PaintCalibrationDataset Tutorial

Note

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

Since multiple calibration items may be required for training an alignment optimization or similar, we have created a custom torch.Dataset that loads calibration items from the PAINT database.

There are three ways of creating a PaintCalibrationDataset:

Direct instantiation from local data

This approach is based on calibration data that has already been downloaded and saved in a root_dir:
```
dataset = PaintCalibrationDataset(
     root_dir=direct_root_dir,
     item_ids=None,
     item_type=args.item_type,
 )
```
- The item_ids can be a list indicating which of the items contained in the root_dir should be used, or if None, all items will be used.
- The item_type specifies what type of calibration item should be loaded (e.g., raw image, cropped image, flux image, flux-centered image, or calibration properties file).
From a benchmark file

You can also create the dataset from a benchmark file (see the :information on dataset splits:splitter for details). In this case, the benchmark_file containing information on the train, validation, and test split must be provided:
```
train, test, val = PaintCalibrationDataset.from_benchmark(
     benchmark_file=benchmark_file,
     root_dir=benchmark_root_dir,
     item_type=args.item_type,
     download=True,
)
```
This class method returns three torch.Dataset instances, one for each split: train, test, and validation.

From heliostat identifiers

Finally, the dataset can be created from a single heliostat or a list of heliostats. All calibration items for the provided heliostats will be used to construct the dataset:

heliostat_dataset = PaintCalibrationDataset.from_heliostats(
     heliostats=heliostats,
     root_dir=heliostat_root_dir,
     item_type=args.item_type,
     download=True,
 )