ad762e682d
- Suppression de generate_solar (éclairage solaire) des visualisations - Accélération GPU de hillshade, slope, aspect, curvature, depressions, anomalies, roughness, texture GLCM, flow (sink filling) - Nettoyage mémoire GPU entre visualisations (gpu_cleanup) - Correction OOM texture GLCM: calcul entropie bin par bin au lieu d'un tableau 3D massif sur GPU - Correction bug: xp_minimum_filter manquant dans imports visualizations - Option --file accepte plusieurs noms complets sans extension - run.sh affiche l'aide si appelé sans arguments - Option --test pour exécuter les tests unitaires dans Docker - Filtre ReturnNumber>=1 intégré dans le pipeline PDAL (plus d'erreur SMRF) - 60 tests unitaires: GPU, visualisations, rendering, DTM, pipeline, CLI - Ajout pytest au Dockerfile Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
320 lines
13 KiB
Python
320 lines
13 KiB
Python
"""Pipeline orchestration for LiDAR archaeological analysis.
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LidarArchaeoPipeline coordinates the full processing chain:
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1. Ground classification (PDAL/SMRF)
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2. DTM generation
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3. Visualization generation (19 products)
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4. Rendering (WebP + PDF report)
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"""
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import logging
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import shutil
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import time
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from pathlib import Path
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import subprocess
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from .dtm import classify_ground, create_dtm_fast
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from .visualizations import (
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generate_hillshade, generate_slope, generate_aspect, generate_curvature,
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generate_lrm, generate_svf, generate_openness,
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generate_mslrm, generate_tpi, generate_depressions, generate_sailore,
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generate_roughness, generate_anomalies, generate_wavelet, generate_texture,
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generate_flow,
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)
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from .gpu import gpu_cleanup
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from .ign import generate_ign_overlay
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from .rendering import tif_to_png, generate_pdf_report
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logger = logging.getLogger("lidar")
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# Ordered list of visualization steps.
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# Each entry: (name, function_or_lambda)
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# Adding a new visualization = add a generate_* function + register here.
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VIZ_STEPS = [
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('hillshade', generate_hillshade),
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('slope', generate_slope),
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('aspect', generate_aspect),
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('curvature', generate_curvature),
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('svf', generate_svf),
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('lrm', generate_lrm),
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('pos_open', lambda d, b, v, r: generate_openness(d, b, v, r, positive=True)),
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('neg_open', lambda d, b, v, r: generate_openness(d, b, v, r, positive=False)),
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('mslrm', generate_mslrm),
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('tpi', generate_tpi),
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('depressions', generate_depressions),
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('sailore', generate_sailore),
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('roughness', generate_roughness),
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('anomalies', generate_anomalies),
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('wavelet', generate_wavelet),
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('texture', generate_texture),
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('flow', generate_flow),
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('ortho', lambda d, b, v, r: generate_ign_overlay(
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d, b, v, r,
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layer='ORTHOIMAGERY.ORTHOPHOTOS',
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title='Photographie Aérienne IGN',
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legend_label='Orthophotographie\nImage aérienne',
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description='Photographie aérienne IGN (Orthophoto)',
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out_suffix='ortho')),
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('topo', lambda d, b, v, r: generate_ign_overlay(
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d, b, v, r,
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layer='GEOGRAPHICALGRIDSYSTEMS.PLANIGNV2',
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title='Carte Topographique IGN',
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legend_label='Carte IGN\nPlan topographique',
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description='Carte topographique IGN (Plan IGN)',
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out_suffix='topo')),
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]
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class LidarArchaeoPipeline:
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"""Orchestrates the LiDAR archaeological analysis pipeline."""
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def __init__(self, input_dir, output_dir, resolution=0.5, workers=1, force=False):
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self.input_dir = Path(input_dir)
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self.output_dir = Path(output_dir)
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self.resolution = resolution
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self.workers = workers
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self.force = force
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self.temp_dir = self.output_dir / "temp"
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if not self.input_dir.exists():
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raise ValueError(f"Répertoire introuvable: {self.input_dir}")
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.temp_dir.mkdir(exist_ok=True)
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self.dtm_dir = self.output_dir / "DTM"
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self.vis_dir = self.output_dir / "visualisations"
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self.pdf_dir = self.output_dir / "rapports"
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for d in [self.dtm_dir, self.vis_dir, self.pdf_dir]:
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d.mkdir(exist_ok=True)
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logger.info("Pipeline initialisé")
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logger.info(f" Entrée : {self.input_dir}")
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logger.info(f" Sortie : {self.output_dir}")
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logger.info(f" Résolution : {resolution}m/px")
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logger.info(f" Workers : {workers}")
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logger.info(f" Force : {'OUI' if self.force else 'non (skip existing)'}")
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def find_laz_files(self):
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"""Find all LAZ/LAS files in input directory."""
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files = list(self.input_dir.glob("*.laz")) + list(self.input_dir.glob("*.las"))
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logger.info(f"{len(files)} fichier(s) LiDAR trouvé(s)")
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for f in sorted(files):
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logger.debug(f" {f.name}")
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return sorted(files)
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def check_tools(self):
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"""Check that required external tools are available."""
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for name, cmd in [('pdal', 'pdal --version'), ('gdal', 'gdalinfo --version')]:
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try:
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result = subprocess.run(cmd.split(), capture_output=True, check=True, text=True)
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version = result.stdout.strip().split('\n')[0]
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logger.info(f" ✓ {name}: {version}")
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except (subprocess.CalledProcessError, FileNotFoundError):
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logger.error(f" ✗ {name} non disponible")
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return False
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return True
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def generate_all_visualizations(self, dtm_file, basename):
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"""Generate all archaeological visualizations for one DTM file.
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Returns a dict of {name: tif_path} for successful generations.
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"""
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logger.info(" Génération visualisations:")
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# Create per-file subdirectory
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file_vis_dir = self.vis_dir / basename
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file_vis_dir.mkdir(exist_ok=True)
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vis_results = {}
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total = len(VIZ_STEPS)
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for idx, (name, func) in enumerate(VIZ_STEPS, 1):
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# Check if output WebP already exists (skip unless --force)
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if not self.force:
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# Determine expected WebP filename from the viz name
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# Special cases for openness and IGN overlays
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if name == 'pos_open':
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expected_webp = file_vis_dir / f"{basename}_positive_openness.webp"
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elif name == 'neg_open':
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expected_webp = file_vis_dir / f"{basename}_negative_openness.webp"
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elif name == 'hillshade':
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expected_webp = file_vis_dir / f"{basename}_hillshade_multi.webp"
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elif name in ('ortho', 'topo'):
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expected_webp = file_vis_dir / f"{basename}_{name}.webp"
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else:
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expected_webp = file_vis_dir / f"{basename}_{name}.webp"
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if expected_webp.exists():
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logger.info(f" [{idx}/{total}] {name}: déjà existant, ignoré")
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vis_results[name] = expected_webp # Track as existing file
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continue
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logger.info(f" [{idx}/{total}] {name}...")
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t0 = time.time()
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try:
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result = func(dtm_file, basename, file_vis_dir, self.resolution)
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vis_results[name] = result
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elapsed = time.time() - t0
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if result:
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logger.info(f" [{idx}/{total}] ✓ {name} ({elapsed:.1f}s)")
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else:
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logger.warning(f" [{idx}/{total}] ✗ {name} — no output ({elapsed:.1f}s)")
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except Exception as e:
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vis_results[name] = None
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logger.error(f" [{idx}/{total}] ✗ {name}: {e}", exc_info=True)
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# Free GPU memory between visualizations to prevent OOM
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gpu_cleanup()
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# Convert to WebP (only newly generated TIFs, not skipped ones)
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logger.info(" Conversion images WebP:")
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for name, tif_file in vis_results.items():
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if tif_file and isinstance(tif_file, Path) and tif_file.suffix == '.tif' and tif_file.exists():
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webp_file = tif_to_png(tif_file, file_vis_dir, self.resolution)
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if webp_file:
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logger.info(f" ✓ {webp_file.name}")
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return vis_results
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def process_file(self, laz_file):
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"""Process a single LAZ file through the full pipeline."""
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basename = laz_file.stem
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t_start = time.time()
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logger.info("=" * 60)
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logger.info(f"FICHIER : {basename}")
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logger.info("=" * 60)
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# Step 1: Ground classification
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logger.info("[1/4] Classification du sol...")
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t1 = time.time()
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las_file = classify_ground(laz_file, self.temp_dir)
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t_classif = time.time() - t1
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if not las_file:
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logger.error(f" ✗ Échec classification ({t_classif:.1f}s)")
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return False
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logger.info(f" ✓ Classification terminée ({t_classif:.1f}s)")
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# Step 2: Generate DTM
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logger.info("[2/4] 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)
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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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return False
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logger.info(f" ✓ DTM terminé ({t_dtm:.1f}s)")
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# Step 3: Visualizations
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logger.info("[3/4] Visualisations archéologiques...")
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self.generate_all_visualizations(dtm_file, basename)
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# Step 4: PDF report
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file_vis_dir = self.vis_dir / basename
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logger.info("[4/4] Rapport PDF A3...")
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t4 = time.time()
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generate_pdf_report(basename, file_vis_dir, self.pdf_dir, self.resolution)
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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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t_total = time.time() - t_start
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logger.info(f"✓ {basename} terminé en {t_total:.1f}s")
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logger.debug(f" Détails: classification={t_classif:.1f}s, DTM={t_dtm:.1f}s, PDF={t_pdf:.1f}s")
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return True
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def process_all(self):
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"""Process all LAZ files in input directory."""
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files = self.find_laz_files()
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if not files:
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logger.error("Aucun fichier LAZ/LAS trouvé !")
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return
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logger.info("=" * 60)
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logger.info("PIPELINE ARCHÉOLOGIQUE LiDAR")
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logger.info("=" * 60)
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logger.info("Vérification des outils...")
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if not self.check_tools():
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logger.error("Outils manquants — abandon")
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return
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results = {}
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t_pipeline_start = time.time()
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if self.workers > 1 and len(files) > 1:
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logger.info(f"Traitement parallèle avec {self.workers} workers...")
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logger.info(f"Fichiers: {len(files)}")
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with ProcessPoolExecutor(max_workers=self.workers) as executor:
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future_to_file = {
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executor.submit(_process_file_standalone, str(laz_file), str(self.input_dir), str(self.output_dir), self.resolution, self.force): laz_file
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for laz_file in files
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}
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for idx, future in enumerate(as_completed(future_to_file), 1):
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laz_file = future_to_file[future]
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try:
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success = future.result()
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results[laz_file.name] = success
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status = "✓" if success else "✗"
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logger.info(f" [{idx}/{len(files)}] {status} {laz_file.name}")
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except Exception as e:
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logger.error(f" [{idx}/{len(files)}] ✗ {laz_file.name}: {e}")
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logger.debug(f" Traceback:", exc_info=True)
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results[laz_file.name] = False
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else:
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total = len(files)
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for idx, laz_file in enumerate(files, 1):
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logger.info(f"--- Fichier {idx}/{total} ---")
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try:
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results[laz_file.name] = self.process_file(laz_file)
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except Exception as e:
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logger.error(f"✗ Erreur traitement {laz_file.name}: {e}")
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logger.debug("Traceback:", exc_info=True)
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results[laz_file.name] = False
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# Summary
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t_pipeline_total = time.time() - t_pipeline_start
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success_count = sum(1 for v in results.values() if v)
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fail_count = sum(1 for v in results.values() if not v)
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logger.info("=" * 60)
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logger.info("RÉSUMÉ")
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logger.info("=" * 60)
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logger.info(f" Succès : {success_count}/{len(results)}")
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if fail_count:
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logger.info(f" Échecs : {fail_count}/{len(results)}")
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for name, ok in results.items():
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if not ok:
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logger.info(f" ✗ {name}")
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logger.info(f" Durée totale : {t_pipeline_total:.1f}s ({t_pipeline_total/60:.1f}min)")
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logger.info(f"\nRésultats dans: {self.output_dir}")
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logger.info(f" • DTM : {self.dtm_dir}")
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logger.info(f" • Visualisations: {self.vis_dir}")
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logger.info(f" • Rapports PDF : {self.pdf_dir}")
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# Clean up temporary files
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logger.info("Nettoyage des fichiers temporaires...")
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try:
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if self.temp_dir.exists():
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shutil.rmtree(self.temp_dir)
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logger.info(" ✓ Fichiers temporaires supprimés")
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except Exception as e:
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logger.warning(f" Note: Impossible de supprimer les fichiers temporaires: {e}")
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def _process_file_standalone(laz_file_str, input_dir, output_dir, resolution, force=False):
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"""Standalone function for multiprocessing — creates its own pipeline instance.
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Each worker gets its own temp directory to avoid file conflicts.
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"""
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pipeline = LidarArchaeoPipeline(input_dir, output_dir, resolution=resolution, workers=1, force=force)
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basename = Path(laz_file_str).stem
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pipeline.temp_dir = pipeline.output_dir / f"temp_{basename}"
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pipeline.temp_dir.mkdir(exist_ok=True)
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laz_file = Path(laz_file_str)
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return pipeline.process_file(laz_file) |