chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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/dataset
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<!--[metadata]
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title = "Drone LiDAR"
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tags = ["3D", "drone", "Lidar"]
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description = "Display drone-based LiDAR data"
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-->
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<picture>
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<img src="https://static.rerun.io/drone_lidar/95c49d78abc01513d344c06e2d9a0c8b84376a0d/full.png" alt="">
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/drone_lidar/95c49d78abc01513d344c06e2d9a0c8b84376a0d/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/drone_lidar/95c49d78abc01513d344c06e2d9a0c8b84376a0d/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/drone_lidar/95c49d78abc01513d344c06e2d9a0c8b84376a0d/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/drone_lidar/95c49d78abc01513d344c06e2d9a0c8b84376a0d/1200w.png">
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</picture>
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## Background
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This example displays drone-based indoor LiDAR data loaded from a [`.las`](https://en.wikipedia.org/wiki/LAS_file_format) file. This dataset contains 18.7M points, acquired at 4013 distinct time points (~4650 points per time point). The point data is loaded using the [laspy](https://laspy.readthedocs.io/en/latest/) Python package, and then sent in one go to the viewer thanks to the [`rr.send_columns()`](https://ref.rerun.io/docs/python/0.18.2/common/columnar_api/#rerun.send_columns) API and its `.partition()` helper. Together, these APIs enable associating subgroups of points with each of their corresponding, non-repeating timestamps.
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[Flyability](https://www.flyability.com) kindly provided the data for this example.
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## Running
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Install the example package:
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```bash
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pip install -e examples/python/drone_lidar
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```
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To experiment with the provided example, simply execute the main Python script:
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```bash
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python -m drone_lidar
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```
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If you wish to customize it, explore additional features, or save it, use the CLI with the `--help` option for guidance:
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```bash
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python -m drone_lidar --help
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```
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from __future__ import annotations
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import io
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import typing
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import zipfile
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from argparse import ArgumentParser
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from pathlib import Path
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import laspy
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import numpy as np
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import numpy.typing as npt
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import requests
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from tqdm import tqdm
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import rerun as rr
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import rerun.blueprint as rrb
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DATASET_DIR = Path(__file__).parent / "dataset"
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if not DATASET_DIR.exists():
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DATASET_DIR.mkdir()
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LIDAR_DATA_FILE = DATASET_DIR / "livemap.las"
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TRAJECTORY_DATA_FILE = DATASET_DIR / "livetraj.csv"
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LIDAR_DATA_URL = "https://storage.googleapis.com/rerun-example-datasets/flyability/basement/livemap.las.zip"
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TRAJECTORY_DATA_URL = "https://storage.googleapis.com/rerun-example-datasets/flyability/basement/livetraj.csv"
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def download_with_progress(url: str, what: str) -> io.BytesIO:
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"""Download a file with a tqdm progress bar."""
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chunk_size = 1024 * 1024
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resp = requests.get(url, stream=True)
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total_size = int(resp.headers.get("content-length", 0))
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with tqdm(
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desc=f"Downloading {what}",
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total=total_size,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as progress:
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download_file = io.BytesIO()
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for data in resp.iter_content(chunk_size):
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download_file.write(data)
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progress.update(len(data))
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download_file.seek(0)
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return download_file
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def unzip_file_from_archive_with_progress(zip_data: typing.BinaryIO, file_name: str, dest_dir: Path) -> None:
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"""Unzip the file named `file_name` from the zip archive contained in `zip_data` to `dest_dir`."""
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with zipfile.ZipFile(zip_data, "r") as zip_ref:
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file_info = zip_ref.getinfo(file_name)
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total_size = file_info.file_size
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with (
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tqdm(
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total=total_size,
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desc=f"Extracting file {file_name}",
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as progress,
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zip_ref.open(file_name) as source,
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open(dest_dir / file_name, "wb") as target,
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):
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for chunk in iter(lambda: source.read(1024 * 1024), b""):
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target.write(chunk)
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progress.update(len(chunk))
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def download_dataset() -> None:
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if not LIDAR_DATA_FILE.exists():
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unzip_file_from_archive_with_progress(
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download_with_progress(LIDAR_DATA_URL, LIDAR_DATA_FILE.name),
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LIDAR_DATA_FILE.name,
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LIDAR_DATA_FILE.parent,
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)
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if not TRAJECTORY_DATA_FILE.exists():
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TRAJECTORY_DATA_FILE.write_bytes(
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download_with_progress(TRAJECTORY_DATA_URL, TRAJECTORY_DATA_FILE.name).getvalue(),
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)
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# TODO(#7333): this utility should be included in the Rerun SDK
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def compute_partitions(
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times: npt.NDArray[np.float64],
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) -> tuple[typing.Sequence[float], typing.Sequence[np.uintp]]:
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"""
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Compute partitions given possibly repeating times.
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This function returns two arrays:
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- Non-repeating times: a filtered version of `times` where repeated times are removed.
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- Partitions: an array of integers where each element indicates the number of elements for the corresponding time
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values in the original `times` array.
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By construction, both arrays should have the same length, and the sum of all elements in `partitions` should be
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equal to the length of `times`.
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"""
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change_indices = (np.argwhere(times != np.concatenate([times[1:], np.array([np.nan])])).T + 1).reshape(-1)
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partitions = np.concatenate([[change_indices[0]], np.diff(change_indices)])
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non_repeating_times = times[change_indices - 1]
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assert np.sum(partitions) == len(times)
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assert len(non_repeating_times) == len(partitions)
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return non_repeating_times, partitions # type: ignore[return-value]
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def log_lidar_data() -> None:
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las_data = laspy.read(LIDAR_DATA_FILE)
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# get positions and convert to meters
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points = las_data.points
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positions = np.column_stack((points.X / 1000.0, points.Y / 1000.0, points.Z / 1000.0))
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times = las_data.gps_time
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non_repeating_times, partitions = compute_partitions(times)
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# log all positions at once using the computed partitions
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rr.send_columns(
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"/lidar",
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[rr.TimeColumn("time", duration=non_repeating_times)],
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rr.Points3D.columns(positions=positions).partition(partitions),
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)
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rr.log(
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"/lidar",
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# negative radii are interpreted in UI units (instead of scene units)
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rr.Points3D.from_fields(colors=(128, 128, 255), radii=-0.1),
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static=True,
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)
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def log_drone_trajectory() -> None:
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data = np.genfromtxt(TRAJECTORY_DATA_FILE, delimiter=" ", skip_header=1)
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timestamp = data[:, 0]
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positions = data[:, 1:4]
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rr.send_columns(
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"/drone",
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[rr.TimeColumn("time", duration=timestamp)],
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rr.Points3D.columns(positions=positions),
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)
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rr.log(
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"/drone",
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rr.Points3D.from_fields(colors=(255, 0, 0), radii=0.5),
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static=True,
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)
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def main() -> None:
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parser = ArgumentParser(description="Visualize drone-based LiDAR data")
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rr.script_add_args(parser)
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args = parser.parse_args()
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download_dataset()
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blueprint = rrb.Spatial3DView(
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origin="/",
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time_ranges=[
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rrb.VisibleTimeRange(
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timeline="time",
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start=rrb.TimeRangeBoundary.cursor_relative(seconds=-60.0),
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end=rrb.TimeRangeBoundary.cursor_relative(),
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),
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],
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)
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rr.script_setup(args, "rerun_example_drone_lidar", default_blueprint=blueprint)
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log_lidar_data()
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log_drone_trajectory()
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if __name__ == "__main__":
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main()
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[project]
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name = "drone_lidar"
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version = "0.1.0"
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readme = "README.md"
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dependencies = ["laspy", "numpy", "requests", "rerun-sdk", "tqdm"]
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[project.scripts]
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drone_lidar = "drone_lidar:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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