Three-piece workflow that imports a self-hosted Immich library and emits new facesets without disturbing existing identity numbering: - work/immich_stage.py (WSL): pages /search/metadata, parallel-fetches /faces?id= per asset, prefilters by face_short>=90 against bbox scaled to original-image coords, downloads originals, sha256-dedups against nl_full.npz and same-run staged files. 8-worker ThreadPoolExecutor doing the full /faces->filter->/original chain per asset; resumable via state.json. API URL + key come from IMMICH_URL / IMMICH_API_KEY env vars, label->UUID map from work/immich/users.json (gitignored). - work/embed_worker.py (Windows venv at C:\face_embed_venv): runs insightface.FaceAnalysis(buffalo_l) with the DmlExecutionProvider on AMD Radeon Vega via onnxruntime-directml. Produces a cache file in the same .npz schema as sort_faces.cmd_embed (loadable via load_cache). ~7.5x speedup over CPU end-to-end; embeddings bit- identical to CPU (cosine similarity 1.0000 across 8 sample faces). - work/cluster_immich.py (WSL): mirrors cluster_osrc.py against an immich_<user>.npz. Builds existing identity centroids from canonical faceset_NNN/ in facesets_swap_ready/, drops matches at <=0.45, clusters the rest at 0.55, applies refine gates, hands off to cmd_export_swap. Numbers new facesets past the existing maximum. - work/finalize_immich.sh: chains queue->Windows embed->cache copy-> cluster_immich, with logging. The 2026-04-26 run on https://fotos.computerliebe.org (Immich v2.7.2) processed 53,842 admin-accessible assets, staged 10,261, embedded 19,462 face records on Vega DML in 64.6 min, matched 8,103 (42%) to existing identities, and emitted 185 new facesets (faceset_026..264 with gaps). facesets_swap_ready/ went from 31 to 216 substantive facesets. Important caveat surfaced: /search/metadata's userIds filter is silently ignored when the API key is bound to a different user, so this run can't enumerate other users' libraries from the admin key. A per-user API key would be required for nic. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2.4 KiB
Executable File
2.4 KiB
Executable File