    Computes a 2-dimensional dense SIFT features image with ``C`` number of
    channels, where
    ``C = num_bins_horizontal * num_bins_vertical * num_or_bins``. The dense
    SIFT [2]_ implementation is taken from Vlfeat [1]_.

    Parameters
    ----------
    pixels : :map:`Image` or subclass or ``(C, Y, X)`` `ndarray`
        Either the image object itself or an array with the pixels. The first
        dimension is interpreted as channels.
    window_step_horizontal : `int`, optional
        Defines the horizontal step by which the window is moved, thus it
        controls the features density. The metric unit is pixels.
    window_step_vertical : `int`, optional
        Defines the vertical step by which the window is moved, thus it
    num_bins_horizontal : `int`, optional
        Defines the number of histogram bins in the X direction.
    num_bins_vertical : `int`, optional
        Defines the number of histogram bins in the Y direction.
    num_or_bins : `int`, optional
        Defines the number of orientation histogram bins.
    cell_size_horizontal : `int`, optional
        Defines cell width in pixels. The cell is the region that is covered by
        a spatial bin.
    cell_size_vertical : `int`, optional
        Defines cell height in pixels. The cell is the region that is covered by
    fast : `bool`, optional
        If ``True``, then the windowing function is a piecewise-flat, rather
        than Gaussian. While this breaks exact SIFT equivalence, in practice it
        is much faster to compute.
    verbose : `bool`, optional
        Flag to print SIFT related information.

    Raises
    ------
    ValueError
        Only 2D arrays are supported
        Size must only contain positive integers.
        Step must only contain positive integers.
        Window size must be a positive integer.
        Geometry must only contain positive integers.

    References
    .. [1] Vedaldi, Andrea, and Brian Fulkerson. "VLFeat: An open and portable
       library of computer vision algorithms." Proceedings of the international
       conference on Multimedia. ACM, 2010.
    .. [2] Lowe, David G. "Distinctive image features from scale-invariant
       keypoints." International journal of computer vision 60.2 (2004): 91-110.