bvandeusen
0ea7ecdea5
The storage backbone both crop jobs write to and read from. image_region =
normalized bbox (rx/ry/rw/rh) + kind ('face'/'figure' → CCIP character id;
'concept' → SigLIP head bag) + the crop's embedding (nullable Vector(768) CCIP /
Vector(1152) SigLIP, one per kind) + version stamps for compute-once gating. The
bbox doubles as grounded-tag provenance. Migration 0061.
RegionService.replace_regions (scoped BY KIND so the figure + concept pipelines
don't clobber each other) + get_regions — the GPU agent's results endpoint will
call the writer; the character matcher + bag scorer read. Server-side, no GPU.
Tests: replace/get round-trip, kind-scoped replacement, CCIP vector round-trip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
58 lines
2.8 KiB
Python
58 lines
2.8 KiB
Python
"""ImageRegion — a detected/proposed sub-region of an image + its crop embedding.
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The storage backbone of the crop pipeline (#114). A region is a normalized bbox
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plus the embedding of its crop:
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- kind='face' / 'figure' → embedded by CCIP for cross-artist character identity.
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- kind='concept' → embedded by SigLIP, a localized instance for a concept head's
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bag-of-embeddings (a concept is "present if ANY instance matches").
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One row carries the embedding appropriate to its kind (the other is null). The
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bbox doubles as grounded-tag provenance (hover a tag → highlight its region; a
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wrong box is a precise negative). The GPU agent writes these via the job API;
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the few-shot character matcher + bag scorer read them — both server-side, no GPU.
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"""
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from datetime import datetime
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from pgvector.sqlalchemy import Vector
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from sqlalchemy import DateTime, Float, ForeignKey, Integer, String, func
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from sqlalchemy.orm import Mapped, mapped_column
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from .base import Base
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CCIP_DIM = 768 # deepghs/imgutils CCIP character embedding
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SIGLIP_DIM = 1152 # matches image_record.siglip_embedding
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class ImageRegion(Base):
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__tablename__ = "image_region"
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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image_record_id: Mapped[int] = mapped_column(
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ForeignKey("image_record.id", ondelete="CASCADE"), index=True
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)
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# 'face' | 'figure' (→ CCIP character id) | 'concept' (→ SigLIP head bag).
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kind: Mapped[str] = mapped_column(String(16), nullable=False)
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# Normalized bbox in [0,1]: top-left (rx, ry) + size (rw, rh). Named rx/ry/…
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# rather than x/y/by to dodge SQL keyword ambiguity ('by').
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rx: Mapped[float] = mapped_column(Float, nullable=False)
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ry: Mapped[float] = mapped_column(Float, nullable=False)
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rw: Mapped[float] = mapped_column(Float, nullable=False)
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rh: Mapped[float] = mapped_column(Float, nullable=False)
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# Proposer/detector confidence (null for deterministic proposers).
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score: Mapped[float | None] = mapped_column(Float, nullable=True)
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# Version stamps so a re-detect / re-crop / re-embed can be gated (compute
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# once; only redo when the producing model version changes).
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detector_version: Mapped[str | None] = mapped_column(String(64), nullable=True)
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crop_version: Mapped[str | None] = mapped_column(String(64), nullable=True)
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embedding_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
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# Exactly one is set, per kind.
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ccip_embedding: Mapped[list[float] | None] = mapped_column(
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Vector(CCIP_DIM), nullable=True
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)
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siglip_embedding: Mapped[list[float] | None] = mapped_column(
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Vector(SIGLIP_DIM), nullable=True
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)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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