convert_to_mcap.py

import argparse
import json
import math
import os
from pathlib import Path
from typing import Dict, Tuple

import numpy as np
import rospy
from diagnostic_msgs.msg import DiagnosticArray, DiagnosticStatus, KeyValue
from mcap.writer import Writer, CompressionType
from nuscenes.can_bus.can_bus_api import NuScenesCanBus
from nuscenes.eval.common.utils import quaternion_yaw
from nuscenes.map_expansion.map_api import NuScenesMap
from nuscenes.nuscenes import NuScenes
from PIL import Image
from pypcd import pypcd

from pyquaternion import Quaternion
from tqdm import tqdm

from foxglove.CameraCalibration_pb2 import CameraCalibration
from foxglove.CompressedImage_pb2 import CompressedImage
from foxglove.FrameTransform_pb2 import FrameTransform
from foxglove.Grid_pb2 import Grid
from foxglove.ImageAnnotations_pb2 import ImageAnnotations
from foxglove.LinePrimitive_pb2 import LinePrimitive
from foxglove.LocationFix_pb2 import LocationFix
from foxglove.PackedElementField_pb2 import PackedElementField
from foxglove.PointCloud_pb2 import PointCloud
from foxglove.PoseInFrame_pb2 import PoseInFrame
from foxglove.PointsAnnotation_pb2 import PointsAnnotation
from foxglove.Quaternion_pb2 import Quaternion as foxglove_Quaternion
from foxglove.SceneUpdate_pb2 import SceneUpdate
from foxglove.Vector3_pb2 import Vector3
from ProtobufWriter import ProtobufWriter
from RosmsgWriter import RosmsgWriter

with open(Path(__file__).parent / "turbomap.json") as f:
    TURBOMAP_DATA = np.array(json.load(f))


# https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes/can_bus/README.md#imu
IMU_JSON_SCHEMA = {
    "type": "object",
    "properties": {
        "linear_accel": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
        "q": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
                "w": {"type": "number"},
            },
        },
        "rotation_rate": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
    },
}

# https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes/can_bus/README.md#pose
ODOM_JSON_SCHEMA = {
    "type": "object",
    "properties": {
        "accel": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
        "orientation": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
                "w": {"type": "number"},
            },
        },
        "pos": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
        "rotation_rate": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
        "vel": {
            "type": "object",
            "properties": {
                "x": {"type": "number"},
                "y": {"type": "number"},
                "z": {"type": "number"},
            },
        },
    },
}


def load_bitmap(dataroot: str, map_name: str, layer_name: str) -> np.ndarray:
    """render bitmap map layers. Currently these are:
    - semantic_prior: The semantic prior (driveable surface and sidewalks) mask from nuScenes 1.0.
    - basemap: The HD lidar basemap used for localization and as general context.

    :param dataroot: Path of the nuScenes dataset.
    :param map_name: Which map out of `singapore-onenorth`, `singepore-hollandvillage`, `singapore-queenstown` and
        'boston-seaport'.
    :param layer_name: The type of bitmap map, `semanitc_prior` or `basemap.
    """
    # Load bitmap.
    if layer_name == "basemap":
        map_path = os.path.join(dataroot, "maps", "basemap", map_name + ".png")
    elif layer_name == "semantic_prior":
        map_hashes = {
            "singapore-onenorth": "53992ee3023e5494b90c316c183be829",
            "singapore-hollandvillage": "37819e65e09e5547b8a3ceaefba56bb2",
            "singapore-queenstown": "93406b464a165eaba6d9de76ca09f5da",
            "boston-seaport": "36092f0b03a857c6a3403e25b4b7aab3",
        }
        map_hash = map_hashes[map_name]
        map_path = os.path.join(dataroot, "maps", map_hash + ".png")
    else:
        raise Exception("Error: Invalid bitmap layer: %s" % layer_name)

    # Convert to numpy.
    if os.path.exists(map_path):
        image = np.array(Image.open(map_path).convert("L"))
    else:
        raise Exception("Error: Cannot find %s %s! Please make sure that the map is correctly installed." % (layer_name, map_path))

    # Invert semantic prior colors.
    if layer_name == "semantic_prior":
        image = image.max() - image

    return image


EARTH_RADIUS_METERS = 6.378137e6
REFERENCE_COORDINATES = {
    "boston-seaport": [42.336849169438615, -71.05785369873047],
    "singapore-onenorth": [1.2882100868743724, 103.78475189208984],
    "singapore-hollandvillage": [1.2993652317780957, 103.78217697143555],
    "singapore-queenstown": [1.2782562240223188, 103.76741409301758],
}


def get_coordinate(ref_lat: float, ref_lon: float, bearing: float, dist: float) -> Tuple[float, float]:
    """
    Using a reference coordinate, extract the coordinates of another point in space given its distance and bearing
    to the reference coordinate. For reference, please see: https://www.movable-type.co.uk/scripts/latlong.html.
    :param ref_lat: Latitude of the reference coordinate in degrees, ie: 42.3368.
    :param ref_lon: Longitude of the reference coordinate in degrees, ie: 71.0578.
    :param bearing: The clockwise angle in radians between target point, reference point and the axis pointing north.
    :param dist: The distance in meters from the reference point to the target point.
    :return: A tuple of lat and lon.
    """
    lat, lon = math.radians(ref_lat), math.radians(ref_lon)
    angular_distance = dist / EARTH_RADIUS_METERS

    target_lat = math.asin(math.sin(lat) * math.cos(angular_distance) + math.cos(lat) * math.sin(angular_distance) * math.cos(bearing))
    target_lon = lon + math.atan2(
        math.sin(bearing) * math.sin(angular_distance) * math.cos(lat),
        math.cos(angular_distance) - math.sin(lat) * math.sin(target_lat),
    )
    return math.degrees(target_lat), math.degrees(target_lon)


def derive_latlon(location: str, pose: Dict[str, float]):
    """
    For each pose value, extract its respective lat/lon coordinate and timestamp.

    This makes the following two assumptions in order to work:
        1. The reference coordinate for each map is in the south-western corner.
        2. The origin of the global poses is also in the south-western corner (and identical to 1).
    :param location: The name of the map the poses correspond to, ie: 'boston-seaport'.
    :param poses: All nuScenes egopose dictionaries of a scene.
    :return: A list of dicts (lat/lon coordinates and timestamps) for each pose.
    """
    assert location in REFERENCE_COORDINATES.keys(), f"Error: The given location: {location}, has no available reference."

    reference_lat, reference_lon = REFERENCE_COORDINATES[location]
    x, y = pose["translation"][:2]
    bearing = math.atan(x / y)
    distance = math.sqrt(x**2 + y**2)
    lat, lon = get_coordinate(reference_lat, reference_lon, bearing, distance)
    return lat, lon


def get_translation(data):
    return Vector3(x=data["translation"][0], y=data["translation"][1], z=data["translation"][2])


def get_rotation(data):
    return foxglove_Quaternion(x=data["rotation"][1], y=data["rotation"][2], z=data["rotation"][3], w=data["rotation"][0])


def get_time(data):
    t = rospy.Time()
    t.secs, msecs = divmod(data["timestamp"], 1_000_000)
    t.nsecs = msecs * 1000

    return t


def get_utime(data):
    t = rospy.Time()
    t.secs, msecs = divmod(data["utime"], 1_000_000)
    t.nsecs = msecs * 1000

    return t


# See:
# https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
# https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f
def turbomap(x):
    np.clip(x, 0, 1, out=x)
    x *= 255
    a = x.astype(np.uint8)
    x -= a  # compute "f" in place
    b = np.minimum(254, a)
    b += 1
    color_a = TURBOMAP_DATA[a]
    color_b = TURBOMAP_DATA[b]
    color_b -= color_a
    color_b *= x[:, np.newaxis]
    return np.add(color_a, color_b, out=color_b)


def get_categories(nusc, first_sample):
    categories = set()
    sample_lidar = first_sample
    while sample_lidar is not None:
        sample = nusc.get("sample", sample_lidar["sample_token"])
        for annotation_id in sample["anns"]:
            ann = nusc.get("sample_annotation", annotation_id)
            categories.add(ann["category_name"])
        sample_lidar = nusc.get("sample_data", sample_lidar["next"]) if sample_lidar.get("next") != "" else None
    return categories


PCD_TO_PACKED_ELEMENT_TYPE_MAP = {
    ("I", 1): PackedElementField.INT8,
    ("U", 1): PackedElementField.UINT8,
    ("I", 2): PackedElementField.INT16,
    ("U", 2): PackedElementField.UINT16,
    ("I", 4): PackedElementField.INT32,
    ("U", 4): PackedElementField.UINT32,
    ("F", 4): PackedElementField.FLOAT32,
    ("F", 8): PackedElementField.FLOAT64,
}


def get_radar(data_path, sample_data, frame_id) -> PointCloud:
    pc_filename = data_path / sample_data["filename"]
    pc = pypcd.PointCloud.from_path(pc_filename)
    msg = PointCloud()
    msg.frame_id = frame_id
    msg.timestamp.FromMicroseconds(sample_data["timestamp"])
    offset = 0
    for name, size, count, ty in zip(pc.fields, pc.size, pc.count, pc.type):
        assert count == 1
        msg.fields.add(name=name, offset=offset, type=PCD_TO_PACKED_ELEMENT_TYPE_MAP[(ty, size)])
        offset += size

    msg.point_stride = offset
    msg.data = pc.pc_data.tobytes()
    return msg


def get_camera(data_path, sample_data, frame_id):
    jpg_filename = data_path / sample_data["filename"]
    msg = CompressedImage()
    msg.timestamp.FromMicroseconds(sample_data["timestamp"])
    msg.frame_id = frame_id
    msg.format = "jpeg"
    with open(jpg_filename, "rb") as jpg_file:
        msg.data = jpg_file.read()
    return msg


def get_camera_info(nusc, sample_data, frame_id):
    calib = nusc.get("calibrated_sensor", sample_data["calibrated_sensor_token"])

    msg_info = CameraCalibration()
    msg_info.timestamp.FromMicroseconds(sample_data["timestamp"])
    msg_info.frame_id = frame_id
    msg_info.height = sample_data["height"]
    msg_info.width = sample_data["width"]
    msg_info.K[:] = (calib["camera_intrinsic"][r][c] for r in range(3) for c in range(3))
    msg_info.R[:] = [1, 0, 0, 0, 1, 0, 0, 0, 1]
    msg_info.P[:] = [msg_info.K[0], msg_info.K[1], msg_info.K[2], 0, msg_info.K[3], msg_info.K[4], msg_info.K[5], 0, 0, 0, 1, 0]
    return msg_info


def get_lidar(data_path, sample_data, frame_id) -> PointCloud:
    pc_filename = data_path / sample_data["filename"]

    with open(pc_filename, "rb") as pc_file:
        msg = PointCloud()
        msg.frame_id = frame_id
        msg.timestamp.FromMicroseconds(sample_data["timestamp"])
        msg.fields.add(name="x", offset=0, type=PackedElementField.FLOAT32),
        msg.fields.add(name="y", offset=4, type=PackedElementField.FLOAT32),
        msg.fields.add(name="z", offset=8, type=PackedElementField.FLOAT32),
        msg.fields.add(name="intensity", offset=12, type=PackedElementField.FLOAT32),
        msg.fields.add(name="ring", offset=16, type=PackedElementField.FLOAT32),
        msg.point_stride = len(msg.fields) * 4  # 4 bytes per field
        msg.data = pc_file.read()
        return msg


def get_lidar_image_annotations(nusc, sample_lidar, sample_data, frame_id):
    # lidar image markers in camera frame
    points, coloring, _ = nusc.explorer.map_pointcloud_to_image(
        pointsensor_token=sample_lidar["token"],
        camera_token=sample_data["token"],
        render_intensity=True,
    )
    points = points.transpose()

    msg = ImageAnnotations()
    ann = msg.points.add()
    ann.timestamp.FromMicroseconds(sample_data["timestamp"])
    ann.type = PointsAnnotation.Type.POINTS
    ann.thickness = 2
    for p in points:
        ann.points.add(x=p[0], y=p[1])
    for c in turbomap(coloring):
        ann.outline_colors.add(r=c[0], g=c[1], b=c[2], a=1)
    return msg


def write_boxes_image_annotations(nusc, protobuf_writer, anns, sample_data, frame_id, topic_ns, stamp):
    # annotation boxes
    collector = Collector()
    _, boxes, camera_intrinsic = nusc.get_sample_data(sample_data["token"])
    for box in boxes:
        c = np.array(nusc.explorer.get_color(box.name)) / 255.0
        box.render(collector, view=camera_intrinsic, normalize=True, colors=(c, c, c))

    msg = ImageAnnotations()

    ann = msg.points.add()
    ann.timestamp.FromMicroseconds(sample_data["timestamp"])
    ann.type = PointsAnnotation.Type.LINE_LIST
    ann.thickness = 2
    for p in collector.points:
        ann.points.add(x=p[0], y=p[1])
    for c in collector.colors:
        ann.outline_colors.add(r=c[0], g=c[1], b=c[2], a=1)

    protobuf_writer.write_message(topic_ns + "/annotations", msg, ann.timestamp.ToNanoseconds())


def write_drivable_area(protobuf_writer, nusc_map, ego_pose, stamp):
    translation = ego_pose["translation"]
    rotation = Quaternion(ego_pose["rotation"])
    yaw_radians = quaternion_yaw(rotation)
    yaw_degrees = yaw_radians / np.pi * 180
    patch_box = (translation[0], translation[1], 32, 32)
    canvas_size = (patch_box[2] * 10, patch_box[3] * 10)

    drivable_area = nusc_map.get_map_mask(patch_box, yaw_degrees, ["drivable_area"], canvas_size)[0]

    msg = Grid()
    msg.timestamp.FromNanoseconds(stamp.to_nsec())
    msg.frame_id = "map"
    msg.cell_size.x = 0.1
    msg.cell_size.y = 0.1
    msg.column_count = drivable_area.shape[1]
    msg.row_stride = drivable_area.shape[1]
    msg.cell_stride = 1
    msg.fields.add(name="drivable_area", offset=0, type=PackedElementField.UINT8)
    msg.pose.position.x = translation[0] - (16 * math.cos(yaw_radians)) + (16 * math.sin(yaw_radians))
    msg.pose.position.y = translation[1] - (16 * math.sin(yaw_radians)) - (16 * math.cos(yaw_radians))
    msg.pose.position.z = 0.01  # Drivable area sits 1cm above the map
    q = Quaternion(axis=(0, 0, 1), radians=yaw_radians)
    msg.pose.orientation.x = q.x
    msg.pose.orientation.y = q.y
    msg.pose.orientation.z = q.z
    msg.pose.orientation.w = q.w
    msg.data = drivable_area.astype(np.uint8).tobytes()

    protobuf_writer.write_message("/drivable_area", msg, stamp.to_nsec())


def get_imu_msg(imu_data):
    timestamp = get_utime(imu_data)

    msg = {
        "linear_accel": {
            "x": imu_data["linear_accel"][0],
            "y": imu_data["linear_accel"][1],
            "z": imu_data["linear_accel"][2],
        },
        "q": {
            "w": imu_data["q"][0],
            "x": imu_data["q"][1],
            "y": imu_data["q"][2],
            "z": imu_data["q"][3],
        },
        "rotation_rate": {
            "x": imu_data["rotation_rate"][0],
            "y": imu_data["rotation_rate"][1],
            "z": imu_data["rotation_rate"][2],
        },
    }

    return (timestamp, "/imu", json.dumps(msg).encode())


def get_odom_msg(pose_data):
    timestamp = get_utime(pose_data)

    msg = {
        "accel": {
            "x": pose_data["accel"][0],
            "y": pose_data["accel"][1],
            "z": pose_data["accel"][2],
        },
        "orientation": {
            "w": pose_data["orientation"][0],
            "x": pose_data["orientation"][1],
            "y": pose_data["orientation"][2],
            "z": pose_data["orientation"][3],
        },
        "pos": {
            "x": pose_data["pos"][0],
            "y": pose_data["pos"][1],
            "z": pose_data["pos"][2],
        },
        "rotation_rate": {
            "x": pose_data["rotation_rate"][0],
            "y": pose_data["rotation_rate"][1],
            "z": pose_data["rotation_rate"][2],
        },
        "vel": {
            "x": pose_data["vel"][0],
            "y": pose_data["vel"][1],
            "z": pose_data["vel"][2],
        },
    }

    return (timestamp, "/odom", json.dumps(msg).encode())


def get_basic_can_msg(name, diag_data):
    values = []
    for (key, value) in diag_data.items():
        if key != "utime":
            values.append(KeyValue(key=key, value=str(round(value, 4))))

    msg = DiagnosticArray()
    msg.header.stamp = get_utime(diag_data)
    msg.status.append(DiagnosticStatus(name=name, level=0, message="OK", values=values))

    return (msg.header.stamp, "/diagnostics", msg)


def get_ego_tf(ego_pose):
    ego_tf = FrameTransform()
    ego_tf.parent_frame_id = "map"
    ego_tf.timestamp.FromMicroseconds(ego_pose["timestamp"])
    ego_tf.child_frame_id = "base_link"
    ego_tf.translation.CopyFrom(get_translation(ego_pose))
    ego_tf.rotation.CopyFrom(get_rotation(ego_pose))
    return ego_tf


def get_sensor_tf(nusc, sensor_id, sample_data):
    sensor_tf = FrameTransform()
    sensor_tf.parent_frame_id = "base_link"
    sensor_tf.timestamp.FromMicroseconds(sample_data["timestamp"])
    sensor_tf.child_frame_id = sensor_id
    calibrated_sensor = nusc.get("calibrated_sensor", sample_data["calibrated_sensor_token"])
    sensor_tf.translation.CopyFrom(get_translation(calibrated_sensor))
    sensor_tf.rotation.CopyFrom(get_rotation(calibrated_sensor))
    return sensor_tf


def scene_bounding_box(nusc, scene, nusc_map, padding=75.0):
    box = [np.inf, np.inf, -np.inf, -np.inf]
    cur_sample = nusc.get("sample", scene["first_sample_token"])
    while cur_sample is not None:
        sample_lidar = nusc.get("sample_data", cur_sample["data"]["LIDAR_TOP"])
        ego_pose = nusc.get("ego_pose", sample_lidar["ego_pose_token"])
        x, y = ego_pose["translation"][:2]
        box[0] = min(box[0], x)
        box[1] = min(box[1], y)
        box[2] = max(box[2], x)
        box[3] = max(box[3], y)
        cur_sample = nusc.get("sample", cur_sample["next"]) if cur_sample.get("next") != "" else None
    box[0] = max(box[0] - padding, 0.0)
    box[1] = max(box[1] - padding, 0.0)
    box[2] = min(box[2] + padding, nusc_map.canvas_edge[0]) - box[0]
    box[3] = min(box[3] + padding, nusc_map.canvas_edge[1]) - box[1]
    return box


def get_scene_map(nusc, scene, nusc_map, image, stamp):
    x, y, w, h = scene_bounding_box(nusc, scene, nusc_map)
    img_x = int(x * 10)
    img_y = int(y * 10)
    img_w = int(w * 10)
    img_h = int(h * 10)
    img = np.flipud(image)[img_y : img_y + img_h, img_x : img_x + img_w]

    # img values are 0-255
    # convert to a color scale, 0=white and 255=black, in packed RGBA format: 0xFFFFFF00 to 0x00000000
    img = (255 - img) * 0x01010100
    # set alpha to 0xFF for all cells except those that are completely black
    img[img != 0x00000000] |= 0x000000FF

    msg = Grid()
    msg.timestamp.FromNanoseconds(stamp.to_nsec())
    msg.frame_id = "map"
    msg.cell_size.x = 0.1
    msg.cell_size.y = 0.1
    msg.column_count = img_w
    msg.row_stride = img_w * 4
    msg.cell_stride = 4
    msg.fields.add(name="alpha", offset=0, type=PackedElementField.UINT8)
    msg.fields.add(name="blue", offset=1, type=PackedElementField.UINT8)
    msg.fields.add(name="green", offset=2, type=PackedElementField.UINT8)
    msg.fields.add(name="red", offset=3, type=PackedElementField.UINT8)
    msg.pose.position.x = x
    msg.pose.position.y = y
    msg.pose.orientation.w = 1
    msg.data = img.astype("<u4").tobytes()

    return msg


def rectContains(rect, point):
    a, b, c, d = rect
    x, y = point[:2]
    return a <= x < a + c and b <= y < b + d


def get_centerline_markers(nusc, scene, nusc_map, stamp):
    pose_lists = nusc_map.discretize_centerlines(1)
    bbox = scene_bounding_box(nusc, scene, nusc_map)

    contained_pose_lists = []
    for pose_list in pose_lists:
        new_pose_list = []
        for pose in pose_list:
            if rectContains(bbox, pose):
                new_pose_list.append(pose)
        if len(new_pose_list) > 1:
            contained_pose_lists.append(new_pose_list)

    scene_update = SceneUpdate()
    for i, pose_list in enumerate(contained_pose_lists):
        entity = scene_update.entities.add()
        entity.frame_id = "map"
        entity.timestamp.FromNanoseconds(stamp.to_nsec())
        entity.id = f"{i}"
        entity.frame_locked = True
        line = entity.lines.add()
        line.type = LinePrimitive.Type.LINE_STRIP
        line.thickness = 0.1
        line.color.r = 51.0 / 255.0
        line.color.g = 160.0 / 255.0
        line.color.b = 44.0 / 255.0
        line.color.a = 1.0
        line.pose.orientation.w = 1.0
        for pose in pose_list:
            line.points.add(x=pose[0], y=pose[1], z=0)

    return scene_update


def find_closest_lidar(nusc, lidar_start_token, stamp_nsec):
    candidates = []

    next_lidar_token = nusc.get("sample_data", lidar_start_token)["next"]
    while next_lidar_token != "":
        lidar_data = nusc.get("sample_data", next_lidar_token)
        if lidar_data["is_key_frame"]:
            break

        dist_abs = abs(stamp_nsec - get_time(lidar_data).to_nsec())
        candidates.append((dist_abs, lidar_data))
        next_lidar_token = lidar_data["next"]

    if len(candidates) == 0:
        return None

    return min(candidates, key=lambda x: x[0])[1]


def get_car_scene_update(stamp) -> SceneUpdate:
    scene_update = SceneUpdate()
    entity = scene_update.entities.add()
    entity.frame_id = "base_link"
    entity.timestamp.FromNanoseconds(stamp)
    entity.id = "car"
    entity.frame_locked = True
    model = entity.models.add()
    model.pose.position.x = 1
    model.pose.orientation.w = 1
    model.scale.x = 1
    model.scale.y = 1
    model.scale.z = 1
    model.url = "https://assets.foxglove.dev/NuScenes_car_uncompressed.glb"
    return scene_update


class Collector:
    """
    Emulates the Matplotlib Axes class to collect line data.
    """

    def __init__(self):
        self.points = []
        self.colors = []

    def plot(self, xx, yy, color, linewidth):
        x1, x2 = xx
        y1, y2 = yy
        self.points.append((x1, y1))
        self.points.append((x2, y2))
        self.colors.append(color)


def get_num_sample_data(nusc: NuScenes, scene):
    num_sample_data = 0
    sample = nusc.get("sample", scene["first_sample_token"])
    for sample_token in sample["data"].values():
        sample_data = nusc.get("sample_data", sample_token)
        while sample_data is not None:
            num_sample_data += 1
            sample_data = nusc.get("sample_data", sample_data["next"]) if sample_data["next"] != "" else None
    return num_sample_data


def write_scene_to_mcap(nusc: NuScenes, nusc_can: NuScenesCanBus, scene, filepath):
    scene_name = scene["name"]
    log = nusc.get("log", scene["log_token"])
    location = log["location"]
    print(f'Loading map "{location}"')
    data_path = Path(nusc.dataroot)
    nusc_map = NuScenesMap(dataroot=data_path, map_name=location)
    print(f'Loading bitmap "{nusc_map.map_name}"')
    image = load_bitmap(nusc_map.dataroot, nusc_map.map_name, "basemap")
    print(f"Loaded {image.shape} bitmap")
    print(f"vehicle is {log['vehicle']}")

    cur_sample = nusc.get("sample", scene["first_sample_token"])
    pbar = tqdm(total=get_num_sample_data(nusc, scene), unit="sample_data", desc=f"{scene_name} Sample Data", leave=False)

    can_parsers = [
        [nusc_can.get_messages(scene_name, "ms_imu"), 0, get_imu_msg],
        [nusc_can.get_messages(scene_name, "pose"), 0, get_odom_msg],
        [
            nusc_can.get_messages(scene_name, "steeranglefeedback"),
            0,
            lambda x: get_basic_can_msg("Steering Angle", x),
        ],
        [
            nusc_can.get_messages(scene_name, "vehicle_monitor"),
            0,
            lambda x: get_basic_can_msg("Vehicle Monitor", x),
        ],
        [
            nusc_can.get_messages(scene_name, "zoesensors"),
            0,
            lambda x: get_basic_can_msg("Zoe Sensors", x),
        ],
        [
            nusc_can.get_messages(scene_name, "zoe_veh_info"),
            0,
            lambda x: get_basic_can_msg("Zoe Vehicle Info", x),
        ],
    ]

    filepath.parent.mkdir(parents=True, exist_ok=True)

    with open(filepath, "wb") as fp:
        print(f"Writing to {filepath}")
        writer = Writer(fp, compression=CompressionType.LZ4)

        imu_schema_id = writer.register_schema(name="IMU", encoding="jsonschema", data=json.dumps(IMU_JSON_SCHEMA).encode())
        imu_channel_id = writer.register_channel(topic="/imu", message_encoding="json", schema_id=imu_schema_id)

        odom_schema_id = writer.register_schema(name="Pose", encoding="jsonschema", data=json.dumps(ODOM_JSON_SCHEMA).encode())
        odom_channel_id = writer.register_channel(topic="/odom", message_encoding="json", schema_id=odom_schema_id)

        protobuf_writer = ProtobufWriter(writer)
        rosmsg_writer = RosmsgWriter(writer)
        writer.start(profile="", library="nuscenes2mcap")

        writer.add_metadata(
            "scene-info",
            {
                "description": scene["description"],
                "name": scene["name"],
                "location": location,
                "vehicle": log["vehicle"],
                "date_captured": log["date_captured"],
            },
        )

        stamp = get_time(
            nusc.get(
                "ego_pose",
                nusc.get("sample_data", cur_sample["data"]["LIDAR_TOP"])["ego_pose_token"],
            )
        )
        map_msg = get_scene_map(nusc, scene, nusc_map, image, stamp)
        centerlines_msg = get_centerline_markers(nusc, scene, nusc_map, stamp)
        protobuf_writer.write_message("/map", map_msg, stamp.to_nsec())
        protobuf_writer.write_message("/semantic_map", centerlines_msg, stamp.to_nsec())

        while cur_sample is not None:
            sample_lidar = nusc.get("sample_data", cur_sample["data"]["LIDAR_TOP"])
            ego_pose = nusc.get("ego_pose", sample_lidar["ego_pose_token"])
            stamp = get_time(ego_pose)

            # write CAN messages to /pose, /odom, and /diagnostics
            can_msg_events = []
            for i in range(len(can_parsers)):
                (can_msgs, index, msg_func) = can_parsers[i]
                while index < len(can_msgs) and get_utime(can_msgs[index]) < stamp:
                    can_msg_events.append(msg_func(can_msgs[index]))
                    index += 1
                    can_parsers[i][1] = index
            can_msg_events.sort(key=lambda x: x[0])
            for (msg_stamp, topic, msg) in can_msg_events:
                if topic == "/imu":
                    writer.add_message(imu_channel_id, msg_stamp.to_nsec(), msg, msg_stamp.to_nsec())
                elif topic == "/odom":
                    writer.add_message(odom_channel_id, msg_stamp.to_nsec(), msg, msg_stamp.to_nsec())
                else:
                    rosmsg_writer.write_message(topic, msg, msg_stamp)

            # publish /tf
            protobuf_writer.write_message("/tf", get_ego_tf(ego_pose), stamp.to_nsec())

            # /driveable_area occupancy grid
            write_drivable_area(protobuf_writer, nusc_map, ego_pose, stamp)

            # iterate sensors
            for (sensor_id, sample_token) in cur_sample["data"].items():
                pbar.update(1)
                sample_data = nusc.get("sample_data", sample_token)
                topic = "/" + sensor_id

                # create sensor transform
                protobuf_writer.write_message("/tf", get_sensor_tf(nusc, sensor_id, sample_data), stamp.to_nsec())

                # write the sensor data
                if sample_data["sensor_modality"] == "radar":
                    msg = get_radar(data_path, sample_data, sensor_id)
                    protobuf_writer.write_message(topic, msg, stamp.to_nsec())
                elif sample_data["sensor_modality"] == "lidar":
                    msg = get_lidar(data_path, sample_data, sensor_id)
                    protobuf_writer.write_message(topic, msg, stamp.to_nsec())
                elif sample_data["sensor_modality"] == "camera":
                    msg = get_camera(data_path, sample_data, sensor_id)
                    protobuf_writer.write_message(topic + "/image_rect_compressed", msg, stamp.to_nsec())
                    msg = get_camera_info(nusc, sample_data, sensor_id)
                    protobuf_writer.write_message(topic + "/camera_info", msg, stamp.to_nsec())

                if sample_data["sensor_modality"] == "camera":
                    msg = get_lidar_image_annotations(nusc, sample_lidar, sample_data, sensor_id)
                    protobuf_writer.write_message(topic + "/lidar", msg, stamp.to_nsec())
                    write_boxes_image_annotations(
                        nusc,
                        protobuf_writer,
                        cur_sample["anns"],
                        sample_data,
                        sensor_id,
                        topic,
                        stamp,
                    )

            # publish /pose
            pose_in_frame = PoseInFrame()
            pose_in_frame.timestamp.FromNanoseconds(stamp.to_nsec())
            pose_in_frame.frame_id = "base_link"
            pose_in_frame.pose.orientation.w = 1
            protobuf_writer.write_message("/pose", pose_in_frame, stamp.to_nsec())

            # publish /gps
            lat, lon = derive_latlon(location, ego_pose)
            gps = LocationFix()
            gps.latitude = lat
            gps.longitude = lon
            gps.altitude = get_translation(ego_pose).z
            protobuf_writer.write_message("/gps", gps, stamp.to_nsec())

            # publish /markers/annotations
            scene_update = SceneUpdate()
            for annotation_id in cur_sample["anns"]:
                ann = nusc.get("sample_annotation", annotation_id)
                marker_id = ann["instance_token"][:4]
                c = np.array(nusc.explorer.get_color(ann["category_name"])) / 255.0

                entity = scene_update.entities.add()
                entity.frame_id = "map"
                entity.timestamp.FromNanoseconds(stamp.to_nsec())
                entity.id = marker_id
                entity.frame_locked = True
                metadata = entity.metadata.add()
                metadata.key = "category"
                metadata.value = ann["category_name"]
                cube = entity.cubes.add()
                cube.pose.position.x = ann["translation"][0]
                cube.pose.position.y = ann["translation"][1]
                cube.pose.position.z = ann["translation"][2]
                cube.pose.orientation.w = ann["rotation"][0]
                cube.pose.orientation.x = ann["rotation"][1]
                cube.pose.orientation.y = ann["rotation"][2]
                cube.pose.orientation.z = ann["rotation"][3]
                cube.size.x = ann["size"][1]
                cube.size.y = ann["size"][0]
                cube.size.z = ann["size"][2]
                cube.color.r = c[0]
                cube.color.g = c[1]
                cube.color.b = c[2]
                cube.color.a = 0.5
            protobuf_writer.write_message("/markers/annotations", scene_update, stamp.to_nsec())

            # publish /markers/car
            protobuf_writer.write_message("/markers/car", get_car_scene_update(stamp.to_nsec()), stamp.to_nsec())

            # collect all sensor frames after this sample but before the next sample
            non_keyframe_sensor_msgs = []
            for (sensor_id, sample_token) in cur_sample["data"].items():
                topic = "/" + sensor_id

                next_sample_token = nusc.get("sample_data", sample_token)["next"]
                while next_sample_token != "":
                    next_sample_data = nusc.get("sample_data", next_sample_token)
                    # if next_sample_data['is_key_frame'] or get_time(next_sample_data).to_nsec() > next_stamp.to_nsec():
                    #     break
                    if next_sample_data["is_key_frame"]:
                        break

                    pbar.update(1)
                    ego_pose = nusc.get("ego_pose", next_sample_data["ego_pose_token"])
                    ego_tf = get_ego_tf(ego_pose)
                    non_keyframe_sensor_msgs.append((ego_tf.timestamp.ToNanoseconds(), "/tf", ego_tf))

                    if next_sample_data["sensor_modality"] == "radar":
                        msg = get_radar(data_path, next_sample_data, sensor_id)
                        non_keyframe_sensor_msgs.append((msg.timestamp.ToNanoseconds(), topic, msg))
                    elif next_sample_data["sensor_modality"] == "lidar":
                        msg = get_lidar(data_path, next_sample_data, sensor_id)
                        non_keyframe_sensor_msgs.append((msg.timestamp.ToNanoseconds(), topic, msg))
                    elif next_sample_data["sensor_modality"] == "camera":
                        msg = get_camera(data_path, next_sample_data, sensor_id)
                        camera_stamp_nsec = msg.timestamp.ToNanoseconds()
                        non_keyframe_sensor_msgs.append((camera_stamp_nsec, topic + "/image_rect_compressed", msg))

                        msg = get_camera_info(nusc, next_sample_data, sensor_id)
                        non_keyframe_sensor_msgs.append((camera_stamp_nsec, topic + "/camera_info", msg))

                        closest_lidar = find_closest_lidar(nusc, cur_sample["data"]["LIDAR_TOP"], camera_stamp_nsec)
                        if closest_lidar is not None:
                            msg = get_lidar_image_annotations(nusc, closest_lidar, next_sample_data, sensor_id)
                            non_keyframe_sensor_msgs.append(
                                (
                                    msg.points[0].timestamp.ToNanoseconds(),
                                    topic + "/lidar",
                                    msg,
                                )
                            )

                    next_sample_token = next_sample_data["next"]

            # sort and publish the non-keyframe sensor msgs
            non_keyframe_sensor_msgs.sort(key=lambda x: x[0])
            for (timestamp, topic, msg) in non_keyframe_sensor_msgs:
                if hasattr(msg, "header"):
                    rosmsg_writer.write_message(topic, msg, msg.header.stamp)
                else:
                    protobuf_writer.write_message(topic, msg, timestamp)

            # move to the next sample
            cur_sample = nusc.get("sample", cur_sample["next"]) if cur_sample.get("next") != "" else None

        pbar.close()
        writer.finish()
        print(f"Finished writing {filepath}")


def convert_all(
    output_dir: Path,
    name: str,
    nusc: NuScenes,
    nusc_can: NuScenesCanBus,
    selected_scenes,
):
    nusc.list_scenes()
    for scene in nusc.scene:
        scene_name = scene["name"]
        if selected_scenes is not None and scene_name not in selected_scenes:
            continue
        mcap_name = f"NuScenes-{name}-{scene_name}.mcap"
        write_scene_to_mcap(nusc, nusc_can, scene, output_dir / mcap_name)


def main():
    parser = argparse.ArgumentParser()
    script_dir = Path(__file__).parent
    parser.add_argument(
        "--data-dir",
        "-d",
        default=script_dir / "data",
        help="path to nuscenes data directory",
    )
    parser.add_argument(
        "--dataset-name",
        "-n",
        default=["v1.0-mini"],
        nargs="+",
        help="dataset to convert",
    )
    parser.add_argument(
        "--output-dir",
        "-o",
        type=Path,
        default=script_dir / "output",
        help="path to write MCAP files into",
    )
    parser.add_argument("--scene", "-s", nargs="*", help="specific scene(s) to write")
    parser.add_argument("--list-only", action="store_true", help="lists the scenes and exits")

    args = parser.parse_args()

    nusc_can = NuScenesCanBus(dataroot=str(args.data_dir))

    for name in args.dataset_name:
        nusc = NuScenes(version=name, dataroot=str(args.data_dir), verbose=True)
        if args.list_only:
            nusc.list_scenes()
            return
        convert_all(args.output_dir, name, nusc, nusc_can, args.scene)


if __name__ == "__main__":
    main()