# coding: utf-8
import numpy as np
from .. import img_as_float
from ..restoration._denoise_cy import _denoise_bilateral, _denoise_tv_bregman
from .._shared.utils import _mode_deprecations
import warnings


def denoise_bilateral(image, win_size=5, sigma_range=None, sigma_spatial=1,
                      bins=10000, mode='constant', cval=0, multichannel=True):
    """Denoise image using bilateral filter.

    This is an edge-preserving and noise reducing denoising filter. It averages
    pixels based on their spatial closeness and radiometric similarity.

    Spatial closeness is measured by the gaussian function of the euclidian
    distance between two pixels and a certain standard deviation
    (`sigma_spatial`).

    Radiometric similarity is measured by the gaussian function of the euclidian
    distance between two color values and a certain standard deviation
    (`sigma_range`).

    Parameters
    ----------
    image : ndarray, shape (M, N[, 3])
        Input image, 2D grayscale or RGB.
    win_size : int
        Window size for filtering.
    sigma_range : float
        Standard deviation for grayvalue/color distance (radiometric
        similarity). A larger value results in averaging of pixels with larger
        radiometric differences. Note, that the image will be converted using
        the `img_as_float` function and thus the standard deviation is in
        respect to the range ``[0, 1]``. If the value is ``None`` the standard
        deviation of the ``image`` will be used.
    sigma_spatial : float
        Standard deviation for range distance. A larger value results in
        averaging of pixels with larger spatial differences.
    bins : int
        Number of discrete values for gaussian weights of color filtering.
        A larger value results in improved accuracy.
    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}
        How to handle values outside the image borders. See
        `numpy.pad` for detail.
    cval : string
        Used in conjunction with mode 'constant', the value outside
        the image boundaries.
    multichannel : bool
        Whether the last axis of the image is to be interpreted as multiple
        channels or another spatial dimension.

    Returns
    -------
    denoised : ndarray
        Denoised image.

    References
    ----------
    .. [1] http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf

    Examples
    --------
    >>> from skimage import data, img_as_float
    >>> astro = img_as_float(data.astronaut())
    >>> astro = astro[220:300, 220:320]
    >>> noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape)
    >>> noisy = np.clip(noisy, 0, 1)
    >>> denoised = denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15)
    """
    if multichannel:
        if image.ndim != 3:
            if image.ndim == 2:
                raise ValueError("Use ``multichannel=False`` for 2D grayscale "
                                 "images. The last axis of the input image "
                                 "must be multiple color channels not another "
                                 "spatial dimension.")
            else:
                raise ValueError("Bilateral filter is only implemented for "
                                 "2D grayscale images (image.ndim == 2) and "
                                 "2D multichannel (image.ndim == 3) images, "
                                 "but the input image has {0} dimensions. "
                                 "".format(image.ndim))
        elif image.shape[2] not in (3, 4):
            if image.shape[2] > 4:
                warnings.warn("The last axis of the input image is interpreted "
                              "as channels. Input image with shape {0} has {1} "
                              "channels in last axis. ``denoise_bilateral`` is "
                              "implemented for 2D grayscale and color images "
                              "only.".format(image.shape, image.shape[2]))
            else:
                msg = "Input image must be grayscale, RGB, or RGBA; but has shape {0}."
                warnings.warn(msg.format(image.shape))
    else:
        if image.ndim > 2:
            raise ValueError("Bilateral filter is not implemented for "
                             "grayscale images of 3 or more dimensions, "
                             "but input image has {0} dimension. Use "
                             "``multichannel=True`` for 2-D RGB "
                             "images.".format(image.shape))


    mode = _mode_deprecations(mode)
    return _denoise_bilateral(image, win_size, sigma_range, sigma_spatial,
                              bins, mode, cval)


def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True):
    """Perform total-variation denoising using split-Bregman optimization.

    Total-variation denoising (also know as total-variation regularization)
    tries to find an image with less total-variation under the constraint
    of being similar to the input image, which is controlled by the
    regularization parameter.

    Parameters
    ----------
    image : ndarray
        Input data to be denoised (converted using img_as_float`).
    weight : float
        Denoising weight. The smaller the `weight`, the more denoising (at
        the expense of less similarity to the `input`). The regularization
        parameter `lambda` is chosen as `2 * weight`.
    eps : float, optional
        Relative difference of the value of the cost function that determines
        the stop criterion. The algorithm stops when::

            SUM((u(n) - u(n-1))**2) < eps

    max_iter : int, optional
        Maximal number of iterations used for the optimization.
    isotropic : boolean, optional
        Switch between isotropic and anisotropic TV denoising.

    Returns
    -------
    u : ndarray
        Denoised image.

    References
    ----------
    .. [1] http://en.wikipedia.org/wiki/Total_variation_denoising
    .. [2] Tom Goldstein and Stanley Osher, "The Split Bregman Method For L1
           Regularized Problems",
           ftp://ftp.math.ucla.edu/pub/camreport/cam08-29.pdf
    .. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising
           using Split Bregman" in Image Processing On Line on 2012–05–19,
           http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf
    .. [4] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf

    """
    return _denoise_tv_bregman(image, weight, max_iter, eps, isotropic)


def _denoise_tv_chambolle_nd(im, weight=0.1, eps=2.e-4, n_iter_max=200):
    """Perform total-variation denoising on n-dimensional images.

    Parameters
    ----------
    im : ndarray
        n-D input data to be denoised.
    weight : float, optional
        Denoising weight. The greater `weight`, the more denoising (at
        the expense of fidelity to `input`).
    eps : float, optional
        Relative difference of the value of the cost function that determines
        the stop criterion. The algorithm stops when:

            (E_(n-1) - E_n) < eps * E_0

    n_iter_max : int, optional
        Maximal number of iterations used for the optimization.

    Returns
    -------
    out : ndarray
        Denoised array of floats.

    Notes
    -----
    Rudin, Osher and Fatemi algorithm.

    """

    ndim = im.ndim
    p = np.zeros((im.ndim, ) + im.shape, dtype=im.dtype)
    g = np.zeros_like(p)
    d = np.zeros_like(im)
    i = 0
    while i < n_iter_max:
        if i > 0:
            # d will be the (negative) divergence of p
            d = -p.sum(0)
            slices_d = [slice(None), ] * ndim
            slices_p = [slice(None), ] * (ndim + 1)
            for ax in range(ndim):
                slices_d[ax] = slice(1, None)
                slices_p[ax+1] = slice(0, -1)
                slices_p[0] = ax
                d[slices_d] += p[slices_p]
                slices_d[ax] = slice(None)
                slices_p[ax+1] = slice(None)
            out = im + d
        else:
            out = im
        E = (d ** 2).sum()

        # g stores the gradients of out along each axis
        # e.g. g[0] is the first order finite difference along axis 0
        slices_g = [slice(None), ] * (ndim + 1)
        for ax in range(ndim):
            slices_g[ax+1] = slice(0, -1)
            slices_g[0] = ax
            g[slices_g] = np.diff(out, axis=ax)
            slices_g[ax+1] = slice(None)

        norm = np.sqrt((g ** 2).sum(axis=0))[np.newaxis, ...]
        E += weight * norm.sum()
        tau = 1. / (2.*ndim)
        norm *= tau / weight
        norm += 1.
        p -= tau * g
        p /= norm
        E /= float(im.size)
        if i == 0:
            E_init = E
            E_previous = E
        else:
            if np.abs(E_previous - E) < eps * E_init:
                break
            else:
                E_previous = E
        i += 1
    return out


def denoise_tv_chambolle(im, weight=0.1, eps=2.e-4, n_iter_max=200,
                         multichannel=False):
    """Perform total-variation denoising on n-dimensional images.

    Parameters
    ----------
    im : ndarray of ints, uints or floats
        Input data to be denoised. `im` can be of any numeric type,
        but it is cast into an ndarray of floats for the computation
        of the denoised image.
    weight : float, optional
        Denoising weight. The greater `weight`, the more denoising (at
        the expense of fidelity to `input`).
    eps : float, optional
        Relative difference of the value of the cost function that
        determines the stop criterion. The algorithm stops when:

            (E_(n-1) - E_n) < eps * E_0

    n_iter_max : int, optional
        Maximal number of iterations used for the optimization.
    multichannel : bool, optional
        Apply total-variation denoising separately for each channel. This
        option should be true for color images, otherwise the denoising is
        also applied in the channels dimension.

    Returns
    -------
    out : ndarray
        Denoised image.

    Notes
    -----
    Make sure to set the multichannel parameter appropriately for color images.

    The principle of total variation denoising is explained in
    http://en.wikipedia.org/wiki/Total_variation_denoising

    The principle of total variation denoising is to minimize the
    total variation of the image, which can be roughly described as
    the integral of the norm of the image gradient. Total variation
    denoising tends to produce "cartoon-like" images, that is,
    piecewise-constant images.

    This code is an implementation of the algorithm of Rudin, Fatemi and Osher
    that was proposed by Chambolle in [1]_.

    References
    ----------
    .. [1] A. Chambolle, An algorithm for total variation minimization and
           applications, Journal of Mathematical Imaging and Vision,
           Springer, 2004, 20, 89-97.

    Examples
    --------
    2D example on astronaut image:

    >>> from skimage import color, data
    >>> img = color.rgb2gray(data.astronaut())[:50, :50]
    >>> img += 0.5 * img.std() * np.random.randn(*img.shape)
    >>> denoised_img = denoise_tv_chambolle(img, weight=60)

    3D example on synthetic data:

    >>> x, y, z = np.ogrid[0:20, 0:20, 0:20]
    >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
    >>> mask = mask.astype(np.float)
    >>> mask += 0.2*np.random.randn(*mask.shape)
    >>> res = denoise_tv_chambolle(mask, weight=100)

    """

    im_type = im.dtype
    if not im_type.kind == 'f':
        im = img_as_float(im)

    if multichannel:
        out = np.zeros_like(im)
        for c in range(im.shape[-1]):
            out[..., c] = _denoise_tv_chambolle_nd(im[..., c], weight, eps,
                                                   n_iter_max)
    else:
        out = _denoise_tv_chambolle_nd(im, weight, eps, n_iter_max)
    return out