Fix corrupted COPC detection, add CSF→SMRF fallback, improve MSRM colormap, add SVF and anisotropic openness
- validate_laz: verify point data accessibility (not just headers) to detect corrupted COPC files that pass header checks but fail on data reads - classify_ground: fallback from CSF to SMRF when CSF produces no ground points or PDAL errors (fixes 2/9 failing tiles) - MSRM: preserve sign in weighted combination so RdBu_r colormap shows both red (elevated) and blue (depressed) instead of red only - Add Sky-View Factor (SVF) visualization: cos²(horizon angle) over 16 directions, excellent for archaeological earthwork detection - Add Anisotropic Openness: directional weighting (NW-SE/NE-SW) enhances linear feature detection aligned with common settlement patterns - Remove anomalies and flow visualizations (replaced by SVF + aniso_open) - Location inset: use IGN topographic map at zoom 10 instead of simplified France outline, with red rectangle marker and fallback - Remove flow (hydrological accumulation) from VIZ_STEPS
This commit is contained in:
Antoine Jacquin
@ -21,12 +21,14 @@ try:
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except ImportError:
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HAS_WARP = False
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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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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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from matplotlib.patches import Polygon as MplPolygon, Rectangle as RectPatch, FancyBboxPatch
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rcParams['figure.dpi'] = 150
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rcParams['savefig.dpi'] = 300
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@ -35,6 +37,32 @@ 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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@ -126,12 +154,20 @@ COLORMAPS = {
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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 (MSRM multi-échelle + 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 MSRM normalisé (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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'svf': {
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'cmap': 'gray_r',
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'title': 'Sky-View Factor (fraction de ciel visible)',
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'legend': 'Proportion de ciel visible depuis chaque point\nBlanc = Ciel dégagé (sommet, plateau, levée)\nNoir = Ciel masqué (vallée, fossé, tranchée)\nMoyenne de cos²(angle horizon) sur 16 directions',
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'description': 'Détection de micro-relief — fossés sombres, levées claires, complémentaire de l\'openness',
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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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'aniso_open': {
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'cmap': 'RdBu_r',
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'title': 'Openness Anisotropique (pondération directionnelle)',
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'legend': 'Openness positive - négative pondérée (degrés)\nRouge = Surélévation dominante (mur, levée)\nBleu = Dépression dominante (fossé, doline)\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': 'symmetric', 'sym_pct': (2, 98),
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},
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'wavelet': {
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'cmap': 'cividis',
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@ -231,6 +267,66 @@ def _apply_colormap(data, tif_file):
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return data, 'terrain', title, 'Altitude normalisée', '', False
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def _download_location_map(min_x, max_x, min_y, max_y):
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"""Download a wide-area IGN topographic map for location context.
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Downloads a zoomed-out IGN PLANIGNV2 tile covering 5-10x the processed
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zone extent, giving a wider geographic context. Results are cached to
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avoid re-downloading for each visualization in the same tile.
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Args:
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min_x, max_x, min_y, max_y: DTM bounds in Lambert 93.
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Returns:
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numpy array (H, W, 3) uint8, or None on failure.
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"""
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import hashlib
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# Cache key based on rounded coordinates (1km grid)
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cache_key = (round(min_x, -3), round(max_x, -3), round(min_y, -3), round(max_y, -3))
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if cache_key in _location_map_cache:
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return _location_map_cache[cache_key]
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from .ign import download_ign_tiles, _optimal_zoom_level
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if not HAS_WARP:
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return None
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try:
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# Compute center coordinates for zoom calculation
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center_x = (min_x + max_x) / 2
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center_y = (min_y + max_y) / 2
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clons, clats = warp_transform('EPSG:2154', 'EPSG:4326', [center_x], [center_y])
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center_lat = clats[0]
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center_lon = clons[0]
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# Use a much lower zoom level for context (wider view)
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# Zoom 10 gives ~150km per 256px tile — perfect for a small location map
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context_zoom = 10
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# Expand bounds by 3x in each direction for wider context
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extent_x = max_x - min_x
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extent_y = max_y - min_y
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context_min_x = center_x - extent_x * 2
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context_max_x = center_x + extent_x * 2
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context_min_y = center_y - extent_y * 2
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context_max_y = center_y + extent_y * 2
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result = download_ign_tiles(
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context_min_x, context_max_x, context_min_y, context_max_y,
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layer='GEOGRAPHICALGRIDSYSTEMS.PLANIGNV2',
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zoom_level=context_zoom
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)
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if result is not None:
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_location_map_cache[cache_key] = result
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return result
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except Exception as e:
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logger.debug(f" Carte de localisation IGN non disponible: {e}")
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return None
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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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@ -397,12 +493,16 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
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# Colorbar/legend area — always at the same position for consistent layout
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# Colorbar/legend area — reduced height to leave room for compass rose above
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cbar_left = data_left + data_width_frac + 0.02
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cbar_width = 0.04
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compass_height = 0.07
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compass_gap = 0.02
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cbar_bottom = data_bottom
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cbar_height = data_height_frac - compass_height - compass_gap
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if is_rgb:
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# RGB: descriptive text label instead of gradient colorbar
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cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
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cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
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cbar_ax.set_xticks([])
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cbar_ax.set_yticks([])
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cbar_ax.text(0.5, 0.5, legend_label, transform=cbar_ax.transAxes,
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@ -411,7 +511,7 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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wrap=True)
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cbar_ax.set_frame_on(False)
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elif is_rgba and saved_cmap is not None:
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cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
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cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
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sm = plt.cm.ScalarMappable(cmap=saved_cmap,
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norm=plt.Normalize(vmin=saved_vmin, vmax=saved_vmax))
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sm.set_array([])
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@ -420,7 +520,7 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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cbar.outline.set_linewidth(1.5)
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cbar.set_label(legend_label, fontsize=10, fontweight='bold')
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else:
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cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
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cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
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cbar = plt.colorbar(im, cax=cbar_ax)
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cbar.ax.tick_params(labelsize=9, width=1.5)
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cbar.outline.set_linewidth(1.5)
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@ -461,13 +561,16 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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spine.set_color('black')
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spine.set_linewidth(0.8)
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# North arrow — compass rose style
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north_ax = inset_axes(ax, width="5%", height="9%", loc='upper right',
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bbox_to_anchor=(-0.03, 0.08, 1, 1), bbox_transform=ax.transAxes)
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# North arrow — compass rose style, positioned above the colorbar
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compass_bottom = data_bottom + data_height_frac + 0.02
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compass_height = 0.07
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compass_width = cbar_width + 0.03
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north_ax = fig.add_axes([cbar_left, compass_bottom, compass_width, compass_height])
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north_ax.set_xlim(-1.2, 1.2)
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north_ax.set_ylim(-0.5, 1.5)
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north_ax.axis('off')
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north_ax.set_aspect('equal')
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north_ax.set_facecolor('white')
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# N arrow
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north_ax.annotate('N', xy=(0, 1.3), fontsize=11, fontweight='bold',
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ha='center', va='bottom', color='#b22222')
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@ -566,6 +669,54 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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[bar_bottom_y - 0.05, bar_top_y + 0.05],
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color='black', linewidth=1, transform=info_ax.transAxes, clip_on=False)
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# Location inset map — IGN topographic background with processed zone marker
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map_ax = fig.add_axes([0.84, 0.02, 0.14, 0.12])
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# Try to download a wide-area IGN topo map for location context
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location_map = _download_location_map(min_x, max_x, min_y, max_y)
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if location_map is not None:
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# Draw IGN topo map as background
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map_ax.imshow(location_map, aspect='auto', extent=[
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min_x - (max_x - min_x) * 2, max_x + (max_x - min_x) * 2,
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min_y - (max_y - min_y) * 2, max_y + (max_y - min_y) * 2
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])
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# Mark the processed zone with a red rectangle
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rect_x1, rect_x2 = min_x, max_x
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rect_y1, rect_y2 = min_y, max_y
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map_ax.add_patch(RectPatch((rect_x1, rect_y1),
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rect_x2 - rect_x1, rect_y2 - rect_y1,
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facecolor='#ff3333', edgecolor='#cc0000',
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linewidth=1.5, alpha=0.6, zorder=5))
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else:
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# Fallback: simplified France outline
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map_ax.set_facecolor('#e8e8e8')
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france = _FRANCE_OUTLINE_L93
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map_ax.fill(france[:, 0] / 1000, france[:, 1] / 1000,
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facecolor='#f5f0e6', edgecolor='#888888', linewidth=0.8)
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rect_x1, rect_x2 = min_x / 1000, max_x / 1000
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rect_y1, rect_y2 = min_y / 1000, max_y / 1000
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map_ax.add_patch(RectPatch((rect_x1, rect_y1),
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rect_x2 - rect_x1, rect_y2 - rect_y1,
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facecolor='#ff3333', edgecolor='#cc0000',
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linewidth=1.2, alpha=0.7, zorder=5))
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map_ax.set_xlim(france[:, 0].min() / 1000 - 50, france[:, 0].max() / 1000 + 50)
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map_ax.set_ylim(france[:, 1].min() / 1000 - 50, france[:, 1].max() / 1000 + 50)
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map_ax.set_aspect('equal')
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map_ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)
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for spine in map_ax.spines.values():
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spine.set_edgecolor('#aaaaaa')
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spine.set_linewidth(0.5)
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# Label with coordinates
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if gps_coords:
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nw_lat, nw_lon = gps_coords['NW']
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se_lat, se_lon = gps_coords['SE']
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map_ax.set_title(f"{nw_lat:.2f}°N {nw_lon:.2f}°E",
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fontsize=6, pad=1, color='#333333')
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else:
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map_ax.set_title(f"X:{min_x/1000:.0f} Y:{min_y/1000:.0f} km L93",
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fontsize=6, pad=1, color='#333333')
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fig.patch.set_facecolor('white')
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# Save as PNG then convert to final format — fixed layout, no bbox_inches='tight'
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