Full Calibration Benchmark Workflow Example

Note

You can find the corresponding Python script for this tutorial here: https://github.com/ARTIST-Association/PAINT/blob/main/scripts/example_benchmark_dataset_full_workflow.py

We provide an executable example of how PAINT calibration data can be used directly in further applications. When executed, the script performs the following steps:

  • Downloads the necessary metadata to generate benchmark splits (if not already downloaded).

  • Generates the dataset benchmark splits.

  • Initializes a torch.Dataset based on these splits (downloading data if necessary).

This is the one script you can use if you want to get your hands on PAINT data as quickly as possible without coding at all yourself!

Script Arguments

The script accepts the following command-line arguments:

  • metadata_input Path to the file containing the metadata required to generate the dataset splits.

  • output_dir Root directory where all outputs and data will be saved.

  • split_type The benchmark dataset split type to apply.

  • train_size The number of training samples required per heliostat. The total training size depends on the number of heliostats.

  • val_size The number of validation samples required per heliostat. The total validation size also depends on the number of heliostats.

  • remove_unused_data Whether to remove metadata not required to load benchmark splits. This data may still be useful for plots or inspection.

  • item_type The type of calibration item to load, e.g., raw image, cropped image, flux image, flux-centered image, or calibration properties.

Have fun experimenting with the workflow! 🚀