fb892ea9f2
Comments, docstrings, logs, CLI help, map UI, legends, PDF sheet, scripts, compose files and AGENTS.md are now English. Data keys stay unchanged (relief_oriente, densite_sol, visualisations/, API JSON keys, link params). Wrong comments and help defaults found along the way are corrected. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
1049 lines
49 KiB
Python
1049 lines
49 KiB
Python
"""Tests for DTM module."""
|
||
|
||
import json
|
||
import numpy as np
|
||
import pytest
|
||
from pathlib import Path
|
||
from unittest.mock import patch, MagicMock
|
||
|
||
|
||
class TestSMRFPipeline:
|
||
def test_pipeline_json_valid(self):
|
||
"""create_smrf_pipeline produces valid JSON with expected stages."""
|
||
from lidar_pipeline.dtm import create_smrf_pipeline
|
||
result = create_smrf_pipeline("/data/input/test.laz", "/data/output/test_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
assert "pipeline" in pipeline
|
||
stages = pipeline["pipeline"]
|
||
|
||
# Should have: reader, range filter (ReturnNumber), assign, ELM, outlier, SMRF, range filter (Classification), writer
|
||
stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
|
||
|
||
# First stage is the filename string (reader)
|
||
assert isinstance(stages[0], str)
|
||
assert "test.laz" in stages[0]
|
||
|
||
# Must contain preprocessing steps
|
||
assert "filters.assign" in stage_types
|
||
assert "filters.elm" in stage_types
|
||
assert "filters.outlier" in stage_types
|
||
|
||
# Must contain SMRF filter
|
||
assert "filters.smrf" in stage_types
|
||
|
||
# Must contain ReturnNumber filter
|
||
range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
assert len(range_stages) >= 1
|
||
# At least one should filter ReturnNumber
|
||
assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
|
||
|
||
def test_pipeline_elm_parameters(self):
|
||
"""ELM filter has terrain-adapted parameters."""
|
||
from lidar_pipeline.dtm import create_smrf_pipeline
|
||
result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
elm_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.elm"][0]
|
||
assert elm_stage["cell"] == 5.0
|
||
assert elm_stage["threshold"] == 2.0
|
||
|
||
def test_pipeline_outlier_parameters(self):
|
||
"""Outlier filter uses statistical method."""
|
||
from lidar_pipeline.dtm import create_smrf_pipeline
|
||
result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
outlier_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.outlier"][0]
|
||
assert outlier_stage["method"] == "statistical"
|
||
assert outlier_stage["mean_k"] == 8
|
||
assert outlier_stage["multiplier"] == 3.0
|
||
|
||
def test_pipeline_output_path(self):
|
||
"""Pipeline output path is set correctly."""
|
||
from lidar_pipeline.dtm import create_smrf_pipeline
|
||
result = create_smrf_pipeline("/input/a.laz", "/output/a_ground.las")
|
||
pipeline = json.loads(result)
|
||
# Last stage should be writer with correct output path
|
||
writer = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "writers.las"][0]
|
||
assert writer["filename"] == "/output/a_ground.las"
|
||
|
||
|
||
class TestCSFPipeline:
|
||
def test_pipeline_json_valid(self):
|
||
"""create_csf_pipeline produces valid JSON with CSF filter."""
|
||
from lidar_pipeline.dtm import create_csf_pipeline
|
||
result = create_csf_pipeline("/data/input/test.laz", "/data/output/test_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
assert "pipeline" in pipeline
|
||
stages = pipeline["pipeline"]
|
||
stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
|
||
|
||
# Must contain CSF filter
|
||
assert "filters.csf" in stage_types
|
||
|
||
# Must contain ReturnNumber filter
|
||
range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
|
||
|
||
def test_csf_parameters(self):
|
||
"""CSF pipeline has expected parameters."""
|
||
from lidar_pipeline.dtm import create_csf_pipeline
|
||
result = create_csf_pipeline("/input/a.laz", "/output/a_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
csf_stage = [s for s in pipeline["pipeline"] if isinstance(s, dict) and s.get("type") == "filters.csf"][0]
|
||
assert csf_stage["resolution"] == 1.0 # 1 m cloth: ~4× faster, DTM unchanged
|
||
assert csf_stage["rigidness"] == 3
|
||
assert csf_stage["smooth"] is True
|
||
assert "hdiff" not in csf_stage # hdiff is not a valid PDAL CSF parameter
|
||
|
||
|
||
class TestInterpolateHoles:
|
||
def test_fills_interior_hole_with_surface(self):
|
||
"""Large interior NaN hole is filled (no NaN left, value is plausible)."""
|
||
from lidar_pipeline.dtm import _interpolate_holes
|
||
# Linear-in-column surface z = 0.02 * x, with a large square hole in the middle.
|
||
x = np.arange(40, dtype=float) * 0.02
|
||
dtm = np.tile(x, (40, 1))
|
||
dtm[16:24, 16:24] = np.nan
|
||
filled, count = _interpolate_holes(dtm)
|
||
assert count == 64
|
||
assert not np.isnan(filled).any()
|
||
# Filled values stay within the surrounding z range (no wild extrapolation).
|
||
zmin, zmax = np.nanmin(dtm), np.nanmax(dtm)
|
||
hole_vals = filled[16:24, 16:24]
|
||
assert np.all(hole_vals >= zmin - 1e-6)
|
||
assert np.all(hole_vals <= zmax + 1e-6)
|
||
# A linear surface is interpolated near-exactly in the interior.
|
||
expected = np.tile(x[16:24], (8, 1))
|
||
assert np.allclose(hole_vals, expected, atol=0.02)
|
||
# Original valid cells are untouched.
|
||
valid = ~np.isnan(dtm)
|
||
assert np.allclose(filled[valid], dtm[valid])
|
||
|
||
def test_no_holes_returns_unchanged(self):
|
||
"""No NaN → returns same array and zero count."""
|
||
from lidar_pipeline.dtm import _interpolate_holes
|
||
dtm = np.arange(64, dtype=float).reshape(8, 8)
|
||
filled, count = _interpolate_holes(dtm)
|
||
assert count == 0
|
||
assert np.shares_memory(filled, dtm)
|
||
|
||
def test_all_nan_returns_unchanged(self):
|
||
"""No valid data → cannot interpolate, returns zeros-free NaN array."""
|
||
from lidar_pipeline.dtm import _interpolate_holes
|
||
dtm = np.full((8, 8), np.nan)
|
||
filled, count = _interpolate_holes(dtm)
|
||
assert count == 0
|
||
assert np.isnan(filled).all()
|
||
|
||
|
||
class TestFillSmallGaps:
|
||
"""Gap fill bounded to the point envelope (no more patches or fringes)."""
|
||
|
||
@staticmethod
|
||
def _grid(n=200, step=2, value=10.0):
|
||
"""Regular point pattern (1 pixel in `step`) over n × n pixels."""
|
||
dtm = np.full((n, n), np.nan)
|
||
dtm[::step, ::step] = value
|
||
return dtm
|
||
|
||
def test_isolated_point_is_removed_not_grown(self):
|
||
from lidar_pipeline.dtm import _fill_small_gaps
|
||
dtm = np.full((100, 100), np.nan)
|
||
dtm[50, 50] = 5.0
|
||
out, filled, removed = _fill_small_gaps(dtm, 0.2)
|
||
assert np.isnan(out).all() # neither a patch nor a lone point
|
||
assert (filled, removed) == (0, 1)
|
||
|
||
def test_gaps_between_points_filled_without_edge_band(self):
|
||
from lidar_pipeline.dtm import _fill_small_gaps
|
||
dtm = self._grid()
|
||
dtm[:, 100:] = np.nan # large hole to the east
|
||
out, filled, _ = _fill_small_gaps(dtm, 0.2)
|
||
assert filled > 0
|
||
assert not np.isnan(out[10:190, 10:99]).any() # gaps between points filled
|
||
assert np.isnan(out[:, 99:]).all() # nothing extrapolated into the hole
|
||
|
||
def test_radius_follows_local_density(self):
|
||
"""Sparse pattern (1 point / 1.8 m, 2.5 m diagonal gaps) filled; 3 m
|
||
hole in a dense area kept; 1.6 m hole filled."""
|
||
from lidar_pipeline.dtm import _fill_small_gaps
|
||
sparse = self._grid(step=9)
|
||
out, _, _ = _fill_small_gaps(sparse, 0.2)
|
||
assert not np.isnan(out[30:170, 30:170]).any()
|
||
dense = self._grid(step=1)
|
||
dense[90:105, 90:105] = np.nan # 3 m hole in a full pattern
|
||
dense[40:48, 40:48] = np.nan # 1.6 m hole (car)
|
||
out, _, _ = _fill_small_gaps(dense, 0.2)
|
||
assert np.isnan(out[95:100, 95:100]).all()
|
||
assert not np.isnan(out[40:48, 40:48]).any()
|
||
|
||
def test_morph_disk_matches_scipy(self):
|
||
from scipy import ndimage as nd
|
||
from lidar_pipeline.dtm import _morph_disk
|
||
rng = np.random.default_rng(0)
|
||
mask = rng.random((60, 70)) < 0.05
|
||
square = np.ones((3, 3), dtype=bool)
|
||
cross = nd.generate_binary_structure(2, 1)
|
||
ref = mask
|
||
for i in range(5):
|
||
ref = nd.binary_dilation(ref, structure=square if i % 2 == 0 else cross)
|
||
assert (_morph_disk(mask, 5) == ref).all()
|
||
ref_e = ref
|
||
for i in range(5):
|
||
ref_e = nd.binary_erosion(ref_e, structure=square if i % 2 == 0 else cross,
|
||
border_value=1)
|
||
assert (_morph_disk(ref, 5, erode=True) == ref_e).all()
|
||
|
||
def test_dtm_records_gap_fill_version(self, tmp_output_dir):
|
||
import laspy
|
||
import rasterio
|
||
from lidar_pipeline.dtm import (create_dtm_fast, read_dtm_gap_fill,
|
||
GAP_FILL_VERSION)
|
||
hdr = laspy.LasHeader(version='1.2', point_format=0)
|
||
las = laspy.LasData(hdr)
|
||
las.x, las.y, las.z = [0.5, 1.5, 0.5, 1.5], [0.5, 0.5, 1.5, 1.5], [10.0] * 4
|
||
las.write(str(tmp_output_dir / "g.las"))
|
||
out = create_dtm_fast(tmp_output_dir / "g.las", "g", tmp_output_dir, 1.0,
|
||
force=True, strip_align=False)
|
||
assert read_dtm_gap_fill(out) == GAP_FILL_VERSION
|
||
with rasterio.open(str(out)) as src:
|
||
src.tags() # readable
|
||
legacy = tmp_output_dir / "legacy.tif"
|
||
with rasterio.open(str(legacy), "w", driver="GTiff", width=2, height=2,
|
||
count=1, dtype="float32") as dst:
|
||
dst.write(np.zeros((1, 2, 2), dtype="float32"))
|
||
assert read_dtm_gap_fill(legacy) == 1
|
||
|
||
|
||
class TestDensitySidecar:
|
||
def test_dtm_writes_ground_density(self, tmp_output_dir):
|
||
"""4 points per m² over 10 × 10 m: density 4 in the core, 1 m grid."""
|
||
import laspy
|
||
import rasterio
|
||
from lidar_pipeline.dtm import create_dtm_fast, density_path
|
||
g = (np.arange(20) + 0.25) / 2.0 # 0.5 m step
|
||
xx, yy = np.meshgrid(g, g)
|
||
hdr = laspy.LasHeader(version='1.2', point_format=0)
|
||
las = laspy.LasData(hdr)
|
||
las.x, las.y, las.z = xx.ravel(), yy.ravel(), np.full(xx.size, 10.0)
|
||
las.write(str(tmp_output_dir / "d.las"))
|
||
out = create_dtm_fast(tmp_output_dir / "d.las", "d", tmp_output_dir, 0.5,
|
||
force=True, strip_align=False)
|
||
with rasterio.open(density_path(out)) as src:
|
||
dens = src.read(1)
|
||
assert abs(src.transform.a - 1.0) < 1e-9
|
||
assert np.allclose(dens[2:-2, 2:-2], 4.0)
|
||
|
||
|
||
class TestDetectGroundMethod:
|
||
def _make_mock_las(self, num_returns, z_values):
|
||
"""Create a mock laspy object with specified NumberOfReturns and z."""
|
||
mock_las = MagicMock()
|
||
mock_las.NumberOfReturns = np.array(num_returns)
|
||
mock_las.z = np.array(z_values)
|
||
mock_las.points = MagicMock()
|
||
mock_las.points.__len__ = lambda self: len(num_returns)
|
||
return mock_las
|
||
|
||
@patch('lidar_pipeline.dtm._read_with_pdal')
|
||
@patch('laspy.read')
|
||
def test_urban_terrain_returns_csf(self, mock_read, mock_pdal):
|
||
"""High single-return ratio (>0.6) should select CSF."""
|
||
from lidar_pipeline.dtm import detect_ground_method
|
||
|
||
# 70% single returns = urban
|
||
n = 10000
|
||
num_returns = np.ones(n, dtype=int)
|
||
num_returns[:int(n * 0.3)] = 2 # 30% multi-return
|
||
z_values = np.random.normal(100, 5, n) # Low variance = flat terrain
|
||
|
||
mock_read.return_value = self._make_mock_las(num_returns, z_values)
|
||
|
||
result = detect_ground_method(Path("/data/input/test.laz"))
|
||
assert result == 'csf'
|
||
|
||
@patch('lidar_pipeline.dtm._read_with_pdal')
|
||
@patch('laspy.read')
|
||
def test_natural_terrain_returns_smrf(self, mock_read, mock_pdal):
|
||
"""Low single-return ratio and moderate variance should select SMRF."""
|
||
from lidar_pipeline.dtm import detect_ground_method
|
||
|
||
# 40% single returns, moderate variance
|
||
n = 10000
|
||
num_returns = np.ones(n, dtype=int)
|
||
num_returns[:int(n * 0.6)] = 2 # 60% multi-return (forest)
|
||
z_values = np.random.normal(100, 15, n) # Moderate variance
|
||
|
||
mock_read.return_value = self._make_mock_las(num_returns, z_values)
|
||
|
||
result = detect_ground_method(Path("/data/input/test.laz"))
|
||
assert result == 'smrf'
|
||
|
||
@patch('lidar_pipeline.dtm._read_with_pdal')
|
||
@patch('laspy.read')
|
||
def test_mountainous_terrain_returns_csf(self, mock_read, mock_pdal):
|
||
"""High variance terrain (>30m std) selects CSF for complex terrain."""
|
||
from lidar_pipeline.dtm import detect_ground_method
|
||
|
||
# Moderate single-return ratio but very high height variance
|
||
n = 10000
|
||
num_returns = np.ones(n, dtype=int)
|
||
num_returns[:int(n * 0.5)] = 2
|
||
z_values = np.random.normal(100, 50, n) # Very high variance = mountainous
|
||
|
||
mock_read.return_value = self._make_mock_las(num_returns, z_values)
|
||
|
||
result = detect_ground_method(Path("/data/input/test.laz"))
|
||
assert result == 'csf'
|
||
|
||
|
||
class TestIGNPipeline:
|
||
def test_pipeline_keeps_supplier_classification(self):
|
||
"""create_ign_pipeline reuses the pre-classification (class 2) without refiltering."""
|
||
from lidar_pipeline.dtm import create_ign_pipeline
|
||
result = create_ign_pipeline("/input/a.laz", "/output/a_ground.las")
|
||
pipeline = json.loads(result)
|
||
|
||
stages = pipeline["pipeline"]
|
||
stage_types = [s.get("type") if isinstance(s, dict) else None for s in stages]
|
||
|
||
# No classification algorithm, no reset, no noise filters
|
||
assert "filters.smrf" not in stage_types
|
||
assert "filters.csf" not in stage_types
|
||
assert "filters.assign" not in stage_types
|
||
assert "filters.elm" not in stage_types
|
||
assert "filters.outlier" not in stage_types
|
||
|
||
# ReturnNumber filter kept + extraction of class 2 points
|
||
range_stages = [s for s in stages if isinstance(s, dict) and s.get("type") == "filters.range"]
|
||
assert any("ReturnNumber" in str(s.get("limits", "")) for s in range_stages)
|
||
assert any(s.get("limits") == "Classification[2:2]" for s in range_stages)
|
||
|
||
writer = [s for s in stages if isinstance(s, dict) and s.get("type") == "writers.las"][0]
|
||
assert writer["filename"] == "/output/a_ground.las"
|
||
|
||
|
||
class TestDetectIGN:
|
||
def _make_mock_las(self, classification, num_returns, z_values):
|
||
mock_las = MagicMock()
|
||
mock_las.classification = classification
|
||
mock_las.NumberOfReturns = np.array(num_returns)
|
||
mock_las.z = np.array(z_values)
|
||
mock_las.points = MagicMock()
|
||
mock_las.points.__len__ = lambda self: len(num_returns)
|
||
return mock_las
|
||
|
||
@patch('lidar_pipeline.dtm._read_with_pdal')
|
||
@patch('laspy.read')
|
||
def test_preclassified_returns_ign(self, mock_read, mock_pdal):
|
||
"""Pre-classified file (mostly class 2) → IGN method."""
|
||
from lidar_pipeline.dtm import detect_ground_method
|
||
|
||
n = 10000
|
||
num_returns = np.ones(n, dtype=int)
|
||
cls = np.zeros(n, dtype=np.uint8)
|
||
cls[int(n * 0.15):] = 2 # 85 % class 2 points
|
||
z_values = np.random.normal(100, 5, n)
|
||
|
||
mock_read.return_value = self._make_mock_las(cls, num_returns, z_values)
|
||
assert detect_ground_method(Path("/data/input/test.laz")) == 'ign'
|
||
|
||
@patch('lidar_pipeline.dtm._read_with_pdal')
|
||
@patch('laspy.read')
|
||
def test_unclassified_falls_back_to_smrf_or_csf(self, mock_read, mock_pdal):
|
||
"""No usable classification → classic SMRF/CSF detection."""
|
||
from lidar_pipeline.dtm import detect_ground_method
|
||
|
||
n = 10000
|
||
num_returns = np.ones(n, dtype=int)
|
||
num_returns[:int(n * 0.6)] = 2 # 60 % multi-return (forest) → not urban
|
||
cls = np.zeros(n, dtype=np.uint8) # no class 2 point
|
||
z_values = np.random.normal(100, 5, n)
|
||
|
||
mock_read.return_value = self._make_mock_las(cls, num_returns, z_values)
|
||
assert detect_ground_method(Path("/data/input/test.laz")) == 'smrf'
|
||
|
||
|
||
class TestClassifyGroundMethod:
|
||
@patch('lidar_pipeline.dtm.subprocess')
|
||
def test_classify_ground_auto_calls_detect(self, mock_subprocess):
|
||
"""classify_ground with method='auto' should call detect_ground_method."""
|
||
from lidar_pipeline.dtm import classify_ground
|
||
|
||
# Mock detect_ground_method to return 'csf'
|
||
with patch('lidar_pipeline.dtm.detect_ground_method', return_value='csf') as mock_detect:
|
||
mock_subprocess.run.return_value = MagicMock(returncode=0)
|
||
result = classify_ground(Path("/data/input/test.laz"), Path("/tmp"), method='auto')
|
||
|
||
mock_detect.assert_called_once()
|
||
|
||
@patch('lidar_pipeline.dtm.subprocess')
|
||
def test_classify_ground_smrf_uses_smrf_pipeline(self, mock_subprocess):
|
||
"""classify_ground with method='smrf' should create SMRF pipeline."""
|
||
from lidar_pipeline.dtm import classify_ground, _create_ground_pipeline
|
||
|
||
mock_subprocess.run.return_value = MagicMock(returncode=0)
|
||
|
||
with patch('lidar_pipeline.dtm.detect_ground_method'):
|
||
# Create temp dir
|
||
import tempfile
|
||
with tempfile.TemporaryDirectory() as tmpdir:
|
||
result = classify_ground(Path("/data/input/test.laz"), Path(tmpdir), method='smrf')
|
||
|
||
# Check the pipeline JSON was written with SMRF
|
||
pipeline_file = Path(tmpdir) / "pipeline_smrf.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.smrf" in stage_types
|
||
|
||
@patch('lidar_pipeline.dtm.subprocess')
|
||
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 only."""
|
||
from lidar_pipeline.dtm import parse_ign_classes
|
||
assert parse_ign_classes("sol") == [2]
|
||
|
||
def test_names_sorted_dedup(self):
|
||
"""Names accepted (EN/FR), sorted and de-duplicated."""
|
||
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):
|
||
"""Raw LAS codes, sorted."""
|
||
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):
|
||
"""Unknown name, out-of-range code or empty list → 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' alone for ground, combination encoded otherwise (cache invalidation)."""
|
||
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):
|
||
"""Several classes → ORed ranges on 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):
|
||
"""Without ign_codes, the IGN path stays ground only (2), backward compatible."""
|
||
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 → ign_1_2 files + multi-class filter."""
|
||
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 (default) → 'ign' names unchanged (cache preserved)."""
|
||
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:
|
||
"""The `pure` flag (IGN classification) no longer changes the DTM: both
|
||
modes fill small gaps and never use a lowest-return floor."""
|
||
|
||
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):
|
||
"""2x2 grid (res=1.0). Ground on 3 cells (z=10), hole at (1,1).
|
||
The full cloud has a lower return (z=7) in the hole."""
|
||
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):
|
||
"""pure=True: the hole is filled from its neighbours, no floor at 7.
|
||
|
||
Gap filling is active in every mode and there is no lowest-return
|
||
floor at all, so `pure` has no effect.
|
||
"""
|
||
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):
|
||
"""pure=False: the hole is filled as well (same behaviour)."""
|
||
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 # bias ~0 < threshold: no 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 TestStripJitterOffsets:
|
||
"""Intra-strip vertical jitter over GPS time windows."""
|
||
|
||
def _synthetic(self, bias_fn, n=800_000, extent=300.0, duration=20.0, seed=0):
|
||
"""Two interleaved strips; strip 1 carries a time-dependent bias."""
|
||
rng = np.random.default_rng(seed)
|
||
x = rng.uniform(0, extent, n)
|
||
y = rng.uniform(0, extent, n)
|
||
clean = 100 + 0.02 * x - 0.01 * y + rng.normal(0, 0.01, n)
|
||
t = rng.uniform(0, duration, n)
|
||
psid = rng.integers(0, 2, n).astype(np.uint16)
|
||
return x, y, clean + bias_fn(t, psid), psid, t, clean
|
||
|
||
def test_recovers_time_varying_offset(self):
|
||
"""A slow ±4 cm oscillation of strip 1 is removed from the true terrain.
|
||
|
||
With a two-strip overlap, the out-of-strip reference assigns a series
|
||
to each strip (each absorbs its share): the residual is checked
|
||
against the clean synthetic terrain, window by window.
|
||
"""
|
||
from lidar_pipeline.dtm import _strip_jitter_offsets, _apply_strip_jitter
|
||
w = 2 * np.pi / 8.0
|
||
x, y, z, psid, t, clean = self._synthetic(
|
||
lambda tt, p: 0.04 * np.sin(w * tt) * (p == 1))
|
||
jitter = _strip_jitter_offsets(x, y, z, psid, t)
|
||
assert set(jitter) == {0, 1}
|
||
resid = z - _apply_strip_jitter(psid, t, jitter) - clean
|
||
assert np.sqrt(np.mean(resid ** 2)) < 0.012
|
||
for lo in np.arange(0, 20.0, 2.0):
|
||
m = (t >= lo) & (t < lo + 2.0)
|
||
assert abs(resid[m].mean()) < 0.012, f"window {lo:.0f}-{lo + 2:.0f} s"
|
||
|
||
def test_tracks_step_offset(self):
|
||
"""A −3 cm step over the second half of the flight is tracked."""
|
||
from lidar_pipeline.dtm import _strip_jitter_offsets, _apply_strip_jitter
|
||
x, y, z, psid, t, clean = self._synthetic(
|
||
lambda tt, p: np.where(tt >= 10.0, -0.03, 0.0) * (p == 1))
|
||
jitter = _strip_jitter_offsets(x, y, z, psid, t)
|
||
resid = z - _apply_strip_jitter(psid, t, jitter) - clean
|
||
assert np.sqrt(np.mean(resid ** 2)) < 0.012
|
||
for lo in (3.0, 6.0, 13.0, 16.0): # away from the smoothed transition
|
||
m = (t >= lo) & (t < lo + 2.0)
|
||
assert abs(resid[m].mean()) < 0.012, f"window {lo:.0f}-{lo + 2:.0f} s"
|
||
|
||
def test_apply_interpolates_linearly(self):
|
||
"""Interpolation between window centres; 0 outside known strips."""
|
||
from lidar_pipeline.dtm import _apply_strip_jitter
|
||
jitter = {7: (np.array([10.0, 11.0]), np.array([0.0, 0.1]))}
|
||
psid = np.array([7, 7, 7, 3], dtype=np.uint16)
|
||
t = np.array([10.0, 10.5, 15.0, 10.5])
|
||
np.testing.assert_allclose(
|
||
_apply_strip_jitter(psid, t, jitter), [0.0, 0.05, 0.1, 0.0])
|
||
|
||
def test_requires_two_sources_and_time(self):
|
||
"""Single strip or non-finite time: nothing to correct."""
|
||
from lidar_pipeline.dtm import _strip_jitter_offsets
|
||
x, y, z, psid, t, _clean = self._synthetic(lambda tt, p: 0.04 * np.sin(tt) * (p == 1))
|
||
assert _strip_jitter_offsets(x, y, z, np.zeros_like(psid), t) == {}
|
||
t_nan = t.copy()
|
||
t_nan[0] = np.nan
|
||
assert _strip_jitter_offsets(x, y, z, psid, t_nan) == {}
|
||
|
||
|
||
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} # JSON keys as strings
|
||
assert data["jitter"] == {}
|
||
|
||
def test_sidecar_records_jitter_series(self, tmp_path):
|
||
"""The sidecar records the jitter series and their parameters."""
|
||
from lidar_pipeline.dtm import (
|
||
_write_strip_align_sidecar, STRIP_JITTER_BIN, STRIP_JITTER_SMOOTH)
|
||
import json
|
||
jitter = {11: (np.array([0.05, 0.15]), np.array([0.012, -0.008]))}
|
||
_write_strip_align_sidecar(tmp_path, "T", "", {}, jitter)
|
||
data = json.loads((tmp_path / "T_dtm_stripalign.json").read_text())
|
||
assert data["jitter_bin"] == STRIP_JITTER_BIN
|
||
assert data["jitter_smooth"] == STRIP_JITTER_SMOOTH
|
||
entry = data["jitter"]["11"]
|
||
assert entry["bins"] == 2
|
||
assert entry["series_m"] == [0.012, -0.008]
|
||
assert entry["max_m"] == 0.012
|
||
|
||
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)
|
||
# Legacy DTM without a sidecar: regenerate
|
||
assert not p._strip_align_matches("TILE", "_r0p2")
|
||
# Matching sidecar: valid
|
||
_write_strip_align_sidecar(p.dtm_dir, "TILE", "_r0p2", {})
|
||
assert p._strip_align_matches("TILE", "_r0p2")
|
||
# Different threshold: regenerate
|
||
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")
|
||
# Different jitter parameters: regenerate
|
||
bad.write_text(json.dumps({"version": 2, "threshold": 0.005, "offsets": {},
|
||
"jitter_bin": 0.5, "jitter_smooth": 5}))
|
||
assert not p._strip_align_matches("TILE", "_r0p2")
|
||
# Alignment disabled + aligned DTM: regenerate
|
||
assert not P(tmp_path, strip_align=False)._strip_align_matches("TILE", "_r0p2")
|
||
# Different line-by-line parameters: regenerate
|
||
_write_strip_align_sidecar(p.dtm_dir, "TILE", "_r0p2", {})
|
||
data = json.loads(bad.read_text())
|
||
data["line_window"] = 99
|
||
bad.write_text(json.dumps(data))
|
||
assert not p._strip_align_matches("TILE", "_r0p2")
|
||
|
||
|
||
|
||
class TestEdgeBuffer:
|
||
"""Edge matching: DTM extended with the ground points of neighbouring tiles."""
|
||
|
||
BASENAME = "LHD_FXX_0638_6628_PTS_LAMB93_IGN69"
|
||
# LHD grid: (col, row) = north-west corner → 0638_6628 covers
|
||
# X ∈ [638000, 639000], Y ∈ [6627000, 6628000] (north edge = 6628 km).
|
||
NOMINAL = (638000.0, 6627000.0, 639000.0, 6628000.0) # 1 km tile
|
||
|
||
def _write_las(self, path, points, classification=None):
|
||
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]
|
||
if classification is not None:
|
||
las.classification = classification
|
||
las.write(str(path))
|
||
return path
|
||
|
||
def _write_clouds(self, root, res=50.0):
|
||
"""Central tile at z=10 + EAST neighbour at z=20 (one point per res cell)."""
|
||
import numpy as np
|
||
input_dir = root / "input"
|
||
input_dir.mkdir(exist_ok=True)
|
||
min_x, min_y, max_x, max_y = self.NOMINAL
|
||
xs = np.arange(min_x + res / 2, max_x, res)
|
||
ys = np.arange(min_y + res / 2, max_y, res)
|
||
gx, gy = np.meshgrid(xs, ys)
|
||
main = list(zip(gx.ravel(), gy.ravel(), np.full(gx.size, 10.0)))
|
||
nxs = xs + 1000.0
|
||
ngx, ngy = np.meshgrid(nxs, ys)
|
||
east = list(zip(ngx.ravel(), ngy.ravel(), np.full(ngx.size, 20.0)))
|
||
ground = self._write_las(root / "ground.las", main)
|
||
source = self._write_las(input_dir / f"{self.BASENAME}.copc.laz", main)
|
||
self._write_las(input_dir / "LHD_FXX_0639_6628_PTS_LAMB93_IGN69.copc.laz",
|
||
east, classification=[2] * len(east))
|
||
return ground, source, input_dir
|
||
|
||
def test_neighbor_discovery(self, tmp_output_dir):
|
||
from lidar_pipeline.dtm import _neighbor_laz_files
|
||
(tmp_output_dir / f"{self.BASENAME}.copc.laz").touch()
|
||
present = [(637, 6627), (639, 6629), (638, 6629)]
|
||
for c, r in present:
|
||
(tmp_output_dir / f"LHD_FXX_{c}_{r}_PTS_LAMB93_IGN69.copc.laz").touch()
|
||
# Non-neighbour noise: never picked
|
||
(tmp_output_dir / "LHD_FXX_0650_6700_PTS_LAMB93_IGN69.copc.laz").touch()
|
||
found = _neighbor_laz_files(tmp_output_dir / f"{self.BASENAME}.copc.laz")
|
||
assert {f.name for f in found} == {
|
||
f"LHD_FXX_{c}_{r}_PTS_LAMB93_IGN69.copc.laz" for c, r in present}
|
||
|
||
def test_neighbor_discovery_non_lhd(self, tmp_output_dir):
|
||
from lidar_pipeline.dtm import _neighbor_laz_files
|
||
src = tmp_output_dir / "nuage_arbitraire.laz"
|
||
src.touch()
|
||
assert _neighbor_laz_files(src) == []
|
||
|
||
def test_neighbor_found_in_edge_subdir(self, tmp_output_dir):
|
||
"""A neighbour kept in edge_neighbors/ is found; a tile directly in
|
||
input/ still takes priority."""
|
||
from lidar_pipeline.dtm import _neighbor_laz_files, EDGE_NEIGHBORS_DIRNAME
|
||
base = "LHD_FXX_0637_6627_PTS_LAMB93_IGN69"
|
||
(tmp_output_dir / f"{base}.copc.laz").touch()
|
||
edge = tmp_output_dir / EDGE_NEIGHBORS_DIRNAME
|
||
edge.mkdir()
|
||
# Neighbour only in the edge-matching subfolder
|
||
(edge / "LHD_FXX_0638_6628_PTS_LAMB93_IGN69.copc.laz").touch()
|
||
# Neighbour present in both places: the input/ copy wins
|
||
(edge / "LHD_FXX_0636_6626_PTS_LAMB93_IGN69.copc.laz").touch()
|
||
(tmp_output_dir / "LHD_FXX_0636_6626_PTS_LAMB93_IGN69.copc.laz").touch()
|
||
found = _neighbor_laz_files(tmp_output_dir / f"{base}.copc.laz")
|
||
by_name = {f.name: f for f in found}
|
||
assert by_name["LHD_FXX_0638_6628_PTS_LAMB93_IGN69.copc.laz"].parent == edge
|
||
assert by_name["LHD_FXX_0636_6626_PTS_LAMB93_IGN69.copc.laz"].parent == tmp_output_dir
|
||
|
||
def test_buffered_dtm_extends_into_neighbor(self, tmp_output_dir):
|
||
"""24x24 DTM (20x20 tile + 100 m band), EAST band filled at z=20 by the neighbour."""
|
||
from lidar_pipeline.dtm import create_dtm_fast, read_dtm_edge_buffer, EDGE_BUFFER_TAG
|
||
import rasterio
|
||
ground, source, _ = self._write_clouds(tmp_output_dir)
|
||
dtm = create_dtm_fast(ground, self.BASENAME, tmp_output_dir, 50.0,
|
||
force=True, source_laz=source, strip_align=False,
|
||
edge_buffer=100.0, neighbor_classes=[2])
|
||
assert dtm is not None
|
||
with rasterio.open(str(dtm)) as src:
|
||
assert (src.width, src.height) == (24, 24)
|
||
assert abs(src.bounds.left - 637900.0) < 1e-6
|
||
assert abs(src.bounds.top - 6628100.0) < 1e-6
|
||
assert src.tags().get(EDGE_BUFFER_TAG) == "100"
|
||
arr = src.read(1)
|
||
assert arr[12, 12] == 10.0 # core: central tile
|
||
assert arr[12, 23] == 20.0 # EAST band: neighbour points
|
||
assert np.isnan(arr[0, 0]) # WEST band without neighbour: empty
|
||
assert read_dtm_edge_buffer(dtm) == 100.0
|
||
|
||
def test_unbuffered_dtm_has_no_tag(self, tmp_output_dir):
|
||
from lidar_pipeline.dtm import create_dtm_fast, read_dtm_edge_buffer, EDGE_BUFFER_TAG
|
||
import rasterio
|
||
ground, source, _ = self._write_clouds(tmp_output_dir)
|
||
dtm = create_dtm_fast(ground, self.BASENAME, tmp_output_dir, 50.0,
|
||
force=True, source_laz=source, strip_align=False)
|
||
assert dtm is not None
|
||
with rasterio.open(str(dtm)) as src:
|
||
assert EDGE_BUFFER_TAG not in src.tags()
|
||
assert read_dtm_edge_buffer(dtm) == 0.0
|
||
|
||
def test_buffered_dtm_non_lhd_falls_back(self, tmp_output_dir):
|
||
"""Non-LHD name: no nominal tile, header bounds kept."""
|
||
from lidar_pipeline.dtm import create_dtm_fast, read_dtm_edge_buffer
|
||
import rasterio
|
||
ground = self._write_las(tmp_output_dir / "ground.las",
|
||
[(0.5, 0.5, 10.0), (0.05, 0.05, 10.0), (1.95, 1.95, 10.0)])
|
||
source = self._write_las(tmp_output_dir / "nuage.laz",
|
||
[(0.5, 0.5, 10.0), (0.05, 0.05, 10.0), (1.95, 1.95, 10.0)])
|
||
dtm = create_dtm_fast(ground, "nuage", tmp_output_dir, 1.0,
|
||
force=True, source_laz=source, strip_align=False,
|
||
edge_buffer=100.0)
|
||
assert dtm is not None
|
||
with rasterio.open(str(dtm)) as src:
|
||
assert abs(src.bounds.left - 0.05) < 1e-6 # header bounds
|
||
assert read_dtm_edge_buffer(dtm) == 0.0
|
||
|
||
|
||
def _synthetic_beam(n_lines=240, spacing=0.4, seed=0, offsets=None, tilts=None):
|
||
"""Synthetic strip: scan lines (scan_angle sawtooth) over sloping terrain
|
||
crossed by a ditch; vertical offsets per line."""
|
||
rng = np.random.default_rng(seed)
|
||
xs, ys, zs, ts, angs, ids = [], [], [], [], [], []
|
||
x_line = np.arange(0, 100, 0.12)
|
||
for k in range(n_lines):
|
||
y = k * spacing + rng.normal(0, 0.02, x_line.size)
|
||
z = 100 + 0.03 * x_line + 0.05 * y
|
||
z = z - 0.5 * (np.abs(x_line - 41) < 1.0) # ditch perpendicular to the lines
|
||
u = np.linspace(-1, 1, x_line.size)
|
||
z = z + offsets[k] + (0 if tilts is None else tilts[k]) * u + rng.normal(0, 0.01, x_line.size)
|
||
xs.append(x_line); ys.append(y); zs.append(z); ids.append(np.full(x_line.size, k))
|
||
ts.append(k * 0.0067 + np.linspace(0, 0.006, x_line.size))
|
||
angs.append(np.linspace(-3300, 3300, x_line.size))
|
||
return (np.concatenate(xs), np.concatenate(ys), np.concatenate(zs),
|
||
np.concatenate(ts), np.concatenate(angs), np.concatenate(ids))
|
||
|
||
|
||
class TestScanLineAlignment:
|
||
"""3rd alignment pass: vertical offset between successive scan lines."""
|
||
|
||
def test_line_ids_follow_sawtooth_not_vegetation_gaps(self):
|
||
from lidar_pipeline.dtm import _scan_line_ids
|
||
t = np.arange(50) * 0.0001
|
||
ang = np.tile(np.linspace(-3000, 3000, 10), 5)
|
||
keep = np.ones(50, bool); keep[13:17] = False # vegetation hole in line 2
|
||
ids = _scan_line_ids(t[keep], ang[keep])
|
||
assert ids.max() == 4
|
||
t2 = t.copy(); t2[30:] += 1.0 # end of pass: new line
|
||
assert _scan_line_ids(t2, np.zeros(50)).max() == 1
|
||
|
||
def test_group_median_matches_numpy(self):
|
||
from lidar_pipeline.dtm import _group_median
|
||
rng = np.random.default_rng(3)
|
||
g = rng.integers(0, 20, 2000)
|
||
v = rng.normal(size=2000); v[::17] = np.nan
|
||
med = _group_median(v, g, 20, 1)
|
||
for k in range(20):
|
||
vals = v[(g == k) & np.isfinite(v)]
|
||
assert med[k] == pytest.approx(np.median(vals))
|
||
|
||
def test_removes_alternating_line_offsets_and_keeps_ditch(self):
|
||
from lidar_pipeline.dtm import _scan_line_corrections_beam
|
||
n = 240
|
||
rng = np.random.default_rng(1)
|
||
offsets = 0.015 * (-1.0) ** np.arange(n) + rng.normal(0, 0.006, n)
|
||
x, y, z, t, ang, ids = _synthetic_beam(n, offsets=offsets)
|
||
corr, per_line, _ = _scan_line_corrections_beam(x, y, z, t, ang)
|
||
assert len(per_line) == n
|
||
from scipy.ndimage import gaussian_filter1d
|
||
hp = lambda v: v - gaussian_filter1d(v, 3, mode="nearest")
|
||
before, after = hp(offsets)[10:-10].std(), hp(offsets - per_line)[10:-10].std()
|
||
assert after < 0.2 * before, f"{before*1000:.1f} → {after*1000:.1f} mm"
|
||
ditch = np.abs(x - 41) < 0.8
|
||
depth = lambda zz: np.median(zz[~ditch & (np.abs(x - 41) < 4)]) - np.median(zz[ditch])
|
||
assert depth(z - corr) == pytest.approx(depth(z), abs=0.01)
|
||
|
||
def test_removes_alternating_line_tilts(self):
|
||
"""Roll: lines tilted alternately (one end high, the other low)."""
|
||
from scipy.ndimage import gaussian_filter1d
|
||
from lidar_pipeline.dtm import _scan_line_corrections_beam
|
||
n = 240
|
||
rng = np.random.default_rng(4)
|
||
tilts = 0.02 * (-1.0) ** np.arange(n) + rng.normal(0, 0.008, n)
|
||
x, y, z, t, ang, ids = _synthetic_beam(n, offsets=np.zeros(n), tilts=tilts)
|
||
corr, _, per_tilt = _scan_line_corrections_beam(x, y, z, t, ang)
|
||
hp = lambda v: v - gaussian_filter1d(v, 3, mode="nearest")
|
||
before, after = hp(tilts)[10:-10].std(), hp(tilts - per_tilt)[10:-10].std()
|
||
assert after < 0.2 * before, f"{before*1000:.1f} → {after*1000:.1f} mm"
|
||
|
||
def test_joint_adjustment_fixes_all_scales_against_other_beam(self):
|
||
"""Two overlapping strips: the faulty strip (slow drift, roll, isolated
|
||
line at −8 cm) is re-aligned on the other at every scale, with no
|
||
drift of the overall elevation."""
|
||
from lidar_pipeline.dtm import _joint_line_corrections
|
||
n = 240
|
||
k = np.arange(n)
|
||
bad_off = 0.03 * np.sin(2 * np.pi * k / 120) # slow drift (not visible on its own surface)
|
||
bad_off[100] -= 0.08 # isolated, strongly shifted line
|
||
bad_tilt = 0.02 * np.cos(2 * np.pi * k / 60) # slow roll
|
||
xa, ya, za, ta, aa, _ = _synthetic_beam(n, seed=1, offsets=bad_off, tilts=bad_tilt)
|
||
xb, yb, zb, tb, ab, _ = _synthetic_beam(n, seed=2, offsets=np.zeros(n))
|
||
tb = tb + 1000.0
|
||
x = np.r_[xa, xb]; y = np.r_[ya, yb]; z = np.r_[za, zb]; t = np.r_[ta, tb]
|
||
ang = np.r_[aa, ab]; psid = np.r_[np.full(len(za), 1), np.full(len(zb), 2)]
|
||
corr, gl, touched, iters = _joint_line_corrections(x, y, z, psid, t, ang)
|
||
truth = np.r_[bad_off[np.repeat(k, len(za) // n)] + bad_tilt[np.repeat(k, len(za) // n)]
|
||
* np.tile(np.linspace(-1, 1, len(za) // n), n), np.zeros(len(zb))]
|
||
# Without ground truth, the error is shared between the strips: it is
|
||
# the offset BETWEEN strips (homologous points, same geometry) that
|
||
# must vanish.
|
||
na = len(za)
|
||
before = truth[:na] - truth[:na].mean()
|
||
rel = (truth[:na] - corr[:na]) - (0.0 - corr[na:])
|
||
after = rel - rel.mean()
|
||
assert np.std(after) < 0.25 * np.std(before), \
|
||
f"{np.std(before)*1000:.1f} → {np.std(after)*1000:.1f} mm"
|
||
assert abs(np.mean(corr)) < 0.002 # no overall drift
|
||
assert touched.mean() > 0.9 and iters <= 8
|
||
|
||
def test_joint_adjustment_removes_static_angle_profile(self):
|
||
"""Arc-shaped calibration error by angle (same for every line):
|
||
non-linear, invisible to offset + tilt, removed by the per-strip,
|
||
per-angle-bin profile."""
|
||
from lidar_pipeline.dtm import _joint_line_corrections
|
||
n = 240
|
||
xa, ya, za, ta, aa, _ = _synthetic_beam(n, seed=5, offsets=np.zeros(n))
|
||
uu = aa / 3300.0
|
||
prof = 0.02 * (uu ** 2 - np.mean(uu ** 2))
|
||
za = za + prof
|
||
xb, yb, zb, tb, ab, _ = _synthetic_beam(n, seed=6, offsets=np.zeros(n))
|
||
x = np.r_[xa, xb]; y = np.r_[ya, yb]; z = np.r_[za, zb]; t = np.r_[ta, tb + 1000.0]
|
||
ang = np.r_[aa, ab]; psid = np.r_[np.full(len(za), 1), np.full(len(zb), 2)]
|
||
corr, _, _, _ = _joint_line_corrections(x, y, z, psid, t, ang)
|
||
na = len(za)
|
||
rel = (prof - corr[:na]) - (0.0 - corr[na:])
|
||
assert np.std(rel - rel.mean()) < 0.3 * np.std(prof), \
|
||
f"{np.std(prof)*1000:.1f} → {np.std(rel - rel.mean())*1000:.1f} mm"
|
||
|
||
def test_clean_beam_left_untouched(self):
|
||
from lidar_pipeline.dtm import _scan_line_corrections
|
||
x, y, z, t, ang, ids = _synthetic_beam(240, offsets=np.zeros(240))
|
||
corr, stats = _scan_line_corrections(x, y, z, np.full(len(z), 7), t, ang)
|
||
assert stats == {} and not corr.any()
|
||
|
||
|
||
class TestIgnDirectExtraction:
|
||
"""IGN classification: direct extraction with laspy (PDAL as fallback)."""
|
||
|
||
def test_keeps_requested_classes_and_valid_returns(self, tmp_path):
|
||
import laspy
|
||
from lidar_pipeline.dtm import _extract_ign_ground
|
||
n = 1000
|
||
rng = np.random.default_rng(0)
|
||
hdr = laspy.LasHeader(point_format=6, version="1.4")
|
||
hdr.scales = np.array([0.01, 0.01, 0.001]); hdr.offsets = np.array([1000.0, 6800000.0, 0.0])
|
||
las = laspy.LasData(hdr)
|
||
las.x = 1000 + rng.uniform(0, 100, n); las.y = 6800000 + rng.uniform(0, 100, n)
|
||
las.z = rng.uniform(100, 110, n)
|
||
cls = rng.choice([1, 2, 3, 6, 9], n); las.classification = cls
|
||
rn = np.ones(n, dtype=np.uint8); rn[:10] = 0; las.return_number = rn
|
||
las.number_of_returns = np.ones(n, dtype=np.uint8)
|
||
las.point_source_id = np.full(n, 42, dtype=np.uint16)
|
||
src = tmp_path / "t.las"; las.write(str(src))
|
||
out = tmp_path / "g.las"
|
||
assert _extract_ign_ground(src, out, [2])
|
||
g = laspy.read(str(out))
|
||
expected = (cls == 2) & (rn >= 1)
|
||
assert len(g.points) == int(expected.sum())
|
||
assert set(np.unique(np.asarray(g.classification))) == {2}
|
||
assert g.header.point_format.id == 6 and np.all(np.asarray(g.point_source_id) == 42)
|
||
assert _extract_ign_ground(src, tmp_path / "e.las", [1, 2])
|
||
assert len(laspy.read(str(tmp_path / "e.las")).points) == int(((np.isin(cls, [1, 2])) & (rn >= 1)).sum())
|