code: def lbp(image, radius, points, ignore_zeros=False):
      """
      features = lbp(image, radius, points, ignore_zeros=False)
  
      Compute Linear Binary Patterns
  
      The return value is a **histogram** of feature counts, where position ``i``
      corresponds to the number of pixels that had code ``i``. The codes are
      compressed so that impossible codes are not used. Therefore, this is the
      ``i``th feature, not just the feature with binary code ``i``.
  
      Parameters
      ----------
      image : ndarray
          input image (2-D numpy ndarray)
      radius : number (integer or floating point)
          radius (in pixels)
      points : integer
          nr of points to consider
      ignore_zeros : boolean, optional
          whether to ignore zeros (default: False)
  
      Returns
      -------
      features : 1-D numpy ndarray
          histogram of features. See above for a caveat on the interpretation of
          these.
  
      References
      ----------
      Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
          Ojala, T. Pietikainen, M. Maenpaa, T. Lecture Notes in Computer Science (Springer)
          2000, ISSU 1842, pages 404-420
      """
      from mahotas.features import _lbp
  
      codes = lbp_transform(
          image,
          radius,
          points,
          ignore_zeros=ignore_zeros,
          preserve_shape=False,
      )
      final = fullhistogram(codes.astype(np.uint32))
  
      codes = np.arange(2**points, dtype=np.uint32)
      iters = codes.copy()
      codes = _lbp.map(codes.astype(np.uint32), points)
      pivots = codes == iters
      npivots = np.sum(pivots)
      compressed = final[pivots[: len(final)]]
      compressed = np.append(
          compressed, np.zeros(npivots - len(compressed))
      )
      return compressed
  
imports: import numpy
  from ..histogram import fullhistogram
label_desc: Use image node_id 3 for calling a global function with example usage: lbp(image, radius, points, ignore_zeros) and returns: compressed