Pipeline LiDAR complet: 9 visualisations + classification sémantique automatique
- 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>
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Jacquin Antoine
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semantic_classifier.py
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293
semantic_classifier.py
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#!/usr/bin/env python3
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"""
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Module de Classification Sémantique Simplifié pour LiDAR Archéologique
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Approche robuste avec K-Means pour classification automatique
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"""
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import numpy as np
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import rasterio
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from rasterio.transform import from_bounds
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from sklearn.cluster import KMeans
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from scipy import ndimage
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import json
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from pathlib import Path
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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class ArchaeoSemanticClassifier:
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"""Classification sémantique automatique robuste"""
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def __init__(self, dtm_file, output_dir):
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self.dtm_file = Path(dtm_file)
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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# Load DTM
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with rasterio.open(self.dtm_file) as src:
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self.dem = src.read(1)
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self.transform = src.transform
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self.crs = src.crs
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self.height, self.width = self.dem.shape
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print(f"✓ Classifieur initialisé (DTM: {self.width}x{self.height})")
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def extract_features_simple(self):
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"""Extraction simplifiée des caractéristiques"""
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print(" → Extraction caractéristiques...")
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# Calculate gradients
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dy, dx = np.gradient(self.dem)
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# Slope
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slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 / np.pi
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# Aspect
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aspect = np.arctan2(-dy, dx) * 180 / np.pi
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aspect = np.mod(aspect, 360)
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# Curvature
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dz_dx = np.gradient(dx, axis=1)
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dz_dy = np.gradient(dy, axis=0)
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curvature = (dz_dx + dz_dy) / 2
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# Local Relief
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from scipy.ndimage import uniform_filter
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local_mean = uniform_filter(self.dem, size=int(15/0.5))
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local_relief = self.dem - local_mean
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return {
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'elevation': self.dem,
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'slope': slope,
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'aspect': aspect,
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'curvature': curvature,
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'local_relief': local_relief
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}
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def classify_kmeans(self, n_clusters=6):
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"""Classification K-Means robuste"""
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print(" → Classification K-Means...")
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features = self.extract_features_simple()
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# Normalize each feature to 0-1
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normalized_features = {}
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for name, data in features.items():
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min_val = np.percentile(data, 2)
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max_val = np.percentile(data, 98)
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normalized = np.clip((data - min_val) / (max_val - min_val + 1e-6), 0, 1)
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normalized_features[name] = normalized
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# Stack features for clustering
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feature_stack = np.stack([
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normalized_features['elevation'].flatten(),
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normalized_features['slope'].flatten(),
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normalized_features['curvature'].flatten(),
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normalized_features['local_relief'].flatten()
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], axis=1)
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# Remove NaN values
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valid_mask = ~np.isnan(feature_stack).any(axis=1)
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feature_stack = feature_stack[valid_mask]
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# K-Means clustering with random_state for reproducibility
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kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10, max_iter=300)
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labels_flat = kmeans.fit_predict(feature_stack)
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# Create full resolution labels
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full_labels = np.zeros(self.height * self.width, dtype=int)
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full_indices = np.where(valid_mask)[0]
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full_labels[full_indices] = labels_flat + 1 # +1 to shift from 0-based
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full_labels = full_labels.reshape(self.height, self.width)
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# Interpret clusters
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self._interpret_clusters(kmeans.cluster_centers_, features)
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return full_labels
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def _interpret_clusters(self, centers, features):
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"""Interprète les clusters selon les centroïdes"""
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interpretations = {}
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# Centers are in normalized feature space
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# Features order: elevation, slope, curvature, local_relief
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for i, center in enumerate(centers):
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elev = center[0]
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slope = center[1]
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curve = center[2]
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relief = center[3]
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# Interpret based on feature values
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if relief > 0.7:
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category = "ÉLEVÉE (Tumulus possible)"
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elif relief < -0.3:
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category = "ENFONCÉ (Fossé, cavité)"
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elif abs(curve) > 0.5:
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if curve > 0:
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category = "CONVEX (Bosse, monticule)"
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else:
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category = "CONCAVE (Creux, dépression)"
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elif slope > 0.6:
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category = "PENTE FORTE (Talus, mur)"
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elif slope < 0.2 and elev < 0.3:
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category = "PLAT (Zone plane)"
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else:
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category = "TOPOGRAPHIE MIXTE"
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interpretations[i + 1] = category # +1 because labels are 1-indexed
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print(f" Interprétation des {len(centers)} clusters :")
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for i, interp in interpretations.items():
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print(f" Classe {i}: {interp}")
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def generate_semantic_map(self, labels):
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"""Génère une carte sémantique colorée"""
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print(" → Génération carte sémantique...")
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# Create color map for semantic classes
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# 0: Background, 1-6: Different semantic classes
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colors = {
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0: [200, 200, 200], # Gray: Background/Unknown
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1: [139, 69, 19], # Brown: Linear/Walls
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2: [128, 0, 128], # Purple: Circular/Mounds
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3: [255, 140, 0], # Orange: Elevated
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4: [0, 200, 255], # Cyan: Depressed
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5: [220, 220, 0], # Yellow: Slope
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6: [0, 128, 0] # Green: Vegetation/Natural
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}
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# Create RGB image
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rgb = np.zeros((self.height, self.width, 3), dtype=np.uint8)
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for class_id, color in colors.items():
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mask = labels == class_id
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for c in range(3):
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rgb[:, :, c][mask] = color[c]
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return rgb
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def process(self, basename):
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"""Pipeline complet de classification sémantique"""
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print(f"\n{'='*60}")
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print(f" CLASSIFICATION SÉMANTIQUE - {basename}")
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print(f"{'='*60}")
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# Run K-Means classification
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labels = self.classify_kmeans(n_clusters=6)
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# Generate semantic map
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rgb = self.generate_semantic_map(labels)
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# Save semantic classification
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output_tif = self.output_dir / f"{basename}_semantic.tif"
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with rasterio.open(
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output_tif,
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'w',
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driver='GTiff',
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height=self.height,
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width=self.width,
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count=1,
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dtype='uint8',
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crs=self.crs,
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transform=self.transform,
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compress='lzw'
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) as dst:
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dst.write(labels, 1)
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# Save visualization
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output_jpg = self.output_dir / f"{basename}_semantic.jpg"
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plt.figure(figsize=(16, 12), facecolor='white')
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plt.imshow(rgb)
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# Create legend
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legend_elements = [
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plt.Rectangle((0, 0), 1, 1, facecolor=np.array(c)/255, edgecolor='black', label=label)
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for label, c in [
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("Inconnu/Fond", [200, 200, 200]),
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("Linéaire (murs)", [139, 69, 19]),
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("Circulaire (tumulus)", [128, 0, 128]),
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("Élevé (monticules)", [255, 140, 0]),
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("Enfoncé (fossés)", [0, 200, 255]),
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("Pente forte (talus)", [220, 220, 0]),
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("Naturel", [0, 128, 0])
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]
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]
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plt.legend(handles=legend_elements, loc='upper right', fontsize=11)
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plt.title(f"Classification Sémantique LiDAR - {basename}\n",
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fontsize=14, fontweight='bold', pad=15)
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plt.axis('off')
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plt.tight_layout()
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plt.savefig(output_jpg, dpi=150, bbox_inches='tight', format='jpg')
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plt.close()
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# Generate statistics
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stats = self._generate_stats(labels, basename)
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print(f"\n✓ Classification terminée !")
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print(f" • Carte sémantique: {output_tif.name}")
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print(f" • Visualisation: {output_jpg.name}")
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print(f" • Statistiques: {self.output_dir / f'{basename}_statistics.json'}")
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return {
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'labels': labels,
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'tif': output_tif,
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'jpg': output_jpg,
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'stats': stats
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}
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def _generate_stats(self, labels, basename):
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"""Génère les statistiques de classification"""
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print(" → Génération statistiques...")
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total_pixels = labels.size
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stats = {}
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class_names = {
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0: "Inconnu/Fond",
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1: "Linéaire",
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2: "Circulaire",
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3: "Élevée",
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4: "Enfoncée",
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5: "Pente forte",
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6: "Naturel"
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}
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for class_id in range(7):
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count = np.sum(labels == class_id)
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percentage = (count / total_pixels) * 100
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if count > 0:
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stats[class_id] = {
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'name': class_names[class_id],
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'count': int(count),
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'percentage': float(percentage)
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}
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# Save as JSON
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stats_file = self.output_dir / f"{basename}_statistics.json"
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with open(stats_file, 'w') as f:
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json.dump(stats, f, indent=2, default=lambda x: float(x) if isinstance(x, (np.floating, np.integer)) else x)
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# Print summary
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print(f"\n 📊 Statistiques :")
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for class_id, info in stats.items():
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print(f" {info['name']}: {info['count']:.0f} px ({info['percentage']:.1f}%)")
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return stats
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def main():
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import sys
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if len(sys.argv) < 2:
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print("Usage: python semantic_classifier.py <dtm_file.tif> [output_dir]")
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sys.exit(1)
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dtm_file = sys.argv[1]
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output_dir = sys.argv[2] if len(sys.argv) > 2 else "semantic_output"
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classifier = ArchaeoSemanticClassifier(dtm_file, output_dir)
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classifier.process(Path(dtm_file).stem)
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if __name__ == "__main__":
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main()
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