Point cloud data processing
Project description
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 PDALGitHub
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.logReading 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)
Requirements
PDAL 2.1+
Python >=3.6
Cython (eg
pip install cython)Packaging (eg
pip install packaging)
Changes
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.
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