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>

CLAUDE.md

@@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-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).
+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).
 
 ## Commands
 
@@ -33,7 +33,7 @@ docker run --rm --gpus all -v $(pwd)/input:/data/input:ro -v $(pwd)/output:/data
 - **`cli.py`** — argparse + logging setup. Entry point via `python -m lidar_pipeline`.
 - **`pipeline.py`** — `LidarArchaeoPipeline` orchestrator. `VIZ_STEPS` registry maps names to generate functions. `FilePrefixFilter` for parallel logging.
 - **`dtm.py`** — PDAL ground classification (SMRF/PMF/CSF + auto-detection) and DTM generation via scipy `binned_statistic_2d`.
-- **`visualizations.py`** — 17 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
+- **`visualizations.py`** — 16 `generate_*` functions + 2 IGN overlay lambdas. All take `(dem_file, basename, vis_dir, resolution)` and return a TIF path or None.
 - **`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.
 - **`ign.py`** — IGN WMTS tile download + overlay generation for orthophoto and topographic maps.
 - **`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.

README.md

@@ -2,7 +2,7 @@
 
 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).
 
-## Visualisations (19 par fichier)
+## Visualisations (18 par fichier)
 
 ### Visualisations principales
 | # | Visualisation | Utilité archéologique |
@@ -26,14 +26,13 @@ Workflow automatisé pour générer des visualisations exploitables à partir de
 | 13 | **Rugosité** | Écart-type de l'élévation | Surfaces anthropiques vs naturelles |
 | 14 | **Anomalies statistiques** | Z-score + Local Moran's I | Anomalies topographiques significatives |
 | 15 | **Ondelette Mexican Hat** | CWT 2D multi-échelle | Tumulus, fossés circulaires |
-| 16 | **Texture GLCM** | Contraste, entropie, homogénéité | Labour, surfaces archéologiques |
+| 16 | **Accumulation de flux** | Algorithme D8 hydrologique | Fossés d'enceinte, routes antiques |
-| 17 | **Accumulation de flux** | Algorithme D8 hydrologique | Fossés d'enceinte, routes antiques |
 
 ### Cartes de référence IGN
 | # | Visualisation | Source |
 |---|--------------|--------|
-| 18 | **Photographie aérienne IGN** | Orthophotographie WMTS |
+| 17 | **Photographie aérienne IGN** | Orthophotographie WMTS |
-| 19 | **Carte topographique IGN** | Plan IGN V2 WMTS |
+| 18 | **Carte topographique IGN** | Plan IGN V2 WMTS |
 
 ## Classification du sol
 

lidar_pipeline/pipeline.py

@@ -44,7 +44,7 @@ from .visualizations import (
     generate_hillshade, generate_slope, generate_aspect, generate_curvature,
     generate_lrm, generate_svf, generate_openness,
     generate_mslrm, generate_tpi, generate_depressions, generate_sailore,
-    generate_roughness, generate_anomalies, generate_wavelet, generate_texture,
+    generate_roughness, generate_anomalies, generate_wavelet,
     generate_flow,
 )
 from .gpu import gpu_cleanup
@@ -71,7 +71,6 @@ VIZ_STEPS = [
     ('roughness',  generate_roughness),
     ('anomalies',  generate_anomalies),
     ('wavelet',    generate_wavelet),
-    ('texture',    generate_texture),
     ('flow',       generate_flow),
     ('ortho',      lambda d, b, v, r: generate_ign_overlay(
                     d, b, v, r,

lidar_pipeline/rendering.py

@@ -156,13 +156,6 @@ COLORMAPS = {
         'description': 'Transformée en ondelette 2D — excellente pour détecter structures circulaires',
         'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
     },
-    'texture': {
-        'cmap': 'inferno',
-        'title': 'Texture GLCM (Contraste + Entropie - Homogénéité)',
-        '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',
-        'description': 'Distingue surfaces anthropiques (labour, chemins) des naturelles',
-        'vmin_mode': 'symmetric', 'sym_pct': (2, 98),
-    },
     'flow': {
         'cmap': 'Blues',
         'title': 'Accumulation de Flux Hydrologique (D8)',
@@ -525,7 +518,7 @@ def generate_pdf_report(basename, vis_dir, pdf_dir, resolution):
     order = ['mslrm', 'svf', 'negative_openness',
               'positive_openness', 'sailore', 'depressions', 'hillshade_multi',
               'lrm', 'tpi', 'slope', 'curvature', 'aspect',
-              'roughness', 'anomalies', 'wavelet', 'texture', 'flow']
+              'roughness', 'anomalies', 'wavelet', 'flow']
 
     def sort_key(f):
         name = f.stem.lower()

lidar_pipeline/tests/test_visualizations.py

@@ -190,14 +190,6 @@ class TestWavelet:
         assert result.exists()
 
 
-class TestTexture:
-    def test_generates_tif(self, synthetic_dem, tmp_output_dir):
-        from lidar_pipeline.visualizations import generate_texture
-        result = generate_texture(synthetic_dem, "test", tmp_output_dir, 5.0)
-        assert result is not None
-        assert result.exists()
-
-
 class TestFlow:
     def test_generates_tif(self, synthetic_dem, tmp_output_dir):
         from lidar_pipeline.visualizations import generate_flow

lidar_pipeline/visualizations.py

@@ -676,88 +676,6 @@ def generate_wavelet(dem_file, basename, vis_dir, resolution):
         return None
 
 
-# ============================================================
-# Texture GLCM
-# ============================================================
-
-def generate_texture(dem_file, basename, vis_dir, resolution):
-    """GLCM-inspired texture analysis — contrast, entropy, homogeneity (GPU-accelerated)."""
-    gpu_tag = " [GPU]" if HAS_GPU else ""
-    logger.info(f"  → Texture GLCM{gpu_tag}...")
-    t0 = time.time()
-    output = vis_dir / f"{basename}_texture.tif"
-
-    try:
-        dem_np, transform, crs = _read_dem(dem_file)
-
-        # Hillshade — compute on CPU to avoid holding DEM on GPU during texture
-        gy, gx = np.gradient(dem_np, resolution)
-        slope = np.arctan(np.sqrt(gx**2 + gy**2))
-        alt_rad = np.radians(45)
-        az_rad = np.radians(315)
-        aspect = np.arctan2(gy, gx)
-        shading = (np.sin(alt_rad) * np.cos(slope) +
-                   np.cos(alt_rad) * np.sin(slope) *
-                   np.cos(az_rad - aspect))
-        hillshade = np.clip(shading, 0, 1)
-
-        valid = np.asarray(hillshade[~np.isnan(hillshade)])
-        if len(valid) == 0:
-            raise ValueError("No valid data for texture analysis")
-        lo, hi = np.percentile(valid, (1, 99))
-        img = np.clip((hillshade - lo) / max(hi - lo, 0.001), 0, 1)
-        del hillshade, shading, slope, aspect, gy, gx  # free memory
-
-        window = int(5 / resolution)
-        if window % 2 == 0:
-            window += 1
-
-        # Contrast (variance) — GPU-accelerated
-        img_gpu = to_gpu(img.astype(np.float32))
-        local_mean = xp_uniform_filter(img_gpu, size=window)
-        local_mean_sq = xp_uniform_filter(img_gpu * img_gpu, size=window)
-        contrast = to_cpu(local_mean_sq - local_mean * local_mean).astype(np.float64)
-        del img_gpu, local_mean, local_mean_sq  # free GPU memory
-
-        # Entropy — compute bin-by-bin to avoid large 3D allocation
-        n_bins = 16
-        img_clean = np.nan_to_num(img, nan=0.0)
-        img_uint8 = np.clip(img_clean * 255, 0, 255).astype(np.uint8)
-        quantized = (img_uint8 // (256 // n_bins)).astype(np.int32)
-        entropy = np.zeros_like(img, dtype=np.float64)
-        win_area = max(window * window, 1)
-
-        for b in range(n_bins):
-            plane = (quantized == b).astype(np.float32)
-            plane_gpu = to_gpu(plane)
-            prob_plane = to_cpu(xp_uniform_filter(plane_gpu, size=window))
-            prob_val = prob_plane / win_area
-            prob_val = np.clip(prob_val, 1e-10, None)
-            entropy -= prob_val * np.log2(prob_val)
-            del plane_gpu  # free GPU memory per bin
-
-        del quantized, img_uint8  # free CPU memory
-
-        # Homogeneity — 1 / (1 + variance)
-        homogeneity = 1.0 / (1.0 + contrast)
-
-        def norm(arr):
-            valid_arr = arr[~np.isnan(arr)]
-            if len(valid_arr) == 0:
-                return arr
-            std_val = max(np.std(valid_arr), 0.01)
-            return (arr - np.mean(valid_arr)) / std_val
-
-        texture_combined = 0.4 * norm(contrast) + 0.4 * norm(entropy) - 0.2 * norm(homogeneity)
-
-        _save_tif(output, texture_combined, transform, crs)
-        logger.info(f"  ✓ Texture terminée ({time.time()-t0:.1f}s){gpu_tag}")
-        return output
-    except Exception as e:
-        logger.error(f"  ✗ Erreur texture GLCM: {e}", exc_info=True)
-        return None
-
-
 # ============================================================
 # Flow accumulation
 # ============================================================