import functools

import numpy as np
from scipy.ndimage import uniform_filter

from skimage._shared import utils
from skimage._shared.filters import gaussian
from skimage._shared.utils import (
        _supported_float_type, check_shape_equality, warn)
from skimage.util.arraycrop import crop
from skimage.util.dtype import dtype_range

__all__ = ['structural_similarity']


@utils.deprecate_multichannel_kwarg()
def structural_similarity(im1, im2,
                          *,
                          win_size=None, gradient=False, data_range=None,
                          channel_axis=None, multichannel=False,
                          gaussian_weights=False, full=False, **kwargs):
    """
    Compute the mean structural similarity index between two images.

    Parameters
    ----------
    im1, im2 : ndarray
        Images. Any dimensionality with same shape.
    win_size : int or None, optional
        The side-length of the sliding window used in comparison. Must be an
        odd value. If `gaussian_weights` is True, this is ignored and the
        window size will depend on `sigma`.
    gradient : bool, optional
        If True, also return the gradient with respect to im2.
    data_range : float, optional
        The data range of the input image (distance between minimum and
        maximum possible values). By default, this is estimated from the image
        data-type.
    channel_axis : int or None, optional
        If None, the image is assumed to be a grayscale (single channel) image.
        Otherwise, this parameter indicates which axis of the array corresponds
        to channels.

        .. versionadded:: 0.19
           ``channel_axis`` was added in 0.19.
    multichannel : bool, optional
        If True, treat the last dimension of the array as channels. Similarity
        calculations are done independently for each channel then averaged.
        This argument is deprecated: specify `channel_axis` instead.
    gaussian_weights : bool, optional
        If True, each patch has its mean and variance spatially weighted by a
        normalized Gaussian kernel of width sigma=1.5.
    full : bool, optional
        If True, also return the full structural similarity image.

    Other Parameters
    ----------------
    use_sample_covariance : bool
        If True, normalize covariances by N-1 rather than, N where N is the
        number of pixels within the sliding window.
    K1 : float
        Algorithm parameter, K1 (small constant, see [1]_).
    K2 : float
        Algorithm parameter, K2 (small constant, see [1]_).
    sigma : float
        Standard deviation for the Gaussian when `gaussian_weights` is True.

    Returns
    -------
    mssim : float
        The mean structural similarity index over the image.
    grad : ndarray
        The gradient of the structural similarity between im1 and im2 [2]_.
        This is only returned if `gradient` is set to True.
    S : ndarray
        The full SSIM image.  This is only returned if `full` is set to True.

    Notes
    -----
    To match the implementation of Wang et. al. [1]_, set `gaussian_weights`
    to True, `sigma` to 1.5, and `use_sample_covariance` to False.

    .. versionchanged:: 0.16
        This function was renamed from ``skimage.measure.compare_ssim`` to
        ``skimage.metrics.structural_similarity``.

    References
    ----------
    .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P.
       (2004). Image quality assessment: From error visibility to
       structural similarity. IEEE Transactions on Image Processing,
       13, 600-612.
       https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
       :DOI:`10.1109/TIP.2003.819861`

    .. [2] Avanaki, A. N. (2009). Exact global histogram specification
       optimized for structural similarity. Optical Review, 16, 613-621.
       :arxiv:`0901.0065`
       :DOI:`10.1007/s10043-009-0119-z`

    """
    check_shape_equality(im1, im2)
    float_type = _supported_float_type(im1.dtype)

    if channel_axis is not None:
        # loop over channels
        args = dict(win_size=win_size,
                    gradient=gradient,
                    data_range=data_range,
                    channel_axis=None,
                    gaussian_weights=gaussian_weights,
                    full=full)
        args.update(kwargs)
        nch = im1.shape[channel_axis]
        mssim = np.empty(nch, dtype=float_type)

        if gradient:
            G = np.empty(im1.shape, dtype=float_type)
        if full:
            S = np.empty(im1.shape, dtype=float_type)
        channel_axis = channel_axis % im1.ndim
        _at = functools.partial(utils.slice_at_axis, axis=channel_axis)
        for ch in range(nch):
            ch_result = structural_similarity(im1[_at(ch)],
                                              im2[_at(ch)], **args)
            if gradient and full:
                mssim[ch], G[_at(ch)], S[_at(ch)] = ch_result
            elif gradient:
                mssim[ch], G[_at(ch)] = ch_result
            elif full:
                mssim[ch], S[_at(ch)] = ch_result
            else:
                mssim[ch] = ch_result
        mssim = mssim.mean()
        if gradient and full:
            return mssim, G, S
        elif gradient:
            return mssim, G
        elif full:
            return mssim, S
        else:
            return mssim

    K1 = kwargs.pop('K1', 0.01)
    K2 = kwargs.pop('K2', 0.03)
    K3 = kwargs.pop('K3', K2 / np.sqrt(2))
    alpha = kwargs.pop('alpha', 1)
    beta = kwargs.pop('beta', 1)
    gamma = kwargs.pop('gamma', 1)
    sigma = kwargs.pop('sigma', 1.5)
    if K1 < 0:
        raise ValueError("K1 must be positive")
    if K2 < 0:
        raise ValueError("K2 must be positive")
    if K3 < 0:
        raise ValueError("K3 must be positive")
    if sigma < 0:
        raise ValueError("sigma must be positive")
    use_sample_covariance = kwargs.pop('use_sample_covariance', True)

    if gaussian_weights:
        # Set to give an 11-tap filter with the default sigma of 1.5 to match
        # Wang et. al. 2004.
        truncate = 3.5

    if win_size is None:
        if gaussian_weights:
            # set win_size used by crop to match the filter size
            r = int(truncate * sigma + 0.5)  # radius as in ndimage
            win_size = 2 * r + 1
        else:
            win_size = 7   # backwards compatibility

    if np.any((np.asarray(im1.shape) - win_size) < 0):
        raise ValueError(
            'win_size exceeds image extent. '
            'Either ensure that your images are '
            'at least 7x7; or pass win_size explicitly '
            'in the function call, with an odd value '
            'less than or equal to the smaller side of your '
            'images. If your images are multichannel '
            '(with color channels), set channel_axis to '
            'the axis number corresponding to the channels.')

    if not (win_size % 2 == 1):
        raise ValueError('Window size must be odd.')

    if data_range is None:
        if im1.dtype != im2.dtype:
            warn("Inputs have mismatched dtype.  Setting data_range based on "
                 "im1.dtype.", stacklevel=2)
        dmin, dmax = dtype_range[im1.dtype.type]
        data_range = dmax - dmin

    ndim = im1.ndim

    if gaussian_weights:
        filter_func = gaussian
        filter_args = {'sigma': sigma, 'truncate': truncate, 'mode': 'reflect'}
    else:
        filter_func = uniform_filter
        filter_args = {'size': win_size}

    # ndimage filters need floating point data
    im1 = im1.astype(float_type, copy=False)
    im2 = im2.astype(float_type, copy=False)

    NP = win_size ** ndim

    # filter has already normalized by NP
    if use_sample_covariance:
        cov_norm = NP / (NP - 1)  # sample covariance
    else:
        cov_norm = 1.0  # population covariance to match Wang et. al. 2004

    # compute (weighted) means
    ux = filter_func(im1, **filter_args)
    uy = filter_func(im2, **filter_args)

    # compute (weighted) variances and covariances
    uxx = filter_func(im1 * im1, **filter_args)
    uyy = filter_func(im2 * im2, **filter_args)
    uxy = filter_func(im1 * im2, **filter_args)
    vx = cov_norm * (uxx - ux * ux)
    vy = cov_norm * (uyy - uy * uy)
    vxy = cov_norm * (uxy - ux * uy)

    R = data_range
    C1 = (K1 * R) ** 2
    C2 = (K2 * R) ** 2
    C3 = (K3 * R) ** 2

    sxsy = np.sqrt(np.clip(vx * vy, 0, None))

    A1, A2, A3, B1, B2, B3 = ((
        2 * ux * uy + C1,
        2 * sxsy + C2,
        vxy + C3,
        ux ** 2 + uy ** 2 + C1,
        vx + vy + C2,
        sxsy + C3,
        ))
    S = (A1/B1)**alpha * (A2/B2)**beta * (A3/B3)**gamma

    # to avoid edge effects will ignore filter radius strip around edges
    pad = (win_size - 1) // 2

    # compute (weighted) mean of ssim. Use float64 for accuracy.
    mssim = crop(S, pad).mean(dtype=np.float64)

    if full:
        return mssim, S
    else:
        return mssim
