Layout uniforme WebP: axes fixes + aspect='equal' pour superposition géolocalisée
- Positions d'axes fixes (data_left/bottom/width/height_frac) pour alignement pixel-parfait entre terrain et ortho/topo - aspect='equal' au lieu de 'auto' pour conserver les proportions géographiques - Colorbar descriptive pour les visualisations RGB (ortho/topo) - Comblage des petits trous DTM (< 1m) via rasterio.fill.fillnodata - Suppression de la visualisation "dépressions" - Hillshade composite: 0.7*hillshade + 0.3*cos(slope) - D8 flow accumulation accéléré par numba JIT (fallback Python) - Flag --keep-tif pour conserver les TIFF intermédiaires - --force supprime aussi les TIF existants avant régénération - ETA affiché pendant la génération des visualisations - Répertoires temp dans temp/ pour traitement parallèle Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -24,7 +24,6 @@ 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.gridspec import GridSpec
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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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@ -119,14 +118,6 @@ COLORMAPS = {
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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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'depressions': {
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'cmap': 'YlOrRd',
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'title': 'Dépressions (Remplissage hydrologique)',
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'legend': 'Profondeur des cuvettes détectées (m)\nTransparent = Pas de dépression\nJaune = Dépression légère | Rouge = Dépression profonde\nMax: {vmax:.2f}m',
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'description': 'Simule le remplissage d\'eau — détecte dolines, sinkholes, cuvettes et zones inondables',
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'vmin_mode': 'fixed', 'vmin_val': 0,
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'vmax_mode': 'percentile', 'vmax_pct': 99,
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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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@ -194,8 +185,9 @@ def _apply_colormap(data, tif_file):
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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
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for key, info in COLORMAPS.items():
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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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@ -246,13 +238,14 @@ def _apply_colormap(data, tif_file):
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return data, 'terrain', title, 'Altitude normalisée', '', False
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def tif_to_png(tif_file, vis_dir, resolution):
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def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False):
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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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@ -359,18 +352,21 @@ def tif_to_png(tif_file, vis_dir, resolution):
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saved_vmin = None
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saved_vmax = None
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# Create figure — adapt width to data resolution for sharp rendering
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# At high res (5000+px wide), we need a larger figure to avoid downsampling artifacts
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# Create figure with FIXED layout for consistent data area position
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# All visualizations use the same axes positions so they can be overlaid
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fig_width = max(20, width / 150)
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fig_width = min(fig_width, 40) # cap at 40 inches
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map_aspect = height / width
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fig = plt.figure(figsize=(fig_width, fig_width * map_aspect * 0.7 + 2.5),
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facecolor='white')
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gs = GridSpec(2, 1, height_ratios=[1.0, 0.06],
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hspace=0.04, figure=fig,
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left=0.06, right=0.88, top=0.93, bottom=0.08)
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fig_width = min(fig_width, 40)
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fig_height = fig_width * 0.7 + 2.0 # Fixed header + footer space
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fig = plt.figure(figsize=(fig_width, fig_height), facecolor='white')
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ax = fig.add_subplot(gs[0])
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# Fixed data area position — identical for ALL visualization types
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# This ensures overlay/superposition works across all WebP images
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data_left = 0.08
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data_bottom = 0.12
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data_width_frac = 0.74
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data_height_frac = 0.78
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ax = fig.add_axes([data_left, data_bottom, data_width_frac, data_height_frac])
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if is_rgba or is_rgb:
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im = ax.imshow(data, aspect='equal', origin='upper',
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interpolation='bilinear')
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@ -380,22 +376,34 @@ def tif_to_png(tif_file, vis_dir, resolution):
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ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
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if not is_rgb:
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if is_rgba and saved_cmap is not None:
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# Create a ScalarMappable for the colorbar from the saved colormap
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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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cbar = plt.colorbar(sm, ax=ax, pad=0.02, shrink=0.85, aspect=30)
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else:
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cbar = plt.colorbar(im, ax=ax, pad=0.02, shrink=0.85, aspect=30)
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# Colorbar/legend area — always at the same position for consistent layout
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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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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.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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fontsize=9, fontweight='bold', rotation=90,
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verticalalignment='center', horizontalalignment='center',
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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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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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cbar = plt.colorbar(sm, 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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cbar.set_label(legend_label, fontsize=10, fontweight='bold')
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else:
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ax.text(1.02, 0.5, legend_label, transform=ax.transAxes,
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fontsize=10, fontweight='bold', rotation=90,
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verticalalignment='center', horizontalalignment='left')
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cbar_ax = fig.add_axes([cbar_left, data_bottom, cbar_width, data_height_frac])
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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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cbar.set_label(legend_label, fontsize=10, fontweight='bold')
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# GPS coordinate ticks
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if gps_coords and 'x_ticks' in gps_coords:
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@ -444,8 +452,8 @@ def tif_to_png(tif_file, vis_dir, resolution):
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north_ax.text(0.5, 0.95, 'N', ha='center', va='top',
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fontsize=13, fontweight='bold', color='black', zorder=11)
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# Bottom info bar
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info_ax = fig.add_subplot(gs[1])
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# Bottom info bar — fixed position
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info_ax = fig.add_axes([data_left, 0.02, data_width_frac + cbar_width + 0.02, 0.07])
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info_ax.axis('off')
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extent_km_x = (max_x - min_x) / 1000
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@ -496,12 +504,11 @@ def tif_to_png(tif_file, vis_dir, resolution):
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fig.patch.set_facecolor('white')
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# Save as PNG then convert to WebP — use higher DPI for large data
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# Save as PNG then convert to WebP — fixed layout, no bbox_inches='tight'
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save_dpi = 200 if width > 3000 else 150
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png_temp = vis_dir / f"{tif_file.stem}_temp.png"
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try:
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plt.savefig(png_temp, dpi=save_dpi, bbox_inches='tight', pad_inches=0.15,
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facecolor='white', format='png')
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plt.savefig(png_temp, dpi=save_dpi, facecolor='white', format='png')
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finally:
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plt.close()
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@ -509,8 +516,9 @@ def tif_to_png(tif_file, vis_dir, resolution):
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img.save(str(webp_file), format='WEBP', lossless=True)
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png_temp.unlink(missing_ok=True)
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# Delete source TIFF
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tif_file.unlink(missing_ok=True)
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# Delete source TIFF (unless --keep-tif)
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if not keep_tif:
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tif_file.unlink(missing_ok=True)
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return webp_file
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@ -565,7 +573,7 @@ def generate_pdf_report(basename, vis_dir, pdf_dir, resolution):
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# Sort analysis files by archaeological priority
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order = ['mslrm', 'svf', 'negative_openness',
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'positive_openness', 'sailore', 'depressions', 'hillshade_multi',
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'positive_openness', 'sailore', 'hillshade_multi',
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'lrm', 'tpi', 'slope', 'curvature', 'aspect',
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'roughness', 'anomalies', 'wavelet', 'flow']
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