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PDAL

PDAL Python support allows you to process data with PDAL into Numpy arrays. It supports embedding Python in PDAL pipelines with the readers.numpy and filters.python stages, and it provides a PDAL extension module to control Python interaction with PDAL.

Additionally, you can use it to fetch schema and metadata from PDAL operations.

Installation

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.

Usage

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

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 those arrays with Numpy for Intensity
  • Pass the filtered array to PDAL to be filtered again
  • Write the filtered array to an LAS file.
data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"


json = """
    {
      "pipeline": [
        {
            "type": "readers.las",
            "filename": "%s"
        }
      ]
    }"""

import pdal
import numpy as np
pipeline = pdal.Pipeline(json % data)
count = pipeline.execute()

# 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]
print (len(arr)) # 1065 points


# 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, and there are 387
clamp =u"""{
  "pipeline":[
    {
      "type":"filters.range",
      "limits":"Intensity[100:300)"
    }
  ]
}"""

p = pdal.Pipeline(clamp, [intensity])
count = p.execute()
clamped = p.arrays[0]
print (count)

# Write our intensity data to an LAS file
output =u"""{
  "pipeline":[
    {
      "type":"writers.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
    }
  ]
}"""

p = pdal.Pipeline(output, [clamped])
count = p.execute()
print (count)

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.

Note

The meshio integration requires that meshio is installed (e.g. pip install meshio). If it is not, then the method fails with an informative RuntimeError.

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.

Note

Like Pipeline.arrays, Pipeline.meshes returns a list of numpy.ndarray to provide for the case where the output from a Pipeline is multiple PointViews

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

import pdal
import json
import psycopg2
import io

pipe = [
    '.../python/test/data/1.2-with-color.las',
    {"type":  "filters.splitter", "length": 1000},
    {"type":  "filters.delaunay"}
]

pl = pdal.Pipeline(json.dumps(pipe))
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()

Requirements

  • PDAL 2.2+
  • Python >=3.6
  • Cython (eg pip install cython)
  • Numpy (eg pip install numpy)
  • Packaging (eg pip install packaging)
  • scikit-build (eg pip install scikit-build)