798 lines
36 KiB
Python
798 lines
36 KiB
Python
from __future__ import annotations
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import uuid
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import json
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from datetime import UTC, datetime
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from pathlib import Path
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from typing import Any
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from typing import Type
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from geoalchemy2.shape import from_shape, to_shape
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from shapely.geometry import mapping, shape
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from sqlalchemy import func
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from app.core.config import Settings, get_settings
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from app.core.errors import AppError
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from app.models import AnalysisRun, Dataset, Detection, Job, Project, VectorFeature
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from app.schemas.detection import DetectionListResponse, DetectionRead, DetectionRunListResponse, DetectionRunRead, DetectionRunResponse
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from app.services.detection_georeferencing import pixel_bbox_to_epsg4326_polygon
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from app.services.detection_qa_service import DetectionQaService
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from app.services.model_asset_catalog_service import ModelAssetCatalogService
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from app.services.model_registry_service import ModelRegistryService
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from app.services.qa_service import QaService
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from app.services.quality_service import QualityService
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from app.services.yolo_adapter import YoloDetectionAdapter
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class DetectionService:
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@staticmethod
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def _now() -> datetime:
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return datetime.now(UTC)
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@staticmethod
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def run_detection(
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db,
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project_id: uuid.UUID,
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dataset_id: uuid.UUID,
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model_id: str,
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confidence_threshold: float,
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model_asset_id: str | None = None,
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class_filter: list[str] | None = None,
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tile_manifest_path: str | None = None,
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parameters_json: dict[str, Any] | None = None,
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settings: Settings | None = None,
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yolo_adapter_class: Type[YoloDetectionAdapter] = YoloDetectionAdapter,
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) -> DetectionRunResponse:
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parameters = dict(parameters_json or {})
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resolved_settings = settings or get_settings()
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project = db.get(Project, project_id)
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if not project:
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raise AppError(code="PROJECT_NOT_FOUND", message="Project not found", status_code=404)
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dataset = db.get(Dataset, dataset_id)
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if not dataset or dataset.project_id != project_id:
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raise AppError(code="DATASET_NOT_FOUND", message="Dataset not found", status_code=404)
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if dataset.dataset_type != "raster":
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raise AppError(
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code="INVALID_DATASET_TYPE",
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message="Detection requires a raster dataset",
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details={"dataset_type": dataset.dataset_type},
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status_code=400,
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)
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selected_model_asset = None
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if model_id == resolved_settings.yolo_model_id and model_asset_id:
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selected_model_asset = ModelAssetCatalogService.resolve_asset(model_asset_id, settings=resolved_settings)
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resolved_settings = ModelAssetCatalogService.settings_for_asset(resolved_settings, selected_model_asset)
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model = ModelRegistryService.get_model_capability(
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model_id,
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settings=resolved_settings,
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yolo_adapter_class=yolo_adapter_class,
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)
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if model is None:
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raise AppError(code="DETECTION_MODEL_NOT_FOUND", message="Detection model not found", status_code=404)
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if model.model_id == "manual-fixture-detector" and parameters.get("fixture_mode") is not True:
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raise AppError(
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code="FIXTURE_MODE_REQUIRED",
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message="Fixture detector requires explicit fixture_mode=true",
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status_code=400,
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)
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if model.model_id == resolved_settings.yolo_model_id and not tile_manifest_path:
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raise AppError(
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code="DETECTION_TILE_MANIFEST_REQUIRED",
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message="Configured YOLO inference requires an existing raster tile manifest path",
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status_code=400,
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)
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run_parameters = {
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"model_id": model.model_id,
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"model_asset_id": selected_model_asset.model_asset_id if selected_model_asset else None,
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"model_asset_path": selected_model_asset.model_path if selected_model_asset else None,
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"model_asset_sha256": selected_model_asset.sha256 if selected_model_asset else None,
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"confidence_threshold": confidence_threshold,
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"class_filter": class_filter or [],
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"tile_manifest_path": tile_manifest_path,
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"parameters_json": parameters,
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}
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job = DetectionService._create_job(db, project_id, dataset_id, run_parameters)
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analysis_run = DetectionService._create_analysis_run(db, project_id, dataset_id, job.id, model, run_parameters)
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if not model.configured:
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message = model.limitation_message
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code = "DETECTION_DEPENDENCY_UNAVAILABLE" if model.status == "dependency_unavailable" else "DETECTION_MODEL_UNAVAILABLE"
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DetectionService._mark_failed(db, analysis_run, job, code=code, message=message)
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return DetectionRunResponse(
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analysis_run_id=analysis_run.id,
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job_id=job.id,
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project_id=project_id,
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dataset_id=dataset_id,
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model_id=model.model_id,
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status="failed",
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detection_count=0,
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error_code=code,
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message=message,
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)
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if model.model_id == "manual-fixture-detector":
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detections = DetectionService._persist_fixture_detections(
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db=db,
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project_id=project_id,
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dataset_id=dataset_id,
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analysis_run=analysis_run,
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job=job,
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model_name=model.model_id,
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model_version=model.version,
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raw_detections=parameters.get("fixture_detections"),
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confidence_threshold=confidence_threshold,
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class_filter=class_filter or [],
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)
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DetectionService._mark_success(db, analysis_run, job, detection_count=len(detections))
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return DetectionRunResponse(
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analysis_run_id=analysis_run.id,
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job_id=job.id,
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project_id=project_id,
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dataset_id=dataset_id,
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model_id=model.model_id,
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status="success",
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detection_count=len(detections),
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message="Fixture detections persisted.",
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)
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if model.model_id == resolved_settings.yolo_model_id:
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try:
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detections, postprocess_summary = DetectionService._run_configured_yolo(
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db=db,
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project_id=project_id,
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dataset_id=dataset_id,
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analysis_run=analysis_run,
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job=job,
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model_name=model.model_id,
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model_version=model.version,
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tile_manifest_path=tile_manifest_path,
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confidence_threshold=confidence_threshold,
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class_filter=class_filter or [],
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settings=resolved_settings,
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yolo_adapter_class=yolo_adapter_class,
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)
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except AppError as exc:
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DetectionService._mark_failed(db, analysis_run, job, code=exc.code, message=exc.message)
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return DetectionRunResponse(
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analysis_run_id=analysis_run.id,
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job_id=job.id,
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project_id=project_id,
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dataset_id=dataset_id,
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model_id=model.model_id,
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status="failed",
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detection_count=0,
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error_code=exc.code,
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message=exc.message,
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)
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DetectionService._mark_success(db, analysis_run, job, detection_count=len(detections), extra_result=postprocess_summary)
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return DetectionRunResponse(
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analysis_run_id=analysis_run.id,
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job_id=job.id,
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project_id=project_id,
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dataset_id=dataset_id,
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model_id=model.model_id,
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status="success",
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detection_count=len(detections),
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message="YOLO detections persisted.",
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)
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raise AppError(code="DETECTION_MODEL_UNAVAILABLE", message="Detection model is unavailable", status_code=503)
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@staticmethod
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def get_run(db, analysis_run_id: uuid.UUID) -> DetectionRunRead:
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run = db.get(AnalysisRun, analysis_run_id)
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if not run or run.analysis_type != "detection":
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raise AppError(code="DETECTION_RUN_NOT_FOUND", message="Detection run not found", status_code=404)
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return DetectionRunRead.model_validate(run)
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@staticmethod
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def list_runs(
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db,
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*,
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project_id: uuid.UUID | None = None,
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dataset_id: uuid.UUID | None = None,
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) -> DetectionRunListResponse:
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query = db.query(AnalysisRun).filter(AnalysisRun.analysis_type == "detection")
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if project_id is not None:
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query = query.filter(AnalysisRun.project_id == project_id)
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if dataset_id is not None:
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query = query.filter(AnalysisRun.dataset_id == dataset_id)
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rows = query.order_by(AnalysisRun.created_at.desc()).all()
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return DetectionRunListResponse(items=[DetectionRunRead.model_validate(row) for row in rows], total=len(rows))
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@staticmethod
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def list_detections(
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db,
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analysis_run_id: uuid.UUID | None = None,
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*,
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dataset_id: uuid.UUID | None = None,
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class_name: str | None = None,
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min_confidence: float | None = None,
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) -> DetectionListResponse:
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if analysis_run_id is not None:
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run = db.get(AnalysisRun, analysis_run_id)
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if not run or run.analysis_type != "detection":
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raise AppError(code="DETECTION_RUN_NOT_FOUND", message="Detection run not found", status_code=404)
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rows = DetectionService._query_detection_rows(
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db,
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analysis_run_id=analysis_run_id,
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dataset_id=dataset_id,
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class_name=class_name,
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min_confidence=min_confidence,
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)
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items = [DetectionRead.model_validate(row) for row in rows]
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return DetectionListResponse(items=items, total=len(items))
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@staticmethod
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def get_detection(db, detection_id: uuid.UUID) -> DetectionRead:
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detection = db.get(Detection, detection_id)
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if not detection:
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raise AppError(code="DETECTION_NOT_FOUND", message="Detection not found", status_code=404)
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return DetectionRead.model_validate(detection)
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@staticmethod
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def detections_to_geojson(
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db,
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*,
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analysis_run_id: uuid.UUID | None = None,
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dataset_id: uuid.UUID | None = None,
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class_name: str | None = None,
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min_confidence: float | None = None,
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) -> dict[str, Any]:
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detections = DetectionService._query_detection_rows(
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db,
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analysis_run_id=analysis_run_id,
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dataset_id=dataset_id,
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class_name=class_name,
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min_confidence=min_confidence,
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)
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return {
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"type": "FeatureCollection",
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"features": [
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{
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"type": "Feature",
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"id": str(detection.id),
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"properties": DetectionService._detection_properties(detection),
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"geometry": mapping(to_shape(detection.geometry)),
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}
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for detection in detections
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],
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}
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@staticmethod
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def compare_detections_with_reference(
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db,
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analysis_run_id: uuid.UUID,
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reference_dataset_id: uuid.UUID,
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iou_threshold: float = 0.5,
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class_name: str | None = None,
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min_confidence: float | None = None,
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) -> dict[str, Any]:
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run = db.get(AnalysisRun, analysis_run_id)
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if not run or run.analysis_type != "detection":
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raise AppError(code="DETECTION_RUN_NOT_FOUND", message="Detection run not found", status_code=404)
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reference_dataset = db.get(Dataset, reference_dataset_id)
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if not reference_dataset:
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raise AppError(code="DATASET_NOT_FOUND", message="Reference dataset not found", status_code=404)
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if reference_dataset.project_id != run.project_id:
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raise AppError(code="INVALID_DATASET_SCOPE", message="Reference dataset does not belong to detection project", status_code=400)
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if reference_dataset.dataset_type not in {"vector", "geojson"}:
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raise AppError(code="INVALID_DATASET_TYPE", message="Reference dataset must be vector data", status_code=400)
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detections = DetectionService._query_detection_rows(
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db,
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analysis_run_id=analysis_run_id,
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dataset_id=run.dataset_id,
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class_name=class_name,
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min_confidence=min_confidence,
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)
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raw_candidate_geometries = [({"id": str(row.id), "class_name": row.class_name}, to_shape(row.geometry)) for row in detections]
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candidate_geometries = raw_candidate_geometries
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run_parameters = run.parameters_json if isinstance(run.parameters_json, dict) else {}
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manifest_path = DetectionQaService.tile_manifest_path(run_parameters)
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resolved_settings = get_settings()
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is_configured_yolo = (
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run_parameters.get("model_id") == resolved_settings.yolo_model_id
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or run.model_name == resolved_settings.yolo_model_id
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)
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if is_configured_yolo and not manifest_path:
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raise AppError(
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code="DETECTION_QA_COVERAGE_UNAVAILABLE",
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message="Configured YOLO QA requires persisted tile manifest provenance",
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status_code=422,
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)
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coverage = None
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if manifest_path:
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manifest = DetectionService._load_tile_manifest(manifest_path, resolved_settings.yolo_max_tiles)
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coverage = DetectionQaService.build_tile_coverage(
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manifest,
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manifest_path=manifest_path,
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expected_dataset_id=run.dataset_id,
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)
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reference_query = db.query(VectorFeature).filter(VectorFeature.dataset_id == reference_dataset_id)
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if coverage is not None and hasattr(reference_query, "count"):
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reference_raw_count = reference_query.count()
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references = reference_query.filter(
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func.ST_Intersects(VectorFeature.geometry, from_shape(coverage.geometry, srid=4326))
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).all()
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else:
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references = reference_query.all()
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reference_raw_count = len(references)
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if reference_raw_count == 0:
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raise AppError(
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code="REFERENCE_FEATURES_NOT_FOUND",
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message="Reference dataset has no persisted vector features for QA",
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status_code=422,
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)
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raw_reference_geometries = [({"id": str(row.id), "feature_class": row.feature_class}, to_shape(row.geometry)) for row in references]
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reference_geometries = raw_reference_geometries
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coverage_summary: dict[str, Any] = {
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"applied": False,
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"mode": "unbounded_no_manifest",
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"manifest_path": None,
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"tile_count": 0,
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"source_crs_values": [],
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"candidate_raw_count": len(raw_candidate_geometries),
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"candidate_evaluated_count": len(raw_candidate_geometries),
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"candidate_excluded_outside_count": 0,
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"candidate_clipped_boundary_count": 0,
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"reference_raw_count": reference_raw_count,
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"reference_evaluated_count": len(raw_reference_geometries),
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"reference_excluded_outside_count": 0,
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"reference_clipped_boundary_count": 0,
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}
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coverage_warnings: list[str] = []
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if coverage is not None:
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candidate_population = DetectionQaService.filter_population(raw_candidate_geometries, coverage)
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reference_population = DetectionQaService.filter_population(
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raw_reference_geometries,
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coverage,
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raw_count=reference_raw_count,
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)
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candidate_geometries = candidate_population.geometries
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reference_geometries = reference_population.geometries
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if not reference_geometries:
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raise AppError(
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code="REFERENCE_FEATURES_OUTSIDE_COVERAGE",
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message="Reference dataset has no polygon features inside persisted inference tile coverage",
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status_code=422,
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)
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coverage_summary = {
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"applied": True,
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"mode": "persisted_tile_manifest_union",
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"manifest_path": coverage.manifest_path,
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"tile_count": coverage.tile_count,
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"source_crs_values": list(coverage.source_crs_values),
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"candidate_raw_count": candidate_population.raw_count,
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"candidate_evaluated_count": candidate_population.evaluated_count,
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"candidate_excluded_outside_count": candidate_population.excluded_outside_count,
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"candidate_clipped_boundary_count": candidate_population.clipped_boundary_count,
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"reference_raw_count": reference_population.raw_count,
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"reference_evaluated_count": reference_population.evaluated_count,
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"reference_excluded_outside_count": reference_population.excluded_outside_count,
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"reference_clipped_boundary_count": reference_population.clipped_boundary_count,
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}
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coverage_warnings.append(
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"QA populations were clipped to the union of persisted inference tile footprints before matching."
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)
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evidence = QaService._match_io_u_evidence(
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candidate_geometries,
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reference_geometries,
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iou_threshold,
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)
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reference_envelopes = [(feature, geometry.envelope) for feature, geometry in reference_geometries]
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envelope_evidence = QaService._match_io_u_evidence(
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candidate_geometries,
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reference_envelopes,
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iou_threshold,
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)
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box_to_footprint_diagnostics = DetectionQaService.box_to_footprint_diagnostics(
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evidence,
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envelope_evidence,
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iou_threshold=iou_threshold,
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)
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mean_iou = None if not evidence.match_iou_values else sum(evidence.match_iou_values) / len(evidence.match_iou_values)
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precision = evidence.matches / (evidence.matches + evidence.false_positives) if evidence.matches + evidence.false_positives > 0 else None
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recall = evidence.matches / (evidence.matches + evidence.false_negatives) if evidence.matches + evidence.false_negatives > 0 else None
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f1_score = None
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if precision is not None and recall is not None:
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f1_score = (2 * precision * recall) / (precision + recall) if precision + recall > 0 else 0.0
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status = "unsupported" if evidence.unsupported else "ok"
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quality_check = QualityService.persist_quality_check(
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db=db,
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project_id=run.project_id,
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analysis_run_id=analysis_run_id,
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candidate_dataset_id=run.dataset_id,
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reference_dataset_id=reference_dataset_id,
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check_type="detections_vs_reference",
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status=status,
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score=f1_score,
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parameters={
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"analysis_run_id": str(analysis_run_id),
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"reference_dataset_id": str(reference_dataset_id),
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"iou_threshold": iou_threshold,
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"class_name": class_name,
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"min_confidence": min_confidence,
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"coverage_policy": coverage_summary["mode"],
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},
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findings={
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"matches": evidence.matches,
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"false_positives": evidence.false_positives,
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"false_negatives": evidence.false_negatives,
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"warnings": coverage_warnings + evidence.warnings,
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"unsupported_geometry": evidence.unsupported,
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"coverage": coverage_summary,
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"box_to_footprint_diagnostics": box_to_footprint_diagnostics,
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"match_evidence": evidence.match_evidence,
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"false_positive_evidence": evidence.false_positive_evidence,
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"false_negative_evidence": evidence.false_negative_evidence,
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},
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metrics={
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"precision": precision,
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"recall": recall,
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"f1": f1_score,
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"mean_iou": mean_iou,
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"false_positive_count": evidence.false_positives,
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"false_negative_count": evidence.false_negatives,
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},
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)
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return {
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"status": status,
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|
"quality_check_id": str(quality_check.id),
|
|
"analysis_run_id": str(analysis_run_id),
|
|
"reference_dataset_id": str(reference_dataset_id),
|
|
"candidate_feature_count": len(candidate_geometries),
|
|
"reference_feature_count": len(reference_geometries),
|
|
"candidate_feature_count_raw": len(raw_candidate_geometries),
|
|
"reference_feature_count_raw": reference_raw_count,
|
|
"matches": evidence.matches,
|
|
"false_positives": evidence.false_positives,
|
|
"false_negatives": evidence.false_negatives,
|
|
"precision": precision,
|
|
"recall": recall,
|
|
"f1_score": f1_score,
|
|
"mean_iou": mean_iou,
|
|
"iou_threshold": iou_threshold,
|
|
"warnings": coverage_warnings + evidence.warnings,
|
|
"coverage": coverage_summary,
|
|
"box_to_footprint_diagnostics": box_to_footprint_diagnostics,
|
|
"match_evidence": evidence.match_evidence,
|
|
"false_positive_evidence": evidence.false_positive_evidence,
|
|
"false_negative_evidence": evidence.false_negative_evidence,
|
|
}
|
|
|
|
@staticmethod
|
|
def _create_job(db, project_id: uuid.UUID, dataset_id: uuid.UUID, parameters: dict[str, Any]) -> Job:
|
|
job = Job(
|
|
id=uuid.uuid4(),
|
|
job_type="detection.run",
|
|
status="running",
|
|
project_id=project_id,
|
|
dataset_id=dataset_id,
|
|
input_dataset_id=dataset_id,
|
|
parameters_json=parameters,
|
|
started_at=DetectionService._now(),
|
|
)
|
|
db.add(job)
|
|
db.commit()
|
|
db.refresh(job)
|
|
return job
|
|
|
|
@staticmethod
|
|
def _query_detection_rows(
|
|
db,
|
|
*,
|
|
analysis_run_id: uuid.UUID | None = None,
|
|
dataset_id: uuid.UUID | None = None,
|
|
class_name: str | None = None,
|
|
min_confidence: float | None = None,
|
|
) -> list[Detection]:
|
|
query = db.query(Detection)
|
|
if analysis_run_id is not None:
|
|
query = query.filter(Detection.analysis_run_id == analysis_run_id)
|
|
if dataset_id is not None:
|
|
query = query.filter(Detection.dataset_id == dataset_id)
|
|
if class_name:
|
|
query = query.filter(Detection.class_name == class_name)
|
|
if min_confidence is not None:
|
|
query = query.filter(Detection.confidence >= min_confidence)
|
|
return query.order_by(Detection.created_at.desc()).all()
|
|
|
|
@staticmethod
|
|
def _detection_properties(detection: Detection) -> dict[str, Any]:
|
|
return {
|
|
"detection_id": str(detection.id),
|
|
"class_name": detection.class_name,
|
|
"confidence": detection.confidence,
|
|
"model_name": detection.model_name,
|
|
"model_version": detection.model_version,
|
|
"analysis_run_id": str(detection.analysis_run_id) if detection.analysis_run_id else None,
|
|
"dataset_id": str(detection.dataset_id) if detection.dataset_id else None,
|
|
"job_id": str(detection.job_id) if detection.job_id else None,
|
|
"source_tile_path": detection.source_tile_path,
|
|
"bbox_json": detection.bbox_json,
|
|
}
|
|
|
|
@staticmethod
|
|
def _create_analysis_run(db, project_id, dataset_id, job_id, model, parameters: dict[str, Any]) -> AnalysisRun:
|
|
analysis_run = AnalysisRun(
|
|
id=uuid.uuid4(),
|
|
project_id=project_id,
|
|
dataset_id=dataset_id,
|
|
job_id=job_id,
|
|
analysis_type="detection",
|
|
status="running",
|
|
model_name=model.model_id,
|
|
model_version=model.version,
|
|
parameters_json=parameters,
|
|
started_at=DetectionService._now(),
|
|
)
|
|
db.add(analysis_run)
|
|
db.commit()
|
|
db.refresh(analysis_run)
|
|
return analysis_run
|
|
|
|
@staticmethod
|
|
def _mark_failed(db, analysis_run: AnalysisRun, job: Job, code: str, message: str) -> None:
|
|
result = {"error_code": code, "message": message, "detection_count": 0}
|
|
analysis_run.status = "failed"
|
|
analysis_run.finished_at = DetectionService._now()
|
|
analysis_run.error_message = message
|
|
analysis_run.result_json = result
|
|
job.status = "failed"
|
|
job.finished_at = analysis_run.finished_at
|
|
job.error_message = message
|
|
job.result_json = result
|
|
db.add(analysis_run)
|
|
db.add(job)
|
|
db.commit()
|
|
db.refresh(analysis_run)
|
|
db.refresh(job)
|
|
|
|
@staticmethod
|
|
def _mark_success(db, analysis_run: AnalysisRun, job: Job, detection_count: int, extra_result: dict[str, Any] | None = None) -> None:
|
|
result = {"detection_count": detection_count}
|
|
if extra_result:
|
|
result.update(extra_result)
|
|
analysis_run.status = "success"
|
|
analysis_run.finished_at = DetectionService._now()
|
|
analysis_run.result_json = result
|
|
job.status = "success"
|
|
job.finished_at = analysis_run.finished_at
|
|
job.result_json = result
|
|
db.add(analysis_run)
|
|
db.add(job)
|
|
db.commit()
|
|
db.refresh(analysis_run)
|
|
db.refresh(job)
|
|
|
|
@staticmethod
|
|
def _persist_fixture_detections(
|
|
db,
|
|
project_id: uuid.UUID,
|
|
dataset_id: uuid.UUID,
|
|
analysis_run: AnalysisRun,
|
|
job: Job,
|
|
model_name: str,
|
|
model_version: str | None,
|
|
raw_detections: Any,
|
|
confidence_threshold: float,
|
|
class_filter: list[str],
|
|
) -> list[Detection]:
|
|
if not isinstance(raw_detections, list):
|
|
raise AppError(code="INVALID_FIXTURE_DETECTIONS", message="fixture_detections must be a list", status_code=400)
|
|
persisted: list[Detection] = []
|
|
allowed_classes = set(class_filter)
|
|
for raw in raw_detections:
|
|
if not isinstance(raw, dict):
|
|
raise AppError(code="INVALID_FIXTURE_DETECTION", message="Each fixture detection must be an object", status_code=400)
|
|
class_name = str(raw.get("class_name") or "")
|
|
confidence = float(raw.get("confidence", 0.0))
|
|
if allowed_classes and class_name not in allowed_classes:
|
|
continue
|
|
if confidence < confidence_threshold:
|
|
continue
|
|
geometry_payload = raw.get("geometry")
|
|
if not isinstance(geometry_payload, dict):
|
|
raise AppError(code="INVALID_FIXTURE_DETECTION", message="Fixture detection geometry is required", status_code=400)
|
|
geometry = shape(geometry_payload)
|
|
if geometry.is_empty or not geometry.is_valid:
|
|
raise AppError(code="INVALID_FIXTURE_GEOMETRY", message="Fixture detection geometry must be valid", status_code=400)
|
|
detection = Detection(
|
|
id=uuid.uuid4(),
|
|
project_id=project_id,
|
|
dataset_id=dataset_id,
|
|
analysis_run_id=analysis_run.id,
|
|
job_id=job.id,
|
|
model_name=model_name,
|
|
model_version=model_version,
|
|
class_name=class_name,
|
|
confidence=confidence,
|
|
geometry=from_shape(geometry, srid=4326),
|
|
bbox_json=raw.get("bbox_json"),
|
|
source_tile_path=raw.get("source_tile_path"),
|
|
properties_json=raw.get("properties_json"),
|
|
)
|
|
db.add(detection)
|
|
persisted.append(detection)
|
|
db.commit()
|
|
for detection in persisted:
|
|
db.refresh(detection)
|
|
return persisted
|
|
|
|
@staticmethod
|
|
def _run_configured_yolo(
|
|
db,
|
|
project_id: uuid.UUID,
|
|
dataset_id: uuid.UUID,
|
|
analysis_run: AnalysisRun,
|
|
job: Job,
|
|
model_name: str,
|
|
model_version: str | None,
|
|
tile_manifest_path: str | None,
|
|
confidence_threshold: float,
|
|
class_filter: list[str],
|
|
settings: Settings,
|
|
yolo_adapter_class: Type[YoloDetectionAdapter],
|
|
) -> tuple[list[Detection], dict[str, Any]]:
|
|
manifest = DetectionService._load_tile_manifest(tile_manifest_path, settings.yolo_max_tiles)
|
|
model_path = Path(settings.yolo_model_path or "").expanduser()
|
|
adapter = yolo_adapter_class(settings)
|
|
model = adapter.load_model(model_path)
|
|
allowed_classes = {DetectionService._canonical_class_name(value) for value in class_filter if DetectionService._canonical_class_name(value)}
|
|
candidates: list[dict[str, Any]] = []
|
|
manifest_crs = manifest.get("crs") or manifest.get("source_crs") or manifest.get("dataset_crs") or "EPSG:4326"
|
|
for tile in manifest["tiles"]:
|
|
tile_path = DetectionService._resolve_tile_path(tile, Path(tile_manifest_path or "").expanduser())
|
|
for raw in adapter.predict_tile(model, tile_path, confidence_threshold):
|
|
model_class_name = str(raw.get("class_name") or "").strip()
|
|
class_name = DetectionService._canonical_class_name(model_class_name)
|
|
confidence = float(raw.get("confidence", 0.0))
|
|
if allowed_classes and class_name not in allowed_classes:
|
|
continue
|
|
if confidence < confidence_threshold:
|
|
continue
|
|
bbox = raw.get("bbox")
|
|
if not isinstance(bbox, list):
|
|
raise AppError(code="DETECTION_INVALID_BBOX", message="YOLO adapter returned a detection without bbox", status_code=422)
|
|
geometry = pixel_bbox_to_epsg4326_polygon(bbox=bbox, tile=tile, crs=tile.get("crs") or manifest_crs)
|
|
properties = dict(raw.get("properties") or {})
|
|
if model_class_name and model_class_name != class_name:
|
|
properties.setdefault("model_class_name", model_class_name)
|
|
candidates.append(
|
|
{
|
|
"class_name": class_name,
|
|
"confidence": confidence,
|
|
"geometry": geometry,
|
|
"bbox": bbox,
|
|
"source_tile_path": str(tile_path),
|
|
"properties": {**properties, "tile_index": tile.get("index")},
|
|
}
|
|
)
|
|
filtered_candidates = DetectionService._suppress_duplicate_candidates(
|
|
candidates,
|
|
iou_threshold=float(settings.yolo_duplicate_iou_threshold),
|
|
)
|
|
persisted: list[Detection] = []
|
|
for candidate in filtered_candidates:
|
|
bbox = candidate["bbox"]
|
|
detection = Detection(
|
|
id=uuid.uuid4(),
|
|
project_id=project_id,
|
|
dataset_id=dataset_id,
|
|
analysis_run_id=analysis_run.id,
|
|
job_id=job.id,
|
|
model_name=model_name,
|
|
model_version=model_version,
|
|
class_name=candidate["class_name"],
|
|
confidence=candidate["confidence"],
|
|
geometry=from_shape(candidate["geometry"], srid=4326),
|
|
bbox_json={
|
|
"x_min": float(bbox[0]),
|
|
"y_min": float(bbox[1]),
|
|
"x_max": float(bbox[2]),
|
|
"y_max": float(bbox[3]),
|
|
},
|
|
source_tile_path=candidate["source_tile_path"],
|
|
properties_json=candidate["properties"],
|
|
)
|
|
db.add(detection)
|
|
persisted.append(detection)
|
|
db.commit()
|
|
for detection in persisted:
|
|
db.refresh(detection)
|
|
return persisted, {
|
|
"raw_detection_count": len(candidates),
|
|
"suppressed_detection_count": len(candidates) - len(filtered_candidates),
|
|
"duplicate_iou_threshold": float(settings.yolo_duplicate_iou_threshold),
|
|
}
|
|
|
|
@staticmethod
|
|
def _canonical_class_name(value: Any) -> str:
|
|
return str(value or "").strip().casefold()
|
|
|
|
@staticmethod
|
|
def _suppress_duplicate_candidates(candidates: list[dict[str, Any]], iou_threshold: float) -> list[dict[str, Any]]:
|
|
if iou_threshold <= 0 or len(candidates) < 2:
|
|
return candidates
|
|
kept: list[dict[str, Any]] = []
|
|
for candidate in sorted(candidates, key=lambda item: float(item["confidence"]), reverse=True):
|
|
duplicate = False
|
|
for kept_candidate in kept:
|
|
if candidate["class_name"] != kept_candidate["class_name"]:
|
|
continue
|
|
if DetectionService._geometry_iou(candidate["geometry"], kept_candidate["geometry"]) >= iou_threshold:
|
|
duplicate = True
|
|
break
|
|
if not duplicate:
|
|
kept.append(candidate)
|
|
return kept
|
|
|
|
@staticmethod
|
|
def _geometry_iou(left, right) -> float:
|
|
if left.is_empty or right.is_empty:
|
|
return 0.0
|
|
intersection_area = left.intersection(right).area
|
|
if intersection_area <= 0:
|
|
return 0.0
|
|
union_area = left.union(right).area
|
|
if union_area <= 0:
|
|
return 0.0
|
|
return intersection_area / union_area
|
|
|
|
@staticmethod
|
|
def _load_tile_manifest(tile_manifest_path: str | None, max_tiles: int) -> dict[str, Any]:
|
|
if not tile_manifest_path:
|
|
raise AppError(
|
|
code="DETECTION_TILE_MANIFEST_REQUIRED",
|
|
message="Configured YOLO inference requires an existing raster tile manifest path",
|
|
status_code=400,
|
|
)
|
|
manifest_path = Path(tile_manifest_path).expanduser()
|
|
if not manifest_path.exists() or not manifest_path.is_file():
|
|
raise AppError(
|
|
code="DETECTION_TILE_MANIFEST_NOT_FOUND",
|
|
message="Raster tile manifest path does not exist",
|
|
details={"tile_manifest_path": str(manifest_path)},
|
|
status_code=422,
|
|
)
|
|
try:
|
|
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
|
|
except json.JSONDecodeError as exc:
|
|
raise AppError(code="DETECTION_TILE_MANIFEST_INVALID", message="Raster tile manifest must be valid JSON", status_code=422) from exc
|
|
tiles = manifest.get("tiles")
|
|
if not isinstance(tiles, list) or not tiles:
|
|
raise AppError(code="DETECTION_TILE_MANIFEST_INVALID", message="Raster tile manifest must contain tiles", status_code=422)
|
|
if len(tiles) > max_tiles:
|
|
raise AppError(
|
|
code="DETECTION_TILE_LIMIT_EXCEEDED",
|
|
message="Raster tile manifest exceeds configured YOLO tile limit",
|
|
details={"tile_count": len(tiles), "max_tiles": max_tiles},
|
|
status_code=422,
|
|
)
|
|
return manifest
|
|
|
|
@staticmethod
|
|
def _resolve_tile_path(tile: dict[str, Any], manifest_path: Path) -> Path:
|
|
raw_path = tile.get("path")
|
|
if not isinstance(raw_path, str) or not raw_path:
|
|
raise AppError(code="DETECTION_TILE_MANIFEST_INVALID", message="Tile manifest entries require a path", status_code=422)
|
|
tile_path = Path(raw_path).expanduser()
|
|
if not tile_path.is_absolute():
|
|
tile_path = manifest_path.parent / tile_path
|
|
if not tile_path.exists() or not tile_path.is_file():
|
|
raise AppError(
|
|
code="DETECTION_TILE_NOT_FOUND",
|
|
message="Tile referenced by manifest does not exist",
|
|
details={"tile_path": str(tile_path)},
|
|
status_code=422,
|
|
)
|
|
return tile_path
|