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rslearn.dataset.remap

remap

Classes to remap raster values.

Remappers module-attribute 

Remappers = _RemapperRegistry()

Registry of Remapper implementations.

Remapper

An abstract class that remaps pixel values based on layer configuration.

Source code in rslearn/dataset/remap.py 
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class Remapper:
    """An abstract class that remaps pixel values based on layer configuration."""

    def __init__(self, config: dict[str, Any]) -> None:
        """Initialize a Remapper.

        Args:
            config: the config dict for this remapper.
        """
        pass

    def __call__(
        self, array: npt.NDArray[Any], dtype: npt.DTypeLike
    ) -> npt.NDArray[Any]:
        """Compute remapped array.

        Args:
            array: the array to remap
            dtype: dtype of output array

        Returns:
            remapped array
        """
        raise NotImplementedError

LinearRemapper

Bases: Remapper

A Remapper that performs a linear remapping.

Source code in rslearn/dataset/remap.py 
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@Remappers.register("linear")
class LinearRemapper(Remapper):
    """A Remapper that performs a linear remapping."""

    def __init__(self, config: dict[str, Any]):
        """Initialize a LinearRemapper.

        Args:
            config: the config dict for this remapper. config["src"] and config["dst"]
                should be tuples (min, max) specifying the source range and the
                destination range, respectively. For example, src=(0, 2550)
                dst=(0, 255) means 1000 would be remapped to 100, and 3000 to 255.
        """
        self.src_min = config["src"][0]
        self.src_max = config["src"][1]
        self.dst_min = config["dst"][0]
        self.dst_max = config["dst"][1]

    def __call__(
        self, array: npt.NDArray[Any], dtype: npt.DTypeLike
    ) -> npt.NDArray[Any]:
        """Compute remapped array.

        Args:
            array: the array to remap
            dtype: dtype of output array

        Returns:
            remapped array
        """
        array = (array.astype(np.float64) - self.src_min) * (
            self.dst_max - self.dst_min
        ) / (self.src_max - self.src_min) + self.dst_min
        array = np.clip(array, self.dst_min, self.dst_max).astype(dtype)
        return array

load_remapper 

load_remapper(config: dict[str, Any]) -> Remapper

Load a remapper from a configuration dictionary.

Source code in rslearn/dataset/remap.py 
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def load_remapper(config: dict[str, Any]) -> Remapper:
    """Load a remapper from a configuration dictionary."""
    cls = Remappers[config["name"]]
    return cls(config)