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Remove PMF, fix NaN in gradient visualizations, fix pos_open/neg_open shared param

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- Remove PMF from ground classification options (PDAL recommends SMRF over PMF)
- Auto-detection now uses CSF for urban/complex terrain instead of PMF
- Add z_std > 30m heuristic to auto-select CSF for complex terrain
- Fix pos_open/neg_open lambda missing 'shared' parameter (NameError in workers)
- Fix NaN mask not restored in hillshade, slope, aspect, curvature
  (gradient-based products computed on filled DEM lost NaN transparency)
- Add nan_mask parameter to _save_tif for centralized NaN restoration
- DTM TIF kept by default (no longer deleted after WebP conversion)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
2026-05-14 00:50:45 +02:00
parent eac482874d
commit 1cf8e1752f
6 changed files with 66 additions and 63 deletions
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13
CLAUDE.md
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@ -16,9 +16,8 @@ All commands run inside Docker. Use `./run.sh` as the primary interface.
./run.sh -g -r 0.2 # High resolution (0.2m/px)
./run.sh --test # Run unit tests
./run.sh -g --file LHD_FXX_1000_6882_PTS_LAMB93_IGN69.copc # Single file
./run.sh --ground-classification pmf # Force PMF ground classification
./run.sh -g --keep-tif # Keep intermediate TIFF files (default: kept)
./run.sh -g --no-keep-tif # Delete intermediate TIFF files
./run.sh --ground-classification csf # Force CSF ground classification (complex terrain)
./run.sh -g --keep-tif # Keep TIFF files (allows WebP regeneration without recalculating DTM)
./run.sh # Print help (no args)
```
@ -34,7 +33,7 @@ docker run --rm --gpus all -v $(pwd)/input:/data/input:ro -v $(pwd)/output:/data
- **`cli.py`** — argparse + logging setup. Entry point via `python -m lidar_pipeline`.
- **`pipeline.py`** — `LidarArchaeoPipeline` orchestrator. `VIZ_STEPS` registry maps names to generate functions. `FilePrefixFilter` for parallel logging. Creates `SharedDEM` once per file and passes it to all visualizations.
- **`dtm.py`** — PDAL ground classification (SMRF/PMF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
- **`dtm.py`** — PDAL ground classification (SMRF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
- **`visualizations.py`** — 15 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution, shared=None)` and return a TIF path or None. `SharedDEM` class pre-computes gradient, NaN mask, LRM to avoid redundant I/O and computation.
- **`gpu.py`** — CuPy/numpy abstraction: `HAS_GPU`, `to_gpu()`, `to_cpu()`, `xp_gaussian_filter()`, `xp_uniform_filter()`, `xp_minimum_filter()`, `gpu_cleanup()`. Falls back to CPU gracefully.
- **`ign.py`** — IGN WMTS tile download + overlay generation for orthophoto and topographic maps.
@ -60,11 +59,11 @@ Three places must be updated:
### Ground classification
Auto-detection in `dtm.py` `detect_ground_method()`:
- Single-return ratio > 0.6 → PMF (urban terrain)
- Single-return ratio > 0.6 → CSF (urban terrain, cloth simulation)
- Height std > 30m → CSF (complex/mountainous terrain)
- Default → SMRF (natural terrain)
Override with `--ground-classification {auto,smrf,pmf,csf}`.
Override with `--ground-classification {auto,smrf,csf}`.
### NaN handling
@ -83,6 +82,6 @@ Uses `ProcessPoolExecutor` with `'spawn'` start method (required for CUDA). Each
- **Language**: UI messages and comments in French. Code identifiers in English.
- **Logging**: Use `logger = logging.getLogger("lidar")`. Prefix per-file logs via `_file_filter.basename`.
- **GPU pattern**: `arr_gpu = to_gpu(arr)` → compute → `result = to_cpu(arr_gpu)` → `gpu_cleanup()` between visualizations.
- **Output format**: Visualizations saved as WebP. DTM TIFF kept by default for reuse (use `--no-keep-tif` to delete). `--force` regenerates WebPs without re-classifying if DTM exists. No COGs or viewer — only WebP + PDF report remain.
- **Output format**: Visualizations saved as WebP. TIFF intermediates deleted by default. Use `--keep-tif` to keep DTM+TIF for WebP regeneration with `--force`. No COGs or viewer.
- **Compression**: TIF intermediates use `deflate` compression (faster than LZW for float32 data).
- **Tests**: Run only inside Docker via `./run.sh --test`. Synthetic DEM fixture in `tests/conftest.py`.

10
lidar_pipeline/cli.py
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@ -118,15 +118,15 @@ Exemples:
help="Reclassifier le sol même si le fichier .las existe déjà"
)
parser.add_argument(
"--no-keep-tif",
"--keep-tif",
action="store_true",
help="Supprimer les fichiers TIFF intermédiaires après conversion WebP (par défaut: conservés)"
help="Conserver les fichiers TIFF (DTM + visualisations) pour pouvoir régénérer les WebP sans recalculer"
)
parser.add_argument(
"--ground-classification",
choices=["auto", "smrf", "pmf", "csf"],
choices=["auto", "smrf", "csf"],
default="auto",
help="Méthode de classification du sol : auto (détection), smrf, pmf, csf (défaut: auto)"
help="Méthode de classification du sol : auto (détection), smrf, csf (défaut: auto)"
)
parser.add_argument(
"--file",
@ -176,7 +176,7 @@ Exemples:
force=args.force,
ground_method=args.ground_classification,
force_classify=args.force_classification,
keep_tif=not args.no_keep_tif,
keep_tif=args.keep_tif,
)
# If --file is specified, process only matching files

41
lidar_pipeline/dtm.py
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@ -1,6 +1,6 @@
"""DTM generation from classified LiDAR point clouds.
Handles ground classification via PDAL/SMRF and DTM rasterisation
Handles ground classification via PDAL (SMRF or CSF) and DTM rasterisation
using scipy binned_statistic_2d. Zones without LiDAR data remain as NaN.
"""
@ -27,13 +27,13 @@ def _create_ground_pipeline(input_laz, output_las, method):
1. Reset Classification to 0
2. ELM (Extended Local Minimum) — mark low outliers as noise (Classification=7)
3. Statistical outlier removal
4. Ground classification (SMRF/PMF/CSF)
4. Ground classification (SMRF or CSF)
5. Extract ground points (Classification=2)
Args:
input_laz: Path to input LAZ/LAS file.
output_las: Path to output classified LAS file.
method: Ground classification method ('smrf', 'pmf', or 'csf').
method: Ground classification method ('smrf' or 'csf').
Returns:
JSON string of the PDAL pipeline.
@ -84,15 +84,6 @@ def _create_ground_pipeline(input_laz, output_las, method):
"threshold": 0.5,
"scalar": 1.25
}
elif method == 'pmf':
ground_step = {
"type": "filters.pmf",
"max_window": 33,
"slope": 0.15,
"max_distance": 2.5,
"initial_distance": 0.15,
"cell_size": 1.0
}
elif method == 'csf':
ground_step = {
"type": "filters.csf",
@ -128,11 +119,6 @@ def create_smrf_pipeline(input_laz, output_las):
return _create_ground_pipeline(input_laz, output_las, 'smrf')
def create_pmf_pipeline(input_laz, output_las):
"""Create a PDAL pipeline JSON for PMF ground classification."""
return _create_ground_pipeline(input_laz, output_las, 'pmf')
def create_csf_pipeline(input_laz, output_las):
"""Create a PDAL pipeline JSON for CSF ground classification."""
return _create_ground_pipeline(input_laz, output_las, 'csf')
@ -141,9 +127,9 @@ def create_csf_pipeline(input_laz, output_las):
def detect_ground_method(laz_file):
"""Detect the best ground classification method based on point cloud statistics.
Auto-selects between SMRF (natural terrain) and PMF (urban) only.
CSF is available only via --ground-classification csf (slow but robust
on complex terrain).
Auto-selects between SMRF and CSF:
- SMRF: fast, robust for most natural terrain (PDAL recommended default)
- CSF: cloth simulation, better for complex/urban terrain
Falls back to SMRF if the file cannot be read or attributes are missing.
@ -151,7 +137,7 @@ def detect_ground_method(laz_file):
laz_file: Path to input LAZ/LAS file.
Returns:
String: 'smrf', 'pmf', or 'csf'
String: 'smrf' or 'csf'
"""
import laspy
@ -182,13 +168,16 @@ def detect_ground_method(laz_file):
f"ratio_retours_uniques={single_return_ratio:.2f}, "
f"écart_Z={z_std:.1f}m, amplitude_Z={z_range:.1f}m")
# Decision logic (auto selects between SMRF and PMF only):
# - High single-return ratio (>0.6) → urban (buildings, roads) → PMF
# Decision logic:
# - High single-return ratio (>0.6) → urban (buildings, roads) → CSF (cloth simulation)
# - High elevation variance (>30m) → complex/mountainous terrain → CSF
# - Default → SMRF (fast, robust for most natural terrain)
# Note: CSF is available only via --ground-classification csf (slow but robust on complex terrain)
if single_return_ratio > 0.6:
method = 'pmf'
method = 'csf'
reason = f"ratio retours uniques={single_return_ratio:.2f} > 0.6 → milieu urbain"
elif z_std > 30:
method = 'csf'
reason = f"écart_Z={z_std:.1f}m > 30m → terrain complexe"
else:
method = 'smrf'
reason = f"terrain naturel standard"
@ -203,7 +192,7 @@ def classify_ground(laz_file, temp_dir, method='auto', force=False):
Args:
laz_file: Path to input LAZ/LAS file.
temp_dir: Directory for temporary files (pipeline.json, ground.las).
method: Ground classification method ('auto', 'smrf', 'pmf', or 'csf').
method: Ground classification method ('auto', 'smrf', or 'csf').
force: If True, reclassify even if output file already exists.
Returns:

7
lidar_pipeline/pipeline.py
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@ -78,8 +78,8 @@ VIZ_STEPS = [
('curvature', generate_curvature),
('svf', generate_svf),
('lrm', generate_lrm),
('pos_open', lambda d, b, v, r: generate_openness(d, b, v, r, positive=True)),
('neg_open', lambda d, b, v, r: generate_openness(d, b, v, r, positive=False)),
('pos_open', lambda d, b, v, r, shared=None: generate_openness(d, b, v, r, positive=True, shared=shared)),
('neg_open', lambda d, b, v, r, shared=None: generate_openness(d, b, v, r, positive=False, shared=shared)),
('mslrm', generate_mslrm),
('tpi', generate_tpi),
('sailore', generate_sailore),
@ -323,9 +323,6 @@ class LidarArchaeoPipeline:
t_pdf = time.time() - t4
logger.info(f" ✓ Rapport PDF terminé ({t_pdf:.1f}s)")
# Step 5: Keep DTM TIF for reuse (regenerating WebPs skips classification)
# Use --no-keep-tif to delete DTM files after processing
t_total = time.time() - t_start
logger.info(f"✓ {basename} terminé en {t_total:.1f}s")
logger.debug(f" Détails: classification={t_classif:.1f}s, DTM={t_dtm:.1f}s, PDF={t_pdf:.1f}s")

30
lidar_pipeline/visualizations.py
View File

@ -100,8 +100,18 @@ def _filter_nanaware_from_filled(shared, filter_func, *args, **kwargs):
return result
def _save_tif(output_path, data, transform, crs, dtype='float32', count=1, nodata=None):
"""Helper to save a 2D or 3D array as GeoTIFF."""
def _save_tif(output_path, data, transform, crs, dtype='float32', count=1, nodata=None, nan_mask=None):
"""Helper to save a 2D or 3D array as GeoTIFF.
Args:
nan_mask: Optional boolean mask (True=NaN) to apply before saving.
Restores NaN zones in gradient-derived products that were
computed on the filled DEM.
"""
if nan_mask is not None:
data = np.array(data, dtype=dtype, copy=True)
data[nan_mask] = np.nan
# Auto-detect nodata for float types with NaN
if nodata is None and dtype.startswith('float') and np.any(np.isnan(data)):
nodata = float('nan')
@ -238,7 +248,9 @@ def generate_hillshade(dem_file, basename, vis_dir, resolution, shared=None):
combined_hillshade = xp.mean(xp.array(hillshades), axis=0)
slope_shaded = cos_slope
combined = 0.7 * combined_hillshade + 0.3 * slope_shaded
_save_tif(output, to_cpu(combined), transform, crs)
nan_mask = shared.nan_mask if shared else np.isnan(to_cpu(dem_np))
_save_tif(output, to_cpu(combined), transform, crs, nan_mask=nan_mask)
logger.info(f" ✓ Hillshade terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:
@ -258,6 +270,7 @@ def generate_slope(dem_file, basename, vis_dir, resolution, shared=None):
transform = shared.transform
crs = shared.crs
slope = shared.slope_deg
nan_mask = shared.nan_mask
if HAS_GPU:
slope = to_gpu(slope)
else:
@ -265,7 +278,8 @@ def generate_slope(dem_file, basename, vis_dir, resolution, shared=None):
dem = to_gpu(dem_np)
dy, dx = xp.gradient(dem)
slope = xp.arctan(xp.sqrt(dx**2 + dy**2)) * 180 / xp.pi
_save_tif(output, to_cpu(slope) if HAS_GPU else slope, transform, crs)
nan_mask = np.isnan(dem_np)
_save_tif(output, to_cpu(slope) if HAS_GPU else slope, transform, crs, nan_mask=nan_mask)
logger.info(f" ✓ Pente terminée ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:
@ -285,6 +299,7 @@ def generate_aspect(dem_file, basename, vis_dir, resolution, shared=None):
transform = shared.transform
crs = shared.crs
aspect = shared.aspect
nan_mask = shared.nan_mask
if HAS_GPU:
aspect = to_gpu(aspect)
else:
@ -293,7 +308,8 @@ def generate_aspect(dem_file, basename, vis_dir, resolution, shared=None):
dy, dx = xp.gradient(dem)
aspect = xp.arctan2(dy, dx) * 180 / xp.pi
aspect = xp.mod(aspect, 360)
_save_tif(output, to_cpu(aspect) if HAS_GPU else aspect, transform, crs)
nan_mask = np.isnan(dem_np)
_save_tif(output, to_cpu(aspect) if HAS_GPU else aspect, transform, crs, nan_mask=nan_mask)
logger.info(f" ✓ Aspect terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:
@ -314,6 +330,7 @@ def generate_curvature(dem_file, basename, vis_dir, resolution, shared=None):
crs = shared.crs
dx = shared.dx
dy = shared.dy
nan_mask = shared.nan_mask
if HAS_GPU:
dx = to_gpu(dx)
dy = to_gpu(dy)
@ -321,10 +338,11 @@ def generate_curvature(dem_file, basename, vis_dir, resolution, shared=None):
dem_np, transform, crs = _read_dem(dem_file)
dem = to_gpu(dem_np)
dy, dx = xp.gradient(dem)
nan_mask = np.isnan(dem_np)
d2z_dx2 = xp.gradient(dx, axis=1)
d2z_dy2 = xp.gradient(dy, axis=0)
curvature = (d2z_dx2 + d2z_dy2) / 2
_save_tif(output, to_cpu(curvature), transform, crs)
_save_tif(output, to_cpu(curvature), transform, crs, nan_mask=nan_mask)
logger.info(f" ✓ Courbure terminée ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:

28
run.sh
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@ -9,10 +9,10 @@
# -v Mode verbeux (timestamps + niveaux)
# --debug Mode debug (détails internes fichier:ligne)
# -f / --force Régénérer tous les fichiers même si existants
# --no-keep-tif Supprimer les TIFF intermédiaires (par défaut: conservés)
# --keep-tif Conserver les fichiers TIFF pour régénérer les WebP
# --force-classification
# Reclassifier le sol même si le fichier .las existe déjà
# --ground-classification {auto,smrf,pmf,csf}
# --ground-classification {auto,smrf,csf}
# Méthode de classification du sol (défaut: auto)
# --file NOM... Traiter un ou plusieurs fichiers LAZ spécifiques
# --test Exécuter les tests unitaires
@ -40,8 +40,8 @@ if [ $# -eq 0 ]; then
echo " -f / --force Régénérer tous les fichiers même si les WebP existent"
echo " --force-classification"
echo " Reclassifier le sol même si le fichier .las existe"
echo " --no-keep-tif Supprimer les TIFF intermédiaires (défaut: conservés)"
echo " --ground-classification {auto,smrf,pmf,csf}"
echo " --keep-tif Conserver les TIFF pour régénérer les WebP"
echo " --ground-classification {auto,smrf,csf}"
echo " Méthode de classification du sol (défaut: auto)"
echo " --file NOM... Traiter un ou plusieurs fichiers LAZ (nom complet sans .laz/.las)"
echo " --test Exécuter les tests unitaires"
@ -52,9 +52,9 @@ if [ $# -eq 0 ]; then
echo " $0 -g -w 4 # GPU + 4 workers"
echo " $0 -g -v # GPU + verbeux"
echo " $0 -g -r 0.2 # Haute résolution"
echo " $0 -g --force # Régénérer les WebP"
echo " $0 -g --force # Régénérer WebP (DTM conservé si --keep-tif)"
echo " $0 -g --force-classification # Reclassifier le sol seulement"
echo " $0 -g --ground-classification pmf # Forcer PMF"
echo " $0 -g --ground-classification csf # Forcer CSF (terrain complexe)"
echo " $0 -g --file LHD_...IGN69.copc # Un fichier"
exit 0
fi
@ -67,7 +67,7 @@ FORCE_FLAG=""
FILE_ARGS=""
GROUND_METHOD=""
FORCE_CLASSIFY_FLAG=""
NO_KEEP_TIF_FLAG=""
KEEP_TIF_FLAG=""
# Parse arguments manually (more robust than getopts for mixed short/long options)
while [ $# -gt 0 ]; do
@ -80,7 +80,7 @@ while [ $# -gt 0 ]; do
--debug) VERBOSE_FLAG="--debug"; shift ;;
--force) FORCE_FLAG="--force"; shift ;;
--force-classification) FORCE_CLASSIFY_FLAG="--force-classification"; shift ;;
--no-keep-tif) NO_KEEP_TIF_FLAG="--no-keep-tif"; shift ;;
--keep-tif) KEEP_TIF_FLAG="--keep-tif"; shift ;;
--ground-classification) GROUND_METHOD="$2"; shift 2 ;;
--ground-classification=*) GROUND_METHOD="${1#--ground-classification=}"; shift ;;
--file) shift; while [ $# -gt 0 ] && [[ ! "$1" =~ ^- ]]; do FILE_ARGS="$FILE_ARGS $1"; shift; done ;;
@ -99,8 +99,8 @@ while [ $# -gt 0 ]; do
echo " -f / --force Régénérer tous les fichiers même si les WebP existent"
echo " --force-classification"
echo " Reclassifier le sol même si le fichier .las existe"
echo " --no-keep-tif Supprimer les TIFF intermédiaires (défaut: conservés)"
echo " --ground-classification {auto,smrf,pmf,csf}"
echo " --keep-tif Conserver les TIFF pour régénérer les WebP"
echo " --ground-classification {auto,smrf,csf}"
echo " Méthode de classification du sol (défaut: auto)"
echo " --file NOM... Traiter un ou plusieurs fichiers LAZ (nom complet sans .laz/.las)"
echo " --test Exécuter les tests unitaires"
@ -111,9 +111,9 @@ while [ $# -gt 0 ]; do
echo " $0 -g -w 4 # GPU + 4 workers"
echo " $0 -g -v # GPU + verbeux"
echo " $0 -g -r 0.2 # Haute résolution"
echo " $0 -g --force # Régénérer les WebP"
echo " $0 -g --force # Régénérer WebP (DTM conservé si --keep-tif)"
echo " $0 -g --force-classification # Reclassifier le sol seulement"
echo " $0 -g --ground-classification pmf # Forcer PMF"
echo " $0 -g --ground-classification csf # Forcer CSF (terrain complexe)"
echo " $0 -g --file LHD_...IGN69.copc # Un fichier"
exit 0
;;
@ -159,14 +159,14 @@ echo " GPU : $([ -n "$GPU_FLAG" ] && echo 'OUI' || echo 'non')"
echo " Verbeux : $([ -n "$VERBOSE_FLAG" ] && echo 'OUI' || echo 'non')"
echo " Force : $([ -n "$FORCE_FLAG" ] && echo 'OUI' || echo 'non')"
echo " Force classif.: $([ -n "$FORCE_CLASSIFY_FLAG" ] && echo 'OUI' || echo 'non')"
echo " Keep TIFF : $([ -n "$NO_KEEP_TIF_FLAG" ] && echo 'non' || echo 'OUI')"
echo " Keep TIFF : $([ -n "$KEEP_TIF_FLAG" ] && echo 'OUI' || echo 'non')"
echo " Classification sol : $([ -n "$GROUND_METHOD" ] && echo "$GROUND_METHOD" || echo 'auto')"
if [ -n "$FILE_ARGS" ]; then
echo " Fichiers :${FILE_ARGS}"
fi
echo "============================================"
CMD_ARGS="-o /data/output -r $RESOLUTION -w $WORKERS $VERBOSE_FLAG $FORCE_FLAG $FORCE_CLASSIFY_FLAG $NO_KEEP_TIF_FLAG"
CMD_ARGS="-o /data/output -r $RESOLUTION -w $WORKERS $VERBOSE_FLAG $FORCE_FLAG $FORCE_CLASSIFY_FLAG $KEEP_TIF_FLAG"
if [ -n "$GROUND_METHOD" ]; then
CMD_ARGS="$CMD_ARGS --ground-classification $GROUND_METHOD"
fi