41206f65de
Trois chantiers liés à la qualité et au coût des rendus : - Calage vertical des faisceaux : les passes d'une tuile peuvent être biaisées de quelques cm (±2,5 cm mesurés sur 1000_6882), créant des marches aux recouvrements. Les offsets par PointSourceId sont mesurés sur les points sol de la tuile (réf. médiane itérée) et retranchés ≥ 0,5 cm avant rastérisation, avec sidecar de cache et application au plancher bare-earth. - Openness décimée ×2 : lancé de rayons sur grille par blocs (max/min) puis rééchantillonnage bilinéaire — 532 s → 40 s par tuile à 0,2 m sur CPU, signal archéologique préservé. Réglable --openness-downsample. - Génération webapp concentrée sur les couches affichées : les défauts /api/generate, /api/preview et le sélecteur génèrent le panneau complet (slope, aspect, pos_open) au lieu d'aspect seul.
656 lines
29 KiB
Python
656 lines
29 KiB
Python
"""Tests for DTM module."""
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import json
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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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from unittest.mock import patch, MagicMock
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class TestSMRFPipeline:
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def test_pipeline_json_valid(self):
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"""create_smrf_pipeline produces valid JSON with expected stages."""
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from lidar_pipeline.dtm import create_smrf_pipeline
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result = create_smrf_pipeline("/data/input/test.laz", "/data/output/test_ground.las")
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pipeline = json.loads(result)
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assert "pipeline" in pipeline
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stages = pipeline["pipeline"]
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# Should have: reader, range filter (ReturnNumber), assign, ELM, outlier, SMRF, range filter (Classification), writer
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stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
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# First stage is the filename string (reader)
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assert isinstance(stages[0], str)
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assert "test.laz" in stages[0]
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# Must contain preprocessing steps
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assert "filters.assign" in stage_types
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assert "filters.elm" in stage_types
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assert "filters.outlier" in stage_types
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# Must contain SMRF filter
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assert "filters.smrf" in stage_types
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# Must contain ReturnNumber filter
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range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
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assert len(range_stages) >= 1
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# At least one should filter ReturnNumber
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assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
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def test_pipeline_elm_parameters(self):
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"""ELM filter has terrain-adapted parameters."""
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from lidar_pipeline.dtm import create_smrf_pipeline
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result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
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pipeline = json.loads(result)
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elm_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.elm"][0]
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assert elm_stage["cell"] == 5.0
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assert elm_stage["threshold"] == 2.0
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def test_pipeline_outlier_parameters(self):
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"""Outlier filter uses statistical method."""
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from lidar_pipeline.dtm import create_smrf_pipeline
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result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
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pipeline = json.loads(result)
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outlier_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.outlier"][0]
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assert outlier_stage["method"] == "statistical"
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assert outlier_stage["mean_k"] == 8
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assert outlier_stage["multiplier"] == 3.0
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def test_pipeline_output_path(self):
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"""Pipeline output path is set correctly."""
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from lidar_pipeline.dtm import create_smrf_pipeline
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result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
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pipeline = json.loads(result)
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# Last stage should be writer with correct output path
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writer = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "writers.las"][0]
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assert writer["filename"] == "/output/a_ground.las"
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class TestCSFPipeline:
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def test_pipeline_json_valid(self):
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"""create_csf_pipeline produces valid JSON with CSF filter."""
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from lidar_pipeline.dtm import create_csf_pipeline
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result = create_csf_pipeline("/data/input/test.laz", "/data/output/test_ground.las")
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pipeline = json.loads(result)
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assert "pipeline" in pipeline
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stages = pipeline["pipeline"]
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stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
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# Must contain CSF filter
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assert "filters.csf" in stage_types
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# Must contain ReturnNumber filter
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range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
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assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
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def test_csf_parameters(self):
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"""CSF pipeline has expected parameters."""
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from lidar_pipeline.dtm import create_csf_pipeline
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result = create_csf_pipeline("/input/a.laz", "/output/a_ground.las")
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pipeline = json.loads(result)
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csf_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.csf"][0]
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assert csf_stage["resolution"] == 1.0 # cloth 1 m : ~4× plus rapide, MNT inchangé
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assert csf_stage["rigidness"] == 3
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assert csf_stage["smooth"] is True
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assert "hdiff" not in csf_stage # hdiff is not a valid PDAL CSF parameter
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class TestInterpolateHoles:
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def test_fills_interior_hole_with_surface(self):
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"""Large interior NaN hole is filled (no NaN left, value is plausible)."""
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from lidar_pipeline.dtm import _interpolate_holes
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# Linear-in-column surface z = 0.02 * x, with a large square hole in the middle.
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x = np.arange(40, dtype=float) * 0.02
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dtm = np.tile(x, (40, 1))
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dtm[16:24, 16:24] = np.nan
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filled, count = _interpolate_holes(dtm)
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assert count == 64
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assert not np.isnan(filled).any()
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# Filled values stay within the surrounding z range (no wild extrapolation).
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zmin, zmax = np.nanmin(dtm), np.nanmax(dtm)
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hole_vals = filled[16:24, 16:24]
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assert np.all(hole_vals >= zmin - 1e-6)
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assert np.all(hole_vals <= zmax + 1e-6)
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# A linear surface is interpolated near-exactly in the interior.
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expected = np.tile(x[16:24], (8, 1))
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assert np.allclose(hole_vals, expected, atol=0.02)
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# Original valid cells are untouched.
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valid = ~np.isnan(dtm)
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assert np.allclose(filled[valid], dtm[valid])
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def test_no_holes_returns_unchanged(self):
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"""No NaN → returns same array and zero count."""
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from lidar_pipeline.dtm import _interpolate_holes
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dtm = np.arange(64, dtype=float).reshape(8, 8)
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filled, count = _interpolate_holes(dtm)
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assert count == 0
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assert np.shares_memory(filled, dtm)
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def test_all_nan_returns_unchanged(self):
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"""No valid data → cannot interpolate, returns zeros-free NaN array."""
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from lidar_pipeline.dtm import _interpolate_holes
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dtm = np.full((8, 8), np.nan)
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filled, count = _interpolate_holes(dtm)
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assert count == 0
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assert np.isnan(filled).all()
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class TestMinReturnGrid:
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def test_takes_lowest_return_per_cell(self, tmp_output_dir):
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"""_min_return_grid rasterise le point le plus bas par cellule (pas la moyenne)."""
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import laspy
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from lidar_pipeline.dtm import _min_return_grid
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out = tmp_output_dir / "pts.las"
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hdr = laspy.LasHeader(version='1.2', point_format=0)
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las = laspy.LasData(hdr)
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# Grille 2x2 sur [0,2]x[0,2]. La cellule (0,0) porte deux points z=5 et
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# z=2 (min=2, moyenne=3.5) ; (1,0) z=3 ; (0,1) z=4 ; (1,1) vide.
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las.x = [0.2, 0.5, 1.2, 0.3]
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las.y = [0.2, 0.3, 0.4, 1.5]
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las.z = [5.0, 2.0, 3.0, 4.0]
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las.write(str(out))
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grid = _min_return_grid(out, 2, 2, (0.0, 0.0, 2.0, 2.0))
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assert grid.shape == (2, 2)
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assert int(np.isnan(grid).sum()) == 1
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# Une seule valeur par cellule, et la cellule (0,0) vaut le MIN (2.0).
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vals = sorted(float(v) for v in grid[~np.isnan(grid)])
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assert vals == [2.0, 3.0, 4.0]
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assert 3.5 not in vals
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class TestBareEarth:
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"""Le plancher « sol nu » ramène le DTM au retour le plus bas de chaque cellule."""
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def _write_las(self, path, points):
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"""points: list of (x, y, z). Écrit un LAS 1.2 format 0 aux bornes [0,2]x[0,2]."""
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import laspy
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hdr = laspy.LasHeader(version='1.2', point_format=0)
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las = laspy.LasData(hdr)
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las.x = [p[0] for p in points]
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las.y = [p[1] for p in points]
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las.z = [p[2] for p in points]
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las.write(str(path))
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return path
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def _make_clouds(self, tmp_output_dir):
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"""Grille 2x2 (res=1.0). Les points « coin » à 0.05/1.95 imposent l'étendue
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[0.05,1.95] (laspy re-déduit les bornes de l'en-tête depuis les points).
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Le sol (las_file) vaut z=10 partout. Le nuage complet (source_laz) a un
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retour plus bas dans les cellules (0,0) -> 2 et (1,0) -> 5 ; les deux autres
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cellules n'ont que z=10."""
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ground = [(0.5, 0.5, 10.0), (1.5, 0.5, 10.0), (0.5, 1.5, 10.0), (1.5, 1.5, 10.0),
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(0.05, 0.05, 10.0), (1.95, 1.95, 10.0)]
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source = list(ground) + [(0.3, 0.3, 2.0), (1.3, 0.3, 5.0)]
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las_file = self._write_las(tmp_output_dir / "ground.las", ground)
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source_laz = self._write_las(tmp_output_dir / "source.las", source)
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return las_file, source_laz
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def _dtm_values(self, tmp_output_dir, bare_earth):
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from lidar_pipeline.dtm import create_dtm_fast
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import rasterio
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las_file, source_laz = self._make_clouds(tmp_output_dir)
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out = create_dtm_fast(las_file, "tile", tmp_output_dir, 1.0,
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force=True, source_laz=source_laz, bare_earth=bare_earth)
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assert out is not None
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with rasterio.open(str(out)) as src:
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arr = src.read(1).astype("float64")
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return arr[~np.isnan(arr)]
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def test_bare_earth_pulls_dtm_to_lowest_return(self, tmp_output_dir):
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"""Avec bare_earth, le DTM descend aux retours les plus bas (2 et 5)."""
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vals = sorted(float(v) for v in self._dtm_values(tmp_output_dir, bare_earth=True))
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# Les cellules sans retour plus bas restent à 10 ; les deux autres descendent.
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assert vals == [2.0, 5.0, 10.0, 10.0]
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assert vals[0] == 2.0 and vals[1] == 5.0
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def test_no_bare_earth_keeps_mean(self, tmp_output_dir):
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"""Sans bare_earth, le DTM garde la moyenne des points sol (10 partout)."""
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vals = sorted(float(v) for v in self._dtm_values(tmp_output_dir, bare_earth=False))
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assert all(v == 10.0 for v in vals)
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class TestDetectGroundMethod:
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def _make_mock_las(self, num_returns, z_values):
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"""Create a mock laspy object with specified NumberOfReturns and z."""
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mock_las = MagicMock()
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mock_las.NumberOfReturns = np.array(num_returns)
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mock_las.z = np.array(z_values)
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mock_las.points = MagicMock()
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mock_las.points.__len__ = lambda self: len(num_returns)
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return mock_las
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@patch('lidar_pipeline.dtm._read_with_pdal')
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@patch('laspy.read')
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def test_urban_terrain_returns_csf(self, mock_read, mock_pdal):
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"""High single-return ratio (>0.6) should select CSF."""
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from lidar_pipeline.dtm import detect_ground_method
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# 70% single returns = urban
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n = 10000
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num_returns = np.ones(n, dtype=int)
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num_returns[:int(n * 0.3)] = 2 # 30% multi-return
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z_values = np.random.normal(100, 5, n) # Low variance = flat terrain
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mock_read.return_value = self._make_mock_las(num_returns, z_values)
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result = detect_ground_method(Path("/data/input/test.laz"))
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assert result == 'csf'
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@patch('lidar_pipeline.dtm._read_with_pdal')
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@patch('laspy.read')
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def test_natural_terrain_returns_smrf(self, mock_read, mock_pdal):
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"""Low single-return ratio and moderate variance should select SMRF."""
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from lidar_pipeline.dtm import detect_ground_method
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# 40% single returns, moderate variance
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n = 10000
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num_returns = np.ones(n, dtype=int)
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num_returns[:int(n * 0.6)] = 2 # 60% multi-return (forest)
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z_values = np.random.normal(100, 15, n) # Moderate variance
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mock_read.return_value = self._make_mock_las(num_returns, z_values)
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result = detect_ground_method(Path("/data/input/test.laz"))
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assert result == 'smrf'
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@patch('lidar_pipeline.dtm._read_with_pdal')
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@patch('laspy.read')
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def test_mountainous_terrain_returns_csf(self, mock_read, mock_pdal):
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"""High variance terrain (>30m std) selects CSF for complex terrain."""
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from lidar_pipeline.dtm import detect_ground_method
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# Moderate single-return ratio but very high height variance
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n = 10000
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num_returns = np.ones(n, dtype=int)
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num_returns[:int(n * 0.5)] = 2
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z_values = np.random.normal(100, 50, n) # Very high variance = mountainous
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mock_read.return_value = self._make_mock_las(num_returns, z_values)
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result = detect_ground_method(Path("/data/input/test.laz"))
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assert result == 'csf'
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class TestIGNPipeline:
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def test_pipeline_keeps_supplier_classification(self):
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"""create_ign_pipeline réutilise la pré-classification (classe 2) sans refiltrer."""
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from lidar_pipeline.dtm import create_ign_pipeline
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result = create_ign_pipeline("/input/a.laz", "/output/a_ground.las")
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pipeline = json.loads(result)
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stages = pipeline["pipeline"]
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stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
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# Aucun algorithme de classification, pas de remise à zéro, pas de filtres de bruit
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assert "filters.smrf" not in stage_types
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assert "filters.csf" not in stage_types
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assert "filters.assign" not in stage_types
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assert "filters.elm" not in stage_types
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assert "filters.outlier" not in stage_types
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# Filtre ReturnNumber conservé + extraction des points classe 2
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range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
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assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
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assert any(s.get("limits") == "Classification[2:2]" for s in range_stages)
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writer = [s for s in stages if isinstance(s, dict) and s.get("type") == "writers.las"][0]
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assert writer["filename"] == "/output/a_ground.las"
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class TestDetectIGN:
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def _make_mock_las(self, classification, num_returns, z_values):
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mock_las = MagicMock()
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mock_las.classification = classification
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mock_las.NumberOfReturns = np.array(num_returns)
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mock_las.z = np.array(z_values)
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mock_las.points = MagicMock()
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mock_las.points.__len__ = lambda self: len(num_returns)
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return mock_las
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@patch('lidar_pipeline.dtm._read_with_pdal')
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@patch('laspy.read')
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def test_preclassified_returns_ign(self, mock_read, mock_pdal):
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"""Fichier pré-classifié (majorité classe 2) → méthode IGN."""
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from lidar_pipeline.dtm import detect_ground_method
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n = 10000
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num_returns = np.ones(n, dtype=int)
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cls = np.zeros(n, dtype=np.uint8)
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cls[int(n * 0.15):] = 2 # 85 % de points classe 2
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z_values = np.random.normal(100, 5, n)
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mock_read.return_value = self._make_mock_las(cls, num_returns, z_values)
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assert detect_ground_method(Path("/data/input/test.laz")) == 'ign'
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@patch('lidar_pipeline.dtm._read_with_pdal')
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@patch('laspy.read')
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def test_unclassified_falls_back_to_smrf_or_csf(self, mock_read, mock_pdal):
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"""Sans classification exploitable → détection SMRF/CSF classique."""
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from lidar_pipeline.dtm import detect_ground_method
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n = 10000
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num_returns = np.ones(n, dtype=int)
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num_returns[:int(n * 0.6)] = 2 # 60 % multi-retours (forêt) → non urbain
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cls = np.zeros(n, dtype=np.uint8) # aucun point classe 2
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z_values = np.random.normal(100, 5, n)
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mock_read.return_value = self._make_mock_las(cls, num_returns, z_values)
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assert detect_ground_method(Path("/data/input/test.laz")) == 'smrf'
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class TestClassifyGroundMethod:
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@patch('lidar_pipeline.dtm.subprocess')
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def test_classify_ground_auto_calls_detect(self, mock_subprocess):
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"""classify_ground with method='auto' should call detect_ground_method."""
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from lidar_pipeline.dtm import classify_ground
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# Mock detect_ground_method to return 'csf'
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with patch('lidar_pipeline.dtm.detect_ground_method', return_value='csf') as mock_detect:
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mock_subprocess.run.return_value = MagicMock(returncode=0)
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result = classify_ground(Path("/data/input/test.laz"), Path("/tmp"), method='auto')
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mock_detect.assert_called_once()
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@patch('lidar_pipeline.dtm.subprocess')
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def test_classify_ground_smrf_uses_smrf_pipeline(self, mock_subprocess):
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"""classify_ground with method='smrf' should create SMRF pipeline."""
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from lidar_pipeline.dtm import classify_ground, _create_ground_pipeline
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mock_subprocess.run.return_value = MagicMock(returncode=0)
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with patch('lidar_pipeline.dtm.detect_ground_method'):
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# Create temp dir
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import tempfile
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with tempfile.TemporaryDirectory() as tmpdir:
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result = classify_ground(Path("/data/input/test.laz"), Path(tmpdir), method='smrf')
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# Check the pipeline JSON was written with SMRF
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pipeline_file = Path(tmpdir) / "pipeline_smrf.json"
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if pipeline_file.exists():
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pipeline = json.loads(pipeline_file.read_text())
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stage_types = [s.get("type") if isinstance(s, dict) else None for s in pipeline["pipeline"]]
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assert "filters.smrf" in stage_types
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@patch('lidar_pipeline.dtm.subprocess')
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def test_classify_ground_csf_uses_csf_pipeline(self, mock_subprocess):
|
||
"""classify_ground with method='csf' should create CSF pipeline."""
|
||
from lidar_pipeline.dtm import classify_ground
|
||
|
||
mock_subprocess.run.return_value = MagicMock(returncode=0)
|
||
|
||
with patch('lidar_pipeline.dtm.detect_ground_method'):
|
||
import tempfile
|
||
with tempfile.TemporaryDirectory() as tmpdir:
|
||
result = classify_ground(Path("/data/input/test.laz"), Path(tmpdir), method='csf')
|
||
|
||
pipeline_file = Path(tmpdir) / "pipeline_csf.json"
|
||
if pipeline_file.exists():
|
||
pipeline = json.loads(pipeline_file.read_text())
|
||
stage_types = [s.get("type") if isinstance(s, dict) else None for s in pipeline["pipeline"]]
|
||
assert "filters.csf" in stage_types
|
||
|
||
class TestParseIgnClasses:
|
||
def test_default_sol(self):
|
||
"""'sol' → code 2 seul."""
|
||
from lidar_pipeline.dtm import parse_ign_classes
|
||
assert parse_ign_classes("sol") == [2]
|
||
|
||
def test_names_sorted_dedup(self):
|
||
"""Noms acceptés (EN/FR), triés et dédupliqués."""
|
||
from lidar_pipeline.dtm import parse_ign_classes
|
||
assert parse_ign_classes("sol,unclassified") == [1, 2]
|
||
assert parse_ign_classes("unclassified,sol") == [1, 2]
|
||
assert parse_ign_classes("non-classe") == [1]
|
||
assert parse_ign_classes("sol,2") == [2]
|
||
|
||
def test_numeric_codes(self):
|
||
"""Codes LAS directs, triés."""
|
||
from lidar_pipeline.dtm import parse_ign_classes
|
||
assert parse_ign_classes("2,1") == [1, 2]
|
||
assert parse_ign_classes("66") == [66]
|
||
|
||
def test_invalid_raises(self):
|
||
"""Nom inconnu, code hors bornes ou liste vide → ValueError."""
|
||
from lidar_pipeline.dtm import parse_ign_classes
|
||
with pytest.raises(ValueError):
|
||
parse_ign_classes("foo")
|
||
with pytest.raises(ValueError):
|
||
parse_ign_classes("300")
|
||
with pytest.raises(ValueError):
|
||
parse_ign_classes("")
|
||
|
||
def test_method_label(self):
|
||
"""'ign' seul pour le sol, combinaison encodée sinon (invalidation cache)."""
|
||
from lidar_pipeline.dtm import ign_method_label
|
||
assert ign_method_label([2]) == "ign"
|
||
assert ign_method_label([1, 2]) == "ign_1_2"
|
||
assert ign_method_label([2, 1]) == "ign_1_2"
|
||
|
||
|
||
class TestIGNPipelineMultiClasses:
|
||
def test_multi_class_limits(self):
|
||
"""Plusieurs classes → plages OU logiques sur Classification."""
|
||
from lidar_pipeline.dtm import _create_ground_pipeline
|
||
result = _create_ground_pipeline("/input/a.laz", "/output/a_ground.las",
|
||
'ign', ign_codes=[1, 2])
|
||
pipeline = json.loads(result)
|
||
range_stages = [s for s in pipeline["pipeline"]
|
||
if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
limits = [str(s.get("limits", "")) for s in range_stages]
|
||
assert any("Classification[1:1]" in l and "Classification[2:2]" in l
|
||
for l in limits)
|
||
|
||
def test_default_sol_only(self):
|
||
"""Sans ign_codes, la voie IGN reste sol seul (2) — rétrocompatible."""
|
||
from lidar_pipeline.dtm import _create_ground_pipeline
|
||
result = _create_ground_pipeline("/input/a.laz", "/output/a_ground.las", 'ign')
|
||
pipeline = json.loads(result)
|
||
range_stages = [s for s in pipeline["pipeline"]
|
||
if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
limits = [str(s.get("limits", "")) for s in range_stages]
|
||
assert any("Classification[2:2]" in l and "Classification[1:1]" not in l
|
||
for l in limits)
|
||
|
||
|
||
class TestClassifyGroundIgnClasses:
|
||
@patch('lidar_pipeline.dtm.subprocess')
|
||
def test_ign_classes_encoded_in_filenames(self, mock_subprocess):
|
||
"""--ign-classes sol,unclassified → fichiers ign_1_2 + filtre multi-classes."""
|
||
import tempfile
|
||
from lidar_pipeline.dtm import classify_ground
|
||
|
||
mock_subprocess.run.return_value = MagicMock(returncode=0)
|
||
|
||
with tempfile.TemporaryDirectory() as tmpdir:
|
||
tmpdir = Path(tmpdir)
|
||
classify_ground(Path("/data/input/test.laz"), tmpdir,
|
||
method='ign', ign_classes="sol,unclassified")
|
||
|
||
pipeline_file = tmpdir / "pipeline_ign_1_2.json"
|
||
assert pipeline_file.exists()
|
||
pipeline = json.loads(pipeline_file.read_text())
|
||
limits = [str(s.get("limits", "")) for s in pipeline["pipeline"]
|
||
if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
assert any("Classification[1:1]" in l and "Classification[2:2]" in l
|
||
for l in limits)
|
||
|
||
@patch('lidar_pipeline.dtm.subprocess')
|
||
def test_ign_default_label_unchanged(self, mock_subprocess):
|
||
"""--ign-classes sol (défaut) → noms 'ign' inchangés (cache préservé)."""
|
||
import tempfile
|
||
from lidar_pipeline.dtm import classify_ground
|
||
|
||
mock_subprocess.run.return_value = MagicMock(returncode=0)
|
||
|
||
with tempfile.TemporaryDirectory() as tmpdir:
|
||
tmpdir = Path(tmpdir)
|
||
classify_ground(Path("/data/input/test.laz"), tmpdir, method='ign')
|
||
|
||
assert (tmpdir / "pipeline_ign.json").exists()
|
||
assert not (tmpdir / "pipeline_ign_1_2.json").exists()
|
||
|
||
|
||
class TestPureDtm:
|
||
"""Mode pur (classification IGN) : aucune retouche, trous en nodata."""
|
||
|
||
def _write_las(self, path, points):
|
||
import laspy
|
||
hdr = laspy.LasHeader(version='1.2', point_format=0)
|
||
las = laspy.LasData(hdr)
|
||
las.x = [p[0] for p in points]
|
||
las.y = [p[1] for p in points]
|
||
las.z = [p[2] for p in points]
|
||
las.write(str(path))
|
||
return path
|
||
|
||
def _make_clouds(self, tmp_output_dir):
|
||
"""Grille 2x2 (res=1.0). Sol sur 3 cellules (z=10), trou en (1,1).
|
||
Le nuage complet a un retour plus bas (z=7) dans le trou."""
|
||
corners = [(0.05, 0.05, 10.0), (1.95, 0.05, 10.0), (0.05, 1.95, 10.0)]
|
||
ground = [(0.5, 0.5, 10.0), (1.5, 0.5, 10.0), (0.5, 1.5, 10.0)] + corners
|
||
source = list(ground) + [(1.5, 1.5, 7.0)]
|
||
las_file = self._write_las(tmp_output_dir / "ground_pure.las", ground)
|
||
source_laz = self._write_las(tmp_output_dir / "source_pure.las", source)
|
||
return las_file, source_laz
|
||
|
||
def _dtm_array(self, tmp_output_dir, pure):
|
||
from lidar_pipeline.dtm import create_dtm_fast
|
||
import rasterio
|
||
las_file, source_laz = self._make_clouds(tmp_output_dir)
|
||
out = create_dtm_fast(las_file, "tile_pure", tmp_output_dir, 1.0,
|
||
force=True, source_laz=source_laz, pure=pure)
|
||
assert out is not None
|
||
with rasterio.open(str(out)) as src:
|
||
return src.read(1).astype("float64")
|
||
|
||
def test_pure_fills_holes_without_floor(self, tmp_output_dir):
|
||
"""pur=True : trous comblés par interpolation, sans plancher à 7.
|
||
|
||
Le comblement est actif dans tous les modes (comportement
|
||
historique) ; « pur » ne désactive que l'abaissement au retour
|
||
le plus bas.
|
||
"""
|
||
arr = self._dtm_array(tmp_output_dir, pure=True)
|
||
assert int(np.isnan(arr).sum()) == 0
|
||
vals = sorted(float(v) for v in arr.flatten())
|
||
assert vals == [10.0, 10.0, 10.0, 10.0]
|
||
|
||
def test_not_pure_fills_holes(self, tmp_output_dir):
|
||
"""pur=False : le trou est comblé (comportement historique conservé)."""
|
||
arr = self._dtm_array(tmp_output_dir, pure=False)
|
||
assert int(np.isnan(arr).sum()) == 0
|
||
vals = sorted(float(v) for v in arr.flatten())
|
||
assert len(vals) == 4
|
||
assert vals[-1] == 10.0
|
||
|
||
|
||
class TestStripLidarExt:
|
||
def test_copc_laz(self):
|
||
from lidar_pipeline.dtm import _strip_lidar_ext
|
||
assert _strip_lidar_ext("LHD_FXX_1000_6881_PTS_LAMB93_IGN69.copc.laz") == "LHD_FXX_1000_6881_PTS_LAMB93_IGN69"
|
||
|
||
def test_laz(self):
|
||
from lidar_pipeline.dtm import _strip_lidar_ext
|
||
assert _strip_lidar_ext("file.laz") == "file"
|
||
|
||
def test_las(self):
|
||
from lidar_pipeline.dtm import _strip_lidar_ext
|
||
assert _strip_lidar_ext("file.las") == "file"
|
||
|
||
def test_path_object(self):
|
||
from lidar_pipeline.dtm import _strip_lidar_ext
|
||
from pathlib import Path
|
||
assert _strip_lidar_ext(Path("/data/input/file.copc.laz")) == "file"
|
||
|
||
|
||
class TestStripVerticalOffsets:
|
||
"""Vertical de-bias of flight-line point sources (strip alignment)."""
|
||
|
||
def _synthetic(self, offsets, n=1_500_000, extent=300.0, seed=0):
|
||
"""Interleaved sources over one tile; each carries a known Z bias."""
|
||
rng = np.random.default_rng(seed)
|
||
x = rng.uniform(0, extent, n)
|
||
y = rng.uniform(0, extent, n)
|
||
z = 100 + 0.02 * x - 0.01 * y + rng.normal(0, 0.01, n)
|
||
psid = rng.integers(0, len(offsets), n)
|
||
return x, y, z + np.asarray(offsets)[psid], psid.astype(np.uint16)
|
||
|
||
def test_recovers_known_offsets(self):
|
||
"""±5 cm biases are recovered; the aligned source stays untouched."""
|
||
from lidar_pipeline.dtm import _strip_vertical_offsets
|
||
x, y, z, psid = self._synthetic((0.0, 0.05, -0.05))
|
||
offs = _strip_vertical_offsets(x, y, z, psid)
|
||
assert abs(offs.get(1, 0.0) - 0.05) < 0.01
|
||
assert abs(offs.get(2, 0.0) + 0.05) < 0.01
|
||
assert 0 not in offs # biais ~0 < seuil : pas de correction
|
||
|
||
def test_small_bias_below_threshold_ignored(self):
|
||
"""Biases under the 0.5 cm threshold trigger no correction."""
|
||
from lidar_pipeline.dtm import _strip_vertical_offsets
|
||
x, y, z, psid = self._synthetic((0.0, 0.003, -0.003))
|
||
assert _strip_vertical_offsets(x, y, z, psid) == {}
|
||
|
||
def test_single_source_returns_empty(self):
|
||
"""A single point source cannot be compared: no offsets."""
|
||
from lidar_pipeline.dtm import _strip_vertical_offsets
|
||
x, y, z, psid = self._synthetic((0.05,))
|
||
assert _strip_vertical_offsets(x, y, z, psid) == {}
|
||
|
||
def test_no_shared_cells_returns_empty(self):
|
||
"""Sources covering disjoint areas (no overlap) are not corrected."""
|
||
from lidar_pipeline.dtm import _strip_vertical_offsets
|
||
rng = np.random.default_rng(1)
|
||
n = 200_000
|
||
x = np.concatenate([rng.uniform(0, 100, n), rng.uniform(200, 300, n)])
|
||
y = rng.uniform(0, 300, 2 * n)
|
||
z = 100 + rng.normal(0, 0.01, 2 * n)
|
||
psid = np.concatenate([np.zeros(n, np.uint16), np.ones(n, np.uint16)])
|
||
z[psid == 1] += 0.10
|
||
assert _strip_vertical_offsets(x, y, z, psid) == {}
|
||
|
||
|
||
class TestStripAlignSidecar:
|
||
def test_sidecar_roundtrip_and_threshold(self, tmp_path):
|
||
"""Sidecar records version/threshold/offsets and matches config."""
|
||
from lidar_pipeline.dtm import (
|
||
_write_strip_align_sidecar, STRIP_ALIGN_VERSION, STRIP_ALIGN_THRESHOLD)
|
||
import json
|
||
offsets = {1049: 0.026, 1147: -0.026}
|
||
_write_strip_align_sidecar(tmp_path, "TILE", "_r0p2", offsets)
|
||
data = json.loads((tmp_path / "TILE_dtm_r0p2_stripalign.json").read_text())
|
||
assert data["version"] == STRIP_ALIGN_VERSION
|
||
assert data["threshold"] == STRIP_ALIGN_THRESHOLD
|
||
assert data["offsets"] == {"1049": 0.026, "1147": -0.026} # clés JSON en chaînes
|
||
|
||
def test_pipeline_match_logic(self, tmp_path):
|
||
"""_strip_align_matches invalidates legacy DTMs and config changes."""
|
||
from lidar_pipeline.pipeline import LidarArchaeoPipeline
|
||
from lidar_pipeline.dtm import _write_strip_align_sidecar
|
||
import json
|
||
|
||
class P(LidarArchaeoPipeline):
|
||
def __init__(self, out, strip_align):
|
||
self.output_dir = out
|
||
self.dtm_dir = out / "DTM"
|
||
self.dtm_dir.mkdir(exist_ok=True)
|
||
self.strip_align = strip_align
|
||
|
||
p = P(tmp_path, strip_align=True)
|
||
# DTM hérité sans sidecar : à régénérer
|
||
assert not p._strip_align_matches("TILE", "_r0p2")
|
||
# Sidecar conforme : valide
|
||
_write_strip_align_sidecar(p.dtm_dir, "TILE", "_r0p2", {})
|
||
assert p._strip_align_matches("TILE", "_r0p2")
|
||
# Seuil différent : à régénérer
|
||
bad = p.dtm_dir / "TILE_dtm_r0p2_stripalign.json"
|
||
bad.write_text(json.dumps({"version": 1, "threshold": 0.02, "offsets": {}}))
|
||
assert not p._strip_align_matches("TILE", "_r0p2")
|
||
# Calage désactivé + DTM calé : à régénérer
|
||
assert not P(tmp_path, strip_align=False)._strip_align_matches("TILE", "_r0p2")
|