44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
"""
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LangGraph state definition for the detection pipeline.
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This TypedDict flows through all graph nodes. Each node reads what
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it needs and writes its outputs. LangGraph manages the state transitions.
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"""
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from __future__ import annotations
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from typing import TypedDict
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from core.detect.models import BoundingBox, BrandDetection, DetectionReport, Frame, PipelineStats, TextCandidate
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class DetectState(TypedDict, total=False):
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# Input
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video_path: str
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job_id: str
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profile_name: str
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source_asset_id: str # UUID of the source MediaAsset
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# Stage outputs
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frames: list[Frame]
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filtered_frames: list[Frame]
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field_masks: dict # {seq: np.ndarray} — pitch mask per frame
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field_boundaries: dict # {seq: [(x,y), ...]} — pitch boundary per frame
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field_coverage: dict # {seq: float} — pitch coverage ratio per frame
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edge_regions_by_frame: dict[int, list[BoundingBox]]
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boxes_by_frame: dict[int, list[BoundingBox]]
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preprocessed_crops: dict # "{frame_seq}_{box_idx}" → np.ndarray
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text_candidates: list[TextCandidate]
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unresolved_candidates: list[TextCandidate]
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detections: list[BrandDetection]
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report: DetectionReport
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# Session brands (accumulated during the run, persisted to DB)
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session_brands: dict # {normalized_name: canonical_name}
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# Running stats (updated by each stage)
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stats: PipelineStats
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# Config overrides for replay (merged into profile configs dict)
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config_overrides: dict
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