#!/usr/bin/env python3
"""
Pipeline LiDAR pour détection archéologique - Version Python pur
Visualisations générées avec numpy/rasterio/matplotlib
"""

import os
import sys
import json
import subprocess
from pathlib import Path
import numpy as np
import rasterio
from rasterio.transform import from_bounds
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import rcParams
from scipy import ndimage
from scipy.stats import binned_statistic_2d
from tqdm import tqdm

try:
    import laspy
except ImportError as e:
    print(f"Erreur import: {e}")
    sys.exit(1)

rcParams['figure.dpi'] = 150
rcParams['savefig.dpi'] = 300
rcParams['font.size'] = 10


class LidarArchaeoPipeline:
    """Pipeline Python pur pour analyse archéologique LiDAR"""

    def __init__(self, input_dir, output_dir, resolution=0.5):
        self.input_dir = Path(input_dir)
        self.output_dir = Path(output_dir)
        self.resolution = resolution
        self.temp_dir = self.output_dir / "temp"

        if not self.input_dir.exists():
            raise ValueError(f"Répertoire introuvable: {self.input_dir}")

        self.output_dir.mkdir(parents=True, exist_ok=True)
        self.temp_dir.mkdir(exist_ok=True)

        self.dtm_dir = self.output_dir / "DTM"
        self.vis_dir = self.output_dir / "visualisations"
        self.report_dir = self.output_dir / "rapports"

        for d in [self.dtm_dir, self.vis_dir, self.report_dir]:
            d.mkdir(exist_ok=True)

        print(f"✓ Pipeline initialisé (Python pur)")
        print(f"  Entrée : {self.input_dir}")
        print(f"  Sortie : {self.output_dir}")
        print(f"  Résolution : {resolution}m/px")

    def find_laz_files(self):
        """Trouve tous les fichiers LAZ/LAS"""
        files = list(self.input_dir.glob("*.laz")) + list(self.input_dir.glob("*.las"))
        print(f"✓ {len(files)} fichier(s) LiDAR trouvé(s)")
        return sorted(files)

    def check_tools(self):
        """Vérifie que les outils sont disponibles"""
        for name, cmd in [('pdal', 'pdal --version'), ('gdal', 'gdalinfo --version')]:
            try:
                subprocess.run(cmd.split(), capture_output=True, check=True)
                print(f"  ✓ {name}")
            except (subprocess.CalledProcessError, FileNotFoundError):
                return False
        return True

    # ============ STEP 1: Classification ============

    def create_pipeline_json(self, input_laz, output_las):
        """Create PDAL pipeline for ground classification"""
        pipeline = {
            "pipeline": [
                str(input_laz),
                {
                    "type": "filters.smrf",
                    "ignore": "Classification[7:7]",
                    "slope": 1.0,
                    "window": 16.0,
                    "threshold": 0.5,
                    "scalar": 1.25
                },
                {
                    "type": "filters.range",
                    "limits": "Classification[2:2]"
                },
                {
                    "type": "writers.las",
                    "filename": str(output_las),
                    "extra_dims": "all"
                }
            ]
        }
        return json.dumps(pipeline)

    def classify_ground(self, laz_file):
        """Classify ground points using SMRF filter"""
        output_las = self.temp_dir / f"{laz_file.stem}_ground.las"

        if output_las.exists():
            print(f"  ✓ Classification déjà effectuée")
            return output_las

        pipeline_json = self.create_pipeline_json(laz_file, output_las)
        pipeline_file = self.temp_dir / "pipeline.json"

        with open(pipeline_file, 'w') as f:
            f.write(pipeline_json)

        try:
            subprocess.run(
                ["pdal", "pipeline", str(pipeline_file)],
                capture_output=True,
                check=True
            )
            print(f"  ✓ Classification sol terminée")
            return output_las
        except subprocess.CalledProcessError as e:
            print(f"  ✗ Erreur PDAL: {e.stderr.decode()}")
            return None

    # ============ STEP 2: DTM Generation ============

    def create_dtm_fast(self, las_file, basename):
        """Create DTM using fast binning method with gap filling"""
        print(f"  → Génération DTM...")

        try:
            import laspy
            from scipy.ndimage import gaussian_filter

            # Read LAZ/LAS file
            las = laspy.read(str(las_file))

            # Get bounds
            min_x, max_x = float(las.header.min[0]), float(las.header.max[0])
            min_y, max_y = float(las.header.min[1]), float(las.header.max[1])

            # Calculate grid dimensions
            width = int(np.ceil((max_x - min_x) / self.resolution))
            height = int(np.ceil((max_y - min_y) / self.resolution))

            print(f"    Bounds: X[{min_x:.1f}, {max_x:.1f}] Y[{min_y:.1f}, {max_y:.1f}]")
            print(f"    Grid: {width}x{height} pixels ({len(las.points):,} points)")

            # Fast binning method
            print(f"    Rasterisation rapide...")

            stat = binned_statistic_2d(
                las.x, las.y, las.z,
                statistic='mean',
                bins=[width, height],
                range=[[min_x, max_x], [min_y, max_y]]
            )

            dtm = stat.statistic.T

            # Fill gaps using interpolation
            print(f"    Remplissage des zones vides...")
            from scipy.ndimage import distance_transform_edt

            # Create mask of empty cells
            mask = np.isnan(dtm)

            if np.any(mask):
                # Use inpainting to fill gaps
                from scipy import interpolate

                # Get coordinates of valid points
                y_coords, x_coords = np.where(~mask)
                z_values = dtm[~mask]

                # Create interpolation function
                if len(y_coords) > 100:  # Only interpolate if enough points
                    # Create grid for interpolation
                    y_all, x_all = np.mgrid[0:dtm.shape[0], 0:dtm.shape[1]]

                    # Use nearest neighbor interpolation for speed
                    interp = interpolate.NearestNDInterpolator(
                        np.column_stack((y_coords, x_coords)),
                        z_values
                    )

                    # Fill missing values
                    dtm_filled = interp(y_all, x_all)

                    # Apply slight smoothing to blend filled areas
                    dtm_filled = gaussian_filter(dtm_filled, sigma=0.5)

                    # Replace NaN values with filled values
                    dtm[mask] = dtm_filled[mask]

            # Save as GeoTIFF
            output_tif = self.dtm_dir / f"{basename}_dtm.tif"
            transform = from_bounds(min_x, min_y, max_x, max_y, width, height)

            with rasterio.open(
                output_tif,
                'w',
                driver='GTiff',
                height=height,
                width=width,
                count=1,
                dtype='float32',
                crs='EPSG:2154',
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(dtm.astype('float32'), 1)

            print(f"    ✓ DTM créé: {output_tif.name}")
            return output_tif

        except Exception as e:
            print(f"  ✗ Erreur DTM: {e}")
            import traceback
            traceback.print_exc()
            return None

    # ============ STEP 3: Visualizations (Python) ============

    def generate_hillshade(self, dem_file, basename):
        """Generate hillshade using numpy"""
        print(f"  → Hillshade multidirectionnel...")

        output = self.vis_dir / f"{basename}_hillshade_multi.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate gradients
            dy, dx = np.gradient(dem)

            # Multi-directional hillshade (NW, NE, SW, SE)
            azimuts = [315, 45, 225, 135]
            altitude = 30
            hillshades = []

            for az in azimuts:
                az_rad = np.radians(az)
                alt_rad = np.radians(altitude)

                # Calculate slope and aspect
                slope = np.arctan(np.sqrt(dx**2 + dy**2))
                aspect = np.arctan2(-dy, dx)

                # Hillshade formula
                hs = np.sin(alt_rad) * np.sin(slope) + \
                     np.cos(alt_rad) * np.cos(slope) * np.cos(az_rad - aspect)
                hillshades.append(np.clip(hs, 0, 1))

            # Combine all directions
            combined = np.mean(hillshades, axis=0)

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=combined.shape[0],
                width=combined.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(combined.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur hillshade: {e}")
            return None

    def generate_slope(self, dem_file, basename):
        """Generate slope map"""
        print(f"  → Pente (Slope)...")

        output = self.vis_dir / f"{basename}_slope.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate slope
            dy, dx = np.gradient(dem)
            slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 / np.pi

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=slope.shape[0],
                width=slope.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(slope.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur slope: {e}")
            return None

    def generate_aspect(self, dem_file, basename):
        """Generate aspect (orientation of slopes) map"""
        print(f"  → Aspect (Orientation)...")

        output = self.vis_dir / f"{basename}_aspect.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate aspect (direction of slope)
            dy, dx = np.gradient(dem)
            aspect = np.arctan2(-dy, dx) * 180 / np.pi
            aspect = np.mod(aspect, 360)  # Convert to 0-360

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=aspect.shape[0],
                width=aspect.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(aspect.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur aspect: {e}")
            return None

    def generate_curvature(self, dem_file, basename):
        """Generate curvature (terrain concavity/convexity) map"""
        print(f"  → Courbure (Curvature)...")

        output = self.vis_dir / f"{basename}_curvature.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate curvature using Laplacian
            dz_dx = np.gradient(dem, axis=1)
            dz_dy = np.gradient(dem, axis=0)
            d2z_dx2 = np.gradient(dz_dx, axis=1)
            d2z_dy2 = np.gradient(dz_dy, axis=0)

            # Mean curvature
            curvature = (d2z_dx2 + d2z_dy2) / 2

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=curvature.shape[0],
                width=curvature.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(curvature.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur curvature: {e}")
            return None

    def generate_solar(self, dem_file, basename):
        """Generate solar irradiance simulation"""
        print(f"  → Éclairage Solaire (Solar Irradiance)...")

        output = self.vis_dir / f"{basename}_solar.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate gradients
            dy, dx = np.gradient(dem)

            # Calculate slope and aspect
            slope = np.arctan(np.sqrt(dx**2 + dy**2))
            aspect = np.arctan2(-dy, dx)

            # Solar irradiance (morning sun - azimuth 90, altitude 30)
            az_rad = np.radians(90)
            alt_rad = np.radians(30)

            # Solar radiation formula
            solar = np.sin(alt_rad) * np.sin(slope) + \
                     np.cos(alt_rad) * np.cos(slope) * np.cos(az_rad - aspect)

            # Clip negative values (shadows)
            solar = np.clip(solar, 0, 1)

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=solar.shape[0],
                width=solar.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(solar.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur solar: {e}")
            return None

    def generate_lrm(self, dem_file, basename):
        """Local Relief Model - deviation from local mean"""
        print(f"  → Local Relief Model...")

        output = self.vis_dir / f"{basename}_lrm.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Calculate local mean with gaussian filter
            from scipy.ndimage import gaussian_filter
            local_mean = gaussian_filter(dem, sigma=15/self.resolution)

            # Deviation from mean
            lrm = dem - local_mean

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=lrm.shape[0],
                width=lrm.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(lrm.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur LRM: {e}")
            return None

    def generate_svf(self, dem_file, basename):
        """Sky-View Factor approximation"""
        print(f"  → Sky-View Factor...")

        output = self.vis_dir / f"{basename}_svf.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            # Simplified SVF using relief inverted
            # Normalize DEM
            dem_norm = (dem - np.nanmin(dem)) / (np.nanmax(dem) - np.nanmin(dem))

            # Apply inverted relief (approximation of SVF)
            svf = 1 - dem_norm

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=svf.shape[0],
                width=svf.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(svf.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur SVF: {e}")
            return None

    def generate_openness(self, dem_file, basename, positive=True):
        """Positive/Negative Openness approximation"""
        name = "positive_openness" if positive else "negative_openness"
        print(f"  → {name.replace('_', ' ').title()}...")

        output = self.vis_dir / f"{basename}_{name}.tif"

        try:
            with rasterio.open(dem_file) as src:
                dem = src.read(1)
                transform = src.transform
                crs = src.crs

            if positive:
                # Positive openness: high points
                from scipy.ndimage import maximum_filter
                openness = maximum_filter(dem, size=int(10/self.resolution))
            else:
                # Negative openness: low points (cavities!)
                from scipy.ndimage import minimum_filter
                openness = minimum_filter(dem, size=int(10/self.resolution))

            # Calculate difference
            result = openness - dem if positive else dem - openness

            # Save
            with rasterio.open(
                output,
                'w',
                driver='GTiff',
                height=result.shape[0],
                width=result.shape[1],
                count=1,
                dtype='float32',
                crs=crs,
                transform=transform,
                compress='lzw'
            ) as dst:
                dst.write(result.astype('float32'), 1)

            return output
        except Exception as e:
            print(f"  ✗ Erreur openness: {e}")
            return None

    # ============ STEP 4: PNG Conversion ============

    def tif_to_png(self, tif_file):
        """Convert GeoTIFF to visualization JPEG with explicit legend"""
        if not tif_file or not tif_file.exists():
            return None

        jpg_file = self.vis_dir / f"{tif_file.stem}.jpg"

        try:
            with rasterio.open(tif_file) as src:
                data = src.read(1)
                nodata = src.nodata

                if nodata is not None:
                    data = np.ma.masked_where((data == nodata) | np.isnan(data), data)

                # Get valid data for normalization
                valid_data = data.compressed() if hasattr(data, 'compressed') else data.flatten()
                valid_data = valid_data[~np.isnan(valid_data)]

                # Determine colormap, normalization, and legend info
                if 'hillshade' in str(tif_file):
                    cmap = 'gray'
                    vmin, vmax = 0, 1
                    data = np.clip(data, vmin, vmax)
                    title = "Hillshade Multidirectionnel"
                    legend_label = "Illumination (0-1)"
                    description = "Ombres → Structures, murs, terrasses visibles"
                elif 'slope' in str(tif_file):
                    cmap = 'inferno'
                    vmin, vmax = 0, np.percentile(valid_data, 95)
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Pente (Slope)"
                    legend_label = f"Pente (°)\nMin: {vmin:.1f}° | Max: {vmax:.1f}°"
                    description = "Orange/Clair = Forte pente (murs, talus) | Foncé = Faible pente"
                elif 'aspect' in str(tif_file):
                    cmap = 'hsv'  # Cyclic colormap for directions
                    # Aspect is 0-360, normalize to 0-1
                    vmin, vmax = 0, 360
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Aspect (Orientation)"
                    legend_label = "Orientation (° du Nord)\nN=0°, E=90°, S=180°, O=270°"
                    description = "Couleur = Direction de la pente (utile pour orientation bâtiments)"
                elif 'curvature' in str(tif_file):
                    cmap = 'RdYlBu_r'  # Diverging for positive/negative curvature
                    vmax = max(abs(np.percentile(valid_data, 5)), abs(np.percentile(valid_data, 95)), 0.001)
                    vmin, vmax = -vmax, vmax
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Courbure (Curvature)"
                    legend_label = f"Courbure\nRouge = Convexe (bosse)\nBleu = Concave (creux)"
                    description = "⭐ Excellent pour fossés, levées, terrasses, talus"
                elif 'solar' in str(tif_file):
                    cmap = 'YlOrBr'  # Sun-like colormap
                    vmin, vmax = 0, 1
                    data = np.clip(data, vmin, vmax)
                    title = "Éclairage Solaire (Matin)"
                    legend_label = "Irradiance Solaire\nFoncé = Ombre | Clair = Éclairé"
                    description = "Simulation soleil matin - révèle structures orientées Est"
                elif 'svf' in str(tif_file):
                    cmap = 'viridis'
                    vmin, vmax = np.percentile(valid_data, (5, 95))
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Sky-View Factor"
                    legend_label = "Ouverture vers le ciel\nFoncé = Cuvette | Clair = Sommet"
                    description = "Excellent pour structures, tumulus, fondations"
                elif 'lrm' in str(tif_file):
                    cmap = 'RdBu_r'
                    vmax = max(abs(np.percentile(valid_data, 2)), abs(np.percentile(valid_data, 98)), 0.1)
                    vmin, vmax = -vmax, vmax
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Local Relief Model (LRM)"
                    legend_label = f"Relief local (m)\nRouge = +{vmax:.1f}m (élévation)\nBleu = {vmin:.1f}m (dépression)"
                    description = "Rouge = Surélévation | Bleu = Creux, fossé"
                elif 'positive' in str(tif_file):
                    cmap = 'YlOrBr'
                    vmin, vmax = np.percentile(valid_data, (10, 98))
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Positive Openness"
                    legend_label = f"Ouverture positive (m)\nMin: {vmin:.1f}m | Max: {vmax:.1f}m"
                    description = "Orange = Zones élevées (tumulus, collines)"
                elif 'negative' in str(tif_file):
                    cmap = 'GnBu_r'
                    vmin, vmax = np.percentile(valid_data, (10, 98))
                    data = np.clip((data - vmin) / (vmax - vmin), 0, 1)
                    title = "Negative Openness"
                    legend_label = f"Ouverture négative (m)\nMin: {vmin:.1f}m | Max: {vmax:.1f}m"
                    description = "Vert/Bleu = Zones basses (cavités, fossés, souterrains)"
                else:
                    cmap = 'terrain'
                    p2, p98 = np.percentile(valid_data, (2, 98))
                    data = np.clip((data - p2) / (p98 - p2), 0, 1)
                    title = tif_file.stem.replace('_', ' ').title()
                    legend_label = "Altitude normalisée"
                    description = ""

                # Create figure with white background for JPEG
                fig, ax = plt.subplots(figsize=(18, 12), facecolor='white')

                # Display data
                im = ax.imshow(data, cmap=cmap, aspect='equal')

                # Enhanced colorbar with explicit values
                cbar = plt.colorbar(im, ax=ax, pad=0.03, shrink=0.75, aspect=30)
                cbar.ax.tick_params(labelsize=10, width=1.5)
                cbar.outline.set_linewidth(1.5)

                # Add colorbar label
                cbar.set_label(legend_label, fontsize=11, fontweight='bold')

                # Enhanced title with description
                full_title = f"{title}\n{description}"
                ax.set_title(full_title, fontsize=16, fontweight='bold', pad=15)

                # Add coordinates info
                ax.text(0.02, 0.98, f"Résolution: {self.resolution}m/px",
                        transform=ax.transAxes, fontsize=9,
                        verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))

                ax.axis('off')
                fig.patch.set_facecolor('white')

                plt.tight_layout()
                # Save as JPEG
                plt.savefig(jpg_file, dpi=150, bbox_inches='tight', facecolor='white', format='jpg')
                plt.close()

                # Delete the source TIFF file to save space
                tif_file.unlink()

                return jpg_file

        except Exception as e:
            print(f"    ✗ Erreur conversion JPEG: {e}")
            import traceback
            traceback.print_exc()
            return None

    def generate_all_visualizations(self, dtm_file, basename):
        """Generate all archaeological visualizations"""
        print(f"\n  Génération visualisations:")

        vis_results = {}

        # Generate rasters (existing + new)
        vis_results['hillshade'] = self.generate_hillshade(dtm_file, basename)
        vis_results['slope'] = self.generate_slope(dtm_file, basename)
        vis_results['aspect'] = self.generate_aspect(dtm_file, basename)
        vis_results['curvature'] = self.generate_curvature(dtm_file, basename)
        vis_results['solar'] = self.generate_solar(dtm_file, basename)
        vis_results['svf'] = self.generate_svf(dtm_file, basename)
        vis_results['lrm'] = self.generate_lrm(dtm_file, basename)
        vis_results['pos_open'] = self.generate_openness(dtm_file, basename, positive=True)
        vis_results['neg_open'] = self.generate_openness(dtm_file, basename, positive=False)

        # Convert to JPEG
        print(f"\n  Conversion images JPEG:")
        for name, tif_file in vis_results.items():
            jpg_file = self.tif_to_png(tif_file)
            if jpg_file:
                print(f"    ✓ {jpg_file.name}")

        return vis_results

    def create_overview(self, basename):
        """Create overview image with all visualizations (JPEG)"""
        patterns = {
            'Hillshade': '*_hillshade_multi.jpg',
            'Pente': '*_slope.jpg',
            'Aspect': '*_aspect.jpg',
            'Courbure': '*_curvature.jpg',
            'Éclairage': '*_solar.jpg',
            'Sky-View Factor': '*_svf.jpg',
            'Local Relief': '*_lrm.jpg',
            'Pos. Openness': '*_positive_openness.jpg',
            'Neg. Openness': '*_negative_openness.jpg'
        }

        images = {}
        for name, pattern in patterns.items():
            files = list(self.vis_dir.glob(pattern))
            if files:
                images[name] = str(files[0])

        if len(images) < 2:
            return None

        # 3x3 grid for 9 visualizations
        fig, axes = plt.subplots(3, 3, figsize=(24, 18), facecolor='white')
        axes = axes.flatten()

        for idx, (name, img_path) in enumerate(images.items()):
            if idx >= 9:
                break
            img = plt.imread(img_path)
            axes[idx].imshow(img)
            axes[idx].set_title(name, fontsize=11, fontweight='bold')
            axes[idx].axis('off')

        # Hide unused subplots
        for idx in range(len(images), 9):
            axes[idx].axis('off')

        # Title
        fig.suptitle(
            f"Analyse Archéologique LiDAR - {basename}",
            fontsize=18,
            fontweight='bold',
            y=0.98
        )

        plt.tight_layout()
        output = self.report_dir / f"{basename}_overview.jpg"
        plt.savefig(output, dpi=120, bbox_inches='tight', facecolor='white', format='jpg')
        plt.close()

        return output

    # ============ Complete Pipeline ============

    def run_semantic_classification(self, dtm_file, basename):
        """Run semantic classification on DTM"""
        print(f"\n[4/4] Classification Sémantique Automatique...")

        try:
            # Import semantic classifier
            from semantic_classifier import ArchaeoSemanticClassifier

            # Create output subdirectory for semantic results
            semantic_dir = self.output_dir / "semantic"
            semantic_dir.mkdir(exist_ok=True)

            # Run classification
            classifier = ArchaeoSemanticClassifier(dtm_file, semantic_dir)
            results = classifier.process(basename)

            print(f"  ✓ Classification sémantique terminée")
            print(f"    → Carte: {results['tif'].name}")
            print(f"    → Visualisation: {results['jpg'].name}")
            stats_file = Path(results['tif']).parent / f"{Path(results['tif']).stem.replace('_semantic', '')}_statistics.json"
            print(f"    → Statistiques: {stats_file.name}")

            return results
        except ImportError as e:
            print(f"  ✗ Module non disponible: {e}")
            return None
        except Exception as e:
            print(f"  ✗ Erreur classification: {e}")
            import traceback
            traceback.print_exc()
            return None

    def process_file(self, laz_file):
        """Process a single LAZ file"""
        basename = laz_file.stem
        print(f"\n{'='*60}")
        print(f" FICHIER : {basename}")
        print(f"{'='*60}")

        # Step 1: Ground classification
        print(f"\n[1/4] Classification du sol...")
        las_file = self.classify_ground(laz_file)
        if not las_file:
            print(f"  ✗ Échec classification")
            return False

        # Step 2: Generate DTM
        print(f"\n[2/4] Génération DTM...")
        dtm_file = self.create_dtm_fast(las_file, basename)
        if not dtm_file:
            print(f"  ✗ Échec DTM")
            return False

        # Step 3: Visualizations
        print(f"\n[3/4] Visualisations archéologiques...")
        vis = self.generate_all_visualizations(dtm_file, basename)

        # Overview
        overview = self.create_overview(basename)
        if overview:
            print(f"\n  ✓ Vue synthétique: {overview}")

        # Step 4: Semantic Classification (NEW!)
        semantic_results = self.run_semantic_classification(dtm_file, basename)

        print(f"\n✓ {basename} traité avec succès !")
        return True

        # Overview
        overview = self.create_overview(basename)
        if overview:
            print(f"\n  ✓ Vue synthétique: {overview}")

        # Step 4: Semantic Classification (NEW!)
        semantic_results = self.run_semantic_classification(dtm_file, basename)

        print(f"\n✓ {basename} traité avec succès !")
        return True

    def process_all(self):
        """Process all LAZ files in input directory"""
        files = self.find_laz_files()

        if not files:
            print("Aucun fichier LAZ/LAS trouvé !")
            return

        print(f"\n{'='*60}")
        print(f" PIPELINE ARCHÉOLOGIQUE LiDAR")
        print(f"{'='*60}")

        # Check tools
        print(f"\nVérification des outils...")
        if not self.check_tools():
            print("Erreur: Certains outils ne sont pas disponibles")
            return

        results = {}
        for laz_file in files:
            try:
                results[laz_file.name] = self.process_file(laz_file)
            except Exception as e:
                print(f"\n✗ Erreur traitement: {e}")
                import traceback
                traceback.print_exc()
                results[laz_file.name] = False

        # Summary
        print(f"\n{'='*60}")
        print(f" RÉSUMÉ")
        print(f"{'='*60}")
        success_count = sum(results.values())
        print(f"✓ {success_count}/{len(results)} fichiers traités avec succès")

        print(f"\nRésultats dans: {self.output_dir}")
        print(f"  • DTM: {self.dtm_dir}")
        print(f"  • Visualisations JPEG: {self.vis_dir}")
        print(f"  • Rapports: {self.report_dir}")

        # Clean up temporary files to save space
        print(f"\nNettoyage des fichiers temporaires...")
        try:
            import shutil
            if self.temp_dir.exists():
                shutil.rmtree(self.temp_dir)
                print(f"  ✓ Fichiers temporaires supprimés ({self.temp_dir})")
        except Exception as e:
            print(f"  Note: Impossible de supprimer les fichiers temporaires")


def main():
    import argparse

    parser = argparse.ArgumentParser(
        description="Pipeline LiDAR pour détection archéologique (Python pur)"
    )
    parser.add_argument(
        "input",
        help="Dossier contenant les fichiers LAZ/LAS"
    )
    parser.add_argument(
        "-o", "--output",
        default="/data/output",
        help="Dossier de sortie"
    )
    parser.add_argument(
        "-r", "--resolution",
        type=float,
        default=0.5,
        help="Résolution en mètres"
    )

    args = parser.parse_args()

    try:
        pipeline = LidarArchaeoPipeline(
            input_dir=args.input,
            output_dir=args.output,
            resolution=args.resolution
        )
        pipeline.process_all()
    except Exception as e:
        print(f"Erreur: {e}")
        sys.exit(1)


if __name__ == "__main__":
    main()