ad762e682d
- Suppression de generate_solar (éclairage solaire) des visualisations - Accélération GPU de hillshade, slope, aspect, curvature, depressions, anomalies, roughness, texture GLCM, flow (sink filling) - Nettoyage mémoire GPU entre visualisations (gpu_cleanup) - Correction OOM texture GLCM: calcul entropie bin par bin au lieu d'un tableau 3D massif sur GPU - Correction bug: xp_minimum_filter manquant dans imports visualizations - Option --file accepte plusieurs noms complets sans extension - run.sh affiche l'aide si appelé sans arguments - Option --test pour exécuter les tests unitaires dans Docker - Filtre ReturnNumber>=1 intégré dans le pipeline PDAL (plus d'erreur SMRF) - 60 tests unitaires: GPU, visualisations, rendering, DTM, pipeline, CLI - Ajout pytest au Dockerfile Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
"""Shared fixtures for LiDAR pipeline tests."""
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import numpy as np
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import rasterio
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from rasterio.transform import from_bounds
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import pytest
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from pathlib import Path
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@pytest.fixture
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def tmp_output_dir(tmp_path):
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"""Create a temporary output directory."""
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out = tmp_path / "output"
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out.mkdir()
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return out
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@pytest.fixture
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def synthetic_dem(tmp_path):
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"""Create a small synthetic DEM GeoTIFF with realistic terrain features.
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Includes:
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- A gaussian hill (tumulus)
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- A linear ridge (wall)
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- A depression (ditch)
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- Background noise
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"""
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size = 200
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x = np.linspace(0, 1000, size)
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y = np.linspace(0, 1000, size)
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X, Y = np.meshgrid(x, y)
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# Base terrain with gentle slope
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dem = 100.0 + 0.01 * X + 0.005 * Y
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# Gaussian hill (tumulus) at center
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dem += 5.0 * np.exp(-((X - 500)**2 + (Y - 500)**2) / (2 * 80**2))
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# Linear ridge (wall) running SW-NE
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dist_to_wall = np.abs((X - 200) * 0.707 + (Y - 300) * 0.707) / np.sqrt(2)
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dem += 1.5 * np.exp(-dist_to_wall**2 / (2 * 10**2))
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# Depression (ditch)
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dist_to_ditch = np.abs(X - 800)
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dem -= 2.0 * np.exp(-dist_to_ditch**2 / (2 * 15**2)) * (Y > 300) * (Y < 700)
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# Small random noise
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rng = np.random.default_rng(42)
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dem += rng.normal(0, 0.05, dem.shape)
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# Save as GeoTIFF (Lambert 93)
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dem_file = tmp_path / "test_dem.tif"
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transform = from_bounds(660000, 6700000, 661000, 6701000, size, size)
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with rasterio.open(
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dem_file, 'w',
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driver='GTiff', height=size, width=size,
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count=1, dtype='float32',
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crs='EPSG:2154', transform=transform,
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compress='lzw'
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) as dst:
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dst.write(dem.astype('float32'), 1)
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return dem_file
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@pytest.fixture
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def synthetic_dem_data(synthetic_dem):
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"""Return (array, transform, crs) from the synthetic DEM."""
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with rasterio.open(synthetic_dem) as src:
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return src.read(1), src.transform, src.crs |