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PDAL Processing Server

A Flask server integrated with Celery for rasterizing .laz files (other pipelines can be used in future)

Setup and Running

  1. Build and run using Docker Compose: app.py
docker-compose up --build
  1. Send a .laz file to turn into a TIFF:
curl -X POST -H "Content-Type: application/json" \
-d '{"input_file":"<input_filename>", "output_file":"<output_filename>", "resolution":<resolution_value>}' \
http://localhost:5000/process-pdal

app.py 3. Check the status of the task:

curl http://localhost:5000/task/<task_id>

Downloading and uploading files

In order to facilitate the download and upload of the files, the blob_helper_mapping_utility is used. See more details at: https://pypi.org/project/blob-mounting-helper-utility/

Environment variables

Variable Description Default
APP_HOST Host of the server
APP_PORT Port of the server 5000
APP_DEBUG Debug mode True
CELERY_BROKER_URL URL of the broker redis://redis:6379/0
CELERY_RESULT_BACKEND URL of the backend redis://redis:6379/0
AZURE_STORAGE_ACCOUNT_KEY Azure storage account key None
BLOB_MOUNTING_CONFIGURATIONS_JSON_PATH Path to the JSON file containing the blob mounting configurations None

About

Leverages PDAL to asynchronously process point cloud data

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