5b74322077
If the DTM .tif exists and --force is not set, skip both ground classification and DTM generation entirely. Previously, the pipeline would spend 3+ minutes reclassifying ground even when the DTM was already present and would be reused anyway. Also includes: SharedDEM cache, enhanced WebP cartouche (compass rose, adaptive scale bar, enriched info bar), removed COG/viewer, UTF-8 fix for parallel workers, skip logic for DTM and PDF. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
720 lines
33 KiB
Python
720 lines
33 KiB
Python
"""Rendering module: colormap registry, GeoTIFF-to-WebP conversion, and PDF report generation.
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Contains:
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- COLORMAPS: registry mapping filename keywords to (cmap, title, legend, description)
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- tif_to_png(): convert a GeoTIFF to a WebP visualization with legend, scale bar, north arrow
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- generate_pdf_report(): generate an A3 PDF report with all visualizations
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"""
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import logging
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import time
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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import rasterio
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from PIL import Image as PILImage
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try:
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from rasterio.warp import transform as warp_transform
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HAS_WARP = True
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except ImportError:
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HAS_WARP = False
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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from matplotlib.patches import Polygon as MplPolygon, Rectangle as RectPatch
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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rcParams['figure.dpi'] = 150
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rcParams['savefig.dpi'] = 300
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rcParams['font.size'] = 10
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logger = logging.getLogger("lidar")
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# ============================================================
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# Colormap registry
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# ============================================================
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# Each entry: keyword → (cmap, title, legend_label, description, vmin_mode, vmax_mode)
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# vmin_mode/vmax_mode: 'percentile_X_Y' or '0_max_X' or 'symmetric_X_Y' or 'fixed_0_1'
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# For RGB images (ortho/topo), special handling is done in tif_to_png.
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COLORMAPS = {
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'hillshade': {
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'cmap': 'gray',
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'title': 'Hillshade Multidirectionnel',
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'legend': 'Illumination combinée de 6 directions\nBlanc = Face éclairée | Noir = Zone d\'ombre',
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'description': 'Ombres portées révélant micro-relief (murs, fossés, terrasses)',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'fixed', 'vmax_val': 1,
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},
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'slope': {
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'cmap': 'inferno',
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'title': 'Pente (Inclinaison du terrain)',
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'legend': 'Inclinaison en degrés\nMin: {vmin:.1f}° | Max: {vmax:.1f}°\nClair = Forte pente | Sombre = Terrain plat',
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'description': 'Murs, talus et bords ressortent en clair — terrain plat en sombre',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'percentile', 'vmax_pct': 95,
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},
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'aspect': {
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'cmap': 'hsv',
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'title': 'Aspect (Direction des pentes)',
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'legend': 'Direction vers laquelle le terrain descend\nRouge=Nord | Vert=Est | Cyan=Sud | Bleu=Ouest',
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'description': 'Orientation des pentes — utile pour distinguer structures des formes naturelles',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'fixed', 'vmax_val': 360,
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},
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'curvature': {
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'cmap': 'RdYlBu_r',
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'title': 'Courbure (Convexité/Concavité du terrain)',
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'legend': 'Changement de pente (1/m)\nRouge = Convexe (sommet de mur, levée)\nBleu = Concave (fond de fossé, dépression)',
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'description': 'Détecte les ruptures de pente — utile pour bords de terrasses et levées',
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'vmin_mode': 'symmetric', 'sym_pct': (5, 95),
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},
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'svf': {
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'cmap': 'viridis',
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'title': 'Sky-View Factor (Ray-tracing 16 directions)',
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'legend': 'Fraction de ciel visible depuis chaque point\nSombre = Encaissé (fossés, vallées, rues)\nClair = Dégagé (sommets, plateformes, plateaux)',
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'description': 'Ray-tracing sur 16 azimuts — élimine l\'éclairage, détecte structures linéaires et enclos',
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'vmin_mode': 'percentile', 'vmin_pct': 5,
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'vmax_mode': 'percentile', 'vmax_pct': 95,
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},
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'mslrm': {
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'cmap': 'RdBu_r',
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'title': 'MSRM - Multi-Scale Relief Model (5 échelles)',
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'legend': 'Relief combiné à 5 échelles (5m à 100m)\nRouge = Surélévation (mur, tumulus, levée)\nBleu = Dépression (fossé, douve, fossé)\n\nDifférence avec LRM:\nLRM = 1 échelle (15m)\nMSRM = 5 échelles combinées\nMSRM détecte du micro au macro',
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'description': 'Combine LRM à 5 échelles — détecte structures de 5m à 100m simultanément',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'lrm': {
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'cmap': 'RdBu_r',
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'title': 'LRM - Local Relief Model (échelle unique 15m)',
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'legend': 'Écart local par rapport au terrain moyen (m)\nRouge = Surélévation (+{vmax:.2f}m)\nBleu = Dépression ({vmin:.2f}m)\nNoyau gaussien unique de 15m',
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'description': 'Micro-relief à 15m seulement — voir MSRM pour toutes les échelles',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'positive_openness': {
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'cmap': 'YlOrBr',
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'title': 'Openness Positive (ouverture vers le haut)',
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'legend': 'Angle d\'ouverture vers le haut (deg)\nClair = Vue dégagée vers le ciel (sommets, plateaux)\nSombre = Vue bloquée (vallées encaissées)',
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'description': 'Ray-tracing 8 directions — complémentaire de la négative pour détecter crêtes',
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'vmin_mode': 'percentile', 'vmin_pct': 10,
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'vmax_mode': 'percentile', 'vmax_pct': 98,
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},
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'negative_openness': {
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'cmap': 'GnBu_r',
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'title': 'Openness Negative (ouverture vers le bas)',
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'legend': 'Angle d\'ouverture vers le bas (deg)\nClair = Surplomb (bords de fossé, grottes)\nSombre = Terrain plat (fonds de vallée)\nMeilleur détecteur de cavités et dolines',
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'description': 'Ray-tracing 8 directions — détecte fossés, dolines, souterrains',
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'vmin_mode': 'percentile', 'vmin_pct': 10,
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'vmax_mode': 'percentile', 'vmax_pct': 98,
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},
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'tpi': {
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'cmap': 'BrBG',
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'title': 'TPI - Topographic Position Index (2 échelles)',
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'legend': 'Position dans le paysage\nBrun/Sombre = Plus bas que le voisinage (fossé, vallée)\nVert/Clair = Plus haut que le voisinage (crête, plateau)\nCombine échelle fine 5m + large 100m',
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'description': 'Identifie la position topographique — utile pour repérer crêtes vs vallées à grande échelle',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'sailore': {
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'cmap': 'seismic',
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'title': 'SAILORE - LRM Auto-Adaptatif',
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'legend': 'Relief local adaptatif (m)\nRouge = Surélévation | Bleu = Dépression\n\nDifférence avec LRM/MSRM:\nLRM = noyau fixe 15m\nMSRM = 5 noyaux fixes\nSAILORE = noyau adapté à la pente\nPlat=grand noyau | Pente=petit noyau',
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'description': 'Noyau qui s\'adapte à la pente locale — terrain plat=grand noyau, pente=petit noyau',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'roughness': {
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'cmap': 'magma',
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'title': 'Rugosité de Surface (Écart-type local 5m)',
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'legend': 'Irrégularité du terrain dans un voisinage de 5m\nSombre = Surface lisse (route, mur, sol plat)\nClair = Surface rugueuse (végétation, ruines, pierres)\nMax: {vmax:.2f}m',
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'description': 'Mesure la variabilité locale — surfaces anthropiques lisses vs naturelles rugueuses',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'percentile', 'vmax_pct': 97,
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},
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'anomalies': {
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'cmap': 'coolwarm',
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'title': 'Anomalies Statistiques (Z-score x Moran\'s I)',
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'legend': 'Anomalies topographiques significatives\nRouge vif = Surélévation anormale (mur, tumulus)\nBleu vif = Dépression anormale (fossé, doline)\nBlanc/gris = Normal\n\nCombine z-score (intensité) et\nMoran\'s I (regroupement spatial)',
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'description': 'Détecte uniquement les anomalies statistiquement significatives — filtre le bruit de fond',
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'vmin_mode': 'symmetric', 'sym_pct': (5, 95),
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},
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'wavelet': {
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'cmap': 'cividis',
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'title': 'Ondelette Mexican Hat (CWT multi-échelle)',
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'legend': 'Réponse de la transformée en ondelette à 5 échelles\nÉchelles: 2m, 5m, 10m, 20m, 50m\n\nClair = Structure détectée à cette échelle\nSombre = Pas de structure\n\nOptimisé pour formes circulaires:\ntumulus, enclos, fossés annulaires',
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'description': 'Transformée en ondelette 2D — excellente pour détecter structures circulaires',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'flow': {
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'cmap': 'Blues',
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'title': 'Accumulation de Flux Hydrologique (D8)',
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'legend': 'Logarithme de l\'accumulation d\'eau\nBlanc = Pas de collecte (sommet, crête)\nBleu foncé = Collecte maximale (thalweg, fossé)\n\nSimule l\'écoulement de l\'eau de pluie\nDétecte fossés d\'enceinte, routes et drainage',
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'description': 'Algorithme D8 — simule le cheminement de l\'eau pour détecter fossés et routes antiques',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'percentile', 'vmax_pct': 98,
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},
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}
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# RGB entries (ortho/topo) are handled specially
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RGB_LEGENDS = {
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'ortho': {
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'title': 'Photographie Aérienne IGN',
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'legend': 'Orthophotographie\nImage aérienne',
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'description': 'Photographie aérienne IGN (Orthophoto)',
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},
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'topo': {
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'title': 'Carte Topographique IGN',
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'legend': 'Carte IGN\nPlan topographique',
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'description': 'Carte topographique IGN (Plan IGN)',
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},
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}
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def _apply_colormap(data, tif_file):
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"""Apply the registered colormap normalization to data based on filename.
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Returns (data, cmap, title, legend_label, description, is_rgb).
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"""
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name = str(tif_file).lower()
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# Check for RGB first
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for key in RGB_LEGENDS:
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if key in name:
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info = RGB_LEGENDS[key]
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return data, None, info['title'], info['legend'], info['description'], True
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# Find matching colormap — sort by key length descending so 'mslrm' matches before 'lrm'
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for key in sorted(COLORMAPS.keys(), key=len, reverse=True):
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info = COLORMAPS[key]
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if key in name:
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valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
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valid_data = valid_data[~np.isnan(valid_data)]
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if len(valid_data) == 0:
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logger.warning(f" Aucune donnée valide pour {Path(tif_file).name} — colormap ignorée")
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return data, 'terrain', Path(tif_file).stem.replace('_', ' ').title(), '', '', False
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vmin = vmax = None
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# Compute vmin/vmax based on mode
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if info['vmin_mode'] == 'fixed':
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vmin = info['vmin_val']
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elif info['vmin_mode'] == 'percentile':
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vmin = np.percentile(valid_data, info['vmin_pct'])
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elif info['vmin_mode'] == 'symmetric':
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vmax_abs = max(abs(np.percentile(valid_data, info['sym_pct'][0])),
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abs(np.percentile(valid_data, info['sym_pct'][1])), 0.001)
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vmin = -vmax_abs
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vmax = vmax_abs # symmetric mode sets both vmin and vmax
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if vmax is None:
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# Only compute vmax if not already set by symmetric mode
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if info.get('vmax_mode') == 'fixed':
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vmax = info['vmax_val']
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elif info.get('vmax_mode') == 'percentile':
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vmax = np.percentile(valid_data, info['vmax_pct'])
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elif info.get('vmax_mode') == 'symmetric':
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vmax_abs = max(abs(np.percentile(valid_data, info['sym_pct'][0])),
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abs(np.percentile(valid_data, info['sym_pct'][1])), 0.001)
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vmax = vmax_abs
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# Apply normalization
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if vmin is not None and vmax is not None:
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data = np.clip((data - vmin) / max(vmax - vmin, 0.001), 0, 1)
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legend = info['legend'].format(vmin=vmin or 0, vmax=vmax or 0)
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return data, info['cmap'], info['title'], legend, info['description'], False
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# Default: terrain colormap with percentile stretch
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valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
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valid_data = valid_data[~np.isnan(valid_data)]
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if len(valid_data) == 0:
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return data, 'terrain', Path(tif_file).stem.replace('_', ' ').title(), '', '', False
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p2, p98 = np.percentile(valid_data, (2, 98))
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data = np.clip((data - p2) / (p98 - p2), 0, 1)
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title = Path(tif_file).stem.replace('_', ' ').title()
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return data, 'terrain', title, 'Altitude normalisée', '', False
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def _nice_scale(extent_m):
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"""Choose a nice round scale distance that fits well in the image.
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Returns (scale_m, label) where label is like '100 m' or '500 m' or '1 km'.
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"""
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nice_scales = [50, 100, 200, 500, 1000, 2000, 5000, 10000]
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# Pick the largest scale that is <= 15% of the extent
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for s in nice_scales:
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if s <= extent_m * 0.15:
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continue
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# s is the first one > 15% — take the one below
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break
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else:
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s = nice_scales[-1]
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# Actually pick the largest scale <= 20% of extent
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chosen = nice_scales[0]
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for s in nice_scales:
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if s <= extent_m * 0.20:
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chosen = s
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if chosen >= 1000:
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return chosen, f"{chosen // 1000} km"
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return chosen, f"{chosen} m"
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def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None):
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"""Convert GeoTIFF to visualization WebP with GPS coordinates, legend, and scale bar.
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Args:
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tif_file: Path to input GeoTIFF.
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vis_dir: Output directory for the WebP file.
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resolution: Grid resolution in m/px.
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keep_tif: If True, keep the source TIFF after conversion.
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Returns:
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Path to output WebP file, or None on failure.
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"""
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if not tif_file or not tif_file.exists():
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return None
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webp_file = vis_dir / f"{tif_file.stem}.webp"
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try:
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with rasterio.open(tif_file) as src:
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is_rgb = src.count >= 3 and ('ortho' in str(tif_file) or 'topo' in str(tif_file))
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if is_rgb:
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data = src.read([1, 2, 3])
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data = np.moveaxis(data, 0, -1)
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else:
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data = src.read(1)
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nodata = src.nodata
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transform = src.transform
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crs = src.crs
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if is_rgb:
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height, width, _ = data.shape
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else:
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height, width = data.shape
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top_left_x = transform.c
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top_left_y = transform.f
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pixel_size_x = transform.a
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pixel_size_y = abs(transform.e)
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min_x = top_left_x
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max_x = top_left_x + width * pixel_size_x
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max_y = top_left_y
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min_y = top_left_y - height * pixel_size_y
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# GPS coordinates
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gps_coords = {}
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if HAS_WARP and crs is not None:
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try:
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l93_xs = [min_x, max_x, min_x, max_x]
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l93_ys = [max_y, max_y, min_y, min_y]
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lons, lats = warp_transform(crs, 'EPSG:4326', l93_xs, l93_ys)
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gps_coords = {
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'NW': (lats[0], lons[0]),
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'NE': (lats[1], lons[1]),
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'SW': (lats[2], lons[2]),
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'SE': (lats[3], lons[3]),
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}
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n_ticks = 5
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tick_l93_x = np.linspace(min_x, max_x, n_ticks)
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tick_l93_y_bottom = np.full(n_ticks, min_y)
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tick_lons, tick_lats = warp_transform(crs, 'EPSG:4326', tick_l93_x, tick_l93_y_bottom)
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gps_coords['x_ticks'] = list(zip(tick_lons, tick_lats))
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tick_l93_y = np.linspace(min_y, max_y, n_ticks)
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tick_l93_x_left = np.full(n_ticks, min_x)
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tick_lons_y, tick_lats_y = warp_transform(crs, 'EPSG:4326', tick_l93_x_left, tick_l93_y)
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gps_coords['y_ticks'] = list(zip(tick_lons_y, tick_lats_y))
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except Exception:
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gps_coords = {}
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if nodata is not None and not is_rgb:
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data = np.ma.masked_where((data == nodata) | np.isnan(data), data)
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if not is_rgb:
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valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
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valid_data = valid_data[~np.isnan(valid_data)]
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# Track NaN mask before converting to plain ndarray
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nan_mask = None
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if not is_rgb:
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if isinstance(data, np.ma.MaskedArray):
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nan_mask = data.mask.copy()
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data = np.ma.filled(data, np.nan)
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elif np.any(np.isnan(data)):
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nan_mask = np.isnan(data)
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# For rendering: replace NaN with neutral value to avoid interpolation halos
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if nan_mask is not None and np.any(nan_mask) and len(valid_data) > 0:
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fill_value = float(np.median(valid_data))
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data[nan_mask] = fill_value
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nan_mask = nan_mask # keep for later
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# Apply colormap
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data, cmap, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)
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# Apply NaN mask: make zones without data transparent
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has_nan_mask = nan_mask is not None and not is_rgb_result
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if has_nan_mask:
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# data is normalized 0-1 from _apply_colormap; apply cmap to get RGBA
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# Save the colormap for colorbar before converting to RGBA
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saved_cmap = plt.get_cmap(cmap) if isinstance(cmap, str) else cmap
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saved_vmin = float(np.nanmin(data)) if not nan_mask.all() else 0
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saved_vmax = float(np.nanmax(data)) if not nan_mask.all() else 1
|
||
rgba = saved_cmap(data) # (H, W, 4) float RGBA
|
||
rgba[nan_mask, 3] = 0.0 # transparent where no data
|
||
data = rgba
|
||
is_rgba = True
|
||
else:
|
||
is_rgba = False
|
||
saved_cmap = None
|
||
saved_vmin = None
|
||
saved_vmax = None
|
||
|
||
# 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)
|
||
fig_height = fig_width * 0.7 + 2.0 # Fixed header + footer space
|
||
fig = plt.figure(figsize=(fig_width, fig_height), facecolor='white')
|
||
|
||
# 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')
|
||
else:
|
||
im = ax.imshow(data, cmap=cmap, aspect='equal', origin='upper',
|
||
interpolation='bilinear')
|
||
|
||
ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
|
||
|
||
# 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:
|
||
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:
|
||
x_pixel_positions = np.linspace(0, width - 1, len(gps_coords['x_ticks']))
|
||
x_labels = [f"{lon:.5f}E" for lon, lat in gps_coords['x_ticks']]
|
||
ax.set_xticks(x_pixel_positions)
|
||
ax.set_xticklabels(x_labels, fontsize=7, rotation=30)
|
||
ax.set_xlabel('Longitude', fontsize=9, fontweight='bold')
|
||
|
||
y_pixel_positions = np.linspace(0, height - 1, len(gps_coords['y_ticks']))
|
||
y_labels = [f"{lat:.5f}N" for lon, lat in gps_coords['y_ticks']]
|
||
ax.set_yticks(y_pixel_positions)
|
||
ax.set_yticklabels(y_labels, fontsize=7)
|
||
ax.set_ylabel('Latitude', fontsize=9, fontweight='bold')
|
||
else:
|
||
x_ticks_count = 5
|
||
x_positions = np.linspace(0, width - 1, x_ticks_count)
|
||
x_labels = [f"{(min_x + xp * pixel_size_x)/1000:.1f}" for xp in x_positions]
|
||
ax.set_xticks(x_positions)
|
||
ax.set_xticklabels(x_labels, fontsize=8)
|
||
ax.set_xlabel('Est (km) - Lambert 93', fontsize=9, fontweight='bold')
|
||
|
||
y_ticks_count = 5
|
||
y_positions = np.linspace(0, height - 1, y_ticks_count)
|
||
y_labels = [f"{(max_y - yp * pixel_size_y)/1000:.1f}" for yp in y_positions]
|
||
ax.set_yticks(y_positions)
|
||
ax.set_yticklabels(y_labels, fontsize=8)
|
||
ax.set_ylabel('Nord (km) - Lambert 93', fontsize=9, fontweight='bold')
|
||
|
||
ax.tick_params(axis='both', which='both', direction='out', length=3,
|
||
width=0.8, colors='black')
|
||
for spine in ax.spines.values():
|
||
spine.set_visible(True)
|
||
spine.set_color('black')
|
||
spine.set_linewidth(0.8)
|
||
|
||
# North arrow — compass rose style
|
||
north_ax = inset_axes(ax, width="5%", height="9%", loc='upper right',
|
||
bbox_to_anchor=(-0.03, 0.08, 1, 1), bbox_transform=ax.transAxes)
|
||
north_ax.set_xlim(-1.2, 1.2)
|
||
north_ax.set_ylim(-0.5, 1.5)
|
||
north_ax.axis('off')
|
||
north_ax.set_aspect('equal')
|
||
# N arrow
|
||
north_ax.annotate('N', xy=(0, 1.3), fontsize=11, fontweight='bold',
|
||
ha='center', va='bottom', color='#b22222')
|
||
north_ax.plot([0, 0], [0.0, 1.0], color='#b22222', linewidth=2.0, zorder=10)
|
||
north_ax.add_patch(MplPolygon([[0, 0.3], [-0.2, 0.7], [0, 1.0], [0.2, 0.7]],
|
||
closed=True, facecolor='#b22222', edgecolor='#b22222', zorder=9))
|
||
# Cardinal ticks
|
||
for angle, label in [(90, ''), (0, 'E'), (180, 'O'), (270, 'S')]:
|
||
rad = np.radians(angle)
|
||
r_text = 1.25
|
||
north_ax.plot([0.85*np.cos(rad), 1.05*np.cos(rad)],
|
||
[0.85*np.sin(rad), 1.05*np.sin(rad)],
|
||
color='#555555', linewidth=0.8, zorder=5)
|
||
if label:
|
||
north_ax.text(r_text*np.cos(rad), r_text*np.sin(rad), label,
|
||
fontsize=7, ha='center', va='center', color='#555555')
|
||
|
||
# Bottom info bar — enriched with source, method, date
|
||
info_ax = fig.add_axes([data_left, 0.015, data_width_frac + cbar_width + 0.02, 0.09])
|
||
info_ax.axis('off')
|
||
|
||
extent_km_x = (max_x - min_x) / 1000
|
||
extent_km_y = (max_y - min_y) / 1000
|
||
|
||
if is_rgb:
|
||
alt_min = alt_max = 0
|
||
else:
|
||
alt_min = float(np.nanmin(valid_data)) if len(valid_data) > 0 else 0
|
||
alt_max = float(np.nanmax(valid_data)) if len(valid_data) > 0 else 0
|
||
|
||
# Build info lines
|
||
line1_parts = []
|
||
if gps_coords:
|
||
nw_lat, nw_lon = gps_coords['NW']
|
||
se_lat, se_lon = gps_coords['SE']
|
||
line1_parts.append(f"GPS: {nw_lat:.5f}°N {nw_lon:.5f}°E — {se_lat:.5f}°N {se_lon:.5f}°E")
|
||
else:
|
||
line1_parts.append(f"X: {min_x:.0f}–{max_x:.0f} Y: {min_y:.0f}–{max_y:.0f}")
|
||
line1_parts.append(f"EPSG:2154")
|
||
line1_parts.append(f"Res: {resolution}m/px")
|
||
line1_parts.append(f"Emprise: {extent_km_x:.1f}×{extent_km_y:.1f}km")
|
||
if not is_rgb:
|
||
line1_parts.append(f"Alt: {alt_min:.1f}–{alt_max:.1f}m")
|
||
|
||
line2_parts = []
|
||
line2_parts.append("Source: LiDAR HD IGN")
|
||
if source_info:
|
||
if source_info.get('method'):
|
||
line2_parts.append(f"Classif.: {source_info['method'].upper()}")
|
||
if source_info.get('date'):
|
||
line2_parts.append(f"Date: {source_info['date']}")
|
||
else:
|
||
line2_parts.append(datetime.now().strftime("Date: %Y-%m-%d"))
|
||
|
||
info_text_line1 = " | ".join(line1_parts)
|
||
info_text_line2 = " | ".join(line2_parts)
|
||
|
||
info_ax.text(0.01, 0.7, info_text_line1,
|
||
transform=info_ax.transAxes, fontsize=8,
|
||
verticalalignment='center', family='monospace',
|
||
bbox=dict(boxstyle='round,pad=0.2', facecolor='#f0f0f0',
|
||
edgecolor='#aaaaaa', alpha=0.95))
|
||
info_ax.text(0.01, 0.2, info_text_line2,
|
||
transform=info_ax.transAxes, fontsize=7.5,
|
||
verticalalignment='center', family='monospace',
|
||
color='#444444',
|
||
bbox=dict(boxstyle='round,pad=0.2', facecolor='#f8f8f8',
|
||
edgecolor='#cccccc', alpha=0.9))
|
||
|
||
# Scale bar — adaptive with alternating black/white segments
|
||
extent_m_x = max_x - min_x
|
||
scale_m, scale_label = _nice_scale(extent_m_x)
|
||
pixels_per_meter = 1.0 / pixel_size_x
|
||
scale_px = int(scale_m * pixels_per_meter)
|
||
n_segments = 5
|
||
segment_px = scale_px / n_segments
|
||
bar_bottom_y = 0.55
|
||
bar_top_y = 0.85
|
||
bar_height = bar_top_y - bar_bottom_y
|
||
scale_start_x = 0.80
|
||
|
||
for seg_i in range(n_segments):
|
||
color = 'black' if seg_i % 2 == 0 else 'white'
|
||
seg_left = scale_start_x + seg_i * segment_px / width
|
||
seg_width_frac = segment_px / width
|
||
info_ax.add_patch(RectPatch((seg_left, bar_bottom_y), seg_width_frac, bar_height,
|
||
facecolor=color, edgecolor='black', linewidth=0.5,
|
||
transform=info_ax.transAxes, clip_on=False))
|
||
info_ax.text(scale_start_x + scale_px / (2 * width), bar_top_y + 0.12,
|
||
f"{scale_label}", ha='center', va='bottom', fontsize=8, fontweight='bold',
|
||
transform=info_ax.transAxes)
|
||
# Scale end ticks
|
||
info_ax.plot([scale_start_x, scale_start_x], [bar_bottom_y - 0.05, bar_top_y + 0.05],
|
||
color='black', linewidth=1, transform=info_ax.transAxes, clip_on=False)
|
||
info_ax.plot([scale_start_x + scale_px / width, scale_start_x + scale_px / width],
|
||
[bar_bottom_y - 0.05, bar_top_y + 0.05],
|
||
color='black', linewidth=1, transform=info_ax.transAxes, clip_on=False)
|
||
|
||
fig.patch.set_facecolor('white')
|
||
|
||
# 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, facecolor='white', format='png')
|
||
finally:
|
||
plt.close()
|
||
|
||
img = PILImage.open(str(png_temp))
|
||
img.save(str(webp_file), format='WEBP', lossless=True)
|
||
png_temp.unlink(missing_ok=True)
|
||
|
||
# Delete source TIFF (unless --keep-tif)
|
||
if not keep_tif:
|
||
tif_file.unlink(missing_ok=True)
|
||
|
||
return webp_file
|
||
|
||
except Exception as e:
|
||
logger.error(f" Erreur conversion WebP: {e}", exc_info=True)
|
||
return None
|
||
|
||
|
||
|
||
def generate_pdf_report(basename, vis_dir, pdf_dir, resolution):
|
||
"""Generate A3 PDF report for a LiDAR file with all visualizations.
|
||
|
||
Page 1: Mise en situation (ortho + topo IGN side by side)
|
||
Pages 2+: Other visualizations (2 per page)
|
||
|
||
Args:
|
||
basename: Base name for the report file.
|
||
vis_dir: Directory containing WebP visualization files.
|
||
pdf_dir: Directory for output PDF.
|
||
resolution: Grid resolution (used in info text).
|
||
|
||
Returns:
|
||
Path to PDF file, or None on failure.
|
||
"""
|
||
from matplotlib.backends.backend_pdf import PdfPages
|
||
|
||
pdf_file = pdf_dir / f"{basename}_rapport.pdf"
|
||
logger.info(f" → Génération rapport PDF A3: {pdf_file.name}")
|
||
t0 = time.time()
|
||
|
||
# Look for WebPs in per-file subdirectory first, then fallback to main dir
|
||
file_vis_dir = vis_dir / basename
|
||
if file_vis_dir.exists():
|
||
png_files = sorted(file_vis_dir.glob("*.webp"))
|
||
else:
|
||
png_files = sorted(vis_dir.glob(f"{basename}_*.webp"))
|
||
if not png_files:
|
||
logger.warning(f" ✗ Aucune image trouvée pour {basename}")
|
||
return None
|
||
|
||
# Categorize
|
||
situ_files = []
|
||
analysis_files = []
|
||
|
||
for f in png_files:
|
||
name = f.stem.lower()
|
||
if 'ortho' in name:
|
||
situ_files.insert(0, f)
|
||
elif 'topo' in name:
|
||
situ_files.append(f)
|
||
else:
|
||
analysis_files.append(f)
|
||
|
||
# Sort analysis files by archaeological priority
|
||
order = ['mslrm', 'svf', 'negative_openness',
|
||
'positive_openness', 'sailore', 'hillshade_multi',
|
||
'lrm', 'tpi', 'slope', 'curvature', 'aspect',
|
||
'roughness', 'anomalies', 'wavelet', 'flow']
|
||
|
||
def sort_key(f):
|
||
name = f.stem.lower()
|
||
for i, key in enumerate(order):
|
||
if key in name:
|
||
return i
|
||
return len(order)
|
||
|
||
analysis_files.sort(key=sort_key)
|
||
|
||
a3_w, a3_h = 16.54, 11.69
|
||
|
||
try:
|
||
with PdfPages(str(pdf_file)) as pdf:
|
||
# Page 1: Mise en situation
|
||
if situ_files:
|
||
fig = plt.figure(figsize=(a3_w, a3_h), facecolor='white')
|
||
n_situ = len(situ_files)
|
||
if n_situ == 2:
|
||
gs = fig.add_gridspec(1, 2, wspace=0.05, left=0.03, right=0.97,
|
||
top=0.92, bottom=0.06)
|
||
else:
|
||
gs = fig.add_gridspec(1, max(n_situ, 1), wspace=0.05,
|
||
left=0.03, right=0.97, top=0.92, bottom=0.06)
|
||
|
||
fig.text(0.5, 0.97, f"Mise en situation - {basename}",
|
||
fontsize=20, fontweight='bold', ha='center', va='top')
|
||
|
||
for i, f in enumerate(situ_files):
|
||
ax = fig.add_subplot(gs[0, i])
|
||
img = np.array(PILImage.open(str(f)).convert('RGB'))
|
||
ax.imshow(img)
|
||
ax.axis('off')
|
||
title = f.stem.replace(basename + '_', '').replace('_', ' ').title()
|
||
ax.set_title(title, fontsize=12, fontweight='bold', pad=5)
|
||
|
||
pdf.savefig(fig, dpi=150)
|
||
plt.close(fig)
|
||
|
||
# Pages 2+: Analysis maps (2 per page)
|
||
for page_start in range(0, len(analysis_files), 2):
|
||
page_files = analysis_files[page_start:page_start + 2]
|
||
|
||
fig = plt.figure(figsize=(a3_w, a3_h), facecolor='white')
|
||
|
||
if len(page_files) == 2:
|
||
gs = fig.add_gridspec(1, 2, wspace=0.08, left=0.03, right=0.97,
|
||
top=0.93, bottom=0.05)
|
||
else:
|
||
gs = fig.add_gridspec(1, 1, left=0.05, right=0.95,
|
||
top=0.93, bottom=0.05)
|
||
|
||
for i, f in enumerate(page_files):
|
||
ax = fig.add_subplot(gs[0, i])
|
||
img = np.array(PILImage.open(str(f)).convert('RGB'))
|
||
ax.imshow(img)
|
||
ax.axis('off')
|
||
title = f.stem.replace(basename + '_', '').replace('_', ' ').title()
|
||
ax.set_title(title, fontsize=11, fontweight='bold', pad=3)
|
||
|
||
page_num = (page_start // 2) + 2
|
||
fig.text(0.99, 0.01, f"Page {page_num}", fontsize=8,
|
||
ha='right', va='bottom', color='gray')
|
||
|
||
pdf.savefig(fig, dpi=150)
|
||
plt.close(fig)
|
||
|
||
logger.info(f" ✓ Rapport PDF terminé ({time.time()-t0:.1f}s)")
|
||
return pdf_file
|
||
|
||
except Exception as e:
|
||
logger.error(f" ✗ Erreur PDF: {e}", exc_info=True)
|
||
return None |