Suppression éclairage solaire, GPU accéléré, --file multi, tests unitaires

- 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>

lidar_pipeline/tests/conftest.py

@@ -0,0 +1,71 @@
+"""Shared fixtures for LiDAR pipeline tests."""
+
+import numpy as np
+import rasterio
+from rasterio.transform import from_bounds
+import pytest
+from pathlib import Path
+
+
+@pytest.fixture
+def tmp_output_dir(tmp_path):
+    """Create a temporary output directory."""
+    out = tmp_path / "output"
+    out.mkdir()
+    return out
+
+
+@pytest.fixture
+def synthetic_dem(tmp_path):
+    """Create a small synthetic DEM GeoTIFF with realistic terrain features.
+
+    Includes:
+    - A gaussian hill (tumulus)
+    - A linear ridge (wall)
+    - A depression (ditch)
+    - Background noise
+    """
+    size = 200
+    x = np.linspace(0, 1000, size)
+    y = np.linspace(0, 1000, size)
+    X, Y = np.meshgrid(x, y)
+
+    # Base terrain with gentle slope
+    dem = 100.0 + 0.01 * X + 0.005 * Y
+
+    # Gaussian hill (tumulus) at center
+    dem += 5.0 * np.exp(-((X - 500)**2 + (Y - 500)**2) / (2 * 80**2))
+
+    # Linear ridge (wall) running SW-NE
+    dist_to_wall = np.abs((X - 200) * 0.707 + (Y - 300) * 0.707) / np.sqrt(2)
+    dem += 1.5 * np.exp(-dist_to_wall**2 / (2 * 10**2))
+
+    # Depression (ditch)
+    dist_to_ditch = np.abs(X - 800)
+    dem -= 2.0 * np.exp(-dist_to_ditch**2 / (2 * 15**2)) * (Y > 300) * (Y < 700)
+
+    # Small random noise
+    rng = np.random.default_rng(42)
+    dem += rng.normal(0, 0.05, dem.shape)
+
+    # Save as GeoTIFF (Lambert 93)
+    dem_file = tmp_path / "test_dem.tif"
+    transform = from_bounds(660000, 6700000, 661000, 6701000, size, size)
+
+    with rasterio.open(
+        dem_file, 'w',
+        driver='GTiff', height=size, width=size,
+        count=1, dtype='float32',
+        crs='EPSG:2154', transform=transform,
+        compress='lzw'
+    ) as dst:
+        dst.write(dem.astype('float32'), 1)
+
+    return dem_file
+
+
+@pytest.fixture
+def synthetic_dem_data(synthetic_dem):
+    """Return (array, transform, crs) from the synthetic DEM."""
+    with rasterio.open(synthetic_dem) as src:
+        return src.read(1), src.transform, src.crs