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|
******************************************************************************
Storing points
******************************************************************************
This tutorial is a basic introduction to pgPointcloud to store points in a
PostgreSQL database hosted on a Docker container.
------------------------------------------------------------------------------
Start Docker container
------------------------------------------------------------------------------
First we download the latest tag of the pgPoincloud Docker image:
.. code-block:: bash
$ docker pull pgpointcloud/pointcloud
This Docker image is based on the official PostgreSQL image and the full
documentation is available `here`_.
For a basic usage, we have to define two environment variables:
+ the PostgreSQL database: ``POSTGRES_DB``
+ the PostgreSQL password: ``POSTGRES_PASSWORD``
Then we can start a new container:
.. code-block:: bash
$ docker run --name pgpointcloud -e POSTGRES_DB=pointclouds -e POSTGRES_PASSWORD=mysecretpassword -d pgpointcloud/pointcloud
Extensions are automatically created in the new database named ``pointclouds``:
.. code-block:: bash
$ docker exec -it pgpointcloud psql -U postgres -d pointclouds -c "\dx"
List of installed extensions
Name | Version | Schema | Description
------------------------+---------+------------+---------------------------------------------------------------------
fuzzystrmatch | 1.1 | public | determine similarities and distance between strings
plpgsql | 1.0 | pg_catalog | PL/pgSQL procedural language
pointcloud | 1.2.1 | public | data type for lidar point clouds
pointcloud_postgis | 1.2.1 | public | integration for pointcloud LIDAR data and PostGIS geometry data
postgis | 3.0.1 | public | PostGIS geometry, geography, and raster spatial types and functions
postgis_tiger_geocoder | 3.0.1 | tiger | PostGIS tiger geocoder and reverse geocoder
postgis_topology | 3.0.1 | topology | PostGIS topology spatial types and functions
(7 rows)
.. _`here`: https://hub.docker.com/_/postgres
------------------------------------------------------------------------------
Run PDAL pipeline
------------------------------------------------------------------------------
For the need of the tutorial, we can download sample data from the `PDAL`_
organization:
.. code-block:: bash
$ wget https://github.com/PDAL/data/raw/master/liblas/LAS12_Sample_withRGB_Quick_Terrain_Modeler_fixed.laz -P /tmp
Thanks to the ``pdal info`` command, we can obtain some information on the dataset:
+ Number of points: 3811489
+ Spatial reference: EPSG:32616
To configure the json PDAL pipeline, we need to set up the ``connection``
parameter for the ``pgpointcloud`` writer. To do that, the Docker container IP
adress on which the PostgreSQL database is running is necessary:
.. code-block:: bash
$ docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' pgpointcloud
172.17.0.2
So the ``pipeline.json`` file looks like:
.. code-block:: json
{
"pipeline":[
{
"type":"readers.las",
"filename":"/tmp/LAS12_Sample_withRGB_Quick_Terrain_Modeler_fixed.laz"
},
{
"type":"filters.chipper",
"capacity":"400"
},
{
"type":"writers.pgpointcloud",
"connection":"host='172.17.0.2' dbname='pointclouds' user='postgres' password='mysecretpassword' port='5432'",
"table":"airport",
"compression":"dimensional",
"srid":"32616"
}
]
}
The PDAL pipeline can finally be execute with ``pdal pipeline pipeline.json``
and an ``airport`` table is created.
.. _`PDAL`: https://github.com/PDAL
------------------------------------------------------------------------------
Configure connection service file
------------------------------------------------------------------------------
To facilitate the access to the database hosted on the Docker container, we can
configure the PostgreSQL connection service file:
.. code-block:: bash
[pgpointcloud]
host=172.17.0.2
port=5432
dbname=pointclouds
user=postgres
password=mysecretpassword
Then we can explore the content of the new ``airport`` table:
.. code-block:: bash
$ psql service=pgpointcloud
psql (12.3)
Type "help" for help.
pointclouds=# SELECT COUNT(*) FROM airport;
count
-------
9529
(1 row)
In this case, we have ``9529`` patchs containing ``400`` points (the size of
the chipper filter), meaning about ``3811600`` points. So the last patch isn't
fully filled.
|
