pdal 3.0.1

PyPI

PDAL Python support is installable via PyPI:

pip install PDAL

GitHub

The repository for PDAL’s Python extension is available at https://github.com/PDAL/python

Python support released independently from PDAL itself as of PDAL 1.7.

Simple

Given the following pipeline, which simply reads an ASPRS LAS file and sorts it by the X dimension:

json = """
{
  "pipeline": [
    "1.2-with-color.las",
    {
        "type": "filters.sort",
        "dimension": "X"
    }
  ]
}"""

import pdal
pipeline = pdal.Pipeline(json)
count = pipeline.execute()
arrays = pipeline.arrays
metadata = pipeline.metadata
log = pipeline.log

Programmatic Pipeline Construction

The previous example specified the pipeline as a JSON string. Alternatively, a pipeline can be constructed by creating Stage instances and piping them together. For example, the previous pipeline can be specified as:

pipeline = pdal.Reader("1.2-with-color.las") | pdal.Filter.sort(dimension="X")

>>> help(pdal.Filter.head)
Help on function head in module pdal.pipeline:

head(**kwargs)
    Return N points from beginning of the point cloud.

    user_data: User JSON
    log: Debug output filename
    option_file: File from which to read additional options
    where: Expression describing points to be passed to this filter
    where_merge='auto': If 'where' option is set, describes how skipped points should be merged with kept points in standard mode.
    count='10': Number of points to return from beginning.  If 'invert' is true, number of points to drop from the beginning.
    invert='false': If true, 'count' specifies the number of points to skip from the beginning.

# update pipeline in-place
pipeline = pdal.Pipeline()
pipeline |= stage
pipeline |= pipeline2

Reading using Numpy Arrays

The following more complex scenario demonstrates the full cycling between PDAL and Python:

• Read a small testfile from GitHub into a Numpy array

• Filters the array with Numpy for Intensity

• Pass the filtered array to PDAL to be filtered again

• Write the final filtered array to a LAS file and a TileDB array via the TileDB-PDAL integration using the TileDB writer plugin

import pdal

data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"

pipeline = pdal.Reader.las(filename=data).pipeline()
print(pipeline.execute())  # 1065 points

# Get the data from the first array
# [array([(637012.24, 849028.31, 431.66, 143, 1,
# 1, 1, 0, 1,  -9., 132, 7326, 245380.78254963,  68,  77,  88),
# dtype=[('X', '<f8'), ('Y', '<f8'), ('Z', '<f8'), ('Intensity', '<u2'),
# ('ReturnNumber', 'u1'), ('NumberOfReturns', 'u1'), ('ScanDirectionFlag', 'u1'),
# ('EdgeOfFlightLine', 'u1'), ('Classification', 'u1'), ('ScanAngleRank', '<f4'),
# ('UserData', 'u1'), ('PointSourceId', '<u2'),
# ('GpsTime', '<f8'), ('Red', '<u2'), ('Green', '<u2'), ('Blue', '<u2')])
arr = pipeline.arrays[0]

# Filter out entries that have intensity < 50
intensity = arr[arr["Intensity"] > 30]
print(len(intensity))  # 704 points

# Now use pdal to clamp points that have intensity 100 <= v < 300
pipeline = pdal.Filter.range(limits="Intensity[100:300)").pipeline(intensity)
print(pipeline.execute())  # 387 points
clamped = pipeline.arrays[0]

# Write our intensity data to a LAS file and a TileDB array. For TileDB it is
# recommended to use Hilbert ordering by default with geospatial point cloud data,
# which requires specifying a domain extent. This can be determined automatically
# from a stats filter that computes statistics about each dimension (min, max, etc.).
pipeline = pdal.Writer.las(
    filename="clamped.las",
    offset_x="auto",
    offset_y="auto",
    offset_z="auto",
    scale_x=0.01,
    scale_y=0.01,
    scale_z=0.01,
).pipeline(clamped)
pipeline |= pdal.Filter.stats() | pdal.Writer.tiledb(array_name="clamped")
print(pipeline.execute())  # 387 points

# Dump the TileDB array schema
import tiledb
with tiledb.open("clamped") as a:
    print(a.schema)

Executing Streamable Pipelines

Streamable pipelines (pipelines that consist exclusively of streamable PDAL stages) can be executed in streaming mode via Pipeline.iterator(). This returns an iterator object that yields Numpy arrays of up to chunk_size size (default=10000) at a time.

import pdal
pipeline = pdal.Reader("test/data/autzen-utm.las") | pdal.Filter.range(limits="Intensity[80:120)")
for array in pipeline.iterator(chunk_size=500):
    print(len(array))
# or to concatenate all arrays into one
# full_array = np.concatenate(list(pipeline))

Pipeline.iterator() also takes an optional prefetch parameter (default=0) to allow prefetching up to to this number of arrays in parallel and buffering them until they are yielded to the caller.

If you just want to execute a streamable pipeline in streaming mode and don’t need to access the data points (typically when the pipeline has Writer stage(s)), you can use the Pipeline.execute_streaming(chunk_size) method instead. This is functionally equivalent to sum(map(len, pipeline.iterator(chunk_size))) but more efficient as it avoids allocating and filling any arrays in memory.

Accessing Mesh Data

Some PDAL stages (for instance filters.delaunay) create TIN type mesh data.

This data can be accessed in Python using the Pipeline.meshes property, which returns a numpy.ndarray of shape (1,n) where n is the number of Triangles in the mesh.

If the PointView contains no mesh data, then n = 0.

Each Triangle is a tuple (A,B,C) where A, B and C are indices into the PointView identifying the point that is the vertex for the Triangle.

Meshio Integration

The meshes property provides the face data but is not easy to use as a mesh. Therefore, we have provided optional Integration into the Meshio library.

The pdal.Pipeline class provides the get_meshio(idx: int) -> meshio.Mesh method. This method creates a Mesh object from the PointView array and mesh properties.

Simple use of the functionality could be as follows:

import pdal

...
pl = pdal.Pipeline(pipeline)
pl.execute()

mesh = pl.get_meshio(0)
mesh.write('test.obj')

Advanced Mesh Use Case

USE-CASE : Take a LiDAR map, create a mesh from the ground points, split into tiles and store the tiles in PostGIS.

(example using 1.2-with-color.las and not doing the ground classification for clarity)

import pdal
import psycopg2
import io

pl = (
    pdal.Reader(".../python/test/data/1.2-with-color.las")
    | pdal.Filter.splitter(length=1000)
    | pdal.Filter.delaunay()
)
pl.execute()

conn = psycopg(%CONNNECTION_STRING%)
buffer = io.StringIO

for idx in range(len(pl.meshes)):
    m =  pl.get_meshio(idx)
    if m:
        m.write(buffer,  file_format = "wkt")
        with conn.cursor() as curr:
          curr.execute(
              "INSERT INTO %table-name% (mesh) VALUES (ST_GeomFromEWKT(%(ewkt)s)",
              { "ewkt": buffer.getvalue()}
          )

conn.commit()
conn.close()
buffer.close()

3.0.0

• Pythonic pipeline creation https://github.com/PDAL/python/pull/91

• Support streaming pipeline execution https://github.com/PDAL/python/pull/94

• Replace Cython with PyBind11 https://github.com/PDAL/python/pull/102

• Remove pdal.pio module https://github.com/PDAL/python/pull/101

• Move readers.numpy and filters.python to separate repository https://github.com/PDAL/python/pull/104

• Miscellaneous refactorings and cleanups

2.3.5

• Fix memory leak https://github.com/PDAL/python/pull/74

• Handle metadata with invalid unicode by erroring https://github.com/PDAL/python/pull/74

2.3.0

• PDAL Python support 2.3.0 requires PDAL 2.1+. Older PDAL base libraries likely will not work.

• Python support built using scikit-build

• readers.numpy and filters.python are installed along with the extension.

• Pipeline can take in a list of arrays that are passed to readers.numpy

• readers.numpy now supports functions that return arrays. See https://pdal.io/stages/readers.numpy.html for more detail.

2.0.0

• PDAL Python extension is now in its own repository on its own release schedule at https://github.com/PDAL/python

• Extension now builds and works under PDAL OSGeo4W64 on Windows.