Commit Graph

7 Commits

Author SHA1 Message Date
fa9b21856f feat: replace face pipeline with InsightFace, add content classifier
Face detection/recognition:
- Replace YuNet + SFace with InsightFace buffalo_l (RetinaFace + ArcFace)
- 512-d ArcFace embeddings (was 128-d SFace), migration 0006 resizes column
- Remove YOLO person-bbox workaround — RetinaFace is accurate enough
- Detection threshold 0.65 cleanly separates real faces (0.72+) from
  false positives on dogs/paintings (0.56-0.61)

Content-type classification:
- CLIP zero-shot classifier using native PyTorch text encoder + ONNX
  image encoder for high-quality text-image similarity
- Categories: photograph, screenshot, document, receipt, meme, artwork
- Writes Tag(kind=content_type) per photo via photo_tags
- Margin-based confidence: top-1 vs top-2 score difference
- New ClassifierSettings in config (enabled, min_confidence)
- Wired into vision_fanout pipeline

Tested: 6 real faces from 4 photos (zero false positives), 11/13 photos
classified (8 photograph, 2 artwork, 1 meme).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 13:49:02 +02:00
2a6661f779 fix: model weights setup — export scripts, ORT compat, bootstrap
- Add export_models.py for OpenCLIP ViT-B/32 and YOLOv8n ONNX export
- Fix ArgMax(13) ORT ARM64 incompatibility by passing eot_indices as a
  separate ONNX input (computed outside the graph in embed.py)
- Use legacy TorchScript exporter (dynamo=False) for IR version 9 compat
- Upgrade onnxruntime to 1.18.1
- Rewrite bootstrap_models.py with clear separation of auto-downloadable
  models (YuNet, SFace) vs manually-exported ones (OpenCLIP, YOLOv8n)
- Wire bootstrap into worker CMD (runs before Celery)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 09:41:25 +02:00
9282a5c734 feat: add vision pipeline scaffolding with ONNX backend
Introduce the app/services/vision/ module with ABC interfaces, ONNX
Runtime backend, model registry, and per-task implementations:
- OpenCLIP ViT-B/32 embedder (image + text, 512-d)
- RapidOCR engine (PP-OCRv4 via ONNX, no PaddlePaddle)
- YOLOv8n object detector (raw ONNX, no ultralytics runtime)
- YuNet + SFace face processor (Apache 2.0, opencv_zoo, 128-d)
- DBSCAN face clustering helper

Add VisionSettings to config (mulita.yml + Pydantic), bootstrap_models.py
for first-boot weight downloads, models_data Docker volume, and ROCm
backend stub for future GPU acceleration.

No Celery tasks wired yet — models load but nothing invokes them.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 09:00:06 +02:00
dea04ceed9 feat: migrate to Postgres + pgvector with Alembic scaffolding
Switch the default database from SQLite to Postgres + pgvector (via
pgvector/pgvector:pg16 Docker image) to support the upcoming vision
pipeline (embeddings, OCR, object detection, face clustering).

- Add `db` service to docker-compose.yml with healthcheck
- Wire `alembic upgrade head` into backend CMD before uvicorn
- Bootstrap empty 0001_baseline revision (schema still owned by create_all)
- Guard SQLite-only PRAGMAs and inline ALTERs behind _is_sqlite flag
- Run `CREATE EXTENSION IF NOT EXISTS vector` on Postgres init
- Add asyncpg, psycopg2-binary, pgvector to requirements
- Provide docker-compose.sqlite.yml escape hatch for legacy SQLite mode

Fresh DB + rescan assumed — no SQLite→Postgres data migration.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 08:46:20 +02:00
733c16bf82 feat: perceptual-hash duplicate detection + grouped picker view
The Duplicates section was useless: SHA-256-only detection only caught
byte-identical files, not the actual duplicates a real library
accumulates (re-encoded JPEGs, screenshots, resized exports), and the
view was a flat date-sorted list with no grouping or actions. This
replaces the whole flow.

Detection
- New phash + duplicate_group_id columns on Photo, added via an
  idempotent ALTER TABLE pass in init_db (the project has no Alembic).
- Thumbs worker computes a 64-bit pHash from the original-resolution
  decoded frame just before the destructive thumbnail loop. Falls back
  silently — phash is nice-to-have, not a blocker for thumbnails.
- backfill_phashes Celery task fills in phashes for photos that
  predated the column, reading the existing thumb_large rather than
  re-decoding the original.
- regroup_duplicates service runs union-find over Hamming distance
  (threshold 6), persists duplicate_group_id, and maintains is_duplicate
  as derived state so existing badges/counts keep working. Chained
  after scan_all_source_roots with a 60s countdown.

API
- GET /library/duplicates/groups returns all groups with members,
  bucketed in Python from one query. Each group has a reason ("exact"
  iff every member shares a SHA-256, "similar" otherwise).
- POST /library/maintenance/{regroup-duplicates,backfill-phashes}.

Frontend
- New DuplicatesView (sectioned grid, one section per cluster) replaces
  the timeline when the user is in the duplicates section. Each section
  shows a "Keep best, discard N" button that picks the highest-pixel
  copy and reuses the existing undoable bulk-discard so Cmd+Z works.
- Manual best override: hover any non-best thumbnail and click "Keep
  this" (Crown icon, top-right) to override the auto-pick. The header
  annotates "(manual)" so it's obvious which copy will be kept.
- Keyboard nav within the duplicates view walks the flat member list,
  with ↑/↓ jumping by the measured column count and scrollIntoView on
  every move. Timeline's keyboard handler now early-returns in the
  duplicates section so the two don't fight.
- BEST pill / Keep-this button live at top-right with a ring outline so
  they don't collide visually with the cyan selection ring around a
  selected cell. Dimensions chip moved to bottom-left to free both
  right corners for the keep affordances.
- New "Duplicates" section in SettingsDialog: shows group/member counts
  and exposes both backfill + re-detect actions, sharing a query cache
  with DuplicatesView via DUPLICATE_GROUPS_QUERY_KEY.
- PhotoInfoPanel "Basic Info" section now shows the photo's full file
  path in monospace below the size/dimensions/date grid.
- New imagehash==4.3.1 dep in requirements.txt.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-09 17:19:08 +02:00
6d1b227fb9 feat: structure 2 2026-04-07 00:15:00 +02:00
46a0d7aba8 feat: structure 2026-04-06 23:30:19 +02:00