Working with LAS and PCD Files

It’s possible to read pointcloud files with the format .las and .pcd. In order to read them just the submodule PointCloud.from_file():

from pathlib import Path

import laspy
import numpy as np

import pointcloudset as pcs

testpcd = Path().cwd().parent.joinpath("../../../tests/testdata/las_files/test_tree.pcd")
testlas = testpcd = Path().cwd().parent.joinpath("../../../tests/testdata/las_files/test_tree.las")

las_pc = pcs.PointCloud.from_file(testlas)
pcd_pc = pcs.PointCloud.from_file(testpcd)

Note:

Coordinates might not be correct yet, since the offset and scale values that are stored within the .las-file are not applied. But now you can use the data as a pcs.PointCloud and analyze + edit it.

las_pc.data

las_pc.plot(color="z")

Combining them to a pointcloudset dataset

You can combine multiple single PointCloud together with a timestep to a pointcloudset dataset.

dataset = pcs.Dataset.from_instance("pointclouds", [las_pc, pcd_pc])

Now you have a regular Pointcloudset Dataset. Note that the timestamps are taken from the pointcloud objects, which by default are the file timestamp.

dataset.timestamps

las_pc.timestamp

dataset.mean()

dataset[0].plot()

dataset[1].plot()

Exporting of Las

you can currently export single pointclouds to las and csv. For the supported formats see:

pcs.io.POINTCLOUD_TO_FILE

dataset[0].to_file(Path("test_tree_export.las"))