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"""Build a compact Foxglove-friendly rosbag from the raw dataset bags.
The raw bags are the source of truth. This script is only for visualization.
It keeps a small set of original topics, adds throttled compressed ToF heatmaps,
and can inject dynamic TF from odometry for Foxglove 3D view.
"""
from __future__ import annotations
import argparse
import glob
import math
import os
import struct
from pathlib import Path
cv2 = None
np = None
TOF_CASCADE_TOPIC = "/nlink_tofsensem_cascade"
TOF_FRAME0_TOPIC = "/nlink_tofsensem_frame0"
LIVOX_LIDAR_TOPIC = "/livox/lidar"
LIVOX_POINTCLOUD_TOPIC = "/foxglove/livox/points"
ODOM_CANDIDATE_TOPICS = (
"/fusion_odometry/current_point_odom",
"/fusion_odometry/lazy_point_odom",
"/ekf_quat/ekf_odom",
"/ekf/ekf_odom",
"/Odometry",
"/vrpn_client_node/crazy/pose",
)
COMPACT_COPY_TOPICS = (
"/tf_static",
"/fusion_odometry/current_point_odom",
"/fusion_odometry/lazy_point_odom",
"/ekf_quat/ekf_odom",
"/mavros/imu/data",
"/mavros/imu/data_raw",
"/livox/imu",
TOF_CASCADE_TOPIC,
TOF_FRAME0_TOPIC,
)
def import_ros_deps():
try:
import genpy
import rosbag
from geometry_msgs.msg import PoseStamped, TransformStamped
from nav_msgs.msg import Path as RosPath
from sensor_msgs.msg import CompressedImage, Image, PointCloud2, PointField
from tf2_msgs.msg import TFMessage
except ImportError as exc:
raise RuntimeError(
"ROS1 Python dependencies are not available. Run this script inside a ROS1 environment "
"that can import rosbag, geometry_msgs, sensor_msgs, and tf2_msgs."
) from exc
return genpy, rosbag, PoseStamped, TransformStamped, RosPath, CompressedImage, Image, PointCloud2, PointField, TFMessage
def import_visual_deps():
global cv2, np
try:
import cv2 as cv2_module
import numpy as np_module
except ImportError as exc:
raise RuntimeError(
"Visualization dependencies are not available. Install python3-opencv and numpy "
"inside the ROS1 environment used to run this script."
) from exc
cv2 = cv2_module
np = np_module
def is_valid_stamp(stamp) -> bool:
return stamp is not None and hasattr(stamp, "to_sec") and stamp.to_sec() > 0.0
def select_time(stamp, fallback_time):
return stamp if is_valid_stamp(stamp) else fallback_time
def parse_topic_csv(text: str) -> list[str]:
if not text:
return []
return [item.strip() for item in text.split(",") if item.strip()]
def bag_stem(path: str) -> str:
name = Path(path).name
return name[:-4] if name.endswith(".bag") else name
class RateLimiter:
def __init__(self, hz: float):
self.period = 0.0 if hz <= 0.0 else 1.0 / float(hz)
self.next_time = None
def allow(self, stamp) -> bool:
if self.period <= 0.0:
return True
if not is_valid_stamp(stamp):
return True
now = stamp.to_sec()
if self.next_time is None or now >= self.next_time:
self.next_time = now + self.period
return True
return False
class TofHeatmapRenderer:
def __init__(
self,
compressed_image_cls,
raw_image_cls,
max_nodes: int,
grid_size: int,
cell_px: int,
min_distance_mm: float,
max_distance_mm: float,
valid_status: int,
colormap_name: str,
output_format: str,
jpeg_quality: int,
draw_distance_text: bool,
show_tables: bool,
include_overview_image: bool,
include_node_images: bool,
overview_topic: str,
node_topic_prefix: str,
):
self.CompressedImage = compressed_image_cls
self.Image = raw_image_cls
self.max_nodes = max(1, int(max_nodes))
self.grid_size = int(grid_size)
self.cell_px = int(cell_px)
self.min_distance_mm = float(min_distance_mm)
self.max_distance_mm = float(max_distance_mm)
self.valid_status = int(valid_status)
self.colormap = getattr(cv2, colormap_name, cv2.COLORMAP_TURBO)
self.output_format = output_format
self.jpeg_quality = int(jpeg_quality)
self.draw_distance_text = bool(draw_distance_text)
self.show_tables = bool(show_tables)
self.include_overview_image = bool(include_overview_image)
self.include_node_images = bool(include_node_images)
self.overview_topic = overview_topic
self.node_topic_prefix = node_topic_prefix
self.latest_frame0_panels = {}
@staticmethod
def get_nodes(msg) -> list:
if hasattr(msg, "nodes"):
return list(msg.nodes)
if hasattr(msg, "node"):
return list(msg.node)
return []
def reshape_or_pad(self, values, fill_value: float = 0.0) -> np.ndarray:
target = self.grid_size * self.grid_size
data = list(values[:target])
if len(data) < target:
data.extend([fill_value] * (target - len(data)))
return np.array(data, dtype=np.float32).reshape(self.grid_size, self.grid_size)
def render_numeric_table(self, grid, title: str, cell_w: int = 44, cell_h: int = 20) -> np.ndarray:
rows, cols = grid.shape
header_h = 24
width = cols * cell_w
height = header_h + rows * cell_h
image = np.full((height, width, 3), 248, dtype=np.uint8)
cv2.putText(image, title, (5, 17), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (20, 20, 20), 1, cv2.LINE_AA)
for row in range(rows + 1):
y = header_h + row * cell_h
cv2.line(image, (0, y), (width, y), (180, 180, 180), 1)
for col in range(cols + 1):
x = col * cell_w
cv2.line(image, (x, header_h), (x, height), (180, 180, 180), 1)
for row in range(rows):
for col in range(cols):
text = str(int(grid[row, col]))
x = col * cell_w + 3
y = header_h + row * cell_h + 14
cv2.putText(image, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.34, (30, 30, 30), 1, cv2.LINE_AA)
return image
def render_node(self, node_id: int, pixels, stamp) -> np.ndarray:
distances = [float(getattr(pixel, "dis", 0.0)) for pixel in pixels]
statuses = [int(getattr(pixel, "dis_status", 255)) for pixel in pixels]
strengths = [int(getattr(pixel, "signal_strength", 0)) for pixel in pixels]
dis = self.reshape_or_pad(distances, fill_value=0.0)
status = self.reshape_or_pad(statuses, fill_value=255).astype(np.int32)
strength = self.reshape_or_pad(strengths, fill_value=0).astype(np.int32)
valid = np.logical_and(status == self.valid_status, dis > 0.0)
clipped = np.clip(dis, self.min_distance_mm, self.max_distance_mm)
scale = max(1e-6, self.max_distance_mm - self.min_distance_mm)
normalized = ((clipped - self.min_distance_mm) / scale * 255.0).astype(np.uint8)
heat = cv2.applyColorMap(normalized, self.colormap)
heat = cv2.cvtColor(heat, cv2.COLOR_BGR2RGB)
heat[~valid] = np.array([88, 88, 88], dtype=np.uint8)
tile_size = self.grid_size * self.cell_px
tile = cv2.resize(heat, (tile_size, tile_size), interpolation=cv2.INTER_NEAREST)
for idx in range(self.grid_size + 1):
offset = idx * self.cell_px
cv2.line(tile, (offset, 0), (offset, tile.shape[0]), (255, 255, 255), 1)
cv2.line(tile, (0, offset), (tile.shape[1], offset), (255, 255, 255), 1)
if self.draw_distance_text:
for row in range(self.grid_size):
for col in range(self.grid_size):
text = str(int(dis[row, col]))
x = col * self.cell_px + 3
y = row * self.cell_px + int(self.cell_px * 0.65)
color = (255, 255, 255) if valid[row, col] else (220, 220, 220)
cv2.putText(tile, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.32, color, 1, cv2.LINE_AA)
body = tile
if self.show_tables:
status_table = self.render_numeric_table(status, "dis_status")
strength_table = self.render_numeric_table(strength, "signal_strength")
table_w = max(status_table.shape[1], strength_table.shape[1])
def pad_width(image, width):
if image.shape[1] == width:
return image
pad = np.full((image.shape[0], width - image.shape[1], 3), 248, dtype=np.uint8)
return np.concatenate([image, pad], axis=1)
status_table = pad_width(status_table, table_w)
strength_table = pad_width(strength_table, table_w)
table_gap = np.full((8, table_w, 3), 245, dtype=np.uint8)
tables = np.concatenate([status_table, table_gap, strength_table], axis=0)
content_h = max(tile.shape[0], tables.shape[0])
tile_pad = np.full((content_h, tile.shape[1], 3), 245, dtype=np.uint8)
table_pad = np.full((content_h, tables.shape[1], 3), 245, dtype=np.uint8)
tile_pad[:tile.shape[0], :tile.shape[1], :] = tile
table_pad[:tables.shape[0], :tables.shape[1], :] = tables
gap = np.full((content_h, 10, 3), 245, dtype=np.uint8)
body = np.concatenate([tile_pad, gap, table_pad], axis=1)
header_h = 32
panel = np.full((header_h + body.shape[0], body.shape[1], 3), 245, dtype=np.uint8)
valid_ratio = float(np.count_nonzero(valid)) / float(valid.size) if valid.size else 0.0
title = "node {} dis(mm) heatmap valid {:.0f}%".format(node_id, valid_ratio * 100.0)
if is_valid_stamp(stamp):
title += " t={:.2f}".format(stamp.to_sec())
cv2.putText(panel, title, (6, 22), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (20, 20, 20), 1, cv2.LINE_AA)
panel[header_h:, :, :] = body
return panel
@staticmethod
def render_overview(node_panels):
if not node_panels:
return np.full((260, 420, 3), 245, dtype=np.uint8)
cols = min(3, len(node_panels))
rows = int(math.ceil(len(node_panels) / float(cols)))
gap = 12
top = 38
tile_h = max(panel.shape[0] for panel in node_panels)
tile_w = max(panel.shape[1] for panel in node_panels)
canvas_h = top + rows * tile_h + max(0, rows - 1) * gap + 12
canvas_w = cols * tile_w + max(0, cols - 1) * gap + 12
canvas = np.full((canvas_h, canvas_w, 3), 245, dtype=np.uint8)
cv2.putText(
canvas,
"TOFSense-M cascade heatmap",
(6, 25),
cv2.FONT_HERSHEY_SIMPLEX,
0.72,
(20, 20, 20),
2,
cv2.LINE_AA,
)
for idx, panel in enumerate(node_panels):
row = idx // cols
col = idx % cols
y = top + row * (tile_h + gap)
x = 6 + col * (tile_w + gap)
canvas[y:y + panel.shape[0], x:x + panel.shape[1], :] = panel
return canvas
def to_ros_image(self, image_rgb: np.ndarray, stamp):
if self.output_format == "raw":
msg = self.Image()
if is_valid_stamp(stamp):
msg.header.stamp = stamp
msg.height = image_rgb.shape[0]
msg.width = image_rgb.shape[1]
msg.encoding = "rgb8"
msg.is_bigendian = 0
msg.step = msg.width * 3
msg.data = np.ascontiguousarray(image_rgb).tobytes()
return msg
msg = self.CompressedImage()
if is_valid_stamp(stamp):
msg.header.stamp = stamp
image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
if self.output_format == "png":
ok, encoded = cv2.imencode(".png", image_bgr, [cv2.IMWRITE_PNG_COMPRESSION, 3])
msg.format = "png"
else:
ok, encoded = cv2.imencode(".jpg", image_bgr, [cv2.IMWRITE_JPEG_QUALITY, self.jpeg_quality])
msg.format = "jpeg"
if not ok:
raise RuntimeError("failed to encode ToF heatmap")
msg.data = encoded.tobytes()
return msg
def consume_cascade(self, msg, fallback_time) -> list[tuple[str, object, object]]:
stamp = fallback_time
if hasattr(msg, "header") and hasattr(msg.header, "stamp"):
stamp = select_time(msg.header.stamp, fallback_time)
node_panels = []
node_outputs = []
for idx, node in enumerate(self.get_nodes(msg)[:self.max_nodes]):
node_id = int(getattr(node, "id", idx))
panel = self.render_node(node_id, list(getattr(node, "pixels", [])), stamp)
node_panels.append(panel)
if self.include_node_images:
node_outputs.append((
self.node_topic_prefix + str(node_id),
self.to_ros_image(panel, stamp),
select_time(stamp, fallback_time),
))
outputs = []
if self.include_overview_image:
overview = self.render_overview(node_panels)
outputs.append((self.overview_topic, self.to_ros_image(overview, stamp), select_time(stamp, fallback_time)))
outputs.extend(node_outputs)
return outputs
def consume_frame0(self, msg, fallback_time) -> list[tuple[str, object, object]]:
stamp = fallback_time
if hasattr(msg, "header") and hasattr(msg.header, "stamp"):
stamp = select_time(msg.header.stamp, fallback_time)
node_id = int(getattr(msg, "id", 0))
panel = self.render_node(node_id, list(getattr(msg, "pixels", [])), stamp)
self.latest_frame0_panels[node_id] = panel
ordered_ids = sorted(self.latest_frame0_panels.keys())[:self.max_nodes]
outputs = []
if self.include_overview_image:
overview = self.render_overview([self.latest_frame0_panels[item] for item in ordered_ids])
outputs.append((self.overview_topic, self.to_ros_image(overview, stamp), select_time(stamp, fallback_time)))
if self.include_node_images:
outputs.append((self.node_topic_prefix + str(node_id), self.to_ros_image(panel, stamp), select_time(stamp, fallback_time)))
return outputs
class Converter:
def __init__(self, args):
self.args = args
import_visual_deps()
(
self.genpy,
self.rosbag,
self.PoseStamped,
self.TransformStamped,
self.RosPath,
self.CompressedImage,
self.Image,
self.PointCloud2,
self.PointField,
self.TFMessage,
) = import_ros_deps()
self.input_bag = Path(args.input_bag).resolve()
self.output_bag = self.resolve_output_bag()
self.tof_limiter = RateLimiter(args.tof_rate_hz)
self.tf_limiter = RateLimiter(args.tf_rate_hz)
self.path_limiter = RateLimiter(args.path_rate_hz)
self.odom_path = None
self.copy_topics = self.resolve_copy_topics()
self.renderer = TofHeatmapRenderer(
compressed_image_cls=self.CompressedImage,
raw_image_cls=self.Image,
max_nodes=args.tof_max_nodes,
grid_size=args.tof_grid_size,
cell_px=args.tof_cell_px,
min_distance_mm=args.tof_min_dis,
max_distance_mm=args.tof_max_dis,
valid_status=args.tof_valid_status,
colormap_name=args.tof_colormap,
output_format=args.tof_output_format,
jpeg_quality=args.tof_jpeg_quality,
draw_distance_text=args.tof_draw_distance_text,
show_tables=args.tof_show_tables,
include_overview_image=args.tof_image_mode in ("overview", "both"),
include_node_images=args.tof_image_mode in ("nodes", "both"),
overview_topic=args.tof_overview_topic,
node_topic_prefix=args.tof_node_topic_prefix,
)
def resolve_output_bag(self) -> Path:
if self.args.output_bag:
output = Path(self.args.output_bag).resolve()
else:
output_dir = Path(self.args.output_dir).resolve() if self.args.output_dir else self.input_bag.parent / "foxglove"
output = output_dir / (bag_stem(str(self.input_bag)) + "_foxglove_compact.bag")
if output == self.input_bag:
raise RuntimeError("output bag path must be different from input bag path")
output.parent.mkdir(parents=True, exist_ok=True)
if output.exists():
if self.args.force:
output.unlink()
else:
raise RuntimeError("output bag already exists: {} (use --force)".format(output))
return output
def resolve_copy_topics(self) -> set[str]:
mode = self.args.copy_mode
if mode == "none":
topics = set()
elif mode == "compact":
topics = set(COMPACT_COPY_TOPICS)
elif mode == "custom":
topics = set()
else:
topics = None
if topics is not None:
topics.update(parse_topic_csv(self.args.copy_topics))
topics.update(parse_topic_csv(self.args.keep_topics))
return topics
def choose_topic(self, topic_info, requested, candidates, label):
if requested and requested != "auto":
if requested not in topic_info:
raise RuntimeError("{} topic not found in bag: {}".format(label, requested))
return requested
for topic in candidates:
if topic in topic_info:
return topic
return None
def build_tf_from_pose(self, msg, bag_time):
if hasattr(msg, "pose") and hasattr(msg.pose, "pose"):
pose = msg.pose.pose
stamp = select_time(msg.header.stamp, bag_time)
parent = self.args.tf_parent_frame or getattr(msg.header, "frame_id", "") or "map"
elif hasattr(msg, "pose"):
pose = msg.pose
stamp = select_time(msg.header.stamp, bag_time)
parent = self.args.tf_parent_frame or getattr(msg.header, "frame_id", "") or "map"
else:
return None, None
transform = self.TransformStamped()
transform.header.stamp = stamp
transform.header.frame_id = parent
transform.child_frame_id = self.args.tf_child_frame
transform.transform.translation.x = pose.position.x
transform.transform.translation.y = pose.position.y
transform.transform.translation.z = pose.position.z
transform.transform.rotation.x = pose.orientation.x
transform.transform.rotation.y = pose.orientation.y
transform.transform.rotation.z = pose.orientation.z
transform.transform.rotation.w = pose.orientation.w
return self.TFMessage(transforms=[transform]), stamp
def build_path_from_odometry(self, msg, bag_time):
if hasattr(msg, "pose") and hasattr(msg.pose, "pose"):
pose = msg.pose.pose
stamp = select_time(msg.header.stamp, bag_time)
elif hasattr(msg, "pose"):
pose = msg.pose
stamp = select_time(msg.header.stamp, bag_time)
else:
return None, None
frame_id = self.args.path_frame
if frame_id == "auto":
frame_id = self.args.tf_parent_frame or getattr(msg.header, "frame_id", "") or "odom"
if self.odom_path is None:
self.odom_path = self.RosPath()
self.odom_path.header.frame_id = frame_id
pose_msg = self.PoseStamped()
pose_msg.header.stamp = stamp
pose_msg.header.frame_id = frame_id
pose_msg.pose = pose
self.odom_path.poses.append(pose_msg)
if self.args.path_max_poses > 0 and len(self.odom_path.poses) > self.args.path_max_poses:
self.odom_path.poses = self.odom_path.poses[-self.args.path_max_poses:]
self.odom_path.header.stamp = stamp
self.odom_path.header.frame_id = frame_id
return self.odom_path, stamp
def build_pointcloud2_from_livox(self, msg, bag_time):
stamp = bag_time
frame_id = "livox_frame"
if hasattr(msg, "header"):
stamp = select_time(getattr(msg.header, "stamp", None), bag_time)
frame_id = getattr(msg.header, "frame_id", "") or frame_id
points = []
for point in getattr(msg, "points", []):
x = float(getattr(point, "x", 0.0))
y = float(getattr(point, "y", 0.0))
z = float(getattr(point, "z", 0.0))
if not self.args.livox_keep_zero_points and x == 0.0 and y == 0.0 and z == 0.0:
continue
points.append((
x,
y,
z,
float(getattr(point, "reflectivity", 0)),
int(getattr(point, "line", 0)) & 0xFF,
int(getattr(point, "tag", 0)) & 0xFF,
int(getattr(point, "offset_time", 0)) & 0xFFFFFFFF,
))
cloud = self.PointCloud2()
cloud.header.stamp = stamp
cloud.header.frame_id = frame_id
cloud.height = 1
cloud.width = len(points)
cloud.fields = [
self.PointField(name="x", offset=0, datatype=self.PointField.FLOAT32, count=1),
self.PointField(name="y", offset=4, datatype=self.PointField.FLOAT32, count=1),
self.PointField(name="z", offset=8, datatype=self.PointField.FLOAT32, count=1),
self.PointField(name="intensity", offset=12, datatype=self.PointField.FLOAT32, count=1),
self.PointField(name="line", offset=16, datatype=self.PointField.UINT8, count=1),
self.PointField(name="tag", offset=17, datatype=self.PointField.UINT8, count=1),
self.PointField(name="offset_time", offset=20, datatype=self.PointField.UINT32, count=1),
]
cloud.is_bigendian = False
cloud.point_step = 24
cloud.row_step = cloud.point_step * cloud.width
cloud.is_dense = False
packer = struct.Struct("<ffffBBxxI")
data = bytearray(cloud.row_step)
for idx, point in enumerate(points):
packer.pack_into(data, idx * cloud.point_step, *point)
cloud.data = bytes(data)
return cloud, stamp
def output_compression(self):
if self.args.bag_compression == "none":
return "none"
return self.args.bag_compression
def resolve_time_window(self, in_bag):
if self.args.start_offset_sec is None and self.args.duration_sec is None:
return None, None
bag_start = float(in_bag.get_start_time())
bag_end = float(in_bag.get_end_time())
start_offset = float(self.args.start_offset_sec or 0.0)
start_sec = bag_start + max(0.0, start_offset)
if self.args.duration_sec is None:
end_sec = bag_end
else:
end_sec = min(bag_end, start_sec + max(0.0, float(self.args.duration_sec)))
if start_sec >= end_sec:
raise RuntimeError("empty time window: start={} end={}".format(start_sec, end_sec))
return self.genpy.Time.from_sec(start_sec), self.genpy.Time.from_sec(end_sec)
def write_static_tf_for_window(self, in_bag, out_bag, window_start):
if window_start is None:
return 0
if self.copy_topics is not None and "/tf_static" not in self.copy_topics:
return 0
written = 0
for _, msg, _ in in_bag.read_messages(topics=["/tf_static"]):
out_bag.write("/tf_static", msg, t=window_start)
written += 1
return written
def resolve_livox_calibration_path(self):
if not self.args.inject_livox_static_tf:
return None
calib_path = Path(self.args.livox_calibration)
if not calib_path.is_absolute():
calib_path = Path(__file__).resolve().parents[1] / calib_path
if not calib_path.is_file():
raise RuntimeError("Livox calibration file not found: {}".format(calib_path))
return calib_path
def load_livox_static_transform(self, stamp):
calib_path = self.resolve_livox_calibration_path()
if calib_path is None:
return None
try:
import yaml
except ImportError as exc:
raise RuntimeError("PyYAML is required to read Livox calibration: {}".format(calib_path)) from exc
with calib_path.open("r", encoding="utf-8") as stream:
calib = yaml.safe_load(stream) or {}
parent = calib.get("parent_frame")
child = calib.get("child_frame")
translation = calib.get("translation_xyz_m")
rotation = calib.get("rotation_xyzw")
if not parent or not child or translation is None or rotation is None:
raise RuntimeError(
"Livox calibration must define parent_frame, child_frame, translation_xyz_m, and rotation_xyzw: {}".format(
calib_path
)
)
if len(translation) != 3 or len(rotation) != 4:
raise RuntimeError("invalid Livox calibration vector length: {}".format(calib_path))
transform = self.TransformStamped()
transform.header.stamp = stamp or self.genpy.Time(0)
transform.header.frame_id = str(parent)
transform.child_frame_id = str(child)
transform.transform.translation.x = float(translation[0])
transform.transform.translation.y = float(translation[1])
transform.transform.translation.z = float(translation[2])
transform.transform.rotation.x = float(rotation[0])
transform.transform.rotation.y = float(rotation[1])
transform.transform.rotation.z = float(rotation[2])
transform.transform.rotation.w = float(rotation[3])
return self.TFMessage(transforms=[transform])
def write_livox_static_tf(self, out_bag, window_start):
if self.copy_topics is not None and "/tf_static" not in self.copy_topics:
return 0
tf_msg = self.load_livox_static_transform(window_start)
if tf_msg is None:
return 0
out_bag.write("/tf_static", tf_msg, t=window_start or self.genpy.Time(0))
return 1
def convert(self):
if not self.input_bag.is_file():
raise RuntimeError("input bag does not exist: {}".format(self.input_bag))
processed = 0
copied = 0
tof_images = 0
tf_inserted = 0
livox_converted = 0
path_inserted = 0
with self.rosbag.Bag(str(self.input_bag), "r") as in_bag:
window_start, window_end = self.resolve_time_window(in_bag)
topic_info = in_bag.get_type_and_topic_info().topics
tof_topic = self.choose_topic(
topic_info,
self.args.tof_input_topic,
(TOF_CASCADE_TOPIC, TOF_FRAME0_TOPIC),
"ToF",
)
odom_topic = self.choose_topic(topic_info, self.args.odom_input_topic, ODOM_CANDIDATE_TOPICS, "odometry")
livox_topic = self.choose_topic(
topic_info,
self.args.livox_input_topic,
(LIVOX_LIDAR_TOPIC,),
"Livox lidar",
) if self.args.convert_livox_pointcloud2 else None
read_topics = None
if self.copy_topics is not None:
read_topics = set(topic for topic in self.copy_topics if topic in topic_info)
if tof_topic:
read_topics.add(tof_topic)
if self.args.inject_dynamic_tf and odom_topic:
read_topics.add(odom_topic)
if livox_topic:
read_topics.add(livox_topic)
read_topics = sorted(read_topics)
print("[INFO] input bag: {}".format(self.input_bag))
print("[INFO] output bag: {}".format(self.output_bag))
print("[INFO] copy mode: {}".format(self.args.copy_mode))
print("[INFO] ToF topic: {}".format(tof_topic or "not found"))
print("[INFO] ToF image mode: {} format={} rate={}Hz".format(
self.args.tof_image_mode, self.args.tof_output_format, self.args.tof_rate_hz
))
print("[INFO] odometry topic for TF: {}".format(odom_topic or "not found"))
print("[INFO] odometry Path: {}".format(
"{} -> {}".format(odom_topic, self.args.odom_path_topic) if odom_topic and self.args.write_odom_path else "disabled/not found"
))
print("[INFO] Livox PointCloud2: {}".format(
"{} -> {}".format(livox_topic, self.args.livox_pointcloud_topic) if livox_topic else "disabled/not found"
))
print("[INFO] Livox static TF calibration: {}".format(
self.resolve_livox_calibration_path() if self.args.inject_livox_static_tf else "disabled"
))
if window_start is not None:
print("[INFO] time window: {:.3f} -> {:.3f} ({:.3f}s)".format(
window_start.to_sec(), window_end.to_sec(), window_end.to_sec() - window_start.to_sec()
))
with self.rosbag.Bag(str(self.output_bag), "w", compression=self.output_compression()) as out_bag:
copied += self.write_static_tf_for_window(in_bag, out_bag, window_start)
copied += self.write_livox_static_tf(out_bag, window_start)
for topic, msg, bag_time in in_bag.read_messages(
topics=read_topics,
start_time=window_start,
end_time=window_end,
):
processed += 1
if self.copy_topics is None or topic in self.copy_topics:
out_bag.write(topic, msg, t=bag_time)
copied += 1
if self.args.inject_dynamic_tf and odom_topic and topic == odom_topic and self.tf_limiter.allow(bag_time):
tf_msg, tf_time = self.build_tf_from_pose(msg, bag_time)
if tf_msg is not None:
out_bag.write(self.args.tf_topic, tf_msg, t=tf_time)
tf_inserted += 1
if self.args.write_odom_path and odom_topic and topic == odom_topic:
path_msg, path_time = self.build_path_from_odometry(msg, bag_time)
if path_msg is not None and self.path_limiter.allow(path_time):
out_bag.write(self.args.odom_path_topic, path_msg, t=path_time)
path_inserted += 1
if livox_topic and topic == livox_topic:
cloud, cloud_time = self.build_pointcloud2_from_livox(msg, bag_time)
out_bag.write(self.args.livox_pointcloud_topic, cloud, t=cloud_time)
livox_converted += 1
if tof_topic and topic == tof_topic and self.args.tof_image_mode != "none" and self.tof_limiter.allow(bag_time):
if topic == TOF_CASCADE_TOPIC or hasattr(msg, "nodes") or hasattr(msg, "node"):
outputs = self.renderer.consume_cascade(msg, bag_time)
else:
outputs = self.renderer.consume_frame0(msg, bag_time)
for out_topic, out_msg, out_time in outputs:
out_bag.write(out_topic, out_msg, t=out_time)
tof_images += 1
if processed % 10000 == 0:
print("[RUNNING] processed={} copied={} tof_images={} tf={} path={} livox_pc2={}".format(
processed, copied, tof_images, tf_inserted, path_inserted, livox_converted
))
input_size = self.input_bag.stat().st_size
output_size = self.output_bag.stat().st_size
ratio = float(output_size) / float(input_size) if input_size else 0.0
print("[DONE] processed={} copied={} tof_images={} tf={} path={} livox_pc2={}".format(
processed, copied, tof_images, tf_inserted, path_inserted, livox_converted
))
print("[DONE] input_size={:.2f} GB output_size={:.2f} GB ratio={:.3f}".format(
input_size / 1e9, output_size / 1e9, ratio
))
def parse_args():
parser = argparse.ArgumentParser(description="Create a compact Foxglove visualization rosbag.")
parser.add_argument("--input-bag", required=True, help="Input ROS1 bag")
parser.add_argument("--output-bag", default=None, help="Output ROS1 bag")
parser.add_argument("--output-dir", default=None, help="Output directory when --output-bag is omitted")
parser.add_argument("--force", action="store_true", help="Overwrite output bag")
parser.add_argument("--start-offset-sec", type=float, default=None, help="Start offset from input bag start")
parser.add_argument("--duration-sec", type=float, default=None, help="Maximum duration to convert")
parser.add_argument(
"--copy-mode",
choices=("compact", "all", "none", "custom"),
default="compact",
help="Original topic copy policy. compact avoids camera/lidar by default.",
)
parser.add_argument("--copy-topics", default="", help="Comma-separated extra original topics to copy")
parser.add_argument(
"--keep-topics",
default="",
help="Alias for --copy-topics. Use with --copy-mode custom to keep exactly the listed original topics.",
)
parser.add_argument("--bag-compression", choices=("none", "bz2", "lz4"), default="bz2")
parser.add_argument("--tof-input-topic", default="auto")
parser.add_argument("--tof-image-mode", choices=("overview", "nodes", "both", "none"), default="overview")
parser.add_argument("--tof-output-format", choices=("jpeg", "png", "raw"), default="jpeg")
parser.add_argument(
"--tof-rate-hz",
type=float,
default=15.0,
help="Visualization image rate. 15 matches TOFSense-M 8x8 nominal rate; 0 disables throttling.",
)
parser.add_argument("--tof-overview-topic", default="/foxglove/tof/overview/compressed")
parser.add_argument("--tof-node-topic-prefix", default="/foxglove/tof/node_")
parser.add_argument("--tof-max-nodes", type=int, default=6)
parser.add_argument("--tof-grid-size", type=int, default=8)
parser.add_argument("--tof-cell-px", type=int, default=28)
parser.add_argument("--tof-min-dis", type=float, default=0.0)
parser.add_argument("--tof-max-dis", type=float, default=5000.0)
parser.add_argument("--tof-valid-status", type=int, default=0)
parser.add_argument("--tof-colormap", default="COLORMAP_TURBO")
parser.add_argument("--tof-jpeg-quality", type=int, default=82)
parser.add_argument("--tof-draw-distance-text", dest="tof_draw_distance_text", action="store_true")
parser.add_argument("--tof-hide-distance-text", dest="tof_draw_distance_text", action="store_false")
parser.set_defaults(tof_draw_distance_text=True)
parser.add_argument("--tof-show-tables", dest="tof_show_tables", action="store_true")
parser.add_argument("--tof-hide-tables", dest="tof_show_tables", action="store_false")
parser.set_defaults(tof_show_tables=True)
parser.add_argument("--convert-livox-pointcloud2", dest="convert_livox_pointcloud2", action="store_true")
parser.add_argument("--no-convert-livox-pointcloud2", dest="convert_livox_pointcloud2", action="store_false")
parser.set_defaults(convert_livox_pointcloud2=True)
parser.add_argument("--livox-input-topic", default="auto")
parser.add_argument("--livox-pointcloud-topic", default=LIVOX_POINTCLOUD_TOPIC)
parser.add_argument("--livox-keep-zero-points", action="store_true")
parser.add_argument("--inject-livox-static-tf", dest="inject_livox_static_tf", action="store_true")
parser.add_argument("--no-inject-livox-static-tf", dest="inject_livox_static_tf", action="store_false")
parser.set_defaults(inject_livox_static_tf=True)
parser.add_argument(
"--livox-calibration",
default="calibration/robot_v1_template/livox_to_base.yaml",
help="YAML file with parent_frame, child_frame, translation_xyz_m, and rotation_xyzw.",
)
parser.add_argument("--inject-dynamic-tf", dest="inject_dynamic_tf", action="store_true")
parser.add_argument("--no-inject-dynamic-tf", dest="inject_dynamic_tf", action="store_false")
parser.set_defaults(inject_dynamic_tf=True)
parser.add_argument("--odom-input-topic", default="auto")
parser.add_argument("--tf-rate-hz", type=float, default=10.0, help="0 disables TF throttling")
parser.add_argument("--tf-topic", default="/tf")
parser.add_argument("--tf-parent-frame", default="map")
parser.add_argument("--tf-child-frame", default="base_link")
parser.add_argument("--write-odom-path", dest="write_odom_path", action="store_true")
parser.add_argument("--no-write-odom-path", dest="write_odom_path", action="store_false")
parser.set_defaults(write_odom_path=True)
parser.add_argument("--odom-path-topic", default="/foxglove/odom/path")
parser.add_argument("--path-rate-hz", type=float, default=10.0, help="0 writes path at every odometry message")
parser.add_argument("--path-frame", default="auto", help="Path frame. auto uses --tf-parent-frame, then odometry frame.")
parser.add_argument("--path-max-poses", type=int, default=0, help="0 keeps the full path")
return parser.parse_args()
def main():
args = parse_args()
Converter(args).convert()
if __name__ == "__main__":
main()
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