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>
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
"""Tests for rendering module (colormaps, tif_to_png)."""
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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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def _make_test_tif(tmp_path, data=None, size=50):
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"""Create a small test GeoTIFF and return its path."""
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if data is None:
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rng = np.random.default_rng(42)
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data = rng.normal(0, 1, (size, size)).astype(np.float32)
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transform = from_bounds(660000, 6700000, 661000, 6701000, size, size)
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tif_file = tmp_path / "test_vis.tif"
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with rasterio.open(
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tif_file, 'w', driver='GTiff', height=size, width=size,
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count=1, dtype='float32', crs='EPSG:2154', transform=transform,
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compress='lzw'
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) as dst:
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dst.write(data, 1)
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return tif_file
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class TestColormaps:
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def test_colormaps_dict_exists(self):
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from lidar_pipeline.rendering import COLORMAPS
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assert isinstance(COLORMAPS, dict)
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def test_all_viz_steps_have_colormaps(self):
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"""Every VIZ_STEPS entry should have a corresponding COLORMAPS entry or render correctly."""
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from lidar_pipeline.pipeline import VIZ_STEPS
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from lidar_pipeline.rendering import COLORMAPS
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# Some viz names differ from colormap keys
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name_map = {
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'pos_open': 'positive_openness',
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'neg_open': 'negative_openness',
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}
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# IGN overlays (ortho, topo) are RGB images — no colormap needed
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skip = {'ortho', 'topo'}
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for name, _ in VIZ_STEPS:
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if name in skip:
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continue
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cmap_key = name_map.get(name, name)
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assert cmap_key in COLORMAPS, f"Missing colormap for: {name} (looked as {cmap_key})"
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def test_colormap_has_required_keys(self):
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"""Each colormap entry must have cmap, title, legend, description."""
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from lidar_pipeline.rendering import COLORMAPS
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required = {'cmap', 'title', 'legend', 'description'}
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for name, entry in COLORMAPS.items():
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missing = required - set(entry.keys())
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assert not missing, f"Colormap '{name}' missing keys: {missing}"
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class TestTifToPng:
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def test_converts_tif_to_webp(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists()
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assert result.suffix == '.webp'
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def test_removes_source_tif(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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assert tif_file.exists()
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tif_to_png(tif_file, tmp_path, 5.0)
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assert not tif_file.exists(), "Source TIF should be deleted after conversion"
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def test_webp_has_content(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result.stat().st_size > 1000 # Must be a real image
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class TestApplyColormap:
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def test_symmetric_mode(self, tmp_path):
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from lidar_pipeline.rendering import COLORMAPS, tif_to_png
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# LRM uses symmetric mode
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data = np.random.default_rng(42).normal(0, 0.5, (50, 50)).astype(np.float32)
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tif_file = _make_test_tif(tmp_path, data)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists()
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def test_percentile_mode(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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# Most visualizations use percentile mode
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data = np.random.default_rng(42).normal(50, 10, (50, 50)).astype(np.float32)
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tif_file = _make_test_tif(tmp_path, data)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists() |