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>
This commit is contained in:
Jacquin Antoine
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lidar_pipeline/tests/test_visualizations.py
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216
lidar_pipeline/tests/test_visualizations.py
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"""Tests for visualization functions.
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Each test creates a small synthetic DEM and runs a visualization function,
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checking that it produces a valid output file.
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"""
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import numpy as np
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import pytest
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from pathlib import Path
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# --- Core terrain visualizations (no GPU required) ---
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class TestHillshade:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_hillshade
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result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 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 == ".tif"
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def test_output_values_valid(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_hillshade
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result = generate_hillshade(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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assert data.shape[0] > 0
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assert np.nanmin(data) >= 0
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assert np.nanmax(data) <= 1
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class TestSlope:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_slope
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result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_slope_values_degrees(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_slope
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result = generate_slope(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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assert np.nanmin(data) >= 0
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assert np.nanmax(data) <= 90
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class TestAspect:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_aspect
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result = generate_aspect(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_aspect_values_0_360(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_aspect
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result = generate_aspect(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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valid = data[~np.isnan(data)]
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assert np.nanmin(valid) >= 0
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assert np.nanmax(valid) <= 360
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class TestCurvature:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_curvature
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result = generate_curvature(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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# --- GPU-accelerated visualizations ---
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class TestLRM:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_lrm
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result = generate_lrm(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_lrm_has_positive_negative(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_lrm
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result = generate_lrm(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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# LRM should have both positive and negative values
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assert np.nanmax(data) > 0
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assert np.nanmin(data) < 0
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class TestSVF:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_svf
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result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_svf_values_0_1(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_svf
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result = generate_svf(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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valid = data[~np.isnan(data)]
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assert np.nanmin(valid) >= 0
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assert np.nanmax(valid) <= 1
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class TestOpenness:
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def test_positive_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_openness
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result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=True)
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assert result is not None
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assert result.exists()
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def test_negative_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_openness
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result = generate_openness(synthetic_dem, "test", tmp_output_dir, 5.0, positive=False)
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assert result is not None
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assert result.exists()
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class TestMSLRM:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_mslrm
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result = generate_mslrm(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestTPI:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_tpi
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result = generate_tpi(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestDepressions:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_depressions
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result = generate_depressions(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestSAILORE:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_sailore
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result = generate_sailore(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestRoughness:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_roughness
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result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_roughness_non_negative(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_roughness
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result = generate_roughness(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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# Standard deviation is always >= 0
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assert np.nanmin(data) >= 0
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class TestAnomalies:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_anomalies
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result = generate_anomalies(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestWavelet:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_wavelet
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result = generate_wavelet(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestTexture:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_texture
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result = generate_texture(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestFlow:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_flow
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result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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def test_flow_log_values(self, synthetic_dem, tmp_output_dir):
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import rasterio
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from lidar_pipeline.visualizations import generate_flow
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result = generate_flow(synthetic_dem, "test", tmp_output_dir, 5.0)
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with rasterio.open(result) as src:
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data = src.read(1)
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# log1p(x) >= 0 for x >= 0
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valid = data[~np.isnan(data)]
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assert np.nanmin(valid) >= 0
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