Refactor pipeline en modules + logging verbose/debug + options CLI
- Découpage du monolithe process_lidar.py (~2750 lignes) en package lidar_pipeline/ avec 9 modules (gpu, dtm, visualizations, ign, rendering, pipeline, cli, __init__, __main__) - Logging configurable: -v (verbose avec timestamps) et --debug (détails internes fichier:ligne) - Option --force pour régénérer tous les fichiers (par défaut skip les WebP existants) - Option --file NOM pour traiter un seul fichier LAZ (tests rapides) - ProcessPoolExecutor avec répertoires temporaires uniques par worker - Suppression du code mort (geomorphons, hillshade_ne, nodata_mask) - Aucun fichier TIFF résiduel après conversion WebP - setup.py pour installation pip, stub process_lidar.py compatible Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
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lidar_pipeline/dtm.py
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209
lidar_pipeline/dtm.py
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"""DTM generation from classified LiDAR point clouds.
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Handles ground classification via PDAL/SMRF and DTM rasterisation
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using scipy binned_statistic_2d with gap-filling interpolation.
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"""
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import json
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import logging
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import subprocess
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from pathlib import Path
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import numpy as np
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import rasterio
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from rasterio.transform import from_bounds
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from scipy import ndimage as scipy_ndimage
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from scipy.ndimage import distance_transform_edt, gaussian_filter
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from scipy.stats import binned_statistic_2d
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from scipy import interpolate
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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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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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Returns:
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JSON string of the PDAL pipeline.
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"""
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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.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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{
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"type": "writers.las",
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"filename": str(output_las),
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"extra_dims": "all"
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}
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]
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}
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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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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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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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if output_las.exists():
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logger.info(" Classification 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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with open(pipeline_file, 'w') as f:
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f.write(pipeline_json)
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try:
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subprocess.run(
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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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return output_las
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except subprocess.CalledProcessError as e:
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error_msg = e.stderr.decode()
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logger.error(f" ✗ Erreur PDAL: {error_msg}")
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# If error is about ReturnNumber=0, try filtering those points
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if "ReturnNumber" in error_msg and "NumberOfReturns" in error_msg:
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logger.info(" → Tentative de filtrage des points ReturnNumber=0...")
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filtered_pipeline = [
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{"type": "readers.las", "filename": str(laz_file)},
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{"type": "filters.range", "limits": "ReturnNumber[1:],NumberOfReturns[1:]"},
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{"type": "filters.smrf", "scalar": 1.25},
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{"type": "filters.range", "limits": "Classification[2:2]"},
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{"type": "writers.las", "filename": str(output_las), "extra_dims": "all"}
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]
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filtered_json = json.dumps(filtered_pipeline)
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with open(pipeline_file, 'w') as f:
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f.write(filtered_json)
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try:
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subprocess.run(
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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 (points filtrés)")
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return output_las
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except subprocess.CalledProcessError as e2:
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logger.error(f" ✗ Erreur même avec filtrage: {e2.stderr.decode()}")
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return None
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return None
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def create_dtm_fast(las_file, basename, dtm_dir, resolution):
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"""Create DTM using fast binning method with gap filling.
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Args:
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las_file: Path to classified ground LAS file.
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basename: Base name for output file.
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dtm_dir: Directory for output DTM GeoTIFF.
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resolution: Grid resolution in meters per pixel.
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Returns:
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Path to output DTM GeoTIFF, or None on failure.
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"""
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import laspy
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logger.info(" → Génération DTM...")
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try:
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las = laspy.read(str(las_file))
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min_x, max_x = float(las.header.min[0]), float(las.header.max[0])
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min_y, max_y = float(las.header.min[1]), float(las.header.max[1])
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width = int(np.ceil((max_x - min_x) / resolution))
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height = int(np.ceil((max_y - min_y) / resolution))
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logger.debug(f" Bounds: X[{min_x:.1f}, {max_x:.1f}] Y[{min_y:.1f}, {max_y:.1f}]")
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logger.debug(f" Grid: {width}x{height} pixels ({len(las.points):,} points)")
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logger.info(f" Rasterisation {width}x{height} ({len(las.points):,} points)...")
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stat = binned_statistic_2d(
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las.x, las.y, las.z,
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statistic='mean',
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bins=[width, height],
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range=[[min_x, max_x], [min_y, max_y]]
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)
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dtm = stat.statistic.T
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dtm = dtm[::-1, :] # Flip Y so north is at top
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# Fill gaps using interpolation
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mask = np.isnan(dtm)
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if np.any(mask):
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logger.info(" Remplissage des zones vides...")
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dist_to_nearest = distance_transform_edt(mask)
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max_fill_distance = max(20, int(10 / resolution))
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y_coords, x_coords = np.where(~mask)
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z_values = dtm[~mask]
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if len(y_coords) > 100:
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interp = interpolate.NearestNDInterpolator(
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np.column_stack((y_coords, x_coords)),
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z_values
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)
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y_missing, x_missing = np.where(mask)
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dtm[y_missing, x_missing] = interp(y_missing, x_missing)
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dtm_smooth = gaussian_filter(dtm, sigma=2.0)
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fill_mask = mask & (dist_to_nearest <= max_fill_distance)
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dtm[fill_mask] = dtm_smooth[fill_mask]
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far_mask = mask & (dist_to_nearest > max_fill_distance)
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dtm[far_mask] = np.nan
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# Save as GeoTIFF
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output_tif = dtm_dir / f"{basename}_dtm.tif"
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transform = from_bounds(min_x, min_y, max_x, max_y, width, height)
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with rasterio.open(
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output_tif, 'w',
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driver='GTiff', height=height, width=width,
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count=1, dtype='float32',
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crs='EPSG:2154', transform=transform,
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compress='lzw'
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) as dst:
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dst.write(dtm.astype('float32'), 1)
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logger.info(f" ✓ DTM créé: {output_tif.name}")
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return output_tif
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except Exception as e:
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logger.error(f" ✗ Erreur DTM: {e}", exc_info=True)
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return None
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