import inspect
import sys
from functools import wraps
from math import atan2, sqrt
from math import pi as PI
from warnings import warn

import numpy as np
from scipy import ndimage as ndi
from scipy.spatial.distance import pdist

from . import _moments
from ._find_contours import find_contours
from ._marching_cubes_lewiner import marching_cubes
from ._regionprops_utils import (
    _normalize_spacing,
    euler_number,
    perimeter,
    perimeter_crofton,
)

__all__ = ['regionprops', 'euler_number', 'perimeter', 'perimeter_crofton']


# All values in this PROPS dict correspond to current scikit-image property
# names. The keys in this PROPS dict correspond to deprecated names used in
# prior releases
PROPS = {
    'Area': 'area',
    'BoundingBox': 'bbox',
    'BoundingBoxArea': 'area_bbox',
    'bbox_area': 'area_bbox',
    'CentralMoments': 'moments_central',
    'Centroid': 'centroid',
    'ConvexArea': 'area_convex',
    'convex_area': 'area_convex',
    # 'ConvexHull',
    'ConvexImage': 'image_convex',
    'convex_image': 'image_convex',
    'Coordinates': 'coords',
    'Eccentricity': 'eccentricity',
    'EquivDiameter': 'equivalent_diameter_area',
    'equivalent_diameter': 'equivalent_diameter_area',
    'EulerNumber': 'euler_number',
    'Extent': 'extent',
    # 'Extrema',
    'FeretDiameter': 'feret_diameter_max',
    'FeretDiameterMax': 'feret_diameter_max',
    'FilledArea': 'area_filled',
    'filled_area': 'area_filled',
    'FilledImage': 'image_filled',
    'filled_image': 'image_filled',
    'HuMoments': 'moments_hu',
    'Image': 'image',
    'InertiaTensor': 'inertia_tensor',
    'InertiaTensorEigvals': 'inertia_tensor_eigvals',
    'IntensityImage': 'image_intensity',
    'intensity_image': 'image_intensity',
    'Label': 'label',
    'LocalCentroid': 'centroid_local',
    'local_centroid': 'centroid_local',
    'MajorAxisLength': 'axis_major_length',
    'major_axis_length': 'axis_major_length',
    'MaxIntensity': 'intensity_max',
    'max_intensity': 'intensity_max',
    'MeanIntensity': 'intensity_mean',
    'mean_intensity': 'intensity_mean',
    'MinIntensity': 'intensity_min',
    'min_intensity': 'intensity_min',
    'std_intensity': 'intensity_std',
    'MinorAxisLength': 'axis_minor_length',
    'minor_axis_length': 'axis_minor_length',
    'Moments': 'moments',
    'NormalizedMoments': 'moments_normalized',
    'Orientation': 'orientation',
    'Perimeter': 'perimeter',
    'CroftonPerimeter': 'perimeter_crofton',
    # 'PixelIdxList',
    # 'PixelList',
    'Slice': 'slice',
    'Solidity': 'solidity',
    # 'SubarrayIdx'
    'WeightedCentralMoments': 'moments_weighted_central',
    'weighted_moments_central': 'moments_weighted_central',
    'WeightedCentroid': 'centroid_weighted',
    'weighted_centroid': 'centroid_weighted',
    'WeightedHuMoments': 'moments_weighted_hu',
    'weighted_moments_hu': 'moments_weighted_hu',
    'WeightedLocalCentroid': 'centroid_weighted_local',
    'weighted_local_centroid': 'centroid_weighted_local',
    'WeightedMoments': 'moments_weighted',
    'weighted_moments': 'moments_weighted',
    'WeightedNormalizedMoments': 'moments_weighted_normalized',
    'weighted_moments_normalized': 'moments_weighted_normalized',
}

COL_DTYPES = {
    'area': float,
    'area_bbox': float,
    'area_convex': float,
    'area_filled': float,
    'axis_major_length': float,
    'axis_minor_length': float,
    'bbox': int,
    'centroid': float,
    'centroid_local': float,
    'centroid_weighted': float,
    'centroid_weighted_local': float,
    'coords': object,
    'coords_scaled': object,
    'eccentricity': float,
    'equivalent_diameter_area': float,
    'euler_number': int,
    'extent': float,
    'feret_diameter_max': float,
    'image': object,
    'image_convex': object,
    'image_filled': object,
    'image_intensity': object,
    'inertia_tensor': float,
    'inertia_tensor_eigvals': float,
    'intensity_max': float,
    'intensity_mean': float,
    'intensity_median': float,
    'intensity_min': float,
    'intensity_std': float,
    'label': int,
    'moments': float,
    'moments_central': float,
    'moments_hu': float,
    'moments_normalized': float,
    'moments_weighted': float,
    'moments_weighted_central': float,
    'moments_weighted_hu': float,
    'moments_weighted_normalized': float,
    'num_pixels': int,
    'orientation': float,
    'perimeter': float,
    'perimeter_crofton': float,
    'slice': object,
    'solidity': float,
}

OBJECT_COLUMNS = [col for col, dtype in COL_DTYPES.items() if dtype == object]

PROP_VALS = set(PROPS.values())

_require_intensity_image = (
    'image_intensity',
    'intensity_max',
    'intensity_mean',
    'intensity_median',
    'intensity_min',
    'intensity_std',
    'moments_weighted',
    'moments_weighted_central',
    'centroid_weighted',
    'centroid_weighted_local',
    'moments_weighted_hu',
    'moments_weighted_normalized',
)


def _infer_number_of_required_args(func):
    """Infer the number of required arguments for a given function.

    Parameters
    ----------
    func : callable
        The function that is being inspected.

    Returns
    -------
    n_args : int
        The number of required arguments for `func`.
    """
    argspec = inspect.getfullargspec(func)
    n_args = len(argspec.args)
    if argspec.defaults is not None:
        n_args -= len(argspec.defaults)
    return n_args


def _infer_regionprop_dtype(func, *, intensity, ndim):
    """Infer the dtype of a region property calculated by `func`.

    If a region property function always returns the same shape and type of
    output regardless of input size, then the dtype is the dtype of the
    returned array. Otherwise, the property has object dtype.

    Parameters
    ----------
    func : callable
        Function to be tested. The signature should be array[bool] -> Any if
        `intensity` is False, or *(array[bool], array[float]) -> Any otherwise.
    intensity : bool
        Whether the regionprop is calculated using an intensity image.
    ndim : int
        The number of dimensions for which to check `func`.

    Returns
    -------
    dtype : NumPy data type
        The data type of the returned property.
    """
    mask_1 = np.ones((1,) * ndim, dtype=bool)
    mask_1 = np.pad(mask_1, (0, 1), constant_values=False)
    mask_2 = np.ones((2,) * ndim, dtype=bool)
    mask_2 = np.pad(mask_2, (1, 0), constant_values=False)
    propmasks = [mask_1, mask_2]

    rng = np.random.default_rng()

    if intensity and _infer_number_of_required_args(func) == 2:

        def _func(mask):
            return func(mask, rng.random(mask.shape))

    else:
        _func = func
    props1, props2 = map(_func, propmasks)
    if (
        np.isscalar(props1)
        and np.isscalar(props2)
        or np.array(props1).shape == np.array(props2).shape
    ):
        dtype = np.array(props1).dtype.type
    else:
        dtype = np.object_
    return dtype


def _cached(f):
    @wraps(f)
    def wrapper(obj):
        cache = obj._cache
        prop = f.__name__

        if not obj._cache_active:
            return f(obj)

        if prop not in cache:
            cache[prop] = f(obj)

        return cache[prop]

    return wrapper


def only2d(method):
    @wraps(method)
    def func2d(self, *args, **kwargs):
        if self._ndim > 2:
            raise NotImplementedError(
                f"Property {method.__name__} is not implemented for 3D images"
            )
        return method(self, *args, **kwargs)

    return func2d


def _inertia_eigvals_to_axes_lengths_3D(inertia_tensor_eigvals):
    """Compute ellipsoid axis lengths from inertia tensor eigenvalues.

    Parameters
    ----------
    inertia_tensor_eigvals : sequence of float
        A sequence of 3 floating point eigenvalues, sorted in descending order.

    Returns
    -------
    axis_lengths : list of float
        The ellipsoid axis lengths sorted in descending order.

    Notes
    -----
    Let a >= b >= c be the ellipsoid semi-axes and s1 >= s2 >= s3 be the
    inertia tensor eigenvalues.

    The inertia tensor eigenvalues are given for a solid ellipsoid in [1]_.
    s1 = 1 / 5 * (a**2 + b**2)
    s2 = 1 / 5 * (a**2 + c**2)
    s3 = 1 / 5 * (b**2 + c**2)

    Rearranging to solve for a, b, c in terms of s1, s2, s3 gives
    a = math.sqrt(5 / 2 * ( s1 + s2 - s3))
    b = math.sqrt(5 / 2 * ( s1 - s2 + s3))
    c = math.sqrt(5 / 2 * (-s1 + s2 + s3))

    We can then simply replace sqrt(5/2) by sqrt(10) to get the full axes
    lengths rather than the semi-axes lengths.

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/List_of_moments_of_inertia#List_of_3D_inertia_tensors
    """
    axis_lengths = []
    for ax in range(2, -1, -1):
        w = sum(v * -1 if i == ax else v for i, v in enumerate(inertia_tensor_eigvals))
        w = max(0, w)  # numerical errors can lead to small negative values
        axis_lengths.append(sqrt(10 * w))
    return axis_lengths


class RegionProperties:
    """Provides properties of a labeled image region.

    Please refer to `skimage.measure.regionprops` for more information
    on the available region properties.

    Examples
    --------
    >>> RegionProperties(
    ...     slice=(slice(0, 2), slice(0, 4)),
    ...     label=2,
    ...     label_image=np.array([[0, 1, 1, 2, 0], [2, 2, 2, 2, 0]]),
    ...     intensity_image=None,
    ...     cache_active=False,
    ... )
    <RegionProperties: label=2, bbox=(0, 0, 2, 4)>
    """

    def __init__(
        self,
        slice,
        label,
        label_image,
        intensity_image,
        cache_active,
        *,
        extra_properties=None,
        spacing=None,
        offset=None,
    ):
        if intensity_image is not None:
            ndim = label_image.ndim
            if not (
                intensity_image.shape[:ndim] == label_image.shape
                and intensity_image.ndim in [ndim, ndim + 1]
            ):
                raise ValueError(
                    'Label and intensity image shapes must match,'
                    ' except for channel (last) axis.'
                )
            multichannel = label_image.shape < intensity_image.shape
        else:
            multichannel = False

        self.label = label
        if offset is None:
            offset = np.zeros((label_image.ndim,), dtype=int)
        self._offset = np.array(offset)

        self._slice = slice
        self.slice = slice
        self._label_image = label_image
        self._intensity_image = intensity_image

        self._cache_active = cache_active
        self._cache = {}
        self._ndim = label_image.ndim
        self._multichannel = multichannel
        self._spatial_axes = tuple(range(self._ndim))
        if spacing is None:
            spacing = np.full(self._ndim, 1.0)
        self._spacing = _normalize_spacing(spacing, self._ndim)
        self._pixel_area = np.prod(self._spacing)

        self._extra_properties = {}
        if extra_properties is not None:
            for func in extra_properties:
                name = func.__name__
                if hasattr(self, name):
                    msg = (
                        f"Extra property '{name}' is shadowed by existing "
                        f"property and will be inaccessible. Consider "
                        f"renaming it."
                    )
                    warn(msg)
            self._extra_properties = {func.__name__: func for func in extra_properties}

    def __getattr__(self, attr):
        if attr == "__setstate__":
            # When deserializing this object with pickle, `__setstate__`
            # is accessed before any other attributes like `self._intensity_image`
            # are available which leads to a RecursionError when trying to
            # access them later on in this function. So guard against this by
            # provoking the default AttributeError (gh-6465).
            return self.__getattribute__(attr)

        if self._intensity_image is None and attr in _require_intensity_image:
            raise AttributeError(
                f"Attribute '{attr}' unavailable when `intensity_image` "
                f"has not been specified."
            )
        if attr in self._extra_properties:
            func = self._extra_properties[attr]
            n_args = _infer_number_of_required_args(func)
            # determine whether func requires intensity image
            if n_args == 2:
                if self._intensity_image is not None:
                    if self._multichannel:
                        multichannel_list = [
                            func(self.image, self.image_intensity[..., i])
                            for i in range(self.image_intensity.shape[-1])
                        ]
                        return np.stack(multichannel_list, axis=-1)
                    else:
                        return func(self.image, self.image_intensity)
                else:
                    raise AttributeError(
                        f'intensity image required to calculate {attr}'
                    )
            elif n_args == 1:
                return func(self.image)
            else:
                raise AttributeError(
                    f'Custom regionprop function\'s number of arguments must '
                    f'be 1 or 2, but {attr} takes {n_args} arguments.'
                )
        elif attr in PROPS and attr.lower() == attr:
            if (
                self._intensity_image is None
                and PROPS[attr] in _require_intensity_image
            ):
                raise AttributeError(
                    f"Attribute '{attr}' unavailable when `intensity_image` "
                    f"has not been specified."
                )
            warn(
                f"`RegionProperties.{attr}` is deprecated starting in "
                "version 0.26 and will be removed in version 2.0. Use "
                f"`RegionProperties.{PROPS[attr]}` instead. ",
                category=FutureWarning,
                stacklevel=2,
            )
            # retrieve deprecated property (excluding old CamelCase ones)
            return getattr(self, PROPS[attr])

        # Fallback to default behavior, potentially raising an attribute error
        return self.__getattribute__(attr)

    def __setattr__(self, name, value):
        if name in PROPS:
            super().__setattr__(PROPS[name], value)
        else:
            super().__setattr__(name, value)

    @property
    @_cached
    def num_pixels(self):
        return np.sum(self.image)

    @property
    @_cached
    def area(self):
        return np.sum(self.image) * self._pixel_area

    @property
    def bbox(self):
        """
        Returns
        -------
        A tuple of the bounding box's start coordinates for each dimension,
        followed by the end coordinates for each dimension.
        """
        return tuple(
            [self.slice[i].start for i in range(self._ndim)]
            + [self.slice[i].stop for i in range(self._ndim)]
        )

    @property
    def area_bbox(self):
        return self.image.size * self._pixel_area

    @property
    def centroid(self):
        return tuple(self.coords_scaled.mean(axis=0))

    @property
    @_cached
    def area_convex(self):
        return np.sum(self.image_convex) * self._pixel_area

    @property
    @_cached
    def image_convex(self):
        from ..morphology.convex_hull import convex_hull_image

        return convex_hull_image(self.image)

    @property
    def coords_scaled(self):
        indices = np.argwhere(self.image)
        object_offset = np.array([self.slice[i].start for i in range(self._ndim)])
        return (object_offset + indices) * self._spacing + self._offset

    @property
    def coords(self):
        indices = np.argwhere(self.image)
        object_offset = np.array([self.slice[i].start for i in range(self._ndim)])
        return object_offset + indices + self._offset

    @property
    @only2d
    def eccentricity(self):
        l1, l2 = self.inertia_tensor_eigvals
        if l1 == 0:
            return 0
        return sqrt(1 - l2 / l1)

    @property
    def equivalent_diameter_area(self):
        return (2 * self._ndim * self.area / PI) ** (1 / self._ndim)

    @property
    def euler_number(self):
        if self._ndim not in [2, 3]:
            raise NotImplementedError(
                'Euler number is implemented for 2D and 3D images only'
            )
        return euler_number(self.image, self._ndim)

    @property
    def extent(self):
        return self.area / self.area_bbox

    @property
    def feret_diameter_max(self):
        identity_convex_hull = np.pad(
            self.image_convex, 2, mode='constant', constant_values=0
        )
        if self._ndim == 2:
            coordinates = np.vstack(
                find_contours(identity_convex_hull, 0.5, fully_connected='high')
            )
        elif self._ndim == 3:
            coordinates, _, _, _ = marching_cubes(identity_convex_hull, level=0.5)
        distances = pdist(coordinates * self._spacing, 'sqeuclidean')
        return sqrt(np.max(distances))

    @property
    def area_filled(self):
        return np.sum(self.image_filled) * self._pixel_area

    @property
    @_cached
    def image_filled(self):
        structure = np.ones((3,) * self._ndim)
        return ndi.binary_fill_holes(self.image, structure)

    @property
    @_cached
    def image(self):
        return self._label_image[self.slice] == self.label

    @property
    @_cached
    def inertia_tensor(self):
        mu = self.moments_central
        return _moments.inertia_tensor(self.image, mu, spacing=self._spacing)

    @property
    @_cached
    def inertia_tensor_eigvals(self):
        return _moments.inertia_tensor_eigvals(self.image, T=self.inertia_tensor)

    @property
    @_cached
    def image_intensity(self):
        if self._intensity_image is None:
            raise AttributeError('No intensity image specified.')
        image = (
            self.image
            if not self._multichannel
            else np.expand_dims(self.image, self._ndim)
        )
        return self._intensity_image[self.slice] * image

    def _image_intensity_double(self):
        return self.image_intensity.astype(np.float64, copy=False)

    @property
    def centroid_local(self):
        M = self.moments
        M0 = M[(0,) * self._ndim]

        def _get_element(axis):
            return (0,) * axis + (1,) + (0,) * (self._ndim - 1 - axis)

        return np.asarray(
            tuple(M[_get_element(axis)] / M0 for axis in range(self._ndim))
        )

    @property
    def intensity_max(self):
        vals = self.image_intensity[self.image]
        return np.max(vals, axis=0).astype(np.float64, copy=False)

    @property
    def intensity_mean(self):
        return np.mean(self.image_intensity[self.image], axis=0)

    @property
    def intensity_median(self):
        return np.median(self.image_intensity[self.image], axis=0)

    @property
    def intensity_min(self):
        vals = self.image_intensity[self.image]
        return np.min(vals, axis=0).astype(np.float64, copy=False)

    @property
    def intensity_std(self):
        vals = self.image_intensity[self.image]
        return np.std(vals, axis=0)

    @property
    def axis_major_length(self):
        if self._ndim == 2:
            l1 = self.inertia_tensor_eigvals[0]
            return 4 * sqrt(l1)
        elif self._ndim == 3:
            # equivalent to _inertia_eigvals_to_axes_lengths_3D(ev)[0]
            ev = self.inertia_tensor_eigvals
            l2 = 10 * (ev[0] + ev[1] - ev[2])
            return sqrt(max(0, l2))
        else:
            raise ValueError("axis_major_length only available in 2D and 3D")

    @property
    def axis_minor_length(self):
        if self._ndim == 2:
            l2 = self.inertia_tensor_eigvals[-1]
            return 4 * sqrt(l2)
        elif self._ndim == 3:
            # equivalent to _inertia_eigvals_to_axes_lengths_3D(ev)[-1]
            ev = self.inertia_tensor_eigvals
            l2 = 10 * (-ev[0] + ev[1] + ev[2])
            # numerical errors can lead to small negative values
            return sqrt(max(0, l2))
        else:
            raise ValueError("axis_minor_length only available in 2D and 3D")

    @property
    @_cached
    def moments(self):
        M = _moments.moments(self.image.astype(np.uint8), 3, spacing=self._spacing)
        return M

    @property
    @_cached
    def moments_central(self):
        mu = _moments.moments_central(
            self.image.astype(np.uint8),
            self.centroid_local,
            order=3,
            spacing=self._spacing,
        )
        return mu

    @property
    @only2d
    def moments_hu(self):
        if any(s != 1.0 for s in self._spacing):
            raise NotImplementedError('`moments_hu` supports spacing = (1, 1) only')
        return _moments.moments_hu(self.moments_normalized)

    @property
    @_cached
    def moments_normalized(self):
        return _moments.moments_normalized(
            self.moments_central, 3, spacing=self._spacing
        )

    @property
    @only2d
    def orientation(self):
        a, b, b, c = self.inertia_tensor.flat
        if a - c == 0:
            if b < 0:
                return PI / 4.0
            else:
                return -PI / 4.0
        else:
            return 0.5 * atan2(-2 * b, c - a)

    @property
    @only2d
    def perimeter(self):
        if len(np.unique(self._spacing)) != 1:
            raise NotImplementedError('`perimeter` supports isotropic spacings only')
        return perimeter(self.image, 4) * self._spacing[0]

    @property
    @only2d
    def perimeter_crofton(self):
        if len(np.unique(self._spacing)) != 1:
            raise NotImplementedError('`perimeter` supports isotropic spacings only')
        return perimeter_crofton(self.image, 4) * self._spacing[0]

    @property
    def solidity(self):
        return self.area / self.area_convex

    @property
    def centroid_weighted(self):
        ctr = self.centroid_weighted_local
        return tuple(
            idx + slc.start * spc
            for idx, slc, spc in zip(ctr, self.slice, self._spacing)
        )

    @property
    def centroid_weighted_local(self):
        M = self.moments_weighted
        M0 = M[(0,) * self._ndim]

        def _get_element(axis):
            return (0,) * axis + (1,) + (0,) * (self._ndim - 1 - axis)

        return np.asarray(
            tuple(M[_get_element(axis)] / M0 for axis in range(self._ndim))
        )

    @property
    @_cached
    def moments_weighted(self):
        image = self._image_intensity_double()
        if self._multichannel:
            moments = np.stack(
                [
                    _moments.moments(image[..., i], order=3, spacing=self._spacing)
                    for i in range(image.shape[-1])
                ],
                axis=-1,
            )
        else:
            moments = _moments.moments(image, order=3, spacing=self._spacing)
        return moments

    @property
    @_cached
    def moments_weighted_central(self):
        ctr = self.centroid_weighted_local
        image = self._image_intensity_double()
        if self._multichannel:
            moments_list = [
                _moments.moments_central(
                    image[..., i], center=ctr[..., i], order=3, spacing=self._spacing
                )
                for i in range(image.shape[-1])
            ]
            moments = np.stack(moments_list, axis=-1)
        else:
            moments = _moments.moments_central(
                image, ctr, order=3, spacing=self._spacing
            )
        return moments

    @property
    @only2d
    def moments_weighted_hu(self):
        if not (np.array(self._spacing) == np.array([1, 1])).all():
            raise NotImplementedError('`moments_hu` supports spacing = (1, 1) only')
        nu = self.moments_weighted_normalized
        if self._multichannel:
            nchannels = self._intensity_image.shape[-1]
            return np.stack(
                [_moments.moments_hu(nu[..., i]) for i in range(nchannels)],
                axis=-1,
            )
        else:
            return _moments.moments_hu(nu)

    @property
    @_cached
    def moments_weighted_normalized(self):
        mu = self.moments_weighted_central
        if self._multichannel:
            nchannels = self._intensity_image.shape[-1]
            return np.stack(
                [
                    _moments.moments_normalized(
                        mu[..., i], order=3, spacing=self._spacing
                    )
                    for i in range(nchannels)
                ],
                axis=-1,
            )
        else:
            return _moments.moments_normalized(mu, order=3, spacing=self._spacing)

    def __iter__(self):
        props = PROP_VALS

        if self._intensity_image is None:
            unavailable_props = _require_intensity_image
            props = props.difference(unavailable_props)

        return iter(sorted(props))

    def __getitem__(self, key):
        if key in PROPS:
            warn(
                f"`RegionProperties[{key!r}]` is deprecated starting in "
                "version 0.26 and will be removed in version 2.0. Use "
                f"`RegionProperties[{PROPS[key]!r}]` instead. ",
                category=FutureWarning,
                stacklevel=2,
            )
            key = PROPS[key]
        return getattr(self, key)

    def __eq__(self, other):
        if not isinstance(other, RegionProperties):
            return False

        for key in PROP_VALS:
            try:
                # so that NaNs are equal
                np.testing.assert_equal(
                    getattr(self, key, None), getattr(other, key, None)
                )
            except AssertionError:
                return False

        return True

    def __repr__(self):
        cls_name = type(self).__qualname__
        out = f"<{cls_name}: label={self.label!r}, bbox={self.bbox}>"
        return out


# For compatibility with code written prior to 0.16
_RegionProperties = RegionProperties


def _props_to_dict(regions, properties=('label', 'bbox'), separator='-'):
    """Convert image region properties list into a column dictionary.

    Parameters
    ----------
    regions : (K,) list
        List of RegionProperties objects as returned by :func:`regionprops`.
    properties : tuple or list of str, optional
        Properties that will be included in the resulting dictionary
        For a list of available properties, please see :func:`regionprops`.
        Users should remember to add "label" to keep track of region
        identities.
    separator : str, optional
        For non-scalar properties not listed in OBJECT_COLUMNS, each element
        will appear in its own column, with the index of that element separated
        from the property name by this separator. For example, the inertia
        tensor of a 2D region will appear in four columns:
        ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``,
        and ``inertia_tensor-1-1`` (where the separator is ``-``).

        Object columns are those that cannot be split in this way because the
        number of columns would change depending on the object. For example,
        ``image`` and ``coords``.

    Returns
    -------
    out_dict : dict
        Dictionary mapping property names to an array of values of that
        property, one value per region. This dictionary can be used as input to
        pandas ``DataFrame`` to map property names to columns in the frame and
        regions to rows.

    Notes
    -----
    Each column contains either a scalar property, an object property, or an
    element in a multidimensional array.

    Properties with scalar values for each region, such as "eccentricity", will
    appear as a float or int array with that property name as key.

    Multidimensional properties *of fixed size* for a given image dimension,
    such as "centroid" (every centroid will have three elements in a 3D image,
    no matter the region size), will be split into that many columns, with the
    name {property_name}{separator}{element_num} (for 1D properties),
    {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D
    properties), and so on.

    For multidimensional properties that don't have a fixed size, such as
    "image" (the image of a region varies in size depending on the region
    size), an object array will be used, with the corresponding property name
    as the key.

    Examples
    --------
    >>> from skimage import data, util, measure
    >>> image = data.coins()
    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
    >>> proplist = regionprops(label_image, image)
    >>> props = _props_to_dict(proplist, properties=['label', 'inertia_tensor',
    ...                                              'inertia_tensor_eigvals'])
    >>> props  # doctest: +ELLIPSIS +SKIP
    {'label': array([ 1,  2, ...]), ...
     'inertia_tensor-0-0': array([  4.012...e+03,   8.51..., ...]), ...
     ...,
     'inertia_tensor_eigvals-1': array([  2.67...e+02,   2.83..., ...])}

    The resulting dictionary can be directly passed to pandas, if installed, to
    obtain a clean DataFrame:

    >>> import pandas as pd  # doctest: +SKIP
    >>> data = pd.DataFrame(props)  # doctest: +SKIP
    >>> data.head()  # doctest: +SKIP
       label  inertia_tensor-0-0  ...  inertia_tensor_eigvals-1
    0      1         4012.909888  ...                267.065503
    1      2            8.514739  ...                  2.834806
    2      3            0.666667  ...                  0.000000
    3      4            0.000000  ...                  0.000000
    4      5            0.222222  ...                  0.111111

    """

    out = {}
    n = len(regions)
    for prop in properties:
        r = regions[0]
        # Copy the original property name so the output will have the
        # user-provided property name in the case of deprecated names.
        orig_prop = prop
        # determine the current property name for any deprecated property.
        prop = PROPS.get(prop, prop)
        rp = getattr(r, prop)
        if prop in COL_DTYPES:
            dtype = COL_DTYPES[prop]
        else:
            func = r._extra_properties[prop]
            dtype = _infer_regionprop_dtype(
                func,
                intensity=r._intensity_image is not None,
                ndim=r.image.ndim,
            )

        # scalars and objects are dedicated one column per prop
        # array properties are raveled into multiple columns
        # for more info, refer to notes 1
        if np.isscalar(rp) or prop in OBJECT_COLUMNS or dtype is np.object_:
            column_buffer = np.empty(n, dtype=dtype)
            for i in range(n):
                column_buffer[i] = regions[i][prop]
            out[orig_prop] = np.copy(column_buffer)
        else:
            # precompute property column names and locations
            modified_props = []
            locs = []
            for ind in np.ndindex(np.shape(rp)):
                modified_props.append(separator.join(map(str, (orig_prop,) + ind)))
                locs.append(ind if len(ind) > 1 else ind[0])

            # fill temporary column data_array
            n_columns = len(locs)
            column_data = np.empty((n, n_columns), dtype=dtype)
            for k in range(n):
                # we coerce to a numpy array to ensure structures like
                # tuple-of-arrays expand correctly into columns
                rp = np.asarray(regions[k][prop])
                for i, loc in enumerate(locs):
                    column_data[k, i] = rp[loc]

            # add the columns to the output dictionary
            for i, modified_prop in enumerate(modified_props):
                out[modified_prop] = column_data[:, i]
    return out


def regionprops_table(
    label_image,
    intensity_image=None,
    properties=('label', 'bbox'),
    *,
    cache=True,
    separator='-',
    extra_properties=None,
    spacing=None,
):
    """Compute region properties and return them as a pandas-compatible table.

    The return value is a dictionary mapping property names to value arrays.
    This dictionary can be used as input to ``pandas.DataFrame`` to result in
    a "tidy" [1]_ table with one region per row and one property per column.

    Use this function typically when you want to do downstream data analysis,
    or save region data to disk in a structured way. One downside of this
    function is that it breaks multi-dimensional properties into independent
    columns; for example, the region centroids of a 3D image end up in three
    different columns, one per dimension. If you need to do complex
    computations with the region properties, using
    :func:`skimage.measure.regionprops` might be more fitting.

    .. versionadded:: 0.16

    Parameters
    ----------
    label_image : (M, N[, P]) ndarray
        Label image. Labels with value 0 are ignored.
    intensity_image : (M, N[, P][, C]) ndarray, optional
        Intensity (input) image of same shape as label image, plus
        optionally an extra dimension for multichannel data. The channel dimension,
        if present, must be the last axis. Default is None.

        .. versionchanged:: 0.18.0
            The ability to provide an extra dimension for channels was added.
    properties : tuple or list of str, optional
        Properties that will be included in the resulting dictionary
        For a list of available properties, please see :func:`regionprops`.
        Users should remember to add "label" to keep track of region
        identities.
    cache : bool, optional
        Determine whether to cache calculated properties. The computation is
        much faster for cached properties, whereas the memory consumption
        increases.
    separator : str, optional
        For non-scalar properties not listed in OBJECT_COLUMNS, each element
        will appear in its own column, with the index of that element separated
        from the property name by this separator. For example, the inertia
        tensor of a 2D region will appear in four columns:
        ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``,
        and ``inertia_tensor-1-1`` (where the separator is ``-``).

        Object columns are those that cannot be split in this way because the
        number of columns would change depending on the object. For example,
        ``image`` and ``coords``.
    extra_properties : iterable of callables
        Add extra property computation functions that are not included with
        skimage. The name of the property is derived from the function name,
        the dtype is inferred by calling the function on a small sample.
        If the name of an extra property clashes with the name of an existing
        property the extra property will not be visible and a UserWarning is
        issued. A property computation function must take a region mask as its
        first argument. If the property requires an intensity image, it must
        accept the intensity image as the second argument.
    spacing : tuple of float, shape (ndim,)
        The pixel spacing along each axis of the image.

    Returns
    -------
    out_dict : dict
        Dictionary mapping property names to an array of values of that
        property, one value per region. This dictionary can be used as input to
        pandas ``DataFrame`` to map property names to columns in the frame and
        regions to rows. If the image has no regions,
        the arrays will have length 0, but the correct type.

    Notes
    -----
    Each column contains either a scalar property, an object property, or an
    element in a multidimensional array.

    Properties with scalar values for each region, such as "eccentricity", will
    appear as a float or int array with that property name as key.

    Multidimensional properties *of fixed size* for a given image dimension,
    such as "centroid" (every centroid will have three elements in a 3D image,
    no matter the region size), will be split into that many columns, with the
    name {property_name}{separator}{element_num} (for 1D properties),
    {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D
    properties), and so on.

    For multidimensional properties that don't have a fixed size, such as
    "image" (the image of a region varies in size depending on the region
    size), an object array will be used, with the corresponding property name
    as the key.

    References
    ----------
    .. [1] Wickham, H (2014) "Tidy Data" Journal of Statistical Software,
           59(10), 1–23. https://doi.org/10.18637/jss.v059.i10
           https://vita.had.co.nz/papers/tidy-data.pdf

    Examples
    --------
    >>> from skimage import data, util, measure
    >>> image = data.coins()
    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
    >>> props = measure.regionprops_table(label_image, image,
    ...                           properties=['label', 'inertia_tensor',
    ...                                       'inertia_tensor_eigvals'])
    >>> props  # doctest: +ELLIPSIS +SKIP
    {'label': array([ 1,  2, ...]), ...
     'inertia_tensor-0-0': array([  4.012...e+03,   8.51..., ...]), ...
     ...,
     'inertia_tensor_eigvals-1': array([  2.67...e+02,   2.83..., ...])}

    The resulting dictionary can be directly passed to pandas, if installed, to
    obtain a clean DataFrame:

    >>> import pandas as pd  # doctest: +SKIP
    >>> data = pd.DataFrame(props)  # doctest: +SKIP
    >>> data.head()  # doctest: +SKIP
       label  inertia_tensor-0-0  ...  inertia_tensor_eigvals-1
    0      1         4012.909888  ...                267.065503
    1      2            8.514739  ...                  2.834806
    2      3            0.666667  ...                  0.000000
    3      4            0.000000  ...                  0.000000
    4      5            0.222222  ...                  0.111111

    [5 rows x 7 columns]

    If we want to measure a feature that does not come as a built-in
    property, we can define custom functions and pass them as
    ``extra_properties``. For example, we can create a custom function
    that measures the intensity quartiles in a region:

    >>> from skimage import data, util, measure
    >>> import numpy as np
    >>> def quartiles(regionmask, intensity):
    ...     return np.percentile(intensity[regionmask], q=(25, 50, 75))
    >>>
    >>> image = data.coins()
    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
    >>> props = measure.regionprops_table(label_image, intensity_image=image,
    ...                                   properties=('label',),
    ...                                   extra_properties=(quartiles,))
    >>> import pandas as pd # doctest: +SKIP
    >>> pd.DataFrame(props).head() # doctest: +SKIP
           label  quartiles-0  quartiles-1  quartiles-2
    0      1       117.00        123.0        130.0
    1      2       111.25        112.0        114.0
    2      3       111.00        111.0        111.0
    3      4       111.00        111.5        112.5
    4      5       112.50        113.0        114.0

    """
    regions = regionprops(
        label_image,
        intensity_image=intensity_image,
        cache=cache,
        extra_properties=extra_properties,
        spacing=spacing,
    )
    if extra_properties is not None:
        properties = list(properties) + [prop.__name__ for prop in extra_properties]
    if len(regions) == 0:
        ndim = label_image.ndim
        label_image = np.zeros((3,) * ndim, dtype=int)
        label_image[(1,) * ndim] = 1
        if intensity_image is not None:
            intensity_image = np.zeros(
                label_image.shape + intensity_image.shape[ndim:],
                dtype=intensity_image.dtype,
            )
        regions = regionprops(
            label_image,
            intensity_image=intensity_image,
            cache=cache,
            extra_properties=extra_properties,
            spacing=spacing,
        )

        out_d = _props_to_dict(regions, properties=properties, separator=separator)
        return {k: v[:0] for k, v in out_d.items()}

    return _props_to_dict(regions, properties=properties, separator=separator)


def regionprops(
    label_image,
    intensity_image=None,
    cache=True,
    *,
    extra_properties=None,
    spacing=None,
    offset=None,
):
    r"""Measure properties of labeled image regions.

    Region properties are evaluated on demand and come in diverse types. If
    you want to do tabular data analysis of specific properties, consider
    using :func:`skimage.measure.regionprops_table` instead.

    Parameters
    ----------
    label_image : (M, N[, P]) ndarray
        Label image. Labels with value 0 are ignored.

        .. versionchanged:: 0.14.1
            Previously, ``label_image`` was processed by ``numpy.squeeze`` and
            so any number of singleton dimensions was allowed. This resulted in
            inconsistent handling of images with singleton dimensions. To
            recover the old behaviour, use
            ``regionprops(np.squeeze(label_image), ...)``.
    intensity_image : (M, N[, P][, C]) ndarray, optional
        Intensity (input) image of same shape as label image, plus
        optionally an extra dimension for multichannel data. Currently,
        this extra channel dimension, if present, must be the last axis.
        Default is None.

        .. versionchanged:: 0.18.0
            The ability to provide an extra dimension for channels was added.
    cache : bool, optional
        Determine whether to cache calculated properties. The computation is
        much faster for cached properties, whereas the memory consumption
        increases.
    extra_properties : iterable of callables
        Add extra property computation functions that are not included with
        skimage. The name of the property is derived from the function name
        and its dtype is inferred by calling the function on a small sample.
        If the name of an extra property clashes with the name of an existing
        property, the extra property will not be visible and a UserWarning will be
        issued. A property computation function must take `label_image` as its
        first argument. If the property requires an intensity image, it must
        accept `intensity_image` as the second argument.
    spacing : tuple of float, shape (ndim,)
        The pixel spacing along each axis of the image.
    offset : array-like of int, shape `(label_image.ndim,)`, optional
        Coordinates of the origin ("top-left" corner) of the label image.
        Normally this is ([0, ]0, 0), but it might be different if one wants
        to obtain regionprops of subvolumes within a larger volume.

    Returns
    -------
    properties : list of RegionProperties
        Each item of the list corresponds to one labeled image region,
        and can be accessed using the attributes listed below.

    Notes
    -----
    The following properties can be accessed as attributes or keys:

    **area** : float
        Area of the region, i.e., number of pixels of the region scaled by
        pixel area (as determined by `spacing`).
    **area_bbox** : float
        Area of the bounding box, i.e., number of pixels of the region's
        bounding box scaled by pixel area (as determined by `spacing`).
    **area_convex** : float
        Area of the convex hull image, which is the smallest convex
        polygon that encloses the region.
    **area_filled** : float
        Area of the region with all the holes filled in.
    **axis_major_length** : float
        The length of the major axis of the ellipse that has the same
        normalized second central moments as the region.
    **axis_minor_length** : float
        The length of the minor axis of the ellipse that has the same
        normalized second central moments as the region.
    **bbox** : tuple
        Bounding box ``(min_row, min_col, max_row, max_col)``.
        Pixels belonging to the bounding box are in the half-open interval
        ``[min_row; max_row)`` and ``[min_col; max_col)``.
    **centroid** : array
        Centroid coordinate tuple ``(row, col)``.
    **centroid_local** : array
        Centroid coordinate tuple ``(row, col)``, relative to region bounding
        box.
    **centroid_weighted** : array
        Centroid coordinate tuple ``(row, col)`` weighted with intensity
        image.
    **centroid_weighted_local** : array
        Centroid coordinate tuple ``(row, col)``, relative to region bounding
        box, weighted with intensity image.
    **coords_scaled** : (K, 2) ndarray
        Coordinate list ``(row, col)`` of the region scaled by `spacing`.
    **coords** : (K, 2) ndarray
        Coordinate list ``(row, col)`` of the region.
    **eccentricity** : float
        Eccentricity of the ellipse that has the same second moments as the
        region. The eccentricity is the ratio of the focal distance
        (distance between focal points) over the major axis length.
        The value is in the interval [0, 1).
        When it is 0, the ellipse becomes a circle.
    **equivalent_diameter_area** : float
        The diameter of a circle with the same area as the region.
    **euler_number** : int
        Euler characteristic of the set of non-zero pixels.
        Computed as number of connected components subtracted by number of
        holes (input.ndim connectivity). In 3D, number of connected
        components plus number of holes subtracted by number of tunnels.
    **extent** : float
        Ratio of pixels in the region to pixels in the total bounding box.
        Computed as ``area / (rows * cols)``.
    **feret_diameter_max** : float
        Maximum Feret's diameter computed as the longest distance between
        points around a region's convex hull contour as determined by
        ``find_contours`` [5]_.
    **image** : (H, J) ndarray
        Binary region image sliced by the bounding box.
    **image_convex** : (H, J) ndarray
        Binary convex hull image sliced by bounding box.
    **image_filled** : (H, J) ndarray
        Binary region image with filled holes sliced by bounding box.
    **image_intensity** : (H, J) ndarray
        Intensity image sliced by bounding box.
    **inertia_tensor** : ndarray
        Inertia tensor of the region for the rotation around its mass.
    **inertia_tensor_eigvals** : tuple
        The eigenvalues of the inertia tensor in decreasing order.
    **intensity_max** : float
        Value of greatest intensity in the region.
    **intensity_mean** : float
        Average of intensity values in the region.
    **intensity_median** : float
        Value of median intensity in the region.
    **intensity_min** : float
        Value of lowest intensity in the region.
    **intensity_std** : float
        Standard deviation of intensity values in the region.
    **label** : int
        The region's label in the input label image.
    **moments** : (3, 3) ndarray
        Spatial moments up to 3rd order::

           m_ij = sum{ array(row, col) * row^i * col^j }

        where the sum is over the ``row, col`` coordinates of the region.
    **moments_central** : (3, 3) ndarray
        Central moments (translation invariant) up to 3rd order::

           mu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j }

        where the sum is over the ``row, col`` coordinates of the region,
        and ``row_c`` and ``col_c`` are the coordinates of the region's centroid.
    **moments_hu** : tuple
        Hu moments (translation, scale and rotation invariant).
    **moments_normalized** : (3, 3) ndarray
        Normalized moments (translation and scale invariant) up to 3rd order::

            nu_ij = mu_ij / m_00^[(i+j)/2 + 1]

        where ``m_00`` is the zeroth spatial moment.
    **moments_weighted** : (3, 3) array
        Spatial moments of intensity image up to 3rd order::

            wm_ij = sum{ array(row, col) * row^i * col^j }

        where the sum is over the ``row, col`` coordinates of the region.
    **moments_weighted_central** : (3, 3) ndarray
        Central moments (translation invariant) of intensity image up to
        3rd order::

            wmu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j }

        where the sum is over the ``row, col`` coordinates of the region,
        and ``row_c`` and ``col_c`` are the coordinates of the region's weighted
        centroid.
    **moments_weighted_hu** : tuple
        Hu moments (translation, scale and rotation invariant) of intensity
        image.
    **moments_weighted_normalized** : (3, 3) ndarray
        Normalized moments (translation and scale invariant) of intensity
        image up to 3rd order::

            wnu_ij = wmu_ij / wm_00^[(i+j)/2 + 1]

        where ``wm_00`` is the zero-th spatial moment (intensity-weighted area).
    **num_pixels** : int
        Number of foreground pixels.
    **orientation** : float
        Angle between the 0th axis (rows) and the major
        axis of the ellipse that has the same second moments as the region,
        ranging from :math:`-\pi/2` to :math:`\pi/2` counter-clockwise.
    **perimeter** : float
        Perimeter of the region which approximates the contour as a line
        through the centers of border pixels using a 4-connectivity.
    **perimeter_crofton** : float
        Perimeter of the region approximated by the Crofton formula in 4
        directions.
    **slice** : tuple of slices
        A slice to extract the region from the input image.
    **solidity** : float
        Ratio of pixels in the region to pixels of the convex hull image.

    `properties` also supports iteration, so that you can do::

      for region in properties:
          print(region, properties[region])

    See Also
    --------
    label

    References
    ----------
    .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
           Core Algorithms. Springer-Verlag, London, 2009.
    .. [2] B. Jähne. Digital Image Processing. Springer-Verlag,
           Berlin-Heidelberg, 6. edition, 2005.
    .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
           Features, from Lecture notes in computer science, p. 676. Springer,
           Berlin, 1993.
    .. [4] https://en.wikipedia.org/wiki/Image_moment
    .. [5] W. Pabst, E. Gregorová. Characterization of particles and particle
           systems, pp. 27-28. ICT Prague, 2007.
           https://old.vscht.cz/sil/keramika/Characterization_of_particles/CPPS%20_English%20version_.pdf

    Examples
    --------
    >>> import skimage as ski
    >>> img = ski.util.img_as_ubyte(ski.data.coins()) > 110
    >>> label_img = ski.measure.label(img, connectivity=img.ndim)
    >>> props = ski.measure.regionprops(label_img)
    >>> # centroid of first labeled region
    >>> props[0].centroid
    (22.72987986048314, 81.91228523446583)
    >>> # centroid of first labeled region
    >>> props[0]['centroid']
    (22.72987986048314, 81.91228523446583)

    Add custom measurements by passing functions as ``extra_properties``:

    >>> import numpy as np
    >>> import skimage as ski
    >>> img = ski.util.img_as_ubyte(ski.data.coins()) > 110
    >>> label_img = ski.measure.label(img, connectivity=img.ndim)
    >>> def pixelcount(regionmask):
    ...     return np.sum(regionmask)
    >>> props = ski.measure.regionprops(label_img, extra_properties=(pixelcount,))
    >>> # pixelcount of first labeled region
    >>> props[0].pixelcount
    7741
    >>> # pixelcount of first labeled region
    >>> props[1]['pixelcount']
    42

    """

    if label_image.ndim not in (2, 3):
        raise TypeError('Only 2-D and 3-D images supported.')

    if not np.issubdtype(label_image.dtype, np.integer):
        if np.issubdtype(label_image.dtype, bool):
            raise TypeError(
                'Non-integer image types are ambiguous: '
                'use skimage.measure.label to label the connected '
                'components of label_image, '
                'or label_image.astype(np.uint8) to interpret '
                'the True values as a single label.'
            )
        else:
            raise TypeError('Non-integer label_image types are ambiguous')

    if offset is None:
        offset_arr = np.zeros((label_image.ndim,), dtype=int)
    else:
        offset_arr = np.asarray(offset)
        if offset_arr.ndim != 1 or offset_arr.size != label_image.ndim:
            raise ValueError(
                'Offset should be an array-like of integers '
                'of shape (label_image.ndim,); '
                f'{offset} was provided.'
            )

    regions = []

    objects = ndi.find_objects(label_image)
    for i, sl in enumerate(objects):
        if sl is None:
            continue

        label = i + 1

        props = RegionProperties(
            sl,
            label,
            label_image,
            intensity_image,
            cache,
            spacing=spacing,
            extra_properties=extra_properties,
            offset=offset_arr,
        )
        regions.append(props)

    return regions


def _parse_docs():
    import re
    import textwrap

    doc = regionprops.__doc__ or ''
    arg_regex = r'\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n    [\*\S]+)'
    if sys.version_info >= (3, 13):
        arg_regex = r'\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n[\*\S]+)'

    matches = re.finditer(arg_regex, doc, flags=re.DOTALL)
    prop_doc = {m.group(1): textwrap.dedent(m.group(2)) for m in matches}

    return prop_doc


def _install_properties_docs():
    prop_doc = _parse_docs()

    for p in [member for member in dir(RegionProperties) if not member.startswith('_')]:
        getattr(RegionProperties, p).__doc__ = prop_doc[p]


if __debug__:
    # don't install docstrings when in optimized/non-debug mode
    _install_properties_docs()
