Add anomaly mask: automatic threshold detection across all viz layers
This commit is contained in:
Antoine Jacquin
@ -61,9 +61,10 @@ from .visualizations import (
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generate_openness,
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generate_mslrm, generate_sailore,
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generate_roughness, generate_wavelet,
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generate_svf, generate_aniso_open,
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generate_flow_accumulation,
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)
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generate_svf, generate_aniso_open,
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generate_flow_accumulation,
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generate_anomaly_mask,
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)
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from .gpu import gpu_cleanup, num_gpus, restrict_gpus, safe_gpu_call
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from .ign import generate_ign_overlay
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from .rendering import tif_to_png
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@ -84,6 +85,7 @@ VIZ_STEPS = [
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('roughness', generate_roughness),
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('wavelet', generate_wavelet),
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('flow_acc', generate_flow_accumulation),
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('anomaly', generate_anomaly_mask),
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('ortho', lambda d, b, v, r: generate_ign_overlay(
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d, b, v, r,
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layer='ORTHOIMAGERY.ORTHOPHOTOS',
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@ -168,6 +168,14 @@ COLORMAPS = {
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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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},
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}
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# RGB entries (ortho/topo) are handled specially
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@ -1112,3 +1112,146 @@ def generate_aniso_open(dem_file, basename, vis_dir, resolution, shared=None):
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except Exception as e:
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logger.error(f" ✗ Erreur openness anisotropique: {e}", exc_info=True)
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return None
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# ============================================================
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# Anomaly Mask — automatic threshold detection
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# ============================================================
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def generate_anomaly_mask(dem_file, basename, vis_dir, resolution, shared=None, n_sigma=2.0):
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"""Composite anomaly mask — automatic threshold detection (GPU if available).
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Reads pre-computed visualization layers (MSRM, SVF, Wavelet, Openness Neg,
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Roughness), normalizes each to z-scores, and combines them into a composite
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anomaly score. Pixels beyond `n_sigma` standard deviations of the local mean
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are flagged as suspicious.
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The output is a continuous score (0–1) where:
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- 0 = no anomaly (flat/natural terrain)
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- 1 = high anomaly (potential archaeological structure)
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This mask is directly usable in GIS for polygon extraction and field survey
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planning.
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Args:
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n_sigma: Number of standard deviations for the anomaly threshold.
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Lower = more sensitive (more false positives).
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Default 2.0 (good balance for archaeological detection).
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"""
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gpu_tag = " [GPU]" if _gpu_mod.HAS_GPU else ""
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logger.info(f" → Détection automatique d'anomalies (seuil {n_sigma}σ){gpu_tag}...")
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t0 = time.time()
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output = vis_dir / f"{basename}_anomaly.tif"
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try:
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if shared:
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transform = shared.transform
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crs = shared.crs
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nan_mask = shared.nan_mask
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else:
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dem_np, transform, crs = _read_dem(dem_file)
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nan_mask = np.isnan(dem_np)
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rows, cols = nan_mask.shape
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# Collect available visualization layers from disk
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# Each is loaded, normalized to z-score, and contributes to the composite
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layer_configs = [
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# (filename_pattern, weight)
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("mslrm", 2.5), # Multi-scale relief — strongest signal
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("negative_openness", 1.8), # Fossés, dolines
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("roughness", 1.5), # Surface irregularity
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("wavelet", 1.5), # Circular + linear structures
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("svf", 1.3), # Sky-view depressions
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("flow_acc", 1.2), # Drainage channels / ditches
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("aniso_open", 1.0), # Anisotropic structures
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("positive_openness", 0.8), # Surélevations
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]
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layers = []
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for pattern, weight in layer_configs:
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layer_path = vis_dir / f"{basename}_{pattern}.tif"
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if not layer_path.exists():
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layer_path = vis_dir / f"{basename}_negative_openness.tif" if "neg" in pattern else None
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if layer_path is None or not layer_path.exists():
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continue
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try:
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with rasterio.open(layer_path) as src:
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data = src.read(1).astype(np.float64)
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# Z-score normalization
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valid = data[~nan_mask]
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if len(valid) == 0:
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continue
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mean_val = np.nanmean(valid)
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std_val = max(np.nanstd(valid), 0.01)
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zscore = (data - mean_val) / std_val
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zscore[nan_mask] = 0.0
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layers.append((zscore, weight))
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except Exception as e:
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logger.debug(f" Couche {pattern} non disponible: {e}")
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continue
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if not layers:
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# Fallback: use MSRM + roughness computed on the fly
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logger.info(" Aucune couche trouvée — calcul MSRM + rugosité en direct...")
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dem_np_safe = shared.dem_np if shared else dem_np
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# Quick MSRM (single scale 10m for speed)
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sigma_px = max(5, 10.0 / resolution)
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local_mean = _filter_nanaware_from_filled(shared, xp_gaussian_filter, sigma=sigma_px) if shared else \
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_filter_nanaware(dem_np_safe, xp_gaussian_filter, sigma=sigma_px)
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quick_mslrm = dem_np_safe - local_mean
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quick_mslrm[nan_mask] = np.nan
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valid = quick_mslrm[~nan_mask]
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std_val = max(np.nanstd(valid), 0.01) if len(valid) > 0 else 0.01
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quick_mslrm = quick_mslrm / std_val
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layers.append((quick_mslrm, 2.5))
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# Quick roughness
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fine_size = max(3, int(3 / resolution))
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if fine_size % 2 == 0:
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fine_size += 1
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if shared:
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fine_mean = _filter_nanaware_from_filled(shared, xp_uniform_filter, size=fine_size)
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fine_mean_sq = _filter_nanaware(shared.filled.astype(np.float64)**2, xp_uniform_filter, size=fine_size)
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else:
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fine_mean = _filter_nanaware(dem_np_safe.astype(np.float64), xp_uniform_filter, size=fine_size)
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fine_mean_sq = _filter_nanaware(dem_np_safe.astype(np.float64)**2, xp_uniform_filter, size=fine_size)
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roughness = np.sqrt(np.maximum(fine_mean_sq - fine_mean * fine_mean, 0))
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roughness[nan_mask] = np.nan
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valid_r = roughness[~nan_mask]
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std_val_r = max(np.nanstd(valid_r), 0.01) if len(valid_r) > 0 else 0.01
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roughness = roughness / std_val_r
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layers.append((roughness, 1.5))
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# Weighted RMS combination (unsigned — all deviations are suspicious)
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combined = np.zeros((rows, cols), dtype=np.float64)
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total_weight = 0.0
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for layer_data, weight in layers:
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combined += (layer_data ** 2) * weight
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total_weight += weight
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if total_weight > 0:
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combined = np.sqrt(combined / total_weight)
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# Apply n_sigma threshold: pixels below n_sigma are suppressed
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combined = np.maximum(combined - n_sigma, 0.0)
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# Rescale to 0–1 for visualization (percentile-based stretch)
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valid_combined = combined[~nan_mask]
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if len(valid_combined) > 0 and np.nanmax(valid_combined) > 0:
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p99 = np.percentile(valid_combined, 99)
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if p99 > 0:
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combined = np.clip(combined / p99, 0, 1)
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combined[nan_mask] = np.nan
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_save_tif(output, combined.astype(np.float32), transform, crs)
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n_anomaly = int(np.sum(combined > 0.1)) if np.any(combined > 0) else 0
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pct_anomaly = n_anomaly / max(np.sum(~nan_mask), 1) * 100
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logger.info(f" ✓ Détection anomalies terminée ({time.time()-t0:.1f}s) — "
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f"{pct_anomaly:.1f}% de la zone ({n_anomaly} px) au-delà de {n_sigma}σ")
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return output
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except Exception as e:
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logger.error(f" ✗ Erreur détection anomalies: {e}", exc_info=True)
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return None
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