code/crawl_multicity.py

"""
STER — Multi-City 3D BAG Crawler (strict 3dSAGER alignment).

For each city, crawl 3D BAG buildings via OGC API Features, extract multi-LoD
geometry (LoD1.2, 1.3, 2.2), compute 25 geometric properties, and build
cross-LoD ground truth via shared BAG pand ID.

Usage:
    python crawl_multicity.py --city rotterdam --n 50000
    python crawl_multicity.py --city amsterdam --n 50000  
    python crawl_multicity.py --city utrecht --n 30000
    python crawl_multicity.py --city eindhoven --n 30000
    python crawl_multicity.py --city tokyo --n 50000  # PLATEAU mode (separate workflow)
"""

import argparse, json, os, sys, time, urllib.request
import numpy as np

API = "https://api.3dbag.nl"
LODS = ["1.2", "1.3", "2.2"]
LOD_KEY = {"1.2": "lod12", "1.3": "lod13", "2.2": "lod22"}

# --- City Bounding Boxes (RDnew / EPSG:28992) ---
# Bounding boxes approximated from city administrative boundaries
CITY_BBOX = {
    "denhaag":     "76000,450000,86000,460000",   # The Hague (for reference)
    "rotterdam":   "88000,433000,98000,444000",   # Rotterdam
    "amsterdam":   "118000,485000,128000,495000",  # Amsterdam
    "utrecht":     "132000,453000,140000,461000",  # Utrecht
    "eindhoven":   "158000,380000,168000,390000",  # Eindhoven
    "groningen":   "232000,580000,240000,588000",  # Groningen
    "maastricht":  "174000,316000,184000,326000",  # Maastricht
}


def _get(url, retries=4, timeout=45):
    """GET with retry. Returns parsed JSON."""
    last = None
    for i in range(retries):
        try:
            req = urllib.request.Request(url, headers={
                "User-Agent": "STER-research/1.0",
                "Accept": "application/json"
            })
            with urllib.request.urlopen(req, timeout=timeout) as r:
                return json.load(r)
        except Exception as e:
            last = e
            time.sleep(1.5 * (i + 1))
    raise RuntimeError(f"GET failed after {retries}: {url}\n{last}")


def transform_vertices(verts, transform):
    s = np.asarray(transform["scale"], dtype=np.float64)
    t = np.asarray(transform["translate"], dtype=np.float64)
    return np.asarray(verts, dtype=np.float64) * s + t


def solid_to_polygon_mesh(geom, real_verts):
    """CityJSON Solid → list of surfaces."""
    if geom.get("type") != "Solid":
        return None
    boundaries = geom.get("boundaries")
    if not boundaries:
        return None
    shell = boundaries[0]
    pm = []
    for surface in shell:
        pts = [real_verts[i] for ring in surface for i in ring]
        pm.append([list(map(float, p)) for p in pts])
    return pm


def extract_building(feature, min_surfaces=10):
    """Extract multi-LoD mesh records from a 3D BAG feature.
    Returns {lod12: {polygon_mesh, vertices, centroid}, ...} or None."""
    real_verts = transform_vertices(feature["vertices"], feature["_transform"])
    lod_geoms = {}
    for oid, obj in feature["CityObjects"].items():
        if obj.get("type") != "BuildingPart":
            continue
        for g in obj.get("geometry", []):
            if g.get("lod") in LODS:
                lod_geoms[g["lod"]] = g
    if "2.2" not in lod_geoms:
        return None
    out = {}
    for lod, g in lod_geoms.items():
        pm = solid_to_polygon_mesh(g, real_verts)
        if pm:
            out[LOD_KEY[lod]] = pm
    if "lod22" not in out or len(out["lod22"]) < min_surfaces:
        return None
    uverts = {}
    centroids = {}
    for k, pm in out.items():
        uv = np.unique(np.array([c for surf in pm for c in surf]), axis=0)
        uverts[k] = uv
        centroids[k] = uv.mean(axis=0)
    return {k: {"polygon_mesh": out[k], "vertices": uverts[k], "centroid": centroids[k]}
            for k in out}


def bag_id_from_feature(feature):
    fid = feature.get("id", "")
    if "NL.IMBAG.Pand." in fid:
        return fid.split("NL.IMBAG.Pand.")[1].split("-")[0]
    return fid


def compute_25_properties(mesh_record):
    """Compute the 25 geometric properties from a mesh record.
    Mirrors 3dSAGER's ObjectPropertiesProcessor.
    Returns dict of {property_name: float}.
    """
    verts = mesh_record["vertices"]
    polys = mesh_record["polygon_mesh"]
    n_verts = len(verts)
    n_faces = len(polys)
    
    # Bounding box
    bbox_min = verts.min(axis=0)
    bbox_max = verts.max(axis=0)
    bbox_dims = bbox_max - bbox_min
    bb_width, bb_length, bb_height = float(bbox_dims[0]), float(bbox_dims[1]), float(bbox_dims[2])
    
    # Area: sum of triangle areas (simplified — triangulate each polygon face)
    area = 0.0
    for poly in polys:
        if len(poly) >= 3:
            p0 = np.array(poly[0])
            for i in range(1, len(poly) - 1):
                v1 = np.array(poly[i]) - p0
                v2 = np.array(poly[i+1]) - p0
                area += 0.5 * float(np.linalg.norm(np.cross(v1, v2)))
    
    # Volume: using divergence theorem / signed volume
    volume = 0.0
    for poly in polys:
        if len(poly) >= 3:
            p = np.array(poly)
            v = 0.0
            for i in range(1, len(p) - 1):
                v += np.dot(p[0], np.cross(p[i], p[i+1]))
            volume += v
    volume = abs(volume) / 6.0
    
    # Convex hull (2D projection onto XY plane)
    from scipy.spatial import ConvexHull
    xy = verts[:, :2]
    try:
        hull2d = ConvexHull(xy)
        convex_hull_area = float(hull2d.volume)  # area in 2D
        convex_hull_volume = convex_hull_area * bb_height  # approximate
    except Exception:
        convex_hull_area = bb_width * bb_length
        convex_hull_volume = convex_hull_area * bb_height
    
    # Perimeter (2D footprint boundary)
    try:
        from scipy.spatial import ConvexHull
        hull = ConvexHull(xy)
        perimeter = float(hull.area)  # perimeter in 2D
    except Exception:
        perimeter = 2 * (bb_width + bb_length)
    
    perimeter_ind = perimeter / max(area, 1e-6)
    
    # Height difference
    height_diff = bb_height
    
    # Floor count estimate (3m per floor)
    num_floors = max(1, int(height_diff / 3.0 + 0.5))
    
    # Centroid
    centroid = verts.mean(axis=0)
    
    # Average centroid distance (2D)
    dists = np.linalg.norm(xy - centroid[:2], axis=1)
    ave_centroid_distance = float(dists.mean())
    
    # Compactness 2D: C2D = 4π·area / perimeter²  (for circles = 1)
    compactness_2d = min(1.0, 4 * np.pi * convex_hull_area / max(perimeter**2, 1e-6))
    
    # Compactness 3D: C3D = 6√π·V / A^{3/2}
    compactness_3d = min(1.0, 6 * np.sqrt(np.pi) * volume / max(area**1.5, 1e-6))
    
    # Density: volume / convex hull volume
    density = volume / max(convex_hull_volume, 1e-6)
    
    # Elongation: bbox length / bbox width
    elongation = max(bb_length, bb_width) / max(min(bb_length, bb_width), 1e-6)
    
    # Shape index: perimeter / (2 * sqrt(pi * area))
    shape_ind = perimeter / max(2 * np.sqrt(np.pi * max(area, 1e-6)), 1e-6)
    
    # Hemisphericality (approximation)
    eq_radius = (volume * 3 / (4 * np.pi)) ** (1/3) if volume > 0 else 0
    hemisphericality = min(1.0, eq_radius / max(height_diff, 1e-6))
    
    # Fractality (simplified: 0 for now — needs perimeter at multiple scales)
    fractality = 0.0
    
    # Cubeness: volume / bbox_volume
    bbox_vol = bb_width * bb_length * bb_height
    cubeness = min(1.0, volume / max(bbox_vol, 1e-6))
    
    # Circumference (2D convex hull perimeter)
    circumference = perimeter
    
    # Aligned bounding box (same as bbox for now, PCA alignment deferred)
    aligned_bb_width = bb_width
    aligned_bb_length = bb_length
    aligned_bb_height = bb_height
    
    # Number of vertices
    num_vertices = n_verts
    
    # Axis symmetry (simplified)
    axes_symmetry = 0.0
    
    return {
        "bounding_box_width": bb_width,
        "bounding_box_length": bb_length,
        "area": area,
        "perimeter": perimeter,
        "perimeter_ind": perimeter_ind,
        "volume": volume,
        "convex_hull_area": convex_hull_area,
        "convex_hull_volume": convex_hull_volume,
        "ave_centroid_distance": ave_centroid_distance,
        "height_diff": height_diff,
        "num_floors": num_floors,
        "axes_symmetry": axes_symmetry,
        "compactness_2d": compactness_2d,
        "compactness_3d": compactness_3d,
        "density": density,
        "elongation": elongation,
        "shape_ind": shape_ind,
        "hemisphericality": hemisphericality,
        "fractality": fractality,
        "cubeness": cubeness,
        "circumference": circumference,
        "aligned_bounding_box_width": aligned_bb_width,
        "aligned_bounding_box_length": aligned_bb_length,
        "aligned_bounding_box_height": aligned_bb_height,
        "num_vertices": num_vertices,
    }


def crawl_city(city, n_target, out_dir, page_size=100, min_surfaces=10, sleep=0.25):
    """Crawl 3D BAG for a city, extract multi-LoD meshes + 25 properties."""
    bbox = CITY_BBOX.get(city)
    if not bbox:
        raise ValueError(f"Unknown city: {city}. Known: {list(CITY_BBOX.keys())}")
    
    os.makedirs(out_dir, exist_ok=True)
    
    per_lod = {LOD_KEY[l]: {} for l in LODS}
    properties_per_lod = {LOD_KEY[l]: {} for l in LODS}
    seen = set()
    
    url = f"{API}/collections/pand/items?limit={page_size}"
    if bbox:
        url += f"&bbox={bbox}"
    
    pages = kept = 0
    t0 = time.time()
    
    print(f"[{city}] Starting crawl: n_target={n_target}, bbox={bbox}")
    
    while url and kept < n_target:
        try:
            page = _get(url)
        except Exception as e:
            print(f"  ERROR page {pages}: {e}")
            break
        
        transform = page.get("metadata", {}).get("transform", {"scale": [1,1,1], "translate": [0,0,0]})
        
        for feat in page.get("features", []):
            bid = bag_id_from_feature(feat)
            if bid in seen:
                continue
            seen.add(bid)
            feat["_transform"] = transform
            
            try:
                blds = extract_building(feat, min_surfaces)
            except Exception:
                continue
            
            if not blds:
                continue
            
            for lod_key, rec in blds.items():
                per_lod[lod_key][bid] = rec
                # Compute 25 properties
                try:
                    props = compute_25_properties(rec)
                    properties_per_lod[lod_key][bid] = props
                except Exception:
                    properties_per_lod[lod_key][bid] = {}
            
            kept += 1
            if kept >= n_target:
                break
        
        pages += 1
        nxt = [l["href"] for l in page.get("links", []) if l.get("rel") == "next"]
        url = nxt[0] if nxt else None
        
        if pages % 10 == 0:
            elapsed = time.time() - t0
            rate = kept / max(elapsed, 1)
            eta = (n_target - kept) / max(rate, 0.01) / 60
            print(f"  [{city}] pages={pages} kept={kept} seen={len(seen)} "
                  f"rate={rate:.0f}/s elapsed={elapsed:.0f}s ETA={eta:.1f}min", flush=True)
        
        time.sleep(sleep)
    
    # Save mesh records
    import joblib
    for lod_key, d in per_lod.items():
        fpath = os.path.join(out_dir, f"3dbag_{lod_key}.joblib")
        joblib.dump(d, fpath)
        print(f"  Saved {len(d)} records → {fpath}")
    
    # Save property vectors as parquet (if pandas available)
    try:
        import pandas as pd
        for lod_key, props_dict in properties_per_lod.items():
            if props_dict:
                df = pd.DataFrame.from_dict(props_dict, orient='index')
                df.index.name = 'bag_id'
                fpath = os.path.join(out_dir, f"properties_{lod_key}.parquet")
                df.to_parquet(fpath)
                print(f"  Saved {len(df)} property vectors → {fpath}")
    except ImportError:
        # Fallback: save as JSON
        for lod_key, props_dict in properties_per_lod.items():
            fpath = os.path.join(out_dir, f"properties_{lod_key}.json")
            with open(fpath, 'w') as f:
                json.dump(props_dict, f)
            print(f"  Saved {len(props_dict)} property vectors → {fpath}")
    
    # Manifest
    common_ids = set(per_lod["lod12"]) & set(per_lod["lod13"]) & set(per_lod["lod22"])
    manifest = {
        "city": city,
        "n_kept": kept,
        "pages": pages,
        "bbox": bbox,
        "min_surfaces": min_surfaces,
        "counts_per_lod": {k: len(v) for k, v in per_lod.items()},
        "n_common_all_lods": len(common_ids),
        "elapsed_sec": round(time.time() - t0, 1),
        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
    }
    with open(os.path.join(out_dir, "manifest.json"), "w") as f:
        json.dump(manifest, f, indent=2)
    
    print(f"[{city}] DONE: kept={kept} common={len(common_ids)} counts={manifest['counts_per_lod']}")
    return manifest


if __name__ == "__main__":
    ap = argparse.ArgumentParser(description="STER Multi-City 3D BAG Crawler")
    ap.add_argument("--city", type=str, required=True, 
                    choices=list(CITY_BBOX.keys()),
                    help="City to crawl")
    ap.add_argument("--n", type=int, default=50000, help="Target buildings")
    ap.add_argument("--out", type=str, default=None, help="Output dir (default: data/<city>/)")
    ap.add_argument("--page_size", type=int, default=100)
    ap.add_argument("--min_surfaces", type=int, default=10)
    ap.add_argument("--sleep", type=float, default=0.25)
    a = ap.parse_args()
    
    out_dir = a.out or os.path.join("data", a.city)
    crawl_city(a.city, a.n, out_dir, a.page_size, a.min_surfaces, a.sleep)