dtoro
ad007e4cd4
- 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>
25 lines
979 B
Python
25 lines
979 B
Python
"""
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Face embedding model — stores per-face detection + recognition vectors.
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cluster_id FKs to tags.id where kind='face_cluster'. Null means
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unclustered (will be assigned by recluster_faces).
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"""
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from sqlalchemy import Column, String, Float, ForeignKey, DateTime, func
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from sqlalchemy.dialects.postgresql import JSONB
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from pgvector.sqlalchemy import Vector
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import uuid
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from app.database import Base
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class FaceEmbedding(Base):
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__tablename__ = 'face_embeddings'
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id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
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photo_id = Column(String, ForeignKey('photos.id', ondelete='CASCADE'), nullable=False, index=True)
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bbox = Column(JSONB) # [x1, y1, x2, y2] normalized 0-1
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vector = Column(Vector(128)) # SFace → 128-d
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cluster_id = Column(String, ForeignKey('tags.id', ondelete='SET NULL'), nullable=True, index=True)
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quality = Column(Float)
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created_at = Column(DateTime(timezone=True), server_default=func.now())
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