ed3e90ea89
- Web map: multi-select picker in the generation bar (aspect, slope, positive openness, anisotropic openness, wavelet) passed to the API; the layer panel is restricted to the same shortlist (PANEL_VIZ) and a refresh button rebuilds the index when new layers appear on disk; jobs started outside the UI are now adopted into the visible queue - Uniform colors across tiles: openness/anisotropic/sailore now store local z-scores, and all renderers use fixed ranges (0-3 sigma, SVF 0-1 physical, slope 0-30 deg) instead of per-tile percentile stretches - Ray-tracing falls back to CPU when VRAM is exhausted so openness and SVF no longer fail silently on shared GPUs - build_index merges visualizations available at only one resolution into the displayed tile so in-progress layers stay visible - 11 new tests (142 passing)
984 lines
46 KiB
Python
984 lines
46 KiB
Python
"""Rendering module: colormap registry, GeoTIFF-to-image 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/AVIF 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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||
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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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|
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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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||
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# Cache for IGN location map tiles (avoid re-downloading for each visualization)
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_location_map_cache = {}
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||
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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, FancyBboxPatch
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from matplotlib.colors import ListedColormap
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from matplotlib.ticker import ScalarFormatter
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try:
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from cmcrameri import cm as cmc
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HAS_CMCRAmeri = True
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except ImportError:
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HAS_CMCRAmeri = False
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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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# Simplified France outline in Lambert 93 (EPSG:2154)
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# Used for location inset map on each visualization
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# ============================================================
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_FRANCE_OUTLINE_L93 = np.array([
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[109000, 6385000], [134000, 6410000], [153000, 6430000], [173000, 6445000],
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[200000, 6460000], [250000, 6475000], [300000, 6490000], [350000, 6500000],
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||
[400000, 6505000], [450000, 6510000], [500000, 6510000], [550000, 6510000],
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[600000, 6505000], [650000, 6500000], [700000, 6495000], [750000, 6485000],
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[800000, 6470000], [840000, 6460000], [880000, 6450000], [920000, 6435000],
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[950000, 6425000], [980000, 6415000], [1010000, 6405000], [1040000, 6395000],
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[1060000, 6385000], [1080000, 6370000], [1100000, 6355000], [1120000, 6340000],
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[1140000, 6320000], [1160000, 6300000], [1175000, 6280000], [1185000, 6260000],
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[1190000, 6240000], [1195000, 6220000], [1198000, 6200000], [1196000, 6180000],
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[1192000, 6160000], [1185000, 6140000], [1175000, 6120000], [1160000, 6100000],
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[1140000, 6085000], [1120000, 6070000], [1095000, 6060000], [1070000, 6050000],
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[1040000, 6040000], [1000000, 6035000], [950000, 6035000], [900000, 6035000],
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[850000, 6040000], [800000, 6045000], [750000, 6050000], [700000, 6055000],
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[650000, 6060000], [600000, 6065000], [550000, 6070000], [500000, 6075000],
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[450000, 6080000], [400000, 6085000], [350000, 6095000], [300000, 6110000],
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[250000, 6125000], [200000, 6145000], [160000, 6170000], [130000, 6200000],
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[110000, 6230000], [100000, 6260000], [95000, 6290000], [100000, 6310000],
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[105000, 6340000], [109000, 6385000],
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])
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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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# === Famille RELIEF : rouge=surélévation, bleu=dépression ===
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# Diverging: rouge vif=positif, bleu vif=négatif, blanc=plat
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'mslrm': {
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'cmap': 'seismic',
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'title': 'MSRM - Multi-Scale Relief Model (échelles adaptatives)',
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'legend': 'Relief combiné multi-échelles (σ locales, échelle fixe ±3σ)\nRouge = Surélévation (mur, tumulus, levée)\nBleu = Dépression (fossé, douve)\n\nÉchelles : 2 à 200m, pondérées 5–20m\nCouleurs homogènes entre tuiles\nDétecte du micro au macro',
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'description': 'Combine LRM à 5–7 échelles — détecte structures de 5m à 100m simultanément',
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'vmin_mode': 'fixed', 'vmin_val': -3,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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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 (σ locales, échelle fixe ±3σ)\nRouge = Surélévation | Bleu = Dépression\nCouleurs homogènes entre tuiles\n\nNoyau adapté à la pente locale\nPlat = grand noyau (25m) | Pente = petit noyau (2m)',
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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': 'fixed', 'vmin_val': -3,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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},
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'aniso_open': {
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'cmap': 'seismic',
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'title': 'Openness Anisotropique (pondération directionnelle)',
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'legend': 'Openness positive − négative pondérée (z-score local)\nRouge = Surélévation dominante (mur, levée)\nBleu = Dépression dominante (fossé, doline)\nÉchelle fixe ±3σ — couleurs homogènes entre tuiles\nPondère les directions NW-SE et NE-SW davantage',
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'description': 'Openness avec pondération anisotropique — détecte mieux les structures alignées NW-SE et NE-SW',
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'vmin_mode': 'fixed', 'vmin_val': -3,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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},
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# === Famille OUVERTURE : séquentiel, valeurs normalisées ===
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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 ciel (z-score local)\nClair = Vue dégagée vers le ciel (sommets, plateaux)\nSombre = Vue bloquée (vallées encaissées)\nÉchelle fixe ±3σ — couleurs homogènes entre tuiles',
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'description': 'Ray-tracing 8 directions, multi-rayon — détecte crêtes et sommets',
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'vmin_mode': 'fixed', 'vmin_val': -3,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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},
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'negative_openness': {
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'cmap': 'PuBu',
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'title': 'Openness Negative (ouverture vers le bas)',
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'legend': 'Angle d\'ouverture vers le bas (z-score local)\nClair = Surplomb (bords de fossé, grottes)\nSombre = Terrain plat (fonds de vallée)\nÉchelle fixe ±3σ — couleurs homogènes entre tuiles\nMeilleur détecteur de cavités et dolines',
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'description': 'Ray-tracing 8 directions, multi-rayon — détecte fossés, dolines, souterrains',
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'vmin_mode': 'fixed', 'vmin_val': -3,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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},
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'svf': {
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'cmap': 'hot_r',
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'title': 'Sky-View Factor (fraction de ciel visible)',
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'legend': 'Proportion de ciel visible (échelle physique fixe 0–1)\nBlanc/jaune = Ciel masqué (vallée, fossé, tranchée)\nNoir = Ciel dégagé (sommet, plateau)\nCouleurs homogènes entre tuiles\nLes fossés ressortent en vif — excellent pour structures linéaires',
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'description': 'Détection de micro-relief — fossés en jaune/blanc, levées en sombre',
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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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# === Famille SCALAIRE : propriétés non divergentes ===
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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 8 directions (échelle fixe 0–1)\nBlanc = Face éclairée | Noir = Zone d\'ombre\nCouleurs homogènes entre tuiles',
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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\nÉchelle fixe 0–30° — couleurs homogènes entre tuiles\nJaune = Forte pente | Violet foncé = Terrain plat',
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'description': 'Murs, talus et bords ressortent en jaune — terrain plat en sombre',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'fixed', 'vmax_val': 30,
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},
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'aspect': {
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||
'cmap': 'twilight',
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'title': 'Aspect (Direction des pentes)',
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'legend': 'Direction vers laquelle le terrain descend\nCycle continu : Nord→Est→Sud→Ouest→Nord\nCouleurs perceptuellement uniformes (pas de saut de teinte)',
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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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'roughness': {
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'cmap': 'plasma',
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'title': 'Rugosité Multi-Échelle (3m + 15m)',
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'legend': 'Irrégularité du terrain combinée fine + large\nViolet foncé = Surface lisse (route, mur, sol plat)\nJaune vif = Surface rugueuse (végétation, ruines, pierres)\nCombine rugosité fine 3m (70%) + large 15m (30%)',
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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': 98,
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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 RMS multi-échelles (σ locales)\nÉchelles adaptées à la résolution\n\nClair = Structure détectée à cette échelle\nSombre = Pas de structure\nÉchelle fixe 0–3σ — couleurs homogènes entre tuiles\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': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'fixed', 'vmax_val': 3,
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},
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'flow_acc': {
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'cmap': 'YlGn',
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'title': 'Accumulation d\'Écoulement (Flow Accumulation)',
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'legend': 'Log10 du nombre de cellules amont\nVert foncé = Forte accumulation (fossé, chenal, drainage)\nJaune = Faible accumulation (terrain plat)\n\nDétection fossés et linéaires hydrologiques',
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'description': 'Priority-flood + D8 — détecte fossés archéologiques et drainages',
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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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||
'anomaly': {
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'cmap': 'YlOrRd',
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'title': 'Carte d\'Anomalies (détection automatique)',
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'legend': 'Score d\'anomalie composite (0–1)\nRouge = Haute anomalie (structures suspectes)\nJaune = Anomalie modérée\nBlanc = Aucun signal (terrain naturel)\n\nSeuil auto: pixels > 2σ de la moyenne locale\nCombiné: MSRM, SVF, Ondelette, Openness, Rugosité',
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'description': 'Détection automatique — cible à vérifier sur terrain',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'fixed', 'vmax_val': 1,
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||
},
|
||
'solar': {
|
||
'cmap': 'gray',
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||
'title': 'Éclairage Solaire',
|
||
'legend': "Illumination solaire (azimut 90°, altitude 30°)\nClair = Face éclairée | Sombre = Zone d'ombre",
|
||
'description': "Simulation de l'éclairage solaire matinal",
|
||
'vmin_mode': 'fixed', 'vmin_val': 0,
|
||
'vmax_mode': 'fixed', 'vmax_val': 1,
|
||
},
|
||
}
|
||
|
||
# RGB entries (ortho/topo) are handled specially
|
||
RGB_LEGENDS = {
|
||
'ortho': {
|
||
'title': 'Photographie Aérienne IGN',
|
||
'legend': 'Orthophotographie\nImage aérienne',
|
||
'description': 'Photographie aérienne IGN (Orthophoto)',
|
||
},
|
||
'topo': {
|
||
'title': 'Carte Topographique IGN',
|
||
'legend': 'Carte IGN\nPlan topographique',
|
||
'description': 'Carte topographique IGN (Plan IGN)',
|
||
},
|
||
}
|
||
|
||
|
||
def _apply_colormap(data, tif_file):
|
||
"""Apply the registered colormap normalization to data based on filename.
|
||
|
||
Returns (data, cmap, title, legend_label, description, is_rgb).
|
||
"""
|
||
name = str(tif_file).lower()
|
||
|
||
# Check for RGB first
|
||
for key in RGB_LEGENDS:
|
||
if key in name:
|
||
info = RGB_LEGENDS[key]
|
||
return data, None, info['title'], info['legend'], info['description'], True
|
||
|
||
# Find matching colormap — sort by key length descending so 'mslrm' matches before 'lrm'
|
||
for key in sorted(COLORMAPS.keys(), key=len, reverse=True):
|
||
info = COLORMAPS[key]
|
||
if key in name:
|
||
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
|
||
valid_data = valid_data[~np.isnan(valid_data)]
|
||
|
||
if len(valid_data) == 0:
|
||
logger.warning(f" Aucune donnée valide pour {Path(tif_file).name} — colormap ignorée")
|
||
return data, 'terrain', Path(tif_file).stem.replace('_', ' ').title(), '', '', False
|
||
|
||
vmin = vmax = None
|
||
|
||
# Compute vmin/vmax based on mode
|
||
if info['vmin_mode'] == 'fixed':
|
||
vmin = info['vmin_val']
|
||
elif info['vmin_mode'] == 'percentile':
|
||
vmin = np.percentile(valid_data, info['vmin_pct'])
|
||
elif info['vmin_mode'] == 'symmetric':
|
||
vmax_abs = max(abs(np.percentile(valid_data, info['sym_pct'][0])),
|
||
abs(np.percentile(valid_data, info['sym_pct'][1])), 0.001)
|
||
vmin = -vmax_abs
|
||
vmax = vmax_abs # symmetric mode sets both vmin and vmax
|
||
|
||
if vmax is None:
|
||
# Only compute vmax if not already set by symmetric mode
|
||
if info.get('vmax_mode') == 'fixed':
|
||
vmax = info['vmax_val']
|
||
elif info.get('vmax_mode') == 'percentile':
|
||
vmax = np.percentile(valid_data, info['vmax_pct'])
|
||
elif info.get('vmax_mode') == 'symmetric':
|
||
vmax_abs = max(abs(np.percentile(valid_data, info['sym_pct'][0])),
|
||
abs(np.percentile(valid_data, info['sym_pct'][1])), 0.001)
|
||
vmax = vmax_abs
|
||
|
||
# Apply normalization
|
||
if vmin is not None and vmax is not None:
|
||
data = np.clip((data - vmin) / max(vmax - vmin, 0.001), 0, 1)
|
||
|
||
legend = info['legend'].format(vmin=vmin or 0, vmax=vmax or 0)
|
||
return data, info['cmap'], info['title'], legend, info['description'], False
|
||
|
||
# Default: terrain colormap with percentile stretch
|
||
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
|
||
valid_data = valid_data[~np.isnan(valid_data)]
|
||
if len(valid_data) == 0:
|
||
return data, 'terrain', Path(tif_file).stem.replace('_', ' ').title(), '', '', False
|
||
p2, p98 = np.percentile(valid_data, (2, 98))
|
||
data = np.clip((data - p2) / (p98 - p2), 0, 1)
|
||
title = Path(tif_file).stem.replace('_', ' ').title()
|
||
return data, 'terrain', title, 'Altitude normalisée', '', False
|
||
|
||
|
||
def _download_location_map(min_x, max_x, min_y, max_y):
|
||
"""Download a wide-area IGN topographic map for location context.
|
||
|
||
Downloads a zoomed-out IGN PLANIGNV2 tile covering ~30km around the
|
||
processed zone, giving regional context. Results are cached to
|
||
avoid re-downloading for each visualization in the same tile.
|
||
|
||
Args:
|
||
min_x, max_x, min_y, max_y: DTM bounds in Lambert 93.
|
||
|
||
Returns:
|
||
Tuple (image_array, bounds_dict) where bounds_dict has keys
|
||
'min_x', 'max_x', 'min_y', 'max_y' in Lambert 93, or None on failure.
|
||
"""
|
||
import hashlib
|
||
|
||
# Cache key based on rounded coordinates (1km grid)
|
||
cache_key = (round(min_x, -3), round(max_x, -3), round(min_y, -3), round(max_y, -3))
|
||
if cache_key in _location_map_cache:
|
||
return _location_map_cache[cache_key]
|
||
|
||
from .ign import download_ign_tiles, _optimal_zoom_level
|
||
|
||
if not HAS_WARP:
|
||
return None
|
||
|
||
try:
|
||
# Compute center coordinates for zoom calculation
|
||
center_x = (min_x + max_x) / 2
|
||
center_y = (min_y + max_y) / 2
|
||
clons, clats = warp_transform('EPSG:2154', 'EPSG:4326', [center_x], [center_y])
|
||
center_lat = clats[0]
|
||
center_lon = clons[0]
|
||
|
||
# Use zoom 10 for context (~150m/px — wide view, fast download)
|
||
context_zoom = 10
|
||
|
||
# Expand bounds to ~30km for regional context
|
||
# Large enough to see surrounding towns/rivers, small enough that
|
||
# the processed zone (typically 1km) is clearly visible as a red rectangle
|
||
context_half = 15000 # 15km each side = 30km total
|
||
context_min_x = center_x - context_half
|
||
context_max_x = center_x + context_half
|
||
context_min_y = center_y - context_half
|
||
context_max_y = center_y + context_half
|
||
|
||
result = download_ign_tiles(
|
||
context_min_x, context_max_x, context_min_y, context_max_y,
|
||
layer='GEOGRAPHICALGRIDSYSTEMS.PLANIGNV2',
|
||
zoom_level=context_zoom,
|
||
min_zoom=8
|
||
)
|
||
|
||
if result is not None:
|
||
bounds = {
|
||
'min_x': context_min_x, 'max_x': context_max_x,
|
||
'min_y': context_min_y, 'max_y': context_max_y,
|
||
}
|
||
cached = (result, bounds)
|
||
_location_map_cache[cache_key] = cached
|
||
return cached
|
||
|
||
return result
|
||
except Exception as e:
|
||
logger.debug(f" Carte de localisation IGN non disponible: {e}")
|
||
return None
|
||
|
||
|
||
def _nice_scale(extent_m):
|
||
"""Choose a nice round scale distance that fits well in the image.
|
||
|
||
Returns (scale_m, label) where label is like '100 m' or '500 m' or '1 km'.
|
||
"""
|
||
nice_scales = [10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000]
|
||
# Pick the largest scale that fits within 20% of extent
|
||
max_scale = extent_m * 0.20
|
||
chosen = nice_scales[0]
|
||
for s in nice_scales:
|
||
if s <= max_scale:
|
||
chosen = s
|
||
else:
|
||
break
|
||
if chosen >= 1000:
|
||
return chosen, f"{chosen // 1000} km"
|
||
return chosen, f"{chosen} m"
|
||
|
||
|
||
def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None, quality=98, output_format='avif'):
|
||
"""Convert GeoTIFF to visualization image (WebP or AVIF) with GPS coordinates, legend, and scale bar.
|
||
|
||
Args:
|
||
tif_file: Path to input GeoTIFF.
|
||
vis_dir: Output directory for the image file.
|
||
resolution: Grid resolution in m/px.
|
||
keep_tif: If True, keep the source TIFF after conversion.
|
||
source_info: Dict with method/date/basename for metadata.
|
||
quality: Image quality (1-100). Use 100 for lossless. Default 95.
|
||
output_format: Output format ('webp' or 'avif'). Default 'webp'.
|
||
|
||
Returns:
|
||
Path to output image file, or None on failure.
|
||
"""
|
||
if not tif_file or not tif_file.exists():
|
||
return None
|
||
|
||
ext = 'avif' if output_format == 'avif' else 'webp'
|
||
output_file = vis_dir / f"{tif_file.stem}.{ext}"
|
||
|
||
try:
|
||
with rasterio.open(tif_file) as src:
|
||
is_rgb = src.count >= 3 and any(k in str(tif_file) for k in ('ortho', 'topo'))
|
||
|
||
if is_rgb:
|
||
data = src.read([1, 2, 3])
|
||
data = np.moveaxis(data, 0, -1)
|
||
else:
|
||
data = src.read(1)
|
||
|
||
nodata = src.nodata
|
||
transform = src.transform
|
||
crs = src.crs
|
||
|
||
if is_rgb:
|
||
height, width, _ = data.shape
|
||
else:
|
||
height, width = data.shape
|
||
|
||
top_left_x = transform.c
|
||
top_left_y = transform.f
|
||
pixel_size_x = transform.a
|
||
pixel_size_y = abs(transform.e)
|
||
|
||
min_x = top_left_x
|
||
max_x = top_left_x + width * pixel_size_x
|
||
max_y = top_left_y
|
||
min_y = top_left_y - height * pixel_size_y
|
||
|
||
# GPS coordinates
|
||
gps_coords = {}
|
||
if HAS_WARP and crs is not None:
|
||
try:
|
||
l93_xs = [min_x, max_x, min_x, max_x]
|
||
l93_ys = [max_y, max_y, min_y, min_y]
|
||
lons, lats = warp_transform(crs, 'EPSG:4326', l93_xs, l93_ys)
|
||
gps_coords = {
|
||
'NW': (lats[0], lons[0]),
|
||
'NE': (lats[1], lons[1]),
|
||
'SW': (lats[2], lons[2]),
|
||
'SE': (lats[3], lons[3]),
|
||
}
|
||
n_ticks = 5
|
||
tick_l93_x = np.linspace(min_x, max_x, n_ticks)
|
||
tick_l93_y_bottom = np.full(n_ticks, min_y)
|
||
tick_lons, tick_lats = warp_transform(crs, 'EPSG:4326', tick_l93_x, tick_l93_y_bottom)
|
||
gps_coords['x_ticks'] = list(zip(tick_lons, tick_lats))
|
||
tick_l93_y = np.linspace(min_y, max_y, n_ticks)
|
||
tick_l93_x_left = np.full(n_ticks, min_x)
|
||
tick_lons_y, tick_lats_y = warp_transform(crs, 'EPSG:4326', tick_l93_x_left, tick_l93_y)
|
||
gps_coords['y_ticks'] = list(zip(tick_lons_y, tick_lats_y))
|
||
except Exception:
|
||
gps_coords = {}
|
||
|
||
if nodata is not None and not is_rgb:
|
||
data = np.ma.masked_where((data == nodata) | np.isnan(data), data)
|
||
|
||
if not is_rgb:
|
||
valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
|
||
valid_data = valid_data[~np.isnan(valid_data)]
|
||
|
||
# Track NaN mask before converting to plain ndarray
|
||
nan_mask = None
|
||
if not is_rgb:
|
||
if isinstance(data, np.ma.MaskedArray):
|
||
nan_mask = data.mask.copy()
|
||
data = np.ma.filled(data, np.nan)
|
||
elif np.any(np.isnan(data)):
|
||
nan_mask = np.isnan(data)
|
||
|
||
# For rendering: replace NaN with neutral value to avoid interpolation halos
|
||
if nan_mask is not None and np.any(nan_mask) and len(valid_data) > 0:
|
||
fill_value = float(np.median(valid_data))
|
||
data[nan_mask] = fill_value
|
||
nan_mask = nan_mask # keep for later
|
||
|
||
# Apply colormap
|
||
data, cmap, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)
|
||
|
||
# Apply NaN mask: make zones without data transparent
|
||
has_nan_mask = nan_mask is not None and not is_rgb_result
|
||
if has_nan_mask:
|
||
# data is normalized 0-1 from _apply_colormap; apply cmap to get RGBA
|
||
# Save the colormap for colorbar before converting to RGBA
|
||
saved_cmap = plt.get_cmap(cmap) if isinstance(cmap, str) else cmap
|
||
saved_vmin = float(np.nanmin(data)) if not nan_mask.all() else 0
|
||
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 = 0.19
|
||
data_width_frac = 0.74
|
||
data_height_frac = 0.71
|
||
|
||
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}", fontsize=14, fontweight='bold', pad=10)
|
||
if description:
|
||
ax.text(0.5, 1.04, description, transform=ax.transAxes,
|
||
fontsize=10, fontstyle='italic', color='#555555',
|
||
ha='center', va='bottom')
|
||
|
||
# Colorbar/legend area — full height alongside data
|
||
cbar_left = data_left + data_width_frac + 0.02
|
||
cbar_width = 0.04
|
||
cbar_bottom = data_bottom
|
||
cbar_height = data_height_frac
|
||
if is_rgb:
|
||
# RGB: descriptive text label instead of gradient colorbar
|
||
cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
|
||
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, cbar_bottom, cbar_width, cbar_height])
|
||
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.ax.yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
|
||
cbar.outline.set_linewidth(1.5)
|
||
cbar.set_label(legend_label, fontsize=10, fontweight='bold')
|
||
else:
|
||
cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
|
||
cbar = plt.colorbar(im, cax=cbar_ax)
|
||
cbar.ax.tick_params(labelsize=9, width=1.5)
|
||
cbar.ax.yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
|
||
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 in bottom-right corner of data area
|
||
# Semi-transparent background for readability over any data
|
||
north_ax = fig.add_axes([data_left + data_width_frac - 0.07,
|
||
data_bottom + 0.01,
|
||
0.06, 0.14],
|
||
facecolor='none')
|
||
north_ax.set_xlim(-1.5, 1.5)
|
||
north_ax.set_ylim(-1.5, 1.5)
|
||
north_ax.axis('off')
|
||
north_ax.set_aspect('equal')
|
||
# Compass rose centered at (0, 0) — all 4 cardinals equidistant from center
|
||
# Semi-transparent white background circle
|
||
circle_bg = plt.Circle((0, 0), 1.0, facecolor='white', edgecolor='#888888',
|
||
linewidth=0.5, alpha=0.7, zorder=1)
|
||
north_ax.add_patch(circle_bg)
|
||
# N arrow (pointing up = North)
|
||
north_ax.annotate('N', xy=(0, 1.35), fontsize=9, fontweight='bold',
|
||
ha='center', va='bottom', color='#b22222', zorder=10)
|
||
north_ax.plot([0, 0], [-0.5, 1.0], color='#b22222', linewidth=2.0, zorder=10)
|
||
north_ax.add_patch(MplPolygon([[0, 0.5], [-0.2, 0.7], [0, 1.0], [0.2, 0.7]],
|
||
closed=True, facecolor='#b22222', edgecolor='#b22222', zorder=9))
|
||
# Cardinal ticks — all centered at (0, 0)
|
||
for angle, label in [(90, 'N'), (0, 'E'), (180, 'O'), (270, 'S')]:
|
||
rad = np.radians(angle)
|
||
north_ax.plot([1.0*np.cos(rad), 1.2*np.cos(rad)],
|
||
[1.0*np.sin(rad), 1.2*np.sin(rad)],
|
||
color='#555555', linewidth=0.8, zorder=5)
|
||
if label:
|
||
north_ax.text(1.35*np.cos(rad), 1.35*np.sin(rad), label,
|
||
fontsize=6, ha='center', va='center', color='#555555', zorder=5)
|
||
|
||
# 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")
|
||
# Round resolution to avoid ugly decimals like 0.499999959
|
||
res_display = round(resolution, 2) if resolution < 1 else round(resolution, 1)
|
||
line1_parts.append(f"Res: {res_display}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
|
||
# Position: left of the location map to avoid overlap
|
||
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
|
||
# Place scale bar so it ends before the location map (map starts at x=0.82 in fig coords)
|
||
# map_ax occupies [0.82, 0.02, 0.16, 0.13] in figure coords
|
||
# info_ax occupies [data_left, 0.015, width, 0.09]
|
||
# Scale bar end in fig coords = info_ax.left + (scale_start_x + scale_px/width) * info_ax.width
|
||
# We need: info_ax.left + (scale_start_x + scale_px/width) * info_ax.width < 0.80
|
||
scale_end_frac = scale_px / width # fraction of info_ax width
|
||
info_ax_width = data_width_frac + cbar_width + 0.02
|
||
# Calculate scale_start_x so scale bar ends at fig_x = 0.78 (leaving gap before map at 0.82)
|
||
max_scale_end_fig = 0.78
|
||
scale_end_in_info = (max_scale_end_fig - data_left) / info_ax_width
|
||
scale_start_x = max(0.05, scale_end_in_info - scale_end_frac)
|
||
|
||
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)
|
||
|
||
# Location inset map — IGN topographic background with processed zone marker
|
||
# Positioned in lower-right corner, above the info bar
|
||
map_ax = fig.add_axes([0.82, 0.02, 0.16, 0.13])
|
||
|
||
# Try to download a wide-area IGN topo map for location context
|
||
location_result = _download_location_map(min_x, max_x, min_y, max_y)
|
||
if location_result is not None:
|
||
location_map, loc_bounds = location_result
|
||
# Draw IGN topo map as background with correct bounds
|
||
map_ax.imshow(location_map, aspect='equal', extent=[
|
||
loc_bounds['min_x'], loc_bounds['max_x'],
|
||
loc_bounds['min_y'], loc_bounds['max_y']
|
||
])
|
||
# Mark the processed zone with a red rectangle
|
||
rect_x1, rect_x2 = min_x, max_x
|
||
rect_y1, rect_y2 = min_y, max_y
|
||
map_ax.add_patch(RectPatch((rect_x1, rect_y1),
|
||
rect_x2 - rect_x1, rect_y2 - rect_y1,
|
||
facecolor='#ff3333', edgecolor='#cc0000',
|
||
linewidth=1.5, alpha=0.6, zorder=5))
|
||
else:
|
||
# Fallback: simplified France outline
|
||
map_ax.set_facecolor('#e8e8e8')
|
||
france = _FRANCE_OUTLINE_L93
|
||
map_ax.fill(france[:, 0] / 1000, france[:, 1] / 1000,
|
||
facecolor='#f5f0e6', edgecolor='#888888', linewidth=0.8)
|
||
rect_x1, rect_x2 = min_x / 1000, max_x / 1000
|
||
rect_y1, rect_y2 = min_y / 1000, max_y / 1000
|
||
map_ax.add_patch(RectPatch((rect_x1, rect_y1),
|
||
rect_x2 - rect_x1, rect_y2 - rect_y1,
|
||
facecolor='#ff3333', edgecolor='#cc0000',
|
||
linewidth=1.2, alpha=0.7, zorder=5))
|
||
map_ax.set_xlim(france[:, 0].min() / 1000 - 50, france[:, 0].max() / 1000 + 50)
|
||
map_ax.set_ylim(france[:, 1].min() / 1000 - 50, france[:, 1].max() / 1000 + 50)
|
||
|
||
map_ax.set_aspect('equal')
|
||
map_ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)
|
||
for spine in map_ax.spines.values():
|
||
spine.set_edgecolor('#aaaaaa')
|
||
spine.set_linewidth(0.5)
|
||
# Label with coordinates
|
||
if gps_coords:
|
||
nw_lat, nw_lon = gps_coords['NW']
|
||
se_lat, se_lon = gps_coords['SE']
|
||
map_ax.set_title(f"{nw_lat:.2f}°N {nw_lon:.2f}°E",
|
||
fontsize=6, pad=1, color='#333333')
|
||
else:
|
||
map_ax.set_title(f"X:{min_x/1000:.0f} Y:{min_y/1000:.0f} km L93",
|
||
fontsize=6, pad=1, color='#333333')
|
||
|
||
fig.patch.set_facecolor('white')
|
||
|
||
# Save figure to in-memory buffer (avoids disk I/O of temp PNG)
|
||
save_dpi = 200 if width > 3000 else 150
|
||
from io import BytesIO
|
||
buf = BytesIO()
|
||
try:
|
||
plt.savefig(buf, dpi=save_dpi, facecolor='white', format='png')
|
||
finally:
|
||
plt.close()
|
||
buf.seek(0)
|
||
|
||
img = PILImage.open(buf)
|
||
pil_format = 'AVIF' if output_format == 'avif' else 'WEBP'
|
||
if quality >= 100:
|
||
img.save(str(output_file), format=pil_format, lossless=True)
|
||
else:
|
||
img.save(str(output_file), format=pil_format, quality=quality)
|
||
|
||
# Delete source TIFF (unless --keep-tif)
|
||
if not keep_tif:
|
||
tif_file.unlink(missing_ok=True)
|
||
|
||
return output_file
|
||
|
||
except Exception as e:
|
||
logger.error(f" Erreur conversion {ext.upper()}: {e}", exc_info=True)
|
||
return None
|
||
|
||
|
||
def tif_to_crop(tif_file, vis_dir, resolution, keep_tif=False, quality=98, output_format='avif'):
|
||
"""Convert GeoTIFF to a cropped visualization image (no legend, no overlay).
|
||
|
||
Applies colormap and saves the image as a pure 1×1 km square.
|
||
Used for grid/map display where images must tile seamlessly.
|
||
|
||
Args:
|
||
tif_file: Path to input GeoTIFF.
|
||
vis_dir: Output directory for the image file.
|
||
resolution: Grid resolution in m/px.
|
||
keep_tif: If True, keep the source TIFF after conversion.
|
||
quality: Image quality (1-100). Use 100 for lossless.
|
||
output_format: Output format ('webp' or 'avif').
|
||
|
||
Returns:
|
||
Path to output image file, or None on failure.
|
||
"""
|
||
if not tif_file or not tif_file.exists():
|
||
return None
|
||
|
||
ext = 'avif' if output_format == 'avif' else 'webp'
|
||
output_file = vis_dir / f"{tif_file.stem}.{ext}"
|
||
|
||
try:
|
||
with rasterio.open(tif_file) as src:
|
||
is_rgb = src.count >= 3 and any(k in str(tif_file) for k in ('ortho', 'topo'))
|
||
|
||
if is_rgb:
|
||
data = src.read([1, 2, 3])
|
||
data = np.moveaxis(data, 0, -1)
|
||
else:
|
||
data = src.read(1)
|
||
|
||
# Apply colormap normalization
|
||
data, cmap_name, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)
|
||
|
||
# Convert to RGB using colormap
|
||
if is_rgb_result:
|
||
# RGB images are already in RGB (uint8 depuis le TIF IGN, ou float 0-1)
|
||
if data.dtype == np.uint8:
|
||
rgb_data = data
|
||
else:
|
||
rgb_data = (np.clip(data, 0, 1) * 255).astype(np.uint8)
|
||
else:
|
||
# Normalize data to 0-1 range for colormap
|
||
cmap = plt.get_cmap(cmap_name)
|
||
rgb_float = cmap(data.clip(0, 1))
|
||
rgb_data = (rgb_float[:, :, :3] * 255).astype(np.uint8)
|
||
|
||
# Save as AVIF/WebP
|
||
img = PILImage.fromarray(rgb_data)
|
||
pil_format = 'AVIF' if output_format == 'avif' else 'WEBP'
|
||
if quality >= 100:
|
||
img.save(str(output_file), format=pil_format, lossless=True)
|
||
else:
|
||
img.save(str(output_file), format=pil_format, quality=quality)
|
||
|
||
# Delete source TIFF (unless --keep-tif)
|
||
if not keep_tif:
|
||
tif_file.unlink(missing_ok=True)
|
||
|
||
return output_file
|
||
|
||
except Exception as e:
|
||
logger.error(f" Erreur conversion crop {ext.upper()}: {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 images in per-file subdirectory first, then fallback to main dir
|
||
file_vis_dir = vis_dir / basename
|
||
png_files = []
|
||
if file_vis_dir.exists():
|
||
png_files = sorted(file_vis_dir.glob("*.avif")) + sorted(file_vis_dir.glob("*.webp"))
|
||
else:
|
||
png_files = sorted(vis_dir.glob(f"{basename}_*.avif")) + sorted(vis_dir.glob(f"{basename}_*.webp"))
|
||
# Deduplicate in case both formats exist
|
||
seen = set()
|
||
unique_files = []
|
||
for f in png_files:
|
||
if f not in seen:
|
||
seen.add(f)
|
||
unique_files.append(f)
|
||
if not unique_files:
|
||
logger.warning(f" ✗ Aucune image trouvée pour {basename}")
|
||
return None
|
||
png_files = unique_files
|
||
|
||
# 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', 'aniso_open', 'sailore', 'hillshade_multi',
|
||
'flow_acc', 'solar', 'slope', 'roughness', 'wavelet']
|
||
|
||
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 |