2986400a0a
- Positions d'axes fixes (data_left/bottom/width/height_frac) pour alignement pixel-parfait entre terrain et ortho/topo - aspect='equal' au lieu de 'auto' pour conserver les proportions géographiques - Colorbar descriptive pour les visualisations RGB (ortho/topo) - Comblage des petits trous DTM (< 1m) via rasterio.fill.fillnodata - Suppression de la visualisation "dépressions" - Hillshade composite: 0.7*hillshade + 0.3*cos(slope) - D8 flow accumulation accéléré par numba JIT (fallback Python) - Flag --keep-tif pour conserver les TIFF intermédiaires - --force supprime aussi les TIF existants avant régénération - ETA affiché pendant la génération des visualisations - Répertoires temp dans temp/ pour traitement parallèle Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
201 lines
7.6 KiB
Python
201 lines
7.6 KiB
Python
"""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 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 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 |