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Pipeline LiDAR: classification sol auto + pré-traitement ELM + fix warnings

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- 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
2026-05-10 03:00:33 +02:00
parent f3026f41c9
commit e2845b9e6d
10 changed files with 545 additions and 95 deletions
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6
lidar_pipeline/rendering.py
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@ -204,7 +204,7 @@ def _apply_colormap(data, tif_file):
# Find matching colormap
for key, info in COLORMAPS.items():
if key in name:
valid_data = data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten()
valid_data = np.asarray(data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
vmin = vmax = None
@ -239,7 +239,7 @@ def _apply_colormap(data, tif_file):
return data, info['cmap'], info['title'], legend, info['description'], False
# Default: terrain colormap with percentile stretch
valid_data = data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten()
valid_data = np.asarray(data[~np.isnan(data)] if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
p2, p98 = np.percentile(valid_data, (2, 98))
data = np.clip((data - p2) / (p98 - p2), 0, 1)
@ -321,7 +321,7 @@ def tif_to_png(tif_file, vis_dir, resolution):
data = np.ma.masked_where((data == nodata) | np.isnan(data), data)
if not is_rgb:
valid_data = data.compressed() if hasattr(data, 'compressed') else data.flatten()
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
# Apply colormap