# Pipeline

Pipelines define the processing of data within PDAL. They describe how point cloud data are read, processed and written. PDAL internally constructs a pipeline to perform data translation operations using translate, for example. While specific applications are useful in many contexts, a pipeline provides useful advantages for many workflows:

1. You have a record of the operation(s) applied to the data

2. You can construct a skeleton of an operation and substitute specific options (filenames, for example)

3. You can construct complex operations using the JSON manipulation facilities of whatever language you want.

Note

pipeline is used to invoke pipeline operations via the command line.

## Introduction

A PDAL processing pipeline is represented in JSON. The structure may either:

- a JSON object, with a key called `pipeline` whose value is an array of inferred or explicit PDAL Stage Objects representations.

- a JSON array, being the array described above without being encapsulated by a JSON object.

### Simple Example

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:

    [
        "input.las",
        {
            "type":"filters.crop",
            "bounds":"([0,100],[0,100])"
        },
        "output.bpf"
    ]

A simple pipeline to convert LAS to BPF while only keeping points inside the box \([0 \leq x \leq 100, 0 \leq y \leq 100]\).

### Reprojection Example

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 writers.gdal writer:

    [
        {
            "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 filters.reprojection to transform the coordinate system of file `B.las` from UTM to Geographic.

### Point Views and Multiple Outputs

Some filters produce sets of points as output. filters.splitter, for example, creates a point set for each tile (rectangular area) in which input points exist. Each of these output sets is called a point view. Point views are carried through a PDAL pipeline individually. Some writers can produce separate output for each input point view. These writers use a placeholder character (#) in the output filename which is replaced by an incrementing integer for each input point view.

The following pipeline provides an example of writing multiple output files from a single pipeline. The crop filter creates two output point views (one for each specified geometry) and the writer creates output files ‘output1.las’ and ‘output2.las’ containing the two sets of points:

    [
        "input.las",
        {
            "type" : "filters.crop",
            "bounds" : [ "([0, 75], [0, 75])", "([50, 125], [50, 125])" ]
        },
        "output#.las"
    ]

## Processing Modes

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 pdal translate or pdal pipeline PDAL uses stream mode if possible. If stream mode can’t be used the applications fall back to standard mode processing. Streamable stages are tagged in the stage documentation with a blue bar. Users can explicitly choose to use standard mode by using the `--nostream` option. Users of the PDAL API can explicitly control the selection of the PDAL processing mode.

## Pipelines

### Pipeline Array

PDAL JSON pipelines are an array of stages.

Note

In versions of PDAL prior to 1.9, the array of stages needed to be the value of a key named “pipeline” which was encapsulated in an object. The earlier format is still accepted for backward compatibility.

Old format:

    {
        "pipeline" :
        [
            "inputfile",
            "outputfile"
        ]
    }

Equivalent new format:

    [
        "inputfile",
        "outputfile"
    ]

- The pipeline array may have any number of string or Stage Objects 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.

### Stage Objects

For more on PDAL stages and their options, check the PDAL documentation on Readers, Writers, and Filters.

- A stage object may have a member with the name `tag` whose value is a string. The purpose of the tag is to cross-reference this stage within other stages. Each `tag` must be unique.

- A stage object may have a member with the name `inputs` whose 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 `inputs` member.

- If `inputs` is not specified for the first non-reader stage, all reader stages leading up to the current stage will be used as inputs.

- If `inputs` is not specified for any subsequent non-reader stages, the previous stage in the array will be used as input.

- A `tag` mentioned in another stage’s `inputs` must have been previously defined in the `pipeline` array.

- A reader or writer stage object may have a member with the name `type` whose value is a string. The `type` must specify a valid PDAL reader or writer name.

- A filter stage object must have a member with the name `type` whose value is a string. The `type` must 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. Some options allow multiple inputs. In those cases, provide the option values as a JSON array.

- A `user_data` option can be added to any stage object and it will be carried through to any serialized pipeline output.

- All stages support the `option_file` option that allows options to be places in a separate file. See Option Files for details.

### Filename Globbing

- 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).

  Filename globbing ONLY works in pipeline file specifications. It doesn’t work when a filename is provided as an option through a command-line application like `pdal pipeline` or `pdal translate`.

### Option Files

All stages accept the `option file` option that allows extra options for a stage to be placed in a separate file. The value of the option is the filename in which the additional options are located.

Option files can be written using either JSON syntax or command line syntax. When using the JSON syntax, the format is a block of options just as if the options were placed in a pipeline:

    {
        "minor_version": 4,
        "out_srs": "EPSG_4326"
    }

When using the command line syntax, the options are specified as they would be on the command line without the need to qualify the option names with the stage name:

    --minor_version=4 --out_srs="EPSG_4326"

## Extended Examples

### BPF to LAS

The following pipeline converts the input file from BPF to LAS, inferring both the reader and writer type, and setting a number of options on the writer stage.

    [
        "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
        }
    ]

### Python HAG

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 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.

See also

filters.hag_nn describes using a specific filter to do this job in more detail.

    [
        "autzen.las",
        {
            "type":"filters.ferry",
            "dimensions":"Z=>HAG"
        },
        {
            "type":"filters.python",
            "script":"hag.py",
            "function":"filter",
            "module":"anything"
        },
        "autzen-hag.las"
    ]

### DTM

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 filters.smrf, filters out all points but the ground returns (classification value of 2) using the filters.range, and then creates the DTM using the writers.gdal.

    [
        "autzen-full.las",
        {
            "type":"filters.smrf",
            "window":33,
            "slope":1.0,
            "threshold":0.15,
            "cell":1.0
        },
        {
            "type":"filters.range",
            "limits":"Classification[2:2]"
        },
        {
            "type":"writers.gdal",
            "filename":"autzen-surface.tif",
            "output_type":"min",
            "gdaldriver":"GTiff",
            "window_size":3,
            "resolution":1.0
        }
    ]

### Decimate & Colorize

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.

    [
        {
            "filename":"1.2-with-color.las",
            "spatialreference":"EPSG:2993"
        },
        {
            "type":"filters.decimation",
            "step":2,
            "offset":1
        },
        {
            "type":"filters.colorization",
            "raster":"autzen.tif",
            "dimensions": ["Red:1:1", "Green:2:1", "Blue:3:1" ]
        },
        {
            "filename":"junk.txt",
            "delimiter":",",
            "write_header":false
        }
    ]

### Reproject

Our first example with multiple readers, this pipeline infers the reader types, and assigns spatial reference information to each. filters.reprojection filter reprojects data to the specified output spatial reference system.

    [
        {
            "filename":"1.2-with-color.las",
            "spatialreference":"EPSG:2027"
        },
        {
            "filename":"1.2-with-color.las",
            "spatialreference":"EPSG:2027"
        },
        {
            "type":"filters.reprojection",
            "out_srs":"EPSG:2028"
        }
    ]

### Globbed Inputs

Finally, we capture another merge pipeline demonstrating the ability to glob multiple input LAS files from a given directory.

    [
        "/path/to/data/\*.las",
        "output.las"
    ]

See also

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.

Note

Issuing the command `pdal info --options` will list all available stages and their options. See info for more.