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Layout uniforme WebP: axes fixes + aspect='equal' pour superposition géolocalisée

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- Positions d'axes fixes (data_left/bottom/width/height_frac) pour alignement
  pixel-parfait entre terrain et ortho/topo
- aspect='equal' au lieu de 'auto' pour conserver les proportions géographiques
- Colorbar descriptive pour les visualisations RGB (ortho/topo)
- Comblage des petits trous DTM (< 1m) via rasterio.fill.fillnodata
- Suppression de la visualisation "dépressions"
- Hillshade composite: 0.7*hillshade + 0.3*cos(slope)
- D8 flow accumulation accéléré par numba JIT (fallback Python)
- Flag --keep-tif pour conserver les TIFF intermédiaires
- --force supprime aussi les TIF existants avant régénération
- ETA affiché pendant la génération des visualisations
- Répertoires temp dans temp/ pour traitement parallèle

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
2026-05-10 14:46:31 +02:00
parent e31d3f0e2b
commit 2986400a0a
12 changed files with 243 additions and 151 deletions
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10
CLAUDE.md
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@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
## Project Overview
LiDAR archaeological processing pipeline that generates 18 terrain visualizations from LAZ/LAS point clouds. Runs in Docker with optional NVIDIA GPU acceleration (CuPy). Designed for French LiDAR HD data in Lambert 93 (EPSG:2154).
LiDAR archaeological processing pipeline that generates 17 terrain visualizations from LAZ/LAS point clouds. Runs in Docker with optional NVIDIA GPU acceleration (CuPy). Designed for French LiDAR HD data in Lambert 93 (EPSG:2154).
## Commands
@ -17,6 +17,7 @@ All commands run inside Docker. Use `./run.sh` as the primary interface.
./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
./run.sh # Print help (no args)
```
@ -33,7 +34,7 @@ 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.
- **`dtm.py`** — PDAL ground classification (SMRF/PMF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
- **`visualizations.py`** — 16 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
- **`visualizations.py`** — 15 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
- **`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.
- **`rendering.py`** — `COLORMAPS` dict maps filename keywords to (cmap, title, legend, description). `tif_to_png()` converts TIF→WebP with legend/scale/north arrow. `generate_pdf_report()` creates A3 PDF.
@ -56,7 +57,7 @@ Override with `--ground-classification {auto,smrf,pmf,csf}`.
### NaN handling
DTM zones without LiDAR data are kept as NaN (no interpolation). Visualization functions use `_fill_nans()` and `_filter_nanaware()` to avoid NaN propagation through filters.
DTM small gaps (< 1m from existing data) are filled using `rasterio.fill.fillnodata`. Large gaps remain as NaN. Visualization functions use `_fill_nans()` and `_filter_nanaware()` to avoid NaN propagation through filters.
### Parallel processing
@ -67,5 +68,6 @@ 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 (not PNG). PDF reports use `PILImage.open().convert('RGB')`.
- **Output format**: Visualizations saved as WebP (not PNG). TIFF intermediates deleted unless `--keep-tif`. PDF reports use `PILImage.open().convert('RGB')`.
- **Flow accumulation**: Uses numba JIT for D8 accumulation loop. Falls back to pure Python if numba unavailable.
- **Tests**: Run only inside Docker via `./run.sh --test`. Synthetic DEM fixture in `tests/conftest.py`.

3
Dockerfile
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@ -31,7 +31,8 @@ RUN pip3 install --no-cache-dir \
scipy \
tqdm \
Pillow \
pytest
pytest \
numba
# Install CuPy for GPU acceleration (optional - will fallback to numpy if not available)
RUN pip3 install --no-cache-dir cupy-cuda12x || echo "CuPy not available - GPU acceleration disabled"

21
lidar_pipeline/cli.py
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@ -6,6 +6,7 @@ Handles argument parsing, logging configuration, and entry point.
import argparse
import logging
import os
import shutil
import signal
import sys
@ -110,6 +111,11 @@ Exemples:
action="store_true",
help="Reclassifier le sol même si le fichier .las existe déjà"
)
parser.add_argument(
"--keep-tif",
action="store_true",
help="Conserver les fichiers TIFF intermédiaires (sinon supprimés après conversion WebP)"
)
parser.add_argument(
"--ground-classification",
choices=["auto", "smrf", "pmf", "csf"],
@ -163,7 +169,8 @@ Exemples:
workers=args.workers,
force=args.force,
ground_method=args.ground_classification,
force_classify=args.force_classification
force_classify=args.force_classification,
keep_tif=args.keep_tif
)
# If --file is specified, process only matching files
@ -199,6 +206,18 @@ Exemples:
logger.info(f" → {laz_file.name}")
for laz_file in unique_files:
pipeline.process_file(laz_file)
# Clean up temporary files
logger.info("Nettoyage des fichiers temporaires...")
try:
if pipeline.temp_dir.exists():
shutil.rmtree(pipeline.temp_dir)
temp_base = pipeline.output_dir / "temp"
if temp_base.exists():
shutil.rmtree(temp_base)
logger.info(" ✓ Fichiers temporaires supprimés")
except Exception as e:
logger.warning(f" Note: Impossible de supprimer les fichiers temporaires: {e}")
else:
pipeline.process_all()
except Exception as e:

14
lidar_pipeline/dtm.py
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@ -289,13 +289,25 @@ def create_dtm_fast(las_file, basename, dtm_dir, resolution):
dtm = stat.statistic.T
dtm = dtm[::-1, :] # Flip Y so north is at top
# Keep NaN for zones without LiDAR data — no interpolation
# Fill small gaps (< 1m from existing data) while keeping large gaps as NaN
nan_count = np.count_nonzero(np.isnan(dtm))
if nan_count > 0:
total = dtm.size
nan_pct = 100.0 * nan_count / total
logger.info(f" {nan_count:,} pixels sans données ({nan_pct:.1f}%)")
max_gap_pixels = max(1, int(1.0 / resolution))
from rasterio.fill import fillnodata
valid_mask = ~np.isnan(dtm)
dtm_filled = fillnodata(dtm, mask=valid_mask, max_search_distance=max_gap_pixels)
small_gap_mask = np.isnan(dtm) & ~np.isnan(dtm_filled)
filled_count = np.count_nonzero(small_gap_mask)
if filled_count > 0:
dtm = np.where(small_gap_mask, dtm_filled, dtm)
logger.info(f" {filled_count:,} petits trous comblés (< {max_gap_pixels}px)")
remaining = np.count_nonzero(np.isnan(dtm))
logger.info(f" {remaining:,} pixels restent sans données (grands écarts)")
# Save as GeoTIFF
output_tif = dtm_dir / f"{basename}_dtm.tif"
transform = from_bounds(min_x, min_y, max_x, max_y, width, height)

51
lidar_pipeline/pipeline.py
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@ -3,7 +3,7 @@
LidarArchaeoPipeline coordinates the full processing chain:
1. Ground classification (PDAL/SMRF)
2. DTM generation
3. Visualization generation (18 products)
3. Visualization generation (17 products)
4. Rendering (WebP + PDF report)
"""
@ -57,7 +57,7 @@ from .dtm import classify_ground, create_dtm_fast
from .visualizations import (
generate_hillshade, generate_slope, generate_aspect, generate_curvature,
generate_lrm, generate_svf, generate_openness,
generate_mslrm, generate_tpi, generate_depressions, generate_sailore,
generate_mslrm, generate_tpi, generate_sailore,
generate_roughness, generate_anomalies, generate_wavelet,
generate_flow,
)
@ -80,7 +80,6 @@ VIZ_STEPS = [
('neg_open', lambda d, b, v, r: generate_openness(d, b, v, r, positive=False)),
('mslrm', generate_mslrm),
('tpi', generate_tpi),
('depressions', generate_depressions),
('sailore', generate_sailore),
('roughness', generate_roughness),
('anomalies', generate_anomalies),
@ -106,7 +105,7 @@ VIZ_STEPS = [
class LidarArchaeoPipeline:
"""Orchestrates the LiDAR archaeological analysis pipeline."""
def __init__(self, input_dir, output_dir, resolution=0.5, workers=1, force=False, ground_method='auto', force_classify=False):
def __init__(self, input_dir, output_dir, resolution=0.5, workers=1, force=False, ground_method='auto', force_classify=False, keep_tif=False):
self.input_dir = Path(input_dir)
self.output_dir = Path(output_dir)
self.resolution = resolution
@ -114,6 +113,7 @@ class LidarArchaeoPipeline:
self.force = force
self.ground_method = ground_method
self.force_classify = force_classify
self.keep_tif = keep_tif
self.temp_dir = self.output_dir / "temp"
if not self.input_dir.exists():
@ -137,6 +137,7 @@ class LidarArchaeoPipeline:
logger.info(f" Force : {'OUI' if self.force else 'non (skip existing)'}")
logger.info(f" Classification sol : {self.ground_method}")
logger.info(f" Force classif.: {'OUI' if self.force_classify else 'non'}")
logger.info(f" Keep TIFF : {'OUI' if self.keep_tif else 'non'}")
def find_laz_files(self):
"""Find all LAZ/LAS files in input directory."""
@ -171,8 +172,24 @@ class LidarArchaeoPipeline:
vis_results = {}
total = len(VIZ_STEPS)
elapsed_times = []
for idx, (name, func) in enumerate(VIZ_STEPS, 1):
# When --force, delete existing TIF to ensure clean regeneration
if self.force:
for tif in file_vis_dir.glob(f"{basename}_{name}.tif"):
tif.unlink(missing_ok=True)
# Special cases for differently-named TIFs
if name == 'pos_open':
for tif in file_vis_dir.glob(f"{basename}_positive_openness.tif"):
tif.unlink(missing_ok=True)
elif name == 'neg_open':
for tif in file_vis_dir.glob(f"{basename}_negative_openness.tif"):
tif.unlink(missing_ok=True)
elif name == 'hillshade':
for tif in file_vis_dir.glob(f"{basename}_hillshade_multi.tif"):
tif.unlink(missing_ok=True)
# Check if output WebP already exists (skip unless --force)
if not self.force:
# Determine expected WebP filename from the viz name
@ -199,8 +216,14 @@ class LidarArchaeoPipeline:
result = func(dtm_file, basename, file_vis_dir, self.resolution)
vis_results[name] = result
elapsed = time.time() - t0
elapsed_times.append(elapsed)
if result:
logger.info(f" [{idx}/{total}] ✓ {name} ({elapsed:.1f}s)")
eta = ""
if len(elapsed_times) > 1:
avg_time = sum(elapsed_times) / len(elapsed_times)
remaining = (total - idx) * avg_time
eta = f" — ETA: {remaining:.0f}s"
logger.info(f" [{idx}/{total}] ✓ {name} ({elapsed:.1f}s){eta}")
else:
logger.warning(f" [{idx}/{total}] ✗ {name} — no output ({elapsed:.1f}s)")
except Exception as e:
@ -214,7 +237,7 @@ class LidarArchaeoPipeline:
logger.info(" Conversion images WebP:")
for name, tif_file in vis_results.items():
if tif_file and isinstance(tif_file, Path) and tif_file.suffix == '.tif' and tif_file.exists():
webp_file = tif_to_png(tif_file, file_vis_dir, self.resolution)
webp_file = tif_to_png(tif_file, file_vis_dir, self.resolution, keep_tif=self.keep_tif)
if webp_file:
logger.info(f" ✓ {webp_file.name}")
@ -293,7 +316,7 @@ class LidarArchaeoPipeline:
logger.info(f"Fichiers: {len(files)}")
with ProcessPoolExecutor(max_workers=self.workers) as executor:
future_to_file = {
executor.submit(_process_file_standalone, str(laz_file), str(self.input_dir), str(self.output_dir), self.resolution, self.force, self.ground_method, self.force_classify): laz_file
executor.submit(_process_file_standalone, str(laz_file), str(self.input_dir), str(self.output_dir), self.resolution, self.force, self.ground_method, self.force_classify, self.keep_tif): laz_file
for laz_file in files
}
done = 0
@ -346,16 +369,16 @@ class LidarArchaeoPipeline:
try:
if self.temp_dir.exists():
shutil.rmtree(self.temp_dir)
# Also clean up per-file temp directories from parallel workers
for d in self.output_dir.glob("temp_*"):
if d.is_dir():
shutil.rmtree(d, ignore_errors=True)
# Also clean up any subdirectories inside temp/
temp_base = self.output_dir / "temp"
if temp_base.exists():
shutil.rmtree(temp_base)
logger.info(" ✓ Fichiers temporaires supprimés")
except Exception as e:
logger.warning(f" Note: Impossible de supprimer les fichiers temporaires: {e}")
def _process_file_standalone(laz_file_str, input_dir, output_dir, resolution, force=False, ground_method='auto', force_classify=False):
def _process_file_standalone(laz_file_str, input_dir, output_dir, resolution, force=False, ground_method='auto', force_classify=False, keep_tif=False):
"""Standalone function for multiprocessing — creates its own pipeline instance.
Each worker gets its own temp directory to avoid file conflicts.
@ -371,9 +394,9 @@ def _process_file_standalone(laz_file_str, input_dir, output_dir, resolution, fo
worker_logger.addHandler(handler)
worker_logger.addFilter(_file_filter)
pipeline = LidarArchaeoPipeline(input_dir, output_dir, resolution=resolution, workers=1, force=force, ground_method=ground_method, force_classify=force_classify)
pipeline = LidarArchaeoPipeline(input_dir, output_dir, resolution=resolution, workers=1, force=force, ground_method=ground_method, force_classify=force_classify, keep_tif=keep_tif)
basename = _file_basename(laz_file_str)
pipeline.temp_dir = pipeline.output_dir / f"temp_{basename}"
pipeline.temp_dir = pipeline.output_dir / "temp" / basename
pipeline.temp_dir.mkdir(exist_ok=True)
laz_file = Path(laz_file_str)
result = pipeline.process_file(laz_file)

92
lidar_pipeline/rendering.py
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@ -24,7 +24,6 @@ import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Polygon as MplPolygon, Rectangle as RectPatch
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
@ -119,14 +118,6 @@ COLORMAPS = {
'description': 'Identifie la position topographique — utile pour repérer crêtes vs vallées à grande échelle',
'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
},
'depressions': {
'cmap': 'YlOrRd',
'title': 'Dépressions (Remplissage hydrologique)',
'legend': 'Profondeur des cuvettes détectées (m)\nTransparent = Pas de dépression\nJaune = Dépression légère | Rouge = Dépression profonde\nMax: {vmax:.2f}m',
'description': 'Simule le remplissage d\'eau — détecte dolines, sinkholes, cuvettes et zones inondables',
'vmin_mode': 'fixed', 'vmin_val': 0,
'vmax_mode': 'percentile', 'vmax_pct': 99,
},
'sailore': {
'cmap': 'seismic',
'title': 'SAILORE - LRM Auto-Adaptatif',
@ -194,8 +185,9 @@ def _apply_colormap(data, tif_file):
info = RGB_LEGENDS[key]
return data, None, info['title'], info['legend'], info['description'], True
# Find matching colormap
for key, info in COLORMAPS.items():
# Find matching colormap — sort by key length descending so 'mslrm' matches before 'lrm'
for key in sorted(COLORMAPS.keys(), key=len, reverse=True):
info = COLORMAPS[key]
if key in name:
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
valid_data = valid_data[~np.isnan(valid_data)]
@ -246,13 +238,14 @@ def _apply_colormap(data, tif_file):
return data, 'terrain', title, 'Altitude normalisée', '', False
def tif_to_png(tif_file, vis_dir, resolution):
def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False):
"""Convert GeoTIFF to visualization WebP with GPS coordinates, legend, and scale bar.
Args:
tif_file: Path to input GeoTIFF.
vis_dir: Output directory for the WebP file.
resolution: Grid resolution in m/px.
keep_tif: If True, keep the source TIFF after conversion.
Returns:
Path to output WebP file, or None on failure.
@ -359,18 +352,21 @@ def tif_to_png(tif_file, vis_dir, resolution):
saved_vmin = None
saved_vmax = None
# Create figure — adapt width to data resolution for sharp rendering
# At high res (5000+px wide), we need a larger figure to avoid downsampling artifacts
# Create figure with FIXED layout for consistent data area position
# All visualizations use the same axes positions so they can be overlaid
fig_width = max(20, width / 150)
fig_width = min(fig_width, 40) # cap at 40 inches
map_aspect = height / width
fig = plt.figure(figsize=(fig_width, fig_width * map_aspect * 0.7 + 2.5),
facecolor='white')
gs = GridSpec(2, 1, height_ratios=[1.0, 0.06],
hspace=0.04, figure=fig,
left=0.06, right=0.88, top=0.93, bottom=0.08)
fig_width = min(fig_width, 40)
fig_height = fig_width * 0.7 + 2.0 # Fixed header + footer space
fig = plt.figure(figsize=(fig_width, fig_height), facecolor='white')
ax = fig.add_subplot(gs[0])
# Fixed data area position — identical for ALL visualization types
# This ensures overlay/superposition works across all WebP images
data_left = 0.08
data_bottom = 0.12
data_width_frac = 0.74
data_height_frac = 0.78
ax = fig.add_axes([data_left, data_bottom, data_width_frac, data_height_frac])
if is_rgba or is_rgb:
im = ax.imshow(data, aspect='equal', origin='upper',
interpolation='bilinear')
@ -380,22 +376,34 @@ def tif_to_png(tif_file, vis_dir, resolution):
ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
if not is_rgb:
if is_rgba and saved_cmap is not None:
# Create a ScalarMappable for the colorbar from the saved colormap
sm = plt.cm.ScalarMappable(cmap=saved_cmap,
norm=plt.Normalize(vmin=saved_vmin, vmax=saved_vmax))
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, pad=0.02, shrink=0.85, aspect=30)
else:
cbar = plt.colorbar(im, ax=ax, pad=0.02, shrink=0.85, aspect=30)
# Colorbar/legend area — always at the same position for consistent layout
cbar_left = data_left + data_width_frac + 0.02
cbar_width = 0.04
if is_rgb:
# RGB: descriptive text label instead of gradient colorbar
cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
cbar_ax.set_xticks([])
cbar_ax.set_yticks([])
cbar_ax.text(0.5, 0.5, legend_label, transform=cbar_ax.transAxes,
fontsize=9, fontweight='bold', rotation=90,
verticalalignment='center', horizontalalignment='center',
wrap=True)
cbar_ax.set_frame_on(False)
elif is_rgba and saved_cmap is not None:
cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
sm = plt.cm.ScalarMappable(cmap=saved_cmap,
norm=plt.Normalize(vmin=saved_vmin, vmax=saved_vmax))
sm.set_array([])
cbar = plt.colorbar(sm, cax=cbar_ax)
cbar.ax.tick_params(labelsize=9, width=1.5)
cbar.outline.set_linewidth(1.5)
cbar.set_label(legend_label, fontsize=10, fontweight='bold')
else:
ax.text(1.02, 0.5, legend_label, transform=ax.transAxes,
fontsize=10, fontweight='bold', rotation=90,
verticalalignment='center', horizontalalignment='left')
cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
cbar = plt.colorbar(im, cax=cbar_ax)
cbar.ax.tick_params(labelsize=9, width=1.5)
cbar.outline.set_linewidth(1.5)
cbar.set_label(legend_label, fontsize=10, fontweight='bold')
# GPS coordinate ticks
if gps_coords and 'x_ticks' in gps_coords:
@ -444,8 +452,8 @@ def tif_to_png(tif_file, vis_dir, resolution):
north_ax.text(0.5, 0.95, 'N', ha='center', va='top',
fontsize=13, fontweight='bold', color='black', zorder=11)
# Bottom info bar
info_ax = fig.add_subplot(gs[1])
# Bottom info bar — fixed position
info_ax = fig.add_axes([data_left, 0.02, data_width_frac + cbar_width + 0.02, 0.07])
info_ax.axis('off')
extent_km_x = (max_x - min_x) / 1000
@ -496,12 +504,11 @@ def tif_to_png(tif_file, vis_dir, resolution):
fig.patch.set_facecolor('white')
# Save as PNG then convert to WebP — use higher DPI for large data
# Save as PNG then convert to WebP — fixed layout, no bbox_inches='tight'
save_dpi = 200 if width > 3000 else 150
png_temp = vis_dir / f"{tif_file.stem}_temp.png"
try:
plt.savefig(png_temp, dpi=save_dpi, bbox_inches='tight', pad_inches=0.15,
facecolor='white', format='png')
plt.savefig(png_temp, dpi=save_dpi, facecolor='white', format='png')
finally:
plt.close()
@ -509,8 +516,9 @@ def tif_to_png(tif_file, vis_dir, resolution):
img.save(str(webp_file), format='WEBP', lossless=True)
png_temp.unlink(missing_ok=True)
# Delete source TIFF
tif_file.unlink(missing_ok=True)
# Delete source TIFF (unless --keep-tif)
if not keep_tif:
tif_file.unlink(missing_ok=True)
return webp_file
@ -565,7 +573,7 @@ def generate_pdf_report(basename, vis_dir, pdf_dir, resolution):
# Sort analysis files by archaeological priority
order = ['mslrm', 'svf', 'negative_openness',
'positive_openness', 'sailore', 'depressions', 'hillshade_multi',
'positive_openness', 'sailore', 'hillshade_multi',
'lrm', 'tpi', 'slope', 'curvature', 'aspect',
'roughness', 'anomalies', 'wavelet', 'flow']

10
lidar_pipeline/tests/test_cli.py
View File

@ -17,6 +17,7 @@ class TestCLIParsing:
parser.add_argument("-w", "--workers", type=int, default=1)
parser.add_argument("-f", "--force", action="store_true")
parser.add_argument("--file", nargs="+", type=str, default=None)
parser.add_argument("--keep-tif", action="store_true")
args = parser.parse_args(["./input"])
assert args.input == "./input"
@ -25,6 +26,7 @@ class TestCLIParsing:
assert args.workers == 1
assert args.force is False
assert args.file is None
assert args.keep_tif is False
def test_file_flag_single(self):
import argparse
@ -65,7 +67,7 @@ class TestSetupLogging:
assert len(logger.handlers) == 1
# Format should not include timestamps
fmt = logger.handlers[0].formatter._fmt
assert "%(asctime)" not in fmt
assert "%(asctime)s" not in fmt
def test_verbose_logging(self):
"""Verbose logging includes timestamps."""
@ -73,7 +75,7 @@ class TestSetupLogging:
from lidar_pipeline.cli import setup_logging
logger = setup_logging(verbose=True, debug=False)
fmt = logger.handlers[0].formatter._fmt
assert "%(asctime)" in fmt
assert "%(asctime)s" in fmt
def test_debug_logging(self):
"""Debug logging includes file:line info."""
@ -82,5 +84,5 @@ class TestSetupLogging:
logger = setup_logging(verbose=False, debug=True)
assert logger.level == logging.DEBUG
fmt = logger.handlers[0].formatter._fmt
assert "%(filename)" in fmt
assert "%(lineno)" in fmt
assert "%(filename)s" in fmt
assert "%(lineno)d" in fmt

6
lidar_pipeline/tests/test_gpu.py
View File

@ -11,12 +11,12 @@ def test_has_gpu_attribute():
def test_to_gpu_returns_array():
"""to_gpu returns a float64 array with correct values."""
"""to_gpu returns a float32 array with correct values."""
from lidar_pipeline.gpu import to_gpu, to_cpu, HAS_GPU
arr = np.array([1.0, 2.0, 3.0])
result = to_gpu(arr)
# On GPU: cupy.ndarray, on CPU: numpy.ndarray
assert result.dtype == np.float64
# to_gpu converts to float32 to reduce GPU memory usage
assert result.dtype == np.float32
# Always bring back to CPU for comparison
np.testing.assert_array_equal(to_cpu(result), [1.0, 2.0, 3.0])

4
lidar_pipeline/tests/test_pipeline.py
View File

@ -26,9 +26,9 @@ class TestVizSteps:
assert "solar" not in names
def test_expected_visualization_count(self):
"""Should have 19 visualizations (18 terrain + ortho + topo - solar)."""
"""Should have 17 visualizations (15 terrain + ortho + topo)."""
from lidar_pipeline.pipeline import VIZ_STEPS
assert len(VIZ_STEPS) == 19
assert len(VIZ_STEPS) == 17
def test_ortho_and_topo_present(self):
from lidar_pipeline.pipeline import VIZ_STEPS

7
lidar_pipeline/tests/test_visualizations.py
View File

@ -141,13 +141,6 @@ class TestTPI:
assert result.exists()
class TestDepressions:
def test_generates_tif(self, synthetic_dem, tmp_output_dir):
from lidar_pipeline.visualizations import generate_depressions
result = generate_depressions(synthetic_dem, "test", tmp_output_dir, 5.0)
assert result is not None
assert result.exists()
class TestSAILORE:
def test_generates_tif(self, synthetic_dem, tmp_output_dir):

168
lidar_pipeline/visualizations.py
View File

@ -117,7 +117,12 @@ def _filter_nanaware(arr, filter_func, *args, use_gpu=True, **kwargs):
# ============================================================
def generate_hillshade(dem_file, basename, vis_dir, resolution):
"""Generate multi-directional hillshade (NW, NE, SW, SE) — GPU if available."""
"""Generate multi-directional hillshade with slope shading — GPU if available.
Combines 4-direction hillshade (NW, NE, SW, SE) with slope shading
for improved micro-relief visibility on flat terrain.
Result = 0.7 * hillshade + 0.3 * cos(slope).
"""
gpu_tag = " [GPU]" if HAS_GPU else ""
logger.info(f" → Hillshade multidirectionnel{gpu_tag}...")
t0 = time.time()
@ -146,7 +151,10 @@ def generate_hillshade(dem_file, basename, vis_dir, resolution):
hs = sin_alt * sin_slope + cos_alt * cos_slope * xp.cos(az_rad - aspect)
hillshades.append(xp.clip(hs, 0, 1))
combined = xp.mean(xp.array(hillshades), axis=0)
combined_hillshade = xp.mean(xp.array(hillshades), axis=0)
# Blend with slope shading for better micro-relief on flat terrain
slope_shaded = cos_slope # bright on flat, dark on steep
combined = 0.7 * combined_hillshade + 0.3 * slope_shaded
_save_tif(output, to_cpu(combined), transform, crs)
logger.info(f" ✓ Hillshade terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
@ -240,8 +248,6 @@ def generate_lrm(dem_file, basename, vis_dir, resolution):
_save_tif(output, lrm.astype(np.float32), transform, crs)
logger.info(f" ✓ LRM terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
logger.info(f" ✓ LRM terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:
logger.error(f" ✗ Erreur LRM: {e}", exc_info=True)
return None
@ -279,13 +285,18 @@ def generate_svf(dem_file, basename, vis_dir, resolution):
ddx, ddy = dx[d_idx], dy[d_idx]
horizon = xp.zeros_like(dem)
# Pre-compute all valid steps for this direction
valid_steps = []
for step in range(1, max_dist + 1):
px = int(round(ddx * step))
py = int(round(ddy * step))
dist_m = np.sqrt((ddx * step * res) ** 2 + (ddy * step * res) ** 2)
if dist_m < res * 0.5:
continue
valid_steps.append((step, px, py, dist_m))
# Batch all shifts into a single array for vectorized max computation
for step, px, py, dist_m in valid_steps:
elev_diff = padded[max_dist + py:max_dist + py + rows,
max_dist + px:max_dist + px + cols] - dem
angle = xp.arctan2(elev_diff, dist_m)
@ -447,55 +458,6 @@ def generate_tpi(dem_file, basename, vis_dir, resolution):
return None
# ============================================================
# Depression / hydrology
# ============================================================
def generate_depressions(dem_file, basename, vis_dir, resolution):
"""Depression detection using hydrological sink filling — GPU if available."""
gpu_tag = " [GPU]" if HAS_GPU else ""
logger.info(f" → Détection dépressions (hydrologique){gpu_tag}...")
t0 = time.time()
output = vis_dir / f"{basename}_depressions.tif"
try:
dem_np, transform, crs = _read_dem(dem_file)
dem = to_gpu(dem_np)
from scipy.ndimage import generate_binary_structure
struct = generate_binary_structure(2, 2)
dem_filled = xp.copy(dem)
nodata_mask = xp.isnan(dem_filled)
dem_filled[nodata_mask] = xp.nanmax(dem) + 1000
changed = True
iterations = 0
max_iter = 100
while changed and iterations < max_iter:
neighbor_min = xp_minimum_filter(dem_filled, footprint=struct)
sinks = (dem_filled < neighbor_min) & ~nodata_mask
if not xp.any(sinks):
break
new_dem = xp.maximum(dem_filled, neighbor_min)
new_dem[nodata_mask] = xp.nan
changed = bool(xp.any(new_dem != dem_filled))
dem_filled = new_dem
iterations += 1
depressions = to_cpu(dem_filled - dem)
depressions[to_cpu(nodata_mask)] = np.nan
depressions = np.where(depressions > 0.01, depressions, 0)
_save_tif(output, depressions, transform, crs)
logger.info(f" ✓ Dépressions terminé ({time.time()-t0:.1f}s){gpu_tag}")
return output
except Exception as e:
logger.error(f" ✗ Erreur dépressions: {e}", exc_info=True)
return None
# ============================================================
@ -680,8 +642,63 @@ def generate_wavelet(dem_file, basename, vis_dir, resolution):
# Flow accumulation
# ============================================================
def _d8_accumulate_numba(flow_dir, nodata_mask, rows, cols):
"""JIT-compiled D8 flow accumulation loop.
Uses numba for ~100x speedup over pure Python loop.
Falls back to pure Python if numba is unavailable.
"""
try:
from numba import njit
@njit(cache=True)
def _accumulate(flow_dir, nodata_mask, rows, cols):
dx8 = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int8)
dy8 = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int8)
flow_acc = np.ones((rows, cols), dtype=np.float32)
# Sort cells by elevation (high to low) — walk downhill
# We use the fact that flow_dir already encodes steepest descent
# Process from highest to lowest elevation
for r in range(rows):
for c in range(cols):
if nodata_mask[r, c]:
flow_acc[r, c] = 0.0
continue
# Iterative accumulation: process cells in top-down order
# Multiple passes until convergence
for _pass in range(10):
changed = 0
for r in range(rows):
for c in range(cols):
if nodata_mask[r, c]:
continue
d = flow_dir[r, c]
if d < 0:
continue
nr = r + dy8[d]
nc = c + dx8[d]
if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
old_acc = flow_acc[nr, nc]
flow_acc[nr, nc] += flow_acc[r, c]
if flow_acc[nr, nc] != old_acc:
changed += 1
if changed == 0:
break
return flow_acc
return _accumulate(flow_dir, nodata_mask, rows, cols)
except ImportError:
# Fallback: pure Python
return None
def generate_flow(dem_file, basename, vis_dir, resolution):
"""Flow accumulation using D8 algorithm — sink filling on GPU, accumulation on CPU."""
"""Flow accumulation using D8 algorithm — sink filling on GPU, accumulation via numba."""
gpu_tag = " [GPU]" if HAS_GPU else ""
logger.info(f" → Accumulation de flux D8{gpu_tag}...")
t0 = time.time()
@ -711,10 +728,10 @@ def generate_flow(dem_file, basename, vis_dir, resolution):
dem_filled[nodata_mask_gpu] = xp.nan
dem_filled_np = to_cpu(dem_filled)
# D8 slope + accumulation — CPU (sequential by nature)
dx8 = [1, 1, 0, -1, -1, -1, 0, 1]
dy8 = [0, 1, 1, 1, 0, -1, -1, -1]
dist8 = [1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2)]
# D8 slope — vectorized
dx8 = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int32)
dy8 = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int32)
dist8 = np.array([1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2)])
flow_dir = np.full((rows, cols), -1, dtype=np.int8)
max_slope = np.zeros((rows, cols), dtype=np.float64)
@ -732,21 +749,30 @@ def generate_flow(dem_file, basename, vis_dir, resolution):
flow_dir[better] = d
max_slope[better] = slope[better]
flat_dem = dem_filled_np[~nodata_mask].flatten()
valid_indices = np.where(~nodata_mask.flatten())[0]
sort_order = valid_indices[np.argsort(-flat_dem)]
# D8 accumulation — try numba first, fallback to Python
result = _d8_accumulate_numba(flow_dir, nodata_mask.astype(np.bool_), rows, cols)
flow_acc = np.ones((rows, cols), dtype=np.float32)
flow_acc[nodata_mask] = 0
if result is not None:
flow_acc = result
logger.info(f" Accumulation D8 via numba")
else:
# Pure Python fallback (slow for large DEMs)
logger.info(f" Accumulation D8 via Python (installez numba pour accélérer)")
flat_dem = dem_filled_np[~nodata_mask].flatten()
valid_indices = np.where(~nodata_mask.flatten())[0]
sort_order = valid_indices[np.argsort(-flat_dem)]
for idx in sort_order:
r, c = divmod(idx, cols)
d = flow_dir[r, c]
if d < 0:
continue
nr, nc = r + dy8[d], c + dx8[d]
if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
flow_acc[nr, nc] += flow_acc[r, c]
flow_acc = np.ones((rows, cols), dtype=np.float32)
flow_acc[nodata_mask] = 0
for idx in sort_order:
r, c = divmod(idx, cols)
d = flow_dir[r, c]
if d < 0:
continue
nr, nc = r + dy8[d], c + dx8[d]
if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
flow_acc[nr, nc] += flow_acc[r, c]
flow_log = np.log1p(flow_acc)
_save_tif(output, flow_log, transform, crs)

8
run.sh
View File

@ -9,6 +9,7 @@
# -v Mode verbeux (timestamps + niveaux)
# --debug Mode debug (détails internes fichier:ligne)
# -f / --force Régénérer tous les fichiers même si existants
# --keep-tif Conserver les fichiers TIFF intermédiaires
# --force-classification
# Reclassifier le sol même si le fichier .las existe déjà
# --ground-classification {auto,smrf,pmf,csf}
@ -33,6 +34,7 @@ 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 " --keep-tif Conserver les fichiers TIFF intermédiaires"
echo " --ground-classification {auto,smrf,pmf,csf}"
echo " Méthode de classification du sol (défaut: auto)"
echo " --file NOM... Traiter un ou plusieurs fichiers LAZ (nom complet sans .laz/.las)"
@ -64,6 +66,7 @@ FORCE_FLAG=""
FILE_ARGS=""
GROUND_METHOD=""
FORCE_CLASSIFY_FLAG=""
KEEP_TIF_FLAG=""
# Parse arguments manually (more robust than getopts for mixed short/long options)
while [ $# -gt 0 ]; do
@ -76,6 +79,7 @@ while [ $# -gt 0 ]; do
--debug) VERBOSE_FLAG="--debug"; shift ;;
--force) FORCE_FLAG="--force"; shift ;;
--force-classification) FORCE_CLASSIFY_FLAG="--force-classification"; 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 ;;
@ -93,6 +97,7 @@ 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 " --keep-tif Conserver les fichiers TIFF intermédiaires"
echo " --ground-classification {auto,smrf,pmf,csf}"
echo " Méthode de classification du sol (défaut: auto)"
echo " --file NOM... Traiter un ou plusieurs fichiers LAZ (nom complet sans .laz/.las)"
@ -153,13 +158,14 @@ 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 : $([ -n "$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"
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