darts_postprocessing

Postprocessing steps for the DARTS dataset.

Functions:

• binarize –

  Binarize the probabilities based on a threshold and a mask.

• erode_mask –

  Erode the mask, also set the edges to invalid.

• prepare_export –

  Prepare the export, e.g. binarizes the data and convert the float probabilities to uint8.

Attributes:

• __version__ –

__version__ module-attribute

__version__ = importlib.metadata.version('darts-nextgen')

binarize

binarize(
    probs: xarray.DataArray,
    threshold: float,
    min_object_size: int,
    mask: xarray.DataArray,
    device: typing.Literal["cuda", "cpu"] | int,
) -> xarray.DataArray

Binarize the probabilities based on a threshold and a mask.

Steps for binarization

1. Dilate the mask. This will dilate the edges of holes in the mask as well as the edges of the tile.
2. Binarize the probabilities based on the threshold.
3. Remove objects at which overlap with either the edge of the tile or the noData mask.
4. Remove small objects.

Parameters:

• probs (xarray.DataArray) –

  Probabilities to binarize.

• threshold (float) –

  Threshold to binarize the probabilities.

• min_object_size (int) –

  Minimum object size to keep.

• mask (xarray.DataArray) –

  Mask to apply to the binarized probabilities. Expects 0=negative, 1=postitive.

• device (typing.Literal['cuda', 'cpu'] | int) –

  The device to use for removing small objects.

Returns:

• xarray.DataArray –

  xr.DataArray: Binarized probabilities.

Source code in darts-postprocessing/src/darts_postprocessing/postprocess.py

@stopuhr.funkuhr("Binarizing probabilities", printer=logger.debug, print_kwargs=["threshold", "min_object_size"])
def binarize(
    probs: xr.DataArray,
    threshold: float,
    min_object_size: int,
    mask: xr.DataArray,
    device: Literal["cuda", "cpu"] | int,
) -> xr.DataArray:
    """Binarize the probabilities based on a threshold and a mask.

    Steps for binarization:
        1. Dilate the mask. This will dilate the edges of holes in the mask as well as the edges of the tile.
        2. Binarize the probabilities based on the threshold.
        3. Remove objects at which overlap with either the edge of the tile or the noData mask.
        4. Remove small objects.

    Args:
        probs (xr.DataArray): Probabilities to binarize.
        threshold (float): Threshold to binarize the probabilities.
        min_object_size (int): Minimum object size to keep.
        mask (xr.DataArray): Mask to apply to the binarized probabilities. Expects 0=negative, 1=postitive.
        device (Literal["cuda", "cpu"] | int): The device to use for removing small objects.

    Returns:
        xr.DataArray: Binarized probabilities.

    """
    use_gpu = device == "cuda" or isinstance(device, int)

    # Warn user if use_gpu is set but no GPU is available
    if use_gpu and not CUCIM_AVAILABLE:
        logger.warning(
            f"Device was set to {device}, but GPU acceleration is not available. Calculating TPI and slope on CPU."
        )
        use_gpu = False

    # Where the output from the ensemble / segmentation is nan turn it into 0, else threshold it
    # Also, where there was no valid input data, turn it into 0
    binarized = (probs.fillna(0) > threshold).astype("uint8")

    # Remove objects at which overlap with either the edge of the tile or the noData mask
    labels = binarized.copy(data=label(binarized, connectivity=2))
    edge_label_ids = np.unique(xr.where(~mask, labels, 0))
    binarized = ~labels.isin(edge_label_ids) & binarized

    # Remove small objects with GPU
    if use_gpu:
        device_nr = device if isinstance(device, int) else 0
        logger.debug(f"Moving binarized to GPU:{device}.")
        # Check if binarized is dask, if not persist it, since remove_small_objects_gpu can't be calculated from
        # cupy-dask arrays
        if binarized.chunks is not None:
            binarized = binarized.persist()
        with cp.cuda.Device(device_nr):
            binarized = binarized.cupy.as_cupy()
            binarized.values = remove_small_objects_gpu(
                binarized.astype(bool).expand_dims("batch", 0).data, min_size=min_object_size
            )[0]
            binarized = binarized.cupy.as_numpy()
            free_cupy()
    else:
        binarized.values = remove_small_objects(
            binarized.astype(bool).expand_dims("batch", 0).values, min_size=min_object_size
        )[0]

    # Convert back to int8
    binarized = binarized.astype("uint8")

    return binarized

erode_mask

erode_mask(
    mask: xarray.DataArray,
    size: int,
    device: typing.Literal["cuda", "cpu"] | int,
) -> xarray.DataArray

Erode the mask, also set the edges to invalid.

Parameters:

• mask (xarray.DataArray) –

  The mask to erode.

• size (int) –

  The size of the disk to use for erosion and the edge-cropping.

• device (typing.Literal['cuda', 'cpu'] | int) –

  The device to use for erosion.

Returns:

• xarray.DataArray –

  xr.DataArray: The dilated and inverted mask.

Source code in darts-postprocessing/src/darts_postprocessing/postprocess.py

@stopuhr.funkuhr("Eroding mask", printer=logger.debug, print_kwargs=["size"])
def erode_mask(mask: xr.DataArray, size: int, device: Literal["cuda", "cpu"] | int) -> xr.DataArray:
    """Erode the mask, also set the edges to invalid.

    Args:
        mask (xr.DataArray): The mask to erode.
        size (int): The size of the disk to use for erosion and the edge-cropping.
        device (Literal["cuda", "cpu"] | int): The device to use for erosion.

    Returns:
        xr.DataArray: The dilated and inverted mask.

    """
    # Clone mask to avoid in-place operations
    mask = mask.copy()

    # Change to dtype uint8 for faster skimage operations
    mask = mask.astype("uint8")

    use_gpu = device == "cuda" or isinstance(device, int)

    # Warn user if use_gpu is set but no GPU is available
    if use_gpu and not CUCIM_AVAILABLE:
        logger.warning(
            f"Device was set to {device}, but GPU acceleration is not available. Calculating TPI and slope on CPU."
        )
        use_gpu = False

    # Dilate the mask with GPU
    if use_gpu:
        device_nr = device if isinstance(device, int) else 0
        logger.debug(f"Moving mask to GPU:{device}.")
        # Check if mask is dask, if not persist it, since dilation can't be calculated from cupy-dask arrays
        if mask.chunks is not None:
            mask = mask.persist()
        with cp.cuda.Device(device_nr):
            mask = mask.cupy.as_cupy()
            mask.values = binary_erosion_gpu(mask.data, disk_gpu(size))
            mask = mask.cupy.as_numpy()
            free_cupy()
    else:
        mask.values = binary_erosion(mask.values, disk(size))

    # Mask edges
    mask[:size, :] = 0
    mask[-size:, :] = 0
    mask[:, :size] = 0
    mask[:, -size:] = 0

    return mask

prepare_export

prepare_export(
    tile: xarray.Dataset,
    bin_threshold: float = 0.5,
    mask_erosion_size: int = 10,
    min_object_size: int = 32,
    quality_level: int
    | typing.Literal[
        "high_quality", "low_quality", "none"
    ] = 0,
    ensemble_subsets: list[str] = [],
    device: typing.Literal["cuda", "cpu"]
    | int = darts_postprocessing.postprocess.DEFAULT_DEVICE,
) -> xarray.Dataset

Prepare the export, e.g. binarizes the data and convert the float probabilities to uint8.

Parameters:

• tile (xarray.Dataset) –

  Input tile from inference and / or an ensemble.

• bin_threshold (float, default: 0.5 ) –

  The threshold to binarize the probabilities. Defaults to 0.5.

• mask_erosion_size (int, default: 10 ) –

  The size of the disk to use for mask erosion and the edge-cropping. Defaults to 10.

• min_object_size (int, default: 32 ) –

  The minimum object size to keep in pixel. Defaults to 32.

• quality_level (int | str, default: 0 ) –

  The quality level to use for the mask. If a string maps to int. high_quality -> 2, low_quality=1, none=0 (apply no masking). Defaults to 0.

• ensemble_subsets (list[str], default: [] ) –

  The ensemble subsets to use for the binarization. Defaults to [].

• device (typing.Literal['cuda', 'cpu'] | int, default: darts_postprocessing.postprocess.DEFAULT_DEVICE ) –

  The device to use for dilation. Defaults to "cuda" if cuda for cucim is available, else "cpu".

Returns:

• xarray.Dataset –

  xr.Dataset: Output tile.

Source code in darts-postprocessing/src/darts_postprocessing/postprocess.py

@stopuhr.funkuhr(
    "Preparing export",
    printer=logger.debug,
    print_kwargs=["bin_threshold", "mask_erosion_size", "min_object_size", "quality_level", "ensemble_subsets"],
)
def prepare_export(
    tile: xr.Dataset,
    bin_threshold: float = 0.5,
    mask_erosion_size: int = 10,
    min_object_size: int = 32,
    quality_level: int | Literal["high_quality", "low_quality", "none"] = 0,
    ensemble_subsets: list[str] = [],
    device: Literal["cuda", "cpu"] | int = DEFAULT_DEVICE,
) -> xr.Dataset:
    """Prepare the export, e.g. binarizes the data and convert the float probabilities to uint8.

    Args:
        tile (xr.Dataset): Input tile from inference and / or an ensemble.
        bin_threshold (float, optional): The threshold to binarize the probabilities. Defaults to 0.5.
        mask_erosion_size (int, optional): The size of the disk to use for mask erosion and the edge-cropping.
            Defaults to 10.
        min_object_size (int, optional): The minimum object size to keep in pixel. Defaults to 32.
        quality_level (int | str, optional): The quality level to use for the mask. If a string maps to int.
            high_quality -> 2, low_quality=1, none=0 (apply no masking). Defaults to 0.
        ensemble_subsets (list[str], optional): The ensemble subsets to use for the binarization.
            Defaults to [].
        device (Literal["cuda", "cpu"] | int, optional): The device to use for dilation.
            Defaults to "cuda" if cuda for cucim is available, else "cpu".

    Returns:
        xr.Dataset: Output tile.

    """
    quality_level = (
        quality_level
        if isinstance(quality_level, int)
        else {"high_quality": 2, "low_quality": 1, "none": 0}[quality_level]
    )
    mask = tile["quality_data_mask"] >= quality_level
    if quality_level > 0:
        mask = erode_mask(mask, mask_erosion_size, device)  # 0=positive, 1=negative
    tile["extent"] = mask.copy()
    tile["extent"].attrs = {
        "long_name": "Extent of the segmentation",
    }

    def _prep_layer(tile, layername, binarized_layer_name):
        # Binarize the segmentation
        tile[binarized_layer_name] = binarize(tile[layername], bin_threshold, min_object_size, mask, device)
        tile[binarized_layer_name].attrs = {
            "long_name": "Binarized Segmentation",
        }

        # Convert the probabilities to uint8
        # Same but this time with 255 as no-data
        # But first check if this step was already run
        if tile[layername].max() > 1:
            return tile

        intprobs = (tile[layername] * 100).fillna(255).astype("uint8")
        tile[layername] = xr.where(mask, intprobs, 255)
        tile[layername].attrs = {
            "long_name": "Probabilities",
            "units": "%",
        }
        tile[layername] = tile[layername].rio.write_nodata(255)
        return tile

    tile = _prep_layer(tile, "probabilities", "binarized_segmentation")

    # get the names of the model probabilities if available
    # for example 'tcvis' from 'probabilities-tcvis'
    for ensemble_subset in ensemble_subsets:
        tile = _prep_layer(tile, f"probabilities-{ensemble_subset}", f"binarized_segmentation-{ensemble_subset}")

    return tile

• binarize
• erode_mask
• prepare_export