bvandeusen
b735432d02
Answers "how are videos/all media handled by the GPU worker": a job is per ITEM, but the agent fans a VIDEO into per-frame instances (ffmpeg in the agent, the existing cadence), each stored with a timestamp — so a video becomes a BAG of frame embeddings (fixes the mean-embedding muddle) instead of one washed-out vector. Stills → frame_time NULL; animated GIF/WebP treated like short video. - image_region.frame_time (migration 0061, not yet deployed so folded in): the source frame's seconds for video/animated media; NULL for stills. RegionService passes it through. A whole frame is just kind='frame'. - gpu_job + GpuJobService (migration 0062): the durable work list that keeps the desktop agent HTTP-only — enqueue (dedupes (image,task)) / lease (FOR UPDATE SKIP LOCKED, re-claims expired leases so the queue self-heals) / heartbeat / complete / fail (re-queues until MAX_ATTEMPTS then 'error'). The server enqueues; the agent leases+submits over the web API; Redis/Postgres stay private. Tests: enqueue dedupe, lease-then-skip-when-held, expired-lease reclaim, scoped heartbeat, complete, fail-requeue-then-error. region test now covers frame_time. NEXT: the thin HTTP API (lease/submit/heartbeat) + bearer-token auth, then the agent container + control UI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
63 lines
3.1 KiB
Python
63 lines
3.1 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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# 'frame' (a whole video frame → SigLIP bag) | 'face' | 'figure' (→ CCIP
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# character id) | 'concept' (→ SigLIP head bag).
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kind: Mapped[str] = mapped_column(String(16), nullable=False)
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# For video/animated media: the source frame's timestamp in SECONDS. NULL for
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# static images. Lets a video be a BAG of per-frame instances (fixes the
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# mean-embedding muddle) + grounds a tag to "appears at 0:42".
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frame_time: Mapped[float | None] = mapped_column(Float, nullable=True)
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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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