#!/usr/bin/env python3
"""Recreate nuScenes-NRS road masks from an authorized nuScenes release.

Only the official nuScenes metadata and sensor files supplied by the user are
read. The source tree is never modified. The implementation mirrors the
historical projection/triangulation post-processing used for nuScenes-NRS.
"""

from __future__ import annotations

import argparse
import json
from pathlib import Path

import cv2
import numpy as np
from scipy.spatial import Delaunay

try:
    import ijson  # type: ignore
except ImportError:  # pragma: no cover - fallback for small metadata exports
    ijson = None


DRIVEABLE_SURFACE_LABEL = 24
IMAGE_WIDTH = 1600
IMAGE_HEIGHT = 900
MAX_EDGE_LENGTH = 40.0
CLOSE_SIZE = 15
CLOSE_ITER = 2
SMOOTH_FACTOR = 0.01
FINAL_ERODE_SIZE = 5
FINAL_ERODE_ITER = 1


def iter_records(path: Path):
    """Yield records from a nuScenes JSON array without requiring a huge RAM load."""
    if ijson is not None:
        with path.open("rb") as handle:
            yield from ijson.items(handle, "item")
        return
    with path.open("r", encoding="utf-8") as handle:
        records = json.load(handle)
    yield from records


def load_json(path: Path):
    with path.open("r", encoding="utf-8") as handle:
        return json.load(handle)


def selected_records(path: Path, wanted: set[str]) -> dict:
    found = {}
    for row in iter_records(path):
        token = row.get("token")
        if token in wanted:
            found[token] = row
            if len(found) == len(wanted):
                break
    missing = wanted - found.keys()
    if missing:
        raise RuntimeError(f"{path.name}: missing {len(missing)} requested records")
    return found


SENSOR_CHANNELS = ("CAM_FRONT", "LIDAR_TOP")


def _channel_from_filename(filename: str) -> str | None:
    """Return a nuScenes channel encoded in a sample-data filename.

    Official nuScenes ``sample_data.json`` records do not carry a ``channel``
    field; their ``samples/`` and ``sweeps/`` paths do.  A few converted
    metadata exports do add the field, and those are handled by
    :func:`index_sample_data` before this helper is called.
    """
    path_parts = Path(filename).parts
    for channel in SENSOR_CHANNELS:
        if channel in path_parts:
            return channel
    return None


def index_sample_data(metadata: Path, wanted: set[str]) -> tuple[dict, dict]:
    """Index CAM_FRONT/LIDAR_TOP records for the requested sample tokens.

    The official nuScenes ``sample.json`` table intentionally contains no
    ``data`` mapping.  That mapping is assembled by the devkit from
    ``sample_data.json`` and the sensor/calibration tables.  This function
    performs the same assembly while streaming ``sample_data.json`` so the
    generator does not need to load that large table into memory.

    Returns ``(records_by_token, channels_by_sample)``.  Each requested sample
    must have exactly one key-frame record for both channels; missing or
    duplicate records raise a descriptive ``RuntimeError``.
    """
    # calibrated_sensor.json and sensor.json are small (dozens of records), so
    # loading them once gives us a reliable channel fallback when a converted
    # filename does not retain the standard ``.../<CHANNEL>/...`` path.
    calibrated_path = metadata / "calibrated_sensor.json"
    sensor_path = metadata / "sensor.json"
    calibrated = {
        row["token"]: row for row in iter_records(calibrated_path)
    }
    sensors = {
        row["token"]: row for row in iter_records(sensor_path)
    } if sensor_path.is_file() else {}

    records_by_token = {}
    channels_by_sample = {token: {} for token in wanted}
    sample_data_path = metadata / "sample_data.json"
    for row in iter_records(sample_data_path):
        sample_token = row.get("sample_token")
        if sample_token not in wanted:
            continue
        # A sample can have many historical sweeps.  Only key-frame records
        # correspond to the samples listed in sample.json.  Some compact
        # exports omit is_key_frame; in that case retain the row and let the
        # channel/duplicate checks below decide.
        if row.get("is_key_frame") is False:
            continue

        candidates = []
        direct_channel = row.get("channel")
        if direct_channel:
            candidates.append(str(direct_channel))
        filename_channel = _channel_from_filename(str(row.get("filename", "")))
        if filename_channel:
            candidates.append(filename_channel)
        calibration = calibrated.get(row.get("calibrated_sensor_token"))
        if calibration is not None:
            sensor = sensors.get(calibration.get("sensor_token"))
            if sensor and sensor.get("channel"):
                candidates.append(str(sensor["channel"]))

        # Keep the first supported channel, but reject contradictory metadata
        # instead of silently associating a LiDAR record with the camera.
        supported = {channel for channel in candidates if channel in SENSOR_CHANNELS}
        if len(supported) > 1:
            raise RuntimeError(
                f"{sample_data_path.name}: conflicting channels for record "
                f"{row.get('token')}: {sorted(supported)}"
            )
        if not supported:
            continue
        channel = next(iter(supported))
        previous_token = channels_by_sample[sample_token].get(channel)
        if previous_token is not None and previous_token != row.get("token"):
            raise RuntimeError(
                f"{sample_data_path.name}: sample {sample_token} has multiple "
                f"key-frame {channel} records ({previous_token}, {row.get('token')})"
            )
        token = row.get("token")
        if not token:
            raise RuntimeError(f"{sample_data_path.name}: record has no token")
        channels_by_sample[sample_token][channel] = token
        records_by_token[token] = row

    missing = {
        sample_token: sorted(set(SENSOR_CHANNELS) - set(channels))
        for sample_token, channels in channels_by_sample.items()
        if set(channels) != set(SENSOR_CHANNELS)
    }
    if missing:
        preview = ", ".join(
            f"{token}: {','.join(channels)}" for token, channels in list(missing.items())[:5]
        )
        raise RuntimeError(
            f"{sample_data_path.name}: missing requested key-frame records ({preview})"
        )
    return records_by_token, channels_by_sample


def quaternion_matrix(rotation) -> np.ndarray:
    w, x, y, z = [float(value) for value in rotation]
    norm = w * w + x * x + y * y + z * z
    if norm < 1e-15:
        raise ValueError("zero-norm quaternion")
    s = 2.0 / norm
    return np.array(
        [
            [1 - s * (y * y + z * z), s * (x * y - z * w), s * (x * z + y * w)],
            [s * (x * y + z * w), 1 - s * (x * x + z * z), s * (y * z - x * w)],
            [s * (x * z - y * w), s * (y * z + x * w), 1 - s * (x * x + y * y)],
        ],
        dtype=np.float64,
    )


def transform_matrix(translation, rotation, inverse=False) -> np.ndarray:
    matrix = np.eye(4, dtype=np.float64)
    rotation_matrix = quaternion_matrix(rotation)
    translation = np.asarray(translation, dtype=np.float64)
    if inverse:
        rotation_matrix = rotation_matrix.T
        matrix[:3, :3] = rotation_matrix
        matrix[:3, 3] = rotation_matrix @ (-translation)
    else:
        matrix[:3, :3] = rotation_matrix
        matrix[:3, 3] = translation
    return matrix


def filter_triangles(points: np.ndarray, simplices: np.ndarray) -> list[np.ndarray]:
    triangles = []
    for simplex in simplices:
        p0, p1, p2 = points[simplex]
        if max(
            np.linalg.norm(p1 - p0),
            np.linalg.norm(p2 - p1),
            np.linalg.norm(p0 - p2),
        ) < MAX_EDGE_LENGTH:
            triangles.append(np.asarray([p0, p1, p2], dtype=np.int32))
    return triangles


def smooth_mask(mask: np.ndarray) -> np.ndarray:
    if not np.any(mask):
        return mask
    close_kernel = cv2.getStructuringElement(
        cv2.MORPH_ELLIPSE, (CLOSE_SIZE, CLOSE_SIZE)
    )
    closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, close_kernel, iterations=CLOSE_ITER)
    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    result = np.zeros_like(mask)
    for contour in contours:
        if cv2.contourArea(contour) < 1000:
            continue
        epsilon = SMOOTH_FACTOR * cv2.arcLength(contour, True)
        polygon = cv2.approxPolyDP(contour, epsilon, True)
        cv2.fillPoly(result, [polygon], 255)
    erode_kernel = cv2.getStructuringElement(
        cv2.MORPH_ELLIPSE, (FINAL_ERODE_SIZE, FINAL_ERODE_SIZE)
    )
    return cv2.erode(result, erode_kernel, iterations=FINAL_ERODE_ITER)


def make_mask(
    dataroot: Path,
    version: str,
    sample: dict,
    sample_data: dict,
    sample_channels: dict[str, str],
    calib: dict,
    poses: dict,
) -> np.ndarray:
    cam_sd = sample_data[sample_channels["CAM_FRONT"]]
    lidar_sd = sample_data[sample_channels["LIDAR_TOP"]]
    cam_calib = calib[cam_sd["calibrated_sensor_token"]]
    lidar_calib = calib[lidar_sd["calibrated_sensor_token"]]
    cam_pose = poses[cam_sd["ego_pose_token"]]
    lidar_pose = poses[lidar_sd["ego_pose_token"]]

    lidar_path = dataroot / lidar_sd["filename"]
    label_path = dataroot / "lidarseg" / version / f"{lidar_sd['token']}_lidarseg.bin"
    if not lidar_path.is_file():
        raise FileNotFoundError(lidar_path)
    if not label_path.is_file():
        raise FileNotFoundError(label_path)

    points = np.fromfile(lidar_path, dtype=np.float32)
    if points.size % 5:
        raise RuntimeError(f"unexpected point record size in {lidar_path}")
    points = points.reshape((-1, 5))[:, :3]
    labels = np.fromfile(label_path, dtype=np.uint8)
    if labels.size != points.shape[0]:
        raise RuntimeError(f"point/label count mismatch for {sample['token']}")
    points = points[labels == DRIVEABLE_SURFACE_LABEL]

    lidar_to_camera = (
        transform_matrix(cam_calib["translation"], cam_calib["rotation"], inverse=True)
        @ transform_matrix(cam_pose["translation"], cam_pose["rotation"], inverse=True)
        @ transform_matrix(lidar_pose["translation"], lidar_pose["rotation"])
        @ transform_matrix(lidar_calib["translation"], lidar_calib["rotation"])
    )
    homogeneous = np.column_stack((points, np.ones(len(points), dtype=np.float64)))
    camera_points = (lidar_to_camera @ homogeneous.T)[:3]
    valid_depth = camera_points[2] > 0.1
    camera_points = camera_points[:, valid_depth]
    intrinsic = np.asarray(cam_calib["camera_intrinsic"], dtype=np.float64)
    projected = intrinsic @ camera_points
    if projected.shape[1]:
        projected[:2] /= projected[2:3]
    inside = (
        (projected[0] >= 0)
        & (projected[0] < IMAGE_WIDTH)
        & (projected[1] >= 0)
        & (projected[1] < IMAGE_HEIGHT)
    ) if projected.shape[1] else np.zeros(0, dtype=bool)
    points_2d = projected[:2, inside].T.astype(np.float32)

    mask = np.zeros((IMAGE_HEIGHT, IMAGE_WIDTH), dtype=np.uint8)
    if len(points_2d) >= 3:
        try:
            triangulation = Delaunay(points_2d)
            for triangle in filter_triangles(points_2d, triangulation.simplices):
                cv2.fillPoly(mask, [triangle], 255)
        except Exception:
            # Degenerate projected point sets produce an empty raw mask in the
            # historical implementation; retain that deterministic behavior.
            pass
    mask = smooth_mask(mask)
    rgb = np.zeros((IMAGE_HEIGHT, IMAGE_WIDTH, 3), dtype=np.uint8)
    rgb[:, :, 2] = mask  # cv2 writes BGR; channel 2 is R in the PNG.
    return rgb


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--dataroot", type=Path, required=True)
    parser.add_argument("--version", default="v1.0-trainval")
    parser.add_argument("--split", choices=("training", "validation"), required=True)
    parser.add_argument("--split-file", type=Path, required=True)
    parser.add_argument("--output-root", type=Path, required=True)
    parser.add_argument("--overwrite", action="store_true")
    args = parser.parse_args()

    dataroot = args.dataroot.resolve()
    metadata = dataroot / args.version
    split_file = args.split_file.resolve()
    tokens = [line.strip() for line in split_file.read_text(encoding="utf-8").splitlines() if line.strip()]
    if len(tokens) != len(set(tokens)):
        raise SystemExit("split file contains duplicate tokens")
    samples = {row["token"]: row for row in load_json(metadata / "sample.json")}
    missing_samples = [token for token in tokens if token not in samples]
    if missing_samples:
        raise SystemExit(f"{len(missing_samples)} split tokens are absent from sample.json")
    sample_data, sample_channels_by_sample = index_sample_data(metadata, set(tokens))
    sample_data_tokens = set(sample_data)
    calib_tokens = {
        sample_data[token]["calibrated_sensor_token"] for token in sample_data_tokens
    }
    pose_tokens = {sample_data[token]["ego_pose_token"] for token in sample_data_tokens}
    calib = selected_records(metadata / "calibrated_sensor.json", calib_tokens)
    poses = selected_records(metadata / "ego_pose.json", pose_tokens)

    out_dir = args.output_root.resolve() / args.split / "masks"
    out_dir.mkdir(parents=True, exist_ok=True)
    failures = []
    for index, token in enumerate(tokens, start=1):
        output = out_dir / f"{token}.png"
        if output.exists() and not args.overwrite:
            continue
        try:
            image = make_mask(
                dataroot,
                args.version,
                samples[token],
                sample_data,
                sample_channels_by_sample[token],
                calib,
                poses,
            )
            if not cv2.imwrite(str(output), image):
                raise OSError(f"cv2.imwrite failed for {output}")
        except Exception as exc:  # keep all missing records visible to the user
            failures.append((token, repr(exc)))
        if index % 100 == 0 or index == len(tokens):
            print(f"{args.split}: {index}/{len(tokens)}")
    if failures:
        for token, error in failures[:20]:
            print(f"FAIL {token}: {error}")
        raise SystemExit(f"generation failed for {len(failures)} samples")
    produced = sorted(path.stem for path in out_dir.glob("*.png"))
    if produced != sorted(tokens):
        raise SystemExit(f"output token set differs from split ({len(produced)} files)")
    print(f"wrote {len(produced)} masks to {out_dir}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())