Pipelines are the operative construct in PDAL, and it is how data are modeled from reading, processing, and writing. PDAL internally constructs a pipeline to perform data translation operations using :ref:`translate_command`, for example. While specific :ref:`applications <apps>` are useful in many contexts, a pipeline provides useful advantages for more complex things:
- You have a record of the operation(s) applied to the data
- You can construct a skeleton of an operation and substitute specific options (filenames, for example)
- You can construct complex operations using the JSON manipulation facilities of whatever language you want.
Note
:ref:`pipeline_command` is used to invoke pipeline operations via the command line.
Warning
As of PDAL 1.2, JSON is the preferred specification language for PDAL pipelines. XML was dropped at the 1.6 release.
A JSON object represents a PDAL processing pipeline. The structure is always a
JSON object, with the primary object called pipeline being an array of
inferred or explicit PDAL :ref:`stage_object` representations.
A simple PDAL pipeline, inferring the appropriate drivers for the reader and writer from filenames, and able to be specified as a set of sequential steps:
{
"pipeline":[
"input.las",
{
"type":"crop",
"bounds":"([0,100],[0,100])"
},
"output.bpf"
]
}
A simple pipeline to convert :ref:`LAS <readers.las>` to :ref:`BPF <readers.bpf>` while only keeping points inside the box [0 \leq x \leq 100, 0 \leq y \leq 100].
A more complex PDAL pipeline reprojects the stage tagged A1, merges
the result with B, and writes the merged output to a GeoTIFF file
with the :ref:`writers.gdal` writer:
{
"pipeline":[
{
"filename":"A.las",
"spatialreference":"EPSG:26916"
},
{
"type":"filters.reprojection",
"in_srs":"EPSG:26916",
"out_srs":"EPSG:4326",
"tag":"A2"
},
{
"filename":"B.las",
"tag":"B"
},
{
"type":"filters.merge",
"tag":"merged",
"inputs":[
"A2",
"B"
]
},
{
"type":"writers.gdal",
"filename":"output.tif"
}
]
}
A more complex pipeline that merges two inputs together but uses
:ref:`filters.reprojection` to transform the coordinate system of
file B.las from UTM to Geographic.
PDAL process data in one of two ways: standard mode or stream mode. With standard mode, all input is read into memory before it is processed. Many algorithms require standard mode processing because they need access to all points. Operations that do sorting or require neighbors of points, for example, require access to all points.
For operations that don't require access to all points, PDAL provides stream mode. Stream mode processes points through a pipeline in chunks, which reduces memory requirements.
When using :ref:`pdal info<info_command>`, :ref:`pdal translate<translate_command>` or :ref:`pdal pipeline<pipeline_command>` PDAL uses stream mode if possible. If stream mode can't use used the applications fall back to standard mode processing.
Users of the PDAL API can explicitly control the selection of the PDAL processing mode.
PDAL JSON pipelines always consist of a single object. This object (referred to as the PDAL JSON object below) represents a processing pipeline.
- The PDAL JSON object may have any number of members (name/value pairs).
- The PDAL JSON object must have a :ref:`pipeline_array`.
- The pipeline array may have any number of string or :ref:`stage_object` elements.
- String elements shall be interpreted as filenames. PDAL will attempt to infer the proper driver from the file extension and position in the array. A writer stage will only be created if the string is the final element in the array.
For more on PDAL stages and their options, check the PDAL documentation on :ref:`readers`, :ref:`writers`, and :ref:`filters`.
- A stage object may have a member with the name
tagwhose value is a string. The purpose of the tag is to cross-reference this stage within other stages. Eachtagmust be unique. - A stage object may have a member with the name
inputswhose value is an array of strings. Each element in the array is the tag of another stage to be set as input to the current stage. - Reader stages will disregard the
inputsmember. - If
inputsis not specified for the first non-reader stage, all reader stages leading up to the current stage will be used as inputs. - If
inputsis not specified for any subsequent non-reader stages, the previous stage in the array will be used as input. - A
tagmentioned in another stage'sinputsmust have been previously defined in thepipelinearray. - A reader or writer stage object may have a member with the name
typewhose value is a string. Thetypemust specify a valid PDAL reader or writer name. - A filter stage object must have a member with the name
typewhose value is a string. Thetypemust specify a valid PDAL filter name. - A stage object may have additional members with names corresponding to stage-specific option names and their respective values. Values provided as JSON objects or arrays will be stringified and parsed within the stage.
- Applications can place a
user_datanode on any stage object and it will be carried through to any serialized pipeline output.
- A filename may contain the wildcard character
*to match any string of characters. This can be useful if working with multiple input files in a directory (e.g., merging all files).
The following pipeline converts the input file from :ref:`BPF <readers.bpf>` to :ref:`LAS <writers.las>`, inferring both the reader and writer type, and setting a number of options on the writer stage.
{
"pipeline":[
"utm15.bpf",
{
"filename":"out2.las",
"scale_x":0.01,
"offset_x":311898.23,
"scale_y":0.01,
"offset_y":4703909.84,
"scale_z":0.01,
"offset_z":7.385474
}
]
}In our next example, the reader and writer types are once again inferred. After reading the input file, the ferry filter is used to copy the Z dimension into a new height above ground (HAG) dimension. Next, the :ref:`filters.python` is used with a Python script to compute height above ground values by comparing the Z values to a surface model. These height above ground values are then written back into the Z dimension for further analysis. See the Python code at hag.py.
.. seealso::
:ref:`filters.hag` describes using a specific filter to do
this job in more detail.
{
"pipeline":[
"autzen.las",
{
"type":"ferry",
"dimensions":"Z=HAG"
},
{
"type":"filters.python",
"script":"hag.py",
"function":"filter",
"module":"anything"
},
"autzen-hag.las"
]
}A common task is to create a digital terrain model (DTM) from the input point cloud. This pipeline infers the reader type, applies an approximate ground segmentation filter using :ref:`filters.pmf`, filters out all points but the ground returns (classification value of 2) using the :ref:`filters.range`, and then creates the DTM using the :ref:`writers.gdal`.
{
"pipeline":[
"autzen-full.las",
{
"type":"ground",
"approximate":true,
"max_window_size":33,
"slope":1.0,
"max_distance":2.5,
"initial_distance":0.15,
"cell_size":1.0
},
{
"type":"range",
"limits":"Classification[2:2]"
},
{
"type":"writers.gdal",
"filename":"autzen-surface.tif",
"output_type":"min",
"output_format":"tif",
"grid_dist_x":1.0,
"grid_dist_y":1.0
}
]
}This example still infers the reader and writer types while applying options on both. The pipeline decimates the input LAS file by keeping every other point, and then colorizes the points using the provided raster image. The output is written as ASCII text.
{
"pipeline":[
{
"filename":"1.2-with-color.las",
"spatialreference":"EPSG:2993"
},
{
"type":"decimation",
"step":2,
"offset":1
},
{
"type":"colorization",
"raster":"autzen.tif",
"dimensions":"Red:1:1, Green:2:1, Blue:3:1"
},
{
"filename":"junk.txt",
"delimiter":",",
"write_header":false
}
]
}Our first example with multiple readers, this pipeline infers the reader types, and assigns spatial reference information to each. Next, the :ref:`filters.merge` merges points from all previous readers, and the :ref:`filters.reprojection` filter reprojects data to the specified output spatial reference system.
{
"pipeline":[
{
"filename":"1.2-with-color.las",
"spatialreference":"EPSG:2027"
},
{
"filename":"1.2-with-color.las",
"spatialreference":"EPSG:2027"
},
{
"type":"filters.merge"
},
{
"type":"reprojection",
"out_srs":"EPSG:2028"
}
]
}Finally, we capture another merge pipeline demonstrating the ability to glob multiple input LAS files from a given directory.
{
"pipeline":[
"/path/to/data/\*.las",
"output.las"
]
}.. seealso::
The PDAL source tree contains a number of example pipelines that
are used for testing. You might find these inspiring. Go to
https://github.com/PDAL/PDAL/tree/master/test/data/pipeline to find
more.
A Pipeline is composed as an array of :cpp:class:`pdal::Stage` , with the first stage at the beginning and the last at the end. There are two primary building blocks in PDAL, :cpp:class:`pdal::Stage` and :cpp:class:`pdal::PointView`. :cpp:class:`pdal::Reader`, :cpp:class:`pdal::Writer`, and :cpp:class:`pdal::Filter` are all subclasses of :cpp:class:`pdal::Stage`.
:cpp:class:`pdal::PointView` is the substrate that flows between stages in a pipeline and transfers the actual data as it moves through the pipeline. A :cpp:class:`pdal::PointView` contains a :cpp:class:`pdal::PointTablePtr`, which itself contains a list of :cpp:class:`pdal::Dimension` objects that define the actual channels that are stored in the :cpp:class:`pdal::PointView`.
PDAL provides four types of stages -- :cpp:class:`pdal::Reader`, :cpp:class:`pdal::Writer`, :cpp:class:`pdal::Filter`, and :cpp:class:`pdal::MultiFilter` -- with the latter being hardly used (just :ref:`filters.merge`) at this point. A Reader is a producer of data, a Writer is a consumer of data, and a Filter is an actor on data.
Note
As a C++ API consumer, you are generally not supposed to worry about the underlying storage of the PointView, but there might be times when you simply just "want the data." In those situations, you can use the :cpp:func:`pdal::PointView::getBytes` method to stream out the raw storage.
While pipeline objects are manipulable through C++ objects, the other, more convenient way is through an JSON syntax. The JSON syntax mirrors the arrangement of the Pipeline, with options and auxiliary metadata added on a per-stage basis.
We have two use cases specifically in mind:
- a :ref:`command-line <pipeline_command>` application that reads an JSON file to allow a user to easily construct arbitrary writer pipelines, as opposed to having to build applications custom to individual needs with arbitrary options, filters, etc.
- a user can provide JSON for a reader pipeline, construct it via a simple call to the PipelineManager API, and then use the :cpp:func:`pdal::Stage::read()` function to perform the read and then do any processing of the points. This style of operation is very appropriate for using PDAL from within environments like Python where the focus is on just getting the points, as opposed to complex pipeline construction.
{
"pipeline":[
"/path/to/my/file/input.las",
"output.las"
]
}Note
https://github.com/PDAL/PDAL/blob/master/test/data/pipeline/ contains test suite pipeline files that provide an excellent example of the currently possible operations.
:cpp:class:`pdal::Reader`, :cpp:class:`pdal::Writer`, and :cpp:class:`pdal::Filter` are the C++ classes that define the stage types in PDAL. Readers follow the pattern of :ref:`readers.las` or :ref:`readers.oci`, Writers follow the pattern of :ref:`writers.las` or :ref:`readers.oci`, with Filters using :ref:`filters.reprojection` or :ref:`filters.crop`.
Note
:ref:`stage_index` contains a full listing of possible stages and descriptions of their options.
Note
Issuing the command pdal info --options will list all available
stages and their options. See :ref:`info_command` for more.