import math
import numpy as np
from scipy import ndimage, spatial
from skimage.util import img_as_float
from ._warps_cy import _warp_fast

from skimage._shared.utils import get_bound_method_class
from skimage._shared import six


class GeometricTransform(object):
    """Perform geometric transformations on a set of coordinates.

    """
    def __call__(self, coords):
        """Apply forward transformation.

        Parameters
        ----------
        coords : (N, 2) array
            Source coordinates.

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """
        raise NotImplementedError()

    def inverse(self, coords):
        """Apply inverse transformation.

        Parameters
        ----------
        coords : (N, 2) array
            Source coordinates.

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """
        raise NotImplementedError()

    def residuals(self, src, dst):
        """Determine residuals of transformed destination coordinates.

        For each transformed source coordinate the euclidean distance to the
        respective destination coordinate is determined.

        Parameters
        ----------
        src : (N, 2) array
            Source coordinates.
        dst : (N, 2) array
            Destination coordinates.

        Returns
        -------
        residuals : (N, ) array
            Residual for coordinate.

        """

        return np.sqrt(np.sum((self(src) - dst)**2, axis=1))

    def __add__(self, other):
        """Combine this transformation with another.

        """
        raise NotImplementedError()


class ProjectiveTransform(GeometricTransform):
    """Matrix transformation.

    Apply a projective transformation (homography) on coordinates.

    For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its
    target position is calculated by multiplying with the given matrix,
    :math:`H`, to give :math:`H \mathbf{x}`::

      [[a0 a1 a2]
       [b0 b1 b2]
       [c0 c1 1 ]].

    E.g., to rotate by theta degrees clockwise, the matrix should be::

      [[cos(theta) -sin(theta) 0]
       [sin(theta)  cos(theta) 0]
       [0            0         1]]

    or, to translate x by 10 and y by 20::

      [[1 0 10]
       [0 1 20]
       [0 0 1 ]].

    Parameters
    ----------
    matrix : (3, 3) array, optional
        Homogeneous transformation matrix.

    """

    _coeffs = range(8)

    def __init__(self, matrix=None):
        if matrix is None:
            # default to an identity transform
            matrix = np.eye(3)
        if matrix.shape != (3, 3):
            raise ValueError("invalid shape of transformation matrix")
        self._matrix = matrix

    @property
    def _inv_matrix(self):
        return np.linalg.inv(self._matrix)

    def _apply_mat(self, coords, matrix):
        coords = np.array(coords, copy=False, ndmin=2)

        x, y = np.transpose(coords)
        src = np.vstack((x, y, np.ones_like(x)))
        dst = np.dot(src.transpose(), matrix.transpose())

        # rescale to homogeneous coordinates
        dst[:, 0] /= dst[:, 2]
        dst[:, 1] /= dst[:, 2]

        return dst[:, :2]

    def __call__(self, coords):
        return self._apply_mat(coords, self._matrix)

    def inverse(self, coords):
        """Apply inverse transformation.

        Parameters
        ----------
        coords : (N, 2) array
            Source coordinates.

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """
        return self._apply_mat(coords, self._inv_matrix)

    def estimate(self, src, dst):
        """Set the transformation matrix with the explicit transformation
        parameters.

        You can determine the over-, well- and under-determined parameters
        with the total least-squares method.

        Number of source and destination coordinates must match.

        The transformation is defined as::

            X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1)
            Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1)

        These equations can be transformed to the following form::

            0 = a0*x + a1*y + a2 - c0*x*X - c1*y*X - X
            0 = b0*x + b1*y + b2 - c0*x*Y - c1*y*Y - Y

        which exist for each set of corresponding points, so we have a set of
        N * 2 equations. The coefficients appear linearly so we can write
        A x = 0, where::

            A   = [[x y 1 0 0 0 -x*X -y*X -X]
                   [0 0 0 x y 1 -x*Y -y*Y -Y]
                    ...
                    ...
                  ]
            x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3]

        In case of total least-squares the solution of this homogeneous system
        of equations is the right singular vector of A which corresponds to the
        smallest singular value normed by the coefficient c3.

        In case of the affine transformation the coefficients c0 and c1 are 0.
        Thus the system of equations is::

            A   = [[x y 1 0 0 0 -X]
                   [0 0 0 x y 1 -Y]
                    ...
                    ...
                  ]
            x.T = [a0 a1 a2 b0 b1 b2 c3]

        Parameters
        ----------
        src : (N, 2) array
            Source coordinates.
        dst : (N, 2) array
            Destination coordinates.

        """
        xs = src[:, 0]
        ys = src[:, 1]
        xd = dst[:, 0]
        yd = dst[:, 1]
        rows = src.shape[0]

        # params: a0, a1, a2, b0, b1, b2, c0, c1
        A = np.zeros((rows * 2, 9))
        A[:rows, 0] = xs
        A[:rows, 1] = ys
        A[:rows, 2] = 1
        A[:rows, 6] = - xd * xs
        A[:rows, 7] = - xd * ys
        A[rows:, 3] = xs
        A[rows:, 4] = ys
        A[rows:, 5] = 1
        A[rows:, 6] = - yd * xs
        A[rows:, 7] = - yd * ys
        A[:rows, 8] = xd
        A[rows:, 8] = yd

        # Select relevant columns, depending on params
        A = A[:, list(self._coeffs) + [8]]

        _, _, V = np.linalg.svd(A)

        H = np.zeros((3, 3))
        # solution is right singular vector that corresponds to smallest
        # singular value
        H.flat[list(self._coeffs) + [8]] = - V[-1, :-1] / V[-1, -1]
        H[2, 2] = 1

        self._matrix = H

    def __add__(self, other):
        """Combine this transformation with another.

        """
        if isinstance(other, ProjectiveTransform):
            # combination of the same types result in a transformation of this
            # type again, otherwise use general projective transformation
            if type(self) == type(other):
                tform = self.__class__
            else:
                tform = ProjectiveTransform
            return tform(other._matrix.dot(self._matrix))
        else:
            raise TypeError("Cannot combine transformations of differing "
                            "types.")


class AffineTransform(ProjectiveTransform):

    """2D affine transformation of the form::

        X = a0*x + a1*y + a2 =
          = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2

        Y = b0*x + b1*y + b2 =
          = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2

    where ``sx`` and ``sy`` are zoom factors in the x and y directions,
    and the homogeneous transformation matrix is::

        [[a0  a1  a2]
         [b0  b1  b2]
         [0   0    1]]

    Parameters
    ----------
    matrix : (3, 3) array, optional
        Homogeneous transformation matrix.
    scale : (sx, sy) as array, list or tuple, optional
        Scale factors.
    rotation : float, optional
        Rotation angle in counter-clockwise direction as radians.
    shear : float, optional
        Shear angle in counter-clockwise direction as radians.
    translation : (tx, ty) as array, list or tuple, optional
        Translation parameters.

    """

    _coeffs = range(6)

    def __init__(self, matrix=None, scale=None, rotation=None, shear=None,
                 translation=None):
        params = any(param is not None
                     for param in (scale, rotation, shear, translation))

        if params and matrix is not None:
            raise ValueError("You cannot specify the transformation matrix and"
                             " the implicit parameters at the same time.")
        elif matrix is not None:
            if matrix.shape != (3, 3):
                raise ValueError("Invalid shape of transformation matrix.")
            self._matrix = matrix
        elif params:
            if scale is None:
                scale = (1, 1)
            if rotation is None:
                rotation = 0
            if shear is None:
                shear = 0
            if translation is None:
                translation = (0, 0)

            sx, sy = scale
            self._matrix = np.array([
                [sx * math.cos(rotation), -sy * math.sin(rotation + shear), 0],
                [sx * math.sin(rotation),  sy * math.cos(rotation + shear), 0],
                [                      0,                                0, 1]
            ])
            self._matrix[0:2, 2] = translation
        else:
            # default to an identity transform
            self._matrix = np.eye(3)

    @property
    def scale(self):
        sx = math.sqrt(self._matrix[0, 0] ** 2 + self._matrix[1, 0] ** 2)
        sy = math.sqrt(self._matrix[0, 1] ** 2 + self._matrix[1, 1] ** 2)
        return sx, sy

    @property
    def rotation(self):
        return math.atan2(self._matrix[1, 0], self._matrix[0, 0])

    @property
    def shear(self):
        beta = math.atan2(- self._matrix[0, 1], self._matrix[1, 1])
        return beta - self.rotation

    @property
    def translation(self):
        return self._matrix[0:2, 2]


class PiecewiseAffineTransform(GeometricTransform):

    """2D piecewise affine transformation.

    Control points are used to define the mapping. The transform is based on
    a Delaunay triangulation of the points to form a mesh. Each triangle is
    used to find a local affine transform.

    """

    def __init__(self):
        self._tesselation = None
        self._inverse_tesselation = None
        self.affines = []
        self.inverse_affines = []

    def estimate(self, src, dst):
        """Set the control points with which to perform the piecewise mapping.

        Number of source and destination coordinates must match.

        Parameters
        ----------
        src : (N, 2) array
            Source coordinates.
        dst : (N, 2) array
            Destination coordinates.

        """

        # forward piecewise affine
        # triangulate input positions into mesh
        self._tesselation = spatial.Delaunay(src)
        # find affine mapping from source positions to destination
        self.affines = []
        for tri in self._tesselation.vertices:
            affine = AffineTransform()
            affine.estimate(src[tri, :], dst[tri, :])
            self.affines.append(affine)

        # inverse piecewise affine
        # triangulate input positions into mesh
        self._inverse_tesselation = spatial.Delaunay(dst)
        # find affine mapping from source positions to destination
        self.inverse_affines = []
        for tri in self._inverse_tesselation.vertices:
            affine = AffineTransform()
            affine.estimate(dst[tri, :], src[tri, :])
            self.inverse_affines.append(affine)

    def __call__(self, coords):
        """Apply forward transformation.

        Coordinates outside of the mesh will be set to `- 1`.

        Parameters
        ----------
        coords : (N, 2) array
            Source coordinates.

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """

        out = np.empty_like(coords, np.double)

        # determine triangle index for each coordinate
        simplex = self._tesselation.find_simplex(coords)

        # coordinates outside of mesh
        out[simplex == -1, :] = -1

        for index in range(len(self._tesselation.vertices)):
            # affine transform for triangle
            affine = self.affines[index]
            # all coordinates within triangle
            index_mask = simplex == index

            out[index_mask, :] = affine(coords[index_mask, :])

        return out

    def inverse(self, coords):
        """Apply inverse transformation.

        Coordinates outside of the mesh will be set to `- 1`.

        Parameters
        ----------
        coords : (N, 2) array
            Source coordinates.

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """

        out = np.empty_like(coords, np.double)

        # determine triangle index for each coordinate
        simplex = self._inverse_tesselation.find_simplex(coords)

        # coordinates outside of mesh
        out[simplex == -1, :] = -1

        for index in range(len(self._inverse_tesselation.vertices)):
            # affine transform for triangle
            affine = self.inverse_affines[index]
            # all coordinates within triangle
            index_mask = simplex == index

            out[index_mask, :] = affine(coords[index_mask, :])

        return out


class SimilarityTransform(ProjectiveTransform):
    """2D similarity transformation of the form::

        X = a0*x - b0*y + a1 =
          = m*x*cos(rotation) + m*y*sin(rotation) + a1

        Y = b0*x + a0*y + b1 =
          = m*x*sin(rotation) + m*y*cos(rotation) + b1

    where ``m`` is a zoom factor and the homogeneous transformation matrix is::

        [[a0  b0  a1]
         [b0  a0  b1]
         [0   0    1]]

    Parameters
    ----------
    matrix : (3, 3) array, optional
        Homogeneous transformation matrix.
    scale : float, optional
        Scale factor.
    rotation : float, optional
        Rotation angle in counter-clockwise direction as radians.
    translation : (tx, ty) as array, list or tuple, optional
        x, y translation parameters.

    """

    def __init__(self, matrix=None, scale=None, rotation=None,
                 translation=None):
        params = any(param is not None
                     for param in (scale, rotation, translation))

        if params and matrix is not None:
            raise ValueError("You cannot specify the transformation matrix and"
                             " the implicit parameters at the same time.")
        elif matrix is not None:
            if matrix.shape != (3, 3):
                raise ValueError("Invalid shape of transformation matrix.")
            self._matrix = matrix
        elif params:
            if scale is None:
                scale = 1
            if rotation is None:
                rotation = 0
            if translation is None:
                translation = (0, 0)

            self._matrix = np.array([
                [math.cos(rotation), - math.sin(rotation), 0],
                [math.sin(rotation),   math.cos(rotation), 0],
                [                 0,                    0, 1]
            ])
            self._matrix[0:2, 0:2] *= scale
            self._matrix[0:2, 2] = translation
        else:
            # default to an identity transform
            self._matrix = np.eye(3)

    def estimate(self, src, dst):
        """Set the transformation matrix with the explicit parameters.

        You can determine the over-, well- and under-determined parameters
        with the total least-squares method.

        Number of source and destination coordinates must match.

        The transformation is defined as::

            X = a0*x - b0*y + a1
            Y = b0*x + a0*y + b1

        These equations can be transformed to the following form::

            0 = a0*x - b0*y + a1 - X
            0 = b0*x + a0*y + b1 - Y

        which exist for each set of corresponding points, so we have a set of
        N * 2 equations. The coefficients appear linearly so we can write
        A x = 0, where::

            A   = [[x 1 -y 0 -X]
                   [y 0  x 1 -Y]
                    ...
                    ...
                  ]
            x.T = [a0 a1 b0 b1 c3]

        In case of total least-squares the solution of this homogeneous system
        of equations is the right singular vector of A which corresponds to the
        smallest singular value normed by the coefficient c3.

        Parameters
        ----------
        src : (N, 2) array
            Source coordinates.
        dst : (N, 2) array
            Destination coordinates.

        """
        xs = src[:, 0]
        ys = src[:, 1]
        xd = dst[:, 0]
        yd = dst[:, 1]
        rows = src.shape[0]

        # params: a0, a1, b0, b1
        A = np.zeros((rows * 2, 5))
        A[:rows, 0] = xs
        A[:rows, 2] = - ys
        A[:rows, 1] = 1
        A[rows:, 2] = xs
        A[rows:, 0] = ys
        A[rows:, 3] = 1
        A[:rows, 4] = xd
        A[rows:, 4] = yd

        _, _, V = np.linalg.svd(A)

        # solution is right singular vector that corresponds to smallest
        # singular value
        a0, a1, b0, b1 = - V[-1, :-1] / V[-1, -1]

        self._matrix = np.array([[a0, -b0, a1],
                                 [b0,  a0, b1],
                                 [ 0,   0,  1]])

    @property
    def scale(self):
        if math.cos(self.rotation) == 0:
            # sin(self.rotation) == 1
            scale = self._matrix[0, 1]
        else:
            scale = self._matrix[0, 0] / math.cos(self.rotation)
        return scale

    @property
    def rotation(self):
        return math.atan2(self._matrix[1, 0], self._matrix[1, 1])

    @property
    def translation(self):
        return self._matrix[0:2, 2]


class PolynomialTransform(GeometricTransform):
    """2D transformation of the form::

        X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
        Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))

    Parameters
    ----------
    params : (2, N) array, optional
        Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So,
        a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`.

    """

    def __init__(self, params=None):
        if params is None:
            # default to transformation which preserves original coordinates
            params = np.array([[0, 1, 0], [0, 0, 1]])
        if params.shape[0] != 2:
            raise ValueError("invalid shape of transformation parameters")
        self._params = params

    def estimate(self, src, dst, order=2):
        """Set the transformation matrix with the explicit transformation
        parameters.

        You can determine the over-, well- and under-determined parameters
        with the total least-squares method.

        Number of source and destination coordinates must match.

        The transformation is defined as::

            X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
            Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))

        These equations can be transformed to the following form::

            0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) - X
            0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) - Y

        which exist for each set of corresponding points, so we have a set of
        N * 2 equations. The coefficients appear linearly so we can write
        A x = 0, where::

            A   = [[1 x y x**2 x*y y**2 ... 0 ...             0 -X]
                   [0 ...                 0 1 x y x**2 x*y y**2 -Y]
                    ...
                    ...
                  ]
            x.T = [a00 a10 a11 a20 a21 a22 ... ann
                   b00 b10 b11 b20 b21 b22 ... bnn c3]

        In case of total least-squares the solution of this homogeneous system
        of equations is the right singular vector of A which corresponds to the
        smallest singular value normed by the coefficient c3.

        Parameters
        ----------
        src : (N, 2) array
            Source coordinates.
        dst : (N, 2) array
            Destination coordinates.
        order : int, optional
            Polynomial order (number of coefficients is order + 1).

        """
        xs = src[:, 0]
        ys = src[:, 1]
        xd = dst[:, 0]
        yd = dst[:, 1]
        rows = src.shape[0]

        # number of unknown polynomial coefficients
        u = (order + 1) * (order + 2)

        A = np.zeros((rows * 2, u + 1))
        pidx = 0
        for j in range(order + 1):
            for i in range(j + 1):
                A[:rows, pidx] = xs ** (j - i) * ys ** i
                A[rows:, pidx + u / 2] = xs ** (j - i) * ys ** i
                pidx += 1

        A[:rows, -1] = xd
        A[rows:, -1] = yd

        _, _, V = np.linalg.svd(A)

        # solution is right singular vector that corresponds to smallest
        # singular value
        params = - V[-1, :-1] / V[-1, -1]

        self._params = params.reshape((2, u / 2))

    def __call__(self, coords):
        """Apply forward transformation.

        Parameters
        ----------
        coords : (N, 2) array
            source coordinates

        Returns
        -------
        coords : (N, 2) array
            Transformed coordinates.

        """
        x = coords[:, 0]
        y = coords[:, 1]
        u = len(self._params.ravel())
        # number of coefficients -> u = (order + 1) * (order + 2)
        order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2)
        dst = np.zeros(coords.shape)

        pidx = 0
        for j in range(order + 1):
            for i in range(j + 1):
                dst[:, 0] += self._params[0, pidx] * x ** (j - i) * y ** i
                dst[:, 1] += self._params[1, pidx] * x ** (j - i) * y ** i
                pidx += 1

        return dst

    def inverse(self, coords):
        raise Exception(
            'There is no explicit way to do the inverse polynomial '
            'transformation. Instead, estimate the inverse transformation '
            'parameters by exchanging source and destination coordinates,'
            'then apply the forward transformation.')


TRANSFORMS = {
    'similarity': SimilarityTransform,
    'affine': AffineTransform,
    'piecewise-affine': PiecewiseAffineTransform,
    'projective': ProjectiveTransform,
    'polynomial': PolynomialTransform,
}
HOMOGRAPHY_TRANSFORMS = (
    SimilarityTransform,
    AffineTransform,
    ProjectiveTransform
)


def estimate_transform(ttype, src, dst, **kwargs):
    """Estimate 2D geometric transformation parameters.

    You can determine the over-, well- and under-determined parameters
    with the total least-squares method.

    Number of source and destination coordinates must match.

    Parameters
    ----------
    ttype : {'similarity', 'affine', 'piecewise-affine', 'projective', \
             'polynomial'}
        Type of transform.
    kwargs : array or int
        Function parameters (src, dst, n, angle)::

            NAME / TTYPE        FUNCTION PARAMETERS
            'similarity'        `src, `dst`
            'affine'            `src, `dst`
            'piecewise-affine'  `src, `dst`
            'projective'        `src, `dst`
            'polynomial'        `src, `dst`, `order` (polynomial order,
                                                      default order is 2)

        Also see examples below.

    Returns
    -------
    tform : :class:`GeometricTransform`
        Transform object containing the transformation parameters and providing
        access to forward and inverse transformation functions.

    Examples
    --------
    >>> import numpy as np
    >>> from skimage import transform as tf

    >>> # estimate transformation parameters
    >>> src = np.array([0, 0, 10, 10]).reshape((2, 2))
    >>> dst = np.array([12, 14, 1, -20]).reshape((2, 2))

    >>> tform = tf.estimate_transform('similarity', src, dst)

    >>> tform.inverse(tform(src)) # == src

    >>> # warp image using the estimated transformation
    >>> from skimage import data
    >>> image = data.camera()

    >>> warp(image, inverse_map=tform.inverse)

    >>> # create transformation with explicit parameters
    >>> tform2 = tf.SimilarityTransform(scale=1.1, rotation=1,
    ...     translation=(10, 20))

    >>> # unite transformations, applied in order from left to right
    >>> tform3 = tform + tform2
    >>> tform3(src) # == tform2(tform(src))

    """
    ttype = ttype.lower()
    if ttype not in TRANSFORMS:
        raise ValueError('the transformation type \'%s\' is not'
                         'implemented' % ttype)

    tform = TRANSFORMS[ttype]()
    tform.estimate(src, dst, **kwargs)

    return tform


def matrix_transform(coords, matrix):
    """Apply 2D matrix transform.

    Parameters
    ----------
    coords : (N, 2) array
        x, y coordinates to transform
    matrix : (3, 3) array
        Homogeneous transformation matrix.

    Returns
    -------
    coords : (N, 2) array
        Transformed coordinates.

    """
    return ProjectiveTransform(matrix)(coords)


def _stackcopy(a, b):
    """Copy b into each color layer of a, such that::

      a[:,:,0] = a[:,:,1] = ... = b

    Parameters
    ----------
    a : (M, N) or (M, N, P) ndarray
        Target array.
    b : (M, N)
        Source array.

    Notes
    -----
    Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.

    """
    if a.ndim == 3:
        a[:] = b[:, :, np.newaxis]
    else:
        a[:] = b


def warp_coords(coord_map, shape, dtype=np.float64):
    """Build the source coordinates for the output pixels of an image warp.

    Parameters
    ----------
    coord_map : callable like GeometricTransform.inverse
        Return input coordinates for given output coordinates.
        Coordinates are in the shape (P, 2), where P is the number
        of coordinates and each element is a ``(x, y)`` pair.
    shape : tuple
        Shape of output image ``(rows, cols[, bands])``.
    dtype : np.dtype or string
        dtype for return value (sane choices: float32 or float64).

    Returns
    -------
    coords : (ndim, rows, cols[, bands]) array of dtype `dtype`
            Coordinates for `scipy.ndimage.map_coordinates`, that will yield
            an image of shape (orows, ocols, bands) by drawing from source
            points according to the `coord_transform_fn`.

    Notes
    -----
    This is a lower-level routine that produces the source coordinates used by
    `warp()`.

    It is provided separately from `warp` to give additional flexibility to
    users who would like, for example, to re-use a particular coordinate
    mapping, to use specific dtypes at various points along the the
    image-warping process, or to implement different post-processing logic
    than `warp` performs after the call to `ndimage.map_coordinates`.


    Examples
    --------
    Produce a coordinate map that Shifts an image up and to the right:

    >>> from skimage import data
    >>> from scipy.ndimage import map_coordinates
    >>>
    >>> def shift_up10_left20(xy):
    ...     return xy - np.array([-20, 10])[None, :]
    >>>
    >>> image = data.lena().astype(np.float32)
    >>> coords = warp_coords(shift_up10_left20, image.shape)
    >>> warped_image = map_coordinates(image, coords)

    """
    rows, cols = shape[0], shape[1]
    coords_shape = [len(shape), rows, cols]
    if len(shape) == 3:
        coords_shape.append(shape[2])
    coords = np.empty(coords_shape, dtype=dtype)

    # Reshape grid coordinates into a (P, 2) array of (x, y) pairs
    tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T

    # Map each (x, y) pair to the source image according to
    # the user-provided mapping
    tf_coords = coord_map(tf_coords)

    # Reshape back to a (2, M, N) coordinate grid
    tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)

    # Place the y-coordinate mapping
    _stackcopy(coords[1, ...], tf_coords[0, ...])

    # Place the x-coordinate mapping
    _stackcopy(coords[0, ...], tf_coords[1, ...])

    if len(shape) == 3:
        coords[2, ...] = range(shape[2])

    return coords


def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
         mode='constant', cval=0., reverse_map=None):
    """Warp an image according to a given coordinate transformation.

    Parameters
    ----------
    image : 2-D or 3-D array
        Input image.
    inverse_map : transformation object, callable ``xy = f(xy, **kwargs)``, (3, 3) array
        Inverse coordinate map. A function that transforms a (N, 2) array of
        ``(x, y)`` coordinates in the *output image* into their corresponding
        coordinates in the *source image* (e.g. a transformation object or its
        inverse). See example section for usage.
    map_args : dict, optional
        Keyword arguments passed to `inverse_map`.
    output_shape : tuple (rows, cols), optional
        Shape of the output image generated. By default the shape of the input
        image is preserved.
    order : int, optional
        The order of interpolation. The order has to be in the range 0-5:
        * 0: Nearest-neighbor
        * 1: Bi-linear (default)
        * 2: Bi-quadratic
        * 3: Bi-cubic
        * 4: Bi-quartic
        * 5: Bi-quintic
    mode : string, optional
        Points outside the boundaries of the input are filled according
        to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
    cval : float, optional
        Used in conjunction with mode 'constant', the value outside
        the image boundaries.

    Notes
    -----
    In case of a `SimilarityTransform`, `AffineTransform` and
    `ProjectiveTransform` and `order` in [0, 3] this function uses the
    underlying transformation matrix to warp the image with a much faster
    routine.

    Examples
    --------
    >>> from skimage.transform import warp
    >>> from skimage import data
    >>> image = data.camera()

    The following image warps are all equal but differ substantially in
    execution time.

    Use a geometric transform to warp an image (fast):

    >>> from skimage.transform import SimilarityTransform
    >>> tform = SimilarityTransform(translation=(0, -10))
    >>> warp(image, tform)

    Shift an image to the right with a callable (slow):

    >>> def shift(xy):
    ...     xy[:, 1] -= 10
    ...     return xy
    >>> warp(image, shift_right)

    Use a transformation matrix to warp an image (fast):

    >>> matrix = np.array([[1, 0, 0], [0, 1, -10], [0, 0, 1]])
    >>> warp(image, matrix)
    >>> from skimage.transform import ProjectiveTransform
    >>> warp(image, ProjectiveTransform(matrix=matrix))

    You can also use the inverse of a geometric transformation (fast):

    >>> warp(image, tform.inverse)

    """
    # Backward API compatibility
    if reverse_map is not None:
        inverse_map = reverse_map

    if image.ndim < 2:
        raise ValueError("Input must have more than 1 dimension.")

    orig_ndim = image.ndim
    image = np.atleast_3d(img_as_float(image))
    ishape = np.array(image.shape)
    bands = ishape[2]

    out = None

    # use fast Cython version for specific interpolation orders and input
    if order in range(4) and not map_args:

        matrix = None

        # inverse_map is a transformation matrix as numpy array
        if isinstance(inverse_map, np.ndarray) and inverse_map.shape == (3, 3):
            matrix = inverse_map

        # inverse_map is a homography
        elif isinstance(inverse_map, HOMOGRAPHY_TRANSFORMS):
            matrix = inverse_map._matrix

        # inverse_map is the inverse of a homography
        elif (hasattr(inverse_map, '__name__')
              and inverse_map.__name__ == 'inverse'
              and isinstance(get_bound_method_class(inverse_map),
                             HOMOGRAPHY_TRANSFORMS)):
            matrix = np.linalg.inv(six.get_method_self(inverse_map)._matrix)

        if matrix is not None:
            matrix = matrix.astype(np.double)
            # transform all bands
            dims = []
            for dim in range(image.shape[2]):
                dims.append(_warp_fast(image[..., dim], matrix,
                            output_shape=output_shape,
                            order=order, mode=mode, cval=cval))
            out = np.dstack(dims)
            if orig_ndim == 2:
                out = out[..., 0]

    if out is None:  # use ndimage.map_coordinates

        if output_shape is None:
            output_shape = ishape

        rows, cols = output_shape[:2]

        # inverse_map is a transformation matrix as numpy array
        if isinstance(inverse_map, np.ndarray) and inverse_map.shape == (3, 3):
            inverse_map = ProjectiveTransform(matrix=inverse_map)

        def coord_map(*args):
            return inverse_map(*args, **map_args)

        coords = warp_coords(coord_map, (rows, cols, bands))

        # Pre-filtering not necessary for order 0, 1 interpolation
        prefilter = order > 1
        out = ndimage.map_coordinates(image, coords, prefilter=prefilter,
                                      mode=mode, order=order, cval=cval)

    # The spline filters sometimes return results outside [0, 1],
    # so clip to ensure valid data
    clipped = np.clip(out, 0, 1)

    if mode == 'constant' and not (0 <= cval <= 1):
        clipped[out == cval] = cval

    out = clipped

    if out.ndim == 3 and orig_ndim == 2:
        # remove singleton dimension introduced by atleast_3d
        return out[..., 0]
    else:
        return out
