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Spectral Library

siapy.entities.images.spectral_lib

SpectralLibImage dataclass 

SpectralLibImage(file: SpectralLibType)

Bases: ImageBase

Source code in siapy/entities/images/spectral_lib.py 
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def __init__(
    self,
    file: "SpectralLibType",
):
    self._file = file

file property 

file: SpectralLibType

filepath property 

filepath: Path

metadata property 

metadata: dict[str, Any]

shape property 

shape: tuple[int, int, int]

rows property 

rows: int

cols property 

cols: int

bands property 

bands: int

default_bands property 

default_bands: list[int]

wavelengths property 

wavelengths: list[float]

description property 

description: dict[str, Any]

camera_id property 

camera_id: str

open classmethod 

open(
    *,
    header_path: str | Path,
    image_path: str | Path | None = None,
) -> SpectralLibImage
Source code in siapy/entities/images/spectral_lib.py 
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@classmethod
def open(cls, *, header_path: str | Path, image_path: str | Path | None = None) -> "SpectralLibImage":
    header_path = Path(header_path)
    if not header_path.exists():
        raise InvalidFilepathError(header_path)

    try:
        sp_file = sp.envi.open(file=header_path, image=image_path)
    except Exception as e:
        raise InvalidInputError({"filepath": str(header_path)}, f"Failed to open spectral file: {e}") from e

    if isinstance(sp_file, sp.io.envi.SpectralLibrary):
        raise InvalidInputError({"file_type": type(sp_file).__name__}, "Expected Image, got SpectralLibrary")

    return cls(sp_file)

to_display 

to_display(equalize: bool = True) -> Image
Source code in siapy/entities/images/spectral_lib.py 
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def to_display(self, equalize: bool = True) -> Image.Image:
    max_uint8 = 255.0
    image_3ch = self.file.read_bands(self.default_bands)
    image_3ch = self._remove_nan(image_3ch, nan_value=0)
    image_3ch[:, :, 0] = image_3ch[:, :, 0] / image_3ch[:, :, 0].max() / max_uint8
    image_3ch[:, :, 1] = image_3ch[:, :, 1] / (image_3ch[:, :, 1].max() / max_uint8)
    image_3ch[:, :, 2] = image_3ch[:, :, 2] / (image_3ch[:, :, 2].max() / max_uint8)
    image = Image.fromarray(image_3ch.astype("uint8"))
    if equalize:
        image = ImageOps.equalize(image)
    return image

to_numpy 

to_numpy(nan_value: float | None = None) -> ndarray
Source code in siapy/entities/images/spectral_lib.py 
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def to_numpy(self, nan_value: float | None = None) -> np.ndarray:
    image = self.file[:, :, :]
    if nan_value is not None:
        image = self._remove_nan(image, nan_value)
    return image

to_xarray 

to_xarray() -> XarrayType
Source code in siapy/entities/images/spectral_lib.py 
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def to_xarray(self) -> "XarrayType":
    data = self._file[:, :, :]
    xarray = xr.DataArray(
        data,
        dims=["y", "x", "band"],
        coords={
            "y": np.arange(self.rows),
            "x": np.arange(self.cols),
            "band": self.wavelengths,
        },
        attrs=self._file.metadata,
    )
    return xarray