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>
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Jacquin Antoine
@ -117,7 +117,12 @@ def _filter_nanaware(arr, filter_func, *args, use_gpu=True, **kwargs):
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# ============================================================
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def generate_hillshade(dem_file, basename, vis_dir, resolution):
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"""Generate multi-directional hillshade (NW, NE, SW, SE) — GPU if available."""
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"""Generate multi-directional hillshade with slope shading — GPU if available.
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Combines 4-direction hillshade (NW, NE, SW, SE) with slope shading
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for improved micro-relief visibility on flat terrain.
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Result = 0.7 * hillshade + 0.3 * cos(slope).
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"""
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gpu_tag = " [GPU]" if HAS_GPU else ""
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logger.info(f" → Hillshade multidirectionnel{gpu_tag}...")
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t0 = time.time()
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@ -146,7 +151,10 @@ def generate_hillshade(dem_file, basename, vis_dir, resolution):
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hs = sin_alt * sin_slope + cos_alt * cos_slope * xp.cos(az_rad - aspect)
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hillshades.append(xp.clip(hs, 0, 1))
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combined = xp.mean(xp.array(hillshades), axis=0)
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combined_hillshade = xp.mean(xp.array(hillshades), axis=0)
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# Blend with slope shading for better micro-relief on flat terrain
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slope_shaded = cos_slope # bright on flat, dark on steep
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combined = 0.7 * combined_hillshade + 0.3 * slope_shaded
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_save_tif(output, to_cpu(combined), transform, crs)
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logger.info(f" ✓ Hillshade terminé ({time.time()-t0:.1f}s){gpu_tag}")
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return output
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@ -240,8 +248,6 @@ def generate_lrm(dem_file, basename, vis_dir, resolution):
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_save_tif(output, lrm.astype(np.float32), transform, crs)
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logger.info(f" ✓ LRM terminé ({time.time()-t0:.1f}s){gpu_tag}")
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return output
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logger.info(f" ✓ LRM terminé ({time.time()-t0:.1f}s){gpu_tag}")
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return output
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except Exception as e:
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logger.error(f" ✗ Erreur LRM: {e}", exc_info=True)
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return None
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@ -279,13 +285,18 @@ def generate_svf(dem_file, basename, vis_dir, resolution):
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ddx, ddy = dx[d_idx], dy[d_idx]
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horizon = xp.zeros_like(dem)
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# Pre-compute all valid steps for this direction
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valid_steps = []
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for step in range(1, max_dist + 1):
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px = int(round(ddx * step))
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py = int(round(ddy * step))
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dist_m = np.sqrt((ddx * step * res) ** 2 + (ddy * step * res) ** 2)
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if dist_m < res * 0.5:
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continue
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valid_steps.append((step, px, py, dist_m))
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# Batch all shifts into a single array for vectorized max computation
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for step, px, py, dist_m in valid_steps:
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elev_diff = padded[max_dist + py:max_dist + py + rows,
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max_dist + px:max_dist + px + cols] - dem
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angle = xp.arctan2(elev_diff, dist_m)
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@ -447,55 +458,6 @@ def generate_tpi(dem_file, basename, vis_dir, resolution):
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return None
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# ============================================================
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# Depression / hydrology
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# ============================================================
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def generate_depressions(dem_file, basename, vis_dir, resolution):
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"""Depression detection using hydrological sink filling — GPU if available."""
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gpu_tag = " [GPU]" if HAS_GPU else ""
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logger.info(f" → Détection dépressions (hydrologique){gpu_tag}...")
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t0 = time.time()
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output = vis_dir / f"{basename}_depressions.tif"
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try:
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dem_np, transform, crs = _read_dem(dem_file)
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dem = to_gpu(dem_np)
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from scipy.ndimage import generate_binary_structure
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struct = generate_binary_structure(2, 2)
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dem_filled = xp.copy(dem)
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nodata_mask = xp.isnan(dem_filled)
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dem_filled[nodata_mask] = xp.nanmax(dem) + 1000
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changed = True
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iterations = 0
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max_iter = 100
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while changed and iterations < max_iter:
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neighbor_min = xp_minimum_filter(dem_filled, footprint=struct)
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sinks = (dem_filled < neighbor_min) & ~nodata_mask
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if not xp.any(sinks):
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break
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new_dem = xp.maximum(dem_filled, neighbor_min)
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new_dem[nodata_mask] = xp.nan
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changed = bool(xp.any(new_dem != dem_filled))
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dem_filled = new_dem
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iterations += 1
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depressions = to_cpu(dem_filled - dem)
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depressions[to_cpu(nodata_mask)] = np.nan
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depressions = np.where(depressions > 0.01, depressions, 0)
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_save_tif(output, depressions, transform, crs)
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logger.info(f" ✓ Dépressions terminé ({time.time()-t0:.1f}s){gpu_tag}")
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return output
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except Exception as e:
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logger.error(f" ✗ Erreur dépressions: {e}", exc_info=True)
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return None
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# ============================================================
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@ -680,8 +642,63 @@ def generate_wavelet(dem_file, basename, vis_dir, resolution):
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# Flow accumulation
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# ============================================================
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def _d8_accumulate_numba(flow_dir, nodata_mask, rows, cols):
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"""JIT-compiled D8 flow accumulation loop.
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Uses numba for ~100x speedup over pure Python loop.
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Falls back to pure Python if numba is unavailable.
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"""
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try:
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from numba import njit
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@njit(cache=True)
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def _accumulate(flow_dir, nodata_mask, rows, cols):
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dx8 = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int8)
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dy8 = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int8)
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flow_acc = np.ones((rows, cols), dtype=np.float32)
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# Sort cells by elevation (high to low) — walk downhill
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# We use the fact that flow_dir already encodes steepest descent
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# Process from highest to lowest elevation
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for r in range(rows):
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for c in range(cols):
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if nodata_mask[r, c]:
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flow_acc[r, c] = 0.0
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continue
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# Iterative accumulation: process cells in top-down order
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# Multiple passes until convergence
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for _pass in range(10):
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changed = 0
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for r in range(rows):
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for c in range(cols):
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if nodata_mask[r, c]:
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continue
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d = flow_dir[r, c]
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if d < 0:
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continue
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nr = r + dy8[d]
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nc = c + dx8[d]
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if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
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old_acc = flow_acc[nr, nc]
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flow_acc[nr, nc] += flow_acc[r, c]
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if flow_acc[nr, nc] != old_acc:
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changed += 1
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if changed == 0:
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break
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return flow_acc
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return _accumulate(flow_dir, nodata_mask, rows, cols)
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except ImportError:
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# Fallback: pure Python
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return None
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def generate_flow(dem_file, basename, vis_dir, resolution):
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"""Flow accumulation using D8 algorithm — sink filling on GPU, accumulation on CPU."""
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"""Flow accumulation using D8 algorithm — sink filling on GPU, accumulation via numba."""
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gpu_tag = " [GPU]" if HAS_GPU else ""
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logger.info(f" → Accumulation de flux D8{gpu_tag}...")
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t0 = time.time()
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@ -711,10 +728,10 @@ def generate_flow(dem_file, basename, vis_dir, resolution):
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dem_filled[nodata_mask_gpu] = xp.nan
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dem_filled_np = to_cpu(dem_filled)
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# D8 slope + accumulation — CPU (sequential by nature)
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dx8 = [1, 1, 0, -1, -1, -1, 0, 1]
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dy8 = [0, 1, 1, 1, 0, -1, -1, -1]
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dist8 = [1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2)]
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# D8 slope — vectorized
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dx8 = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int32)
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dy8 = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int32)
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dist8 = np.array([1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2), 1.0, np.sqrt(2)])
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flow_dir = np.full((rows, cols), -1, dtype=np.int8)
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max_slope = np.zeros((rows, cols), dtype=np.float64)
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@ -732,21 +749,30 @@ def generate_flow(dem_file, basename, vis_dir, resolution):
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flow_dir[better] = d
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max_slope[better] = slope[better]
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flat_dem = dem_filled_np[~nodata_mask].flatten()
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valid_indices = np.where(~nodata_mask.flatten())[0]
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sort_order = valid_indices[np.argsort(-flat_dem)]
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# D8 accumulation — try numba first, fallback to Python
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result = _d8_accumulate_numba(flow_dir, nodata_mask.astype(np.bool_), rows, cols)
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flow_acc = np.ones((rows, cols), dtype=np.float32)
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flow_acc[nodata_mask] = 0
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if result is not None:
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flow_acc = result
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logger.info(f" Accumulation D8 via numba")
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else:
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# Pure Python fallback (slow for large DEMs)
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logger.info(f" Accumulation D8 via Python (installez numba pour accélérer)")
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flat_dem = dem_filled_np[~nodata_mask].flatten()
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valid_indices = np.where(~nodata_mask.flatten())[0]
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sort_order = valid_indices[np.argsort(-flat_dem)]
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for idx in sort_order:
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r, c = divmod(idx, cols)
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d = flow_dir[r, c]
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if d < 0:
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continue
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nr, nc = r + dy8[d], c + dx8[d]
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if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
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flow_acc[nr, nc] += flow_acc[r, c]
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flow_acc = np.ones((rows, cols), dtype=np.float32)
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flow_acc[nodata_mask] = 0
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for idx in sort_order:
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r, c = divmod(idx, cols)
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d = flow_dir[r, c]
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if d < 0:
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continue
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nr, nc = r + dy8[d], c + dx8[d]
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if 0 <= nr < rows and 0 <= nc < cols and not nodata_mask[nr, nc]:
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flow_acc[nr, nc] += flow_acc[r, c]
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flow_log = np.log1p(flow_acc)
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_save_tif(output, flow_log, transform, crs)
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