Pipeline LiDAR optimisé: suppression classification sémantique, ajout flèche nord vectorielle
- Suppression module classification sémantique (non fonctionnel) - Suppression section rapports (vue synthétique) - Ajout flèche du nord vectorielle noire (coin supérieur droit, au-dessus légende) - Pipeline simplifié à 3 étapes: classification sol, génération DTM, visualisations - Prétraitement ReturnNumber pour fichiers LAZ corrompus - Orientation nord garantie sur toutes les cartes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
200
process_lidar.py
200
process_lidar.py
@ -48,9 +48,8 @@ class LidarArchaeoPipeline:
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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.report_dir = self.output_dir / "rapports"
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for d in [self.dtm_dir, self.vis_dir, self.report_dir]:
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for d in [self.dtm_dir, self.vis_dir]:
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d.mkdir(exist_ok=True)
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print(f"✓ Pipeline initialisé (Python pur)")
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@ -110,7 +109,53 @@ class LidarArchaeoPipeline:
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print(f" ✓ Classification déjà effectuée")
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return output_las
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pipeline_json = self.create_pipeline_json(laz_file, output_las)
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# First, try to fix ReturnNumber values if needed
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fixed_las = self.temp_dir / f"{laz_file.stem}_fixed.las"
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# Create preprocessing pipeline to fix ReturnNumber=0 issue using expression
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fix_pipeline = [
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{
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"type": "readers.las",
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"filename": str(laz_file)
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},
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{
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"type": "filters.expression",
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"expression": "ReturnNumber = MAX(ReturnNumber, 1)"
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},
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{
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"type": "filters.expression",
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"expression": "NumberOfReturns = MAX(NumberOfReturns, 1)"
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},
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{
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"type": "writers.las",
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"filename": str(fixed_las),
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"extra_dims": "all"
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}
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]
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try:
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# Try with fixed data first
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fix_json = json.dumps(fix_pipeline)
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fix_pipe_file = self.temp_dir / "fix_pipeline.json"
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with open(fix_pipe_file, 'w') as f:
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f.write(fix_json)
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import subprocess
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result = subprocess.run(['pdal', 'pipeline', str(fix_pipe_file)],
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capture_output=True, text=True)
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if result.returncode == 0:
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print(f" → Correction ReturnNumber effectuée")
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input_for_smrf = fixed_las
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else:
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print(f" → Erreur correction, utilisation fichier original")
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input_for_smrf = laz_file
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if fixed_las.exists():
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fixed_las.unlink()
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except Exception as e:
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print(f" → Erreur prétraitement: {e}")
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input_for_smrf = laz_file
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pipeline_json = self.create_pipeline_json(input_for_smrf, output_las)
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pipeline_file = self.temp_dir / "pipeline.json"
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with open(pipeline_file, 'w') as f:
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@ -670,8 +715,8 @@ class LidarArchaeoPipeline:
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# Create figure with white background for JPEG
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fig, ax = plt.subplots(figsize=(18, 12), facecolor='white')
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# Display data
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im = ax.imshow(data, cmap=cmap, aspect='equal')
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# Display data with north at the top
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im = ax.imshow(data, cmap=cmap, aspect='equal', origin='upper')
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# Enhanced colorbar with explicit values
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cbar = plt.colorbar(im, ax=ax, pad=0.03, shrink=0.75, aspect=30)
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@ -691,11 +736,42 @@ class LidarArchaeoPipeline:
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
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ax.axis('off')
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# Add vector north arrow above the colorbar on the right side (black lines)
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from matplotlib.patches import FancyArrow, Polygon
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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# Create inset axes positioned above the colorbar on the right
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# The colorbar is typically at the right, so we place north arrow above it
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north_ax = inset_axes(ax, width="5%", height="8%", loc='upper right',
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bbox_to_anchor=(-0.02, 0.15, 1, 1), bbox_transform=ax.transAxes)
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north_ax.set_xlim(0, 1)
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north_ax.set_ylim(0, 1)
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north_ax.axis('off')
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# Draw vector arrow pointing north (black lines)
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# Main arrow shaft
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north_ax.plot([0.5, 0.5], [0.15, 0.65], color='black', linewidth=2.5, zorder=10)
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# Arrow head (triangle outline)
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arrow_head_outline = [[0.5, 0.25], [0.35, 0.45], [0.5, 0.65], [0.65, 0.45]]
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for i in range(len(arrow_head_outline) - 1):
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north_ax.plot([arrow_head_outline[i][0], arrow_head_outline[i+1][0]],
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[arrow_head_outline[i][1], arrow_head_outline[i+1][1]],
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color='black', linewidth=2.5, zorder=10)
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# Fill the arrow head
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north_ax.add_patch(Polygon([[0.5, 0.25], [0.35, 0.45], [0.5, 0.65], [0.65, 0.45]],
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closed=True, facecolor='black', edgecolor='black', zorder=9))
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# Add "N" label above the arrow
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north_ax.text(0.5, 0.92, 'N', ha='center', va='top',
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fontsize=14, fontweight='bold', color='black', zorder=11)
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fig.patch.set_facecolor('white')
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plt.tight_layout()
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# Save as JPEG
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plt.savefig(jpg_file, dpi=150, bbox_inches='tight', facecolor='white', format='jpg')
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plt.savefig(jpg_file, dpi=150, bbox_inches='tight', pad_inches=0.1, facecolor='white', format='jpg')
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plt.close()
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# Delete the source TIFF file to save space
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@ -735,94 +811,8 @@ class LidarArchaeoPipeline:
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return vis_results
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def create_overview(self, basename):
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"""Create overview image with all visualizations (JPEG)"""
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patterns = {
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'Hillshade': '*_hillshade_multi.jpg',
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'Pente': '*_slope.jpg',
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'Aspect': '*_aspect.jpg',
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'Courbure': '*_curvature.jpg',
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'Éclairage': '*_solar.jpg',
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'Sky-View Factor': '*_svf.jpg',
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'Local Relief': '*_lrm.jpg',
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'Pos. Openness': '*_positive_openness.jpg',
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'Neg. Openness': '*_negative_openness.jpg'
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}
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images = {}
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for name, pattern in patterns.items():
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files = list(self.vis_dir.glob(pattern))
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if files:
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images[name] = str(files[0])
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if len(images) < 2:
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return None
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# 3x3 grid for 9 visualizations
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fig, axes = plt.subplots(3, 3, figsize=(24, 18), facecolor='white')
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axes = axes.flatten()
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for idx, (name, img_path) in enumerate(images.items()):
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if idx >= 9:
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break
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img = plt.imread(img_path)
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axes[idx].imshow(img)
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axes[idx].set_title(name, fontsize=11, fontweight='bold')
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axes[idx].axis('off')
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# Hide unused subplots
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for idx in range(len(images), 9):
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axes[idx].axis('off')
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# Title
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fig.suptitle(
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f"Analyse Archéologique LiDAR - {basename}",
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fontsize=18,
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fontweight='bold',
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y=0.98
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)
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plt.tight_layout()
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output = self.report_dir / f"{basename}_overview.jpg"
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plt.savefig(output, dpi=120, bbox_inches='tight', facecolor='white', format='jpg')
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plt.close()
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return output
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# ============ Complete Pipeline ============
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def run_semantic_classification(self, dtm_file, basename):
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"""Run semantic classification on DTM"""
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print(f"\n[4/4] Classification Sémantique Automatique...")
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try:
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# Import semantic classifier
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from semantic_classifier import ArchaeoSemanticClassifier
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# Create output subdirectory for semantic results
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semantic_dir = self.output_dir / "semantic"
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semantic_dir.mkdir(exist_ok=True)
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# Run classification
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classifier = ArchaeoSemanticClassifier(dtm_file, semantic_dir)
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results = classifier.process(basename)
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print(f" ✓ Classification sémantique terminée")
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print(f" → Carte: {results['tif'].name}")
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print(f" → Visualisation: {results['jpg'].name}")
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stats_file = Path(results['tif']).parent / f"{Path(results['tif']).stem.replace('_semantic', '')}_statistics.json"
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print(f" → Statistiques: {stats_file.name}")
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return results
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except ImportError as e:
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print(f" ✗ Module non disponible: {e}")
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return None
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except Exception as e:
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print(f" ✗ Erreur classification: {e}")
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import traceback
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traceback.print_exc()
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return None
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def process_file(self, laz_file):
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"""Process a single LAZ file"""
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basename = laz_file.stem
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@ -831,42 +821,23 @@ class LidarArchaeoPipeline:
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print(f"{'='*60}")
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# Step 1: Ground classification
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print(f"\n[1/4] Classification du sol...")
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print(f"\n[1/3] Classification du sol...")
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las_file = self.classify_ground(laz_file)
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if not las_file:
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print(f" ✗ Échec classification")
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return False
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# Step 2: Generate DTM
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print(f"\n[2/4] Génération DTM...")
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print(f"\n[2/3] Génération DTM...")
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dtm_file = self.create_dtm_fast(las_file, basename)
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if not dtm_file:
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print(f" ✗ Échec DTM")
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return False
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# Step 3: Visualizations
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print(f"\n[3/4] Visualisations archéologiques...")
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print(f"\n[3/3] Visualisations archéologiques...")
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vis = self.generate_all_visualizations(dtm_file, basename)
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# Overview
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overview = self.create_overview(basename)
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if overview:
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print(f"\n ✓ Vue synthétique: {overview}")
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# Step 4: Semantic Classification (NEW!)
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semantic_results = self.run_semantic_classification(dtm_file, basename)
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print(f"\n✓ {basename} traité avec succès !")
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return True
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# Overview
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overview = self.create_overview(basename)
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if overview:
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print(f"\n ✓ Vue synthétique: {overview}")
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# Step 4: Semantic Classification (NEW!)
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semantic_results = self.run_semantic_classification(dtm_file, basename)
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print(f"\n✓ {basename} traité avec succès !")
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return True
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@ -908,7 +879,6 @@ class LidarArchaeoPipeline:
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print(f"\nRésultats dans: {self.output_dir}")
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print(f" • DTM: {self.dtm_dir}")
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print(f" • Visualisations JPEG: {self.vis_dir}")
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print(f" • Rapports: {self.report_dir}")
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# Clean up temporary files to save space
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print(f"\nNettoyage des fichiers temporaires...")
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