Cross-reference face detections with YOLO 'person' bounding boxes —
only keep faces that overlap >= 50% with a detected human body. This
eliminates false positives on dogs, paintings, and cartoons without
needing an aggressive score threshold.
Lower face detection threshold back to 0.6 since the person-overlap
check is now the primary precision filter.
Tested: 6 verified faces from 4 photos, zero false positives.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- recluster_faces now writes photo_tags rows for each face cluster so
the tag count and tag_ids filter work (previously count was always 0)
- Old cluster tags and photo_tags are cleaned up before re-clustering
- Raise face detection threshold from 0.4 to 0.7 to reduce false
positives (was detecting dog faces as people)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Rewrite faces.py to use cv2.FaceDetectorYN instead of raw ONNX
(handles multi-scale anchor decoding and NMS internally)
- Load original photo files at up to 4000px for face detection instead
of 240px thumbnails — faces were too small to detect at thumbnail res
- Falls back to thumbnail if original is unavailable
Tested: 33 faces extracted from 13 photos, clustered into 1 person.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Rewrite all vision tasks to use sync psycopg2 sessions instead of
asyncpg — fixes 'another operation in progress' and event loop errors
when Celery forks workers sharing the async connection pool
- Letterbox-pad images to exactly 640x640 for YuNet face detector
(was crashing on non-square thumbnails)
- Deduplicate object detections per label per photo — keep highest
confidence only to avoid photo_tags PK violation on multiple
detections of the same class
- Add all queues (-Q default,high,low,vision) to worker command
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Create face_embeddings table with pgvector Vector(128) + HNSW index
- Implement extract_faces task (YuNet detection + SFace recognition)
- Implement recluster_faces task (DBSCAN clustering → Tag(kind=face_cluster))
- Clusters are named "Person N" and get representative_photo_id
- cluster_id FK → tags.id, SET NULL on delete for merge/rename support
Migration 0005 creates the face_embeddings table.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Implement detect_objects Celery task:
- Runs YOLOv8n on 640px thumbnail via ONNX Runtime
- Creates Tag(kind=object) rows for each COCO class detected
- Writes photo_tags associations with confidence, bbox, and source
- Wipes previous detections per source model on re-run
No new tables/migrations — uses the unified Tag model from PR3.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Create ocr_text table for storing per-region OCR results
- Add tsvector search_vector column to photos with GIN index and
auto-update trigger on filename/user_title/user_notes
- Implement ocr_photo Celery task using rapidocr-onnxruntime
- Add FTS leg to hybrid search: queries photos.search_vector and
ocr_text via UNION, fused with semantic results via RRF (k=60)
Migration 0004 backfills search_vector for existing rows.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Wire the full embedding flow:
- Rewrite Embedding model to use pgvector Vector(512) with HNSW index
- Add embed_photo, vision_fanout, backfill_vision Celery tasks on
dedicated `vision` queue
- Hook vision_fanout into generate_thumbnails completion
- Add POST /api/v1/photos/search with hybrid RRF ranking (semantic-only
for now; FTS leg added in PR5)
- Stub ocr_photo, detect_objects, extract_faces tasks for later PRs
Migration 0003 drops/recreates the embeddings table (was never populated).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
