_WRIST, R_WRIST = 15, 16
|
||||
L_HIP, R_HIP = 23, 24
|
||||
L_KNEE, R_KNEE = 25, 26
|
||||
L_ANKLE, R_ANKLE = 27, 28
|
||||
L_FOOT, R_FOOT = 31, 32
|
||||
|
||||
# (parent_idx, joint_idx, child_idx, min_deg, max_deg)
|
||||
JOINT_LIMITS: tuple[tuple[int, int, int, float, float], ...] = (
|
||||
(L_SHOULDER, L_ELBOW, L_WRIST, 0.0, 175.0),
|
||||
(R_SHOULDER, R_ELBOW, R_WRIST, 0.0, 175.0),
|
||||
(L_HIP, L_KNEE, L_ANKLE, 0.0, 175.0),
|
||||
(R_HIP, R_KNEE, R_ANKLE, 0.0, 175.0),
|
||||
(L_KNEE, L_ANKLE, L_FOOT, 60.0, 135.0),
|
||||
(R_KNEE, R_ANKLE, R_FOOT, 60.0, 135.0),
|
||||
)
|
||||
|
||||
|
||||
# ----------------------------- utilities --------------------------------
|
||||
|
||||
def _is_finite(v: float) -> bool:
|
||||
return v == v and v not in (float("inf"), float("-inf"))
|
||||
|
||||
|
||||
def _kp_finite(kp: Kp3D) -> bool:
|
||||
return _is_finite(kp.x) and _is_finite(kp.y) and _is_finite(kp.z)
|
||||
|
||||
|
||||
def _median(values: list[float]) -> float:
|
||||
s = sorted(values)
|
||||
n = len(s)
|
||||
if n == 0:
|
||||
return 0.0
|
||||
if n % 2 == 1:
|
||||
return s[n // 2]
|
||||
return 0.5 * (s[n // 2 - 1] + s[n // 2])
|
||||
|
||||
|
||||
def _std(values: list[float], mu: float) -> float:
|
||||
if not values:
|
||||
return 0.0
|
||||
var = sum((v - mu) ** 2 for v in values) / len(values)
|
||||
return math.sqrt(var)
|
||||
|
||||
|
||||
# ----------------------------- median filter ----------------------------
|
||||
|
||||
class MedianFilter:
|
||||
"""Per (pid, joint) ring buffer ; replaces spikes outside 3σ by median."""
|
||||
|
||||
def __init__(self, window: int = 3) -> None:
|
||||
self.window = max(1, window)
|
||||
self._buf: dict[tuple[int, int], deque[tuple[float, float, float]]] = {}
|
||||
|
||||
def reset(self) -> None:
|
||||
self._buf.clear()
|
||||
|
||||
def apply(self, pid: int, joint_idx: int, x: float, y: float, z: float
|
||||
) -> tuple[float, float, float]:
|
||||
key = (pid, joint_idx)
|
||||
buf = self._buf.get(key)
|
||||
if buf is None:
|
||||
buf = deque(maxlen=self.window)
|
||||
self._buf[key] = buf
|
||||
|
||||
# Spike detection requires history.
|
||||
out = (x, y, z)
|
||||
if not (_is_finite(x) and _is_finite(y) and _is_finite(z)):
|
||||
if buf:
|
||||
med = (_median([v[0] for v in buf]),
|
||||
_median([v[1] for v in buf]),
|
||||
_median([v[2] for v in buf]))
|
||||
out = med
|
||||
else:
|
||||
out = (0.0, 0.0, 0.0)
|
||||
elif len(buf) >= self.window:
|
||||
for axis_idx, val in enumerate(out):
|
||||
col = [v[axis_idx] for v in buf]
|
||||
med = _median(col)
|
||||
sigma = _std(col, med)
|
||||
if sigma > 1e-6 and abs(val - med) > 3.0 * sigma:
|
||||
out = tuple(med if i == axis_idx else out[i]
|
||||
for i in range(3)) # type: ignore[assignment]
|
||||
buf.append(out)
|
||||
return out
|
||||
|
||||
|
||||
# ----------------------------- Kalman CV --------------------------------
|
||||
|
||||
@dataclass
|
||||
class _KalmanState:
|
||||
# State vector [x, y, z, vx, vy, vz]
|
||||
x: list[float] = field(default_factory=lambda: [0.0] * 6)
|
||||
# 6x6 covariance flattened
|
||||
P: list[list[float]] = field(default_factory=lambda: [[0.0] * 6 for _ in range(6)])
|
||||
initialised: bool = False
|
||||
last_t: float = 0.0
|
||||
|
||||
|
||||
def _mat_eye(n: int, s: float = 1.0) -> list[list[float]]:
|
||||
return [[s if i == j else 0.0 for j in range(n)] for i in range(n)]
|
||||
|
||||
|
||||
def _mat_mul(A: list[list[float]], B: list[list[float]]) -> list[list[float]]:
|
||||
ra, ca = len(A), len(A[0])
|
||||
cb = len(B[0])
|
||||
out = [[0.0] * cb for _ in range(ra)]
|
||||
for i in range(ra):
|
||||
Ai = A[i]
|
||||
for k in range(ca):
|
||||
aik = Ai[k]
|
||||
if aik == 0.0:
|
||||
continue
|
||||
Bk = B[k]
|
||||
for j in range(cb):
|
||||
out[i][j] += aik * Bk[j]
|
||||
return out
|
||||
|
||||
|
||||
def _mat_add(A: list[list[float]], B: list[list[float]]) -> list[list[float]]:
|
||||
return [[A[i][j] + B[i][j] for j in range(len(A[0]))] for i in range(len(A))]
|
||||
|
||||
|
||||
def _mat_sub(A: list[list[float]], B: list[list[float]]) -> list[list[float]]:
|
||||
return [[A[i][j] - B[i][j] for j in range(len(A[0]))] for i in range(len(A))]
|
||||
|
||||
|
||||
def _mat_T(A: list[list[float]]) -> list[list[float]]:
|
||||
return [[A[i][j] for i in range(len(A))] for j in range(len(A[0]))]
|
||||
|
||||
|
||||
def _mat_inv3(M: list[list[float]]) -> list[list[float]]:
|
||||
a, b, c = M[0]
|
||||
d, e, f = M[1]
|
||||
g, h, i = M[2]
|
||||
A = e * i - f * h
|
||||
B = -(d * i - f * g)
|
||||
C = d * h - e * g
|
||||
det = a * A + b * B + c * C
|
||||
if abs(det) < 1e-12:
|
||||
return _mat_eye(3, 1.0)
|
||||
inv_det = 1.0 / det
|
||||
return [
|
||||
[A * inv_det, -(b * i - c * h) * inv_det, (b * f - c * e) * inv_det],
|
||||
[B * inv_det, (a * i - c * g) * inv_det, -(a * f - c * d) * inv_det],
|
||||
[C * inv_det, -(a * h - b * g) * inv_det, (a * e - b * d) * inv_det],
|
||||
]
|
||||
|
||||
|
||||
class KalmanCV:
|
||||
"""Constant-velocity Kalman per (pid, joint_idx) on R^3."""
|
||||
|
||||
def __init__(self, q: float = 1e-3, r: float = 1e-2) -> None:
|
||||
self.q = q
|
||||
self.r = r
|
||||
self._states: dict[tuple[int, int], _KalmanState] = {}
|
||||
self._H = [
|
||||
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
|
||||
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
|
||||
]
|
||||
|
||||
def reset(self) -> None:
|
||||
self._states.clear()
|
||||
|
||||
def get_velocity(self, pid: int, joint_idx: int) -> tuple[float, float, float]:
|
||||
st = self._states.get((pid, joint_idx))
|
||||
if st is None or not st.initialised:
|
||||
return (0.0, 0.0, 0.0)
|
||||
return (st.x[3], st.x[4], st.x[5])
|
||||
|
||||
def step(self, pid: int, joint_idx: int, mx: float, my: float, mz: float,
|
||||
t_now: float) -> tuple[float, float, float]:
|
||||
key = (pid, joint_idx)
|
||||
st = self._states.get(key)
|
||||
if st is None:
|
||||
st = _KalmanState()
|
||||
self._states[key] = st
|
||||
|
||||
if not st.initialised:
|
||||
st.x = [mx, my, mz, 0.0, 0.0, 0.0]
|
||||
st.P = _mat_eye(6, 1.0)
|
||||
st.initialised = True
|
||||
st.last_t = t_now
|
||||
return (mx, my, mz)
|
||||
|
||||
dt = max(1e-3, min(0.2, t_now - st.last_t))
|
||||
st.last_t = t_now
|
||||
|
||||
# Predict
|
||||
F = _mat_eye(6, 1.0)
|
||||
F[0][3] = dt
|
||||
F[1][4] = dt
|
||||
F[2][5] = dt
|
||||
x_pred = [
|
||||
st.x[0] + dt * st.x[3],
|
||||
st.x[1] + dt * st.x[4],
|
||||
st.x[2] + dt * st.x[5],
|
||||
st.x[3], st.x[4], st.x[5],
|
||||
]
|
||||
Q = _mat_eye(6, self.q)
|
||||
P_pred = _mat_add(_mat_mul(_mat_mul(F, st.P), _mat_T(F)), Q)
|
||||
|
||||
# Update
|
||||
z = [mx, my, mz]
|
||||
# y = z - H x_pred
|
||||
Hx = [x_pred[0], x_pred[1], x_pred[2]]
|
||||
y = [z[i] - Hx[i] for i in range(3)]
|
||||
# S = H P H^T + R (3x3)
|
||||
HP = _mat_mul(self._H, P_pred)
|
||||
S = [[HP[i][j] for j in range(3)] for i in range(3)]
|
||||
# add HP*H^T rest cols (cols 3..5) -> 0 contribution since H rest zero
|
||||
for i in range(3):
|
||||
S[i][i] += self.r
|
||||
S_inv = _mat_inv3(S)
|
||||
# K = P H^T S^-1 (6x3)
|
||||
PHt = [[P_pred[i][j] for j in range(3)] for i in range(6)]
|
||||
K = _mat_mul(PHt, S_inv)
|
||||
# x = x_pred + K y
|
||||
x_new = [x_pred[i] + sum(K[i][j] * y[j] for j in range(3))
|
||||
for i in range(6)]
|
||||
# P = (I - K H) P_pred
|
||||
KH = [[K[i][0] if j == 0 else (K[i][1] if j == 1 else (K[i][2] if j == 2 else 0.0))
|
||||
for j in range(6)] for i in range(6)]
|
||||
I6 = _mat_eye(6, 1.0)
|
||||
st.P = _mat_mul(_mat_sub(I6, KH), P_pred)
|
||||
st.x = x_new
|
||||
return (x_new[0], x_new[1], x_new[2])
|
||||
|
||||
|
||||
# --------------------------- spring damper ------------------------------
|
||||
|
||||
class SpringDamper:
|
||||
"""Critically-tunable spring-damper per (pid, joint_idx) on R^3."""
|
||||
|
||||
def __init__(self, stiffness: float = 200.0, damping: float = 15.0,
|
||||
mass: float = 1.0, enabled: bool = True) -> None:
|
||||
self.k = stiffness
|
||||
self.c = damping
|
||||
self.m = max(1e-3, mass)
|
||||
self.enabled = enabled
|
||||
self._pos: dict[tuple[int, int], list[float]] = {}
|
||||
self._vel: dict[tuple[int, int], list[float]] = {}
|
||||
self._last_t: dict[tuple[int, int], float] = {}
|
||||
|
||||
def reset(self) -> None:
|
||||
self._pos.clear()
|
||||
self._vel.clear()
|
||||
self._last_t.clear()
|
||||
|
||||
def step(self, pid: int, joint_idx: int, tx: float, ty: float, tz: float,
|
||||
t_now: float) -> tuple[float, float, float]:
|
||||
if not self.enabled:
|
||||
return (tx, ty, tz)
|
||||
key = (pid, joint_idx)
|
||||
pos = self._pos.get(key)
|
||||
if pos is None:
|
||||
self._pos[key] = [tx, ty, tz]
|
||||
self._vel[key] = [0.0, 0.0, 0.0]
|
||||
self._last_t[key] = t_now
|
||||
return (tx, ty, tz)
|
||||
dt = max(1e-3, min(0.1, t_now - self._last_t[key]))
|
||||
self._last_t[key] = t_now
|
||||
vel = self._vel[key]
|
||||
target = (tx, ty, tz)
|
||||
for i in range(3):
|
||||
# F = k(target - pos) - c * vel
|
||||
f = self.k * (target[i] - pos[i]) - self.c * vel[i]
|
||||
a = f / self.m
|
||||
vel[i] += a * dt
|
||||
pos[i] += vel[i] * dt
|
||||
return (pos[0], pos[1], pos[2])
|
||||
|
||||
|
||||
# --------------------------- lookahead ----------------------------------
|
||||
|
||||
class LookaheadPredictor:
|
||||
"""Linear extrapolation using Kalman velocities, capped to avoid blow-ups."""
|
||||
|
||||
def __init__(self, lookahead_ms: float = 50.0, max_velocity: float = 5.0
|
||||
) -> None:
|
||||
self.lookahead_s = lookahead_ms / 1000.0
|
||||
self.max_v = max_velocity
|
||||
|
||||
def step(self, x: float, y: float, z: float,
|
||||
vx: float, vy: float, vz: float) -> tuple[float, float, float]:
|
||||
def clamp(v: float) -> float:
|
||||
if v > self.max_v:
|
||||
return self.max_v
|
||||
if v < -self.max_v:
|
||||
return -self.max_v
|
||||
return v
|
||||
dt = self.lookahead_s
|
||||
return (x + clamp(vx) * dt, y + clamp(vy) * dt, z + clamp(vz) * dt)
|
||||
|
||||
|
||||
# --------------------------- IK constraints -----------------------------
|
||||
|
||||
def _vec_sub(a: tuple[float, float, float], b: tuple[float, float, float]
|
||||
) -> tuple[float, float, float]:
|
||||
return (a[0] - b[0], a[1] - b[1], a[2] - b[2])
|
||||
|
||||
|
||||
def _vec_add(a: tuple[float, float, float], b: tuple[float, float, float]
|
||||
) -> tuple[float, float, float]:
|
||||
return (a[0] + b[0], a[1] + b[1], a[2] + b[2])
|
||||
|
||||
|
||||
def _vec_scale(a: tuple[float, float, float], s: float
|
||||
) -> tuple[float, float, float]:
|
||||
return (a[0] * s, a[1] * s, a[2] * s)
|
||||
|
||||
|
||||
def _vec_dot(a: tuple[float, float, float], b: tuple[float, float, float]
|
||||
) -> float:
|
||||
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]
|
||||
|
||||
|
||||
def _vec_norm(a: tuple[float, float, float]) -> float:
|
||||
return math.sqrt(_vec_dot(a, a))
|
||||
|
||||
|
||||
def _vec_normalize(a: tuple[float, float, float], eps: float = 1e-9
|
||||
) -> tuple[float, float, float]:
|
||||
n = _vec_norm(a)
|
||||
if n < eps:
|
||||
return (1.0, 0.0, 0.0)
|
||||
return (a[0] / n, a[1] / n, a[2] / n)
|
||||
|
||||
|
||||
def _slerp_dir(d_from: tuple[float, float, float],
|
||||
d_to: tuple[float, float, float],
|
||||
t: float) -> tuple[float, float, float]:
|
||||
"""Slerp between two unit-ish vectors."""
|
||||
a = _vec_normalize(d_from)
|
||||
b = _vec_normalize(d_to)
|
||||
cos_a = max(-1.0, min(1.0, _vec_dot(a, b)))
|
||||
ang = math.acos(cos_a)
|
||||
if ang < 1e-6:
|
||||
return a
|
||||
sa = math.sin(ang)
|
||||
if abs(sa) < 1e-6:
|
||||
# antiparallel : pick an arbitrary perpendicular, then rotate.
|
||||
ortho = (1.0, 0.0, 0.0) if abs(a[0]) < 0.9 else (0.0, 1.0, 0.0)
|
||||
# Gram-Schmidt
|
||||
d = _vec_dot(ortho, a)
|
||||
perp = (ortho[0] - d * a[0], ortho[1] - d * a[1], ortho[2] - d * a[2])
|
||||
perp = _vec_normalize(perp)
|
||||
# rotate a by t*pi around perp axis : Rodrigues for angle = t*pi
|
||||
theta = t * ang
|
||||
cs, sn = math.cos(theta), math.sin(theta)
|
||||
# cross(perp, a)
|
||||
cx = perp[1] * a[2] - perp[2] * a[1]
|
||||
cy = perp[2] * a[0] - perp[0] * a[2]
|
||||
cz = perp[0] * a[1] - perp[1] * a[0]
|
||||
dot_pa = _vec_dot(perp, a)
|
||||
return (a[0] * cs + cx * sn + perp[0] * dot_pa * (1 - cs),
|
||||
a[1] * cs + cy * sn + perp[1] * dot_pa * (1 - cs),
|
||||
a[2] * cs + cz * sn + perp[2] * dot_pa * (1 - cs))
|
||||
w1 = math.sin((1.0 - t) * ang) / sa
|
||||
w2 = math.sin(t * ang) / sa
|
||||
return (a[0] * w1 + b[0] * w2,
|
||||
a[1] * w1 + b[1] * w2,
|
||||
a[2] * w1 + b[2] * w2)
|
||||
|
||||
|
||||
class IKConstraints:
|
||||
"""Clamp interior joint angles for elbows, knees, ankles."""
|
||||
|
||||
def __init__(self, limits: Iterable[tuple[int, int, int, float, float]]
|
||||
= JOINT_LIMITS) -> None:
|
||||
self.limits = tuple(limits)
|
||||
|
||||
def apply(self, kps: list[Kp3D]) -> list[Kp3D]:
|
||||
if len(kps) < NUM_JOINTS:
|
||||
return kps
|
||||
out = list(kps)
|
||||
for parent_i, joint_i, child_i, min_deg, max_deg in self.limits:
|
||||
if max(parent_i, joint_i, child_i) >= len(out):
|
||||
continue
|
||||
p = (out[parent_i].x, out[parent_i].y, out[parent_i].z)
|
||||
j = (out[joint_i].x, out[joint_i].y, out[joint_i].z)
|
||||
c = (out[child_i].x, out[child_i].y, out[child_i].z)
|
||||
v_pj = _vec_sub(p, j) # from joint to parent
|
||||
v_cj = _vec_sub(c, j) # from joint to child
|
||||
n_pj = _vec_norm(v_pj)
|
||||
n_cj = _vec_norm(v_cj)
|
||||
if n_pj < 1e-6 or n_cj < 1e-6:
|
||||
continue
|
||||
cos_a = max(-1.0, min(1.0, _vec_dot(v_pj, v_cj) / (n_pj * n_cj)))
|
||||
ang_deg = math.degrees(math.acos(cos_a))
|
||||
min_r = math.radians(min_deg)
|
||||
max_r = math.radians(max_deg)
|
||||
target_r: float | None = None
|
||||
if ang_deg < min_deg:
|
||||
target_r = min_r
|
||||
elif ang_deg > max_deg:
|
||||
target_r = max_r
|
||||
if target_r is None:
|
||||
continue
|
||||
# Interpolate child direction toward parent direction (or away)
|
||||
# so the new angle matches target_r.
|
||||
cur_r = math.acos(cos_a)
|
||||
# t such that new_angle = (1-t)*cur + t*pi between dirs ; use slerp.
|
||||
# Find t in [0,1] s.t. slerp(d_cj, d_pj, t) makes angle = target_r
|
||||
# The angle between slerp result and d_pj is (1-t)*cur_r.
|
||||
# So target_r = (1 - t) * cur_r -> t = 1 - target_r / cur_r
|
||||
if cur_r < 1e-6:
|
||||
continue
|
||||
t = 1.0 - (target_r / cur_r)
|
||||
t = max(0.0, min(1.0, t))
|
||||
d_cj = _vec_normalize(v_cj)
|
||||
d_pj = _vec_normalize(v_pj)
|
||||
new_dir = _slerp_dir(d_cj, d_pj, t)
|
||||
new_child = _vec_add(j, _vec_scale(new_dir, n_cj))
|
||||
old = out[child_i]
|
||||
out[child_i] = Kp3D(x=new_child[0], y=new_child[1],
|
||||
z=new_child[2], c=old.c)
|
||||
return out
|
||||
|
||||
|
||||
# --------------------------- chain wrapper ------------------------------
|
||||
|
||||
def _parse_env_stages() -> tuple[str, ...]:
|
||||
raw = os.environ.get("POSE_FILTER")
|
||||
if raw is None:
|
||||
return DEFAULT_STAGES
|
||||
raw = raw.strip().lower()
|
||||
if raw in ("off", "none", "0", "false"):
|
||||
return ()
|
||||
parts = tuple(p.strip() for p in raw.replace(",", "+").split("+") if p.strip())
|
||||
return tuple(p for p in parts if p in ALL_STAGES)
|
||||
|
||||
|
||||
class PoseFilterChain:
|
||||
"""Chain : median → kalman → spring → lookahead → ik."""
|
||||
|
||||
def __init__(self, state: State | None = None,
|
||||
enabled_stages: Iterable[str] | None = None) -> None:
|
||||
self.state = state
|
||||
if enabled_stages is None:
|
||||
stages = _parse_env_stages()
|
||||
else:
|
||||
stages = tuple(s for s in enabled_stages if s in ALL_STAGES)
|
||||
self.enabled = stages
|
||||
self.median = MedianFilter(window=3)
|
||||
self.kalman = KalmanCV()
|
||||
self.spring = SpringDamper(enabled="spring" in self.enabled)
|
||||
self.lookahead = LookaheadPredictor()
|
||||
self.ik = IKConstraints()
|
||||
# One Euro filters (CHI 2012) — adaptive low-pass driven by speed.
|
||||
# Joints variant: applied in joint-space, per (pid, joint_idx).
|
||||
# Bones variant: applied to bone vectors (child - parent) along
|
||||
# the MediaPipe Pose 33 subset that overlaps SMPL-X fused joints.
|
||||
self.one_euro_joints = SkeletonFilter(min_cutoff=1.2, beta=0.08)
|
||||
self.one_euro_bones = BoneOneEuroFilter(min_cutoff=1.0, beta=0.05)
|
||||
self.arkit_fuse = ArkitFuse()
|
||||
self.last_apply_ms: float = 0.0
|
||||
self.last_apply_bones_ms: float = 0.0
|
||||
LOG.info("PoseFilterChain stages=%s", self.enabled or ("off",))
|
||||
|
||||
def reset(self) -> None:
|
||||
self.median.reset()
|
||||
self.kalman.reset()
|
||||
self.spring.reset()
|
||||
self.one_euro_joints.reset_all()
|
||||
self.one_euro_bones.reset_all()
|
||||
|
||||
def apply(self, bodies3d: list[list[Kp3D]], ids: list[int],
|
||||
t_now: float) -> list[list[Kp3D]]:
|
||||
if not bodies3d or not self.enabled:
|
||||
self.last_apply_ms = 0.0
|
||||
return bodies3d
|
||||
t0 = time.perf_counter()
|
||||
out: list[list[Kp3D]] = []
|
||||
use_median = "median" in self.enabled
|
||||
use_kalman = "kalman" in self.enabled
|
||||
use_spring = "spring" in self.enabled
|
||||
use_lookahead = "lookahead" in self.enabled
|
||||
use_ik = "ik" in self.enabled
|
||||
use_one_euro_joints = "one_euro_joints" in self.enabled
|
||||
use_arkit_fuse = "arkit_fuse" in self.enabled
|
||||
|
||||
for body_i, kps in enumerate(bodies3d):
|
||||
pid = ids[body_i] if body_i < len(ids) else -1
|
||||
if use_arkit_fuse and self.state is not None:
|
||||
kps = self.arkit_fuse.apply(self.state, pid, kps, t_now)
|
||||
new_kps: list[Kp3D] = []
|
||||
for j_idx, kp in enumerate(kps):
|
||||
x, y, z, c = kp.x, kp.y, kp.z, kp.c
|
||||
if use_median:
|
||||
x, y, z = self.median.apply(pid, j_idx, x, y, z)
|
||||
if use_one_euro_joints:
|
||||
x, y, z = self.one_euro_joints.smooth(
|
||||
pid, j_idx, x, y, z, t_now)
|
||||
if use_kalman:
|
||||
x, y, z = self.kalman.step(pid, j_idx, x, y, z, t_now)
|
||||
if use_spring:
|
||||
x, y, z = self.spring.step(pid, j_idx, x, y, z, t_now)
|
||||
if use_lookahead and use_kalman:
|
||||
vx, vy, vz = self.kalman.get_velocity(pid, j_idx)
|
||||
x, y, z = self.lookahead.step(x, y, z, vx, vy, vz)
|
||||
new_kps.append(Kp3D(x=x, y=y, z=z, c=c))
|
||||
if use_ik:
|
||||
new_kps = self.ik.apply(new_kps)
|
||||
out.append(new_kps)
|
||||
|
||||
self.last_apply_ms = (time.perf_counter() - t0) * 1000.0
|
||||
return out
|
||||
|
||||
# ---- Face / hand smoothing entry points ---------------------------
|
||||
def apply_face(self, faces: list[list], ids: list[int],
|
||||
t_now: float) -> list[list]:
|
||||
if not hasattr(self, "_face_chain"):
|
||||
self._face_chain = FaceFilterChain()
|
||||
return self._face_chain.apply(faces, ids, t_now)
|
||||
|
||||
def apply_hand(self, hands: list[list], ids: list[int],
|
||||
handedness: list[str] | None,
|
||||
t_now: float) -> list[list]:
|
||||
if not hasattr(self, "_hand_chain"):
|
||||
self._hand_chain = HandFilterChain()
|
||||
return self._hand_chain.apply(hands, ids, handedness, t_now)
|
||||
|
||||
# ---- Bone-space One Euro (Point B) --------------------------------
|
||||
def apply_bones(self, bodies3d: list[list[Kp3D]], ids: list[int],
|
||||
t_now: float) -> list[list[Kp3D]]:
|
||||
"""Filter bone vectors (child - parent) for the body skeleton.
|
||||
|
||||
Called *after* SMPL-X fusion in multi.py. No-op unless
|
||||
``one_euro_bones`` is in POSE_FILTER. Mutates the child slot
|
||||
of each bone in-place — parents are walked in topological
|
||||
order (root → leaves) so children always see updated parents.
|
||||
"""
|
||||
if not bodies3d or "one_euro_bones" not in self.enabled:
|
||||
self.last_apply_bones_ms = 0.0
|
||||
return bodies3d
|
||||
t0 = time.perf_counter()
|
||||
for body_i, kps in enumerate(bodies3d):
|
||||
pid = ids[body_i] if body_i < len(ids) else -1
|
||||
self.one_euro_bones.apply_body(pid, kps, t_now)
|
||||
self.last_apply_bones_ms = (time.perf_counter() - t0) * 1000.0
|
||||
return bodies3d
|
||||
|
||||
def forget_person(self, pid: int) -> None:
|
||||
"""Drop per-pid state on track loss (caller responsibility)."""
|
||||
try:
|
||||
self.one_euro_joints.forget(pid)
|
||||
self.one_euro_bones.forget(pid)
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
|
||||
|
||||
# ============================ bone One Euro ===============================
|
||||
|
||||
# Body skeleton bones expressed as (parent_idx, child_idx) over the
|
||||
# MediaPipe Pose 33 indexing — chosen to overlap with the 14
|
||||
# SMPL-X-fused slots (cf. multi.py SMPLX_TO_MP33). Topological order:
|
||||
# legs first, then arms, then bridges (clavicle, pelvis), then torso.
|
||||
BODY_BONES: tuple[tuple[int, int], ...] = (
|
||||
(L_HIP, L_KNEE), # 23 -> 25
|
||||
(L_KNEE, L_ANKLE), # 25 -> 27
|
||||
(L_ANKLE, L_FOOT), # 27 -> 31
|
||||
(R_HIP, R_KNEE), # 24 -> 26
|
||||
(R_KNEE, R_ANKLE), # 26 -> 28
|
||||
(R_ANKLE, R_FOOT), # 28 -> 32
|
||||
(L_SHOULDER, L_ELBOW), # 11 -> 13
|
||||
(L_ELBOW, L_WRIST), # 13 -> 15
|
||||
(R_SHOULDER, R_ELBOW), # 12 -> 14
|
||||
(R_ELBOW, R_WRIST), # 14 -> 16
|
||||
(L_SHOULDER, R_SHOULDER), # clavicle bridge
|
||||
(L_HIP, R_HIP), # pelvis bridge
|
||||
(L_SHOULDER, L_HIP), # left torso
|
||||
(R_SHOULDER, R_HIP), # right torso
|
||||
)
|
||||
|
||||
|
||||
class BoneOneEuroFilter:
|
||||
"""One Euro filter applied to bone vectors of the body skeleton.
|
||||
|
||||
For each bone (parent, child), the vector ``child - parent`` is
|
||||
smoothed component-wise. Child position is then reconstructed as
|
||||
``parent + smoothed_bone``. This preserves bone *direction*
|
||||
stability frame-to-frame while remaining responsive to genuine
|
||||
pose changes (One Euro adaptive cutoff).
|
||||
|
||||
State is keyed by ``(pid, bone_idx)`` and lives in three
|
||||
OneEuroFilter instances per bone (one per axis).
|
||||
"""
|
||||
|
||||
def __init__(self, min_cutoff: float = 1.0, beta: float = 0.05) -> None:
|
||||
self._min_cutoff = min_cutoff
|
||||
self._beta = beta
|
||||
# (pid, bone_idx) -> (fx, fy, fz)
|
||||
self._table: dict[tuple[int, int], tuple[
|
||||
OneEuroFilter, OneEuroFilter, OneEuroFilter]] = {}
|
||||
|
||||
def _filters_for(self, pid: int, bone_idx: int) -> tuple[
|
||||
OneEuroFilter, OneEuroFilter, OneEuroFilter]:
|
||||
key = (pid, bone_idx)
|
||||
f = self._table.get(key)
|
||||
if f is None:
|
||||
f = (
|
||||
OneEuroFilter(self._min_cutoff, self._beta),
|
||||
OneEuroFilter(self._min_cutoff, self._beta),
|
||||
OneEuroFilter(self._min_cutoff, self._beta),
|
||||
)
|
||||
self._table[key] = f
|
||||
return f
|
||||
|
||||
def apply_body(self, pid: int, kps: list[Kp3D], t: float) -> None:
|
||||
n = len(kps)
|
||||
for bone_idx, (p_idx, c_idx) in enumerate(BODY_BONES):
|
||||
if p_idx >= n or c_idx >= n:
|
||||
continue
|
||||
p = kps[p_idx]
|
||||
c = kps[c_idx]
|
||||
if not (_kp_finite(p) and _kp_finite(c)):
|
||||
continue
|
||||
dx = c.x - p.x
|
||||
dy = c.y - p.y
|
||||
dz = c.z - p.z
|
||||
fx, fy, fz = self._filters_for(pid, bone_idx)
|
||||
sx = fx(dx, t)
|
||||
sy = fy(dy, t)
|
||||
sz = fz(dz, t)
|
||||
kps[c_idx] = Kp3D(
|
||||
x=p.x + sx, y=p.y + sy, z=p.z + sz, c=c.c)
|
||||
|
||||
def forget(self, pid: int) -> None:
|
||||
self._table = {k: v for k, v in self._table.items() if k[0] != pid}
|
||||
|
||||
def reset_all(self) -> None:
|
||||
self._table.clear()
|
||||
|
||||
|
||||
class ArkitFuse:
|
||||
"""Splice ARKit 91-joint world-space data into MediaPipe Pose 33.
|
||||
|
||||
Reads ``state.persons_arkit_joints[pid]`` (shape (91, 3)) when fresh
|
||||
(last_t within FRESH_SEC). Writes the 14 body slots covered by
|
||||
ARKIT91_TO_MP33 ; everything else (face landmarks, finger tips)
|
||||
stays MediaPipe-driven.
|
||||
"""
|
||||
|
||||
FRESH_SEC: float = 1.0
|
||||
|
||||
def apply(self, state: "State", pid: int,
|
||||
kps: list[Kp3D], t_now: float) -> list[Kp3D]:
|
||||
with state.lock():
|
||||
arr = state.persons_arkit_joints.get(pid)
|
||||
last_t = state.persons_arkit_last_t.get(pid, 0.0)
|
||||
if arr is None:
|
||||
return kps
|
||||
if t_now - last_t > self.FRESH_SEC:
|
||||
return kps
|
||||
out = list(kps)
|
||||
n = len(out)
|
||||
for arkit_idx, mp33_idx in ARKIT91_TO_MP33:
|
||||
if mp33_idx >= n:
|
||||
continue
|
||||
x = float(arr[arkit_idx, 0])
|
||||
y = float(arr[arkit_idx, 1])
|
||||
z = float(arr[arkit_idx, 2])
|
||||
old = out[mp33_idx]
|
||||
out[mp33_idx] = Kp3D(x=x, y=y, z=z, c=getattr(old, "c", 1.0))
|
||||
return out
|
||||
|
||||
|
||||
# ============================ face / hand =================================
|
||||
|
||||
# Face and hand filtering operate on PoseKp lists (normalized x,y in [0,1]
|
||||
# + z relative depth + confidence). We only apply temporal smoothing
|
||||
# (median + Kalman 2D + lookahead) — no IK, no spring.
|
||||
|
||||
def _parse_env_face_stages() -> tuple[str, ...]:
|
||||
raw = os.environ.get("POSE_FILTER_FACE")
|
||||
if raw is None:
|
||||
return ("median", "kalman", "lookahead")
|
||||
raw = raw.strip().lower()
|
||||
if raw in ("off", "none", "0", "false"):
|
||||
return ()
|
||||
parts = tuple(p.strip() for p in raw.replace(",", "+").split("+") if p.strip())
|
||||
return tuple(p for p in parts if p in ("median", "kalman", "lookahead"))
|
||||
|
||||
|
||||
def _parse_env_hand_stages() -> tuple[str, ...]:
|
||||
raw = os.environ.get("POSE_FILTER_HAND")
|
||||
if raw is None:
|
||||
return ("median", "kalman", "lookahead")
|
||||
raw = raw.strip().lower()
|
||||
if raw in ("off", "none", "0", "false"):
|
||||
return ()
|
||||
parts = tuple(p.strip() for p in raw.replace(",", "+").split("+") if p.strip())
|
||||
return tuple(p for p in parts if p in ("median", "kalman", "lookahead"))
|
||||
|
||||
|
||||
class AlphaBetaCV:
|
||||
"""Lightweight alpha-beta filter (scalar Kalman approximation).
|
||||
|
||||
Far cheaper than the 6x6 KalmanCV : O(1) per joint per axis with no
|
||||
matrix algebra. Suited to face/hand smoothing where the full CV
|
||||
Kalman is overkill.
|
||||
"""
|
||||
|
||||
def __init__(self, alpha: float = 0.55, beta: float = 0.15) -> None:
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
# state[key] = [x, y, z, vx, vy, vz, last_t]
|
||||
self._st: dict[tuple[int, int], list[float]] = {}
|
||||
|
||||
def reset(self) -> None:
|
||||
self._st.clear()
|
||||
|
||||
def get_velocity(self, pid: int, joint_idx: int
|
||||
) -> tuple[float, float, float]:
|
||||
s = self._st.get((pid, joint_idx))
|
||||
if s is None:
|
||||
return (0.0, 0.0, 0.0)
|
||||
return (s[3], s[4], s[5])
|
||||
|
||||
def step(self, pid: int, joint_idx: int, mx: float, my: float,
|
||||
mz: float, t_now: float) -> tuple[float, float, float]:
|
||||
key = (pid, joint_idx)
|
||||
s = self._st.get(key)
|
||||
if s is None:
|
||||
self._st[key] = [mx, my, mz, 0.0, 0.0, 0.0, t_now]
|
||||
return (mx, my, mz)
|
||||
dt = max(1e-3, min(0.2, t_now - s[6]))
|
||||
s[6] = t_now
|
||||
# Predict
|
||||
x_pred = s[0] + s[3] * dt
|
||||
y_pred = s[1] + s[4] * dt
|
||||
z_pred = s[2] + s[5] * dt
|
||||
# Residual
|
||||
rx = mx - x_pred
|
||||
ry = my - y_pred
|
||||
rz = mz - z_pred
|
||||
# Update
|
||||
s[0] = x_pred + self.alpha * rx
|
||||
s[1] = y_pred + self.alpha * ry
|
||||
s[2] = z_pred + self.alpha * rz
|
||||
s[3] += (self.beta / dt) * rx
|
||||
s[4] += (self.beta / dt) * ry
|
||||
s[5] += (self.beta / dt) * rz
|
||||
return (s[0], s[1], s[2])
|
||||
|
||||
|
||||
class FaceFilterChain:
|
||||
"""Per-pid temporal smoothing for face landmarks (median + Kalman + lookahead).
|
||||
|
||||
Lookahead 30 ms ; max velocity in normalized units/s.
|
||||
"""
|
||||
|
||||
def __init__(self, lookahead_ms: float = 30.0,
|
||||
enabled_stages: Iterable[str] | None = None) -> None:
|
||||
if enabled_stages is None:
|
||||
stages = _parse_env_face_stages()
|
||||
else:
|
||||
stages = tuple(s for s in enabled_stages
|
||||
if s in ("median", "kalman", "lookahead"))
|
||||
self.enabled = stages
|
||||
self.median = MedianFilter(window=3)
|
||||
self.kalman = AlphaBetaCV(alpha=0.55, beta=0.15)
|
||||
self.lookahead = LookaheadPredictor(
|
||||
lookahead_ms=lookahead_ms, max_velocity=2.0)
|
||||
self.last_apply_ms: float = 0.0
|
||||
|
||||
def reset(self) -> None:
|
||||
self.median.reset()
|
||||
self.kalman.reset()
|
||||
|
||||
def apply(self, faces: list[list], ids: list[int],
|
||||
t_now: float) -> list[list]:
|
||||
if not faces or not self.enabled:
|
||||
self.last_apply_ms = 0.0
|
||||
return faces
|
||||
t0 = time.perf_counter()
|
||||
use_median = "median" in self.enabled
|
||||
use_kalman = "kalman" in self.enabled
|
||||
use_lookahead = "lookahead" in self.enabled
|
||||
out: list[list] = []
|
||||
for f_i, kps in enumerate(faces):
|
||||
pid = ids[f_i] if f_i < len(ids) else -1
|
||||
# Encode pid with a face-side namespace to avoid colliding with
|
||||
# body and hand kalman/median caches.
|
||||
key_pid = pid * 13 + 1 if pid >= 0 else pid
|
||||
new_kps = []
|
||||
for j_idx, kp in enumerate(kps):
|
||||
x, y, z, c = kp.x, kp.y, kp.z, kp.c
|
||||
if use_median:
|
||||
x, y, z = self.median.apply(key_pid, j_idx, x, y, z)
|
||||
if use_kalman:
|
||||
x, y, z = self.kalman.step(key_pid, j_idx, x, y, z, t_now)
|
||||
if use_lookahead and use_kalman:
|
||||
vx, vy, vz = self.kalman.get_velocity(key_pid, j_idx)
|
||||
x, y, z = self.lookahead.step(x, y, z, vx, vy, vz)
|
||||
new_kps.append(type(kp)(x=x, y=y, z=z, c=c))
|
||||
out.append(new_kps)
|
||||
self.last_apply_ms = (time.perf_counter() - t0) * 1000.0
|
||||
return out
|
||||
|
||||
|
||||
class HandFilterChain:
|
||||
"""Per-pid+side temporal smoothing for hand landmarks.
|
||||
|
||||
Left and right hands keep independent filter state via a namespaced
|
||||
pid (pid*2 for left, pid*2+1 for right). When handedness is not
|
||||
provided, hands fall back to a side-agnostic namespace.
|
||||
"""
|
||||
|
||||
def __init__(self, lookahead_ms: float = 30.0,
|
||||
enabled_stages: Iterable[str] | None = None) -> None:
|
||||
if enabled_stages is None:
|
||||
stages = _parse_env_hand_stages()
|
||||
else:
|
||||
stages = tuple(s for s in enabled_stages
|
||||
if s in ("median", "kalman", "lookahead"))
|
||||
self.enabled = stages
|
||||
self.median = MedianFilter(window=3)
|
||||
self.kalman = AlphaBetaCV(alpha=0.6, beta=0.2)
|
||||
self.lookahead = LookaheadPredictor(
|
||||
lookahead_ms=lookahead_ms, max_velocity=4.0)
|
||||
self.last_apply_ms: float = 0.0
|
||||
|
||||
def reset(self) -> None:
|
||||
self.median.reset()
|
||||
self.kalman.reset()
|
||||
|
||||
def apply(self, hands: list[list], ids: list[int],
|
||||
handedness: list[str] | None,
|
||||
t_now: float) -> list[list]:
|
||||
if not hands or not self.enabled:
|
||||
self.last_apply_ms = 0.0
|
||||
return hands
|
||||
t0 = time.perf_counter()
|
||||
use_median = "median" in self.enabled
|
||||
use_kalman = "kalman" in self.enabled
|
||||
use_lookahead = "lookahead" in self.enabled
|
||||
out: list[list] = []
|
||||
for h_i, kps in enumerate(hands):
|
||||
pid = ids[h_i] if h_i < len(ids) else -1
|
||||
side = (handedness[h_i] if handedness and h_i < len(handedness)
|
||||
else "u").lower()
|
||||
side_bit = 0 if side.startswith("l") else (1 if side.startswith("r") else 2)
|
||||
# Namespace : (pid << 2) | side_bit — keeps L/R independent.
|
||||
key_pid = (pid * 4 + side_bit + 7) if pid >= 0 else pid
|
||||
new_kps = []
|
||||
for j_idx, kp in enumerate(kps):
|
||||
x, y, z, c = kp.x, kp.y, kp.z, kp.c
|
||||
if use_median:
|
||||
x, y, z = self.median.apply(key_pid, j_idx, x, y, z)
|
||||
if use_kalman:
|
||||
x, y, z = self.kalman.step(key_pid, j_idx, x, y, z, t_now)
|
||||
if use_lookahead and use_kalman:
|
||||
vx, vy, vz = self.kalman.get_velocity(key_pid, j_idx)
|
||||
x, y, z = self.lookahead.step(x, y, z, vx, vy, vz)
|
||||
new_kps.append(type(kp)(x=x, y=y, z=z, c=c))
|
||||
out.append(new_kps)
|
||||
self.last_apply_ms = (time.perf_counter() - t0) * 1000.0
|
||||
return out
|
||||
@@ -38,6 +38,11 @@ detrpose = [
|
||||
"iopath>=0.1.10",
|
||||
"opencv-python>=4.10",
|
||||
]
|
||||
# Open3D for ICP fusion between iPhone LiDAR and Multi-HMR SMPL-X meshes.
|
||||
# CPU-only is sufficient at 5-10 Hz LiDAR cadence.
|
||||
lidar = [
|
||||
"open3d>=0.18,<0.20",
|
||||
]
|
||||
nlf = [
|
||||
"torch>=2.4",
|
||||
"torchvision>=0.19",
|
||||
|
||||
@@ -0,0 +1,77 @@
|
||||
"""Latency / convergence bench for the ICP fusion worker.
|
||||
|
||||
Usage:
|
||||
|
||||
cd data_only_viz
|
||||
uv run --extra lidar python -m data_only_viz.scripts.bench_icp_fusion \
|
||||
--n-frames 200 --n-people 2 --seed 0
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.icp_fusion import FusionWorker, IcpConfig
|
||||
from data_only_viz.lidar_calib import Extrinsic
|
||||
from data_only_viz.state import SMPLXPerson, State
|
||||
|
||||
|
||||
def _synth_person(seed: int, offset_x: float) -> SMPLXPerson:
|
||||
rng = np.random.RandomState(seed)
|
||||
verts = np.zeros((10475, 3), dtype=np.float32)
|
||||
pts = rng.randn(2000, 3).astype(np.float32) * 0.1
|
||||
verts[: pts.shape[0]] = pts + np.array([offset_x, 0, 1.5], dtype=np.float32)
|
||||
verts[5559] = pts.mean(axis=0) + np.array([offset_x, 0, 1.5], dtype=np.float32)
|
||||
return SMPLXPerson(pid=seed, vertices_3d=verts)
|
||||
|
||||
|
||||
def main(argv: list[str] | None = None) -> int:
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--n-frames", type=int, default=200)
|
||||
p.add_argument("--n-people", type=int, default=2)
|
||||
p.add_argument("--seed", type=int, default=0)
|
||||
args = p.parse_args(argv)
|
||||
|
||||
rng = np.random.RandomState(args.seed)
|
||||
persons = [_synth_person(i, offset_x=-0.6 + 1.2 * i) for i in range(args.n_people)]
|
||||
state = State()
|
||||
state.persons_smplx = persons
|
||||
|
||||
worker = FusionWorker(extrinsic=Extrinsic.identity(), config=IcpConfig())
|
||||
|
||||
latencies_ms: list[float] = []
|
||||
accepted = 0
|
||||
pelvis_delta_m: list[float] = []
|
||||
for _ in range(args.n_frames):
|
||||
all_pts = np.concatenate([
|
||||
pers.vertices_3d[: 2000] + np.array([0, 0.05, 0], dtype=np.float32) +
|
||||
0.02 * rng.randn(2000, 3).astype(np.float32)
|
||||
for pers in persons
|
||||
])
|
||||
state.lidar_points = all_pts
|
||||
before = np.stack([p.vertices_3d[5559].copy() for p in state.persons_smplx])
|
||||
t0 = time.perf_counter()
|
||||
meta = worker.run_once(state)
|
||||
latencies_ms.append((time.perf_counter() - t0) * 1000.0)
|
||||
accepted += len(meta.applied)
|
||||
after = np.stack([p.vertices_3d[5559] for p in state.persons_smplx])
|
||||
pelvis_delta_m.extend(np.linalg.norm(after - before, axis=1).tolist())
|
||||
|
||||
report = {
|
||||
"n_frames": args.n_frames,
|
||||
"n_people": args.n_people,
|
||||
"latency_ms_p50": float(np.percentile(latencies_ms, 50)),
|
||||
"latency_ms_p95": float(np.percentile(latencies_ms, 95)),
|
||||
"acceptance_rate": accepted / (args.n_frames * args.n_people),
|
||||
"pelvis_delta_m_mean": float(np.mean(pelvis_delta_m)),
|
||||
"pelvis_delta_m_max": float(np.max(pelvis_delta_m)),
|
||||
}
|
||||
print(json.dumps(report, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,235 @@
|
||||
"""Bench Multi-HMR CoreML — compute_units sweep + section split.
|
||||
|
||||
Bench Multi-HMR `.mlpackage` inference latency on M5 (or any Apple
|
||||
Silicon). Decomposes the per-frame cost into copy_in / predict /
|
||||
copy_out so we can see where time goes, then sweeps compute_units
|
||||
(CPU_AND_GPU vs ALL vs CPU_AND_NE vs CPU_ONLY) and tests the
|
||||
"reused MLMultiArray buffer" optimization.
|
||||
|
||||
Usage:
|
||||
uv run --project data_only_viz \
|
||||
python -m data_only_viz.scripts.bench_multihmr_coreml
|
||||
|
||||
The result reproduces the 2026-05-14 finding: predict() is ~99% of
|
||||
latency, copy_in is <2 ms, copy_out is <1 ms. None of the I/O
|
||||
micro-optims (reused buffer, vImage preprocess, async copy) can
|
||||
help meaningfully — only changing the model itself does (INT8 quant
|
||||
via `scripts/quantize_multihmr_int8.py`, lower resolution, or a
|
||||
smaller architecture).
|
||||
|
||||
Pause the live worker before running for clean numbers:
|
||||
pgrep -f 'data_only_viz.main.*multi-hmr' | xargs kill -STOP
|
||||
# ...run bench...
|
||||
pgrep -f 'data_only_viz.main.*multi-hmr' | xargs kill -CONT
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import ctypes
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from Foundation import NSURL
|
||||
|
||||
from data_only_viz.multihmr_coreml import (
|
||||
DEFAULT_MLPACKAGE,
|
||||
_load_frameworks,
|
||||
_mlarray_to_np,
|
||||
_np_to_mlarray,
|
||||
)
|
||||
|
||||
H = W = 672
|
||||
NITER = 30
|
||||
NWARM = 5
|
||||
|
||||
|
||||
def _make_inputs():
|
||||
img = np.random.rand(1, 3, H, W).astype(np.float32)
|
||||
focal = float(H)
|
||||
K = np.array(
|
||||
[[[focal, 0, H / 2], [0, focal, H / 2], [0, 0, 1.0]]],
|
||||
dtype=np.float32,
|
||||
)
|
||||
return img, K
|
||||
|
||||
|
||||
def _load_model(compute_units: int, mlpackage: Path):
|
||||
ns = _load_frameworks()
|
||||
MLModel = ns["MLModel"]
|
||||
MLModelConfiguration = ns["MLModelConfiguration"]
|
||||
cfg = MLModelConfiguration.alloc().init()
|
||||
cfg.setComputeUnits_(compute_units)
|
||||
url = NSURL.fileURLWithPath_(str(mlpackage))
|
||||
compiled = MLModel.compileModelAtURL_error_(url, None)
|
||||
if compiled is None:
|
||||
raise RuntimeError(f"compile failed cu={compute_units}")
|
||||
model = MLModel.modelWithContentsOfURL_configuration_error_(
|
||||
compiled, cfg, None)
|
||||
if model is None:
|
||||
raise RuntimeError(f"load failed cu={compute_units}")
|
||||
return model, ns
|
||||
|
||||
|
||||
def _stats(ts):
|
||||
ts = sorted(ts)
|
||||
return (ts[len(ts) // 2],
|
||||
ts[len(ts) // 10],
|
||||
ts[(len(ts) * 9) // 10])
|
||||
|
||||
|
||||
def bench_basic(label: str, compute_units: int, mlpackage: Path):
|
||||
try:
|
||||
model, ns = _load_model(compute_units, mlpackage)
|
||||
except Exception as e: # noqa: BLE001
|
||||
print(f"[{label}] LOAD FAILED: {e}")
|
||||
return None
|
||||
MLDictionaryFeatureProvider = ns["MLDictionaryFeatureProvider"]
|
||||
MLFeatureValue = ns["MLFeatureValue"]
|
||||
img, K = _make_inputs()
|
||||
for _ in range(NWARM):
|
||||
img_ml = _np_to_mlarray(img); k_ml = _np_to_mlarray(K)
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml),
|
||||
"cam_K": MLFeatureValue.featureValueWithMultiArray_(k_ml)}
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
out = model.predictionFromFeatures_error_(prov, None)
|
||||
if out is None:
|
||||
print(f"[{label}] predict returned None")
|
||||
return None
|
||||
ts = []
|
||||
for _ in range(NITER):
|
||||
t0 = time.perf_counter()
|
||||
img_ml = _np_to_mlarray(img); k_ml = _np_to_mlarray(K)
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml),
|
||||
"cam_K": MLFeatureValue.featureValueWithMultiArray_(k_ml)}
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
out = model.predictionFromFeatures_error_(prov, None)
|
||||
for name in out.featureNames():
|
||||
fv = out.featureValueForName_(name)
|
||||
ml = fv.multiArrayValue()
|
||||
if ml is None:
|
||||
continue
|
||||
_ = _mlarray_to_np(ml)
|
||||
ts.append((time.perf_counter() - t0) * 1e3)
|
||||
med, p10, p90 = _stats(ts)
|
||||
print(f"[{label:34s}] med={med:6.1f}ms p10={p10:6.1f} "
|
||||
f"p90={p90:6.1f} fps={1000/med:5.1f}")
|
||||
return med
|
||||
|
||||
|
||||
def bench_reused_input(label: str, compute_units: int, mlpackage: Path):
|
||||
try:
|
||||
model, ns = _load_model(compute_units, mlpackage)
|
||||
except Exception as e: # noqa: BLE001
|
||||
print(f"[{label}] LOAD FAILED: {e}")
|
||||
return None
|
||||
MLDictionaryFeatureProvider = ns["MLDictionaryFeatureProvider"]
|
||||
MLFeatureValue = ns["MLFeatureValue"]
|
||||
img, K = _make_inputs()
|
||||
img_ml = _np_to_mlarray(img); k_ml = _np_to_mlarray(K)
|
||||
ptr_img = img_ml.dataPointer()
|
||||
addr_img = int(ptr_img) if isinstance(ptr_img, int) else \
|
||||
ctypes.cast(ptr_img, ctypes.c_void_p).value
|
||||
ptr_k = k_ml.dataPointer()
|
||||
addr_k = int(ptr_k) if isinstance(ptr_k, int) else \
|
||||
ctypes.cast(ptr_k, ctypes.c_void_p).value
|
||||
img_bytes = img.nbytes
|
||||
k_bytes = K.nbytes
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml),
|
||||
"cam_K": MLFeatureValue.featureValueWithMultiArray_(k_ml)}
|
||||
for _ in range(NWARM):
|
||||
ctypes.memmove(addr_img, img.ctypes.data, img_bytes)
|
||||
ctypes.memmove(addr_k, K.ctypes.data, k_bytes)
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
_ = model.predictionFromFeatures_error_(prov, None)
|
||||
ts = []
|
||||
for _ in range(NITER):
|
||||
t0 = time.perf_counter()
|
||||
ctypes.memmove(addr_img, img.ctypes.data, img_bytes)
|
||||
ctypes.memmove(addr_k, K.ctypes.data, k_bytes)
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
out = model.predictionFromFeatures_error_(prov, None)
|
||||
for name in out.featureNames():
|
||||
fv = out.featureValueForName_(name)
|
||||
ml = fv.multiArrayValue()
|
||||
if ml is None:
|
||||
continue
|
||||
_ = _mlarray_to_np(ml)
|
||||
ts.append((time.perf_counter() - t0) * 1e3)
|
||||
med, p10, p90 = _stats(ts)
|
||||
print(f"[{label:34s}] med={med:6.1f}ms p10={p10:6.1f} "
|
||||
f"p90={p90:6.1f} fps={1000/med:5.1f}")
|
||||
return med
|
||||
|
||||
|
||||
def bench_section_split(compute_units: int, mlpackage: Path):
|
||||
model, ns = _load_model(compute_units, mlpackage)
|
||||
MLDictionaryFeatureProvider = ns["MLDictionaryFeatureProvider"]
|
||||
MLFeatureValue = ns["MLFeatureValue"]
|
||||
img, K = _make_inputs()
|
||||
for _ in range(NWARM):
|
||||
img_ml = _np_to_mlarray(img); k_ml = _np_to_mlarray(K)
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml),
|
||||
"cam_K": MLFeatureValue.featureValueWithMultiArray_(k_ml)}
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
_ = model.predictionFromFeatures_error_(prov, None)
|
||||
t_in, t_pred, t_out = [], [], []
|
||||
for _ in range(NITER):
|
||||
t0 = time.perf_counter()
|
||||
img_ml = _np_to_mlarray(img); k_ml = _np_to_mlarray(K)
|
||||
feats = {"image": MLFeatureValue.featureValueWithMultiArray_(img_ml),
|
||||
"cam_K": MLFeatureValue.featureValueWithMultiArray_(k_ml)}
|
||||
prov = MLDictionaryFeatureProvider.alloc(
|
||||
).initWithDictionary_error_(feats, None)
|
||||
t1 = time.perf_counter()
|
||||
out = model.predictionFromFeatures_error_(prov, None)
|
||||
t2 = time.perf_counter()
|
||||
for name in out.featureNames():
|
||||
fv = out.featureValueForName_(name)
|
||||
ml = fv.multiArrayValue()
|
||||
if ml is None:
|
||||
continue
|
||||
_ = _mlarray_to_np(ml)
|
||||
t3 = time.perf_counter()
|
||||
t_in.append((t1 - t0) * 1e3)
|
||||
t_pred.append((t2 - t1) * 1e3)
|
||||
t_out.append((t3 - t2) * 1e3)
|
||||
mi = lambda a: sorted(a)[len(a) // 2]
|
||||
print("[section-split CPU_AND_GPU]")
|
||||
print(f" copy_in : {mi(t_in):6.2f} ms")
|
||||
print(f" predict : {mi(t_pred):6.2f} ms")
|
||||
print(f" copy_out : {mi(t_out):6.2f} ms")
|
||||
print(f" total : {mi(t_in)+mi(t_pred)+mi(t_out):6.2f} ms")
|
||||
|
||||
|
||||
def main(argv: list[str]) -> int:
|
||||
mlpackage = DEFAULT_MLPACKAGE
|
||||
if len(argv) > 1:
|
||||
mlpackage = Path(argv[1])
|
||||
if not mlpackage.exists():
|
||||
print(f"mlpackage missing: {mlpackage}", file=sys.stderr)
|
||||
return 1
|
||||
print(f"bench target: {mlpackage}")
|
||||
print("=" * 70)
|
||||
print("Section split (alloc/predict/copy)")
|
||||
print("=" * 70)
|
||||
bench_section_split(1, mlpackage)
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("Compute-units sweep (30 iter median)")
|
||||
print("=" * 70)
|
||||
bench_basic("A. CPU_AND_GPU (baseline)", 1, mlpackage)
|
||||
bench_basic("B. ALL (ANE+GPU+CPU)", 2, mlpackage)
|
||||
bench_basic("C. CPU_AND_NE (ANE-only)", 3, mlpackage)
|
||||
bench_basic("D. CPU_ONLY", 0, mlpackage)
|
||||
bench_reused_input("E. CPU_AND_GPU + reused buffer", 1, mlpackage)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main(sys.argv))
|
||||
@@ -0,0 +1,97 @@
|
||||
"""Interactive one-shot extrinsic calibration between iPhone LiDAR and webcam.
|
||||
|
||||
Usage:
|
||||
|
||||
cd data_only_viz
|
||||
uv run --extra lidar python -m data_only_viz.scripts.calibrate_lidar \
|
||||
--lidar-host 192.168.0.42 --lidar-port 5500 --webcam-index 0
|
||||
|
||||
The script prompts the user to assume 4 stances (front, left, right, back),
|
||||
captures paired pelvis points (webcam: Multi-HMR vertex 5559; LiDAR: centroid
|
||||
of the largest mesh anchor), solves Kabsch, and writes the result to
|
||||
ICP_LIDAR_EXTRINSIC or the default path.
|
||||
|
||||
Multi-HMR worker is launched in-process for this script (single-shot mode).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import datetime as dt
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.lidar_calib import Extrinsic, kabsch_rigid, save_extrinsic
|
||||
from data_only_viz.lidar_receiver import LidarTCPReader
|
||||
|
||||
_LOG = logging.getLogger("calibrate_lidar")
|
||||
_PELVIS_VERT_INDEX = 5559 # SMPL-X canonical pelvis vertex
|
||||
|
||||
|
||||
def _wait_for_lidar(reader: LidarTCPReader, timeout_s: float = 5.0):
|
||||
deadline = time.monotonic() + timeout_s
|
||||
while time.monotonic() < deadline:
|
||||
latest = reader.latest()
|
||||
if latest is not None and latest.points.shape[0] > 50:
|
||||
return latest
|
||||
time.sleep(0.05)
|
||||
raise RuntimeError("LiDAR frame never arrived")
|
||||
|
||||
|
||||
def _capture_one_pair(reader: LidarTCPReader, get_smplx_pelvis_cam) -> tuple[np.ndarray, np.ndarray]:
|
||||
input("Hold still, then press ENTER to capture...")
|
||||
lidar = _wait_for_lidar(reader)
|
||||
pelvis_cam = get_smplx_pelvis_cam()
|
||||
pelvis_arkit = lidar.points.mean(axis=0)
|
||||
_LOG.info("captured: cam=%s arkit=%s", pelvis_cam, pelvis_arkit)
|
||||
return pelvis_cam, pelvis_arkit
|
||||
|
||||
|
||||
def main(argv: list[str] | None = None) -> int:
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--lidar-host", required=True)
|
||||
p.add_argument("--lidar-port", type=int, default=5500)
|
||||
p.add_argument("--webcam-index", type=int, default=0)
|
||||
p.add_argument("--stances", type=int, default=4)
|
||||
args = p.parse_args(argv)
|
||||
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s")
|
||||
|
||||
reader = LidarTCPReader(host=args.lidar_host, port=args.lidar_port)
|
||||
reader.start()
|
||||
|
||||
# Task 9 added the ``MultiHMRWorker.predict_once`` API surface but
|
||||
# left the body as ``NotImplementedError`` — the existing PyTorch
|
||||
# path is too coupled to the worker thread for a clean extraction.
|
||||
# When ``predict_once`` is wired (follow-up task), replace this
|
||||
# placeholder by opening cv2.VideoCapture(args.webcam_index),
|
||||
# running ``worker.predict_once(rgb)`` and returning
|
||||
# ``person.vertices_3d[_PELVIS_VERT_INDEX]``.
|
||||
def _placeholder_pelvis_cam() -> np.ndarray:
|
||||
raise SystemExit(
|
||||
"calibrate_lidar needs MultiHMRWorker.predict_once to be "
|
||||
"implemented (currently NotImplementedError)")
|
||||
|
||||
pairs_cam, pairs_arkit = [], []
|
||||
try:
|
||||
for i in range(args.stances):
|
||||
_LOG.info("stance %d/%d", i + 1, args.stances)
|
||||
cam, arkit = _capture_one_pair(reader, _placeholder_pelvis_cam)
|
||||
pairs_cam.append(cam)
|
||||
pairs_arkit.append(arkit)
|
||||
finally:
|
||||
reader.stop()
|
||||
|
||||
T = kabsch_rigid(np.asarray(pairs_arkit), np.asarray(pairs_cam))
|
||||
path = save_extrinsic(Extrinsic(
|
||||
T_arkit_to_cam=T,
|
||||
confidence=1.0,
|
||||
captured_at_iso=dt.datetime.now(dt.timezone.utc).isoformat(),
|
||||
))
|
||||
_LOG.info("extrinsic saved to %s", path)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,202 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Convert DINOv2 ViT-S/14 to a CoreML .mlpackage for ANE-friendly inference.
|
||||
|
||||
The wrapped module takes (1, 3, 224, 224) RGB float32 in [0, 1], applies
|
||||
ImageNet normalization internally, runs the ViT, and returns the CLS
|
||||
embedding (1, 384) L2-normalised. We trace + convert with
|
||||
``coremltools.convert(... compute_units=ComputeUnit.ALL, compute_precision=FP16)``.
|
||||
|
||||
Run with the Python 3.12 venv that has coremltools and torch::
|
||||
|
||||
/tmp/coreml312/bin/python -m data_only_viz.scripts.convert_dinov2 [--force]
|
||||
|
||||
Output:
|
||||
~/.cache/av-live-multihmr/dinov2_vits14.mlpackage
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
import types
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
LOG = logging.getLogger("convert_dinov2")
|
||||
|
||||
OUT_DIR = Path.home() / ".cache" / "av-live-multihmr"
|
||||
OUT_PATH = OUT_DIR / "dinov2_vits14.mlpackage"
|
||||
|
||||
_IMAGENET_MEAN = (0.485, 0.456, 0.406)
|
||||
_IMAGENET_STD = (0.229, 0.224, 0.225)
|
||||
|
||||
|
||||
def _build_wrapper():
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
backbone = torch.hub.load(
|
||||
"facebookresearch/dinov2",
|
||||
"dinov2_vits14",
|
||||
source="github",
|
||||
trust_repo=True,
|
||||
)
|
||||
backbone.eval()
|
||||
|
||||
# Pretrained pos_embed is at 37x37 (518/14). We pre-resample to
|
||||
# 16x16 (224/14) once so the traced graph never needs an upsample.
|
||||
pe = backbone.pos_embed.data # (1, 1+37*37, 384)
|
||||
cls_pe = pe[:, :1]
|
||||
patch_pe = pe[:, 1:]
|
||||
n_old = int(round((patch_pe.shape[1]) ** 0.5))
|
||||
dim = patch_pe.shape[-1]
|
||||
patch_pe = patch_pe.reshape(1, n_old, n_old, dim).permute(0, 3, 1, 2)
|
||||
patch_pe = F.interpolate(patch_pe, size=(16, 16), mode="bilinear",
|
||||
align_corners=False)
|
||||
patch_pe = patch_pe.permute(0, 2, 3, 1).reshape(1, 16 * 16, dim)
|
||||
new_pe = torch.cat([cls_pe, patch_pe], dim=1).contiguous()
|
||||
backbone.pos_embed = nn.Parameter(new_pe, requires_grad=False)
|
||||
|
||||
mean = torch.tensor(_IMAGENET_MEAN, dtype=torch.float32).view(1, 3, 1, 1)
|
||||
std = torch.tensor(_IMAGENET_STD, dtype=torch.float32).view(1, 3, 1, 1)
|
||||
|
||||
class DinoV2Wrapper(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.backbone = backbone
|
||||
self.register_buffer("mean", mean)
|
||||
self.register_buffer("std", std)
|
||||
|
||||
def forward(self, x):
|
||||
x = (x - self.mean) / self.std
|
||||
bb = self.backbone
|
||||
x = bb.patch_embed(x)
|
||||
# cls_token is (1,1,384). Concat directly (B=1 fixed).
|
||||
x = torch.cat((bb.cls_token, x), dim=1)
|
||||
x = x + bb.pos_embed
|
||||
for blk in bb.blocks:
|
||||
x = blk(x)
|
||||
x = bb.norm(x)
|
||||
cls = x[:, 0]
|
||||
cls = cls / (cls.norm(dim=-1, keepdim=True) + 1e-8)
|
||||
return cls
|
||||
|
||||
return DinoV2Wrapper().eval()
|
||||
|
||||
|
||||
def _patch_coremltools_cast():
|
||||
"""coremltools 9.0 _cast assumes x.val is a 0-d scalar. With recent
|
||||
torch (2.12) some aten::Int args land as 1-D length-1 arrays. Patch
|
||||
the helper to flatten before scalar-casting."""
|
||||
from coremltools.converters.mil.frontend.torch import ops as _ops
|
||||
from coremltools.converters.mil.mil import Builder as mb
|
||||
|
||||
_orig = _ops._cast
|
||||
|
||||
def _patched_cast(context, node, dtype, dtype_name):
|
||||
# Inputs are read inside _orig from context; we wrap the failure
|
||||
# path by checking the first input's val first.
|
||||
inputs = _ops._get_inputs(context, node, expected=1)
|
||||
x = inputs[0]
|
||||
if x.can_be_folded_to_const():
|
||||
val = x.val
|
||||
if hasattr(val, "shape") and getattr(val, "shape", ()) != ():
|
||||
# 1-D length-1 (or all-ones shape) -> extract scalar
|
||||
import numpy as _np
|
||||
arr = _np.asarray(val).reshape(-1)
|
||||
if arr.size == 1:
|
||||
res = mb.const(val=dtype(arr[0]), name=node.name)
|
||||
context.add(res, node.name)
|
||||
return
|
||||
return _orig(context, node, dtype, dtype_name)
|
||||
|
||||
_ops._cast = _patched_cast
|
||||
|
||||
|
||||
def convert(force: bool = False) -> Path:
|
||||
import torch
|
||||
import coremltools as ct
|
||||
_patch_coremltools_cast()
|
||||
|
||||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
if OUT_PATH.exists() and not force:
|
||||
LOG.info("already converted: %s", OUT_PATH)
|
||||
return OUT_PATH
|
||||
|
||||
LOG.info("loading DINOv2 ViT-S/14 ...")
|
||||
wrap = _build_wrapper()
|
||||
example = torch.rand(1, 3, 224, 224, dtype=torch.float32)
|
||||
with torch.no_grad():
|
||||
ref_out = wrap(example)
|
||||
LOG.info("torch out shape=%s norm=%.4f", tuple(ref_out.shape),
|
||||
float(ref_out.norm(dim=-1).mean()))
|
||||
|
||||
LOG.info("tracing ...")
|
||||
with torch.no_grad():
|
||||
traced = torch.jit.trace(wrap, example, strict=False)
|
||||
|
||||
LOG.info("ct.convert (mlprogram FP16, computeUnits=ALL) ...")
|
||||
mlmodel = ct.convert(
|
||||
traced,
|
||||
source="pytorch",
|
||||
convert_to="mlprogram",
|
||||
inputs=[ct.TensorType(name="image", shape=example.shape,
|
||||
dtype=np.float32)],
|
||||
outputs=[ct.TensorType(name="embedding", dtype=np.float32)],
|
||||
compute_precision=ct.precision.FLOAT16,
|
||||
compute_units=ct.ComputeUnit.ALL,
|
||||
minimum_deployment_target=ct.target.macOS14,
|
||||
)
|
||||
mlmodel.short_description = "DINOv2 ViT-S/14 person re-id (384-D, L2)"
|
||||
mlmodel.save(str(OUT_PATH))
|
||||
LOG.info("saved %s", OUT_PATH)
|
||||
|
||||
pred = mlmodel.predict({"image": example.numpy().astype(np.float32)})
|
||||
coreml_out = list(pred.values())[0].reshape(-1)
|
||||
ref_np = ref_out.numpy().reshape(-1)
|
||||
cos = float(np.dot(coreml_out, ref_np) /
|
||||
(np.linalg.norm(coreml_out) * np.linalg.norm(ref_np) + 1e-8))
|
||||
LOG.info("CoreML vs Torch cosine on random input: %.4f", cos)
|
||||
return OUT_PATH
|
||||
|
||||
|
||||
def bench(n_iter: int = 30) -> None:
|
||||
import coremltools as ct
|
||||
LOG.info("bench: load mlpackage ...")
|
||||
m = ct.models.MLModel(str(OUT_PATH),
|
||||
compute_units=ct.ComputeUnit.ALL)
|
||||
crop = np.random.rand(1, 3, 224, 224).astype(np.float32)
|
||||
for _ in range(3):
|
||||
m.predict({"image": crop})
|
||||
times = []
|
||||
for _ in range(n_iter):
|
||||
t0 = time.perf_counter()
|
||||
m.predict({"image": crop})
|
||||
times.append((time.perf_counter() - t0) * 1e3)
|
||||
times.sort()
|
||||
p50 = times[len(times) // 2]
|
||||
p95 = times[int(len(times) * 0.95)]
|
||||
LOG.info("bench %d iter: p50=%.2f ms p95=%.2f ms mean=%.2f ms (~%.1f fps)",
|
||||
n_iter, p50, p95, sum(times) / len(times), 1000.0 / p50)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
logging.basicConfig(level=logging.INFO,
|
||||
format="%(asctime)s %(name)s %(message)s")
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--force", action="store_true")
|
||||
ap.add_argument("--bench-only", action="store_true")
|
||||
ap.add_argument("--n-iter", type=int, default=30)
|
||||
args = ap.parse_args()
|
||||
|
||||
if not args.bench_only:
|
||||
convert(force=args.force)
|
||||
bench(n_iter=args.n_iter)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -524,10 +524,10 @@ try:
|
||||
compute_units=ct.ComputeUnit.CPU_AND_GPU,
|
||||
minimum_deployment_target=ct.target.macOS15,
|
||||
convert_to="mlprogram",
|
||||
# FP16 OK depuis le patch roma branchless (cf rapport bisection
|
||||
# 2026-05-13) : la source du NaN etait torch.empty + index_put_
|
||||
# dans roma.rotmat_to_rotvec, pas la precision.
|
||||
compute_precision=ct.precision.FLOAT16,
|
||||
# FP32 mandatory : FP16 (global ou hybride op_selector) degrade
|
||||
# visiblement le mesh sur poses extremes. INT8 weight quant
|
||||
# teste 2026-05-14 : aucun gain sur GPU compute-bound.
|
||||
compute_precision=ct.precision.FLOAT32,
|
||||
)
|
||||
out_path = "/tmp/multihmr_full_672_s.mlpackage"
|
||||
mlmodel.save(out_path)
|
||||
|
||||
@@ -0,0 +1,81 @@
|
||||
"""Quantize Multi-HMR mlpackage to INT8 (weight-only) for M5 speedup.
|
||||
|
||||
Run in the Python 3.12 conversion venv (coremltools cannot run on 3.14):
|
||||
|
||||
/tmp/coreml312/.venv/bin/python \
|
||||
data_only_viz/scripts/quantize_multihmr_int8.py
|
||||
|
||||
Produces `multihmr_full_672_s_int8.mlpackage` next to the FP32 file.
|
||||
Bench after with `scripts/coreml_full_probe.py` or just load with
|
||||
`MultiHMRCoreMLBackend(path=...new path...)`.
|
||||
|
||||
Strategy:
|
||||
- Linear 8-bit weight palettization (per-tensor symmetric). Activations
|
||||
stay FP16 — that's the "weight-only quant" path, lowest accuracy
|
||||
hit and what CoreML's GPU runtime accelerates best.
|
||||
- Skip the SMPL-X decoder branch ops that are sensitive to numeric
|
||||
drift (skipped by name pattern below — adjust if v3d shows mesh
|
||||
artefacts after quantization).
|
||||
|
||||
Validation:
|
||||
- After producing the int8 mlpackage, run the live worker briefly
|
||||
with COREML_MLPACKAGE pointing to the new file and visually check
|
||||
the mesh. If v3d shows tearing on extreme poses, retry with
|
||||
`granularity="per_channel"` instead of `per_tensor`.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
import coremltools as ct
|
||||
from coremltools.optimize.coreml import (
|
||||
linear_quantize_weights,
|
||||
OptimizationConfig,
|
||||
OpLinearQuantizerConfig,
|
||||
)
|
||||
except ImportError as e:
|
||||
print(f"coremltools missing in this venv: {e}", file=sys.stderr)
|
||||
print("Run from the Python 3.12 conversion venv (coremltools "
|
||||
"is not available on 3.14).", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
SRC = Path.home() / ".cache" / "av-live-multihmr" / \
|
||||
"multihmr_full_672_s.mlpackage"
|
||||
DST = Path.home() / ".cache" / "av-live-multihmr" / \
|
||||
"multihmr_full_672_s_int8.mlpackage"
|
||||
|
||||
|
||||
def main() -> int:
|
||||
if not SRC.exists():
|
||||
print(f"source mlpackage missing: {SRC}", file=sys.stderr)
|
||||
return 1
|
||||
print(f"loading FP32 model from {SRC}")
|
||||
model = ct.models.MLModel(str(SRC))
|
||||
|
||||
# Per-tensor symmetric int8 weight quant. Per-tensor keeps the
|
||||
# quantized model small and GPU-friendly; per-channel is a safer
|
||||
# fallback if mesh quality degrades.
|
||||
op_cfg = OpLinearQuantizerConfig(
|
||||
mode="linear_symmetric",
|
||||
dtype="int8",
|
||||
granularity="per_tensor",
|
||||
)
|
||||
cfg = OptimizationConfig(global_config=op_cfg)
|
||||
print("running linear_quantize_weights (per_tensor int8)...")
|
||||
quant = linear_quantize_weights(model, config=cfg)
|
||||
print(f"saving quantized model to {DST}")
|
||||
quant.save(str(DST))
|
||||
print("done. Test with:")
|
||||
print(f" COREML_MLPACKAGE={DST} \\\n"
|
||||
f" MULTIHMR_BACKEND=coreml \\\n"
|
||||
f" uv run --project data_only_viz \\\n"
|
||||
f" python -m data_only_viz.main --multi-hmr "
|
||||
f"--motion-gate 0")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -25,9 +25,16 @@ from typing import Sequence
|
||||
|
||||
import numpy as np
|
||||
|
||||
import os
|
||||
|
||||
from .mesh_rigger import MeshRigger
|
||||
from .state import SMPLXPerson, State
|
||||
|
||||
try:
|
||||
from .dino_reid import DinoReid
|
||||
except Exception: # noqa: BLE001
|
||||
DinoReid = None # type: ignore[assignment]
|
||||
|
||||
LOG = logging.getLogger("smplx_tcp")
|
||||
|
||||
MAGIC = b"SMPX"
|
||||
@@ -47,7 +54,25 @@ class SMPLXTCPSender:
|
||||
self._sock: socket.socket | None = None
|
||||
# Hybrid keyframe rigging : entre deux keyframes Multi-HMR (~3 fps),
|
||||
# on translate le mesh via le delta pelvis Apple Vision (30 fps).
|
||||
self._rigger = MeshRigger(state) if enable_rigging else None
|
||||
# MULTIHMR_REID: 'dino' (try DINOv2 + IoU fusion, fallback IoU) /
|
||||
# 'iou' (pure IoU). Default: 'dino' if mlpackage exists.
|
||||
reid_mode = os.environ.get("MULTIHMR_REID", "dino").lower()
|
||||
dino = None
|
||||
if enable_rigging and reid_mode == "dino" and DinoReid is not None:
|
||||
try:
|
||||
if DinoReid.is_available():
|
||||
dino = DinoReid()
|
||||
LOG.info("MeshRigger: DINOv2 reid enabled")
|
||||
else:
|
||||
LOG.info(
|
||||
"MeshRigger: dino mlpackage absent, IoU only")
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("MeshRigger: dino load failed (%s), IoU only", e)
|
||||
dino = None
|
||||
dino_weight = float(os.environ.get("MULTIHMR_REID_ALPHA", "0.5"))
|
||||
self._rigger = MeshRigger(
|
||||
state, dino_weight=dino_weight,
|
||||
dino_reid=dino) if enable_rigging else None
|
||||
|
||||
def start(self) -> None:
|
||||
self._thread = threading.Thread(
|
||||
|
||||
@@ -119,6 +119,43 @@ class State:
|
||||
# Multi-HMR (SMPL-X 10475 verts x N personnes)
|
||||
persons_smplx: list = field(default_factory=list) # list[SMPLXPerson]
|
||||
smplx_last_t: float = 0.0
|
||||
# SMPL-X joint positions (127 joints incl. body + jaw + eyes + hands)
|
||||
# per pid, shape (127, 3) float32, camera coords (z>0 forward).
|
||||
# Indices 25-39 = left hand 15 finger joints, 40-54 = right hand.
|
||||
persons_smplx_joints: dict = field(default_factory=dict)
|
||||
|
||||
# HaMeR MANO hand meshes (v1.2 task #26-28). Keyed by pid -> side
|
||||
# (0=left, 1=right) -> ndarray shape (778, 3) in camera-space metres.
|
||||
# Companion arrays per pid/side:
|
||||
# persons_hands_mesh_t : last_update timestamp (perf_counter)
|
||||
# persons_hands_mesh_cam_t : (3,) translation of the hand mesh root.
|
||||
persons_hands_mesh: dict = field(default_factory=dict)
|
||||
persons_hands_mesh_cam_t: dict = field(default_factory=dict)
|
||||
persons_hands_mesh_last_t: float = 0.0
|
||||
|
||||
|
The comments describe per-pid/per-side "companion arrays" for hand meshes (including a per-hand last-update timestamp), but The comments describe per-pid/per-side "companion arrays" for hand meshes (including a per-hand last-update timestamp), but `persons_hands_mesh_last_t` is a single float. If the intent is per-pid/per-side freshness tracking, this should likely be a dict keyed by pid/side (or the comment should be updated to reflect a single global timestamp).
|
||||
# ARKit body tracking (iOS ARBodyTracker app) : 91 joints world
|
||||
# space per pid. Same units as MediaPipe pose_world_landmarks
|
||||
# (metres, hip-centered). Fresh = updated within < 1 s.
|
||||
persons_arkit_joints: dict = field(default_factory=dict)
|
||||
persons_arkit_last_t: dict = field(default_factory=dict)
|
||||
|
||||
# ---- LiDAR / ICP mesh fusion (Task 8 - 2026-05-14) ----
|
||||
# Set by the LidarTCPReader poller; consumed by FusionWorker.run_once.
|
||||
# The mesh-level fusion is complementary to the ARKit *joint* fusion
|
||||
# above: joints are sparse + 60 Hz, LiDAR is dense + 5-10 Hz.
|
||||
lidar_points: object = None # np.ndarray (N, 3) float32 ARKit world; None if no frame
|
||||
lidar_timestamp_ns: int = 0
|
||||
icp_metadata: object = None # FusionMetadata from icp_fusion or None
|
||||
|
||||
# v1.3: centralised webcam source. WebcamSource owns the single
|
||||
# cv2.VideoCapture on the host and writes BGR frames here so all
|
||||
# consumers (MediaPipe Multi, Apple Vision, Multi-HMR worker,
|
||||
# HaMeR) read from one shared buffer instead of fighting over the
|
||||
# camera device. ``latest_bgr_id`` is a monotonic counter so a
|
||||
# consumer can detect new frames vs. re-reads.
|
||||
latest_bgr: object = None # np.ndarray (H, W, 3) BGR uint8
|
||||
latest_bgr_id: int = 0
|
||||
latest_bgr_t: float = 0.0
|
||||
|
||||
# Renderer
|
||||
width: int = 1280
|
||||
@@ -140,6 +177,11 @@ class State:
|
||||
# Derniere frame webcam au format JPEG bytes (pour NSImageView overlay).
|
||||
# Le pose worker la met a jour ; le HUD timer lit et l'affiche.
|
||||
last_webcam_jpeg: bytes | None = None
|
||||
# Last full RGB frame fed to Multi-HMR (uint8 HxWx3, typ. 672x672).
|
||||
# Updated by multi_hmr_worker right before inference. Read by
|
||||
# MeshRigger for DINOv2-based person re-id. None when absent.
|
||||
last_frame_rgb: np.ndarray | None = None
|
||||
last_frame_rgb_t: float = 0.0
|
||||
|
||||
_lock: threading.RLock = field(default_factory=threading.RLock, repr=False)
|
||||
|
||||
|
||||
@@ -0,0 +1,42 @@
|
||||
"""ArkitFuse stage overrides 14 body slots with ARKit data when fresh."""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import Kp3D, State
|
||||
from data_only_viz.pose_filter import PoseFilterChain
|
||||
|
||||
|
||||
def _mp33_zero_body():
|
||||
return [Kp3D(x=0.0, y=0.0, z=0.0, c=1.0) for _ in range(33)]
|
||||
|
||||
|
||||
def test_arkit_fuse_overrides_shoulder():
|
||||
state = State()
|
||||
# ARKit publishes joint 50 (left shoulder) with (1.0, 2.0, 3.0)
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[50] = (1.0, 2.0, 3.0)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter()
|
||||
chain = PoseFilterChain(state=state, enabled_stages=("arkit_fuse",))
|
||||
bodies = [_mp33_zero_body()]
|
||||
out = chain.apply(bodies, ids=[0], t_now=time.perf_counter())
|
||||
# Slot 11 = L_SHOULDER (from ARKIT91_TO_MP33).
|
||||
assert out[0][11].x == 1.0
|
||||
assert out[0][11].y == 2.0
|
||||
assert out[0][11].z == 3.0
|
||||
|
||||
|
||||
def test_arkit_fuse_skips_stale():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[50] = (9.0, 9.0, 9.0)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter() - 5.0
|
||||
chain = PoseFilterChain(state=state, enabled_stages=("arkit_fuse",))
|
||||
bodies = [_mp33_zero_body()]
|
||||
out = chain.apply(bodies, ids=[0], t_now=time.perf_counter())
|
||||
# Stale -> not applied, MediaPipe zero left intact.
|
||||
assert out[0][11].x == 0.0
|
||||
@@ -0,0 +1,32 @@
|
||||
"""ARKit 91 joints → MediaPipe Pose 33 mapping integrity."""
|
||||
from data_only_viz.arkit_joint_map import (
|
||||
ARKIT91_TO_MP33, ARKIT_PELVIS_IDX, MP33_NUM_LANDMARKS,
|
||||
)
|
||||
|
||||
|
||||
def test_mapping_is_tuple_of_pairs():
|
||||
assert isinstance(ARKIT91_TO_MP33, tuple)
|
||||
assert len(ARKIT91_TO_MP33) > 0
|
||||
for pair in ARKIT91_TO_MP33:
|
||||
assert isinstance(pair, tuple)
|
||||
assert len(pair) == 2
|
||||
|
||||
|
||||
def test_mapping_indices_in_range():
|
||||
for arkit_idx, mp33_idx in ARKIT91_TO_MP33:
|
||||
assert 0 <= arkit_idx < 91, f"arkit idx out of range: {arkit_idx}"
|
||||
assert 0 <= mp33_idx < MP33_NUM_LANDMARKS, \
|
||||
f"mp33 idx out of range: {mp33_idx}"
|
||||
|
||||
|
||||
def test_pelvis_index_valid():
|
||||
assert 0 <= ARKIT_PELVIS_IDX < 91
|
||||
|
||||
|
||||
def test_no_duplicate_mp33_targets():
|
||||
"""Each MediaPipe slot must be written by at most one ARKit joint."""
|
||||
mp33_seen = set()
|
||||
for _, mp33_idx in ARKIT91_TO_MP33:
|
||||
assert mp33_idx not in mp33_seen, \
|
||||
f"mp33 slot {mp33_idx} mapped twice"
|
||||
mp33_seen.add(mp33_idx)
|
||||
@@ -0,0 +1,77 @@
|
||||
"""Tests for the DINOv2 reid backend.
|
||||
|
||||
These tests are skipped automatically if the .mlpackage is not present
|
||||
(`scripts/convert_dinov2.py` was never run) or pyobjc is unavailable.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from data_only_viz.dino_reid import DEFAULT_MLPACKAGE, EMBED_DIM, DinoReid
|
||||
|
||||
|
||||
pytestmark = pytest.mark.skipif(
|
||||
not DEFAULT_MLPACKAGE.exists(),
|
||||
reason=f"DINOv2 mlpackage missing at {DEFAULT_MLPACKAGE}; "
|
||||
"run scripts/convert_dinov2.py first",
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def reid() -> DinoReid:
|
||||
return DinoReid()
|
||||
|
||||
|
||||
def test_is_available() -> None:
|
||||
assert DinoReid.is_available() is True
|
||||
|
||||
|
||||
def test_load(reid: DinoReid) -> None:
|
||||
assert reid is not None
|
||||
assert reid._out_name
|
||||
|
||||
|
||||
def test_embed_random_crops_different(reid: DinoReid) -> None:
|
||||
# Two crops with very different visual content. DINOv2 CLS tokens
|
||||
# for two iid noise patches are surprisingly close (~0.98), so we
|
||||
# build crops that are visually distinct: one is mostly red, the
|
||||
# other is mostly green with a striped pattern.
|
||||
a = np.zeros((224, 224, 3), dtype=np.uint8)
|
||||
a[..., 0] = 220 # red
|
||||
a[40:80, 40:180] = (240, 30, 30)
|
||||
b = np.zeros((224, 224, 3), dtype=np.uint8)
|
||||
b[..., 1] = 200 # green
|
||||
for i in range(0, 224, 16):
|
||||
b[i:i + 8] = (10, 30, 220) # blue stripes
|
||||
embs = reid.embed_crops([a, b])
|
||||
assert embs.shape == (2, EMBED_DIM)
|
||||
norms = np.linalg.norm(embs, axis=1)
|
||||
assert np.allclose(norms, 1.0, atol=1e-3)
|
||||
cos = float(np.dot(embs[0], embs[1]))
|
||||
assert cos < 0.95, f"distinct crops too similar: cos={cos:.3f}"
|
||||
|
||||
|
||||
def test_embed_identical_crops_same(reid: DinoReid) -> None:
|
||||
rng = np.random.default_rng(7)
|
||||
a = rng.integers(0, 255, size=(224, 224, 3), dtype=np.uint8)
|
||||
embs = reid.embed_crops([a, a.copy()])
|
||||
assert embs.shape == (2, EMBED_DIM)
|
||||
cos = float(np.dot(embs[0], embs[1]))
|
||||
assert cos > 0.999, f"identical crops cos={cos:.4f} (expected ~1.0)"
|
||||
|
||||
|
||||
def test_latency_batch4(reid: DinoReid) -> None:
|
||||
rng = np.random.default_rng(0)
|
||||
crops = [rng.integers(0, 255, size=(180, 90, 3), dtype=np.uint8)
|
||||
for _ in range(4)]
|
||||
# warmup
|
||||
reid.embed_crops(crops)
|
||||
t0 = time.perf_counter()
|
||||
reid.embed_crops(crops)
|
||||
dt_ms = (time.perf_counter() - t0) * 1e3
|
||||
# Spec target: < 30 ms for batch=4 on M5.
|
||||
assert dt_ms < 80.0, f"batch=4 too slow: {dt_ms:.1f} ms"
|
||||
@@ -0,0 +1,214 @@
|
||||
"""Tests for FaceFilterChain, HandFilterChain, and multi.py discrimination."""
|
||||
from __future__ import annotations
|
||||
|
||||
import random
|
||||
import time
|
||||
|
||||
import pytest
|
||||
|
||||
from data_only_viz.pose_filter import (
|
||||
FaceFilterChain,
|
||||
HandFilterChain,
|
||||
PoseFilterChain,
|
||||
)
|
||||
from data_only_viz.state import Kp3D, PoseKp
|
||||
|
||||
|
||||
def _jitter_face(n_pts: int, base_x: float, base_y: float,
|
||||
amp: float, rng: random.Random) -> list[PoseKp]:
|
||||
return [
|
||||
PoseKp(
|
||||
x=base_x + rng.uniform(-amp, amp),
|
||||
y=base_y + rng.uniform(-amp, amp),
|
||||
z=rng.uniform(-amp, amp),
|
||||
c=1.0,
|
||||
)
|
||||
for _ in range(n_pts)
|
||||
]
|
||||
|
||||
|
||||
def test_face_filter_reduces_jitter() -> None:
|
||||
chain = FaceFilterChain()
|
||||
rng = random.Random(42)
|
||||
n_pts = 68
|
||||
base_x, base_y = 0.5, 0.5
|
||||
amp = 0.01
|
||||
outputs: list[list[PoseKp]] = []
|
||||
t = 0.0
|
||||
for k in range(8):
|
||||
t += 1.0 / 30.0
|
||||
faces = [_jitter_face(n_pts, base_x, base_y, amp, rng)]
|
||||
out = chain.apply(faces, [0], t)
|
||||
outputs.append(out[0])
|
||||
# Compute variance on x of joint 0 across the last 5 frames.
|
||||
last = outputs[-5:]
|
||||
xs = [f[0].x for f in last]
|
||||
mean = sum(xs) / len(xs)
|
||||
var = sum((v - mean) ** 2 for v in xs) / len(xs)
|
||||
assert var < 0.005, f"face filter variance too high: {var}"
|
||||
|
||||
|
||||
def test_hand_filter_left_right_independent() -> None:
|
||||
chain = HandFilterChain()
|
||||
rng = random.Random(7)
|
||||
n_pts = 21
|
||||
t = 0.0
|
||||
last_l: list[PoseKp] = []
|
||||
last_r: list[PoseKp] = []
|
||||
for k in range(6):
|
||||
t += 1.0 / 30.0
|
||||
left_hand = _jitter_face(n_pts, 0.2, 0.5, 0.008, rng)
|
||||
right_hand = _jitter_face(n_pts, 0.8, 0.5, 0.008, rng)
|
||||
out = chain.apply([left_hand, right_hand], [0, 0],
|
||||
["Left", "Right"], t)
|
||||
last_l, last_r = out[0], out[1]
|
||||
# Left and right hands keep distinct positions despite same pid.
|
||||
assert abs(last_l[0].x - last_r[0].x) > 0.4
|
||||
# Filter reduced jitter on each side.
|
||||
assert 0.1 < last_l[0].x < 0.35
|
||||
assert 0.65 < last_r[0].x < 0.9
|
||||
|
||||
|
||||
def test_hand_filter_chain_wrapper_smoke() -> None:
|
||||
chain = PoseFilterChain()
|
||||
rng = random.Random(0)
|
||||
hands = [_jitter_face(21, 0.5, 0.5, 0.01, rng) for _ in range(2)]
|
||||
out = chain.apply_hand(hands, [0, 1], ["Left", "Right"], t_now=0.1)
|
||||
assert len(out) == 2
|
||||
assert len(out[0]) == 21
|
||||
|
||||
|
||||
def test_face_filter_disabled_passthrough() -> None:
|
||||
chain = FaceFilterChain(enabled_stages=())
|
||||
faces = [[PoseKp(x=0.5, y=0.5, z=0.0, c=1.0) for _ in range(68)]]
|
||||
out = chain.apply(faces, [0], t_now=0.0)
|
||||
assert out[0][0].x == 0.5
|
||||
|
||||
|
||||
def test_face_hand_latency_under_5ms() -> None:
|
||||
"""Full chain (body 33 + face 68 + hand 21x2) < 5 ms per frame."""
|
||||
body_chain = PoseFilterChain(
|
||||
enabled_stages=("median", "kalman", "lookahead", "ik"))
|
||||
face_chain = FaceFilterChain()
|
||||
hand_chain = HandFilterChain()
|
||||
rng = random.Random(0)
|
||||
body = [Kp3D(x=i * 0.01, y=i * 0.02, z=i * 0.03, c=1.0)
|
||||
for i in range(33)]
|
||||
face = _jitter_face(68, 0.5, 0.5, 0.01, rng)
|
||||
hand_l = _jitter_face(21, 0.2, 0.5, 0.01, rng)
|
||||
hand_r = _jitter_face(21, 0.8, 0.5, 0.01, rng)
|
||||
# Warm-up
|
||||
for k in range(5):
|
||||
t = k * 0.033
|
||||
body_chain.apply([body], [0], t)
|
||||
face_chain.apply([face], [0], t)
|
||||
hand_chain.apply([hand_l, hand_r], [0, 0], ["Left", "Right"], t)
|
||||
# Measure
|
||||
durs: list[float] = []
|
||||
for k in range(30):
|
||||
t = (k + 5) * 0.033
|
||||
t0 = time.perf_counter()
|
||||
body_chain.apply([body], [0], t)
|
||||
face_chain.apply([face], [0], t)
|
||||
hand_chain.apply([hand_l, hand_r], [0, 0], ["Left", "Right"], t)
|
||||
durs.append((time.perf_counter() - t0) * 1000.0)
|
||||
avg = sum(durs) / len(durs)
|
||||
# CI margin : actual M-class target is < 5 ms ; allow 25 ms in tests.
|
||||
assert avg < 25.0, f"chain too slow: {avg:.2f} ms"
|
||||
|
||||
|
||||
# ----------------------- multi.py discrimination ---------------------------
|
||||
|
||||
|
||||
def _make_body(n_visible: int) -> list[PoseKp]:
|
||||
"""Make a 33-joint body with `n_visible` high-conf joints, rest low."""
|
||||
out: list[PoseKp] = []
|
||||
for i in range(33):
|
||||
c = 1.0 if i < n_visible else 0.05
|
||||
# Spread across both x and y so the bbox has non-zero area.
|
||||
out.append(PoseKp(x=0.1 + i * 0.01, y=0.2 + i * 0.005, z=0.0, c=c))
|
||||
return out
|
||||
|
||||
|
||||
def _make_body3d(n: int = 33) -> list[Kp3D]:
|
||||
return [Kp3D(x=0.0, y=0.0, z=0.0, c=1.0) for _ in range(n)]
|
||||
|
||||
|
||||
def _instantiate_worker():
|
||||
"""Build a MultiWorker without starting the thread (skip if cv2 missing)."""
|
||||
pytest.importorskip("cv2", reason="opencv not installed")
|
||||
from data_only_viz.multi import MultiWorker
|
||||
from data_only_viz.state import State
|
||||
return MultiWorker(state=State(), camera_index=-1)
|
||||
|
||||
|
||||
def test_ghost_rejection_drops_low_visibility_body() -> None:
|
||||
w = _instantiate_worker()
|
||||
bodies = [_make_body(n_visible=5), _make_body(n_visible=25)]
|
||||
b3d = [_make_body3d(), _make_body3d()]
|
||||
ids = [0, 1]
|
||||
new_bodies, new_b3d, new_ids = w._reject_ghosts_and_nms(bodies, b3d, ids)
|
||||
assert len(new_bodies) == 1
|
||||
assert len(new_b3d) == 1
|
||||
assert new_ids == [1]
|
||||
assert w._n_ghost_dropped == 1
|
||||
|
||||
|
||||
def test_nms_keeps_best_score() -> None:
|
||||
w = _instantiate_worker()
|
||||
# Two heavily overlapping bodies, second has higher mean confidence.
|
||||
b1 = _make_body(n_visible=20)
|
||||
b2 = _make_body(n_visible=33)
|
||||
new_bodies, _, new_ids = w._reject_ghosts_and_nms([b1, b2], [], [0, 1])
|
||||
# IoU of identical bbox => one dropped, the higher-score one kept.
|
||||
assert len(new_bodies) == 1
|
||||
assert new_ids == [1]
|
||||
|
||||
|
||||
def test_pid_persistence_through_short_absence() -> None:
|
||||
w = _instantiate_worker()
|
||||
body = _make_body(n_visible=30)
|
||||
# Frame 1..30 : pid 0 present.
|
||||
for _ in range(30):
|
||||
new_ids = w._apply_pid_hysteresis([body], [0])
|
||||
assert new_ids == [0]
|
||||
# Frames 31..35 : pid 0 absent (no detection).
|
||||
for _ in range(5):
|
||||
w._apply_pid_hysteresis([], [])
|
||||
# Frame 36 : a NEW pid 9 appears at the same bbox -> should be remapped.
|
||||
new_ids = w._apply_pid_hysteresis([body], [9])
|
||||
assert new_ids == [0], f"expected hysteresis remap to 0, got {new_ids}"
|
||||
|
||||
|
||||
def test_drop_low_visibility_face() -> None:
|
||||
w = _instantiate_worker()
|
||||
# 30 valid (non-zero) + 38 zeros.
|
||||
face_bad = [
|
||||
PoseKp(x=(0.1 if i < 30 else 0.0),
|
||||
y=(0.1 if i < 30 else 0.0), z=0.0, c=1.0)
|
||||
for i in range(68)
|
||||
]
|
||||
face_ok = [
|
||||
PoseKp(x=0.1 + i * 0.001, y=0.2, z=0.0, c=1.0)
|
||||
for i in range(68)
|
||||
]
|
||||
kept, ids = w._drop_low_visibility(
|
||||
[face_bad, face_ok], [0, 1], min_visible=50, which="face")
|
||||
assert len(kept) == 1
|
||||
assert ids == [1]
|
||||
assert w._n_face_dropped == 1
|
||||
|
||||
|
||||
def test_drop_low_visibility_hand() -> None:
|
||||
w = _instantiate_worker()
|
||||
hand_bad = [PoseKp(x=0.0, y=0.0, z=0.0, c=1.0) for _ in range(21)]
|
||||
# Only 10 visible (others are zero) -> drop.
|
||||
for i in range(10):
|
||||
hand_bad[i] = PoseKp(x=0.5, y=0.5, z=0.0, c=1.0)
|
||||
hand_ok = [PoseKp(x=0.1 + i * 0.01, y=0.2, z=0.0, c=1.0)
|
||||
for i in range(21)]
|
||||
kept, ids = w._drop_low_visibility(
|
||||
[hand_bad, hand_ok], [0, 1], min_visible=15, which="hand")
|
||||
assert len(kept) == 1
|
||||
assert ids == [1]
|
||||
assert w._n_hand_dropped == 1
|
||||
@@ -0,0 +1,144 @@
|
||||
"""Tests for ICP registration of SMPL-X verts onto LiDAR point clouds."""
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
pytest.importorskip("open3d")
|
||||
|
||||
|
||||
def _synthetic_smplx_torso(n: int = 1500, seed: int = 0) -> np.ndarray:
|
||||
"""Generate a coarse capsule-like point cloud standing in for SMPL-X verts."""
|
||||
rng = np.random.RandomState(seed)
|
||||
z = rng.uniform(0.0, 1.7, size=n)
|
||||
r = 0.12 + 0.02 * rng.randn(n)
|
||||
theta = rng.uniform(0, 2 * np.pi, size=n)
|
||||
x = r * np.cos(theta)
|
||||
y = r * np.sin(theta)
|
||||
return np.stack([x, y, z], axis=1).astype(np.float32)
|
||||
|
||||
|
||||
def test_icp_recovers_small_translation() -> None:
|
||||
from data_only_viz.icp_fusion import IcpConfig, register_mesh_to_lidar
|
||||
|
||||
src = _synthetic_smplx_torso(seed=1)
|
||||
translation = np.array([0.05, 0.02, 0.10], dtype=np.float32)
|
||||
tgt = src + translation + 0.005 * np.random.RandomState(2).randn(*src.shape).astype(np.float32)
|
||||
|
||||
out = register_mesh_to_lidar(src, tgt, config=IcpConfig())
|
||||
|
||||
assert out.accepted, f"ICP should accept, got fitness={out.fitness:.3f}"
|
||||
truth = src + translation
|
||||
err_before = np.linalg.norm(src - truth, axis=1).mean()
|
||||
err_after = np.linalg.norm(out.vertices_registered - truth, axis=1).mean()
|
||||
assert err_after < err_before * 0.5, f"err before={err_before:.4f} after={err_after:.4f}"
|
||||
|
||||
|
||||
def test_icp_rejects_when_lidar_too_sparse() -> None:
|
||||
from data_only_viz.icp_fusion import IcpConfig, register_mesh_to_lidar
|
||||
|
||||
src = _synthetic_smplx_torso(seed=3)
|
||||
tgt = src[:5]
|
||||
|
||||
out = register_mesh_to_lidar(src, tgt, config=IcpConfig())
|
||||
assert not out.accepted
|
||||
np.testing.assert_array_equal(out.vertices_registered, src)
|
||||
|
||||
|
||||
def test_icp_rejects_on_nan_input() -> None:
|
||||
from data_only_viz.icp_fusion import IcpConfig, register_mesh_to_lidar
|
||||
|
||||
src = _synthetic_smplx_torso(seed=4)
|
||||
src[10, 1] = np.nan
|
||||
tgt = src.copy()
|
||||
tgt = np.nan_to_num(tgt, nan=0.0)
|
||||
|
||||
out = register_mesh_to_lidar(src, tgt, config=IcpConfig())
|
||||
assert not out.accepted
|
||||
np.testing.assert_array_equal(out.vertices_registered, src)
|
||||
|
||||
|
||||
def test_icp_preserves_dtype_and_shape() -> None:
|
||||
from data_only_viz.icp_fusion import IcpConfig, register_mesh_to_lidar
|
||||
|
||||
src = _synthetic_smplx_torso(seed=5)
|
||||
tgt = src + np.array([0.0, 0.0, 0.02], dtype=np.float32)
|
||||
out = register_mesh_to_lidar(src, tgt, config=IcpConfig())
|
||||
assert out.vertices_registered.shape == src.shape
|
||||
assert out.vertices_registered.dtype == np.float32
|
||||
|
||||
|
||||
def test_partition_lidar_by_pid_two_people() -> None:
|
||||
from data_only_viz.icp_fusion import partition_lidar_by_pid
|
||||
|
||||
src_a = _synthetic_smplx_torso(seed=10) + np.array([-0.75, 0.0, 0.0], dtype=np.float32)
|
||||
src_b = _synthetic_smplx_torso(seed=11) + np.array([+0.75, 0.0, 0.0], dtype=np.float32)
|
||||
pelvis_a = src_a.mean(axis=0)
|
||||
pelvis_b = src_b.mean(axis=0)
|
||||
|
||||
lidar = np.concatenate([
|
||||
src_a + 0.01 * np.random.RandomState(20).randn(*src_a.shape).astype(np.float32),
|
||||
src_b + 0.01 * np.random.RandomState(21).randn(*src_b.shape).astype(np.float32),
|
||||
np.array([[10.0, 10.0, 10.0]] * 100, dtype=np.float32),
|
||||
])
|
||||
|
||||
parts = partition_lidar_by_pid(lidar, pelvises={0: pelvis_a, 1: pelvis_b}, max_dist_m=1.0)
|
||||
|
||||
assert set(parts.keys()) == {0, 1}
|
||||
assert parts[0].shape[0] > 1000
|
||||
assert parts[1].shape[0] > 1000
|
||||
assert not np.any(np.linalg.norm(parts[0] - np.array([10, 10, 10]), axis=1) < 0.5)
|
||||
assert not np.any(np.linalg.norm(parts[1] - np.array([10, 10, 10]), axis=1) < 0.5)
|
||||
|
||||
|
||||
def test_partition_returns_empty_dict_when_no_pelvises() -> None:
|
||||
from data_only_viz.icp_fusion import partition_lidar_by_pid
|
||||
|
||||
out = partition_lidar_by_pid(np.zeros((100, 3), dtype=np.float32), pelvises={}, max_dist_m=1.0)
|
||||
assert out == {}
|
||||
|
||||
|
||||
def test_fusion_worker_in_place_update(monkeypatch) -> None:
|
||||
from data_only_viz.icp_fusion import FusionWorker, IcpConfig
|
||||
from data_only_viz.lidar_calib import Extrinsic
|
||||
from data_only_viz.state import SMPLXPerson, State
|
||||
|
||||
src = _synthetic_smplx_torso(seed=30)
|
||||
verts = np.zeros((10475, 3), dtype=np.float32)
|
||||
verts[: src.shape[0]] = src
|
||||
verts[5559] = src.mean(axis=0)
|
||||
|
||||
person = SMPLXPerson(pid=0, vertices_3d=verts.copy())
|
||||
state = State()
|
||||
state.persons_smplx = [person]
|
||||
|
||||
lidar_pts = src + np.array([0.0, 0.04, 0.0], dtype=np.float32)
|
||||
state.lidar_points = lidar_pts
|
||||
state.lidar_timestamp_ns = 1
|
||||
|
||||
worker = FusionWorker(
|
||||
extrinsic=Extrinsic.identity(),
|
||||
config=IcpConfig(),
|
||||
)
|
||||
metadata = worker.run_once(state)
|
||||
|
||||
assert metadata.applied == {0}
|
||||
delta = state.persons_smplx[0].vertices_3d[5559] - verts[5559]
|
||||
assert 0.02 <= delta[1] <= 0.06
|
||||
|
||||
|
||||
def test_fusion_worker_skips_when_no_lidar() -> None:
|
||||
from data_only_viz.icp_fusion import FusionWorker, IcpConfig
|
||||
from data_only_viz.lidar_calib import Extrinsic
|
||||
from data_only_viz.state import SMPLXPerson, State
|
||||
|
||||
verts = np.zeros((10475, 3), dtype=np.float32)
|
||||
verts[5559] = [0.0, 1.0, 2.0]
|
||||
state = State()
|
||||
state.persons_smplx = [SMPLXPerson(pid=0, vertices_3d=verts.copy())]
|
||||
state.lidar_points = None
|
||||
|
||||
worker = FusionWorker(extrinsic=Extrinsic.identity(), config=IcpConfig())
|
||||
metadata = worker.run_once(state)
|
||||
assert metadata.applied == set()
|
||||
np.testing.assert_array_equal(state.persons_smplx[0].vertices_3d, verts)
|
||||
@@ -0,0 +1,51 @@
|
||||
"""IphoneOSCListener writes ARKit joints to state from OSC packets."""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
from pythonosc.udp_client import SimpleUDPClient
|
||||
|
||||
from data_only_viz.state import State
|
||||
from data_only_viz.iphone_osc_listener import (
|
||||
IphoneOSCListener, IPHONE_OSC_PORT,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def listener():
|
||||
state = State()
|
||||
listener = IphoneOSCListener(state, port=IPHONE_OSC_PORT + 100)
|
||||
listener.start()
|
||||
yield state, listener
|
||||
listener.stop()
|
||||
|
||||
|
||||
def test_kp_message_updates_state(listener):
|
||||
state, lst = listener
|
||||
client = SimpleUDPClient("127.0.0.1", lst.port)
|
||||
client.send_message("/body3d/kp", [0, 1, 0.1, 0.2, 0.3])
|
||||
# Settle
|
||||
deadline = time.monotonic() + 1.0
|
||||
while time.monotonic() < deadline:
|
||||
with state.lock():
|
||||
if 0 in state.persons_arkit_joints:
|
||||
arr = state.persons_arkit_joints[0]
|
||||
if arr[1, 0] != 0.0:
|
||||
break
|
||||
time.sleep(0.02)
|
||||
with state.lock():
|
||||
assert 0 in state.persons_arkit_joints, \
|
||||
"OSC /body3d/kp message not received within 1s"
|
||||
arr = state.persons_arkit_joints[0]
|
||||
assert arr.shape == (91, 3)
|
||||
assert np.allclose(arr[1], [0.1, 0.2, 0.3])
|
||||
|
||||
|
||||
def test_gc_drops_stale_pids(listener):
|
||||
state, lst = listener
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[7] = np.zeros((91, 3), dtype=np.float32)
|
||||
state.persons_arkit_last_t[7] = time.perf_counter() - 5.0
|
||||
lst._gc_stale()
|
||||
with state.lock():
|
||||
assert 7 not in state.persons_arkit_joints
|
||||
@@ -0,0 +1,76 @@
|
||||
"""Tests for LiDAR <-> webcam extrinsic calibration persistence."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
def test_extrinsic_default_is_identity() -> None:
|
||||
from data_only_viz.lidar_calib import Extrinsic
|
||||
|
||||
e = Extrinsic.identity()
|
||||
np.testing.assert_allclose(e.T_arkit_to_cam, np.eye(4))
|
||||
assert e.confidence == 0.0
|
||||
assert e.captured_at_iso == ""
|
||||
|
||||
|
||||
def test_extrinsic_roundtrip_json(tmp_path: Path) -> None:
|
||||
from data_only_viz.lidar_calib import Extrinsic, load_extrinsic, save_extrinsic
|
||||
|
||||
T = np.eye(4)
|
||||
T[:3, 3] = [0.1, -0.05, 0.30]
|
||||
e = Extrinsic(T_arkit_to_cam=T, confidence=0.95, captured_at_iso="2026-05-14T12:00:00Z")
|
||||
|
||||
path = tmp_path / "extrinsic.json"
|
||||
save_extrinsic(e, path)
|
||||
loaded = load_extrinsic(path)
|
||||
|
||||
np.testing.assert_allclose(loaded.T_arkit_to_cam, T, atol=1e-10)
|
||||
assert loaded.confidence == pytest.approx(0.95)
|
||||
assert loaded.captured_at_iso == "2026-05-14T12:00:00Z"
|
||||
|
||||
|
||||
def test_load_extrinsic_missing_path_returns_identity(tmp_path: Path) -> None:
|
||||
from data_only_viz.lidar_calib import load_extrinsic
|
||||
|
||||
e = load_extrinsic(tmp_path / "does-not-exist.json")
|
||||
np.testing.assert_allclose(e.T_arkit_to_cam, np.eye(4))
|
||||
assert e.confidence == 0.0
|
||||
|
||||
|
||||
def test_kabsch_recovers_known_rigid_transform() -> None:
|
||||
from data_only_viz.lidar_calib import kabsch_rigid
|
||||
|
||||
rng = np.random.RandomState(7)
|
||||
src = rng.randn(20, 3)
|
||||
theta = np.deg2rad(30.0)
|
||||
R = np.array([
|
||||
[np.cos(theta), 0, np.sin(theta)],
|
||||
[0, 1, 0],
|
||||
[-np.sin(theta), 0, np.cos(theta)],
|
||||
])
|
||||
t = np.array([0.1, -0.2, 0.5])
|
||||
tgt = src @ R.T + t
|
||||
|
||||
T = kabsch_rigid(src, tgt)
|
||||
R_est = T[:3, :3]
|
||||
t_est = T[:3, 3]
|
||||
np.testing.assert_allclose(R_est, R, atol=1e-6)
|
||||
np.testing.assert_allclose(t_est, t, atol=1e-6)
|
||||
|
||||
|
||||
def test_kabsch_requires_at_least_three_pairs() -> None:
|
||||
from data_only_viz.lidar_calib import kabsch_rigid
|
||||
|
||||
with pytest.raises(ValueError, match="at least 3"):
|
||||
kabsch_rigid(np.zeros((2, 3)), np.zeros((2, 3)))
|
||||
|
||||
|
||||
def test_kabsch_rejects_mismatched_shapes() -> None:
|
||||
from data_only_viz.lidar_calib import kabsch_rigid
|
||||
|
||||
with pytest.raises(ValueError, match="shape"):
|
||||
kabsch_rigid(np.zeros((5, 3)), np.zeros((4, 3)))
|
||||
@@ -0,0 +1,111 @@
|
||||
"""Unit tests for the iPhone LiDAR TCP frame decoder."""
|
||||
from __future__ import annotations
|
||||
|
||||
import struct
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
def _encode_frame(points: np.ndarray, timestamp_ns: int) -> bytes:
|
||||
"""Mimic the iPhone-side encoder for round-trip testing."""
|
||||
n = points.shape[0]
|
||||
body = struct.pack(">Q", timestamp_ns) + struct.pack(">I", n) + points.astype("<f4").tobytes()
|
||||
header = struct.pack(">I", len(body))
|
||||
return header + body
|
||||
|
||||
|
||||
def test_decode_lidar_frame_roundtrip() -> None:
|
||||
from data_only_viz.lidar_receiver import LidarFrame, decode_frame
|
||||
|
||||
pts = np.array([[0.1, 0.2, 0.3], [-1.0, 2.0, 5.5]], dtype=np.float32)
|
||||
payload = _encode_frame(pts, timestamp_ns=1_700_000_000_000_000_000)
|
||||
|
||||
body = payload[4:]
|
||||
frame = decode_frame(body)
|
||||
|
||||
assert isinstance(frame, LidarFrame)
|
||||
assert frame.timestamp_ns == 1_700_000_000_000_000_000
|
||||
np.testing.assert_allclose(frame.points, pts, atol=1e-6)
|
||||
|
||||
|
||||
def test_decode_lidar_frame_rejects_truncated() -> None:
|
||||
from data_only_viz.lidar_receiver import decode_frame
|
||||
|
||||
pts = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
|
||||
body = (
|
||||
struct.pack(">Q", 0) +
|
||||
struct.pack(">I", 1) +
|
||||
pts.astype("<f4").tobytes()[:8] # truncated
|
||||
)
|
||||
with pytest.raises(ValueError, match="truncated"):
|
||||
decode_frame(body)
|
||||
|
||||
|
||||
def test_decode_lidar_frame_rejects_zero_vertex_count() -> None:
|
||||
from data_only_viz.lidar_receiver import decode_frame
|
||||
|
||||
body = struct.pack(">Q", 0) + struct.pack(">I", 0)
|
||||
with pytest.raises(ValueError, match="vertex_count"):
|
||||
decode_frame(body)
|
||||
|
||||
|
||||
import socket
|
||||
import threading
|
||||
import time
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def unused_tcp_port() -> int:
|
||||
"""Bind to port 0 to grab a free port from the OS, then release it."""
|
||||
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
s.bind(("127.0.0.1", 0))
|
||||
port = s.getsockname()[1]
|
||||
s.close()
|
||||
return port
|
||||
|
||||
|
||||
def _serve_one_frame(port: int, frame_bytes: bytes) -> None:
|
||||
srv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
|
||||
srv.bind(("127.0.0.1", port))
|
||||
srv.listen(1)
|
||||
conn, _ = srv.accept()
|
||||
conn.sendall(frame_bytes)
|
||||
time.sleep(0.1)
|
||||
conn.close()
|
||||
srv.close()
|
||||
|
||||
|
||||
def test_reader_grabs_latest_frame(unused_tcp_port: int) -> None:
|
||||
from data_only_viz.lidar_receiver import LidarTCPReader
|
||||
|
||||
pts = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
|
||||
frame = _encode_frame(pts, timestamp_ns=42)
|
||||
t = threading.Thread(target=_serve_one_frame, args=(unused_tcp_port, frame), daemon=True)
|
||||
t.start()
|
||||
time.sleep(0.05)
|
||||
|
||||
reader = LidarTCPReader(host="127.0.0.1", port=unused_tcp_port, connect_timeout_s=2.0)
|
||||
reader.start()
|
||||
deadline = time.monotonic() + 2.0
|
||||
latest = None
|
||||
while time.monotonic() < deadline:
|
||||
latest = reader.latest()
|
||||
if latest is not None:
|
||||
break
|
||||
time.sleep(0.02)
|
||||
reader.stop()
|
||||
t.join(timeout=1.0)
|
||||
|
||||
assert latest is not None
|
||||
assert latest.timestamp_ns == 42
|
||||
np.testing.assert_allclose(latest.points, pts, atol=1e-6)
|
||||
|
||||
|
||||
def test_reader_returns_none_before_first_frame(unused_tcp_port: int) -> None:
|
||||
from data_only_viz.lidar_receiver import LidarTCPReader
|
||||
|
||||
reader = LidarTCPReader(host="127.0.0.1", port=unused_tcp_port, connect_timeout_s=0.05)
|
||||
# Do not start it; latest() must be None.
|
||||
assert reader.latest() is None
|
||||
@@ -51,3 +51,16 @@ def test_state_mutations_are_all_under_lock():
|
||||
f"line {lineno} mutates persons_smplx without a nearby `state.lock()` context:\n"
|
||||
f"{lines[lineno - 1]}"
|
||||
)
|
||||
|
||||
|
||||
def test_predict_once_returns_none_when_coreml_unavailable(monkeypatch):
|
||||
from data_only_viz.multi_hmr_worker import MultiHMRWorker
|
||||
from data_only_viz.state import State
|
||||
# Force CoreML loader to return None
|
||||
state = State()
|
||||
worker = MultiHMRWorker(state, num_persons=1)
|
||||
monkeypatch.setattr(worker, "_get_or_load_coreml_backend", lambda: None)
|
||||
import pytest, numpy as np
|
||||
rgb = np.zeros((480, 640, 3), dtype=np.uint8)
|
||||
with pytest.raises(NotImplementedError):
|
||||
worker.predict_once(rgb)
|
||||
|
This test name says This test name says `returns_none_when_coreml_unavailable`, but the assertion expects `predict_once` to raise `NotImplementedError`. Either rename the test to match the current behavior, or (if the intended API is to return `None`) update the implementation and assert accordingly so the test communicates the contract clearly.
|
||||
|
||||
@@ -0,0 +1,41 @@
|
||||
"""arkit_pelvis_z_override : if ARKit pelvis z is fresh, replace
|
||||
the Multi-HMR pred_cam_t.z so the SMPL-X mesh sits at the actual
|
||||
distance instead of HaMeR's monocular guess.
|
||||
"""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import State
|
||||
from data_only_viz.multi_hmr_worker import arkit_pelvis_z_override
|
||||
|
||||
|
||||
def test_returns_arkit_z_when_fresh():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[1] = (0.0, 0.0, 2.5) # ARKIT_PELVIS_IDX=1, z=2.5 m
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter()
|
||||
z_pred = 5.0 # Multi-HMR ambiguous guess
|
||||
z_out = arkit_pelvis_z_override(state, pid=0, z_pred=z_pred)
|
||||
assert z_out == 2.5
|
||||
|
||||
|
||||
def test_keeps_pred_when_stale():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[1] = (0.0, 0.0, 2.5)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter() - 5.0
|
||||
z_pred = 5.0
|
||||
z_out = arkit_pelvis_z_override(state, pid=0, z_pred=z_pred)
|
||||
assert z_out == 5.0
|
||||
|
||||
|
||||
def test_keeps_pred_when_pid_missing():
|
||||
state = State()
|
||||
z_pred = 4.2
|
||||
z_out = arkit_pelvis_z_override(state, pid=99, z_pred=z_pred)
|
||||
assert z_out == 4.2
|
||||
@@ -80,8 +80,12 @@ def test_infer_latency_under_target():
|
||||
times.sort()
|
||||
median_ms = times[n // 2]
|
||||
print(f"median latency: {median_ms:.1f} ms (n={n})")
|
||||
# Target 50ms = 20fps. M5 bench shows ~29ms. Generous margin.
|
||||
assert median_ms < 80.0, f"median {median_ms:.1f}ms > 80ms target"
|
||||
# Full Multi-HMR CoreML on M5: ~120-140 ms standalone (7-8 fps),
|
||||
# see scripts/bench_multihmr_coreml.py and multihmr_coreml.py
|
||||
# docstring. The earlier 80 ms target was a backbone-only probe
|
||||
# estimate that does not hold for the full model. 250 ms gives
|
||||
# headroom for thermal/contention without masking a regression.
|
||||
assert median_ms < 250.0, f"median {median_ms:.1f}ms > 250ms target"
|
||||
|
||||
|
||||
def test_filter_threshold():
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
"""Smoke test for the Open3D dependency used by ICP fusion."""
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
open3d = pytest.importorskip("open3d")
|
||||
|
||||
|
||||
def test_open3d_pointcloud_roundtrip() -> None:
|
||||
pts = np.random.RandomState(0).randn(100, 3).astype(np.float32)
|
||||
pcd = open3d.geometry.PointCloud()
|
||||
pcd.points = open3d.utility.Vector3dVector(pts)
|
||||
out = np.asarray(pcd.points)
|
||||
assert out.shape == (100, 3)
|
||||
np.testing.assert_allclose(out, pts, atol=1e-5)
|
||||
|
||||
|
||||
def test_open3d_icp_converges_on_translated_copy() -> None:
|
||||
rng = np.random.RandomState(1)
|
||||
src = rng.randn(500, 3).astype(np.float64)
|
||||
translation = np.array([0.10, -0.05, 0.20])
|
||||
tgt = src + translation
|
||||
|
||||
src_pcd = open3d.geometry.PointCloud()
|
||||
src_pcd.points = open3d.utility.Vector3dVector(src)
|
||||
tgt_pcd = open3d.geometry.PointCloud()
|
||||
tgt_pcd.points = open3d.utility.Vector3dVector(tgt)
|
||||
|
||||
result = open3d.pipelines.registration.registration_icp(
|
||||
src_pcd, tgt_pcd, max_correspondence_distance=0.5,
|
||||
init=np.eye(4),
|
||||
estimation_method=open3d.pipelines.registration.TransformationEstimationPointToPoint(),
|
||||
)
|
||||
np.testing.assert_allclose(result.transformation[:3, 3], translation, atol=1e-3)
|
||||
@@ -0,0 +1,127 @@
|
||||
"""Tests for the 3D pose filter chain."""
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
import pytest
|
||||
|
||||
from data_only_viz.pose_filter import (
|
||||
IKConstraints,
|
||||
KalmanCV,
|
||||
LookaheadPredictor,
|
||||
MedianFilter,
|
||||
PoseFilterChain,
|
||||
L_ELBOW,
|
||||
L_SHOULDER,
|
||||
L_WRIST,
|
||||
)
|
||||
from data_only_viz.state import Kp3D
|
||||
|
||||
|
||||
def _body(values: list[tuple[float, float, float]]) -> list[Kp3D]:
|
||||
"""Build a 33-joint body, fill remaining with zeros."""
|
||||
out = [Kp3D(x=v[0], y=v[1], z=v[2], c=1.0) for v in values]
|
||||
while len(out) < 33:
|
||||
out.append(Kp3D(x=0.0, y=0.0, z=0.0, c=1.0))
|
||||
return out
|
||||
|
||||
|
||||
def test_median_filter_kills_spike() -> None:
|
||||
mf = MedianFilter(window=3)
|
||||
pid, j = 0, 0
|
||||
# Warm up
|
||||
mf.apply(pid, j, 0.0, 0.0, 0.0)
|
||||
mf.apply(pid, j, 0.01, 0.0, 0.0)
|
||||
mf.apply(pid, j, 0.02, 0.0, 0.0)
|
||||
# Spike (NaN)
|
||||
x, y, z = mf.apply(pid, j, float("nan"), float("nan"), float("nan"))
|
||||
assert math.isfinite(x) and math.isfinite(y) and math.isfinite(z)
|
||||
assert abs(x) < 0.1
|
||||
# Big outlier in x
|
||||
x2, _, _ = mf.apply(pid, j, 10.0, 0.0, 0.0)
|
||||
assert x2 < 1.0
|
||||
|
||||
|
||||
def test_kalman_converges() -> None:
|
||||
# Use a noisy constant-velocity signal : Kalman CV should converge.
|
||||
import random
|
||||
rng = random.Random(0)
|
||||
kf = KalmanCV(q=1e-3, r=1e-2)
|
||||
pid, j = 0, 0
|
||||
t = 0.0
|
||||
dt = 1.0 / 30.0
|
||||
vel = 0.3 # m/s
|
||||
errs: list[float] = []
|
||||
for i in range(120):
|
||||
t += dt
|
||||
true_pos = vel * t
|
||||
meas = true_pos + rng.gauss(0.0, 0.01) # 1 cm gaussian noise
|
||||
out = kf.step(pid, j, meas, 0.0, 0.0, t)
|
||||
if i > 30:
|
||||
errs.append(abs(out[0] - true_pos))
|
||||
mean_err = sum(errs) / len(errs)
|
||||
assert mean_err < 0.01 # ±1 cm post warmup
|
||||
|
||||
|
||||
def test_lookahead_extrapolates_constant_velocity() -> None:
|
||||
pred = LookaheadPredictor(lookahead_ms=50.0, max_velocity=5.0)
|
||||
x, y, z = pred.step(0.0, 0.0, 0.0, 1.0, 0.0, 0.0)
|
||||
assert abs(x - 0.05) < 1e-6
|
||||
assert abs(y) < 1e-9 and abs(z) < 1e-9
|
||||
# Velocity cap
|
||||
x2, _, _ = pred.step(0.0, 0.0, 0.0, 100.0, 0.0, 0.0)
|
||||
assert abs(x2 - 5.0 * 0.050) < 1e-6
|
||||
|
||||
|
||||
def test_ik_clamps_elbow_180_plus() -> None:
|
||||
ik = IKConstraints()
|
||||
# Shoulder at origin, elbow at (1,0,0), wrist BEHIND elbow at (2,0,0)
|
||||
# -> shoulder-elbow-wrist angle is 180 deg, exceeds 175 deg limit.
|
||||
coords: list[tuple[float, float, float]] = [(0.0, 0.0, 0.0)] * 33
|
||||
coords[L_SHOULDER] = (0.0, 0.0, 0.0)
|
||||
coords[L_ELBOW] = (1.0, 0.0, 0.0)
|
||||
coords[L_WRIST] = (2.0, 0.0, 0.0)
|
||||
body = _body(coords)
|
||||
out = ik.apply(body)
|
||||
p = (out[L_SHOULDER].x, out[L_SHOULDER].y, out[L_SHOULDER].z)
|
||||
e = (out[L_ELBOW].x, out[L_ELBOW].y, out[L_ELBOW].z)
|
||||
w = (out[L_WRIST].x, out[L_WRIST].y, out[L_WRIST].z)
|
||||
v_pj = (p[0] - e[0], p[1] - e[1], p[2] - e[2])
|
||||
v_cj = (w[0] - e[0], w[1] - e[1], w[2] - e[2])
|
||||
n_pj = math.sqrt(sum(c * c for c in v_pj))
|
||||
n_cj = math.sqrt(sum(c * c for c in v_cj))
|
||||
cos_a = (v_pj[0] * v_cj[0] + v_pj[1] * v_cj[1] + v_pj[2] * v_cj[2]
|
||||
) / (n_pj * n_cj)
|
||||
cos_a = max(-1.0, min(1.0, cos_a))
|
||||
ang_deg = math.degrees(math.acos(cos_a))
|
||||
assert ang_deg <= 175.5
|
||||
# Bone length preserved
|
||||
assert abs(n_cj - 1.0) < 1e-6
|
||||
|
||||
|
||||
def test_chain_no_op_when_disabled() -> None:
|
||||
chain = PoseFilterChain(enabled_stages=())
|
||||
body = _body([(0.1, 0.2, 0.3), (0.4, 0.5, 0.6)])
|
||||
out = chain.apply([body], [0], t_now=0.0)
|
||||
assert len(out) == 1
|
||||
for i in range(len(body)):
|
||||
assert out[0][i].x == body[i].x
|
||||
assert out[0][i].y == body[i].y
|
||||
assert out[0][i].z == body[i].z
|
||||
|
||||
|
||||
def test_chain_latency_under_2ms() -> None:
|
||||
chain = PoseFilterChain(
|
||||
enabled_stages=("median", "kalman", "lookahead", "ik"))
|
||||
body = _body([(i * 0.01, i * 0.02, i * 0.03) for i in range(33)])
|
||||
# Warm up internal state
|
||||
for k in range(5):
|
||||
chain.apply([body, body], [0, 1], t_now=k * 0.033)
|
||||
# Measure
|
||||
times: list[float] = []
|
||||
for k in range(30):
|
||||
chain.apply([body, body], [0, 1], t_now=(k + 5) * 0.033)
|
||||
times.append(chain.last_apply_ms)
|
||||
avg = sum(times) / len(times)
|
||||
# Generous bound for CI ; live target is <2 ms but allow 10 ms in tests.
|
||||
assert avg < 10.0
|
||||
@@ -0,0 +1,22 @@
|
||||
"""State must expose persons_arkit_joints + persons_arkit_last_t."""
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import State
|
||||
|
||||
|
||||
def test_state_has_arkit_joint_fields():
|
||||
s = State()
|
||||
assert hasattr(s, "persons_arkit_joints")
|
||||
assert hasattr(s, "persons_arkit_last_t")
|
||||
assert isinstance(s.persons_arkit_joints, dict)
|
||||
assert isinstance(s.persons_arkit_last_t, dict)
|
||||
|
||||
|
||||
def test_state_arkit_joints_writable_under_lock():
|
||||
s = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
with s.lock():
|
||||
s.persons_arkit_joints[0] = arr
|
||||
s.persons_arkit_last_t[0] = 1.5
|
||||
assert 0 in s.persons_arkit_joints
|
||||
assert s.persons_arkit_last_t[0] == 1.5
|
||||
Generated
+738
-5
@@ -2,11 +2,15 @@ version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.11"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version < '3.12' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
|
||||
"python_full_version >= '3.14' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
|
||||
"(python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
|
||||
"python_full_version < '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
|
||||
@@ -21,6 +25,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "addict"
|
||||
version = "2.4.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186, upload-time = "2020-11-21T16:21:31.416Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
version = "0.0.4"
|
||||
@@ -87,6 +100,9 @@ detrpose = [
|
||||
{ name = "transformers" },
|
||||
{ name = "xtcocotools" },
|
||||
]
|
||||
lidar = [
|
||||
{ name = "open3d" },
|
||||
]
|
||||
multihmr = [
|
||||
{ name = "einops" },
|
||||
{ name = "huggingface-hub" },
|
||||
@@ -115,6 +131,21 @@ pose = [
|
||||
{ name = "opencv-python" },
|
||||
{ name = "ultralytics" },
|
||||
]
|
||||
smplerx = [
|
||||
{ name = "einops" },
|
||||
{ name = "mmcv-lite" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python" },
|
||||
{ name = "pillow" },
|
||||
{ name = "scipy" },
|
||||
{ name = "smplx" },
|
||||
{ name = "timm" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "ultralytics" },
|
||||
{ name = "yacs" },
|
||||
]
|
||||
|
||||
[package.dev-dependencies]
|
||||
dev = [
|
||||
@@ -126,19 +157,25 @@ requires-dist = [
|
||||
{ name = "cloudpickle", marker = "extra == 'detrpose'", specifier = ">=3.0" },
|
||||
{ name = "coremltools", marker = "extra == 'pose'", specifier = ">=9.0" },
|
||||
{ name = "einops", marker = "extra == 'multihmr'", specifier = ">=0.8" },
|
||||
{ name = "einops", marker = "extra == 'smplerx'", specifier = ">=0.8" },
|
||||
{ name = "huggingface-hub", marker = "extra == 'multihmr'", specifier = ">=0.24" },
|
||||
{ name = "iopath", marker = "extra == 'detrpose'", specifier = ">=0.1.10" },
|
||||
{ name = "iopath", marker = "extra == 'multihmr'", specifier = ">=0.1.10" },
|
||||
{ name = "mediapipe", marker = "extra == 'pose'", specifier = ">=0.10.35" },
|
||||
{ name = "mmcv-lite", marker = "extra == 'smplerx'", specifier = ">=2.1" },
|
||||
{ name = "numpy", specifier = ">=1.26,<2" },
|
||||
{ name = "numpy", marker = "extra == 'multihmr'", specifier = ">=1.26,<2" },
|
||||
{ name = "numpy", marker = "extra == 'nlf'", specifier = ">=1.26" },
|
||||
{ name = "numpy", marker = "extra == 'smplerx'", specifier = ">=1.26,<2" },
|
||||
{ name = "omegaconf", marker = "extra == 'detrpose'", specifier = ">=2.3" },
|
||||
{ name = "open3d", marker = "extra == 'lidar'", specifier = ">=0.18,<0.20" },
|
||||
{ name = "opencv-python", marker = "extra == 'detrpose'", specifier = ">=4.10" },
|
||||
{ name = "opencv-python", marker = "extra == 'multihmr'", specifier = ">=4.10" },
|
||||
{ name = "opencv-python", marker = "extra == 'nlf'", specifier = ">=4.10" },
|
||||
{ name = "opencv-python", marker = "extra == 'pose'", specifier = ">=4.10" },
|
||||
{ name = "opencv-python", marker = "extra == 'smplerx'", specifier = ">=4.10" },
|
||||
{ name = "pillow", marker = "extra == 'multihmr'", specifier = ">=10.0" },
|
||||
{ name = "pillow", marker = "extra == 'smplerx'", specifier = ">=10.0" },
|
||||
{ name = "pycocotools", marker = "extra == 'detrpose'", specifier = ">=2.0" },
|
||||
{ name = "pyobjc-core", specifier = ">=10.3" },
|
||||
{ name = "pyobjc-framework-avfoundation", specifier = ">=10.3" },
|
||||
@@ -151,25 +188,42 @@ requires-dist = [
|
||||
{ name = "scipy", specifier = ">=1.13" },
|
||||
{ name = "scipy", marker = "extra == 'detrpose'", specifier = ">=1.13" },
|
||||
{ name = "scipy", marker = "extra == 'multihmr'", specifier = ">=1.13" },
|
||||
{ name = "scipy", marker = "extra == 'smplerx'", specifier = ">=1.13" },
|
||||
{ name = "smplx", marker = "extra == 'multihmr'", specifier = ">=0.1.28" },
|
||||
{ name = "smplx", marker = "extra == 'smplerx'", specifier = ">=0.1.28" },
|
||||
{ name = "timm", marker = "extra == 'smplerx'", specifier = ">=1.0" },
|
||||
{ name = "torch", marker = "extra == 'detrpose'", specifier = ">=2.4" },
|
||||
{ name = "torch", marker = "extra == 'multihmr'", specifier = ">=2.4" },
|
||||
{ name = "torch", marker = "extra == 'nlf'", specifier = ">=2.4" },
|
||||
{ name = "torch", marker = "extra == 'smplerx'", specifier = ">=2.4" },
|
||||
{ name = "torchgeometry", marker = "extra == 'multihmr'", specifier = ">=0.1.2" },
|
||||
{ name = "torchvision", marker = "extra == 'detrpose'", specifier = ">=0.19" },
|
||||
{ name = "torchvision", marker = "extra == 'multihmr'", specifier = ">=0.19" },
|
||||
{ name = "torchvision", marker = "extra == 'nlf'", specifier = ">=0.19" },
|
||||
{ name = "torchvision", marker = "extra == 'smplerx'", specifier = ">=0.19" },
|
||||
{ name = "tqdm", marker = "extra == 'multihmr'", specifier = ">=4.65" },
|
||||
{ name = "tqdm", marker = "extra == 'smplerx'", specifier = ">=4.65" },
|
||||
{ name = "transformers", marker = "extra == 'detrpose'", specifier = ">=4.40" },
|
||||
{ name = "trimesh", marker = "extra == 'multihmr'", specifier = ">=4.4" },
|
||||
{ name = "ultralytics", marker = "extra == 'pose'", specifier = ">=8.3" },
|
||||
{ name = "ultralytics", marker = "extra == 'smplerx'", specifier = ">=8.3" },
|
||||
{ name = "xtcocotools", marker = "extra == 'detrpose'", specifier = ">=1.14" },
|
||||
{ name = "yacs", marker = "extra == 'smplerx'", specifier = ">=0.1.8" },
|
||||
]
|
||||
provides-extras = ["pose", "detrpose", "nlf", "multihmr"]
|
||||
provides-extras = ["pose", "detrpose", "lidar", "nlf", "multihmr", "smplerx"]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
dev = [{ name = "pytest", specifier = ">=9.0.3" }]
|
||||
|
||||
[[package]]
|
||||
name = "blinker"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cattrs"
|
||||
version = "26.1.0"
|
||||
@@ -381,6 +435,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "configargparse"
|
||||
version = "1.7.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/3f/0b/30328302903c55218ffc5199646d0e9d28348ff26c02ba77b2ffc58d294a/configargparse-1.7.5.tar.gz", hash = "sha256:e3f9a7bb6be34d66b2e3c4a2f58e3045f8dfae47b0dc039f87bcfaa0f193fb0f", size = 53548, upload-time = "2026-03-11T02:19:38.144Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fe/19/3ba5e1b0bcc7b91aeab6c258afd70e4907d220fed3972febe38feb40db30/configargparse-1.7.5-py3-none-any.whl", hash = "sha256:1e63fdffedf94da9cd435fc13a1cd24777e76879dd2343912c1f871d4ac8c592", size = 27692, upload-time = "2026-03-11T02:19:36.442Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.3"
|
||||
@@ -604,6 +667,26 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/fa/d3c15189f7c52aaefbaea76fb012119b04b9013f4bf446cb4eb4c26c4e6b/cython-3.2.4-py3-none-any.whl", hash = "sha256:732fc93bc33ae4b14f6afaca663b916c2fdd5dcbfad7114e17fb2434eeaea45c", size = 1257078, upload-time = "2026-01-04T14:14:12.373Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dash"
|
||||
version = "4.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "flask" },
|
||||
{ name = "importlib-metadata" },
|
||||
{ name = "nest-asyncio" },
|
||||
{ name = "plotly" },
|
||||
{ name = "requests" },
|
||||
{ name = "retrying" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "werkzeug" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/44/da/a13ae3a6528bd51a6901461dbff4549c6009de203d6249a89b9a09ac5cfb/dash-4.1.0.tar.gz", hash = "sha256:17a92a87b0c1eacc025079a705e44e72cd4c5794629c0a2909942b611faeb595", size = 6927689, upload-time = "2026-03-23T20:39:47.578Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/2a/00/10b1f8b3885fc4add1853e9603af15c593fa0be20d37c158c4d811e868dc/dash-4.1.0-py3-none-any.whl", hash = "sha256:1af9f302bc14061061012cdb129b7e370d3604b12a7f730b252ad8e4966f01f7", size = 7232489, upload-time = "2026-03-23T20:39:40.658Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "einops"
|
||||
version = "0.8.2"
|
||||
@@ -613,6 +696,15 @@ wheels = [
|
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||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
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||||
|
||||
[[package]]
|
||||
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|
||||
version = "5.15.0"
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source = { registry = "https://pypi.org/simple" }
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|
||||
|
||||
[[package]]
|
||||
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|
||||
version = "5.8.0"
|
||||
@@ -2371,6 +3050,15 @@ wheels = [
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||||
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|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2026.2"
|
||||
source = { registry = "https://pypi.org/simple" }
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ultralytics"
|
||||
version = "8.4.49"
|
||||
@@ -2416,6 +3104,18 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" },
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source = { registry = "https://pypi.org/simple" }
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{ name = "markupsafe" },
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/93/8c/2e650f2afeb7ee576912636c23ddb621c91ac6a98e66dc8d29c3c69446e1/werkzeug-3.1.8-py3-none-any.whl", hash = "sha256:63a77fb8892bf28ebc3178683445222aa500e48ebad5ec77b0ad80f8726b1f50", size = 226459, upload-time = "2026-04-02T18:49:12.72Z" },
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||||
|
||||
[[package]]
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||||
name = "xtcocotools"
|
||||
version = "1.14.3"
|
||||
@@ -2432,3 +3132,36 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/80/e0/01cf7f8b3f4229568b37de680d0eaadc651d5ee36bd483b83658f49dc6c2/xtcocotools-1.14.3-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:126ca596229b2016552bf27cad01f3a2f70a3ff7576a58305a00499cb9e0057d", size = 464351, upload-time = "2023-10-19T07:52:33.199Z" },
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[[package]]
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{ name = "pyyaml" },
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[[package]]
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version = "0.43.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "platformdirs" },
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wheels = [
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[[package]]
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name = "zipp"
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version = "3.23.1"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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]
|
||||
|
||||
@@ -0,0 +1,69 @@
|
||||
# ICP LiDAR ↔ SMPL-X Dense Fusion
|
||||
|
||||
Refines Multi-HMR SMPL-X meshes using live iPhone LiDAR via point-to-plane ICP.
|
||||
|
||||
## Env vars
|
||||
|
||||
| Var | Default | Effect |
|
||||
|-----|---------|--------|
|
||||
| `ICP_FUSION` | `0` | `1` enables LiDAR receiver + FusionWorker |
|
||||
| `ICP_LIDAR_HOST` | _(required when on)_ | iPhone ARBodyTracker IP on the LAN |
|
||||
| `ICP_LIDAR_PORT` | `5500` | TCP port the iOS app publishes ARMesh on |
|
||||
| `ICP_LIDAR_EXTRINSIC` | `~/.config/av-live/lidar_extrinsic.json` | Path to persisted extrinsic JSON |
|
||||
|
||||
## Relation to ARKit joint fusion
|
||||
|
||||
ICP LiDAR fusion is **mesh-level** and complementary to the existing **joint-level** ARKit fusion (`iphone_osc_listener.py` + `pose_filter.py::ArkitFuse` + `multi_hmr_worker.arkit_pelvis_z_override`). The two run independently:
|
||||
|
||||
- **ARKit joints** (OSC :57128) — sparse (14 mapped joints), 60 Hz, fast, used to override MediaPipe pose joint slots and lock Multi-HMR pelvis Z.
|
||||
- **ICP LiDAR mesh** (TCP :5500) — dense (~thousand points), 5–10 Hz, used to register Multi-HMR SMPL-X vertices onto the real-world geometry captured by the iPhone LiDAR.
|
||||
|
||||
They can be enabled together or separately. ICP runs only when `ICP_FUSION=1`.
|
||||
|
||||
## Calibration
|
||||
|
||||
1. Launch the iPhone ARBodyTracker app and note its LAN IP.
|
||||
2. From `data_only_viz/`:
|
||||
```bash
|
||||
uv run --extra lidar python -m data_only_viz.scripts.calibrate_lidar \
|
||||
--lidar-host <iPhone IP> --lidar-port 5500 --webcam-index 0
|
||||
```
|
||||
3. The script asks for 4 stances (front / left / right / back). Hold still each time and press ENTER.
|
||||
4. The estimated extrinsic is written to `ICP_LIDAR_EXTRINSIC` (or the default path). Re-run any time the camera or iPhone moves.
|
||||
|
||||
> NOTE — as of the initial ICP MVP (Task 9), `multi_hmr_worker.predict_once` is a stub raising `NotImplementedError`. The calibration CLI runs the LiDAR reader and 4-stance loop scaffold but cannot capture the webcam pelvis side until a follow-up wires `predict_once` to the existing inference path. Track this in the next planning round.
|
||||
|
||||
## Runtime
|
||||
|
||||
```bash
|
||||
ICP_FUSION=1 ICP_LIDAR_HOST=192.168.0.42 \
|
||||
uv run --extra lidar python -m data_only_viz.main
|
||||
```
|
||||
|
||||
## Architecture (summary)
|
||||
|
||||
```
|
||||
iPhone ARBodyTracker app
|
||||
├── OSC :57128 /body3d/kp → IphoneOSCListener (ARKit joint fusion)
|
||||
└── TCP :5500 ARMeshAnchors → LidarTCPReader (ICP mesh fusion)
|
||||
↓
|
||||
FusionWorker.run_once(state)
|
||||
↓
|
||||
state.persons_smplx[*].vertices_3d
|
||||
(replaced in place when ICP accepts)
|
||||
```
|
||||
|
||||
ICP fusion runs in its own daemon thread (`IcpFusionThread`, target 8 Hz). It is opt-in (off by default) and a no-op if the LiDAR stream is absent.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- **`open3d` missing** → `cd data_only_viz && uv sync --extra lidar`
|
||||
- **No LiDAR frames** → check that the iPhone app is publishing on the expected port and that nothing else is bound to it. `nc -l 5500` from the Mac should not succeed while the app runs.
|
||||
- **ICP always rejected (`fitness < 0.30`)** → the extrinsic is likely stale; re-run calibration. Verify the iPhone is facing the same scene as the webcam.
|
||||
- **Mesh appears scaled wrong** → SMPL-X is in metres; the iPhone publishes metres. If you see a factor-1000 mismatch the iOS encoder is sending millimetres — patch the iOS app, not this code.
|
||||
- **Bench shows `latency_ms_p95 > 100`** → reduce `IcpConfig.voxel_size_m` (e.g. 0.03 m) or `max_iterations` (e.g. 20).
|
||||
- **Python `cp314` wheel failure on `uv sync --extra lidar`** → open3d does not ship cp313+ wheels yet. Use Python 3.12 (`uv venv --python 3.12`).
|
||||
|
||||
## Implementation note (Task 6 deviation)
|
||||
|
||||
`register_mesh_to_lidar` uses a two-stage coarse-to-fine ICP internally: a warm-start pass at `max(0.25 m, 5× threshold)` correspondence, then a strict pass at `IcpConfig.max_correspondence_m` (default 0.05 m). The accept/reject gate (`fitness ≥ 0.30`, `rmse ≤ 0.05 m`) is evaluated **only on the strict pass** so the contract is preserved. The warm-start makes ICP converge reliably when Multi-HMR's initial mesh sits more than 5 cm off the LiDAR surface (typical with a fresh calibration or pose change).
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,921 @@
|
||||
# iPhone LiDAR → Multi-HMR Fusion Implementation Plan
|
||||
|
||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
||||
|
||||
**Goal:** Wire the iOS ARBodyTracker app's `/body3d/kp` OSC stream into the Python pipeline so ARKit 91-joint LiDAR ground truth fixes Multi-HMR's scale ambiguity and reduces SMPL-X joint jitter via Kalman fusion.
|
||||
|
||||
**Architecture:** A new `iphone_osc_listener.py` worker subscribes to `/body3d/kp` on UDP `:57128` (port distinct from existing `:57126` body output) and writes per-pid 91-joint arrays into `state.persons_arkit_joints`. `pose_filter.py` gains an `arkit_fuse` stage that pulls the ARKit joints (low-noise ground truth) and overrides matching MediaPipe Pose 33 slots before the existing kalman/one_euro chain runs. `multi_hmr_worker.py` post-inference reads the ARKit pelvis world-z and rewrites `pred_cam_t` of the SMPL-X mesh so it lands at the actual depth instead of HaMeR's per-frame guess.
|
||||
|
||||
**Tech Stack:** Python 3.14, python-osc (OSCDispatcher), numpy, threading, pytest. Existing modules: `state.State`, `pose_filter.PoseFilterChain`, `multi_hmr_worker.MultiHMRWorker`.
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
| File | Responsibility |
|
||||
|---|---|
|
||||
| `data_only_viz/iphone_osc_listener.py` | **NEW**. ThreadingOSCUDPServer on `:57128`, routes `/body3d/kp pid joint_idx x y z` → `state.persons_arkit_joints[pid][joint_idx] = (x,y,z)`. GC entries older than 1.0s. |
|
||||
| `data_only_viz/state.py` | **MODIFY**. Add fields `persons_arkit_joints: dict[int, np.ndarray]` (91×3 per pid) + `persons_arkit_last_t: dict[int, float]`. |
|
||||
| `data_only_viz/arkit_joint_map.py` | **NEW**. Constant tuple `ARKIT91_TO_MP33` mapping ARKit joint indices → MediaPipe Pose 33 indices. |
|
||||
| `data_only_viz/pose_filter.py` | **MODIFY**. Add `"arkit_fuse"` to `ALL_STAGES`, add `ArkitFuse` class, splice in `PoseFilterChain.apply` before kalman. |
|
||||
| `data_only_viz/multi_hmr_worker.py` | **MODIFY**. After Multi-HMR inference, if `state.persons_arkit_joints[pid]` is fresh, override `pred_cam_t.z` with ARKit pelvis world-z. |
|
||||
| `data_only_viz/main.py` | **MODIFY**. Start `IphoneOSCListener` in `_start_pose_worker` regardless of any --flag (always-on, harmless if no iPhone). |
|
||||
| `data_only_viz/tests/test_iphone_osc_listener.py` | **NEW**. Unit tests: send fake OSC packets, assert state updated. |
|
||||
| `data_only_viz/tests/test_arkit_fuse.py` | **NEW**. Unit tests: fake state, run PoseFilterChain.apply, assert MP33 slots overwritten. |
|
||||
| `data_only_viz/tests/test_multihmr_arkit_z.py` | **NEW**. Unit test: fake ARKit pelvis z, assert pred_cam_t corrected. |
|
||||
|
||||
---
|
||||
|
||||
## Task 1: State fields for ARKit joints
|
||||
|
||||
**Files:**
|
||||
- Modify: `data_only_viz/state.py` (add fields)
|
||||
- Test: `data_only_viz/tests/test_state_arkit.py` (new)
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
Create `data_only_viz/tests/test_state_arkit.py`:
|
||||
|
||||
```python
|
||||
"""State must expose persons_arkit_joints + persons_arkit_last_t."""
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import State
|
||||
|
||||
|
||||
def test_state_has_arkit_joint_fields():
|
||||
s = State()
|
||||
assert hasattr(s, "persons_arkit_joints")
|
||||
assert hasattr(s, "persons_arkit_last_t")
|
||||
assert isinstance(s.persons_arkit_joints, dict)
|
||||
assert isinstance(s.persons_arkit_last_t, dict)
|
||||
|
||||
|
||||
def test_state_arkit_joints_writable_under_lock():
|
||||
s = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
with s.lock():
|
||||
s.persons_arkit_joints[0] = arr
|
||||
s.persons_arkit_last_t[0] = 1.5
|
||||
assert 0 in s.persons_arkit_joints
|
||||
assert s.persons_arkit_last_t[0] == 1.5
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run test to verify it fails**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_state_arkit.py -v`
|
||||
Expected: FAIL with `AttributeError: 'State' object has no attribute 'persons_arkit_joints'`
|
||||
|
||||
- [ ] **Step 3: Add fields to State**
|
||||
|
||||
Edit `data_only_viz/state.py`. Find the existing line with `persons_hands_mesh_last_t: float = 0.0` (around line 134) and insert below it:
|
||||
|
||||
```python
|
||||
# ARKit body tracking (iOS ARBodyTracker app) : 91 joints world
|
||||
# space per pid. Same units as MediaPipe pose_world_landmarks
|
||||
# (metres, hip-centered). Fresh = updated within < 1 s.
|
||||
persons_arkit_joints: dict = field(default_factory=dict)
|
||||
persons_arkit_last_t: dict = field(default_factory=dict)
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run test to verify it passes**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_state_arkit.py -v`
|
||||
Expected: PASS (2 passed)
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
cd /Users/electron/Documents/Projets/AV-Live
|
||||
git add data_only_viz/state.py data_only_viz/tests/test_state_arkit.py
|
||||
git commit -m "feat(state): add persons_arkit_joints + persons_arkit_last_t"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 2: ARKit → MediaPipe joint index mapping
|
||||
|
||||
**Files:**
|
||||
- Create: `data_only_viz/arkit_joint_map.py`
|
||||
- Test: `data_only_viz/tests/test_arkit_joint_map.py` (new)
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
Create `data_only_viz/tests/test_arkit_joint_map.py`:
|
||||
|
||||
```python
|
||||
"""ARKit 91 joints → MediaPipe Pose 33 mapping integrity."""
|
||||
from data_only_viz.arkit_joint_map import (
|
||||
ARKIT91_TO_MP33, ARKIT_PELVIS_IDX, MP33_NUM_LANDMARKS,
|
||||
)
|
||||
|
||||
|
||||
def test_mapping_is_tuple_of_pairs():
|
||||
assert isinstance(ARKIT91_TO_MP33, tuple)
|
||||
assert len(ARKIT91_TO_MP33) > 0
|
||||
for pair in ARKIT91_TO_MP33:
|
||||
assert isinstance(pair, tuple)
|
||||
assert len(pair) == 2
|
||||
|
||||
|
||||
def test_mapping_indices_in_range():
|
||||
for arkit_idx, mp33_idx in ARKIT91_TO_MP33:
|
||||
assert 0 <= arkit_idx < 91, f"arkit idx out of range: {arkit_idx}"
|
||||
assert 0 <= mp33_idx < MP33_NUM_LANDMARKS, \
|
||||
f"mp33 idx out of range: {mp33_idx}"
|
||||
|
||||
|
||||
def test_pelvis_index_valid():
|
||||
assert 0 <= ARKIT_PELVIS_IDX < 91
|
||||
|
||||
|
||||
def test_no_duplicate_mp33_targets():
|
||||
"""Each MediaPipe slot must be written by at most one ARKit joint."""
|
||||
mp33_seen = set()
|
||||
for _, mp33_idx in ARKIT91_TO_MP33:
|
||||
assert mp33_idx not in mp33_seen, \
|
||||
f"mp33 slot {mp33_idx} mapped twice"
|
||||
mp33_seen.add(mp33_idx)
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run test to verify it fails**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_arkit_joint_map.py -v`
|
||||
Expected: FAIL with `ModuleNotFoundError: No module named 'data_only_viz.arkit_joint_map'`
|
||||
|
||||
- [ ] **Step 3: Create the mapping module**
|
||||
|
||||
Create `data_only_viz/arkit_joint_map.py`:
|
||||
|
||||
```python
|
||||
"""ARKit ARSkeleton3D 91-joint indices → MediaPipe Pose 33 indices.
|
||||
|
||||
The ARKit ARSkeleton.JointName enum (Apple SDK) orders 91 joints
|
||||
starting with the root, hips, spine chain, shoulders, etc. We pick
|
||||
only the joints with a clear 1:1 anatomical correspondence to the
|
||||
MediaPipe Pose 33 landmark set (which is what AVLiveBody renders).
|
||||
Face/hand sub-joints (fingers, eyes) are skipped — those keep their
|
||||
existing data sources (MediaPipe Face/Hand + HaMeR MANO).
|
||||
|
||||
Reference for ARKit joint order : Apple developer docs
|
||||
"ARSkeleton.JointName" — the canonical 91-joint list runs from
|
||||
root_joint=0 down to right_handThumbEndJoint=90.
|
||||
|
||||
The selection here mirrors `multi.py::SMPLX_TO_MP33` so the same 14
|
||||
body slots are overridden by ARKit when fresh. Confidence comes
|
||||
from ARKit's tracking state but is not currently fanned out — we
|
||||
trust ARKit body tracking when its OSC frame is present.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
# MediaPipe Pose 33 cardinality (cf. mediapipe pose_world_landmarks).
|
||||
MP33_NUM_LANDMARKS = 33
|
||||
|
||||
# Pelvis = ARKit hips_joint, slot 1 in the canonical enum order.
|
||||
# Used by multi_hmr_worker for cam-translation z lock.
|
||||
ARKIT_PELVIS_IDX = 1
|
||||
|
||||
# (arkit_joint_idx, mediapipe_pose_idx). Match the body slots used
|
||||
# by the SMPL-X body fusion in multi.py.
|
||||
ARKIT91_TO_MP33: tuple[tuple[int, int], ...] = (
|
||||
(50, 11), # left_shoulder_1_joint -> L_SHOULDER
|
||||
(32, 12), # right_shoulder_1_joint -> R_SHOULDER
|
||||
(53, 13), # left_arm_joint -> L_ELBOW
|
||||
(35, 14), # right_arm_joint -> R_ELBOW
|
||||
(54, 15), # left_forearm_joint -> L_WRIST
|
||||
(36, 16), # right_forearm_joint -> R_WRIST
|
||||
(62, 23), # left_upLeg_joint -> L_HIP
|
||||
(57, 24), # right_upLeg_joint -> R_HIP
|
||||
(63, 25), # left_leg_joint -> L_KNEE
|
||||
(58, 26), # right_leg_joint -> R_KNEE
|
||||
(64, 27), # left_foot_joint -> L_ANKLE
|
||||
(59, 28), # right_foot_joint -> R_ANKLE
|
||||
(65, 31), # left_toes_joint -> L_FOOT_INDEX
|
||||
(60, 32), # right_toes_joint -> R_FOOT_INDEX
|
||||
)
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run test to verify it passes**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_arkit_joint_map.py -v`
|
||||
Expected: PASS (4 passed)
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add data_only_viz/arkit_joint_map.py data_only_viz/tests/test_arkit_joint_map.py
|
||||
git commit -m "feat(viz): arkit 91-joint -> mediapipe 33 mapping"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 3: iPhone OSC listener worker
|
||||
|
||||
**Files:**
|
||||
- Create: `data_only_viz/iphone_osc_listener.py`
|
||||
- Test: `data_only_viz/tests/test_iphone_osc_listener.py` (new)
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
Create `data_only_viz/tests/test_iphone_osc_listener.py`:
|
||||
|
||||
```python
|
||||
"""IphoneOSCListener writes ARKit joints to state from OSC packets."""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
from pythonosc.udp_client import SimpleUDPClient
|
||||
|
||||
from data_only_viz.state import State
|
||||
from data_only_viz.iphone_osc_listener import (
|
||||
IphoneOSCListener, IPHONE_OSC_PORT,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def listener():
|
||||
state = State()
|
||||
listener = IphoneOSCListener(state, port=IPHONE_OSC_PORT + 100)
|
||||
listener.start()
|
||||
yield state, listener
|
||||
listener.stop()
|
||||
|
||||
|
||||
def test_kp_message_updates_state(listener):
|
||||
state, lst = listener
|
||||
client = SimpleUDPClient("127.0.0.1", lst.port)
|
||||
client.send_message("/body3d/kp", [0, 1, 0.1, 0.2, 0.3])
|
||||
# Settle
|
||||
deadline = time.monotonic() + 1.0
|
||||
while time.monotonic() < deadline:
|
||||
with state.lock():
|
||||
if 0 in state.persons_arkit_joints:
|
||||
arr = state.persons_arkit_joints[0]
|
||||
if arr[1, 0] != 0.0:
|
||||
break
|
||||
time.sleep(0.02)
|
||||
with state.lock():
|
||||
arr = state.persons_arkit_joints[0]
|
||||
assert arr.shape == (91, 3)
|
||||
assert np.allclose(arr[1], [0.1, 0.2, 0.3])
|
||||
|
||||
|
||||
def test_gc_drops_stale_pids(listener):
|
||||
state, lst = listener
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[7] = np.zeros((91, 3), dtype=np.float32)
|
||||
state.persons_arkit_last_t[7] = time.perf_counter() - 5.0
|
||||
lst._gc_stale()
|
||||
with state.lock():
|
||||
assert 7 not in state.persons_arkit_joints
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run test to verify it fails**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_iphone_osc_listener.py -v`
|
||||
Expected: FAIL with `ModuleNotFoundError: No module named 'data_only_viz.iphone_osc_listener'`
|
||||
|
||||
- [ ] **Step 3: Implement listener**
|
||||
|
||||
Create `data_only_viz/iphone_osc_listener.py`:
|
||||
|
||||
```python
|
||||
"""OSC UDP listener for the iOS ARBodyTracker app.
|
||||
|
||||
Subscribes to /body3d/kp on UDP :57128 (distinct from MediaPipe
|
||||
output :57126). Each /body3d/kp pid joint_idx x y z message stores
|
||||
one joint of ARKit's 91-joint ARSkeleton3D into
|
||||
state.persons_arkit_joints[pid] (np.ndarray shape (91, 3), float32).
|
||||
A background GC drops pids whose last_t is older than 1.0 s.
|
||||
|
||||
Worker pattern mirrors osc_listener.OscListener.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
from pythonosc import dispatcher, osc_server
|
||||
|
||||
from .state import State
|
||||
|
||||
LOG = logging.getLogger("iphone_osc")
|
||||
|
||||
IPHONE_OSC_PORT = 57128
|
||||
ARKIT_NUM_JOINTS = 91
|
||||
STALE_SEC = 1.0
|
||||
|
||||
|
||||
class IphoneOSCListener:
|
||||
def __init__(self, state: State, host: str = "0.0.0.0",
|
||||
port: int = IPHONE_OSC_PORT) -> None:
|
||||
self.state = state
|
||||
self.host = host
|
||||
self.port = port
|
||||
self._server: osc_server.ThreadingOSCUDPServer | None = None
|
||||
self._server_thread: threading.Thread | None = None
|
||||
self._gc_thread: threading.Thread | None = None
|
||||
self._stop = threading.Event()
|
||||
|
||||
def start(self) -> None:
|
||||
d = dispatcher.Dispatcher()
|
||||
d.map("/body3d/kp", self._on_kp)
|
||||
d.map("/body3d/count", self._on_count)
|
||||
self._server = osc_server.ThreadingOSCUDPServer(
|
||||
(self.host, self.port), d)
|
||||
self._server_thread = threading.Thread(
|
||||
target=self._server.serve_forever,
|
||||
name="iphone_osc", daemon=True)
|
||||
self._server_thread.start()
|
||||
self._gc_thread = threading.Thread(
|
||||
target=self._gc_loop, name="iphone_gc", daemon=True)
|
||||
self._gc_thread.start()
|
||||
LOG.info("iphone OSC listening on %s:%d", self.host, self.port)
|
||||
|
||||
def stop(self) -> None:
|
||||
self._stop.set()
|
||||
if self._server is not None:
|
||||
self._server.shutdown()
|
||||
self._server.server_close()
|
||||
self._server = None
|
||||
|
||||
def _on_kp(self, _addr: str, *args: Any) -> None:
|
||||
if len(args) < 5:
|
||||
return
|
||||
try:
|
||||
pid = int(args[0])
|
||||
joint_idx = int(args[1])
|
||||
x = float(args[2])
|
||||
y = float(args[3])
|
||||
z = float(args[4])
|
||||
except (TypeError, ValueError):
|
||||
return
|
||||
if not (0 <= joint_idx < ARKIT_NUM_JOINTS):
|
||||
return
|
||||
with self.state.lock():
|
||||
arr = self.state.persons_arkit_joints.get(pid)
|
||||
if arr is None or arr.shape != (ARKIT_NUM_JOINTS, 3):
|
||||
arr = np.zeros((ARKIT_NUM_JOINTS, 3), dtype=np.float32)
|
||||
self.state.persons_arkit_joints[pid] = arr
|
||||
arr[joint_idx] = (x, y, z)
|
||||
self.state.persons_arkit_last_t[pid] = time.perf_counter()
|
||||
|
||||
def _on_count(self, _addr: str, *args: Any) -> None:
|
||||
# Optional : we currently don't gate on count, but parse for log.
|
||||
if not args:
|
||||
return
|
||||
try:
|
||||
n = int(args[0])
|
||||
except (TypeError, ValueError):
|
||||
return
|
||||
if not hasattr(self, "_last_hb") or \
|
||||
time.monotonic() - self._last_hb > 5.0:
|
||||
self._last_hb = time.monotonic()
|
||||
LOG.info("hb: %d ARKit bodies live", n)
|
||||
|
||||
def _gc_stale(self) -> None:
|
||||
cutoff = time.perf_counter() - STALE_SEC
|
||||
with self.state.lock():
|
||||
drop = [
|
||||
pid for pid, t in self.state.persons_arkit_last_t.items()
|
||||
if t < cutoff
|
||||
]
|
||||
for pid in drop:
|
||||
self.state.persons_arkit_joints.pop(pid, None)
|
||||
self.state.persons_arkit_last_t.pop(pid, None)
|
||||
|
||||
def _gc_loop(self) -> None:
|
||||
while not self._stop.is_set():
|
||||
self._gc_stale()
|
||||
time.sleep(0.5)
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run test to verify it passes**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_iphone_osc_listener.py -v`
|
||||
Expected: PASS (2 passed)
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add data_only_viz/iphone_osc_listener.py data_only_viz/tests/test_iphone_osc_listener.py
|
||||
git commit -m "feat(viz): iphone OSC listener -> state.persons_arkit_joints"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 4: ArkitFuse stage in PoseFilterChain
|
||||
|
||||
**Files:**
|
||||
- Modify: `data_only_viz/pose_filter.py`
|
||||
- Test: `data_only_viz/tests/test_arkit_fuse.py` (new)
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
Create `data_only_viz/tests/test_arkit_fuse.py`:
|
||||
|
||||
```python
|
||||
"""ArkitFuse stage overrides 14 body slots with ARKit data when fresh."""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import Kp3D, State
|
||||
from data_only_viz.pose_filter import PoseFilterChain
|
||||
|
||||
|
||||
def _mp33_zero_body():
|
||||
return [Kp3D(x=0.0, y=0.0, z=0.0, c=1.0) for _ in range(33)]
|
||||
|
||||
|
||||
def test_arkit_fuse_overrides_shoulder():
|
||||
state = State()
|
||||
# ARKit publishes joint 50 (left shoulder) with (1.0, 2.0, 3.0)
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[50] = (1.0, 2.0, 3.0)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter()
|
||||
chain = PoseFilterChain(state=state, enabled_stages=("arkit_fuse",))
|
||||
bodies = [_mp33_zero_body()]
|
||||
out = chain.apply(bodies, ids=[0], t_now=time.perf_counter())
|
||||
# Slot 11 = L_SHOULDER (from ARKIT91_TO_MP33).
|
||||
assert out[0][11].x == 1.0
|
||||
assert out[0][11].y == 2.0
|
||||
assert out[0][11].z == 3.0
|
||||
|
||||
|
||||
def test_arkit_fuse_skips_stale():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[50] = (9.0, 9.0, 9.0)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter() - 5.0
|
||||
chain = PoseFilterChain(state=state, enabled_stages=("arkit_fuse",))
|
||||
bodies = [_mp33_zero_body()]
|
||||
out = chain.apply(bodies, ids=[0], t_now=time.perf_counter())
|
||||
# Stale -> not applied, MediaPipe zero left intact.
|
||||
assert out[0][11].x == 0.0
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run test to verify it fails**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_arkit_fuse.py -v`
|
||||
Expected: FAIL — `arkit_fuse` not in `ALL_STAGES` so chain.enabled is empty, no fuse happens.
|
||||
|
||||
- [ ] **Step 3: Add ArkitFuse class + register stage**
|
||||
|
||||
Edit `data_only_viz/pose_filter.py`. Find the line `ALL_STAGES = (...)` near the top and replace:
|
||||
|
||||
```python
|
||||
ALL_STAGES = (
|
||||
"median", "kalman", "spring", "lookahead", "ik",
|
||||
"one_euro_joints", "one_euro_bones", "arkit_fuse",
|
||||
)
|
||||
```
|
||||
|
||||
Find the import block at the top (after `from .euro_filter import ...`) and add:
|
||||
|
||||
```python
|
||||
from .arkit_joint_map import ARKIT91_TO_MP33
|
||||
```
|
||||
|
||||
Find the `PoseFilterChain.__init__` method and after the line `self.one_euro_bones = BoneOneEuroFilter(...)` add:
|
||||
|
||||
```python
|
||||
self.arkit_fuse = ArkitFuse()
|
||||
```
|
||||
|
||||
In `PoseFilterChain.apply`, find the block defining `use_one_euro_joints = "one_euro_joints" in self.enabled` and add right after it:
|
||||
|
||||
```python
|
||||
use_arkit_fuse = "arkit_fuse" in self.enabled
|
||||
```
|
||||
|
||||
In the same method, find the outer `for body_i, kps in enumerate(bodies3d):` loop. The fuse happens BEFORE per-joint filtering (so kalman sees the fused signal). Insert this immediately after the `pid = ids[body_i] if body_i < len(ids) else -1` line:
|
||||
|
||||
```python
|
||||
if use_arkit_fuse and self.state is not None:
|
||||
kps = self.arkit_fuse.apply(self.state, pid, kps, t_now)
|
||||
```
|
||||
|
||||
Then add the `ArkitFuse` class definition. Find the line `# ============================ face / hand =================================` and insert right BEFORE it:
|
||||
|
||||
```python
|
||||
class ArkitFuse:
|
||||
"""Splice ARKit 91-joint world-space data into MediaPipe Pose 33.
|
||||
|
||||
Reads ``state.persons_arkit_joints[pid]`` (shape (91, 3)) when fresh
|
||||
(last_t within FRESH_SEC). Writes the 14 body slots covered by
|
||||
ARKIT91_TO_MP33 ; everything else (face landmarks, finger tips)
|
||||
stays MediaPipe-driven.
|
||||
"""
|
||||
|
||||
FRESH_SEC: float = 1.0
|
||||
|
||||
def apply(self, state: "State", pid: int,
|
||||
kps: list[Kp3D], t_now: float) -> list[Kp3D]:
|
||||
with state.lock():
|
||||
arr = state.persons_arkit_joints.get(pid)
|
||||
last_t = state.persons_arkit_last_t.get(pid, 0.0)
|
||||
if arr is None:
|
||||
return kps
|
||||
if t_now - last_t > self.FRESH_SEC:
|
||||
return kps
|
||||
out = list(kps)
|
||||
n = len(out)
|
||||
for arkit_idx, mp33_idx in ARKIT91_TO_MP33:
|
||||
if mp33_idx >= n:
|
||||
continue
|
||||
x = float(arr[arkit_idx, 0])
|
||||
y = float(arr[arkit_idx, 1])
|
||||
z = float(arr[arkit_idx, 2])
|
||||
old = out[mp33_idx]
|
||||
out[mp33_idx] = Kp3D(x=x, y=y, z=z, c=getattr(old, "c", 1.0))
|
||||
return out
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run test to verify it passes**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_arkit_fuse.py -v`
|
||||
Expected: PASS (2 passed)
|
||||
|
||||
- [ ] **Step 5: Regression check existing filter tests**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_pose_filter.py -v`
|
||||
Expected: PASS (all existing pose_filter tests still green)
|
||||
|
||||
- [ ] **Step 6: Commit**
|
||||
|
||||
```bash
|
||||
git add data_only_viz/pose_filter.py data_only_viz/tests/test_arkit_fuse.py
|
||||
git commit -m "feat(viz): arkit_fuse stage in PoseFilterChain"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 5: Cam-translation z lock in multi_hmr_worker
|
||||
|
||||
**Files:**
|
||||
- Modify: `data_only_viz/multi_hmr_worker.py`
|
||||
- Test: `data_only_viz/tests/test_multihmr_arkit_z.py` (new)
|
||||
|
||||
- [ ] **Step 1: Locate post-inference cam_t write site**
|
||||
|
||||
Run: `grep -n "pred_cam_t\|cam_t =" /Users/electron/Documents/Projets/AV-Live/data_only_viz/multi_hmr_worker.py | head -15`
|
||||
|
||||
You will see lines where `pred_cam_t` is read from model output and copied into `state.persons_smplx`. Look for the loop after model inference that assigns `transl=...` or `translation=...` per pid. Save the exact line numbers — Step 3 inserts the lock just AFTER the existing cam_t computation but BEFORE the state write.
|
||||
|
||||
- [ ] **Step 2: Write the failing test**
|
||||
|
||||
Create `data_only_viz/tests/test_multihmr_arkit_z.py`:
|
||||
|
||||
```python
|
||||
"""arkit_pelvis_z_override : if ARKit pelvis z is fresh, replace
|
||||
the Multi-HMR pred_cam_t.z so the SMPL-X mesh sits at the actual
|
||||
distance instead of HaMeR's monocular guess.
|
||||
"""
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
|
||||
from data_only_viz.state import State
|
||||
from data_only_viz.multi_hmr_worker import arkit_pelvis_z_override
|
||||
|
||||
|
||||
def test_returns_arkit_z_when_fresh():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[1] = (0.0, 0.0, 2.5) # ARKIT_PELVIS_IDX=1, z=2.5 m
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter()
|
||||
z_pred = 5.0 # Multi-HMR ambiguous guess
|
||||
z_out = arkit_pelvis_z_override(state, pid=0, z_pred=z_pred)
|
||||
assert z_out == 2.5
|
||||
|
||||
|
||||
def test_keeps_pred_when_stale():
|
||||
state = State()
|
||||
arr = np.zeros((91, 3), dtype=np.float32)
|
||||
arr[1] = (0.0, 0.0, 2.5)
|
||||
with state.lock():
|
||||
state.persons_arkit_joints[0] = arr
|
||||
state.persons_arkit_last_t[0] = time.perf_counter() - 5.0
|
||||
z_pred = 5.0
|
||||
z_out = arkit_pelvis_z_override(state, pid=0, z_pred=z_pred)
|
||||
assert z_out == 5.0
|
||||
|
||||
|
||||
def test_keeps_pred_when_pid_missing():
|
||||
state = State()
|
||||
z_pred = 4.2
|
||||
z_out = arkit_pelvis_z_override(state, pid=99, z_pred=z_pred)
|
||||
assert z_out == 4.2
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Run test to verify it fails**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_multihmr_arkit_z.py -v`
|
||||
Expected: FAIL — `arkit_pelvis_z_override` does not exist in multi_hmr_worker.
|
||||
|
||||
- [ ] **Step 4: Add the override function + import**
|
||||
|
||||
Edit `data_only_viz/multi_hmr_worker.py`. At the top of the file, add to the imports block:
|
||||
|
||||
```python
|
||||
from .arkit_joint_map import ARKIT_PELVIS_IDX
|
||||
```
|
||||
|
||||
Then at module level (after imports, before any class), add:
|
||||
|
||||
```python
|
||||
def arkit_pelvis_z_override(state, pid: int, z_pred: float,
|
||||
fresh_sec: float = 1.0) -> float:
|
||||
"""Return ARKit pelvis world-z if a fresh ARKit frame exists for
|
||||
this pid, otherwise return the Multi-HMR predicted z unchanged.
|
||||
|
||||
Used to resolve Multi-HMR's monocular scale ambiguity: ARKit's
|
||||
LiDAR-anchored pelvis position is ground truth in the iPhone
|
||||
world frame, which (after extrinsics calibration) is the same
|
||||
metric scale as the SMPL-X cam-space output.
|
||||
"""
|
||||
import time as _time
|
||||
with state.lock():
|
||||
arr = state.persons_arkit_joints.get(pid)
|
||||
last_t = state.persons_arkit_last_t.get(pid, 0.0)
|
||||
if arr is None:
|
||||
return float(z_pred)
|
||||
if _time.perf_counter() - last_t > fresh_sec:
|
||||
return float(z_pred)
|
||||
return float(arr[ARKIT_PELVIS_IDX, 2])
|
||||
```
|
||||
|
||||
- [ ] **Step 5: Run test to verify it passes**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/test_multihmr_arkit_z.py -v`
|
||||
Expected: PASS (3 passed)
|
||||
|
||||
- [ ] **Step 6: Wire override at the cam_t write site**
|
||||
|
||||
Now use the line numbers from Step 1. In `multi_hmr_worker.py`, find the per-person loop where `pred_cam_t` (or equivalent transl) is being written into the SMPL-X person record. For each person, after the model output's z is computed but before assignment to state, wrap the z value:
|
||||
|
||||
```python
|
||||
z_locked = arkit_pelvis_z_override(
|
||||
self.state, pid, float(transl[2]))
|
||||
transl = np.array([transl[0], transl[1], z_locked],
|
||||
dtype=transl.dtype)
|
||||
```
|
||||
|
||||
(Adapt variable names to the exact context — `transl`, `t_full`, or whatever is used. The pattern is: take the existing z, replace via the override, repack.)
|
||||
|
||||
- [ ] **Step 7: Regression smoke**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && ~/avlive-venv/bin/python -m pytest data_only_viz/tests/ -q -x`
|
||||
Expected: All tests pass (no regressions introduced).
|
||||
|
||||
- [ ] **Step 8: Commit**
|
||||
|
||||
```bash
|
||||
git add data_only_viz/multi_hmr_worker.py data_only_viz/tests/test_multihmr_arkit_z.py
|
||||
git commit -m "feat(viz): arkit pelvis z locks Multi-HMR cam translation"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 6: Always-on listener in main.py
|
||||
|
||||
**Files:**
|
||||
- Modify: `data_only_viz/main.py`
|
||||
|
||||
- [ ] **Step 1: Locate _start_pose_worker function**
|
||||
|
||||
Run: `grep -n "_start_pose_worker\|_maybe_start_webcam_source" /Users/electron/Documents/Projets/AV-Live/data_only_viz/main.py | head -5`
|
||||
|
||||
Note the line number where `_start_pose_worker` begins (around line 287).
|
||||
|
||||
- [ ] **Step 2: Insert listener startup**
|
||||
|
||||
Edit `data_only_viz/main.py`. In the body of `_start_pose_worker`, immediately after the call to `self._maybe_start_webcam_source()` (line ~289), insert:
|
||||
|
||||
```python
|
||||
# iPhone ARBodyTracker (option 2 LiDAR fusion) : always-on
|
||||
# listener on :57128. Harmless if no iPhone is broadcasting ;
|
||||
# state.persons_arkit_joints stays empty and the arkit_fuse
|
||||
# stage no-ops. Activated via POSE_FILTER=...+arkit_fuse.
|
||||
try:
|
||||
from .iphone_osc_listener import IphoneOSCListener
|
||||
self._iphone_osc = IphoneOSCListener(self._state)
|
||||
self._iphone_osc.start()
|
||||
LOG.info("worker: + iPhone OSC listener :57128")
|
||||
except Exception as e: # noqa: BLE001
|
||||
LOG.warning("iphone OSC listener start failed (%s)", e)
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Smoke test the listener starts**
|
||||
|
||||
Run: `cd /Users/electron/Documents/Projets/AV-Live && AV_LIVE_INFERENCE_OFF=1 timeout 8 ~/avlive-venv/bin/python -u -c "
|
||||
import os, sys, time
|
||||
os.environ.setdefault('AV_SHARED_CAM', '0')
|
||||
sys.path.insert(0, '/Users/electron/Documents/Projets/AV-Live')
|
||||
import threading, logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
from data_only_viz.state import State
|
||||
from data_only_viz.iphone_osc_listener import IphoneOSCListener
|
||||
s = State()
|
||||
l = IphoneOSCListener(s)
|
||||
l.start()
|
||||
time.sleep(2)
|
||||
print('listener up :', l.port)
|
||||
l.stop()
|
||||
print('stopped clean')
|
||||
" 2>&1 | tail -5`
|
||||
|
||||
Expected output:
|
||||
```
|
||||
... iphone OSC listening on 0.0.0.0:57128
|
||||
listener up : 57128
|
||||
stopped clean
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Commit**
|
||||
|
||||
```bash
|
||||
git add data_only_viz/main.py
|
||||
git commit -m "feat(viz): start iphone OSC listener in main pose worker"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 7: Documentation update
|
||||
|
||||
**Files:**
|
||||
- Modify: `CLAUDE.md` (top-level env var table)
|
||||
- Modify: `data_only_viz/CLAUDE.md` (POSE_FILTER stages table)
|
||||
|
||||
- [ ] **Step 1: Update top-level CLAUDE.md env var table**
|
||||
|
||||
Edit `/Users/electron/Documents/Projets/AV-Live/CLAUDE.md`. Find the row with `POSE_FILTER` and replace its description so it lists `arkit_fuse` as an available stage. Also append a new row for the iPhone OSC port. Use these exact replacements:
|
||||
|
||||
For the `POSE_FILTER` row, replace whatever is there with:
|
||||
|
||||
```
|
||||
| `POSE_FILTER` | `median+kalman+lookahead+ik` | filter chain stages — extra: `one_euro_joints` (joint-space CHI 2012 One Euro, inserted before kalman), `one_euro_bones` (bone-vector One Euro applied after SMPL-X fusion in multi.py), `arkit_fuse` (overrides 14 body slots with ARKit ARSkeleton3D from the iOS app, expects /body3d/kp on :57128) |
|
||||
```
|
||||
|
||||
Then below the `POSE_FILTER` row add:
|
||||
|
||||
```
|
||||
| `IPHONE_OSC_PORT` | `57128` | UDP port the iPhone ARBodyTracker app pushes /body3d/kp to (always-on listener in data_only_viz) |
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Update data_only_viz/CLAUDE.md**
|
||||
|
||||
Edit `/Users/electron/Documents/Projets/AV-Live/data_only_viz/CLAUDE.md`. Find the "Conventions" section's filtering bullet (mentions `euro_filter.py`) and append after it:
|
||||
|
||||
```
|
||||
- ARKit fusion : `iphone_osc_listener.py` consume /body3d/kp UDP :57128
|
||||
→ `state.persons_arkit_joints`. `pose_filter.py::ArkitFuse` (stage
|
||||
`arkit_fuse`) splices the 14 mapped body slots into MediaPipe pose
|
||||
before kalman ; `multi_hmr_worker::arkit_pelvis_z_override` locks the
|
||||
SMPL-X cam translation z to the ARKit pelvis. Mapping in
|
||||
`arkit_joint_map.py`.
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add CLAUDE.md data_only_viz/CLAUDE.md
|
||||
git commit -m "docs: iphone arkit fusion env + filter stage"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 8: End-to-end live smoke
|
||||
|
||||
This is a manual verification step run once after the iOS app is
|
||||
deployed to a real iPhone Pro and broadcasting on the LAN. No new
|
||||
code ; just confirm wiring + telemetry.
|
||||
|
||||
- [ ] **Step 1: Start the GrosMac pipeline (already wired)**
|
||||
|
||||
Run: `bash /Users/electron/Documents/Projets/AV-Live/launcher/apps/dist/GrosMac-AVLive.app/Contents/MacOS/bootstrap &`
|
||||
|
||||
Wait ~8 s, then verify the listener line appeared:
|
||||
|
||||
```
|
||||
grep "iphone OSC listening" ~/Library/Logs/AVLive/GrosMac-AVLive.python.log
|
||||
```
|
||||
|
||||
Expected: a line `iphone OSC listening on 0.0.0.0:57128`.
|
||||
|
||||
- [ ] **Step 2: Start ARBodyTracker on iPhone**
|
||||
|
||||
In the iOS app (deployed via Xcode):
|
||||
1. Host = your GrosMac LAN IP (`192.168.0.159`)
|
||||
2. Port = `57128`
|
||||
3. Tap **Start**
|
||||
|
||||
Stand 2 m in front of the iPhone with body fully visible. The app
|
||||
status label should say "running (LiDAR depth, env mesh)".
|
||||
|
||||
- [ ] **Step 3: Confirm ARKit state on GrosMac**
|
||||
|
||||
Run on GrosMac while iPhone is broadcasting:
|
||||
|
||||
```bash
|
||||
~/avlive-venv/bin/python -u -c "
|
||||
import time, sys
|
||||
sys.path.insert(0, '/Users/electron/Documents/Projets/AV-Live')
|
||||
from data_only_viz.iphone_osc_listener import IphoneOSCListener
|
||||
from data_only_viz.state import State
|
||||
s = State()
|
||||
l = IphoneOSCListener(s, port=57130) # alt port to avoid clash
|
||||
l.start()
|
||||
time.sleep(3)
|
||||
with s.lock():
|
||||
print('pids :', list(s.persons_arkit_joints.keys()))
|
||||
if s.persons_arkit_joints:
|
||||
pid = next(iter(s.persons_arkit_joints))
|
||||
print('pelvis :', s.persons_arkit_joints[pid][1])
|
||||
l.stop()
|
||||
"
|
||||
```
|
||||
|
||||
Expected: `pids : [0]` and `pelvis : [x, y, z]` with z > 0.
|
||||
|
||||
NOTE: this snippet uses port 57130 to avoid clashing with the live
|
||||
listener already bound to 57128. To test against the live listener,
|
||||
just open Activity Monitor's network panel for the Python process —
|
||||
you should see UDP packets flowing in on :57128.
|
||||
|
||||
- [ ] **Step 4: Enable arkit_fuse in live pipeline**
|
||||
|
||||
The pipeline currently uses `POSE_FILTER` from the bootstrap env. To
|
||||
add `arkit_fuse`, edit the GrosMac bootstrap and append the stage:
|
||||
|
||||
Edit `/Users/electron/Documents/Projets/AV-Live/launcher/apps/dist/GrosMac-AVLive.app/Contents/MacOS/bootstrap`. Find any existing `export POSE_FILTER=...` line (if absent, look around the `if [ "${ROLE}" = "source" ]; then` section for where envs are exported in the source branch) and add:
|
||||
|
||||
```bash
|
||||
export POSE_FILTER="median+kalman+lookahead+ik+one_euro_joints+one_euro_bones+arkit_fuse"
|
||||
```
|
||||
|
||||
Restart GrosMac bundle:
|
||||
|
||||
```bash
|
||||
pkill -9 -f "AVLiveBody|data_only_viz"
|
||||
open /Users/electron/Documents/Projets/AV-Live/launcher/apps/dist/GrosMac-AVLive.app
|
||||
```
|
||||
|
||||
- [ ] **Step 5: Verify fusion in log**
|
||||
|
||||
After the live launch, tail the python log:
|
||||
|
||||
```bash
|
||||
grep "PoseFilterChain stages" ~/Library/Logs/AVLive/GrosMac-AVLive.python.log
|
||||
```
|
||||
|
||||
Expected: a line ending in `'arkit_fuse')`.
|
||||
|
||||
- [ ] **Step 6: Visual confirmation**
|
||||
|
||||
In AVLiveBody window (release build), the body wireframe should
|
||||
visibly stabilise compared to a session without ARKit (shoulders/hips
|
||||
no longer wobble between MediaPipe predictions ; the mesh sits at
|
||||
the real-world depth from the camera instead of HaMeR's monocular
|
||||
guess). If you see persistent jitter, double-check via Activity
|
||||
Monitor that UDP :57128 traffic is non-zero, and that
|
||||
`state.persons_arkit_joints` has fresh entries (Step 3 snippet).
|
||||
|
||||
---
|
||||
|
||||
## Self-Review
|
||||
|
||||
Spec coverage check :
|
||||
- iphone_osc_listener.py ✅ Task 3
|
||||
- state fields ✅ Task 1
|
||||
- arkit_joint_map.py ✅ Task 2
|
||||
- pose_filter arkit_fuse ✅ Task 4
|
||||
- multi_hmr cam-z lock ✅ Task 5
|
||||
- main.py startup ✅ Task 6
|
||||
- Docs ✅ Task 7
|
||||
- Live verification ✅ Task 8
|
||||
|
||||
ICP mesh fitting is intentionally deferred — that's a separate plan
|
||||
once the joint-level fusion is proven stable.
|
||||
|
||||
Type consistency : `ARKIT91_TO_MP33` declared in Task 2, used in
|
||||
Task 4 (pose_filter import) and Task 5 (multi_hmr import of
|
||||
`ARKIT_PELVIS_IDX`). `IphoneOSCListener` defined Task 3, instantiated
|
||||
Task 6. State fields `persons_arkit_joints` and
|
||||
`persons_arkit_last_t` declared Task 1, consumed Tasks 3, 4, 5.
|
||||
|
||||
No placeholders, no TBD, every step is concrete with code or a
|
||||
copy-pasteable command. The plan compiles a hot-loop story without
|
||||
ICP, which keeps it bite-sized and shippable in a single working
|
||||
session (~1 day for a fresh engineer).
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,430 @@
|
||||
# iPhone Capture Implementation Plan (Plan 2 of 3)
|
||||
|
||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
||||
|
||||
**Goal:** Make the iOS `ARBodyTracker` app stream the camera RGB video (HEVC) over the USB transport alongside the ARKit skeleton, and retire the legacy OSC/UDP sender — so the iPhone is a self-contained, network-free capture source.
|
||||
|
||||
**Architecture:** `ARBodySession` already captures the ARKit 91-joint skeleton and sends it as `AVLiveWire` `.skeleton` frames through `USBServer` (built in Plan 1). This plan adds a `VideoEncoder` (VideoToolbox hardware HEVC) that encodes each `ARFrame.capturedImage` and sends it as `.video` frames through the same `USBServer`. The OSC/UDP fanout (`/body3d/kp` to `host:57128/57129`) and its `ContentView` config fields are removed.
|
||||
|
||||
**Tech Stack:** Swift 5.10, ARKit, VideoToolbox, CoreMedia, `AVLiveWire` (local package), iOS 17. Build verification via `xcodebuild`.
|
||||
|
||||
**Companion spec:** `docs/superpowers/specs/2026-05-18-iphone-usb-body-link-design.md`
|
||||
**Prerequisite:** Plan 1 (`docs/superpowers/plans/2026-05-18-iphone-usb-transport.md`) — merged.
|
||||
|
||||
---
|
||||
|
||||
## Verification note
|
||||
|
||||
The iOS app is an iOS-only target; it cannot be built with `swift build`
|
||||
on a macOS host. The verification command for every task is:
|
||||
|
||||
```bash
|
||||
cd iphone-arbody && xcodegen generate && \
|
||||
xcodebuild -project ARBodyTracker.xcodeproj -scheme ARBodyTracker \
|
||||
-sdk iphonesimulator -destination 'generic/platform=iOS Simulator' \
|
||||
-configuration Debug build
|
||||
```
|
||||
|
||||
Expected: `** BUILD SUCCEEDED **`. VideoToolbox HEVC encoding and ARKit
|
||||
body tracking only run fully on a physical device — runtime behavior is
|
||||
an owner on-device check, out of this plan's automated scope.
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
| File | Responsibility |
|
||||
|------|----------------|
|
||||
| `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/VideoEncoder.swift` | NEW. VideoToolbox HEVC hardware encoder: `CVPixelBuffer` → `VideoPayload` via callback |
|
||||
| `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ARBodySession.swift` | MODIFY. Add video encoding in `session(_:didUpdate:)`; remove OSC fanout |
|
||||
| `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ContentView.swift` | MODIFY. Remove OSC host/port config UI |
|
||||
|
||||
---
|
||||
|
||||
## Task 1: VideoEncoder
|
||||
|
||||
`VideoEncoder` wraps a `VTCompressionSession` configured for HEVC. It
|
||||
accepts `CVPixelBuffer`s and invokes `onPayload` with a `VideoPayload`
|
||||
(keyframe flag + the access-unit bytes; for keyframes the HEVC
|
||||
parameter sets are prepended so the Mac decoder is self-sufficient).
|
||||
|
||||
**Files:**
|
||||
- Create: `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/VideoEncoder.swift`
|
||||
|
||||
- [ ] **Step 1: Create the file**
|
||||
|
||||
```swift
|
||||
import AVLiveWire
|
||||
import CoreMedia
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
import VideoToolbox
|
||||
|
||||
/// Hardware HEVC encoder. Feed `CVPixelBuffer`s from ARKit frames in;
|
||||
/// receive one `VideoPayload` per encoded access unit via `onPayload`.
|
||||
/// Keyframe payloads carry the VPS/SPS/PPS parameter sets prepended,
|
||||
/// each as a 4-byte-length-prefixed NAL unit, so the Mac decoder can
|
||||
/// build its format description without a side channel.
|
||||
final class VideoEncoder {
|
||||
var onPayload: ((VideoPayload) -> Void)?
|
||||
|
||||
private var session: VTCompressionSession?
|
||||
private let lock = NSLock()
|
||||
|
||||
/// Create the compression session for a given frame size.
|
||||
func start(width: Int32, height: Int32) {
|
||||
stop()
|
||||
var s: VTCompressionSession?
|
||||
let status = VTCompressionSessionCreate(
|
||||
allocator: kCFAllocatorDefault,
|
||||
width: width, height: height,
|
||||
codecType: kCMVideoCodecType_HEVC,
|
||||
encoderSpecification: nil,
|
||||
imageBufferAttributes: nil,
|
||||
compressedDataAllocator: nil,
|
||||
outputCallback: nil,
|
||||
refcon: nil,
|
||||
compressionSessionOut: &s)
|
||||
guard status == noErr, let s else {
|
||||
NSLog("VideoEncoder: VTCompressionSessionCreate failed %d",
|
||||
status)
|
||||
return
|
||||
}
|
||||
VTSessionSetProperty(s, key: kVTCompressionPropertyKey_RealTime,
|
||||
value: kCFBooleanTrue)
|
||||
VTSessionSetProperty(s,
|
||||
key: kVTCompressionPropertyKey_AllowFrameReordering,
|
||||
value: kCFBooleanFalse)
|
||||
VTSessionSetProperty(s,
|
||||
key: kVTCompressionPropertyKey_MaxKeyFrameInterval,
|
||||
value: 30 as CFNumber)
|
||||
VTCompressionSessionPrepareToEncodeFrames(s)
|
||||
session = s
|
||||
}
|
||||
|
||||
/// Encode one frame. `pts` is the capture timestamp in seconds.
|
||||
func encode(_ pixelBuffer: CVPixelBuffer, pts: Double) {
|
||||
lock.lock(); let s = session; lock.unlock()
|
||||
guard let s else { return }
|
||||
let time = CMTime(seconds: pts, preferredTimescale: 1_000_000)
|
||||
VTCompressionSessionEncodeFrame(
|
||||
s, imageBuffer: pixelBuffer, presentationTimeStamp: time,
|
||||
duration: .invalid, frameProperties: nil,
|
||||
infoFlagsOut: nil) { [weak self] status, _, sample in
|
||||
guard status == noErr, let sample else { return }
|
||||
self?.handle(sample)
|
||||
}
|
||||
}
|
||||
|
||||
func stop() {
|
||||
lock.lock(); let s = session; session = nil; lock.unlock()
|
||||
if let s {
|
||||
VTCompressionSessionInvalidate(s)
|
||||
}
|
||||
}
|
||||
|
||||
deinit { stop() }
|
||||
|
||||
// MARK: - Sample → VideoPayload
|
||||
|
||||
private func handle(_ sample: CMSampleBuffer) {
|
||||
let isKeyframe = !Self.notSync(sample)
|
||||
var out = Data()
|
||||
if isKeyframe, let fmt = CMSampleBufferGetFormatDescription(sample) {
|
||||
out.append(Self.parameterSets(fmt))
|
||||
}
|
||||
if let block = CMSampleBufferGetDataBuffer(sample) {
|
||||
var lengthOut = 0
|
||||
var ptr: UnsafeMutablePointer<Int8>?
|
||||
if CMBlockBufferGetDataPointer(
|
||||
block, atOffset: 0, lengthAtOffsetOut: nil,
|
||||
totalLengthOut: &lengthOut,
|
||||
dataPointerOut: &ptr) == noErr, let ptr {
|
||||
out.append(UnsafeBufferPointer(
|
||||
start: UnsafeRawPointer(ptr)
|
||||
.assumingMemoryBound(to: UInt8.self),
|
||||
count: lengthOut))
|
||||
}
|
||||
}
|
||||
guard !out.isEmpty else { return }
|
||||
onPayload?(VideoPayload(isKeyframe: isKeyframe, data: out))
|
||||
}
|
||||
|
||||
/// True if the sample is NOT a sync (key) frame.
|
||||
private static func notSync(_ sample: CMSampleBuffer) -> Bool {
|
||||
guard let arr = CMSampleBufferGetSampleAttachmentsArray(
|
||||
sample, createIfNecessary: false),
|
||||
CFArrayGetCount(arr) > 0 else { return false }
|
||||
let dict = unsafeBitCast(CFArrayGetValueAtIndex(arr, 0),
|
||||
to: CFDictionary.self)
|
||||
let key = Unmanaged.passUnretained(
|
||||
kCMSampleAttachmentKey_NotSync).toOpaque()
|
||||
return CFDictionaryContainsKey(dict, key)
|
||||
}
|
||||
|
||||
/// Concatenate the HEVC VPS/SPS/PPS parameter sets, each as a
|
||||
/// 4-byte big-endian length prefix followed by the NAL bytes.
|
||||
private static func parameterSets(
|
||||
_ fmt: CMFormatDescription) -> Data {
|
||||
var count = 0
|
||||
CMVideoFormatDescriptionGetHEVCParameterSetAtIndex(
|
||||
fmt, parameterSetIndex: 0, parameterSetPointerOut: nil,
|
||||
parameterSetSizeOut: nil, parameterSetCountOut: &count,
|
||||
nalUnitHeaderLengthOut: nil)
|
||||
var data = Data()
|
||||
for i in 0..<count {
|
||||
var ptr: UnsafePointer<UInt8>?
|
||||
var size = 0
|
||||
guard CMVideoFormatDescriptionGetHEVCParameterSetAtIndex(
|
||||
fmt, parameterSetIndex: i,
|
||||
parameterSetPointerOut: &ptr,
|
||||
parameterSetSizeOut: &size,
|
||||
parameterSetCountOut: nil,
|
||||
nalUnitHeaderLengthOut: nil) == noErr,
|
||||
let ptr else { continue }
|
||||
var be = UInt32(size).bigEndian
|
||||
withUnsafeBytes(of: &be) { data.append(contentsOf: $0) }
|
||||
data.append(UnsafeBufferPointer(start: ptr, count: size))
|
||||
}
|
||||
return data
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Verify it compiles**
|
||||
|
||||
Run the verification command from the "Verification note" section above.
|
||||
Expected: `** BUILD SUCCEEDED **` (the new file compiles within the
|
||||
target).
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/VideoEncoder.swift
|
||||
git commit -m "feat(ios): VideoToolbox HEVC encoder"
|
||||
```
|
||||
|
||||
(subject ≤50 chars; add a short body — the commit hook rejects
|
||||
subject-only messages; no AI attribution.)
|
||||
|
||||
---
|
||||
|
||||
## Task 2: Stream video from ARBodySession
|
||||
|
||||
Wire `VideoEncoder` into the ARKit frame loop. On each `didUpdate`
|
||||
frame already processed for skeletons, also encode `capturedImage` and
|
||||
send the resulting `VideoPayload` over the existing `USBServer`.
|
||||
|
||||
**Files:**
|
||||
- Modify: `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ARBodySession.swift`
|
||||
|
||||
- [ ] **Step 1: Add the encoder property and its payload wiring**
|
||||
|
||||
In `ARBodySession`, next to `private let usb = USBServer()` (currently
|
||||
line 45), add:
|
||||
|
||||
```swift
|
||||
private let videoEncoder = VideoEncoder()
|
||||
private var videoStarted = false
|
||||
```
|
||||
|
||||
In `init()`, after the `usb.onState = { ... }` block, add the encoder
|
||||
output wiring:
|
||||
|
||||
```swift
|
||||
videoEncoder.onPayload = { [weak self] payload in
|
||||
Task { @MainActor in
|
||||
guard let self, self.usbState == .connected else {
|
||||
return
|
||||
}
|
||||
self.usb.send(tag: .video, pid: -1,
|
||||
timestamp: self.lastFrameTime,
|
||||
payload: payload.encoded())
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Encode the captured image in the frame loop**
|
||||
|
||||
In `session(_:didUpdate:)`, inside the `Task { @MainActor in ... }`
|
||||
block, after `self.lastFrameTime = t` and before the anchor loop,
|
||||
add video encoding:
|
||||
|
||||
```swift
|
||||
// Start the encoder lazily once the first frame size is
|
||||
// known, then encode every (throttled) frame.
|
||||
let img = frame.capturedImage
|
||||
let w = Int32(CVPixelBufferGetWidth(img))
|
||||
let h = Int32(CVPixelBufferGetHeight(img))
|
||||
if !self.videoStarted, w > 0, h > 0 {
|
||||
self.videoEncoder.start(width: w, height: h)
|
||||
self.videoStarted = true
|
||||
}
|
||||
if self.videoStarted {
|
||||
self.videoEncoder.encode(img, pts: t)
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Stop the encoder on stop()**
|
||||
|
||||
In `stop()`, after `usb.stop()`, add:
|
||||
|
||||
```swift
|
||||
videoEncoder.stop()
|
||||
videoStarted = false
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Verify it compiles**
|
||||
|
||||
Run the verification command. Expected: `** BUILD SUCCEEDED **`.
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ARBodySession.swift
|
||||
git commit -m "feat(ios): stream HEVC video over USB"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 3: Remove the legacy OSC sender
|
||||
|
||||
The OSC/UDP fanout is the network dependency the autonomous USB design
|
||||
removes. Delete it from `ARBodySession`.
|
||||
|
||||
**Files:**
|
||||
- Modify: `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ARBodySession.swift`
|
||||
|
||||
- [ ] **Step 1: Delete OSC members and methods**
|
||||
|
||||
In `ARBodySession.swift`, delete:
|
||||
- the stored properties `host`, `pythonPort`, `swiftPort` (currently
|
||||
lines 39-41) and `conns` (line 44);
|
||||
- the `configure(host:pythonPort:swiftPort:sendEnvMesh:)` method —
|
||||
replace it with a parameterless `configure(sendEnvMesh:)`:
|
||||
```swift
|
||||
func configure(sendEnvMesh: Bool) {
|
||||
self.sendEnvMesh = sendEnvMesh
|
||||
}
|
||||
```
|
||||
- the call `openUDP()` in `start()`;
|
||||
- in `stop()`, the lines `for c in conns { c.cancel() }` and
|
||||
`conns.removeAll()`;
|
||||
- the entire `// MARK: - UDP fanout` section: `openUDP()` and
|
||||
`sendDatagram(_:)`;
|
||||
- the `publishJoints(pid:body:)` method and its call site in
|
||||
`session(_:didUpdate:)` (`self.publishJoints(pid: count, body: body)`);
|
||||
- the `sendOSC(addr:args:)` call for `/body3d/count` in
|
||||
`session(_:didUpdate:)`;
|
||||
- the `// MARK: - OSC minimal encoder` section: the `OSCArg` enum,
|
||||
`sendOSC(addr:args:)`, and `appendOSCString(_:into:)`.
|
||||
|
||||
After deletion, `Network` is still needed (`USBServer` uses it
|
||||
indirectly — actually `USBServer` imports its own `Network`). Remove
|
||||
`import Network` from `ARBodySession.swift` only if no symbol from it
|
||||
remains; if `NWConnection`/`NWEndpoint` no longer appear in the file,
|
||||
remove the import.
|
||||
|
||||
- [ ] **Step 2: Verify it compiles**
|
||||
|
||||
Run the verification command. Expected: `** BUILD SUCCEEDED **`. If the
|
||||
build reports an unused `import` or an unresolved symbol, fix it
|
||||
minimally (remove the dead import, or keep it if still referenced).
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ARBodySession.swift
|
||||
git commit -m "refactor(ios): drop legacy OSC sender"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 4: Simplify ContentView
|
||||
|
||||
`ContentView` exposes OSC host/port text fields that no longer have a
|
||||
backing. Remove them; keep the USB status indicator and Start/Stop.
|
||||
|
||||
**Files:**
|
||||
- Modify: `iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ContentView.swift`
|
||||
|
||||
- [ ] **Step 1: Remove OSC state and UI**
|
||||
|
||||
In `ContentView`:
|
||||
- delete the `@State` properties `host`, `pythonPort`, `swiftPort`
|
||||
(currently lines 7-9);
|
||||
- in `controlPanel`, delete the `HStack { Text("Host") ... }` block and
|
||||
the `HStack { Text("Py") ... Text("Swift") ... }` block (the two
|
||||
rows of OSC text fields, currently lines 85-102);
|
||||
- in the Start/Stop button action, replace the `session.configure(
|
||||
host:pythonPort:swiftPort:sendEnvMesh:)` call with
|
||||
`session.configure(sendEnvMesh: sendEnvMesh)`.
|
||||
|
||||
Keep: `sendEnvMesh` toggle, Start/Stop button, status text, the USB
|
||||
status dot/label, and the bodies/frames/jointsPerSec line.
|
||||
|
||||
- [ ] **Step 2: Verify it compiles**
|
||||
|
||||
Run the verification command. Expected: `** BUILD SUCCEEDED **`. The
|
||||
three `#Preview` blocks at the end of the file construct
|
||||
`ContentView(useMockBackground:useMockSkeleton:)` — those parameters
|
||||
are unaffected; the previews must still compile.
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/ContentView.swift
|
||||
git commit -m "refactor(ios): drop OSC config from ContentView"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 5: Final build verification
|
||||
|
||||
- [ ] **Step 1: Full clean build**
|
||||
|
||||
```bash
|
||||
cd iphone-arbody && xcodegen generate && \
|
||||
xcodebuild -project ARBodyTracker.xcodeproj -scheme ARBodyTracker \
|
||||
-sdk iphonesimulator -destination 'generic/platform=iOS Simulator' \
|
||||
-configuration Debug clean build
|
||||
```
|
||||
|
||||
Expected: `** BUILD SUCCEEDED **`, zero errors.
|
||||
|
||||
- [ ] **Step 2: Confirm no OSC references remain**
|
||||
|
||||
```bash
|
||||
grep -rn -E "OSC|57128|57129|openUDP|sendDatagram" \
|
||||
iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/
|
||||
```
|
||||
|
||||
Expected: no matches in `ARBodySession.swift` or `ContentView.swift`.
|
||||
(`USBServer.swift` and `VideoEncoder.swift` never had OSC.) Comments
|
||||
mentioning history are acceptable; live OSC code is not.
|
||||
|
||||
- [ ] **Step 3: Commit any cleanup** (only if Step 2 found stragglers)
|
||||
|
||||
---
|
||||
|
||||
## Self-Review
|
||||
|
||||
- **Spec coverage:** This plan implements the spec's `VideoEncoder`
|
||||
unit and the `ARBodySession` "exposes video frames / OSC sender
|
||||
removed" requirement. `ARBodySession` already builds and sends
|
||||
`SkeletonPayload` over `USBServer` (delivered via the recovery
|
||||
branch + Plan 1), so no skeleton-path task is needed. `ContentView`
|
||||
simplification follows from OSC removal.
|
||||
- **Placeholders:** none — every step has concrete code or an exact
|
||||
command and expected output.
|
||||
- **Type consistency:** `VideoPayload`, `FrameTag.video`,
|
||||
`USBServer.send(tag:pid:timestamp:payload:)` are used consistently
|
||||
with their Plan 1 / `AVLiveWire` definitions. `VideoEncoder.start`
|
||||
takes `Int32` width/height matching `CVPixelBufferGetWidth`'s `Int`
|
||||
cast to `Int32`.
|
||||
- **Known risk:** the `VideoEncoder` VideoToolbox code compiles on the
|
||||
simulator but HEVC hardware encoding and the exact access-unit /
|
||||
parameter-set byte layout can only be validated on a physical
|
||||
device. Plan 3's `VideoDecoder` must agree with the framing chosen
|
||||
here (length-prefixed parameter sets prepended to keyframe payloads);
|
||||
this is the integration seam to verify when Plan 3 is built.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,549 @@
|
||||
# macOS Multi-HMR Mesh Implementation Plan (Plan 3b of 3)
|
||||
|
||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
||||
|
||||
**Goal:** Add the dense-mesh half of the macOS pipeline — run Multi-HMR (CoreML) on the USB video stream inside `AVLiveBody`, fuse the result with the ARKit skeleton, and render the SMPL-X body mesh.
|
||||
|
||||
**Architecture:** `VideoDecoder` (Plan 3a) already turns `.video` frames into `CVPixelBuffer`s. This plan adds `MultiHMRCoreML`, a Swift wrapper around the bundled `multihmr_full_672_s.mlpackage`: it preprocesses a pixel buffer into the model's two `MLMultiArray` inputs, runs inference, and parses up to 4 detected persons (10475-vertex SMPL-X meshes). `BodyFusion` associates each mesh with the ARKit skeleton from `USBSkeletonConsumer` and corrects pelvis depth. The existing `MeshRenderer` (which already renders 10475-vertex SMPL-X meshes from its OSC server) is fed from the fusion output.
|
||||
|
||||
**Tech Stack:** Swift 5, macOS 15, CoreML, CoreVideo/CoreImage, RealityKit, `AVLiveWire`, `XCTest`. Build verifies on the host with `swift build` / `swift test`.
|
||||
|
||||
**Companion spec:** `docs/superpowers/specs/2026-05-18-iphone-usb-body-link-design.md`
|
||||
**Prerequisites:** Plan 1, 2, 3a (merged); the working CoreML model (voie 2).
|
||||
|
||||
---
|
||||
|
||||
## The model — exact I/O contract
|
||||
|
||||
The reference implementation is `data_only_viz/multihmr_coreml.py` (Python, validated). The Swift wrapper must mirror it:
|
||||
|
||||
- **File:** `~/.cache/av-live-multihmr/multihmr_full_672_s.mlpackage` (204 MB, FP32). Not in git (`*.mlpackage` is gitignored).
|
||||
- **Load:** an `.mlpackage` must be compiled to `.mlmodelc` (`MLModel.compileModel(at:)`) before `MLModel(contentsOf:configuration:)`. Use `MLComputeUnits.cpuAndGPU` (benched best: ~139 ms standalone).
|
||||
- **Inputs** (an `MLDictionaryFeatureProvider` with two `MLMultiArray`s):
|
||||
- `"image"` — shape `[1, 3, 672, 672]`, Float32, RGB, **ImageNet-normalized**: `(v - mean) / std`, mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]` per channel. Feeding raw `[0,1]` collapses all scores (the "0 detections" bug).
|
||||
- `"cam_K"` — shape `[1, 3, 3]`, Float32, camera intrinsics.
|
||||
- **Outputs** (fixed K=4 persons):
|
||||
- `var_2420` — v3d `[4, 10475, 3]` vertices
|
||||
- `var_2423` — transl `[4, 1, 3]` pelvis translation
|
||||
- `var_2436` — scores `[4]`
|
||||
- `var_2439` — betas `[4, 10]`, `var_2442` — expression `[4, 10]` (unused here)
|
||||
- **Detection:** keep person `k` when `scores[k] >= 0.3`.
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
| File | Responsibility |
|
||||
|------|----------------|
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/Resources/multihmr_full_672_s.mlpackage` | NEW (build input, gitignored). Copied from `~/.cache/av-live-multihmr/` by a setup step |
|
||||
| `launcher/AV-Live-Body/Package.swift` | MODIFY. Declare the `.mlpackage` as a `.copy` resource |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/MultiHMRCoreML.swift` | NEW. Load the model; `CVPixelBuffer` → inputs → inference → `[MultiHMRPerson]` |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/BodyFusion.swift` | NEW. Associate ARKit skeleton ↔ Multi-HMR person; pelvis-depth correction |
|
||||
| `launcher/AV-Live-Body/Tests/AVLiveBodyTests/BodyFusionTests.swift` | NEW. Pure association/correction logic tests |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift` | MODIFY. Drive `VideoDecoder` → `MultiHMRCoreML` → `BodyFusion` → `MeshRenderer` |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/MeshRenderer.swift` | REFERENCE — reuse its existing `updatePersons`-style entry point for 10475-vertex meshes |
|
||||
|
||||
---
|
||||
|
||||
## Task 1: Bundle the model + loader
|
||||
|
||||
**Files:**
|
||||
- Create (copy): `launcher/AV-Live-Body/Sources/AVLiveBody/Resources/multihmr_full_672_s.mlpackage`
|
||||
- Modify: `launcher/AV-Live-Body/Package.swift`
|
||||
|
||||
- [ ] **Step 1: Copy the model into the package resources**
|
||||
|
||||
The model is a build input that cannot live in git. Copy it:
|
||||
|
||||
```bash
|
||||
mkdir -p launcher/AV-Live-Body/Sources/AVLiveBody/Resources
|
||||
cp -R ~/.cache/av-live-multihmr/multihmr_full_672_s.mlpackage \
|
||||
launcher/AV-Live-Body/Sources/AVLiveBody/Resources/
|
||||
```
|
||||
|
||||
Verify it is gitignored (root `.gitignore` has `*.mlpackage`):
|
||||
|
||||
```bash
|
||||
git check-ignore launcher/AV-Live-Body/Sources/AVLiveBody/Resources/multihmr_full_672_s.mlpackage
|
||||
```
|
||||
|
||||
Expected: the path is printed (it is ignored — it must NOT be committed).
|
||||
|
||||
If the source file is absent, STOP — Plan 3b is blocked until voie 2's
|
||||
`.mlpackage` is regenerated (`data_only_viz/scripts/coreml_full_probe.py`).
|
||||
|
||||
- [ ] **Step 2: Declare the resource in Package.swift**
|
||||
|
||||
In `launcher/AV-Live-Body/Package.swift`, add to the `AVLiveBody`
|
||||
executable target's `resources:` array (next to the existing
|
||||
`smplx_faces.bin` / `scene.metal` copies):
|
||||
|
||||
```swift
|
||||
.copy("Resources/multihmr_full_672_s.mlpackage"),
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Verify the build still resolves resources**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build`
|
||||
Expected: build succeeds; the `.mlpackage` is copied into the bundle.
|
||||
|
||||
- [ ] **Step 4: Commit (Package.swift only — the model is gitignored)**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Package.swift
|
||||
git commit -m "build(av-live-body): bundle Multi-HMR mlpackage"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 2: MultiHMRCoreML
|
||||
|
||||
`MultiHMRCoreML` loads the bundled model, preprocesses a `CVPixelBuffer`
|
||||
into the two model inputs, runs inference, and returns detected persons.
|
||||
|
||||
**Files:**
|
||||
- Create: `launcher/AV-Live-Body/Sources/AVLiveBody/MultiHMRCoreML.swift`
|
||||
|
||||
- [ ] **Step 1: Write the implementation**
|
||||
|
||||
`launcher/AV-Live-Body/Sources/AVLiveBody/MultiHMRCoreML.swift`:
|
||||
|
||||
```swift
|
||||
import CoreML
|
||||
import CoreVideo
|
||||
import CoreImage
|
||||
import Foundation
|
||||
|
||||
/// One detected SMPL-X body from Multi-HMR.
|
||||
struct MultiHMRPerson {
|
||||
var vertices: [SIMD3<Float>] // 10475 SMPL-X verts, model space
|
||||
var translation: SIMD3<Float> // pelvis translation
|
||||
var score: Float
|
||||
}
|
||||
|
||||
/// CoreML wrapper around the bundled `multihmr_full_672_s.mlpackage`.
|
||||
/// Mirrors `data_only_viz/multihmr_coreml.py`: two MLMultiArray inputs
|
||||
/// (`image` 1x3x672x672 ImageNet-normalized, `cam_K` 1x3x3), fixed
|
||||
/// K=4 person outputs.
|
||||
final class MultiHMRCoreML {
|
||||
static let inputSize = 672
|
||||
static let vertexCount = 10475
|
||||
static let maxPersons = 4
|
||||
private static let detThreshold: Float = 0.3
|
||||
private static let normMean: [Float] = [0.485, 0.456, 0.406]
|
||||
private static let normStd: [Float] = [0.229, 0.224, 0.225]
|
||||
|
||||
private let model: MLModel
|
||||
private let ciContext = CIContext()
|
||||
|
||||
/// Loads the bundled model. Returns nil if the resource or load
|
||||
/// fails — callers fall back to skeleton-only rendering.
|
||||
init?() {
|
||||
guard let url = Bundle.module.url(
|
||||
forResource: "multihmr_full_672_s",
|
||||
withExtension: "mlpackage") else {
|
||||
NSLog("MultiHMRCoreML: mlpackage resource missing")
|
||||
return nil
|
||||
}
|
||||
let cfg = MLModelConfiguration()
|
||||
cfg.computeUnits = .cpuAndGPU
|
||||
do {
|
||||
let compiled = try MLModel.compileModel(at: url)
|
||||
model = try MLModel(contentsOf: compiled, configuration: cfg)
|
||||
} catch {
|
||||
NSLog("MultiHMRCoreML: load failed %@",
|
||||
String(describing: error))
|
||||
return nil
|
||||
}
|
||||
}
|
||||
|
||||
/// Run inference on one camera frame. `cameraK` is the 3x3 camera
|
||||
/// intrinsics row-major.
|
||||
func infer(_ pixelBuffer: CVPixelBuffer,
|
||||
cameraK: [Float]) -> [MultiHMRPerson] {
|
||||
guard let image = makeImageInput(pixelBuffer),
|
||||
let k = makeKInput(cameraK) else { return [] }
|
||||
let inputs: [String: MLFeatureValue] = [
|
||||
"image": MLFeatureValue(multiArray: image),
|
||||
"cam_K": MLFeatureValue(multiArray: k),
|
||||
]
|
||||
guard let provider = try? MLDictionaryFeatureProvider(
|
||||
dictionary: inputs),
|
||||
let out = try? model.prediction(from: provider) else {
|
||||
return []
|
||||
}
|
||||
return parse(out)
|
||||
}
|
||||
|
||||
// MARK: - Input preprocessing
|
||||
|
||||
/// `CVPixelBuffer` -> [1,3,672,672] Float32, RGB, ImageNet-normed.
|
||||
private func makeImageInput(_ pb: CVPixelBuffer) -> MLMultiArray? {
|
||||
let n = Self.inputSize
|
||||
// Resize to n x n BGRA via CoreImage.
|
||||
let ci = CIImage(cvPixelBuffer: pb)
|
||||
let sx = CGFloat(n) / ci.extent.width
|
||||
let sy = CGFloat(n) / ci.extent.height
|
||||
let scaled = ci.transformed(
|
||||
by: CGAffineTransform(scaleX: sx, y: sy))
|
||||
var dst: CVPixelBuffer?
|
||||
CVPixelBufferCreate(kCFAllocatorDefault, n, n,
|
||||
kCVPixelFormatType_32BGRA, nil, &dst)
|
||||
guard let dst else { return nil }
|
||||
ciContext.render(scaled, to: dst)
|
||||
CVPixelBufferLockBaseAddress(dst, .readOnly)
|
||||
defer { CVPixelBufferUnlockBaseAddress(dst, .readOnly) }
|
||||
guard let base = CVPixelBufferGetBaseAddress(dst) else {
|
||||
return nil
|
||||
}
|
||||
let rowBytes = CVPixelBufferGetBytesPerRow(dst)
|
||||
let px = base.assumingMemoryBound(to: UInt8.self)
|
||||
guard let arr = try? MLMultiArray(
|
||||
shape: [1, 3, NSNumber(value: n), NSNumber(value: n)],
|
||||
dataType: .float32) else { return nil }
|
||||
let ptr = arr.dataPointer.assumingMemoryBound(to: Float.self)
|
||||
let plane = n * n
|
||||
for y in 0..<n {
|
||||
for x in 0..<n {
|
||||
let p = y * rowBytes + x * 4 // BGRA
|
||||
let b = Float(px[p]) / 255.0
|
||||
let g = Float(px[p + 1]) / 255.0
|
||||
let r = Float(px[p + 2]) / 255.0
|
||||
let idx = y * n + x
|
||||
ptr[idx] =
|
||||
(r - Self.normMean[0]) / Self.normStd[0]
|
||||
ptr[plane + idx] =
|
||||
(g - Self.normMean[1]) / Self.normStd[1]
|
||||
ptr[2 * plane + idx] =
|
||||
(b - Self.normMean[2]) / Self.normStd[2]
|
||||
}
|
||||
}
|
||||
return arr
|
||||
}
|
||||
|
||||
/// 9 row-major intrinsics -> [1,3,3] Float32.
|
||||
private func makeKInput(_ k: [Float]) -> MLMultiArray? {
|
||||
guard k.count == 9,
|
||||
let arr = try? MLMultiArray(
|
||||
shape: [1, 3, 3], dataType: .float32) else { return nil }
|
||||
let ptr = arr.dataPointer.assumingMemoryBound(to: Float.self)
|
||||
for i in 0..<9 { ptr[i] = k[i] }
|
||||
return arr
|
||||
}
|
||||
|
||||
// MARK: - Output parsing
|
||||
|
||||
private func parse(_ out: MLFeatureProvider) -> [MultiHMRPerson] {
|
||||
guard let v3d = out.featureValue(for: "var_2420")?
|
||||
.multiArrayValue,
|
||||
let transl = out.featureValue(for: "var_2423")?
|
||||
.multiArrayValue,
|
||||
let scores = out.featureValue(for: "var_2436")?
|
||||
.multiArrayValue else { return [] }
|
||||
var persons: [MultiHMRPerson] = []
|
||||
let vc = Self.vertexCount
|
||||
for k in 0..<Self.maxPersons {
|
||||
let score = scores[k].floatValue
|
||||
if score < Self.detThreshold { continue }
|
||||
var verts = [SIMD3<Float>](
|
||||
repeating: .zero, count: vc)
|
||||
let base = k * vc * 3
|
||||
for i in 0..<vc {
|
||||
let o = base + i * 3
|
||||
verts[i] = SIMD3(v3d[o].floatValue,
|
||||
v3d[o + 1].floatValue,
|
||||
v3d[o + 2].floatValue)
|
||||
}
|
||||
let tb = k * 3
|
||||
persons.append(MultiHMRPerson(
|
||||
vertices: verts,
|
||||
translation: SIMD3(transl[tb].floatValue,
|
||||
transl[tb + 1].floatValue,
|
||||
transl[tb + 2].floatValue),
|
||||
score: score))
|
||||
}
|
||||
return persons
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Verify it compiles**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build`
|
||||
Expected: build succeeds. `Bundle.module` exists because the target
|
||||
has resources. If a CoreML signature differs on this SDK, fix
|
||||
minimally; the I/O contract (two named MLMultiArray inputs, the three
|
||||
named outputs) must be preserved.
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/MultiHMRCoreML.swift
|
||||
git commit -m "feat(av-live-body): Multi-HMR CoreML wrapper"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 3: BodyFusion
|
||||
|
||||
`BodyFusion` is pure logic: given the ARKit 91-joint skeleton frames
|
||||
(from `USBSkeletonConsumer`) and the Multi-HMR persons, associate each
|
||||
mesh with the nearest skeleton and lock the mesh pelvis depth to the
|
||||
ARKit pelvis Z (the LiDAR-anchored, metrically-correct depth).
|
||||
|
||||
**Files:**
|
||||
- Create: `launcher/AV-Live-Body/Sources/AVLiveBody/BodyFusion.swift`
|
||||
- Test: `launcher/AV-Live-Body/Tests/AVLiveBodyTests/BodyFusionTests.swift`
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
`launcher/AV-Live-Body/Tests/AVLiveBodyTests/BodyFusionTests.swift`:
|
||||
|
||||
```swift
|
||||
import XCTest
|
||||
import AVLiveWire
|
||||
@testable import AVLiveBody
|
||||
|
||||
final class BodyFusionTests: XCTestCase {
|
||||
private func skeleton(pelvisZ: Float)
|
||||
-> ArkitOSCListener.ArkitBodyFrame {
|
||||
var f = ArkitOSCListener.ArkitBodyFrame()
|
||||
f.pid = 0
|
||||
// ARKit body skeleton joint 0 is the hips/pelvis root.
|
||||
f.joints[0] = SIMD3(0, 0, pelvisZ)
|
||||
f.hasJoint[0] = true
|
||||
return f
|
||||
}
|
||||
|
||||
func testPelvisDepthOverride() {
|
||||
let mesh = MultiHMRPerson(
|
||||
vertices: [SIMD3<Float>](repeating: .zero, count: 1),
|
||||
translation: SIMD3(0, 0, -1.0), score: 0.9)
|
||||
let fused = BodyFusion.fuse(
|
||||
persons: [mesh], skeletons: [0: skeleton(pelvisZ: -2.5)])
|
||||
XCTAssertEqual(fused.count, 1)
|
||||
XCTAssertEqual(fused[0].translation.z, -2.5, accuracy: 1e-4)
|
||||
}
|
||||
|
||||
func testPassthroughWhenNoSkeleton() {
|
||||
let mesh = MultiHMRPerson(
|
||||
vertices: [SIMD3<Float>](repeating: .zero, count: 1),
|
||||
translation: SIMD3(0, 0, -1.0), score: 0.9)
|
||||
let fused = BodyFusion.fuse(persons: [mesh], skeletons: [:])
|
||||
XCTAssertEqual(fused[0].translation.z, -1.0, accuracy: 1e-4)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run the test to verify it fails**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test --filter BodyFusionTests`
|
||||
Expected: FAIL — `BodyFusion` undefined.
|
||||
|
||||
- [ ] **Step 3: Write the implementation**
|
||||
|
||||
`launcher/AV-Live-Body/Sources/AVLiveBody/BodyFusion.swift`:
|
||||
|
||||
```swift
|
||||
import AVLiveWire
|
||||
import Foundation
|
||||
import simd
|
||||
|
||||
/// Associates Multi-HMR meshes with ARKit skeletons and corrects the
|
||||
/// mesh pelvis depth. Pure, stateless — unit-testable.
|
||||
enum BodyFusion {
|
||||
/// ARKit body skeleton root (hips) joint index.
|
||||
static let pelvisJoint = 0
|
||||
|
||||
/// Returns the persons with `translation.z` of each replaced by
|
||||
/// the matching ARKit skeleton's pelvis Z when one is available.
|
||||
/// Association is nearest-translation; with a single skeleton and
|
||||
/// a single dominant person this is exact.
|
||||
static func fuse(persons: [MultiHMRPerson],
|
||||
skeletons: [Int: ArkitOSCListener.ArkitBodyFrame])
|
||||
-> [MultiHMRPerson] {
|
||||
// Collect candidate ARKit pelvis depths.
|
||||
let pelvisZs: [Float] = skeletons.values.compactMap { s in
|
||||
guard pelvisJoint < s.hasJoint.count,
|
||||
s.hasJoint[pelvisJoint] else { return nil }
|
||||
return s.joints[pelvisJoint].z
|
||||
}
|
||||
guard !pelvisZs.isEmpty else { return persons }
|
||||
// Highest-scoring person is the primary; lock its depth to the
|
||||
// single ARKit skeleton (ARKit tracks one body). Others pass
|
||||
// through unchanged.
|
||||
guard let primaryIdx = persons.indices.max(by: {
|
||||
persons[$0].score < persons[$1].score
|
||||
}) else { return persons }
|
||||
var out = persons
|
||||
out[primaryIdx].translation.z = pelvisZs[0]
|
||||
return out
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run the test to verify it passes**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test --filter BodyFusionTests`
|
||||
Expected: PASS, 2 tests.
|
||||
|
||||
- [ ] **Step 5: Run the full suite + commit**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test` — Expected: all pass
|
||||
(9: prior 7 + 2).
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/BodyFusion.swift launcher/AV-Live-Body/Tests/AVLiveBodyTests/BodyFusionTests.swift
|
||||
git commit -m "feat(av-live-body): ARKit-to-mesh body fusion"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 4: Wire the mesh pipeline
|
||||
|
||||
Drive the chain: `USBSkeletonConsumer.onVideo` → `VideoDecoder` →
|
||||
`MultiHMRCoreML` → `BodyFusion` → `MeshRenderer`.
|
||||
|
||||
**Files:**
|
||||
- Modify: `launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift`
|
||||
- Reference: `launcher/AV-Live-Body/Sources/AVLiveBody/MeshRenderer.swift`
|
||||
|
||||
- [ ] **Step 1: Read `MeshRenderer.swift`**
|
||||
|
||||
Identify the method that ingests SMPL-X persons (the OSC `SMPX` server
|
||||
path calls it — likely `updatePersons(_:)` taking per-person 10475
|
||||
vertex arrays). Note its exact signature and the vertex/coordinate
|
||||
convention it expects.
|
||||
|
||||
- [ ] **Step 2: Add the mesh pipeline to `USBSkeletonConsumer`**
|
||||
|
||||
Give `USBSkeletonConsumer` an optional mesh pipeline. Add stored
|
||||
properties:
|
||||
|
||||
```swift
|
||||
private let videoDecoder = VideoDecoder()
|
||||
private let multiHMR = MultiHMRCoreML()
|
||||
/// Set by the app to receive fused mesh persons on the main queue.
|
||||
var onMeshPersons: (([MultiHMRPerson]) -> Void)?
|
||||
/// Camera intrinsics (row-major 3x3) for Multi-HMR; a sane default
|
||||
/// is the iPhone main-camera focal at 672 px until a `.meta` frame
|
||||
/// supplies the real values.
|
||||
private var cameraK: [Float] = [
|
||||
672, 0, 336,
|
||||
0, 672, 336,
|
||||
0, 0, 1,
|
||||
]
|
||||
```
|
||||
|
||||
In `init()` (or `start()`), wire the decoder to the model:
|
||||
|
||||
```swift
|
||||
videoDecoder.onFrame = { [weak self] pixelBuffer in
|
||||
guard let self else { return }
|
||||
guard let hmr = self.multiHMR else { return }
|
||||
let raw = hmr.infer(pixelBuffer, cameraK: self.cameraK)
|
||||
let latestSkeletons = self.bodies
|
||||
let fused = BodyFusion.fuse(
|
||||
persons: raw, skeletons: latestSkeletons)
|
||||
DispatchQueue.main.async {
|
||||
self.onMeshPersons?(fused)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Change the `.video` branch of `route(_:)` so it feeds the decoder
|
||||
instead of only forwarding the payload:
|
||||
|
||||
```swift
|
||||
case .video:
|
||||
guard let payload =
|
||||
VideoPayload(decoding: frame.payload) else { return }
|
||||
videoDecoder.decode(payload)
|
||||
```
|
||||
|
||||
(`onVideo` may be kept for diagnostics or removed — keeping it is
|
||||
harmless; if removed, delete its declaration too.)
|
||||
|
||||
- [ ] **Step 3: Feed `MeshRenderer` from the app**
|
||||
|
||||
In `AVLiveBodyApp.swift`'s `ContentView` `.onAppear` (or where the
|
||||
renderers are wired), set `usbConsumer.onMeshPersons` to call the
|
||||
`MeshRenderer` ingest method identified in Step 1, converting
|
||||
`[MultiHMRPerson]` (vertices + fused translation) into whatever shape
|
||||
that method expects. The translation from `BodyFusion` positions each
|
||||
mesh; the 10475 vertices are the SMPL-X surface.
|
||||
|
||||
If `MeshRenderer`'s ingest method is not reachable from `ContentView`
|
||||
(it may be owned by `BodyView`), thread an `onMeshPersons` closure the
|
||||
same way `usbConsumer` itself was threaded in Plan 3a Task 4.
|
||||
|
||||
- [ ] **Step 4: Verify build + tests**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build && swift test`
|
||||
Expected: build succeeds; all tests pass (9).
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift launcher/AV-Live-Body/Sources/AVLiveBody/AVLiveBodyApp.swift
|
||||
git commit -m "feat(av-live-body): wire Multi-HMR mesh pipeline"
|
||||
```
|
||||
|
||||
(Include `BodyView.swift` in the commit if Step 3 threaded a closure
|
||||
through it.)
|
||||
|
||||
---
|
||||
|
||||
## Task 5: Final verification
|
||||
|
||||
- [ ] **Step 1: Clean build + full test suite**
|
||||
|
||||
```bash
|
||||
cd launcher/AV-Live-Body && swift build && swift test
|
||||
```
|
||||
|
||||
Expected: build succeeds; all 9 tests pass.
|
||||
|
||||
- [ ] **Step 2: Confirm the model is bundled, not committed**
|
||||
|
||||
```bash
|
||||
git status --porcelain | grep mlpackage || echo "model not staged — correct"
|
||||
ls -d launcher/AV-Live-Body/Sources/AVLiveBody/Resources/multihmr_full_672_s.mlpackage
|
||||
```
|
||||
|
||||
Expected: the model directory exists on disk but is NOT staged in git.
|
||||
|
||||
---
|
||||
|
||||
## Self-Review
|
||||
|
||||
- **Spec coverage:** This plan implements the spec's `MultiHMRCoreML`,
|
||||
`BodyFusion`, and the mesh-render wiring — the dense-mesh half
|
||||
deferred from Plan 3a. With Plan 3b done, the full spec
|
||||
(`USBClient`/`StreamDemuxer`/`VideoDecoder`/`MultiHMRCoreML`/
|
||||
`BodyFusion` + renderers) is covered.
|
||||
- **Placeholders:** none — new files carry complete code; modify tasks
|
||||
cite exact files and instruct reading `MeshRenderer.swift` for the
|
||||
one signature this plan cannot reproduce blind.
|
||||
- **Type consistency:** `MultiHMRPerson` is produced by
|
||||
`MultiHMRCoreML.infer` and consumed by `BodyFusion.fuse` and
|
||||
`onMeshPersons`. The model I/O names (`image`, `cam_K`, `var_2420`,
|
||||
`var_2423`, `var_2436`) match `multihmr_coreml.py` exactly.
|
||||
- **Known risks:**
|
||||
1. **Bundling 204 MB** — `swift build` copies the `.mlpackage` into
|
||||
the app bundle; build is slower and the app is large. Acceptable
|
||||
per the owner's decision (FP32, validated).
|
||||
2. **`CVPixelBuffer` → tensor** — the CoreImage resize + manual
|
||||
BGRA→normalized-CHW packing is the most error-prone code here and
|
||||
needs on-device validation against `multihmr_coreml.py`'s output
|
||||
on the same frame. It also runs per-frame on the CPU — a perf
|
||||
hotspot; revisit with `vImage`/Metal if frame rate suffers.
|
||||
3. **~7.6 fps** — Multi-HMR is far below 30 fps; the mesh layer is
|
||||
slow while the skeleton (Plan 3a) stays real-time. `MeshRenderer`
|
||||
already interpolates meshes to ~60 fps between worker frames —
|
||||
reuse that, do not block the USB read loop on inference (the
|
||||
`videoDecoder.onFrame` callback already runs off the main queue).
|
||||
4. **`cameraK`** — a placeholder intrinsics matrix is used until a
|
||||
`.meta` frame carries the real values; absolute depth scale will
|
||||
be approximate until then. A future iteration should send camera
|
||||
intrinsics from the iPhone in a `.meta` frame.
|
||||
@@ -0,0 +1,655 @@
|
||||
# macOS USB Consumer Implementation Plan (Plan 3a of 3)
|
||||
|
||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
||||
|
||||
**Goal:** Make the macOS `AVLiveBody` app consume the iPhone's USB stream — connect via `usbmuxd`, demux `AVLiveWire` frames, render the 91-joint skeleton on screen, and HEVC-decode the video — without the Multi-HMR dense-mesh step (deferred to Plan 3b).
|
||||
|
||||
**Architecture:** A new `USBSkeletonConsumer` runs the blocking `UnixMuxTransport`/`USBClient` read loop on a dedicated background thread, feeds bytes through `StreamDemuxer`, and republishes `.skeleton` frames as `@Published` ARKit-shaped body frames plus a `.video` callback. `Skeleton3DRenderer`'s long-standing `// TODO: render yellow ARKit markers` (line 138) is completed so the 91-joint USB skeleton actually draws. A new `VideoDecoder` turns `.video` `VideoPayload`s into `CVPixelBuffer`s via `VTDecompressionSession`.
|
||||
|
||||
**Tech Stack:** Swift 5 (language mode v5), macOS 15, RealityKit, VideoToolbox, `AVLiveWire` (already a dependency of `AV-Live-Body`), `XCTest`.
|
||||
|
||||
**Companion spec:** `docs/superpowers/specs/2026-05-18-iphone-usb-body-link-design.md`
|
||||
**Prerequisites:** Plan 1 (transport, merged), Plan 2 (iOS capture, merged).
|
||||
**Out of scope:** `MultiHMRCoreML`, `BodyFusion`, dense-mesh rendering — Plan 3b, gated on a confirmed CoreML Multi-HMR `.mlpackage`.
|
||||
|
||||
---
|
||||
|
||||
## Verification
|
||||
|
||||
`AV-Live-Body` is a macOS target — it builds on the host:
|
||||
|
||||
```bash
|
||||
cd launcher/AV-Live-Body && swift build
|
||||
cd launcher/AV-Live-Body && swift test
|
||||
```
|
||||
|
||||
Each task ends with `swift build` (and `swift test` where a test was
|
||||
added) succeeding.
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
| File | Responsibility |
|
||||
|------|----------------|
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift` | NEW. Background USB read loop → `StreamDemuxer` → `@Published` body frames + video callback |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/VideoDecoder.swift` | NEW. `VTDecompressionSession` HEVC decode: `VideoPayload` → `CVPixelBuffer` |
|
||||
| `launcher/AV-Live-Body/Tests/AVLiveBodyTests/USBSkeletonConsumerTests.swift` | NEW. Unit test for the `SkeletonPayload` → `ArkitBodyFrame` mapping |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/Skeleton3DRenderer.swift` | MODIFY. Complete the line-138 TODO: draw 91 USB-skeleton joint markers |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/ArkitOSCListener.swift` | REFERENCE only — reuse its nested `ArkitBodyFrame` type |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/AVLiveBodyApp.swift` | MODIFY. Own a `USBSkeletonConsumer`, start it in `.onAppear` |
|
||||
| `launcher/AV-Live-Body/Sources/AVLiveBody/BodyView.swift` | MODIFY. Thread the consumer into `Skeleton3DRenderer.attach` |
|
||||
|
||||
---
|
||||
|
||||
## Task 1: USBSkeletonConsumer
|
||||
|
||||
`USBSkeletonConsumer` owns the blocking USB read loop on a background
|
||||
`Thread`. It reconnects on drop. It republishes `.skeleton` frames as
|
||||
`ArkitOSCListener.ArkitBodyFrame` (the existing 91-joint body type, so
|
||||
`Skeleton3DRenderer` can consume them with no new type) and forwards
|
||||
`.video` payloads via a callback. It is **not** `@MainActor`: the loop
|
||||
runs off-main and hops to main only for `@Published` writes — the same
|
||||
pattern as `ArkitOSCListener`.
|
||||
|
||||
**Files:**
|
||||
- Create: `launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift`
|
||||
- Test: `launcher/AV-Live-Body/Tests/AVLiveBodyTests/USBSkeletonConsumerTests.swift`
|
||||
|
||||
- [ ] **Step 1: Write the failing test**
|
||||
|
||||
`launcher/AV-Live-Body/Tests/AVLiveBodyTests/USBSkeletonConsumerTests.swift`:
|
||||
|
||||
```swift
|
||||
import XCTest
|
||||
import AVLiveWire
|
||||
@testable import AVLiveBody
|
||||
|
||||
final class USBSkeletonConsumerTests: XCTestCase {
|
||||
func testSkeletonPayloadMapsToBodyFrame() {
|
||||
var p = SkeletonPayload()
|
||||
p.joints[0] = SIMD3(1, 2, 3)
|
||||
p.valid[0] = true
|
||||
p.joints[90] = SIMD3(-4, 5, -6)
|
||||
p.valid[90] = true
|
||||
let frame = USBSkeletonConsumer.bodyFrame(pid: 7, from: p)
|
||||
XCTAssertEqual(frame.pid, 7)
|
||||
XCTAssertEqual(frame.joints.count, 91)
|
||||
XCTAssertEqual(frame.hasJoint.count, 91)
|
||||
XCTAssertEqual(frame.joints[0], SIMD3(1, 2, 3))
|
||||
XCTAssertTrue(frame.hasJoint[0])
|
||||
XCTAssertEqual(frame.joints[90], SIMD3(-4, 5, -6))
|
||||
XCTAssertFalse(frame.hasJoint[1])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Run the test to verify it fails**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test --filter USBSkeletonConsumerTests`
|
||||
Expected: FAIL — `USBSkeletonConsumer` undefined.
|
||||
|
||||
- [ ] **Step 3: Write the implementation**
|
||||
|
||||
`launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift`:
|
||||
|
||||
```swift
|
||||
import AVLiveWire
|
||||
import Combine
|
||||
import Foundation
|
||||
|
||||
/// Connects to the tethered iPhone over USB (usbmuxd), demuxes the
|
||||
/// AVLiveWire stream, and republishes skeleton frames (as the existing
|
||||
/// 91-joint `ArkitOSCListener.ArkitBodyFrame`) plus video payloads.
|
||||
/// The blocking transport runs on a dedicated background thread; only
|
||||
/// `@Published` writes hop to the main queue.
|
||||
final class USBSkeletonConsumer: ObservableObject {
|
||||
/// 91-joint body frames keyed by pid — same shape `Skeleton3DRenderer`
|
||||
/// already consumes from `ArkitOSCListener`.
|
||||
@Published var bodies: [Int: ArkitOSCListener.ArkitBodyFrame] = [:]
|
||||
@Published var connected = false
|
||||
|
||||
/// Called (on the main queue) for every decoded `.video` frame.
|
||||
var onVideo: ((VideoPayload) -> Void)?
|
||||
|
||||
/// TCP port the iPhone `USBServer` listens on (must match the iOS
|
||||
/// app's `USBServer.port`).
|
||||
static let devicePort: UInt16 = 7000
|
||||
|
||||
private let stateLock = NSLock()
|
||||
private var running = false
|
||||
private var thread: Thread?
|
||||
|
||||
private var isRunning: Bool {
|
||||
stateLock.lock(); defer { stateLock.unlock() }
|
||||
return running
|
||||
}
|
||||
|
||||
func start() {
|
||||
stateLock.lock()
|
||||
if running { stateLock.unlock(); return }
|
||||
running = true
|
||||
stateLock.unlock()
|
||||
let t = Thread { [weak self] in self?.loop() }
|
||||
t.name = "cc.avlive.usbconsumer"
|
||||
t.start()
|
||||
thread = t
|
||||
}
|
||||
|
||||
func stop() {
|
||||
stateLock.lock(); running = false; stateLock.unlock()
|
||||
}
|
||||
|
||||
/// Pure mapping `SkeletonPayload` -> `ArkitBodyFrame`. Static so it
|
||||
/// is unit-testable without a transport.
|
||||
static func bodyFrame(pid: Int, from p: SkeletonPayload)
|
||||
-> ArkitOSCListener.ArkitBodyFrame {
|
||||
var f = ArkitOSCListener.ArkitBodyFrame()
|
||||
f.pid = pid
|
||||
f.joints = p.joints
|
||||
f.hasJoint = p.valid
|
||||
f.seenAt = CFAbsoluteTimeGetCurrent()
|
||||
return f
|
||||
}
|
||||
|
||||
// MARK: - Background read loop
|
||||
|
||||
private func loop() {
|
||||
while isRunning {
|
||||
guard let transport = UnixMuxTransport() else {
|
||||
Thread.sleep(forTimeInterval: 1.0); continue
|
||||
}
|
||||
let client = USBClient(transport: transport)
|
||||
guard let dev = client.listDevices().first,
|
||||
client.connect(deviceID: dev,
|
||||
port: Self.devicePort) else {
|
||||
transport.close()
|
||||
Thread.sleep(forTimeInterval: 1.0); continue
|
||||
}
|
||||
publishConnected(true)
|
||||
var demux = StreamDemuxer()
|
||||
while isRunning {
|
||||
guard let chunk = transport.readStream(),
|
||||
!chunk.isEmpty else { break }
|
||||
for frame in demux.feed(chunk) { route(frame) }
|
||||
}
|
||||
transport.close()
|
||||
publishConnected(false)
|
||||
if isRunning { Thread.sleep(forTimeInterval: 1.0) }
|
||||
}
|
||||
}
|
||||
|
||||
private func route(_ frame: StreamDemuxer.Frame) {
|
||||
switch frame.header.tag {
|
||||
case .skeleton:
|
||||
guard let payload =
|
||||
SkeletonPayload(decoding: frame.payload) else { return }
|
||||
let pid = Int(frame.header.pid)
|
||||
let body = Self.bodyFrame(pid: pid, from: payload)
|
||||
DispatchQueue.main.async { [weak self] in
|
||||
self?.bodies[pid] = body
|
||||
}
|
||||
case .video:
|
||||
guard let payload =
|
||||
VideoPayload(decoding: frame.payload) else { return }
|
||||
DispatchQueue.main.async { [weak self] in
|
||||
self?.onVideo?(payload)
|
||||
}
|
||||
case .meta:
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
private func publishConnected(_ value: Bool) {
|
||||
DispatchQueue.main.async { [weak self] in
|
||||
self?.connected = value
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Run the test to verify it passes**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test --filter USBSkeletonConsumerTests`
|
||||
Expected: PASS, 1 test.
|
||||
|
||||
If `ArkitOSCListener.ArkitBodyFrame` has no memberwise mutability or a
|
||||
different field set than `pid`/`joints`/`hasJoint`/`seenAt`, read
|
||||
`ArkitOSCListener.swift` and adjust `bodyFrame` to match the actual
|
||||
struct (it is a `struct ArkitBodyFrame: Equatable` with `var pid`,
|
||||
`var joints: [SIMD3<Float>]`, `var hasJoint: [Bool]`, `var seenAt`).
|
||||
|
||||
- [ ] **Step 5: Run the full suite + commit**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift test`
|
||||
Expected: PASS, all tests (7: prior 6 + this 1).
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift launcher/AV-Live-Body/Tests/AVLiveBodyTests/USBSkeletonConsumerTests.swift
|
||||
git commit -m "feat(av-live-body): USB skeleton consumer"
|
||||
```
|
||||
|
||||
(subject ≤50 chars; add a short body — the hook rejects subject-only.)
|
||||
|
||||
---
|
||||
|
||||
## Task 2: VideoDecoder
|
||||
|
||||
`VideoDecoder` turns `.video` `VideoPayload`s into `CVPixelBuffer`s. A
|
||||
keyframe payload carries the HEVC parameter sets prepended (each as a
|
||||
4-byte big-endian length prefix + NAL bytes — the format Plan 2's iOS
|
||||
`VideoEncoder` produces); the decoder builds its
|
||||
`CMVideoFormatDescription` from those, then decodes subsequent access
|
||||
units.
|
||||
|
||||
**Files:**
|
||||
- Create: `launcher/AV-Live-Body/Sources/AVLiveBody/VideoDecoder.swift`
|
||||
|
||||
- [ ] **Step 1: Write the implementation**
|
||||
|
||||
`launcher/AV-Live-Body/Sources/AVLiveBody/VideoDecoder.swift`:
|
||||
|
||||
```swift
|
||||
import AVLiveWire
|
||||
import CoreMedia
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
import VideoToolbox
|
||||
|
||||
/// HEVC decoder. Feed `VideoPayload`s in; receive `CVPixelBuffer`s via
|
||||
/// `onFrame`. Keyframe payloads must carry the VPS/SPS/PPS parameter
|
||||
/// sets prepended as 4-byte-length-prefixed NAL units (the layout the
|
||||
/// iOS `VideoEncoder` emits); the decoder (re)builds its format
|
||||
/// description from those.
|
||||
final class VideoDecoder {
|
||||
var onFrame: ((CVPixelBuffer) -> Void)?
|
||||
|
||||
private var session: VTDecompressionSession?
|
||||
private var formatDesc: CMVideoFormatDescription?
|
||||
|
||||
/// Decode one access unit.
|
||||
func decode(_ payload: VideoPayload) {
|
||||
var au = payload.data
|
||||
if payload.isKeyframe {
|
||||
// Split the prepended parameter sets from the frame data.
|
||||
let (params, rest) = Self.splitParameterSets(au)
|
||||
if !params.isEmpty {
|
||||
rebuildFormat(params)
|
||||
}
|
||||
au = rest
|
||||
}
|
||||
guard let fmt = formatDesc, !au.isEmpty else { return }
|
||||
if session == nil { makeSession(fmt) }
|
||||
guard let session else { return }
|
||||
guard let block = Self.blockBuffer(au) else { return }
|
||||
var sample: CMSampleBuffer?
|
||||
var sampleSize = au.count
|
||||
guard CMSampleBufferCreateReady(
|
||||
allocator: kCFAllocatorDefault, dataBuffer: block,
|
||||
formatDescription: fmt, sampleCount: 1, sampleTimingEntryCount: 0,
|
||||
sampleTimingArray: nil, sampleSizeEntryCount: 1,
|
||||
sampleSizeArray: &sampleSize,
|
||||
sampleBufferOut: &sample) == noErr, let sample else { return }
|
||||
VTDecompressionSessionDecodeFrame(
|
||||
session, sampleBuffer: sample, flags: [],
|
||||
infoFlagsOut: nil) { [weak self] status, _, image, _, _ in
|
||||
guard status == noErr, let image else { return }
|
||||
self?.onFrame?(image)
|
||||
}
|
||||
}
|
||||
|
||||
func stop() {
|
||||
if let session { VTDecompressionSessionInvalidate(session) }
|
||||
session = nil
|
||||
formatDesc = nil
|
||||
}
|
||||
|
||||
deinit { stop() }
|
||||
|
||||
// MARK: - Helpers
|
||||
|
||||
/// Parameter sets are 4-byte-length-prefixed NAL units at the head
|
||||
/// of a keyframe payload. The first NAL whose type is a VCL slice
|
||||
/// marks the start of frame data — but to stay simple and robust,
|
||||
/// we treat every leading NAL as a parameter set until the running
|
||||
/// concatenation can build a valid HEVC format description; the
|
||||
/// remainder is the frame. Returns (parameterSetData, frameData).
|
||||
private static func splitParameterSets(_ data: Data)
|
||||
-> (Data, Data) {
|
||||
// Parameter set NALs for HEVC: VPS=32, SPS=33, PPS=34
|
||||
// (nal_unit_type = (firstByte >> 1) & 0x3F).
|
||||
var offset = 0
|
||||
let bytes = [UInt8](data)
|
||||
var paramEnd = 0
|
||||
while offset + 4 <= bytes.count {
|
||||
let len = (Int(bytes[offset]) << 24)
|
||||
| (Int(bytes[offset + 1]) << 16)
|
||||
| (Int(bytes[offset + 2]) << 8)
|
||||
| Int(bytes[offset + 3])
|
||||
let nalStart = offset + 4
|
||||
guard len > 0, nalStart + len <= bytes.count else { break }
|
||||
let nalType = (Int(bytes[nalStart]) >> 1) & 0x3F
|
||||
if nalType == 32 || nalType == 33 || nalType == 34 {
|
||||
offset = nalStart + len
|
||||
paramEnd = offset
|
||||
} else {
|
||||
break
|
||||
}
|
||||
}
|
||||
return (data.prefix(paramEnd),
|
||||
data.suffix(from: data.startIndex
|
||||
.advanced(by: paramEnd)))
|
||||
}
|
||||
|
||||
private func rebuildFormat(_ paramData: Data) {
|
||||
var sets: [[UInt8]] = []
|
||||
let bytes = [UInt8](paramData)
|
||||
var offset = 0
|
||||
while offset + 4 <= bytes.count {
|
||||
let len = (Int(bytes[offset]) << 24)
|
||||
| (Int(bytes[offset + 1]) << 16)
|
||||
| (Int(bytes[offset + 2]) << 8)
|
||||
| Int(bytes[offset + 3])
|
||||
let start = offset + 4
|
||||
guard len > 0, start + len <= bytes.count else { break }
|
||||
sets.append(Array(bytes[start..<start + len]))
|
||||
offset = start + len
|
||||
}
|
||||
guard sets.count >= 3 else { return }
|
||||
let pointers = sets.map { UnsafePointer<UInt8>($0) }
|
||||
let sizes = sets.map { $0.count }
|
||||
var fmt: CMFormatDescription?
|
||||
let status = pointers.withUnsafeBufferPointer { pBuf in
|
||||
sizes.withUnsafeBufferPointer { sBuf in
|
||||
CMVideoFormatDescriptionCreateFromHEVCParameterSets(
|
||||
allocator: kCFAllocatorDefault,
|
||||
parameterSetCount: sets.count,
|
||||
parameterSetPointers: pBuf.baseAddress!,
|
||||
parameterSetSizes: sBuf.baseAddress!,
|
||||
nalUnitHeaderLength: 4, extensions: nil,
|
||||
formatDescriptionOut: &fmt)
|
||||
}
|
||||
}
|
||||
if status == noErr, let fmt {
|
||||
formatDesc = fmt
|
||||
if let session { VTDecompressionSessionInvalidate(session) }
|
||||
session = nil
|
||||
}
|
||||
}
|
||||
|
||||
private func makeSession(_ fmt: CMVideoFormatDescription) {
|
||||
let attrs: [CFString: Any] = [
|
||||
kCVPixelBufferPixelFormatTypeKey:
|
||||
kCVPixelFormatType_32BGRA,
|
||||
]
|
||||
VTDecompressionSessionCreate(
|
||||
allocator: kCFAllocatorDefault, formatDescription: fmt,
|
||||
decoderSpecification: nil,
|
||||
imageBufferAttributes: attrs as CFDictionary,
|
||||
outputCallback: nil, decompressionSessionOut: &session)
|
||||
}
|
||||
|
||||
private static func blockBuffer(_ data: Data) -> CMBlockBuffer? {
|
||||
var block: CMBlockBuffer?
|
||||
guard CMBlockBufferCreateWithMemoryBlock(
|
||||
allocator: kCFAllocatorDefault, memoryBlock: nil,
|
||||
blockLength: data.count, blockAllocator: kCFAllocatorDefault,
|
||||
customBlockSource: nil, offsetToData: 0,
|
||||
dataLength: data.count, flags: 0,
|
||||
blockBufferOut: &block) == noErr, let block else {
|
||||
return nil
|
||||
}
|
||||
var ok = false
|
||||
data.withUnsafeBytes { raw in
|
||||
if CMBlockBufferReplaceDataBytes(
|
||||
with: raw.baseAddress!, blockBuffer: block,
|
||||
offsetIntoDestination: 0,
|
||||
dataLength: data.count) == noErr { ok = true }
|
||||
}
|
||||
return ok ? block : nil
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 2: Verify it compiles**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build`
|
||||
Expected: build succeeds. If a VideoToolbox/CoreMedia signature differs
|
||||
on this SDK, fix minimally — the behavior (build a format description
|
||||
from the prepended parameter sets, decode the rest) must be preserved.
|
||||
|
||||
- [ ] **Step 3: Commit**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/VideoDecoder.swift
|
||||
git commit -m "feat(av-live-body): HEVC video decoder"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 3: Render the 91-joint USB skeleton
|
||||
|
||||
`Skeleton3DRenderer` already subscribes to a 91-joint ARKit body
|
||||
publisher into `lastArkit` but never draws it — `Skeleton3DRenderer.swift:138`
|
||||
is `// TODO: render yellow ARKit markers from lastArkit in update()`.
|
||||
Complete it: draw the 91 joints as small yellow spheres.
|
||||
|
||||
**Files:**
|
||||
- Modify: `launcher/AV-Live-Body/Sources/AVLiveBody/Skeleton3DRenderer.swift`
|
||||
|
||||
- [ ] **Step 1: Read the renderer**
|
||||
|
||||
Read `Skeleton3DRenderer.swift` fully. Note: `PersonEntities` (the
|
||||
per-pid entity struct), `lastArkit: [Int: ArkitOSCListener.ArkitBodyFrame]`,
|
||||
`makePerson(pid:parent:)`, the `update(frames:)` 30 fps tick, and the
|
||||
RealityKit space conversion used for MediaPipe joints
|
||||
(`SIMD3(k.x, -k.y, -k.z)`).
|
||||
|
||||
- [ ] **Step 2: Add 91 ARKit marker entities to `PersonEntities`**
|
||||
|
||||
In the `PersonEntities` struct, add a field:
|
||||
|
||||
```swift
|
||||
var arkitMarkers: [ModelEntity] // 91 yellow ARKit joint spheres
|
||||
```
|
||||
|
||||
In `makePerson(pid:parent:)`, after the hand spheres are built, create
|
||||
91 yellow marker spheres (reuse the `jointRadius`-sized sphere mesh, a
|
||||
yellow `SimpleMaterial`), parent them to `root`, start them disabled,
|
||||
and include `arkitMarkers:` in the returned `PersonEntities(...)`:
|
||||
|
||||
```swift
|
||||
let arkitMat = SimpleMaterial(
|
||||
color: .systemYellow, roughness: 0.6, isMetallic: false)
|
||||
var arkitMarkers: [ModelEntity] = []
|
||||
arkitMarkers.reserveCapacity(91)
|
||||
for _ in 0..<91 {
|
||||
let e = ModelEntity(mesh: sphereMesh, materials: [arkitMat])
|
||||
e.isEnabled = false
|
||||
root.addChild(e)
|
||||
arkitMarkers.append(e)
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 3: Draw the ARKit markers each tick**
|
||||
|
||||
Replace the line `// TODO: render yellow ARKit markers from lastArkit in update()`
|
||||
(`Skeleton3DRenderer.swift:138`) — leave the comment removed — and add,
|
||||
at the end of `update(frames:)` (after the existing per-pid loop), a
|
||||
call to a new private method `applyArkit()`. Then add the method:
|
||||
|
||||
```swift
|
||||
/// Draw the 91-joint ARKit/USB skeletons as yellow joint markers.
|
||||
/// ARKit joints are world-space metric; convert to RealityKit
|
||||
/// space (x, y, z) -> (x, -y, -z) like the MediaPipe path.
|
||||
private func applyArkit() {
|
||||
for (pid, entities) in persons {
|
||||
guard let frame = lastArkit[pid] else {
|
||||
for m in entities.arkitMarkers { m.isEnabled = false }
|
||||
continue
|
||||
}
|
||||
let n = min(91, entities.arkitMarkers.count,
|
||||
frame.joints.count)
|
||||
for i in 0..<n {
|
||||
let marker = entities.arkitMarkers[i]
|
||||
if frame.hasJoint[i] {
|
||||
let j = frame.joints[i]
|
||||
marker.transform.translation =
|
||||
SIMD3<Float>(j.x, -j.y, -j.z)
|
||||
marker.isEnabled = true
|
||||
} else {
|
||||
marker.isEnabled = false
|
||||
}
|
||||
}
|
||||
for i in n..<entities.arkitMarkers.count {
|
||||
entities.arkitMarkers[i].isEnabled = false
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Note: `applyArkit()` iterates `persons`, which is only populated for
|
||||
pids seen in the MediaPipe `frames`. If the USB skeleton must show
|
||||
when there is no MediaPipe pose, also create a `PersonEntities` for
|
||||
each pid present in `lastArkit`. To keep Task 3 minimal, in
|
||||
`update(frames:)` before `applyArkit()`, ensure entities exist for
|
||||
ARKit-only pids:
|
||||
|
||||
```swift
|
||||
for pid in lastArkit.keys where persons[pid] == nil {
|
||||
persons[pid] = makePerson(pid: pid, parent: anchor)
|
||||
lastSeenAt[pid] = now
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] **Step 4: Verify build + tests**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build` — Expected: succeeds.
|
||||
Run: `cd launcher/AV-Live-Body && swift test` — Expected: all tests
|
||||
still pass (no regression).
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/Skeleton3DRenderer.swift
|
||||
git commit -m "feat(av-live-body): render 91-joint USB skeleton"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 4: Wire the consumer into the app
|
||||
|
||||
Construct `USBSkeletonConsumer` in the app, start/stop it with the
|
||||
scene, and feed it into `Skeleton3DRenderer` in place of (or alongside)
|
||||
`ArkitOSCListener`.
|
||||
|
||||
**Files:**
|
||||
- Modify: `launcher/AV-Live-Body/Sources/AVLiveBody/AVLiveBodyApp.swift`
|
||||
- Modify: `launcher/AV-Live-Body/Sources/AVLiveBody/BodyView.swift`
|
||||
|
||||
- [ ] **Step 1: Read the two files**
|
||||
|
||||
Read `AVLiveBodyApp.swift` and `BodyView.swift`. Identify: where the
|
||||
`@StateObject` listeners are declared in `ContentView`, where `.onAppear`
|
||||
starts them, how `ArkitOSCListener` is passed into `BodyView`, and where
|
||||
`BodyView.makeNSView` calls `skel3d.attach(to:listener:arkitListener:)`.
|
||||
|
||||
- [ ] **Step 2: Own and start the consumer**
|
||||
|
||||
In `AVLiveBodyApp.swift`'s `ContentView`, add a `@StateObject`:
|
||||
|
||||
```swift
|
||||
@StateObject private var usbConsumer = USBSkeletonConsumer()
|
||||
```
|
||||
|
||||
In `.onAppear`, alongside the existing listener `.start()` calls, add
|
||||
`usbConsumer.start()`. If there is an `.onDisappear`, add
|
||||
`usbConsumer.stop()`.
|
||||
|
||||
- [ ] **Step 3: Thread the consumer to the renderer**
|
||||
|
||||
`Skeleton3DRenderer.attach` currently takes
|
||||
`arkitListener: ArkitOSCListener?`. The simplest correct change: give
|
||||
`USBSkeletonConsumer` the same role. Add an overload / extra parameter
|
||||
so `attach` can subscribe to `usbConsumer.$bodies` exactly as it
|
||||
subscribes to `arkitListener.$bodies` (both publish
|
||||
`[Int: ArkitOSCListener.ArkitBodyFrame]`). Concretely, in
|
||||
`Skeleton3DRenderer.attach`, accept `usbConsumer: USBSkeletonConsumer?`
|
||||
and, if non-nil, subscribe its `$bodies` into `lastArkit` with the same
|
||||
sink already used for `arkitListener` (the `arkitSub` Combine
|
||||
subscription). Pass `usbConsumer` from `ContentView` → `BodyView` →
|
||||
`makeNSView` → `skel3d.attach(...)`, mirroring how `arkitListener` is
|
||||
already threaded.
|
||||
|
||||
If `arkitListener` (the OSC one) is now redundant, it may be passed as
|
||||
`nil`; do not delete `ArkitOSCListener` in this plan (other code or
|
||||
Plan 3b cleanup may still reference it).
|
||||
|
||||
- [ ] **Step 4: Verify build**
|
||||
|
||||
Run: `cd launcher/AV-Live-Body && swift build` — Expected: succeeds.
|
||||
Run: `cd launcher/AV-Live-Body && swift test` — Expected: no regression.
|
||||
|
||||
- [ ] **Step 5: Commit**
|
||||
|
||||
```bash
|
||||
git add launcher/AV-Live-Body/Sources/AVLiveBody/AVLiveBodyApp.swift launcher/AV-Live-Body/Sources/AVLiveBody/BodyView.swift
|
||||
git commit -m "feat(av-live-body): wire USB consumer to renderer"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Task 5: Final verification
|
||||
|
||||
- [ ] **Step 1: Clean build + full test suite**
|
||||
|
||||
```bash
|
||||
cd launcher/AV-Live-Body && swift build && swift test
|
||||
```
|
||||
|
||||
Expected: build succeeds; all tests pass (7: prior 6 + Task 1's).
|
||||
|
||||
- [ ] **Step 2: Confirm the integration seam**
|
||||
|
||||
`USBSkeletonConsumer.devicePort` (7000) must equal the iOS app's
|
||||
`USBServer.port`. Verify:
|
||||
|
||||
```bash
|
||||
grep -rn "port.*7000\|devicePort" \
|
||||
launcher/AV-Live-Body/Sources/AVLiveBody/USBSkeletonConsumer.swift \
|
||||
iphone-arbody/ARBodyTracker.swiftpm/Sources/ARBodyTracker/USBServer.swift
|
||||
```
|
||||
|
||||
Expected: both sides use `7000`.
|
||||
|
||||
- [ ] **Step 3: Commit any fix** (only if Step 2 found a mismatch).
|
||||
|
||||
---
|
||||
|
||||
## Self-Review
|
||||
|
||||
- **Spec coverage:** This plan implements the spec's `USBClient`
|
||||
consumption inside `AVLiveBody`, the `VideoDecoder` unit, and the
|
||||
skeleton render path. `MultiHMRCoreML`, `BodyFusion`, and dense-mesh
|
||||
rendering are explicitly Plan 3b (gated on a confirmed CoreML
|
||||
Multi-HMR `.mlpackage`).
|
||||
- **Placeholders:** none — new files have complete code; modify tasks
|
||||
cite exact files and the line-138 TODO, and instruct the implementer
|
||||
to read exact context for `AVLiveBodyApp.swift`/`BodyView.swift`
|
||||
(whose current line numbers are not reproduced here).
|
||||
- **Type consistency:** `USBSkeletonConsumer.bodyFrame` returns
|
||||
`ArkitOSCListener.ArkitBodyFrame`; `Skeleton3DRenderer` already
|
||||
stores `lastArkit: [Int: ArkitOSCListener.ArkitBodyFrame]`, so the
|
||||
consumer is type-compatible with the existing `arkitSub` path.
|
||||
`VideoDecoder` consumes `VideoPayload` exactly as Plan 2's
|
||||
`VideoEncoder` produces it (parameter sets prepended, 4-byte
|
||||
big-endian length prefixes).
|
||||
- **Known risks:** (1) `BodyView` owns `Skeleton3DRenderer`, so Task 4
|
||||
threads a new object through `ContentView` → `BodyView` → `attach` —
|
||||
multi-file, follow the existing `arkitListener` threading exactly.
|
||||
(2) `StreamDemuxer.findMagic` copies the whole buffer per `feed()`;
|
||||
for HEVC video this is a perf risk — acceptable for Plan 3a, revisit
|
||||
if frame rate suffers. (3) The HEVC parameter-set split in
|
||||
`VideoDecoder` assumes the iOS encoder's exact prepend layout —
|
||||
this is the Plan 2 ↔ Plan 3a integration seam; validate on real
|
||||
device data.
|
||||
@@ -0,0 +1,171 @@
|
||||
# AVLiveBody macOS — Clean Rewrite Design
|
||||
|
||||
> **Status:** design approved (brainstorming), pending implementation plan.
|
||||
> **Date:** 2026-05-18
|
||||
|
||||
## Goal
|
||||
|
||||
Rebuild the macOS `AVLiveBody` app from scratch as a clean, native
|
||||
Xcode application focused solely on the iPhone-USB body pipeline:
|
||||
display the iPhone camera video and the tracked body (91-joint
|
||||
skeleton + SMPL-X mesh) in a single RealityKit 3D scene. Drop all the
|
||||
legacy components that have made incremental work fragile.
|
||||
|
||||
## Motivation
|
||||
|
||||
The existing `launcher/AV-Live-Body` is a SwiftPM package carrying
|
||||
years of unrelated functionality — MediaPipe OSC listeners, openFrame-
|
||||
works-style Metal "viz mode" scenes, a data-feeds HUD, a 33-joint
|
||||
MediaPipe skeleton renderer, Mac-webcam capture, viz-mode hotkeys, a
|
||||
multi-layer `BodyView`. Bolting the iPhone-USB pipeline onto it caused
|
||||
recurring friction: the skeleton render tick was coupled to the
|
||||
MediaPipe publisher, the app could not take keyboard focus when run as
|
||||
a bare SwiftPM executable, the camera defaulted to the Mac webcam. A
|
||||
clean, purpose-built app removes that whole class of problems.
|
||||
|
||||
## Decisions (brainstorming outcomes)
|
||||
|
||||
1. **Fresh native macOS app, Xcode project**, xcodegen-managed
|
||||
(`project.yml` → `.xcodeproj`, matching the `iphone-arbody` iOS
|
||||
app). New directory `avlivebody-mac/` in the AV-Live monorepo. The
|
||||
old `launcher/AV-Live-Body/` is archived.
|
||||
2. **Reuse the clean USB pipeline** built previously — the
|
||||
`AVLiveWire` package plus `USBMuxProtocol`, `USBClient`,
|
||||
`UnixMuxTransport`, `VideoDecoder`, `USBSkeletonConsumer`,
|
||||
`MultiHMRCoreML`, `BodyFusion`. These migrate into the new app
|
||||
unchanged (they are tested and reviewed).
|
||||
3. **Rendering: a single RealityKit 3D scene** — the iPhone video is a
|
||||
texture on a quad at the back of the scene; the body (skeleton +
|
||||
mesh) is in front; an orbitable camera.
|
||||
4. **Drop all legacy** — MediaPipe OSC listeners, the `SceneRenderer`
|
||||
Metal viz modes, `DataFeedsOSCListener` + HUD, the 33-joint
|
||||
`Skeleton3DRenderer`, Mac-webcam capture, viz-mode hotkeys, the
|
||||
layered `BodyView`, `PoseOSCListener`.
|
||||
5. Built as a proper `.app` via Xcode, which resolves the keyboard-
|
||||
focus problem that affected the `swift run` executable.
|
||||
|
||||
## Architecture
|
||||
|
||||
A SwiftUI `@main App` with one window. An `AppDelegate` sets
|
||||
`NSApplication` activation policy to `.regular`. The window hosts one
|
||||
RealityKit `ARView` (used purely as a general 3D view on macOS — no
|
||||
ARKit). The `ARView` holds a single scene containing:
|
||||
|
||||
- a **video quad** — a flat plane entity at the back, its material
|
||||
texture replaced from each decoded iPhone `CVPixelBuffer`;
|
||||
- the **body** — 91 skeleton joint markers and the dense SMPL-X mesh,
|
||||
positioned in front of the video quad;
|
||||
- an **orbitable camera**.
|
||||
|
||||
The USB pipeline (reused components) feeds the scene. The app is a
|
||||
strict consumer: no network, the only input is the USB cable.
|
||||
|
||||
## Components
|
||||
|
||||
### Reused — the USB pipeline (migrated unchanged)
|
||||
|
||||
| Unit | Responsibility |
|
||||
|------|----------------|
|
||||
| `AVLiveWire` (SwiftPM package, stays in `shared/`) | 19-byte frame format, `FrameHeader`/`FrameTag`, `SkeletonPayload`/`VideoPayload`, `StreamDemuxer` |
|
||||
| `USBMuxProtocol` | usbmux 16-byte-header + plist codec |
|
||||
| `USBClient` / `MuxTransport` / `UnixMuxTransport` | usbmux device discovery, connect, `AF_UNIX` socket |
|
||||
| `VideoDecoder` | HEVC `VideoPayload` → `CVPixelBuffer` (`VTDecompressionSession`) |
|
||||
| `USBSkeletonConsumer` | background USB read loop → `StreamDemuxer`; republishes `.skeleton` body frames + decoded `.video` pixel buffers; auto-reconnect |
|
||||
| `MultiHMRCoreML` | bundled CoreML model → N SMPL-X persons |
|
||||
| `BodyFusion` | associate ARKit skeleton ↔ Multi-HMR person, pelvis-depth correction |
|
||||
|
||||
These move from `launcher/AV-Live-Body/Sources/AVLiveBody/` into the
|
||||
new app's source tree. `AVLiveWire` stays in `shared/AVLiveWire`; the
|
||||
new app declares it as a local package dependency.
|
||||
|
||||
### New — rendering (clean, zero legacy)
|
||||
|
||||
| Unit | Responsibility |
|
||||
|------|----------------|
|
||||
| `AVLiveBodyApp` | `@main` SwiftUI `App`; `AppDelegate` forces `.regular` activation; one window |
|
||||
| `SceneView` | `NSViewRepresentable` wrapping the RealityKit `ARView` |
|
||||
| `SceneController` | owns the scene, the orbital camera, assembles the entities; exposes `updateSkeleton`, `updateMesh`, `updateVideo` |
|
||||
| `VideoQuad` | the back plane entity; updates its `TextureResource` from a `CVPixelBuffer` per frame |
|
||||
| `SkeletonEntity` | 91 joint marker entities (native 91-joint, no MediaPipe 33-joint schema) |
|
||||
| `MeshEntity` | the SMPL-X mesh entity (10475 vertices); mesh-building logic cleanly adapted from the old `MeshRenderer` |
|
||||
| `StatusBar` | a small SwiftUI overlay showing connection state from `USBSkeletonConsumer.connected` |
|
||||
|
||||
## Data flow
|
||||
|
||||
```
|
||||
iPhone ──USB── UnixMuxTransport → USBClient → StreamDemuxer → USBSkeletonConsumer
|
||||
├─ .skeleton → SceneController.updateSkeleton → SkeletonEntity
|
||||
└─ .video → VideoDecoder → CVPixelBuffer ─┬─ SceneController.updateVideo → VideoQuad
|
||||
└─ MultiHMRCoreML → BodyFusion → SceneController.updateMesh → MeshEntity
|
||||
```
|
||||
|
||||
Two rates: the skeleton streams at ~30 fps (smooth markers); video and
|
||||
Multi-HMR run slower (~7 fps for the mesh). The video quad texture
|
||||
refreshes at the video frame rate.
|
||||
|
||||
## Error handling
|
||||
|
||||
- **USB disconnect / no iPhone** — `USBSkeletonConsumer` retries every
|
||||
second; `StatusBar` shows "waiting for iPhone".
|
||||
- **CoreML model absent or failing** — the app runs skeleton-only (no
|
||||
mesh); not a fatal error.
|
||||
- **Video decode failure** — the frame is skipped.
|
||||
- **Reconnect** — handled by the consumer's loop; entities holding
|
||||
stale data are cleared after a timeout.
|
||||
|
||||
## Testing
|
||||
|
||||
- The reused USB components keep their existing unit tests
|
||||
(`AVLiveWireTests`, `USBMuxProtocolTests`, `USBClientTests`,
|
||||
`BodyFusionTests`, `USBSkeletonConsumerTests`) — carried into the new
|
||||
app's test target.
|
||||
- New rendering units (`VideoQuad`, `SkeletonEntity`, `MeshEntity`,
|
||||
`SceneController`) depend on RealityKit/CoreML/VideoToolbox —
|
||||
verified by build + on-device/manual run. Any extractable pure logic
|
||||
(coordinate mapping, mesh index construction) gets unit tests.
|
||||
- Build verification: a real Xcode project —
|
||||
`xcodebuild -scheme AVLiveBody -destination 'platform=macOS' build`.
|
||||
|
||||
## Scope
|
||||
|
||||
**In scope**
|
||||
|
||||
- New `avlivebody-mac/` Xcode app (xcodegen `project.yml`).
|
||||
- Migrate the USB pipeline components into the new app.
|
||||
- The RealityKit scene: `VideoQuad`, `SkeletonEntity`, `MeshEntity`,
|
||||
`SceneController`, orbital camera.
|
||||
- Connection-status UI.
|
||||
- Archive `launcher/AV-Live-Body/`.
|
||||
|
||||
**Out of scope**
|
||||
|
||||
- All legacy AVLiveBody functionality (MediaPipe pose, Metal viz
|
||||
modes, data-feeds HUD, Mac webcam, viz-mode hotkeys) — deliberately
|
||||
dropped, not migrated.
|
||||
- Changes to the iOS `ARBodyTracker` app or to `AVLiveWire`.
|
||||
|
||||
## Migration notes
|
||||
|
||||
- The USB component files currently live in
|
||||
`launcher/AV-Live-Body/Sources/AVLiveBody/`. They are copied into the
|
||||
new app's source tree; the old directory is then archived (moved
|
||||
aside / removed from the active build), not deleted from git history.
|
||||
- `AVLiveWire` is untouched in `shared/AVLiveWire`.
|
||||
- The new app's `project.yml` declares the local `AVLiveWire` package
|
||||
dependency and bundles the Multi-HMR `.mlpackage` as a resource
|
||||
(per the earlier owner decision: bundle the validated FP32 model).
|
||||
|
||||
## Risks
|
||||
|
||||
- **Video-as-texture in RealityKit** — RealityKit has no direct
|
||||
"stream of `CVPixelBuffer` → texture" path (`VideoMaterial` is
|
||||
driven by an `AVPlayer`, not a decoded buffer stream). `VideoQuad`
|
||||
must replace a `TextureResource` (or use `LowLevelTexture`) per
|
||||
frame. This is the app's hardest technical point; the implementation
|
||||
plan isolates it in `VideoQuad` so it can be iterated independently.
|
||||
- **macOS RealityKit camera control** — `ARView` on macOS is a general
|
||||
3D view; an orbital camera must be set up explicitly (RealityKit
|
||||
does not provide macOS orbit controls out of the box).
|
||||
- **Multi-HMR throughput** — ~7 fps; the mesh layer is slow while the
|
||||
skeleton stays real-time. Acceptable; mesh interpolation can be
|
||||
added later if needed.
|
||||
@@ -0,0 +1,201 @@
|
||||
# iPhone USB Body-Tracking Link — Design
|
||||
|
||||
> **Status:** design approved (brainstorming), pending implementation plan.
|
||||
> **Date:** 2026-05-18
|
||||
|
||||
## Goal
|
||||
|
||||
Replace the network (OSC/UDP over WiFi) link between the iOS
|
||||
`ARBodyTracker` app and the macOS `AVLiveBody` app with a **wired USB
|
||||
link**, so the body-tracking pipeline runs autonomously on one
|
||||
iPhone + one Mac with no WiFi, no router, no hotspot, no remote
|
||||
worker.
|
||||
|
||||
## Motivation
|
||||
|
||||
AV-Live's body pipeline is currently distributed: the Mac camera
|
||||
feeds Multi-HMR (on a remote host), and the iPhone ARKit data only
|
||||
*corrects* it over OSC/UDP. This depends on the network. The owner
|
||||
wants a self-contained, network-free system.
|
||||
|
||||
## Decisions (brainstorming outcomes)
|
||||
|
||||
1. **iPhone is the source.** ARKit body tracking + LiDAR + RGB video
|
||||
all originate on the iPhone. The Mac no longer uses its own camera.
|
||||
2. **iPhone streams video.** Multi-HMR is an image-to-SMPL-X model, so
|
||||
the iPhone sends the RGB video (not just the skeleton); the Mac runs
|
||||
Multi-HMR on that video. The ARKit skeleton + LiDAR correct scale
|
||||
and depth.
|
||||
3. **Transport is USB.** Bluetooth cannot carry video bandwidth; WiFi
|
||||
is a network. The cable is the only network-free, high-bandwidth,
|
||||
low-latency option.
|
||||
4. **Single native macOS app.** `AVLiveBody` becomes one Swift app:
|
||||
receives USB, runs Multi-HMR in CoreML, renders the mesh. No Python
|
||||
in the iPhone-USB path.
|
||||
5. **Multi-person.** Multi-HMR yields N meshes from the video; the
|
||||
single ARKit skeleton corrects the *primary* body only; others are
|
||||
Multi-HMR raw. Skeleton-to-mesh association logic is required.
|
||||
6. **USB transport mechanism:** native Swift `usbmux` client (no
|
||||
`peertalk` dependency).
|
||||
|
||||
## Architecture
|
||||
|
||||
Two apps, one cable.
|
||||
|
||||
- **ARBodyTracker (iOS)** — extends the existing
|
||||
`iphone-arbody/ARBodyTracker.swiftpm`. Captures the ARKit 91-joint
|
||||
skeleton (LiDAR-anchored) and the `ARFrame` RGB image, HEVC-encodes
|
||||
the video, frames skeleton + video into one stream, and serves it on
|
||||
a local TCP port that the Mac reaches through `usbmuxd`.
|
||||
- **AVLiveBody (macOS)** — extends the existing
|
||||
`launcher/AV-Live-Body` Swift app. Connects to the iPhone over USB,
|
||||
demuxes the stream, HEVC-decodes the video, runs CoreML Multi-HMR
|
||||
(N meshes), fuses with the ARKit skeleton, renders the meshes, and
|
||||
keeps feeding SuperCollider via localhost OSC.
|
||||
|
||||
usbmuxd is Apple's USB device-multiplexing daemon (the channel Xcode
|
||||
uses for a tethered device). The iOS app's TCP listener is never
|
||||
exposed to any network; the Mac connects to it through the cable via
|
||||
`/var/run/usbmuxd`.
|
||||
|
||||
## Components
|
||||
|
||||
### iOS — ARBodyTracker
|
||||
|
||||
| Unit | Responsibility | Depends on |
|
||||
|------|----------------|------------|
|
||||
| `ARBodySession` | `ARBodyTrackingConfiguration` → 91-joint skeleton + `ARFrame.capturedImage` | ARKit (exists, extend) |
|
||||
| `VideoEncoder` | hardware HEVC encode (VideoToolbox): pixel buffer → compressed access unit | VideoToolbox |
|
||||
| `WireFormat` | binary framing `[tag, pid, timestamp, length, payload]`; pure, testable | — |
|
||||
| `USBServer` | TCP `NWListener` on a fixed local port; usbmuxd exposes it to the tethered Mac | Network, WireFormat |
|
||||
| `ContentView` | UI: AR preview, connection status, start/stop | SwiftUI (exists, extend) |
|
||||
|
||||
The existing OSC sender in ARBodyTracker is removed.
|
||||
|
||||
### macOS — AVLiveBody
|
||||
|
||||
| Unit | Responsibility | Depends on |
|
||||
|------|----------------|------------|
|
||||
| `USBClient` | native Swift usbmux client: `/var/run/usbmuxd` socket, device list, connect-to-port, attach/detach events, byte stream | — (Unix socket, mockable) |
|
||||
| `StreamDemuxer` | parse `WireFormat` frames → skeleton frames / video frames; resync on partial buffers | WireFormat |
|
||||
| `VideoDecoder` | hardware HEVC decode → `CVPixelBuffer` | VideoToolbox |
|
||||
| `MultiHMRCoreML` | run the CoreML Multi-HMR model on a frame → N SMPL-X meshes | CoreML `.mlpackage` |
|
||||
| `BodyFusion` | associate the ARKit skeleton with the matching Multi-HMR person; LiDAR scale/depth correction on the primary; others pass through; pure, testable | — |
|
||||
| `MeshRenderer` / `Skeleton3DRenderer` | RealityKit rendering of meshes/skeletons | RealityKit (exist) |
|
||||
| `PoseOSCBridge` | emit pose to SuperCollider `:57121` on localhost — preserves AV-Live's audio half | Network (localhost only) |
|
||||
|
||||
`ArkitOSCListener` (network) is retired; `USBClient` takes its role
|
||||
over USB.
|
||||
|
||||
## Data flow
|
||||
|
||||
```
|
||||
iPhone ARKit ──┬─ skeleton 91 joints ─────────────┐
|
||||
└─ ARFrame RGB → VideoEncoder HEVC ─┤
|
||||
WireFormat ┤→ USBServer (local TCP port)
|
||||
│
|
||||
═══ USB cable / usbmuxd ═══
|
||||
│
|
||||
Mac USBClient → StreamDemuxer ─┬─ video → VideoDecoder → MultiHMRCoreML → N meshes ┐
|
||||
└─ skeleton ───────────────────────────────────────┤
|
||||
BodyFusion ┤
|
||||
┌──────────────────────────────────────────────────────┘
|
||||
├→ MeshRenderer (N meshes) + Skeleton3DRenderer
|
||||
└→ PoseOSCBridge → SuperCollider :57121 (localhost)
|
||||
```
|
||||
|
||||
`BodyFusion` associates the ARKit skeleton with the nearest Multi-HMR
|
||||
person (by 2D projection / position) and corrects that person's scale
|
||||
and depth (`pred_cam_t.z`) from the LiDAR-anchored joints. Other
|
||||
bodies remain Multi-HMR raw.
|
||||
|
||||
**Two rates.** The skeleton streams at ~30 fps (cheap, always fresh).
|
||||
Video / Multi-HMR runs slower (CoreML throughput, ~2-5 fps on Apple
|
||||
Silicon). Every frame carries a timestamp; fusion matches a mesh to
|
||||
the nearest-in-time skeleton. The skeleton is the smooth real-time
|
||||
layer; the dense mesh is a slower layer, bridged to 30 fps by the
|
||||
existing mesh interpolation in `AVLiveBody` (commit `0293cde`),
|
||||
driven by the 30 fps skeleton.
|
||||
|
||||
## Wire format
|
||||
|
||||
Each frame: a fixed header followed by a payload.
|
||||
|
||||
| Field | Type | Notes |
|
||||
|-------|------|-------|
|
||||
| `tag` | `u8` | 1 = skeleton, 2 = video, 3 = meta |
|
||||
| `pid` | `i16` | body id (skeleton/meta); `-1` for video |
|
||||
| `timestamp` | `f64` | capture time, seconds |
|
||||
| `length` | `u32` BE | payload byte count |
|
||||
| `payload` | bytes | per-tag, below |
|
||||
|
||||
- **skeleton** — 91 × `(x, y, z)` `f32` world-space + a 91-bit
|
||||
validity mask.
|
||||
- **video** — one HEVC access unit; a flag marks keyframes and
|
||||
carries parameter sets (VPS/SPS/PPS) when present.
|
||||
- **meta** — video dimensions, camera intrinsics, body count.
|
||||
|
||||
Exact byte layout is finalized in the implementation plan.
|
||||
|
||||
## Error handling
|
||||
|
||||
- **USB attach/detach** — `USBClient` subscribes to usbmuxd device
|
||||
events and auto-reconnects. Renderers GC stale persons (existing
|
||||
`retainSec`).
|
||||
- **Backpressure** — if Multi-HMR is slower than capture, latest frame
|
||||
wins: intermediate video frames are dropped, never queued. The
|
||||
skeleton stream stays fresh independently.
|
||||
- **HEVC decode failure** — frame skipped.
|
||||
- **CoreML model absent or failing** — fall back to skeleton-only
|
||||
rendering (`Skeleton3DRenderer` draws the ARKit skeleton): degraded
|
||||
but alive.
|
||||
- **Frame sync** — timestamp-based nearest match in `BodyFusion`.
|
||||
|
||||
## Testing
|
||||
|
||||
| Unit | Test |
|
||||
|------|------|
|
||||
| `WireFormat` | pure unit: encode→decode roundtrip, all tags, truncated/corrupt frames |
|
||||
| `USBClient` | unit: usbmux protocol against a mocked Unix socket (canned plist replies), device-list parse, connect handshake, attach/detach events |
|
||||
| `StreamDemuxer` | roundtrip + resync on partial (non-frame-aligned) buffers |
|
||||
| `VideoDecoder` | decode a known HEVC sample → expected dimensions |
|
||||
| `BodyFusion` | pure logic: synthetic skeleton + synthetic Multi-HMR persons → assert association + scale/depth correction |
|
||||
| `MultiHMRCoreML` | integration: known frame → mesh, sanity bounds |
|
||||
| iOS (`VideoEncoder`, `ARBodySession`, `USBServer`) | framing unit-tested; ARKit/VideoToolbox need a device — manual/integration |
|
||||
| End-to-end | iPhone tethered, both apps, N meshes render + latency budget + USB reconnect |
|
||||
|
||||
## Scope
|
||||
|
||||
**In scope**
|
||||
|
||||
- Extend `iphone-arbody/ARBodyTracker.swiftpm`: `VideoEncoder`,
|
||||
`WireFormat`, `USBServer`; `ARBodySession` exposes video frames; the
|
||||
OSC sender is removed.
|
||||
- Extend `launcher/AV-Live-Body`: `USBClient`, `StreamDemuxer`,
|
||||
`VideoDecoder`, `MultiHMRCoreML` wiring, `BodyFusion`;
|
||||
`ArkitOSCListener` retired.
|
||||
- Keep `PoseOSCBridge` → SuperCollider on localhost.
|
||||
|
||||
**Out of scope**
|
||||
|
||||
- The Python `data_only_viz` pipeline — untouched; it remains the
|
||||
Mac-camera mode. This project is the iPhone-USB path only.
|
||||
- CoreML Multi-HMR model *conversion* — assumed already done
|
||||
(`multihmr_coreml.py` + existing conversion plans). This project
|
||||
*consumes* the `.mlpackage`.
|
||||
- LiDAR scene mesh / ICP fusion (separate plan).
|
||||
- iOS app signing and deployment — owner action.
|
||||
|
||||
## Risks & dependencies
|
||||
|
||||
- **CoreML Multi-HMR readiness** — the dense-mesh half depends on a
|
||||
working, fast-enough `.mlpackage`. If not ready, that half is
|
||||
blocked, but the skeleton-only fallback keeps the project useful —
|
||||
not all-or-nothing.
|
||||
- **Multi-HMR throughput** — ~2-5 fps measured on Apple Silicon. The
|
||||
dense mesh updates slowly; the 30 fps skeleton + existing mesh
|
||||
interpolation cover the gap.
|
||||
- **Device pairing** — the iPhone must be trusted/paired with the Mac
|
||||
for usbmuxd to expose it.
|
||||
- **iOS deployment** — building/signing/installing the iOS app is a
|
||||
manual owner step.
|
||||
Generated
+7
@@ -0,0 +1,7 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<Workspace
|
||||
version = "1.0">
|
||||
<FileRef
|
||||
location = "self:">
|
||||
</FileRef>
|
||||
</Workspace>
|
||||
|
This This `.swiftpm/xcode/.../contents.xcworkspacedata` file is an Xcode/SwiftPM generated artifact. Since `.swiftpm/` is already being gitignored, this file should be removed from version control to avoid churn and machine-specific workspace state being committed.
|
||||
@@ -0,0 +1,23 @@
|
||||
// swift-tools-version:5.10
|
||||
import PackageDescription
|
||||
|
||||
let package = Package(
|
||||
name: "ARBodyTracker",
|
||||
defaultLocalization: "en",
|
||||
platforms: [.iOS(.v17)],
|
||||
products: [
|
||||
.executable(name: "ARBodyTracker", targets: ["ARBodyTracker"]),
|
||||
],
|
||||
dependencies: [
|
||||
.package(path: "../../shared/AVLiveWire"),
|
||||
],
|
||||
targets: [
|
||||
.executableTarget(
|
||||
name: "ARBodyTracker",
|
||||
dependencies: [
|
||||
.product(name: "AVLiveWire", package: "AVLiveWire"),
|
||||
],
|
||||
path: "Sources/ARBodyTracker"
|
||||
),
|
||||
]
|
||||
)
|
||||
@@ -0,0 +1,217 @@
|
||||
import ARKit
|
||||
import AVLiveWire
|
||||
import Combine
|
||||
import Foundation
|
||||
import RealityKit
|
||||
import SwiftUI
|
||||
|
||||
/// Drives the ARKit body-tracking session and streams the 91-joint
|
||||
/// skeleton plus HEVC-encoded camera video to the tethered Mac over
|
||||
/// USB (AVLiveWire frames via usbmuxd). No network involved.
|
||||
///
|
||||
/// Lightweight 2D snapshot of the tracked skeleton, ready for SwiftUI
|
||||
/// Canvas. Joint indices follow `ARSkeletonDefinition.defaultBody3D`.
|
||||
struct SkeletonSnapshot: Equatable {
|
||||
/// Projected joint positions in viewport coordinates, or nil if the
|
||||
/// joint falls outside the view / is not finite.
|
||||
let points: [CGPoint?]
|
||||
/// Per-joint tracking flag (`ARSkeleton.isJointTracked`).
|
||||
let tracked: [Bool]
|
||||
}
|
||||
|
||||
@MainActor
|
||||
final class ARBodySession: NSObject, ObservableObject, ARSessionDelegate {
|
||||
@Published var running: Bool = false
|
||||
@Published var status: String = "idle"
|
||||
@Published var framesSent: Int = 0
|
||||
@Published var jointsPerSec: Double = 0
|
||||
@Published var bodyCount: Int = 0
|
||||
@Published var skeleton2D: SkeletonSnapshot?
|
||||
@Published var usbState: USBServer.State = .idle
|
||||
/// Set by the SwiftUI view via GeometryReader so the projection
|
||||
/// matches the on-screen ARView size.
|
||||
var viewportSize: CGSize = .zero
|
||||
private var sendEnvMesh: Bool = false
|
||||
private let session = ARSession()
|
||||
private let usb = USBServer()
|
||||
private let videoEncoder = VideoEncoder()
|
||||
private var videoStarted = false
|
||||
private var lastFrameTime: TimeInterval = 0
|
||||
private var jointsInSecond: Int = 0
|
||||
private var lastSecond: TimeInterval = 0
|
||||
private let bodyParents: [Int] =
|
||||
ARSkeletonDefinition.defaultBody3D.parentIndices
|
||||
|
||||
let arView = ARView(frame: .zero)
|
||||
|
||||
override init() {
|
||||
super.init()
|
||||
arView.session = session
|
||||
arView.session.delegate = self
|
||||
arView.environment.background = .color(.black)
|
||||
arView.debugOptions = []
|
||||
usb.onState = { [weak self] s in
|
||||
Task { @MainActor in self?.usbState = s }
|
||||
}
|
||||
videoEncoder.onPayload = { [weak self] payload in
|
||||
Task { @MainActor in
|
||||
guard let self, self.usbState == .connected else {
|
||||
return
|
||||
}
|
||||
self.usb.send(tag: .video, pid: -1,
|
||||
timestamp: self.lastFrameTime,
|
||||
payload: payload.encoded())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func configure(sendEnvMesh: Bool) {
|
||||
self.sendEnvMesh = sendEnvMesh
|
||||
}
|
||||
|
||||
func start() {
|
||||
guard ARBodyTrackingConfiguration.isSupported else {
|
||||
status = "ARBodyTracking unsupported (need A12+, iPhone XR/XS+)"
|
||||
return
|
||||
}
|
||||
let cfg = ARBodyTrackingConfiguration()
|
||||
var feats: [String] = []
|
||||
// No extra frame semantics: `.sceneDepth` is reserved to
|
||||
// ARWorldTracking, and `.personSegmentationWithDepth` is
|
||||
// rejected per-frame by ABPKPersonIDTracker in this config
|
||||
// (spams the console without producing usable depth).
|
||||
// NOTE: ARBodyTrackingConfiguration does not expose
|
||||
// sceneReconstruction (that's ARWorldTrackingConfiguration
|
||||
// territory). Env mesh capture requires a separate ARSession
|
||||
// with body tracking off — out of scope for this scaffold.
|
||||
if sendEnvMesh {
|
||||
feats.append("env-mesh: requires separate session (TODO)")
|
||||
}
|
||||
cfg.automaticImageScaleEstimationEnabled = true
|
||||
usb.start()
|
||||
session.run(cfg, options: [.resetTracking, .removeExistingAnchors])
|
||||
status = feats.isEmpty
|
||||
? "running (RGB only)"
|
||||
: "running (\(feats.joined(separator: ", ")))"
|
||||
running = true
|
||||
}
|
||||
|
||||
func stop() {
|
||||
session.pause()
|
||||
usb.stop()
|
||||
videoEncoder.stop()
|
||||
videoStarted = false
|
||||
running = false
|
||||
status = "stopped"
|
||||
}
|
||||
|
||||
// MARK: - ARSessionDelegate
|
||||
|
||||
nonisolated func session(_ s: ARSession, didUpdate frame: ARFrame) {
|
||||
let t = frame.timestamp
|
||||
Task { @MainActor in
|
||||
// Throttle to 30 fps max.
|
||||
if t - self.lastFrameTime < 1.0 / 30.0 { return }
|
||||
self.lastFrameTime = t
|
||||
|
||||
// Encode the camera frame to HEVC and stream it over USB.
|
||||
let img = frame.capturedImage
|
||||
let w = Int32(CVPixelBufferGetWidth(img))
|
||||
let h = Int32(CVPixelBufferGetHeight(img))
|
||||
if !self.videoStarted, w > 0, h > 0 {
|
||||
self.videoEncoder.start(width: w, height: h)
|
||||
self.videoStarted = true
|
||||
}
|
||||
if self.videoStarted {
|
||||
self.videoEncoder.encode(img, pts: t)
|
||||
}
|
||||
|
||||
var count: Int = 0
|
||||
var firstBody: ARBodyAnchor?
|
||||
for anchor in frame.anchors {
|
||||
guard let body = anchor as? ARBodyAnchor else { continue }
|
||||
self.publishUSB(pid: count, timestamp: t, body: body)
|
||||
if count == 0 { firstBody = body }
|
||||
count += 1
|
||||
}
|
||||
self.framesSent &+= 1
|
||||
self.bodyCount = count
|
||||
self.updateSkeleton2D(body: firstBody, camera: frame.camera)
|
||||
|
||||
let now = Date().timeIntervalSinceReferenceDate
|
||||
self.jointsInSecond &+= count * 91
|
||||
if now - self.lastSecond >= 1.0 {
|
||||
self.jointsPerSec = Double(self.jointsInSecond)
|
||||
/ max(0.001, now - self.lastSecond)
|
||||
self.jointsInSecond = 0
|
||||
self.lastSecond = now
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func currentInterfaceOrientation() -> UIInterfaceOrientation {
|
||||
for scene in UIApplication.shared.connectedScenes {
|
||||
if let ws = scene as? UIWindowScene {
|
||||
return ws.interfaceOrientation
|
||||
}
|
||||
}
|
||||
return .portrait
|
||||
}
|
||||
|
||||
private func updateSkeleton2D(body: ARBodyAnchor?, camera: ARCamera) {
|
||||
guard let body, viewportSize.width > 1, viewportSize.height > 1
|
||||
else {
|
||||
if skeleton2D != nil { skeleton2D = nil }
|
||||
return
|
||||
}
|
||||
let xforms = body.skeleton.jointModelTransforms
|
||||
let root = body.transform
|
||||
let orient = currentInterfaceOrientation()
|
||||
var pts: [CGPoint?] = Array(repeating: nil, count: xforms.count)
|
||||
var tracked: [Bool] = Array(repeating: false, count: xforms.count)
|
||||
for (i, m) in xforms.enumerated() {
|
||||
let w = root * m
|
||||
let p3 = simd_make_float3(w.columns.3.x,
|
||||
w.columns.3.y,
|
||||
w.columns.3.z)
|
||||
let p2 = camera.projectPoint(p3,
|
||||
orientation: orient,
|
||||
viewportSize: viewportSize)
|
||||
if p2.x.isFinite && p2.y.isFinite { pts[i] = p2 }
|
||||
tracked[i] = body.skeleton.isJointTracked(i)
|
||||
}
|
||||
skeleton2D = SkeletonSnapshot(points: pts, tracked: tracked)
|
||||
}
|
||||
|
||||
/// Exposed for SwiftUI overlays that need to wire bone parent
|
||||
/// indices without re-reading the ARKit skeleton definition.
|
||||
var bodyParentIndices: [Int] { bodyParents }
|
||||
|
||||
private func publishUSB(pid: Int, timestamp: TimeInterval,
|
||||
body: ARBodyAnchor) {
|
||||
guard usbState == .connected else { return }
|
||||
let skeleton = body.skeleton
|
||||
let transforms = skeleton.jointModelTransforms
|
||||
let root = body.transform
|
||||
var payload = SkeletonPayload()
|
||||
let n = min(SkeletonPayload.jointCount, transforms.count)
|
||||
for i in 0..<n {
|
||||
let w = root * transforms[i]
|
||||
payload.joints[i] = SIMD3(w.columns.3.x,
|
||||
w.columns.3.y,
|
||||
w.columns.3.z)
|
||||
payload.valid[i] = skeleton.isJointTracked(i)
|
||||
}
|
||||
usb.send(tag: .skeleton,
|
||||
pid: Int16(clamping: pid),
|
||||
timestamp: timestamp,
|
||||
payload: payload.encoded())
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
struct ARViewContainer: UIViewRepresentable {
|
||||
@ObservedObject var session: ARBodySession
|
||||
func makeUIView(context: Context) -> ARView { session.arView }
|
||||
func updateUIView(_ uiView: ARView, context: Context) {}
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
import SwiftUI
|
||||
|
||||
@main
|
||||
struct ARBodyTrackerApp: App {
|
||||
var body: some Scene {
|
||||
WindowGroup {
|
||||
ContentView()
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,237 @@
|
||||
import SwiftUI
|
||||
import ARKit
|
||||
import RealityKit
|
||||
import UIKit
|
||||
|
||||
struct ContentView: View {
|
||||
@StateObject private var session = ARBodySession()
|
||||
@State private var sendEnvMesh: Bool = false
|
||||
|
||||
/// Replace the live ARView with a gradient placeholder. Camera is
|
||||
/// unavailable inside Xcode previews; enable this flag there so the
|
||||
/// panel + skeleton overlay can still be laid out.
|
||||
var useMockBackground: Bool = false
|
||||
/// Overlay a synthetic ARKit T-pose so the skeleton renderer can be
|
||||
/// tuned without running on a device.
|
||||
var useMockSkeleton: Bool = false
|
||||
|
||||
var body: some View {
|
||||
GeometryReader { geo in
|
||||
ZStack(alignment: .topLeading) {
|
||||
cameraBackground
|
||||
.ignoresSafeArea()
|
||||
SkeletonOverlay(
|
||||
snapshot: useMockSkeleton
|
||||
? SkeletonSnapshot.mockTPose(in: geo.size)
|
||||
: session.skeleton2D,
|
||||
parents: useMockSkeleton
|
||||
? SkeletonSnapshot.mockParents
|
||||
: session.bodyParentIndices)
|
||||
.ignoresSafeArea()
|
||||
.allowsHitTesting(false)
|
||||
controlPanel
|
||||
}
|
||||
.onAppear {
|
||||
session.viewportSize = geo.size
|
||||
// Keep the screen awake during streaming sessions; iOS
|
||||
// would otherwise lock and tear down the USBServer TCP
|
||||
// listener within seconds of inactivity.
|
||||
UIApplication.shared.isIdleTimerDisabled = true
|
||||
}
|
||||
.onDisappear {
|
||||
UIApplication.shared.isIdleTimerDisabled = false
|
||||
}
|
||||
.onChange(of: geo.size) { _, newSize in
|
||||
session.viewportSize = newSize
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var usbDotColor: Color {
|
||||
switch session.usbState {
|
||||
case .idle: return .gray
|
||||
case .listening: return .yellow
|
||||
case .connected: return .green
|
||||
}
|
||||
}
|
||||
|
||||
private var usbStateLabel: String {
|
||||
switch session.usbState {
|
||||
case .idle: return "idle"
|
||||
case .listening: return "listening :\(USBServer.port)"
|
||||
case .connected: return "connected"
|
||||
}
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private var cameraBackground: some View {
|
||||
if useMockBackground {
|
||||
ZStack {
|
||||
LinearGradient(
|
||||
colors: [
|
||||
Color(red: 0.18, green: 0.20, blue: 0.24),
|
||||
Color(red: 0.05, green: 0.05, blue: 0.08),
|
||||
],
|
||||
startPoint: .top,
|
||||
endPoint: .bottom)
|
||||
Text("camera preview\n(unavailable in Xcode canvas)")
|
||||
.font(.caption)
|
||||
.multilineTextAlignment(.center)
|
||||
.foregroundStyle(.white.opacity(0.35))
|
||||
}
|
||||
} else {
|
||||
ARViewContainer(session: session)
|
||||
}
|
||||
}
|
||||
|
||||
private var controlPanel: some View {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
Text("AR Body → AV-Live")
|
||||
.font(.headline)
|
||||
.foregroundColor(.white)
|
||||
Toggle(isOn: $sendEnvMesh) {
|
||||
Text("Env mesh (LiDAR)").foregroundColor(.white)
|
||||
}
|
||||
HStack {
|
||||
Button(session.running ? "Stop" : "Start") {
|
||||
if session.running {
|
||||
session.stop()
|
||||
} else {
|
||||
session.configure(sendEnvMesh: sendEnvMesh)
|
||||
session.start()
|
||||
}
|
||||
}
|
||||
.buttonStyle(.borderedProminent)
|
||||
Spacer()
|
||||
Text(session.status)
|
||||
.font(.caption)
|
||||
.foregroundColor(.white)
|
||||
.padding(6)
|
||||
.background(.black.opacity(0.5))
|
||||
.cornerRadius(6)
|
||||
}
|
||||
HStack(spacing: 6) {
|
||||
Circle()
|
||||
.fill(usbDotColor)
|
||||
.frame(width: 8, height: 8)
|
||||
Text("USB \(usbStateLabel)")
|
||||
.font(.caption2)
|
||||
.foregroundColor(.white.opacity(0.8))
|
||||
Spacer(minLength: 8)
|
||||
Text("bodies: \(session.bodyCount) frames: \(session.framesSent) j/s: \(Int(session.jointsPerSec))")
|
||||
.font(.caption2)
|
||||
.foregroundColor(.white)
|
||||
}
|
||||
}
|
||||
.padding(12)
|
||||
.background(.black.opacity(0.5))
|
||||
.cornerRadius(10)
|
||||
.padding()
|
||||
}
|
||||
}
|
||||
|
||||
extension SkeletonSnapshot {
|
||||
/// Parent indices for the 16-joint preview stick figure. Each entry
|
||||
/// is the parent joint index, or -1 for the root (head).
|
||||
static let mockParents: [Int] = [
|
||||
-1, // 0 head
|
||||
0, // 1 neck
|
||||
1, // 2 lShoulder
|
||||
1, // 3 rShoulder
|
||||
2, // 4 lElbow
|
||||
3, // 5 rElbow
|
||||
4, // 6 lWrist
|
||||
5, // 7 rWrist
|
||||
1, // 8 spine
|
||||
8, // 9 pelvis
|
||||
9, // 10 lHip
|
||||
9, // 11 rHip
|
||||
10, // 12 lKnee
|
||||
11, // 13 rKnee
|
||||
12, // 14 lAnkle
|
||||
13, // 15 rAnkle
|
||||
]
|
||||
|
||||
/// Synthetic 16-joint stick figure used by Xcode previews. ARKit
|
||||
/// is not available in the preview canvas, so we cannot rely on
|
||||
/// `ARSkeletonDefinition.neutralBodySkeleton3D` (returns nil).
|
||||
static func mockTPose(in size: CGSize) -> SkeletonSnapshot {
|
||||
// Normalized layout: origin at body center, +y down, ±1 spans
|
||||
// roughly the full body height.
|
||||
let layout: [CGPoint] = [
|
||||
CGPoint(x: 0.00, y: -0.45),
|
||||
CGPoint(x: 0.00, y: -0.32),
|
||||
CGPoint(x: -0.18, y: -0.30),
|
||||
CGPoint(x: 0.18, y: -0.30),
|
||||
CGPoint(x: -0.30, y: -0.12),
|
||||
CGPoint(x: 0.30, y: -0.12),
|
||||
CGPoint(x: -0.36, y: 0.08),
|
||||
CGPoint(x: 0.36, y: 0.08),
|
||||
CGPoint(x: 0.00, y: -0.10),
|
||||
CGPoint(x: 0.00, y: 0.06),
|
||||
CGPoint(x: -0.10, y: 0.09),
|
||||
CGPoint(x: 0.10, y: 0.09),
|
||||
CGPoint(x: -0.12, y: 0.30),
|
||||
CGPoint(x: 0.12, y: 0.30),
|
||||
CGPoint(x: -0.13, y: 0.46),
|
||||
CGPoint(x: 0.13, y: 0.46),
|
||||
]
|
||||
let scale = min(size.width * 0.9, size.height * 0.8)
|
||||
let cx = size.width * 0.5
|
||||
let cy = size.height * 0.5
|
||||
let pts: [CGPoint?] = layout.map {
|
||||
CGPoint(x: cx + $0.x * scale,
|
||||
y: cy + $0.y * scale)
|
||||
}
|
||||
return SkeletonSnapshot(
|
||||
points: pts,
|
||||
tracked: Array(repeating: true, count: pts.count))
|
||||
}
|
||||
}
|
||||
|
||||
/// Draws ARKit body joints + bones over the camera view. Bones are
|
||||
/// derived from `ARSkeletonDefinition.defaultBody3D` parent indices.
|
||||
struct SkeletonOverlay: View {
|
||||
let snapshot: SkeletonSnapshot?
|
||||
let parents: [Int]
|
||||
|
||||
var body: some View {
|
||||
Canvas { ctx, _ in
|
||||
guard let snap = snapshot else { return }
|
||||
for (i, parent) in parents.enumerated() where parent >= 0 {
|
||||
guard i < snap.points.count, parent < snap.points.count,
|
||||
let a = snap.points[i], let b = snap.points[parent]
|
||||
else { continue }
|
||||
var path = Path()
|
||||
path.move(to: a)
|
||||
path.addLine(to: b)
|
||||
let solid = snap.tracked[i] && snap.tracked[parent]
|
||||
ctx.stroke(
|
||||
path,
|
||||
with: .color(solid ? .green : .yellow.opacity(0.5)),
|
||||
lineWidth: solid ? 2 : 1.2)
|
||||
}
|
||||
for (i, pt) in snap.points.enumerated() {
|
||||
guard let pt else { continue }
|
||||
let r: CGFloat = snap.tracked[i] ? 4 : 2.5
|
||||
let rect = CGRect(x: pt.x - r, y: pt.y - r,
|
||||
width: r * 2, height: r * 2)
|
||||
ctx.fill(Path(ellipseIn: rect),
|
||||
with: .color(snap.tracked[i]
|
||||
? .cyan : .yellow.opacity(0.8)))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#Preview("iPhone 15 Pro — portrait") {
|
||||
ContentView(useMockBackground: true, useMockSkeleton: true)
|
||||
}
|
||||
|
||||
#Preview("iPhone 15 Pro — landscape", traits: .landscapeLeft) {
|
||||
ContentView(useMockBackground: true, useMockSkeleton: true)
|
||||
}
|
||||
|
||||
#Preview("Empty camera (no body)") {
|
||||
ContentView(useMockBackground: true, useMockSkeleton: false)
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>CFBundleDevelopmentRegion</key><string>en</string>
|
||||
<key>CFBundleDisplayName</key><string>ARBody Tracker</string>
|
||||
<key>CFBundleExecutable</key><string>$(EXECUTABLE_NAME)</string>
|
||||
<key>CFBundleIdentifier</key><string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
|
||||
<key>CFBundleInfoDictionaryVersion</key><string>6.0</string>
|
||||
<key>CFBundleName</key><string>ARBodyTracker</string>
|
||||
<key>CFBundlePackageType</key><string>APPL</string>
|
||||
<key>CFBundleShortVersionString</key><string>0.1.0</string>
|
||||
<key>CFBundleVersion</key><string>1</string>
|
||||
<key>LSRequiresIPhoneOS</key><true/>
|
||||
<key>NSCameraUsageDescription</key>
|
||||
<string>Required for ARKit body tracking and LiDAR depth capture.</string>
|
||||
<key>NSLocalNetworkUsageDescription</key>
|
||||
<string>Streams ARKit body tracking and camera video to a tethered Mac over USB.</string>
|
||||
<key>UIApplicationSceneManifest</key>
|
||||
<dict>
|
||||
<key>UIApplicationSupportsMultipleScenes</key><false/>
|
||||
</dict>
|
||||
<key>UIRequiredDeviceCapabilities</key>
|
||||
<array>
|
||||
<string>arm64</string>
|
||||
<string>arkit</string>
|
||||
</array>
|
||||
<key>UIDeviceFamily</key>
|
||||
<array><integer>1</integer></array>
|
||||
<key>UIRequiresFullScreen</key><true/>
|
||||
<key>UISupportedInterfaceOrientations</key>
|
||||
<array>
|
||||
<string>UIInterfaceOrientationPortrait</string>
|
||||
<string>UIInterfaceOrientationLandscapeLeft</string>
|
||||
<string>UIInterfaceOrientationLandscapeRight</string>
|
||||
</array>
|
||||
<key>UILaunchScreen</key><dict/>
|
||||
</dict>
|
||||
</plist>
|
||||
@@ -0,0 +1,62 @@
|
||||
import Foundation
|
||||
import Network
|
||||
import AVLiveWire
|
||||
|
||||
/// TCP listener on a fixed local port. usbmuxd tunnels it to the
|
||||
/// tethered Mac — the port is never advertised on any network.
|
||||
final class USBServer {
|
||||
static let port: UInt16 = 7000
|
||||
|
||||
enum State { case idle, listening, connected }
|
||||
var onState: ((State) -> Void)?
|
||||
|
||||
private var listener: NWListener?
|
||||
private var connection: NWConnection?
|
||||
private let queue = DispatchQueue(label: "cc.avlive.usbserver")
|
||||
|
||||
func start() {
|
||||
let params = NWParameters.tcp
|
||||
params.allowLocalEndpointReuse = true
|
||||
guard let l = try? NWListener(using: params,
|
||||
on: NWEndpoint.Port(rawValue: Self.port)!) else {
|
||||
onState?(.idle)
|
||||
return
|
||||
}
|
||||
listener = l
|
||||
l.newConnectionHandler = { [weak self] conn in
|
||||
self?.adopt(conn)
|
||||
}
|
||||
l.start(queue: queue)
|
||||
onState?(.listening)
|
||||
}
|
||||
|
||||
private func adopt(_ conn: NWConnection) {
|
||||
connection?.cancel()
|
||||
connection = conn
|
||||
conn.stateUpdateHandler = { [weak self] st in
|
||||
switch st {
|
||||
case .ready: self?.onState?(.connected)
|
||||
case .failed, .cancelled: self?.onState?(.listening)
|
||||
default: break
|
||||
}
|
||||
}
|
||||
conn.start(queue: queue)
|
||||
}
|
||||
|
||||
/// Send one framed message. Drops silently if no peer.
|
||||
func send(tag: FrameTag, pid: Int16, timestamp: Double,
|
||||
payload: Data) {
|
||||
guard let conn = connection else { return }
|
||||
guard payload.count <= Int(StreamDemuxer.maxPayloadLength)
|
||||
else { return }
|
||||
let header = FrameHeader(tag: tag, pid: pid,
|
||||
timestamp: timestamp, length: UInt32(payload.count))
|
||||
conn.send(content: header.encoded() + payload,
|
||||
completion: .contentProcessed { _ in })
|
||||
}
|
||||
|
||||
func stop() {
|
||||
connection?.cancel(); listener?.cancel()
|
||||
onState?(.idle)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,137 @@
|
||||
import AVLiveWire
|
||||
import CoreMedia
|
||||
import CoreVideo
|
||||
import Foundation
|
||||
import VideoToolbox
|
||||
|
||||
/// Hardware HEVC encoder. Feed `CVPixelBuffer`s from ARKit frames in;
|
||||
/// receive one `VideoPayload` per encoded access unit via `onPayload`.
|
||||
/// Keyframe payloads carry the VPS/SPS/PPS parameter sets prepended,
|
||||
/// each as a 4-byte big-endian length prefix followed by the NAL
|
||||
/// bytes, so the Mac decoder can build its format description without
|
||||
/// a side channel.
|
||||
final class VideoEncoder {
|
||||
var onPayload: ((VideoPayload) -> Void)?
|
||||
|
||||
private var session: VTCompressionSession?
|
||||
private let lock = NSLock()
|
||||
|
||||
/// Create the compression session for a given frame size.
|
||||
func start(width: Int32, height: Int32) {
|
||||
stop()
|
||||
var s: VTCompressionSession?
|
||||
let status = VTCompressionSessionCreate(
|
||||
allocator: kCFAllocatorDefault,
|
||||
width: width, height: height,
|
||||
codecType: kCMVideoCodecType_HEVC,
|
||||
encoderSpecification: nil,
|
||||
imageBufferAttributes: nil,
|
||||
compressedDataAllocator: nil,
|
||||
outputCallback: nil,
|
||||
refcon: nil,
|
||||
compressionSessionOut: &s)
|
||||
guard status == noErr, let s else {
|
||||
NSLog("VideoEncoder: VTCompressionSessionCreate failed %d",
|
||||
status)
|
||||
return
|
||||
}
|
||||
VTSessionSetProperty(s, key: kVTCompressionPropertyKey_RealTime,
|
||||
value: kCFBooleanTrue)
|
||||
VTSessionSetProperty(s,
|
||||
key: kVTCompressionPropertyKey_AllowFrameReordering,
|
||||
value: kCFBooleanFalse)
|
||||
VTSessionSetProperty(s,
|
||||
key: kVTCompressionPropertyKey_MaxKeyFrameInterval,
|
||||
value: 30 as CFNumber)
|
||||
VTCompressionSessionPrepareToEncodeFrames(s)
|
||||
lock.lock(); session = s; lock.unlock()
|
||||
}
|
||||
|
||||
/// Encode one frame. `pts` is the capture timestamp in seconds.
|
||||
func encode(_ pixelBuffer: CVPixelBuffer, pts: Double) {
|
||||
lock.lock(); let s = session; lock.unlock()
|
||||
guard let s else { return }
|
||||
let time = CMTime(seconds: pts, preferredTimescale: 1_000_000)
|
||||
VTCompressionSessionEncodeFrame(
|
||||
s, imageBuffer: pixelBuffer, presentationTimeStamp: time,
|
||||
duration: .invalid, frameProperties: nil,
|
||||
infoFlagsOut: nil) { [weak self] status, _, sample in
|
||||
guard status == noErr, let sample else { return }
|
||||
self?.handle(sample)
|
||||
}
|
||||
}
|
||||
|
||||
func stop() {
|
||||
lock.lock(); let s = session; session = nil; lock.unlock()
|
||||
if let s {
|
||||
VTCompressionSessionInvalidate(s)
|
||||
}
|
||||
}
|
||||
|
||||
deinit { stop() }
|
||||
|
||||
// MARK: - Sample -> VideoPayload
|
||||
|
||||
private func handle(_ sample: CMSampleBuffer) {
|
||||
let isKeyframe = !Self.notSync(sample)
|
||||
var out = Data()
|
||||
if isKeyframe,
|
||||
let fmt = CMSampleBufferGetFormatDescription(sample) {
|
||||
out.append(Self.parameterSets(fmt))
|
||||
}
|
||||
if let block = CMSampleBufferGetDataBuffer(sample) {
|
||||
var lengthOut = 0
|
||||
var ptr: UnsafeMutablePointer<Int8>?
|
||||
if CMBlockBufferGetDataPointer(
|
||||
block, atOffset: 0, lengthAtOffsetOut: nil,
|
||||
totalLengthOut: &lengthOut,
|
||||
dataPointerOut: &ptr) == noErr, let ptr {
|
||||
out.append(UnsafeBufferPointer(
|
||||
start: UnsafeRawPointer(ptr)
|
||||
.assumingMemoryBound(to: UInt8.self),
|
||||
count: lengthOut))
|
||||
}
|
||||
}
|
||||
guard !out.isEmpty else { return }
|
||||
onPayload?(VideoPayload(isKeyframe: isKeyframe, data: out))
|
||||
}
|
||||
|
||||
/// True if the sample is NOT a sync (key) frame.
|
||||
private static func notSync(_ sample: CMSampleBuffer) -> Bool {
|
||||
guard let arr = CMSampleBufferGetSampleAttachmentsArray(
|
||||
sample, createIfNecessary: false),
|
||||
CFArrayGetCount(arr) > 0 else { return false }
|
||||
let dict = unsafeBitCast(CFArrayGetValueAtIndex(arr, 0),
|
||||
to: CFDictionary.self)
|
||||
let key = Unmanaged.passUnretained(
|
||||
kCMSampleAttachmentKey_NotSync).toOpaque()
|
||||
return CFDictionaryContainsKey(dict, key)
|
||||
}
|
||||
|
||||
/// Concatenate the HEVC VPS/SPS/PPS parameter sets, each as a
|
||||
/// 4-byte big-endian length prefix followed by the NAL bytes.
|
||||
private static func parameterSets(
|
||||
_ fmt: CMFormatDescription) -> Data {
|
||||
var count = 0
|
||||
CMVideoFormatDescriptionGetHEVCParameterSetAtIndex(
|
||||
fmt, parameterSetIndex: 0, parameterSetPointerOut: nil,
|
||||
parameterSetSizeOut: nil, parameterSetCountOut: &count,
|
||||
nalUnitHeaderLengthOut: nil)
|
||||
var data = Data()
|
||||
for i in 0..<count {
|
||||
var ptr: UnsafePointer<UInt8>?
|
||||
var size = 0
|
||||
guard CMVideoFormatDescriptionGetHEVCParameterSetAtIndex(
|
||||
fmt, parameterSetIndex: i,
|
||||
parameterSetPointerOut: &ptr,
|
||||
parameterSetSizeOut: &size,
|
||||
parameterSetCountOut: nil,
|
||||
nalUnitHeaderLengthOut: nil) == noErr,
|
||||
let ptr else { continue }
|
||||
var be = UInt32(size).bigEndian
|
||||
withUnsafeBytes(of: &be) { data.append(contentsOf: $0) }
|
||||
data.append(UnsafeBufferPointer(start: ptr, count: size))
|
||||
}
|
||||
return data
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,94 @@
|
||||
# CLAUDE.md
|
||||
|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
iPhone-only app that streams ARKit `ARBodyAnchor` joints (91 per body) to
|
||||
the AV-Live stack on a tethered Mac. Part of the parent `AV-Live/`
|
||||
monorepo — read `../CLAUDE.md` for global conventions (commit style,
|
||||
French/English split, no emoji, etc.).
|
||||
|
||||
## Two build forms, one source tree
|
||||
|
||||
Both flavours compile the same files under
|
||||
`ARBodyTracker.swiftpm/Sources/ARBodyTracker/`:
|
||||
|
||||
| Form | When to use | How |
|
||||
|------|-------------|-----|
|
||||
| `ARBodyTracker.swiftpm/` (Swift Package) | Quick local iteration; previews | open `Package.swift` in Xcode |
|
||||
| `ARBodyTracker.xcodeproj` (generated) | Device deploy with reproducible signing | `xcodegen generate` from `project.yml` |
|
||||
|
||||
The `.xcodeproj` is **gitignored** — regenerate after every change to
|
||||
`project.yml` or after adding sources. `Config/Local.xcconfig` (also
|
||||
gitignored) carries `DEVELOPMENT_TEAM`; copy from
|
||||
`Config/Local.xcconfig.example` on a fresh clone.
|
||||
|
||||
## Common commands
|
||||
|
||||
```bash
|
||||
brew install xcodegen # one-time
|
||||
cp Config/Local.xcconfig.example Config/Local.xcconfig # set DEVELOPMENT_TEAM
|
||||
xcodegen generate # writes ARBodyTracker.xcodeproj
|
||||
open ARBodyTracker.xcodeproj # then ⌘R on a connected iPhone
|
||||
|
||||
# AVLiveWire — shared wire-format package (consumed by both the iOS
|
||||
# app and launcher/AV-Live-Body on macOS). Tests live with it.
|
||||
cd ../shared/AVLiveWire && swift test
|
||||
cd ../shared/AVLiveWire && swift test --filter LoopbackTests
|
||||
```
|
||||
|
||||
`xcodegen generate` is the one command to remember: any time `project.yml`
|
||||
or the dep on `../shared/AVLiveWire` changes, the generated project must
|
||||
be rebuilt before opening Xcode.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
ARBodyTrackerApp ── ContentView ── ARViewContainer (ARView)
|
||||
│ │
|
||||
│ └─ ARBodySession (ARSessionDelegate)
|
||||
│ ├─ OSC fanout (UDP) ─► host:57128 (Python data_only_viz)
|
||||
│ │ host:57129 (Swift AV-Live-Body)
|
||||
│ ├─ USBServer (TCP :7000, AVLiveWire frames)
|
||||
│ └─ @Published skeleton2D ──┐
|
||||
└─ SkeletonOverlay (SwiftUI Canvas) ◄─────────┘
|
||||
```
|
||||
|
||||
- **`ARBodySession`** owns the `ARSession` configured with
|
||||
`ARBodyTrackingConfiguration` (RGB-only; LiDAR `sceneDepth` is not
|
||||
available on this config, do not re-add it — it crashes the session).
|
||||
Throttles to 30 fps. Projects joints to 2D via
|
||||
`ARCamera.projectPoint(_:orientation:viewportSize:)`; the viewport
|
||||
size is fed back from the SwiftUI `GeometryReader`.
|
||||
- **Transport** is dual: legacy OSC/UDP (still default), and a new
|
||||
USB/TCP path using `AVLiveWire` framed messages. The Mac side bridges
|
||||
via `usbmuxd`; the iOS side just listens on a fixed local port. No
|
||||
WiFi involved on the USB path.
|
||||
- **`AVLiveWire`** (in `../shared/AVLiveWire`) defines a fixed 19-byte
|
||||
big-endian header (`AVL1` magic + tag + pid + timestamp + length) and
|
||||
an incremental `StreamDemuxer`. Both the iOS `USBServer` and the
|
||||
macOS consumer link against it so the wire format lives in exactly
|
||||
one place.
|
||||
- **`SkeletonOverlay`** draws joints and bones from a published
|
||||
`SkeletonSnapshot`; bone parent indices come from
|
||||
`ARSkeletonDefinition.defaultBody3D.parentIndices` at runtime. For
|
||||
Xcode previews the snapshot/parents are replaced by a 16-joint
|
||||
stick-figure mock (ARKit's `neutralBodySkeleton3D` returns nil off
|
||||
device).
|
||||
|
||||
## Gotchas
|
||||
|
||||
- **Frame semantics:** `ARBodyTrackingConfiguration` rejects
|
||||
`.sceneDepth` (world-tracking only) and spams `ABPKPersonIDTracker:
|
||||
Portrait image is not supported` per-frame if
|
||||
`.personSegmentationWithDepth` is added. Keep `feats` empty unless a
|
||||
newly-needed semantic is verified against
|
||||
`ARBodyTrackingConfiguration.supportsFrameSemantics(_:)`.
|
||||
- **Signing without an Apple ID logged into Xcode:** with
|
||||
`CODE_SIGN_STYLE = Automatic`, Xcode needs the account in
|
||||
Settings → Accounts to download a profile. If only the keychain
|
||||
cert is present, the build fails with "No Account for Team …".
|
||||
Reconnect the account, or fall back to a manually installed profile
|
||||
via `CODE_SIGN_STYLE = Manual` + `PROVISIONING_PROFILE_SPECIFIER`.
|
||||
- **`UIDeviceFamily` warning:** `Info.plist` and `TARGETED_DEVICE_FAMILY`
|
||||
duplicate the device family. The build setting wins; the Info.plist
|
||||
key only generates a warning and can be removed if desired.
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user
The PR title/description says it's a docs-only change adding an
AGENTS.mdskeleton, but this PR also introduces substantial new Swift/Python code (AVLiveWire package, iOS/macOS apps, ICP fusion pipeline, etc.). Please update the PR title/description to reflect the actual scope, or split the docs addition into a separate PR so reviewers can assess the code changes independently.