330 lines
15 KiB
Python
330 lines
15 KiB
Python
from __future__ import annotations
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from typing import Any
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from uuid import UUID
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from geoalchemy2.shape import to_shape
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from shapely.geometry import mapping
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from sqlalchemy import or_
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from sqlalchemy.orm import Session
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from app.core.errors import AppError
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from app.models import Detection, DetectionReview, QualityCheck, Segmentation, VectorFeature
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class QualityEvidenceService:
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@staticmethod
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def evidence_geojson(db: Session, *, project_id: UUID, quality_check_id: UUID) -> dict[str, Any]:
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quality_check = db.get(QualityCheck, quality_check_id)
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if not quality_check or quality_check.project_id != project_id:
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raise AppError(code="QUALITY_CHECK_NOT_FOUND", message="Quality check not found", status_code=404)
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findings = quality_check.findings_json or {}
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features: list[dict[str, Any]] = []
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warnings: list[str] = []
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candidate_ids, reference_ids = QualityEvidenceService._evidence_identifiers(findings)
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candidate_index = QualityEvidenceService._candidate_feature_index(db, quality_check, candidate_ids)
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reference_index = QualityEvidenceService._reference_feature_index(db, quality_check, reference_ids)
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for evidence in QualityEvidenceService._evidence_items(findings.get("match_evidence")):
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candidate_id = QualityEvidenceService._string_value(evidence.get("candidate_feature_id"))
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reference_id = QualityEvidenceService._string_value(evidence.get("reference_feature_id"))
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iou = evidence.get("iou")
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if candidate_id:
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row = candidate_index.get(candidate_id)
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if row is not None:
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features.append(
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QualityEvidenceService._row_to_feature(
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row,
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role="match_candidate",
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quality_check=quality_check,
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evidence=evidence,
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)
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)
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else:
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warnings.append(f"Candidate evidence feature not found: {candidate_id}")
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if reference_id:
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row = reference_index.get(reference_id)
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if row is not None:
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features.append(
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QualityEvidenceService._row_to_feature(
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row,
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role="match_reference",
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quality_check=quality_check,
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evidence={"candidate_feature_id": candidate_id, "reference_feature_id": reference_id, "iou": iou},
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)
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)
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else:
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warnings.append(f"Reference evidence feature not found: {reference_id}")
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for evidence in QualityEvidenceService._evidence_items(findings.get("false_positive_evidence")):
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candidate_id = QualityEvidenceService._string_value(evidence.get("candidate_feature_id"))
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if not candidate_id:
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continue
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row = candidate_index.get(candidate_id)
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if row is not None:
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features.append(
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QualityEvidenceService._row_to_feature(
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row,
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role="false_positive",
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quality_check=quality_check,
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evidence=evidence,
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)
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)
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else:
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warnings.append(f"False-positive evidence feature not found: {candidate_id}")
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for evidence in QualityEvidenceService._evidence_items(findings.get("false_negative_evidence")):
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reference_id = QualityEvidenceService._string_value(evidence.get("reference_feature_id"))
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if not reference_id:
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continue
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row = reference_index.get(reference_id)
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if row is not None:
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features.append(
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QualityEvidenceService._row_to_feature(
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row,
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role="false_negative",
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quality_check=quality_check,
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evidence=evidence,
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)
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)
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else:
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warnings.append(f"False-negative evidence feature not found: {reference_id}")
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QualityEvidenceService._annotate_reviews(db, quality_check, features)
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return {
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"quality_check_id": str(quality_check.id),
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"project_id": str(quality_check.project_id),
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"candidate_dataset_id": str(quality_check.candidate_dataset_id) if quality_check.candidate_dataset_id else None,
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"reference_dataset_id": str(quality_check.reference_dataset_id),
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"analysis_run_id": str(quality_check.analysis_run_id) if quality_check.analysis_run_id else None,
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"feature_count": len(features),
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"warnings": warnings,
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"geojson": {
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"type": "FeatureCollection",
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"features": features,
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},
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}
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@staticmethod
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def _evidence_items(value: Any) -> list[dict[str, Any]]:
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if not isinstance(value, list):
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return []
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return [item for item in value if isinstance(item, dict)]
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@staticmethod
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def _string_value(value: Any) -> str | None:
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if value is None:
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return None
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text = str(value).strip()
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return text or None
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@staticmethod
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def _evidence_identifiers(findings: dict[str, Any]) -> tuple[set[str], set[str]]:
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candidate_ids: set[str] = set()
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reference_ids: set[str] = set()
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for evidence in QualityEvidenceService._evidence_items(findings.get("match_evidence")):
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candidate_id = QualityEvidenceService._string_value(evidence.get("candidate_feature_id"))
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reference_id = QualityEvidenceService._string_value(evidence.get("reference_feature_id"))
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if candidate_id:
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candidate_ids.add(candidate_id)
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if reference_id:
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reference_ids.add(reference_id)
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for evidence in QualityEvidenceService._evidence_items(findings.get("false_positive_evidence")):
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candidate_id = QualityEvidenceService._string_value(evidence.get("candidate_feature_id"))
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if candidate_id:
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candidate_ids.add(candidate_id)
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for evidence in QualityEvidenceService._evidence_items(findings.get("false_negative_evidence")):
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reference_id = QualityEvidenceService._string_value(evidence.get("reference_feature_id"))
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if reference_id:
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reference_ids.add(reference_id)
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return candidate_ids, reference_ids
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@staticmethod
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def _uuid_identifiers(identifiers: set[str]) -> list[UUID]:
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values: list[UUID] = []
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for identifier in identifiers:
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try:
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values.append(UUID(identifier))
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except (TypeError, ValueError):
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continue
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return values
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@staticmethod
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def _vector_feature_rows(db: Session, dataset_id: UUID, identifiers: set[str]) -> list[VectorFeature]:
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if not identifiers:
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return []
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conditions = [VectorFeature.source_feature_id.in_(identifiers)]
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uuid_identifiers = QualityEvidenceService._uuid_identifiers(identifiers)
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if uuid_identifiers:
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conditions.append(VectorFeature.id.in_(uuid_identifiers))
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return (
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db.query(VectorFeature)
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.filter(VectorFeature.dataset_id == dataset_id, or_(*conditions))
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.all()
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)
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@staticmethod
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def _candidate_feature_index(
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db: Session,
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quality_check: QualityCheck,
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identifiers: set[str],
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) -> dict[str, Any]:
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index: dict[str, Any] = {}
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if not identifiers:
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return index
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uuid_identifiers = QualityEvidenceService._uuid_identifiers(identifiers)
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if quality_check.candidate_dataset_id:
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for row in QualityEvidenceService._vector_feature_rows(db, quality_check.candidate_dataset_id, identifiers):
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QualityEvidenceService._add_index_keys(index, row)
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if uuid_identifiers:
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for row in db.query(Detection).filter(
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Detection.dataset_id == quality_check.candidate_dataset_id,
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Detection.id.in_(uuid_identifiers),
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).all():
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QualityEvidenceService._add_index_keys(index, row)
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for row in db.query(Segmentation).filter(
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Segmentation.dataset_id == quality_check.candidate_dataset_id,
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Segmentation.id.in_(uuid_identifiers),
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).all():
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QualityEvidenceService._add_index_keys(index, row)
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if quality_check.analysis_run_id and uuid_identifiers:
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for row in db.query(Detection).filter(
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Detection.analysis_run_id == quality_check.analysis_run_id,
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Detection.id.in_(uuid_identifiers),
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).all():
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QualityEvidenceService._add_index_keys(index, row)
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for row in db.query(Segmentation).filter(
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Segmentation.analysis_run_id == quality_check.analysis_run_id,
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Segmentation.id.in_(uuid_identifiers),
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).all():
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QualityEvidenceService._add_index_keys(index, row)
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return index
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@staticmethod
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def _reference_feature_index(
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db: Session,
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quality_check: QualityCheck,
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identifiers: set[str],
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) -> dict[str, Any]:
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index: dict[str, Any] = {}
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for row in QualityEvidenceService._vector_feature_rows(db, quality_check.reference_dataset_id, identifiers):
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QualityEvidenceService._add_index_keys(index, row)
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return index
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@staticmethod
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def _annotate_reviews(
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db: Session,
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quality_check: QualityCheck,
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features: list[dict[str, Any]],
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) -> None:
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if quality_check.check_type != "detections_vs_reference":
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return
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reviews = db.query(DetectionReview).filter(DetectionReview.quality_check_id == quality_check.id).all()
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review_index = {(row.evidence_role, row.evidence_feature_id): row for row in reviews}
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for feature in features:
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properties = feature.get("properties")
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if not isinstance(properties, dict):
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continue
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role = QualityEvidenceService._string_value(properties.get("qa_evidence_role"))
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if role == "false_positive":
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evidence_id = QualityEvidenceService._string_value(properties.get("candidate_feature_id"))
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elif role == "false_negative":
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evidence_id = QualityEvidenceService._string_value(properties.get("reference_feature_id"))
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else:
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continue
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review = review_index.get((role, evidence_id or ""))
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properties.update(
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{
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"review_decision": review.decision if review else "unreviewed",
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"review_notes": review.notes if review else None,
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"reviewed_by": review.reviewed_by if review else None,
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"reviewed_at": review.updated_at.isoformat() if review and review.updated_at else None,
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}
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)
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@staticmethod
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def _add_index_keys(index: dict[str, Any], row: Any) -> None:
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for key in QualityEvidenceService._row_identifiers(row):
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index.setdefault(key, row)
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@staticmethod
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def _row_identifiers(row: Any) -> set[str]:
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identifiers = {str(row.id)}
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source_feature_id = getattr(row, "source_feature_id", None)
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if source_feature_id:
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identifiers.add(str(source_feature_id))
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properties = getattr(row, "properties_json", None) or {}
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if isinstance(properties, dict):
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for property_key in ("vector_feature_id", "source_feature_id", "detection_id", "segmentation_id", "id", "name"):
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value = properties.get(property_key)
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if value is not None:
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identifiers.add(str(value))
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return identifiers
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@staticmethod
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def _row_to_feature(row: Any, *, role: str, quality_check: QualityCheck, evidence: dict[str, Any]) -> dict[str, Any]:
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try:
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geometry = to_shape(row.geometry)
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except Exception as exc:
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raise AppError(
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code="INVALID_QA_EVIDENCE_GEOMETRY",
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message="Persisted QA evidence geometry could not be converted to GeoJSON",
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details={"feature_id": str(getattr(row, "id", ""))},
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status_code=500,
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) from exc
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properties = dict(getattr(row, "properties_json", None) or {})
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properties.update(
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{
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"qa_evidence_role": role,
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"quality_check_id": str(quality_check.id),
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"project_id": str(quality_check.project_id),
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"candidate_dataset_id": str(quality_check.candidate_dataset_id) if quality_check.candidate_dataset_id else None,
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"reference_dataset_id": str(quality_check.reference_dataset_id),
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"analysis_run_id": str(quality_check.analysis_run_id) if quality_check.analysis_run_id else None,
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"feature_id": str(row.id),
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"dataset_id": str(getattr(row, "dataset_id", "")) if getattr(row, "dataset_id", None) else None,
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"source_feature_id": getattr(row, "source_feature_id", None),
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"feature_class": getattr(row, "feature_class", None) or getattr(row, "class_name", None),
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"candidate_feature_id": QualityEvidenceService._string_value(evidence.get("candidate_feature_id")),
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"reference_feature_id": QualityEvidenceService._string_value(evidence.get("reference_feature_id")),
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"iou": evidence.get("iou"),
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}
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)
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properties.update(QualityEvidenceService._row_provenance(row))
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return {
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"type": "Feature",
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"id": f"{role}:{row.id}",
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"geometry": mapping(geometry),
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"properties": properties,
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}
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@staticmethod
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def _row_provenance(row: Any) -> dict[str, Any]:
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if isinstance(row, Detection):
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return {
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"detection_id": str(row.id),
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"job_id": str(row.job_id) if row.job_id else None,
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"confidence": row.confidence,
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"model_name": row.model_name,
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"model_version": row.model_version,
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"source_tile_path": row.source_tile_path,
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"bbox_json": row.bbox_json,
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}
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if isinstance(row, Segmentation):
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return {
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"segmentation_id": str(row.id),
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"job_id": str(row.job_id) if row.job_id else None,
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"confidence": row.confidence,
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"model_name": row.model_name,
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"model_version": row.model_version,
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"source_tile_path": row.source_tile_path,
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"bbox_json": row.bbox_json,
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"mask_path": row.mask_path,
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"area_m2": row.area_m2,
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}
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return {}
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