Fix bugs and improve pipeline flexibility
- Fix gpu_cleanup import missing in visualizations.py (NameError in workers) - Fix t_pdf referenced before assignment when PDF is skipped - Skip classification+DTM when DTM exists regardless of --force - --force now only regenerates WebP/PDF, not classification/DTM - --force-classification forces reclassification when needed - Add laspy repair fallback for corrupt LAZ files (EVLR errors) - Keep DTM TIF by default for reuse (--no-keep-tif to delete) - Increase space between image and bottom cartouche (0.12→0.19) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -118,9 +118,9 @@ Exemples:
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help="Reclassifier le sol même si le fichier .las existe déjà"
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)
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parser.add_argument(
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"--keep-tif",
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"--no-keep-tif",
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action="store_true",
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help="Conserver les fichiers TIFF intermédiaires (sinon supprimés après conversion WebP)"
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help="Supprimer les fichiers TIFF intermédiaires après conversion WebP (par défaut: conservés)"
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)
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parser.add_argument(
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"--ground-classification",
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@ -176,7 +176,7 @@ Exemples:
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force=args.force,
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ground_method=args.ground_classification,
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force_classify=args.force_classification,
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keep_tif=args.keep_tif,
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keep_tif=not args.no_keep_tif,
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)
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# If --file is specified, process only matching files
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@ -246,10 +246,57 @@ def classify_ground(laz_file, temp_dir, method='auto', force=False):
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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 ({method.upper()}): {e.stderr.decode()}")
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error_msg = e.stderr.decode() if e.stderr else str(e)
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logger.warning(f" ✗ Erreur classification PDAL ({method.upper()}): {error_msg}")
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# Try repairing file with laspy if PDAL fails on EVLR/VLR
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if 'VLR' in error_msg or 'Invalid' in error_msg:
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logger.info(f" → Tentative de réparation du fichier avec laspy...")
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repaired_las = temp_dir / f"{laz_base}_repaired.las"
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if _repair_laz_with_laspy(laz_file, repaired_las):
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# Retry PDAL pipeline with repaired file
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pipeline_json = _create_ground_pipeline(repaired_las, output_las, method)
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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(f" ✓ Classification sol {method.upper()} terminée (fichier réparé)")
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return output_las
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except subprocess.CalledProcessError as e2:
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error_msg2 = e2.stderr.decode() if e2.stderr else str(e2)
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logger.error(f" ✗ Échec classification même après réparation: {error_msg2}")
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else:
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logger.error(f" ✗ Impossible de réparer le fichier")
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return None
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def _repair_laz_with_laspy(input_laz, output_las):
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"""Try to repair a corrupt LAZ file by re-reading with laspy and saving as LAS.
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Works around PDAL errors like 'Invalid Extended VLR size' by stripping
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problematic VLR/EVLR metadata during re-save.
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Args:
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input_laz: Path to corrupt LAZ/LAS file.
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output_las: Path for repaired LAS output.
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Returns:
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True if repair succeeded, False otherwise.
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"""
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import laspy
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try:
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las = laspy.read(str(input_laz))
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las.write(str(output_las))
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logger.info(f" ✓ Fichier réparé via laspy ({len(las.points):,} points)")
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return True
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except Exception as e:
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logger.warning(f" ✗ Réparation laspy échouée: {e}")
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return False
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def create_dtm_fast(las_file, basename, dtm_dir, resolution, force=False):
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"""Create DTM using fast binning method with gap filling.
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@ -275,9 +275,11 @@ class LidarArchaeoPipeline:
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logger.info(f"FICHIER : {basename}")
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logger.info("=" * 60)
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# Skip ground classification + DTM if DTM already exists (unless --force)
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# Skip ground classification + DTM if DTM already exists
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# --force only affects visualizations/PDF, not classification/DTM
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# Use --force-classification to force reclassification
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dtm_path = self.dtm_dir / f"{basename}_dtm.tif"
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if dtm_path.exists() and not self.force:
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if dtm_path.exists():
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logger.info("[1/5] Classification du sol — sautée (DTM existant)")
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logger.info("[2/5] Génération DTM — sautée (DTM existant)")
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dtm_file = dtm_path
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@ -297,7 +299,7 @@ class LidarArchaeoPipeline:
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# Step 2: Generate DTM
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logger.info("[2/5] Génération DTM...")
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t2 = time.time()
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dtm_file = create_dtm_fast(las_file, basename, self.dtm_dir, self.resolution, force=self.force)
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dtm_file = create_dtm_fast(las_file, basename, self.dtm_dir, self.resolution)
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t_dtm = time.time() - t2
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if not dtm_file:
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logger.error(f" ✗ Échec DTM ({t_dtm:.1f}s)")
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@ -309,6 +311,7 @@ class LidarArchaeoPipeline:
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self.generate_all_visualizations(dtm_file, basename)
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# Step 4: PDF report
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t_pdf = 0
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file_vis_dir = self.vis_dir / basename
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pdf_file = self.pdf_dir / f"{basename}_rapport.pdf"
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if pdf_file.exists() and not self.force:
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@ -320,10 +323,8 @@ class LidarArchaeoPipeline:
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t_pdf = time.time() - t4
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logger.info(f" ✓ Rapport PDF terminé ({t_pdf:.1f}s)")
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# Step 5: Clean up DTM TIF unless --keep-tif
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if not self.keep_tif:
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for dtm_file in sorted(self.dtm_dir.glob(f"{basename}_dtm.tif")):
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dtm_file.unlink(missing_ok=True)
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# Step 5: Keep DTM TIF for reuse (regenerating WebPs skips classification)
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# Use --no-keep-tif to delete DTM files after processing
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t_total = time.time() - t_start
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logger.info(f"✓ {basename} terminé en {t_total:.1f}s")
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@ -387,9 +387,9 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None):
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# Fixed data area position — identical for ALL visualization types
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# This ensures overlay/superposition works across all WebP images
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data_left = 0.08
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data_bottom = 0.12
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data_bottom = 0.19
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data_width_frac = 0.74
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data_height_frac = 0.78
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data_height_frac = 0.71
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ax = fig.add_axes([data_left, data_bottom, data_width_frac, data_height_frac])
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if is_rgba or is_rgb:
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@ -18,7 +18,7 @@ import rasterio
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from scipy.ndimage import generic_filter
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from scipy.stats import binned_statistic_2d
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from .gpu import HAS_GPU, to_gpu, to_cpu, xp_gaussian_filter, xp_uniform_filter, xp_minimum_filter
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from .gpu import HAS_GPU, to_gpu, to_cpu, xp_gaussian_filter, xp_uniform_filter, xp_minimum_filter, gpu_cleanup
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logger = logging.getLogger("lidar")
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