Pipeline LiDAR: classification sol auto + pré-traitement ELM + fix warnings
- Ajout classification automatique du sol (SMRF/PMF/CSF) avec détection
heuristique (ratio retours uniques > 0.6 → PMF urbain, sinon SMRF)
- Pré-traitement PDAL recommandé avant classification: ELM + outlier
removal (cell=5.0, threshold=2.0 adapté au calcaire rocailleux)
- Options CLI: --ground-classification {auto,smrf,pmf,csf} et
--force-classification pour forcer la reclassification
- Fix double logging (logger.propagate = False)
- Fix --force non transmis dans run.sh (réécriture parsing arguments)
- Fix warning numpy 'partition will ignore mask': conversion MaskedArray
en ndarray avant np.percentile()
- Ajout liblaszip8 + lazrs pour support LAZ dans Docker et laspy
- Tests unitaires pour PMF, CSF et auto-détection
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -17,38 +17,102 @@ from scipy.stats import binned_statistic_2d
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logger = logging.getLogger("lidar")
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def create_smrf_pipeline(input_laz, output_las):
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"""Create a PDAL pipeline JSON for SMRF ground classification.
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def _create_ground_pipeline(input_laz, output_las, method):
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"""Create a PDAL pipeline JSON for ground classification.
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Includes a filter for ReturnNumber/NumberOfReturns >= 1 to handle
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All methods include a ReturnNumber/NumberOfReturns >= 1 filter to handle
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LiDAR HD files that may contain points with invalid return numbers.
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Pre-processing steps (PDAL recommended workflow):
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1. Reset Classification to 0
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2. ELM (Extended Local Minimum) — mark low outliers as noise (Classification=7)
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3. Statistical outlier removal
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4. Ground classification (SMRF/PMF/CSF)
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5. Extract ground points (Classification=2)
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Args:
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input_laz: Path to input LAZ/LAS file.
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output_las: Path to output classified LAS file.
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method: Ground classification method ('smrf', 'pmf', or 'csf').
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Returns:
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JSON string of the PDAL pipeline.
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"""
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# Common ReturnNumber filter for LiDAR HD compatibility
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return_filter = {
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"type": "filters.range",
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"limits": "ReturnNumber[1:],NumberOfReturns[1:]"
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}
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# Reset Classification to 0 before preprocessing
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reset_classification = {
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"type": "filters.assign",
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"assignment": "Classification[:]=0"
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}
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# ELM (Extended Local Minimum) — mark low outliers as noise
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# Parameters tuned for rocky limestone terrain with low vegetation:
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# - cell=5.0m: fine resolution to capture rocky relief
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# - threshold=2.0m: high threshold to avoid marking rock outcrops as noise
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elm_filter = {
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"type": "filters.elm",
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"cell": 5.0,
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"threshold": 2.0
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}
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# Statistical outlier removal
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outlier_filter = {
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"type": "filters.outlier",
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"method": "statistical",
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"mean_k": 8,
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"multiplier": 3.0
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}
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# Classification filter (ground points only)
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ground_filter = {
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"type": "filters.range",
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"limits": "Classification[2:2]"
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}
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# Method-specific ground classification filter
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if method == 'smrf':
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ground_step = {
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"type": "filters.smrf",
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"ignore": "Classification[7:7]",
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"slope": 1.0,
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"window": 16.0,
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"threshold": 0.5,
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"scalar": 1.25
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}
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elif method == 'pmf':
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ground_step = {
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"type": "filters.pmf",
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"max_window": 33,
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"slope": 0.15,
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"max_distance": 2.5,
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"initial_distance": 0.15,
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"cell_size": 1.0
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}
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elif method == 'csf':
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ground_step = {
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"type": "filters.csf",
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"resolution": 0.5,
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"rigidness": 3,
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"smooth": True,
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"threshold": 0.5
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}
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else:
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raise ValueError(f"Méthode de classification inconnue: {method}")
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pipeline = {
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"pipeline": [
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str(input_laz),
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{
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"type": "filters.range",
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"limits": "ReturnNumber[1:],NumberOfReturns[1:]"
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},
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{
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"type": "filters.smrf",
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"ignore": "Classification[7:7]",
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"slope": 1.0,
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"window": 16.0,
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"threshold": 0.5,
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"scalar": 1.25
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},
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{
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"type": "filters.range",
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"limits": "Classification[2:2]"
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},
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return_filter,
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reset_classification,
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elm_filter,
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outlier_filter,
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ground_step,
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ground_filter,
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{
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"type": "writers.las",
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"filename": str(output_las),
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@ -59,26 +123,113 @@ def create_smrf_pipeline(input_laz, output_las):
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return json.dumps(pipeline)
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def classify_ground(laz_file, temp_dir):
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"""Classify ground points using PDAL SMRF filter.
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def create_smrf_pipeline(input_laz, output_las):
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"""Create a PDAL pipeline JSON for SMRF ground classification."""
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return _create_ground_pipeline(input_laz, output_las, 'smrf')
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def create_pmf_pipeline(input_laz, output_las):
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"""Create a PDAL pipeline JSON for PMF ground classification."""
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return _create_ground_pipeline(input_laz, output_las, 'pmf')
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def create_csf_pipeline(input_laz, output_las):
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"""Create a PDAL pipeline JSON for CSF ground classification."""
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return _create_ground_pipeline(input_laz, output_las, 'csf')
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def detect_ground_method(laz_file):
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"""Detect the best ground classification method based on point cloud statistics.
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Auto-selects between SMRF (natural terrain) and PMF (urban) only.
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CSF is available only via --ground-classification csf (slow but robust
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on complex terrain).
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Falls back to SMRF if the file cannot be read or attributes are missing.
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Args:
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laz_file: Path to input LAZ/LAS file.
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Returns:
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String: 'smrf', 'pmf', or 'csf'
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"""
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import laspy
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try:
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las = laspy.read(str(laz_file))
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except Exception as e:
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logger.warning(f" Impossible de lire le nuage pour auto-détection: {e}")
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logger.info(f" → Méthode: SMRF (défaut — lecture impossible)")
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return 'smrf'
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total_points = len(las.points)
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z = np.array(las.z)
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# Height variance (always available)
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z_std = float(np.std(z))
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z_range = float(np.max(z) - np.min(z))
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# Try to get NumberOfReturns (may not exist in all point formats)
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single_return_ratio = 0.0
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try:
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num_returns = np.array(las.NumberOfReturns)
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single_return_count = int(np.sum(num_returns == 1))
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single_return_ratio = single_return_count / total_points if total_points > 0 else 0
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except AttributeError:
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logger.debug(" NumberOfReturns non disponible — utilisation de la variance Z uniquement")
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logger.info(f" Analyse du nuage: {total_points:,} points, "
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f"ratio_retours_uniques={single_return_ratio:.2f}, "
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f"écart_Z={z_std:.1f}m, amplitude_Z={z_range:.1f}m")
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# Decision logic (auto selects between SMRF and PMF only):
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# - High single-return ratio (>0.6) → urban (buildings, roads) → PMF
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# - Default → SMRF (fast, robust for most natural terrain)
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# Note: CSF is available only via --ground-classification csf (slow but robust on complex terrain)
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if single_return_ratio > 0.6:
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method = 'pmf'
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reason = f"ratio retours uniques={single_return_ratio:.2f} > 0.6 → milieu urbain"
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else:
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method = 'smrf'
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reason = f"terrain naturel standard"
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logger.info(f" → Méthode: {method.upper()} ({reason})")
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return method
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def classify_ground(laz_file, temp_dir, method='auto', force=False):
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"""Classify ground points using PDAL ground classification filter.
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Args:
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laz_file: Path to input LAZ/LAS file.
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temp_dir: Directory for temporary files (pipeline.json, ground.las).
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method: Ground classification method ('auto', 'smrf', 'pmf', or 'csf').
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force: If True, reclassify even if output file already exists.
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Returns:
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Path to classified ground LAS file, or None on failure.
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"""
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import laspy # noqa: ensure available
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output_las = temp_dir / f"{laz_file.stem}_ground.las"
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# Auto-detect method if requested
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if method == 'auto':
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method = detect_ground_method(laz_file)
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logger.info(f" Classification sol: {method.upper()} (auto)")
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else:
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logger.info(f" Classification sol: {method.upper()} (forcé)")
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if output_las.exists():
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logger.info(" Classification déjà effectuée — fichier existant réutilisé")
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output_las = temp_dir / f"{laz_file.stem}_ground_{method}.las"
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if output_las.exists() and not force:
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logger.info(f" Classification {method.upper()} déjà effectuée — fichier existant réutilisé")
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return output_las
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pipeline_json = create_smrf_pipeline(laz_file, output_las)
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pipeline_file = temp_dir / "pipeline.json"
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if force and output_las.exists():
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logger.info(f" Reclassification forcée — suppression de {output_las.name}")
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output_las.unlink()
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pipeline_json = _create_ground_pipeline(laz_file, output_las, method)
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pipeline_file = temp_dir / f"pipeline_{method}.json"
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with open(pipeline_file, 'w') as f:
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f.write(pipeline_json)
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@ -88,10 +239,10 @@ def classify_ground(laz_file, temp_dir):
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["pdal", "pipeline", str(pipeline_file)],
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capture_output=True, check=True
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)
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logger.info(" ✓ Classification sol terminée")
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logger.info(f" ✓ Classification sol {method.upper()} terminée")
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return output_las
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except subprocess.CalledProcessError as e:
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logger.error(f" ✗ Erreur classification PDAL: {e.stderr.decode()}")
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logger.error(f" ✗ Erreur classification PDAL ({method.upper()}): {e.stderr.decode()}")
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return None
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