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>

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
 # ============================================================