#cython: cdivision=True
#cython: boundscheck=False
#cython: nonecheck=False
#cython: wraparound=False
import numpy as np
cimport numpy as cnp
from libc.float cimport DBL_MAX

from skimage.color import rgb2grey
from skimage.util import img_as_float


def corner_moravec(image, Py_ssize_t window_size=1):
    """Compute Moravec corner measure response image.

    This is one of the simplest corner detectors and is comparatively fast but
    has several limitations (e.g. not rotation invariant).

    Parameters
    ----------
    image : ndarray
        Input image.
    window_size : int, optional (default 1)
        Window size.

    Returns
    -------
    response : ndarray
        Moravec response image.

    References
    ----------
    ..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm
    ..[2] http://en.wikipedia.org/wiki/Corner_detection

    Examples
    --------
    >>> from skimage.feature import corner_moravec, peak_local_max
    >>> square = np.zeros([7, 7])
    >>> square[3, 3] = 1
    >>> square
    array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  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.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]])
    >>> corner_moravec(square)
    array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  1.,  1.,  1.,  0.,  0.],
           [ 0.,  0.,  1.,  2.,  1.,  0.,  0.],
           [ 0.,  0.,  1.,  1.,  1.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]])
    """

    cdef Py_ssize_t rows = image.shape[0]
    cdef Py_ssize_t cols = image.shape[1]

    cdef double[:, ::1] cimage = np.ascontiguousarray(img_as_float(image))
    cdef double[:, ::1] out = np.zeros(image.shape, dtype=np.double)

    cdef double msum, min_msum
    cdef Py_ssize_t r, c, br, bc, mr, mc, a, b
    for r in range(2 * window_size, rows - 2 * window_size):
        for c in range(2 * window_size, cols - 2 * window_size):
            min_msum = DBL_MAX
            for br in range(r - window_size, r + window_size + 1):
                for bc in range(c - window_size, c + window_size + 1):
                    if br != r and bc != c:
                        msum = 0
                        for mr in range(- window_size, window_size + 1):
                            for mc in range(- window_size, window_size + 1):
                                msum += (cimage[r + mr, c + mc]
                                         - cimage[br + mr, bc + mc]) ** 2
                        min_msum = min(msum, min_msum)

            out[r, c] = min_msum

    return np.asarray(out)