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Rasterio Library

siapy.entities.images.rasterio_lib

RasterioLibImage dataclass 

RasterioLibImage(file: XarrayType)

Bases: ImageBase

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

file property 

file: XarrayType

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]

camera_id property 

camera_id: str

open classmethod 

open(filepath: str | Path) -> RasterioLibImage
Source code in siapy/entities/images/rasterio_lib.py 
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@classmethod
def open(cls, filepath: str | Path) -> "RasterioLibImage":
    filepath = Path(filepath)
    if not filepath.exists():
        raise InvalidFilepathError(filepath)

    try:
        raster = rioxarray.open_rasterio(filepath)
    except Exception as e:
        raise InvalidInputError({"filepath": str(filepath)}, f"Failed to open raster file: {e}") from e

    if isinstance(raster, list):
        raise InvalidInputError({"file_type": type(raster).__name__}, "Expected DataArray or Dataset, got list")

    return cls(raster)

to_display 

to_display(equalize: bool = True) -> Image
Source code in siapy/entities/images/rasterio_lib.py 
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def to_display(self, equalize: bool = True) -> Image.Image:
    bands_data = self.file.sel(band=self.default_bands)
    image_3ch = bands_data.transpose("y", "x", "band").values
    image_3ch_clean = np.nan_to_num(np.asarray(image_3ch))
    min_val = np.nanmin(image_3ch_clean)
    max_val = np.nanmax(image_3ch_clean)

    image_scaled = ((image_3ch_clean - min_val) * (255.0 / (max_val - min_val))).astype(np.uint8)

    image = Image.fromarray(image_scaled)
    if equalize:
        image = ImageOps.equalize(image)
    return image

to_numpy 

to_numpy(
    nan_value: float | None = None,
) -> NDArray[floating[Any]]
Source code in siapy/entities/images/rasterio_lib.py 
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def to_numpy(self, nan_value: float | None = None) -> NDArray[np.floating[Any]]:
    image = np.asarray(self.file.transpose("y", "x", "band").values)
    if nan_value is not None:
        image = np.nan_to_num(image, nan=nan_value)
    return image

to_xarray 

to_xarray() -> XarrayType
Source code in siapy/entities/images/rasterio_lib.py 
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def to_xarray(self) -> "XarrayType":
    return self.file