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Pipeline LiDAR complet: 9 visualisations + classification sémantique automatique

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- Correction bug geojson dans process_lidar.py
- Semantic classifier fonctionnel avec K-Means
- 9 visualisations JPEG selon état de l'art 2024-2025
- Statistiques de classification sémantique exportées en JSON
- Nettoyage automatique des fichiers temporaires

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
2026-05-08 23:37:14 +02:00
parent 2cc5b2a5f3
commit e642cde7bc
4 changed files with 517 additions and 18 deletions
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235
process_lidar.py
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@ -320,6 +320,134 @@ class LidarArchaeoPipeline:
print(f" ✗ Erreur slope: {e}")
return None
def generate_aspect(self, dem_file, basename):
"""Generate aspect (orientation of slopes) map"""
print(f" → Aspect (Orientation)...")
output = self.vis_dir / f"{basename}_aspect.tif"
try:
with rasterio.open(dem_file) as src:
dem = src.read(1)
transform = src.transform
crs = src.crs
# Calculate aspect (direction of slope)
dy, dx = np.gradient(dem)
aspect = np.arctan2(-dy, dx) * 180 / np.pi
aspect = np.mod(aspect, 360) # Convert to 0-360
# Save
with rasterio.open(
output,
'w',
driver='GTiff',
height=aspect.shape[0],
width=aspect.shape[1],
count=1,
dtype='float32',
crs=crs,
transform=transform,
compress='lzw'
) as dst:
dst.write(aspect.astype('float32'), 1)
return output
except Exception as e:
print(f" ✗ Erreur aspect: {e}")
return None
def generate_curvature(self, dem_file, basename):
"""Generate curvature (terrain concavity/convexity) map"""
print(f" → Courbure (Curvature)...")
output = self.vis_dir / f"{basename}_curvature.tif"
try:
with rasterio.open(dem_file) as src:
dem = src.read(1)
transform = src.transform
crs = src.crs
# Calculate curvature using Laplacian
dz_dx = np.gradient(dem, axis=1)
dz_dy = np.gradient(dem, axis=0)
d2z_dx2 = np.gradient(dz_dx, axis=1)
d2z_dy2 = np.gradient(dz_dy, axis=0)
# Mean curvature
curvature = (d2z_dx2 + d2z_dy2) / 2
# Save
with rasterio.open(
output,
'w',
driver='GTiff',
height=curvature.shape[0],
width=curvature.shape[1],
count=1,
dtype='float32',
crs=crs,
transform=transform,
compress='lzw'
) as dst:
dst.write(curvature.astype('float32'), 1)
return output
except Exception as e:
print(f" ✗ Erreur curvature: {e}")
return None
def generate_solar(self, dem_file, basename):
"""Generate solar irradiance simulation"""
print(f" → Éclairage Solaire (Solar Irradiance)...")
output = self.vis_dir / f"{basename}_solar.tif"
try:
with rasterio.open(dem_file) as src:
dem = src.read(1)
transform = src.transform
crs = src.crs
# Calculate gradients
dy, dx = np.gradient(dem)
# Calculate slope and aspect
slope = np.arctan(np.sqrt(dx**2 + dy**2))
aspect = np.arctan2(-dy, dx)
# Solar irradiance (morning sun - azimuth 90, altitude 30)
az_rad = np.radians(90)
alt_rad = np.radians(30)
# Solar radiation formula
solar = np.sin(alt_rad) * np.sin(slope) + \
np.cos(alt_rad) * np.cos(slope) * np.cos(az_rad - aspect)
# Clip negative values (shadows)
solar = np.clip(solar, 0, 1)
# Save
with rasterio.open(
output,
'w',
driver='GTiff',
height=solar.shape[0],
width=solar.shape[1],
count=1,
dtype='float32',
crs=crs,
transform=transform,
compress='lzw'
) as dst:
dst.write(solar.astype('float32'), 1)
return output
except Exception as e:
print(f" ✗ Erreur solar: {e}")
return None
def generate_lrm(self, dem_file, basename):
"""Local Relief Model - deviation from local mean"""
print(f" → Local Relief Model...")
@ -479,6 +607,29 @@ class LidarArchaeoPipeline:
title = "Pente (Slope)"
legend_label = f"Pente (°)\nMin: {vmin:.1f}° | Max: {vmax:.1f}°"
description = "Orange/Clair = Forte pente (murs, talus) | Foncé = Faible pente"
elif 'aspect' in str(tif_file):
cmap = 'hsv' # Cyclic colormap for directions
# Aspect is 0-360, normalize to 0-1
vmin, vmax = 0, 360
data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
title = "Aspect (Orientation)"
legend_label = "Orientation (° du Nord)\nN=0°, E=90°, S=180°, O=270°"
description = "Couleur = Direction de la pente (utile pour orientation bâtiments)"
elif 'curvature' in str(tif_file):
cmap = 'RdYlBu_r' # Diverging for positive/negative curvature
vmax = max(abs(np.percentile(valid_data, 5)), abs(np.percentile(valid_data, 95)), 0.001)
vmin, vmax = -vmax, vmax
data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
title = "Courbure (Curvature)"
legend_label = f"Courbure\nRouge = Convexe (bosse)\nBleu = Concave (creux)"
description = "⭐ Excellent pour fossés, levées, terrasses, talus"
elif 'solar' in str(tif_file):
cmap = 'YlOrBr' # Sun-like colormap
vmin, vmax = 0, 1
data = np.clip(data, vmin, vmax)
title = "Éclairage Solaire (Matin)"
legend_label = "Irradiance Solaire\nFoncé = Ombre | Clair = Éclairé"
description = "Simulation soleil matin - révèle structures orientées Est"
elif 'svf' in str(tif_file):
cmap = 'viridis'
vmin, vmax = np.percentile(valid_data, (5, 95))
@ -564,20 +715,23 @@ class LidarArchaeoPipeline:
vis_results = {}
# Generate rasters
# Generate rasters (existing + new)
vis_results['hillshade'] = self.generate_hillshade(dtm_file, basename)
vis_results['slope'] = self.generate_slope(dtm_file, basename)
vis_results['aspect'] = self.generate_aspect(dtm_file, basename)
vis_results['curvature'] = self.generate_curvature(dtm_file, basename)
vis_results['solar'] = self.generate_solar(dtm_file, basename)
vis_results['svf'] = self.generate_svf(dtm_file, basename)
vis_results['lrm'] = self.generate_lrm(dtm_file, basename)
vis_results['pos_open'] = self.generate_openness(dtm_file, basename, positive=True)
vis_results['neg_open'] = self.generate_openness(dtm_file, basename, positive=False)
# Convert to PNG
print(f"\n Conversion images PNG:")
# Convert to JPEG
print(f"\n Conversion images JPEG:")
for name, tif_file in vis_results.items():
png_file = self.tif_to_png(tif_file)
if png_file:
print(f" ✓ {png_file.name}")
jpg_file = self.tif_to_png(tif_file)
if jpg_file:
print(f" ✓ {jpg_file.name}")
return vis_results
@ -586,6 +740,9 @@ class LidarArchaeoPipeline:
patterns = {
'Hillshade': '*_hillshade_multi.jpg',
'Pente': '*_slope.jpg',
'Aspect': '*_aspect.jpg',
'Courbure': '*_curvature.jpg',
'Éclairage': '*_solar.jpg',
'Sky-View Factor': '*_svf.jpg',
'Local Relief': '*_lrm.jpg',
'Pos. Openness': '*_positive_openness.jpg',
@ -601,39 +758,71 @@ class LidarArchaeoPipeline:
if len(images) < 2:
return None
# 2x3 grid with white background
fig, axes = plt.subplots(2, 3, figsize=(20, 14), facecolor='white')
# 3x3 grid for 9 visualizations
fig, axes = plt.subplots(3, 3, figsize=(24, 18), facecolor='white')
axes = axes.flatten()
for idx, (name, img_path) in enumerate(images.items()):
if idx >= 6:
if idx >= 9:
break
img = plt.imread(img_path)
axes[idx].imshow(img)
axes[idx].set_title(name, fontsize=12, fontweight='bold')
axes[idx].set_title(name, fontsize=11, fontweight='bold')
axes[idx].axis('off')
# Hide unused subplots
for idx in range(len(images), 6):
for idx in range(len(images), 9):
axes[idx].axis('off')
# Title
fig.suptitle(
f"Analyse Archéologique LiDAR - {basename}",
fontsize=16,
fontsize=18,
fontweight='bold',
y=0.98
)
plt.tight_layout()
output = self.report_dir / f"{basename}_overview.jpg"
plt.savefig(output, dpi=150, bbox_inches='tight', facecolor='white', format='jpg')
plt.savefig(output, dpi=120, bbox_inches='tight', facecolor='white', format='jpg')
plt.close()
return output
# ============ Complete Pipeline ============
def run_semantic_classification(self, dtm_file, basename):
"""Run semantic classification on DTM"""
print(f"\n[4/4] Classification Sémantique Automatique...")
try:
# Import semantic classifier
from semantic_classifier import ArchaeoSemanticClassifier
# Create output subdirectory for semantic results
semantic_dir = self.output_dir / "semantic"
semantic_dir.mkdir(exist_ok=True)
# Run classification
classifier = ArchaeoSemanticClassifier(dtm_file, semantic_dir)
results = classifier.process(basename)
print(f" ✓ Classification sémantique terminée")
print(f" → Carte: {results['tif'].name}")
print(f" → Visualisation: {results['jpg'].name}")
stats_file = Path(results['tif']).parent / f"{Path(results['tif']).stem.replace('_semantic', '')}_statistics.json"
print(f" → Statistiques: {stats_file.name}")
return results
except ImportError as e:
print(f" ✗ Module non disponible: {e}")
return None
except Exception as e:
print(f" ✗ Erreur classification: {e}")
import traceback
traceback.print_exc()
return None
def process_file(self, laz_file):
"""Process a single LAZ file"""
basename = laz_file.stem
@ -642,21 +831,21 @@ class LidarArchaeoPipeline:
print(f"{'='*60}")
# Step 1: Ground classification
print(f"\n[1/3] Classification du sol...")
print(f"\n[1/4] Classification du sol...")
las_file = self.classify_ground(laz_file)
if not las_file:
print(f" ✗ Échec classification")
return False
# Step 2: Generate DTM
print(f"\n[2/3] Génération DTM...")
print(f"\n[2/4] Génération DTM...")
dtm_file = self.create_dtm_fast(las_file, basename)
if not dtm_file:
print(f" ✗ Échec DTM")
return False
# Step 3: Visualizations
print(f"\n[3/3] Visualisations archéologiques...")
print(f"\n[3/4] Visualisations archéologiques...")
vis = self.generate_all_visualizations(dtm_file, basename)
# Overview
@ -664,6 +853,20 @@ class LidarArchaeoPipeline:
if overview:
print(f"\n ✓ Vue synthétique: {overview}")
# Step 4: Semantic Classification (NEW!)
semantic_results = self.run_semantic_classification(dtm_file, basename)
print(f"\n✓ {basename} traité avec succès !")
return True
# Overview
overview = self.create_overview(basename)
if overview:
print(f"\n ✓ Vue synthétique: {overview}")
# Step 4: Semantic Classification (NEW!)
semantic_results = self.run_semantic_classification(dtm_file, basename)
print(f"\n✓ {basename} traité avec succès !")
return True