"""
Grayscale morphological operations
"""
import functools
import numpy as np
from scipy import ndimage as ndi
from .misc import default_selem
from ..util import crop

__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
           'black_tophat']


def _shift_selem(selem, shift_x, shift_y):
    """Shift the binary image `selem` in the left and/or up.

    This only affects 2D structuring elements with even number of rows
    or columns.

    Parameters
    ----------
    selem : 2D array, shape (M, N)
        The input structuring element.
    shift_x, shift_y : bool
        Whether to move `selem` along each axis.

    Returns
    -------
    out : 2D array, shape (M + int(shift_x), N + int(shift_y))
        The shifted structuring element.
    """
    if selem.ndim != 2:
        # do nothing for 1D or 3D or higher structuring elements
        return selem
    m, n = selem.shape
    if m % 2 == 0:
        extra_row = np.zeros((1, n), selem.dtype)
        if shift_x:
            selem = np.vstack((selem, extra_row))
        else:
            selem = np.vstack((extra_row, selem))
        m += 1
    if n % 2 == 0:
        extra_col = np.zeros((m, 1), selem.dtype)
        if shift_y:
            selem = np.hstack((selem, extra_col))
        else:
            selem = np.hstack((extra_col, selem))
    return selem


def _invert_selem(selem):
    """Change the order of the values in `selem`.

    This is a patch for the *weird* footprint inversion in
    `ndi.grey_morphology` [1]_.

    Parameters
    ----------
    selem : array
        The input structuring element.

    Returns
    -------
    inverted : array, same shape and type as `selem`
        The structuring element, in opposite order.

    Examples
    --------
    >>> selem = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], np.uint8)
    >>> _invert_selem(selem)
    array([[1, 1, 0],
           [1, 1, 0],
           [0, 0, 0]], dtype=uint8)

    References
    ----------
    .. [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
    """
    inverted = selem[(slice(None, None, -1),) * selem.ndim]
    return inverted


def pad_for_eccentric_selems(func):
    """Pad input images for certain morphological operations.

    Parameters
    ----------
    func : callable
        A morphological function, either opening or closing, that
        supports eccentric structuring elements. Its parameters must
        include at least `image`, `selem`, and `out`.

    Returns
    -------
    func_out : callable
        The same function, but correctly padding the input image before
        applying the input function.

    See Also
    --------
    opening, closing.
    """
    @functools.wraps(func)
    def func_out(image, selem, out=None, *args, **kwargs):
        pad_widths = []
        padding = False
        if out is None:
            out = np.empty_like(image)
        for axis_len in selem.shape:
            if axis_len % 2 == 0:
                axis_pad_width = axis_len - 1
                padding = True
            else:
                axis_pad_width = 0
            pad_widths.append((axis_pad_width,) * 2)
        if padding:
            image = np.pad(image, pad_widths, mode='edge')
            out_temp = np.empty_like(image)
        else:
            out_temp = out
        out_temp = func(image, selem, out=out_temp, *args, **kwargs)
        if padding:
            out[:] = crop(out_temp, pad_widths)
        else:
            out = out_temp
        return out
    return func_out

@default_selem
def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
    """Return greyscale morphological erosion of an image.

    Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
    in the neighborhood centered at (i,j). Erosion shrinks bright regions and
    enlarges dark regions.

    Parameters
    ----------
    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as an array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarrays, optional
        The array to store the result of the morphology. If None is
        passed, a new array will be allocated.
    shift_x, shift_y : bool, optional
        shift structuring element about center point. This only affects
        eccentric structuring elements (i.e. selem with even numbered sides).

    Returns
    -------
    eroded : array, same shape as `image`
        The result of the morphological erosion.

    Notes
    -----
    For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
    lower algorithm complexity makes the `skimage.filters.rank.minimum`
    function more efficient for larger images and structuring elements.

    Examples
    --------
    >>> # Erosion shrinks bright regions
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> bright_square = np.array([[0, 0, 0, 0, 0],
    ...                           [0, 1, 1, 1, 0],
    ...                           [0, 1, 1, 1, 0],
    ...                           [0, 1, 1, 1, 0],
    ...                           [0, 0, 0, 0, 0]], dtype=np.uint8)
    >>> erosion(bright_square, square(3))
    array([[0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0],
           [0, 0, 1, 0, 0],
           [0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    selem = np.array(selem)
    selem = _shift_selem(selem, shift_x, shift_y)
    if out is None:
        out = np.empty_like(image)
    ndi.grey_erosion(image, footprint=selem, output=out)
    return out


@default_selem
def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
    """Return greyscale morphological dilation of an image.

    Morphological dilation sets a pixel at (i,j) to the maximum over all pixels
    in the neighborhood centered at (i,j). Dilation enlarges bright regions
    and shrinks dark regions.

    Parameters
    ----------

    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as a 2-D array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarray, optional
        The array to store the result of the morphology. If None, is
        passed, a new array will be allocated.
    shift_x, shift_y : bool, optional
        shift structuring element about center point. This only affects
        eccentric structuring elements (i.e. selem with even numbered sides).

    Returns
    -------
    dilated : uint8 array, same shape and type as `image`
        The result of the morphological dilation.

    Notes
    -----
    For `uint8` (and `uint16` up to a certain bit-depth) data, the lower
    algorithm complexity makes the `skimage.filters.rank.maximum` function more
    efficient for larger images and structuring elements.

    Examples
    --------
    >>> # Dilation enlarges bright regions
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> bright_pixel = np.array([[0, 0, 0, 0, 0],
    ...                          [0, 0, 0, 0, 0],
    ...                          [0, 0, 1, 0, 0],
    ...                          [0, 0, 0, 0, 0],
    ...                          [0, 0, 0, 0, 0]], dtype=np.uint8)
    >>> dilation(bright_pixel, square(3))
    array([[0, 0, 0, 0, 0],
           [0, 1, 1, 1, 0],
           [0, 1, 1, 1, 0],
           [0, 1, 1, 1, 0],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    selem = np.array(selem)
    selem = _shift_selem(selem, shift_x, shift_y)
    # Inside ndimage.grey_dilation, the structuring element is inverted,
    # eg. `selem = selem[::-1, ::-1]` for 2D [1]_, for reasons unknown to
    # this author (@jni). To "patch" this behaviour, we invert our own
    # selem before passing it to `ndi.grey_dilation`.
    # [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
    selem = _invert_selem(selem)
    if out is None:
        out = np.empty_like(image)
    ndi.grey_dilation(image, footprint=selem, output=out)
    return out


@default_selem
@pad_for_eccentric_selems
def opening(image, selem=None, out=None):
    """Return greyscale morphological opening of an image.

    The morphological opening on an image is defined as an erosion followed by
    a dilation. Opening can remove small bright spots (i.e. "salt") and connect
    small dark cracks. This tends to "open" up (dark) gaps between (bright)
    features.

    Parameters
    ----------
    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as an array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarray, optional
        The array to store the result of the morphology. If None
        is passed, a new array will be allocated.

    Returns
    -------
    opening : array, same shape and type as `image`
        The result of the morphological opening.

    Examples
    --------
    >>> # Open up gap between two bright regions (but also shrink regions)
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> bad_connection = np.array([[1, 0, 0, 0, 1],
    ...                            [1, 1, 0, 1, 1],
    ...                            [1, 1, 1, 1, 1],
    ...                            [1, 1, 0, 1, 1],
    ...                            [1, 0, 0, 0, 1]], dtype=np.uint8)
    >>> opening(bad_connection, square(3))
    array([[0, 0, 0, 0, 0],
           [1, 1, 0, 1, 1],
           [1, 1, 0, 1, 1],
           [1, 1, 0, 1, 1],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    eroded = erosion(image, selem)
    # note: shift_x, shift_y do nothing if selem side length is odd
    out = dilation(eroded, selem, out=out, shift_x=True, shift_y=True)
    return out


@default_selem
@pad_for_eccentric_selems
def closing(image, selem=None, out=None):
    """Return greyscale morphological closing of an image.

    The morphological closing on an image is defined as a dilation followed by
    an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
    small bright cracks. This tends to "close" up (dark) gaps between (bright)
    features.

    Parameters
    ----------
    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as an array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarray, optional
        The array to store the result of the morphology. If None,
        is passed, a new array will be allocated.

    Returns
    -------
    closing : array, same shape and type as `image`
        The result of the morphological closing.

    Examples
    --------
    >>> # Close a gap between two bright lines
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> broken_line = np.array([[0, 0, 0, 0, 0],
    ...                         [0, 0, 0, 0, 0],
    ...                         [1, 1, 0, 1, 1],
    ...                         [0, 0, 0, 0, 0],
    ...                         [0, 0, 0, 0, 0]], dtype=np.uint8)
    >>> closing(broken_line, square(3))
    array([[0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0],
           [1, 1, 1, 1, 1],
           [0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    dilated = dilation(image, selem)
    # note: shift_x, shift_y do nothing if selem side length is odd
    out = erosion(dilated, selem, out=out, shift_x=True, shift_y=True)
    return out


@default_selem
def white_tophat(image, selem=None, out=None):
    """Return white top hat of an image.

    The white top hat of an image is defined as the image minus its
    morphological opening. This operation returns the bright spots of the image
    that are smaller than the structuring element.

    Parameters
    ----------
    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as an array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarray, optional
        The array to store the result of the morphology. If None
        is passed, a new array will be allocated.

    Returns
    -------
    out : array, same shape and type as `image`
        The result of the morphological white top hat.

    See also
    --------
    black_tophat

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Top-hat_transform

    Examples
    --------
    >>> # Subtract grey background from bright peak
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> bright_on_grey = np.array([[2, 3, 3, 3, 2],
    ...                            [3, 4, 5, 4, 3],
    ...                            [3, 5, 9, 5, 3],
    ...                            [3, 4, 5, 4, 3],
    ...                            [2, 3, 3, 3, 2]], dtype=np.uint8)
    >>> white_tophat(bright_on_grey, square(3))
    array([[0, 0, 0, 0, 0],
           [0, 0, 1, 0, 0],
           [0, 1, 5, 1, 0],
           [0, 0, 1, 0, 0],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    selem = np.array(selem)
    if out is image:
        opened = opening(image, selem)
        if np.issubdtype(opened.dtype, np.bool_):
            np.logical_xor(out, opened, out=out)
        else:
            out -= opened
        return out
    elif out is None:
        out = np.empty_like(image)
    # work-around for NumPy deprecation warning for arithmetic 
    # operations on bool arrays
    if isinstance(image, np.ndarray) and image.dtype == np.bool:
        image_ = image.view(dtype=np.uint8)
    else:
        image_ = image
    if isinstance(out, np.ndarray) and out.dtype == np.bool:
        out_ = out.view(dtype=np.uint8)
    else:
        out_ = out
    out_ = ndi.white_tophat(image_, footprint=selem, output=out_)
    return out


@default_selem
def black_tophat(image, selem=None, out=None):
    """Return black top hat of an image.

    The black top hat of an image is defined as its morphological closing minus
    the original image. This operation returns the dark spots of the image that
    are smaller than the structuring element. Note that dark spots in the
    original image are bright spots after the black top hat.

    Parameters
    ----------
    image : ndarray
        Image array.
    selem : ndarray, optional
        The neighborhood expressed as a 2-D array of 1's and 0's.
        If None, use cross-shaped structuring element (connectivity=1).
    out : ndarray, optional
        The array to store the result of the morphology. If None
        is passed, a new array will be allocated.

    Returns
    -------
    out : array, same shape and type as `image`
        The result of the morphological black top hat.

    See also
    --------
    white_tophat

    References
    ----------
    .. [1] https://en.wikipedia.org/wiki/Top-hat_transform

    Examples
    --------
    >>> # Change dark peak to bright peak and subtract background
    >>> import numpy as np
    >>> from skimage.morphology import square
    >>> dark_on_grey = np.array([[7, 6, 6, 6, 7],
    ...                          [6, 5, 4, 5, 6],
    ...                          [6, 4, 0, 4, 6],
    ...                          [6, 5, 4, 5, 6],
    ...                          [7, 6, 6, 6, 7]], dtype=np.uint8)
    >>> black_tophat(dark_on_grey, square(3))
    array([[0, 0, 0, 0, 0],
           [0, 0, 1, 0, 0],
           [0, 1, 5, 1, 0],
           [0, 0, 1, 0, 0],
           [0, 0, 0, 0, 0]], dtype=uint8)

    """
    if out is image:
        original = image.copy()
    else:
        original = image
    out = closing(image, selem, out=out)
    if np.issubdtype(out.dtype, np.bool_):
        np.logical_xor(out, original, out=out)
    else:
        out -= original
    return out