397 lines
19 KiB
Python
397 lines
19 KiB
Python
from __future__ import annotations
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import io
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import math
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from datetime import UTC, datetime
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from pathlib import Path
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from uuid import UUID
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from geoalchemy2.shape import to_shape
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from pyproj import Transformer
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from shapely.geometry import box, mapping
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from shapely.ops import transform as shapely_transform
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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 Area, Dataset
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from app.schemas.dhmv import (
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TerrainMetric,
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TerrainPartitionSelectionRequest,
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TerrainSelectionRequest,
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TerrainSelectionResponse,
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TerrainSelectionSummary,
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)
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from app.services.dhmv_acquisition_service import DhmvAcquisitionService
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from app.services.raster_partition_analysis_service import RasterPartitionAnalysisService
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class TerrainAnalysisService:
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UNSUPPORTED_METRICS = ["water_depth_m", "water_volume_m3"]
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LIMITATION = (
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"Hoogte, reliëf en helling zijn afgeleid uit DHMV II. Afstroming vraagt bijkomende hydrologische modellering. "
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"Waterdiepte en watervolume zijn niet beschikbaar uit DTM/DSM alleen."
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)
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@staticmethod
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def _load_dataset(db, project_id: UUID, dataset_id: UUID) -> Dataset:
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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" or dataset.source_name != DhmvAcquisitionService.PROVIDER:
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raise AppError(
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code="INVALID_TERRAIN_DATASET",
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message="Terrain analysis requires a governed DHMV raster dataset",
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status_code=400,
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)
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if dataset.status != "ready" or not dataset.storage_path or not Path(dataset.storage_path).is_file():
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raise AppError(code="DATASET_FILE_MISSING", message="Persisted DHMV raster file is unavailable", status_code=404)
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return dataset
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@staticmethod
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def _selection_geometry(db, project_id: UUID, payload: TerrainSelectionRequest):
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selection = box(payload.bbox.min_x, payload.bbox.min_y, payload.bbox.max_x, payload.bbox.max_y)
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if payload.area_id is None:
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return selection
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area = db.get(Area, payload.area_id)
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if not area:
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raise AppError(code="AREA_NOT_FOUND", message="Area not found", status_code=404)
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if area.project_id != project_id:
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raise AppError(code="INVALID_DATASET_SCOPE", message="Area does not belong to this project", status_code=400)
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selection = selection.intersection(to_shape(area.geometry))
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if selection.is_empty or selection.area <= 0:
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raise AppError(code="TERRAIN_SELECTION_OUTSIDE_AREA", message="Selection does not overlap the selected work area", status_code=422)
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return selection
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@staticmethod
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def analyze(
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db,
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project_id: UUID,
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dataset_id: UUID,
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payload: TerrainSelectionRequest,
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*,
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settings: Settings | None = None,
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) -> dict:
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resolved_settings = settings or get_settings()
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dataset = TerrainAnalysisService._load_dataset(db, project_id, dataset_id)
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selection_4326 = TerrainAnalysisService._selection_geometry(db, project_id, payload)
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try:
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import numpy as np
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import rasterio
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from rasterio.features import geometry_mask
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from rasterio.mask import mask
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except ImportError as exc:
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raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio and numpy are required for terrain analysis", status_code=503) from exc
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source_metadata = dataset.source_metadata or {}
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product_key = str(source_metadata.get("product_key") or "")
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surface_model = str(source_metadata.get("surface_model") or "")
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if product_key not in DhmvAcquisitionService._products() or surface_model not in {"terrain", "surface"}:
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raise AppError(code="INVALID_TERRAIN_METADATA", message="DHMV product provenance is incomplete", status_code=409)
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try:
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with rasterio.open(dataset.storage_path) as source:
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if source.crs is None:
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raise AppError(code="INVALID_DATASET_CRS", message="DHMV raster CRS is missing", status_code=409)
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transformer = Transformer.from_crs("EPSG:4326", source.crs, always_xy=True)
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selection_metric = shapely_transform(transformer.transform, selection_4326)
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source_extent = box(*source.bounds)
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analysis_geometry = selection_metric.intersection(source_extent)
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if analysis_geometry.is_empty or analysis_geometry.area <= 0:
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raise AppError(
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code="TERRAIN_SELECTION_OUTSIDE_DATASET",
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message="Selection does not overlap the persisted DHMV raster",
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status_code=422,
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)
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min_x, min_y, max_x, max_y = analysis_geometry.bounds
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expected_cells = math.ceil((max_x - min_x) / abs(source.res[0])) * math.ceil((max_y - min_y) / abs(source.res[1]))
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if expected_cells > resolved_settings.dhmv_max_pixels:
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raise AppError(
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code="TERRAIN_SELECTION_TOO_LARGE",
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message="Terrain analysis exceeds the configured raster cell limit",
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details={"pixel_count": expected_cells, "max_pixels": resolved_settings.dhmv_max_pixels},
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status_code=422,
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)
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clipped, clipped_transform = mask(
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source,
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[mapping(analysis_geometry)],
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crop=True,
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filled=False,
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indexes=[1],
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)
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elevation = np.ma.asarray(clipped[0], dtype="float64")
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raw = elevation.filled(np.nan)
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nodata = source.nodata
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invalid = ~np.isfinite(raw)
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if nodata is not None:
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invalid |= raw == float(nodata)
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selected_cells = geometry_mask(
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[mapping(analysis_geometry)],
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out_shape=elevation.shape,
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transform=clipped_transform,
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invert=True,
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)
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valid_mask = selected_cells & ~np.ma.getmaskarray(elevation) & ~invalid
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values = raw[valid_mask]
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if values.size == 0:
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raise AppError(code="TERRAIN_NO_VALID_DATA", message="No valid DHMV height cells occur in this selection", status_code=422)
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resolution_x = abs(float(source.res[0]))
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resolution_y = abs(float(source.res[1]))
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slope_values = np.asarray([], dtype="float64")
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if raw.shape[0] >= 2 and raw.shape[1] >= 2:
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surface = np.where(valid_mask, raw, np.nan)
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gradient_y, gradient_x = np.gradient(surface, resolution_y, resolution_x)
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slope = np.degrees(np.arctan(np.hypot(gradient_x, gradient_y)))
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slope_values = slope[np.isfinite(slope) & valid_mask]
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except AppError:
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raise
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except Exception as exc:
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raise AppError(
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code="TERRAIN_ANALYSIS_FAILED",
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message="The persisted DHMV raster could not be analysed",
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details={"reason": str(exc)},
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status_code=500,
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) from exc
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def metric(key: str, label: str, value: float, unit: str, method: str) -> TerrainMetric:
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return TerrainMetric(
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metric_key=key,
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metric_label=label,
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metric_value=round(float(value), 4),
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metric_unit=unit,
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aggregation_method=method,
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)
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prefix = "terrain" if surface_model == "terrain" else "surface"
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elevation_label = "Gemiddelde maaiveldhoogte" if surface_model == "terrain" else "Gemiddelde oppervlaktehoogte"
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metrics = [
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metric(f"{prefix}_elevation_mean_m", elevation_label, values.mean(), "m TAW", "mean_valid_cells"),
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metric(f"{prefix}_elevation_min_m", "Laagste hoogte", values.min(), "m TAW", "minimum_valid_cells"),
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metric(f"{prefix}_elevation_max_m", "Hoogste hoogte", values.max(), "m TAW", "maximum_valid_cells"),
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metric(f"{prefix}_elevation_p10_m", "10e percentiel hoogte", np.percentile(values, 10), "m TAW", "percentile_10_valid_cells"),
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metric(f"{prefix}_elevation_p90_m", "90e percentiel hoogte", np.percentile(values, 90), "m TAW", "percentile_90_valid_cells"),
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metric("relief_m", "Reliëfverschil", values.max() - values.min(), "m", "maximum_minus_minimum"),
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]
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if slope_values.size:
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metrics.extend(
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[
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metric("slope_mean_deg", "Gemiddelde helling", slope_values.mean(), "°", "mean_finite_gradient"),
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metric("slope_p90_deg", "90e percentiel helling", np.percentile(slope_values, 90), "°", "percentile_90_finite_gradient"),
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metric("slope_max_deg", "Steilste helling", slope_values.max(), "°", "maximum_finite_gradient"),
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]
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)
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primary = metrics[0]
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selected_cell_count = int(selected_cells.sum())
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response = TerrainSelectionResponse(
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dataset_id=dataset.id,
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dataset_ids=[dataset.id],
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partition_count=1,
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product_key=product_key,
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surface_model=surface_model,
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selection_bbox=payload.bbox,
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selection_area_id=payload.area_id,
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sample_count=int(values.size),
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slope_sample_count=int(slope_values.size),
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coverage_ratio=round(float(values.size / max(1, selected_cell_count)), 6),
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resolution_m=round(max(resolution_x, resolution_y), 4),
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vertical_reference=str(source_metadata.get("vertical_reference") or DhmvAcquisitionService.VERTICAL_REFERENCE),
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summary=TerrainSelectionSummary(
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metric_label=primary.metric_label,
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metric_value=primary.metric_value,
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metric_unit=primary.metric_unit,
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aggregation_method=primary.aggregation_method,
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primary_metric_key=primary.metric_key,
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metrics=metrics,
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),
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unsupported_metrics=TerrainAnalysisService.UNSUPPORTED_METRICS,
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limitation_message=TerrainAnalysisService.LIMITATION,
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generated_at=datetime.now(UTC).isoformat(),
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)
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return response.model_dump(mode="json")
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@staticmethod
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def analyze_partitions(
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db,
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project_id: UUID,
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payload: TerrainPartitionSelectionRequest,
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*,
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settings: Settings | None = None,
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) -> dict:
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resolved_settings = settings or get_settings()
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product = DhmvAcquisitionService._products().get(payload.product_key.strip().lower())
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if product is None:
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raise AppError(
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code="DHMV_PRODUCT_NOT_SUPPORTED",
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message="Select a governed DHMV terrain or surface product",
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details={"product_key": payload.product_key},
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status_code=422,
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)
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selection_4326 = TerrainAnalysisService._selection_geometry(db, project_id, payload)
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partition = RasterPartitionAnalysisService.select(
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db,
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project_id,
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source_name=DhmvAcquisitionService.PROVIDER,
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product_key=product.key,
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selection_geometry_4326=selection_4326,
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nodata=DhmvAcquisitionService.NODATA,
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max_pixels=resolved_settings.dhmv_max_pixels,
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)
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surface_models = {
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str((dataset.source_metadata or {}).get("surface_model") or "")
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for dataset in partition.datasets
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}
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if surface_models != {product.surface_model}:
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raise AppError(
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code="INVALID_TERRAIN_METADATA",
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message="DHMV partition provenance is incomplete",
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details={"surface_models": sorted(surface_models)},
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status_code=409,
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)
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try:
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import numpy as np
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except ImportError as exc:
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raise AppError(
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code="RASTER_PROCESSING_UNAVAILABLE",
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message="Numpy is required for partitioned terrain analysis",
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status_code=503,
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) from exc
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raw = partition.values
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invalid = ~np.isfinite(raw) | (raw == DhmvAcquisitionService.NODATA)
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valid_mask = partition.selected_cells & ~invalid
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values = raw[valid_mask]
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if values.size == 0:
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raise AppError(
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code="TERRAIN_NO_VALID_DATA",
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message="No valid DHMV height cells occur in this selection",
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status_code=422,
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)
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slope_values = np.asarray([], dtype="float64")
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if raw.shape[0] >= 2 and raw.shape[1] >= 2:
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surface = np.where(valid_mask, raw, np.nan)
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gradient_y, gradient_x = np.gradient(
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surface,
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partition.resolution_y,
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partition.resolution_x,
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)
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slope = np.degrees(np.arctan(np.hypot(gradient_x, gradient_y)))
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slope_values = slope[np.isfinite(slope) & valid_mask]
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def metric(key: str, label: str, value: float, unit: str, method: str) -> TerrainMetric:
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return TerrainMetric(
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metric_key=key,
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metric_label=label,
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metric_value=round(float(value), 4),
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metric_unit=unit,
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aggregation_method=method,
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)
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prefix = "terrain" if product.surface_model == "terrain" else "surface"
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elevation_label = (
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"Gemiddelde maaiveldhoogte"
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if product.surface_model == "terrain"
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else "Gemiddelde oppervlaktehoogte"
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)
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metrics = [
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metric(f"{prefix}_elevation_mean_m", elevation_label, values.mean(), "m TAW", "mean_valid_cells"),
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metric(f"{prefix}_elevation_min_m", "Laagste hoogte", values.min(), "m TAW", "minimum_valid_cells"),
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metric(f"{prefix}_elevation_max_m", "Hoogste hoogte", values.max(), "m TAW", "maximum_valid_cells"),
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metric(f"{prefix}_elevation_p10_m", "10e percentiel hoogte", np.percentile(values, 10), "m TAW", "percentile_10_valid_cells"),
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metric(f"{prefix}_elevation_p90_m", "90e percentiel hoogte", np.percentile(values, 90), "m TAW", "percentile_90_valid_cells"),
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metric("relief_m", "Reliëfverschil", values.max() - values.min(), "m", "maximum_minus_minimum"),
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]
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if slope_values.size:
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metrics.extend(
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[
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metric("slope_mean_deg", "Gemiddelde helling", slope_values.mean(), "°", "mean_finite_gradient"),
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metric("slope_p90_deg", "90e percentiel helling", np.percentile(slope_values, 90), "°", "percentile_90_finite_gradient"),
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metric("slope_max_deg", "Steilste helling", slope_values.max(), "°", "maximum_finite_gradient"),
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]
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)
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primary = metrics[0]
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selected_cell_count = int(partition.selected_cells.sum())
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first_dataset = partition.datasets[0]
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response = TerrainSelectionResponse(
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dataset_id=first_dataset.id,
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dataset_ids=[dataset.id for dataset in partition.datasets],
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partition_count=len(partition.datasets),
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product_key=product.key,
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surface_model=product.surface_model,
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selection_bbox=payload.bbox,
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selection_area_id=payload.area_id,
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sample_count=int(values.size),
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slope_sample_count=int(slope_values.size),
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coverage_ratio=round(float(values.size / max(1, selected_cell_count)), 6),
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resolution_m=round(max(partition.resolution_x, partition.resolution_y), 4),
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vertical_reference=DhmvAcquisitionService.VERTICAL_REFERENCE,
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summary=TerrainSelectionSummary(
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metric_label=primary.metric_label,
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metric_value=primary.metric_value,
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metric_unit=primary.metric_unit,
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aggregation_method=primary.aggregation_method,
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primary_metric_key=primary.metric_key,
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metrics=metrics,
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),
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unsupported_metrics=TerrainAnalysisService.UNSUPPORTED_METRICS,
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limitation_message=(
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f"{TerrainAnalysisService.LIMITATION} De selectie werd exact berekend over "
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f"{len(partition.datasets)} persistente gemeentelijke rasterpartities."
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),
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generated_at=datetime.now(UTC).isoformat(),
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)
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return response.model_dump(mode="json")
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@staticmethod
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def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1800) -> bytes:
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dataset = TerrainAnalysisService._load_dataset(db, project_id, dataset_id)
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try:
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import numpy as np
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import rasterio
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from PIL import Image
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from rasterio.enums import Resampling
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except ImportError as exc:
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raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio, numpy and Pillow are required for terrain rendering", status_code=503) from exc
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try:
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with rasterio.open(dataset.storage_path) as source:
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scale = min(1.0, max_dimension / max(source.width, source.height))
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width = max(1, round(source.width * scale))
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height = max(1, round(source.height * scale))
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data = source.read(1, out_shape=(height, width), masked=True, resampling=Resampling.bilinear)
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values = np.asarray(data.filled(np.nan), dtype="float64")
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valid = np.isfinite(values) & ~np.ma.getmaskarray(data)
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if not valid.any():
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raise AppError(code="TERRAIN_NO_VALID_DATA", message="DHMV raster contains no renderable cells", status_code=422)
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low, high = np.percentile(values[valid], [2, 98])
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if high <= low:
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high = low + 1.0
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normalized = np.clip((values - low) / (high - low), 0.0, 1.0)
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stops = np.asarray([0.0, 0.25, 0.5, 0.75, 1.0])
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colors = np.asarray(
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[
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[30, 94, 91],
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[79, 139, 102],
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[194, 183, 105],
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[173, 121, 79],
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[105, 94, 108],
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],
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dtype="float64",
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)
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rgba = np.zeros((height, width, 4), dtype="uint8")
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for channel in range(3):
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rgba[:, :, channel] = np.interp(normalized, stops, colors[:, channel]).astype("uint8")
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rgba[:, :, 3] = np.where(valid, 225, 0).astype("uint8")
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output = io.BytesIO()
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Image.fromarray(rgba).save(output, format="PNG", optimize=True)
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return output.getvalue()
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except AppError:
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raise
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except Exception as exc:
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raise AppError(
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code="TERRAIN_PREVIEW_FAILED",
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message="The persisted DHMV raster could not be rendered",
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details={"reason": str(exc)},
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status_code=500,
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) from exc
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