panoptica.utils

panoptica.utils.constants

class panoptica.utils.constants.CCABackend(value)

Bases: _Enum_Compare

Enumeration representing different connected component analysis (CCA) backends.

This enumeration defines options for CCA backends, which are used for labeling connected components in segmentation masks.

Members:

• cc3d: Represents the Connected Components in 3D (CC3D) backend for CCA. [CC3D Website](https://github.com/seung-lab/connected-components-3d)

• scipy: Represents the SciPy backend for CCA. [SciPy Website](https://www.scipy.org/)

cc3d = 1

scipy = 2

class panoptica.utils.constants._Enum_Compare(value)

Bases: Enum

An enumeration.

panoptica.utils.datatypes

panoptica.util.numpy_utils

panoptica.utils.numpy_utils._count_unique_without_zeros(arr: ndarray) → int

Count the number of unique elements in the input NumPy array, excluding zeros.

Parameters:

arr (np.ndarray) – Input array.

Returns:

Number of unique elements excluding zeros.

Return type:

int

panoptica.utils.numpy_utils._get_bbox_nd(img: ndarray, px_dist: int | tuple[int, ...] = 0) → tuple[slice, ...]

calculates a bounding box in n dimensions given a image (factor ~2 times faster than compute_crop_slice)

Parameters:

• img – input array

• px_dist – int | tuple[int]: dist (int): The amount of padding to be added to the cropped image. If int, will apply the same padding to each dim. Default value is 0.

Returns:

list of boundary coordinates [x_min, x_max, y_min, y_max, z_min, z_max]

panoptica.utils.numpy_utils._get_smallest_fitting_uint(max_value: int) → type

Determine the smallest unsigned integer type that can accommodate the given maximum value.

Parameters:

max_value (int) – The maximum value to be accommodated.

Returns:

The NumPy data type (e.g., np.uint8, np.uint16, np.uint32, np.uint64).

Return type:

type

Example: >>> _get_smallest_fitting_uint(255) <class ‘numpy.uint8’>

panoptica.utils.numpy_utils._unique_without_zeros(arr: ndarray) → ndarray

Get unique non-zero values from a NumPy array.

Parameters:

arr (np.ndarray) – Input NumPy array.

Returns:

Unique non-zero values from the input array.

Return type:

np.ndarray

Issues a warning if negative values are present.