Suppression de la visualisation Texture GLCM
- Suppression de generate_texture() de visualizations.py - Suppression de l'entrée 'texture' de VIZ_STEPS et COLORMAPS - Suppression du test TestTexture - Mise à jour README (19 → 18 visualisations) - Mise à jour CLAUDE.md (17 → 16 fonctions generate_*) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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## Project Overview
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LiDAR archaeological processing pipeline that generates 19 terrain visualizations from LAZ/LAS point clouds. Runs in Docker with optional NVIDIA GPU acceleration (CuPy). Designed for French LiDAR HD data in Lambert 93 (EPSG:2154).
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LiDAR archaeological processing pipeline that generates 18 terrain visualizations from LAZ/LAS point clouds. Runs in Docker with optional NVIDIA GPU acceleration (CuPy). Designed for French LiDAR HD data in Lambert 93 (EPSG:2154).
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## Commands
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@ -33,7 +33,7 @@ docker run --rm --gpus all -v $(pwd)/input:/data/input:ro -v $(pwd)/output:/data
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- **`cli.py`** — argparse + logging setup. Entry point via `python -m lidar_pipeline`.
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- **`pipeline.py`** — `LidarArchaeoPipeline` orchestrator. `VIZ_STEPS` registry maps names to generate functions. `FilePrefixFilter` for parallel logging.
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- **`dtm.py`** — PDAL ground classification (SMRF/PMF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
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- **`visualizations.py`** — 17 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
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- **`visualizations.py`** — 16 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
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- **`gpu.py`** — CuPy/numpy abstraction: `HAS_GPU`, `to_gpu()`, `to_cpu()`, `xp_gaussian_filter()`, `xp_uniform_filter()`, `xp_minimum_filter()`, `gpu_cleanup()`. Falls back to CPU gracefully.
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- **`ign.py`** — IGN WMTS tile download + overlay generation for orthophoto and topographic maps.
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- **`rendering.py`** — `COLORMAPS` dict maps filename keywords to (cmap, title, legend, description). `tif_to_png()` converts TIF→WebP with legend/scale/north arrow. `generate_pdf_report()` creates A3 PDF.
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@ -2,7 +2,7 @@
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Workflow automatisé pour générer des visualisations exploitables à partir de données LiDAR HD (IGN) pour la détection de structures archéologiques. Tourne en Docker avec accélération GPU optionnelle (NVIDIA/CuPy).
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## Visualisations (19 par fichier)
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## Visualisations (18 par fichier)
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### Visualisations principales
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| # | Visualisation | Utilité archéologique |
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@ -26,14 +26,13 @@ Workflow automatisé pour générer des visualisations exploitables à partir de
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| 13 | **Rugosité** | Écart-type de l'élévation | Surfaces anthropiques vs naturelles |
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| 14 | **Anomalies statistiques** | Z-score + Local Moran's I | Anomalies topographiques significatives |
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| 15 | **Ondelette Mexican Hat** | CWT 2D multi-échelle | Tumulus, fossés circulaires |
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| 16 | **Texture GLCM** | Contraste, entropie, homogénéité | Labour, surfaces archéologiques |
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| 17 | **Accumulation de flux** | Algorithme D8 hydrologique | Fossés d'enceinte, routes antiques |
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| 16 | **Accumulation de flux** | Algorithme D8 hydrologique | Fossés d'enceinte, routes antiques |
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### Cartes de référence IGN
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| # | Visualisation | Source |
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|---|--------------|--------|
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| 18 | **Photographie aérienne IGN** | Orthophotographie WMTS |
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| 19 | **Carte topographique IGN** | Plan IGN V2 WMTS |
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| 17 | **Photographie aérienne IGN** | Orthophotographie WMTS |
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| 18 | **Carte topographique IGN** | Plan IGN V2 WMTS |
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## Classification du sol
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@ -44,7 +44,7 @@ from .visualizations import (
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generate_hillshade, generate_slope, generate_aspect, generate_curvature,
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generate_lrm, generate_svf, generate_openness,
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generate_mslrm, generate_tpi, generate_depressions, generate_sailore,
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generate_roughness, generate_anomalies, generate_wavelet, generate_texture,
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generate_roughness, generate_anomalies, generate_wavelet,
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generate_flow,
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)
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from .gpu import gpu_cleanup
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@ -71,7 +71,6 @@ VIZ_STEPS = [
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('roughness', generate_roughness),
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('anomalies', generate_anomalies),
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('wavelet', generate_wavelet),
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('texture', generate_texture),
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('flow', generate_flow),
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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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@ -156,13 +156,6 @@ COLORMAPS = {
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'description': 'Transformée en ondelette 2D — excellente pour détecter structures circulaires',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'texture': {
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'cmap': 'inferno',
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'title': 'Texture GLCM (Contraste + Entropie - Homogénéité)',
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'legend': 'Analyse de la texture du relief (fenêtre 5m)\nClair = Texture hétérogène (labour, ruines, sol perturbé)\nSombre = Texture homogène (sol nu, route, zone plate)\n\nCombine contraste, entropie et homogénéité',
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'description': 'Distingue surfaces anthropiques (labour, chemins) des naturelles',
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'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
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},
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'flow': {
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'cmap': 'Blues',
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'title': 'Accumulation de Flux Hydrologique (D8)',
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@ -525,7 +518,7 @@ def generate_pdf_report(basename, vis_dir, pdf_dir, resolution):
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order = ['mslrm', 'svf', 'negative_openness',
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'positive_openness', 'sailore', 'depressions', 'hillshade_multi',
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'lrm', 'tpi', 'slope', 'curvature', 'aspect',
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'roughness', 'anomalies', 'wavelet', 'texture', 'flow']
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'roughness', 'anomalies', 'wavelet', 'flow']
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def sort_key(f):
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name = f.stem.lower()
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@ -190,14 +190,6 @@ class TestWavelet:
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assert result.exists()
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class TestTexture:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_texture
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result = generate_texture(synthetic_dem, "test", tmp_output_dir, 5.0)
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assert result is not None
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assert result.exists()
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class TestFlow:
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def test_generates_tif(self, synthetic_dem, tmp_output_dir):
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from lidar_pipeline.visualizations import generate_flow
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@ -676,88 +676,6 @@ def generate_wavelet(dem_file, basename, vis_dir, resolution):
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return None
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# ============================================================
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# Texture GLCM
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# ============================================================
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def generate_texture(dem_file, basename, vis_dir, resolution):
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"""GLCM-inspired texture analysis — contrast, entropy, homogeneity (GPU-accelerated)."""
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gpu_tag = " [GPU]" if HAS_GPU else ""
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logger.info(f" → Texture GLCM{gpu_tag}...")
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t0 = time.time()
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output = vis_dir / f"{basename}_texture.tif"
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try:
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dem_np, transform, crs = _read_dem(dem_file)
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# Hillshade — compute on CPU to avoid holding DEM on GPU during texture
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gy, gx = np.gradient(dem_np, resolution)
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slope = np.arctan(np.sqrt(gx**2 + gy**2))
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alt_rad = np.radians(45)
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az_rad = np.radians(315)
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aspect = np.arctan2(gy, gx)
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shading = (np.sin(alt_rad) * np.cos(slope) +
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np.cos(alt_rad) * np.sin(slope) *
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np.cos(az_rad - aspect))
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hillshade = np.clip(shading, 0, 1)
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valid = np.asarray(hillshade[~np.isnan(hillshade)])
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if len(valid) == 0:
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raise ValueError("No valid data for texture analysis")
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lo, hi = np.percentile(valid, (1, 99))
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img = np.clip((hillshade - lo) / max(hi - lo, 0.001), 0, 1)
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del hillshade, shading, slope, aspect, gy, gx # free memory
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window = int(5 / resolution)
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if window % 2 == 0:
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window += 1
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# Contrast (variance) — GPU-accelerated
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img_gpu = to_gpu(img.astype(np.float32))
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local_mean = xp_uniform_filter(img_gpu, size=window)
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local_mean_sq = xp_uniform_filter(img_gpu * img_gpu, size=window)
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contrast = to_cpu(local_mean_sq - local_mean * local_mean).astype(np.float64)
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del img_gpu, local_mean, local_mean_sq # free GPU memory
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# Entropy — compute bin-by-bin to avoid large 3D allocation
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n_bins = 16
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img_clean = np.nan_to_num(img, nan=0.0)
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img_uint8 = np.clip(img_clean * 255, 0, 255).astype(np.uint8)
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quantized = (img_uint8 // (256 // n_bins)).astype(np.int32)
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entropy = np.zeros_like(img, dtype=np.float64)
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win_area = max(window * window, 1)
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for b in range(n_bins):
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plane = (quantized == b).astype(np.float32)
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plane_gpu = to_gpu(plane)
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prob_plane = to_cpu(xp_uniform_filter(plane_gpu, size=window))
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prob_val = prob_plane / win_area
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prob_val = np.clip(prob_val, 1e-10, None)
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entropy -= prob_val * np.log2(prob_val)
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del plane_gpu # free GPU memory per bin
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del quantized, img_uint8 # free CPU memory
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# Homogeneity — 1 / (1 + variance)
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homogeneity = 1.0 / (1.0 + contrast)
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def norm(arr):
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valid_arr = arr[~np.isnan(arr)]
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if len(valid_arr) == 0:
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return arr
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std_val = max(np.std(valid_arr), 0.01)
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return (arr - np.mean(valid_arr)) / std_val
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texture_combined = 0.4 * norm(contrast) + 0.4 * norm(entropy) - 0.2 * norm(homogeneity)
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_save_tif(output, texture_combined, transform, crs)
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logger.info(f" ✓ Texture terminée ({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 texture GLCM: {e}", exc_info=True)
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return None
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# ============================================================
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# Flow accumulation
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# ============================================================
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