code: def dense(
      f,
      spacing,
      scale=None,
      is_integral=False,
      include_interest_point=False,
  ):
      """
      desc_array = dense(f, spacing, scale={np.sqrt(spacing)}, is_integral=False, include_interest_point=False)
  
      Parameters
      ----------
      f : image
          original image
      spacing : integer
          Distance between points
      scale : float, optional
          Scale of interest points. By default, it is set to ``np.sqrt(spacing)``
      is_integral : boolean, optional
          Whether `f` is an integral image
      include_interest_point : bool, optional
          Whether to return interest point information. Default is False
  
      Returns
      -------
      descriptors : ndarray
          Descriptors at dense points. Note that the interest point is **not
          returned by default**.
  
      See Also
      --------
      surf : function
          Find interest points and then compute descriptors
      descriptors : function
          Compute descriptors at user provided interest points
      """
      if scale is None:
          scale = np.sqrt(spacing)
      s0, s1 = f.shape
      x = np.arange(int(spacing / 2), s0, int(spacing))
      y = np.arange(int(spacing / 2), s1, int(spacing))
      X, Y = np.meshgrid(x, y)
      S = np.zeros(X.shape, dtype=float)
      S += scale
      ips = np.vstack(
          [
              X.ravel(),
              Y.ravel(),
              S.ravel(),
              np.ones(X.size),
              np.ones(X.size),
          ]
      )
      return descriptors(
          f,
          ips.T,
          is_integral=is_integral,
          descriptor_only=(not include_interest_point),
      )
  
file_path: mahotas/mahotas/features/surf.py
imports: from __future__ import division
  import numpy
  from .None import _surf
label_desc: Use image node_id 5 for calling a global function with example usage: dense(f, spacing, scale, is_integral, include_interest_point) and returns: descriptors