Use scipy directly where possible

bob/bio/vein/utils.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-import numpy
-import scipy.signal
-import bob.sp
-import bob.core
-
-
-def imfilter(a, b):
-  """Applies a 2D filtering between images
-
-  This implementation was created to work similarly like the Matlab one when
-  ``boundary = replicate``. It filters the image ``a`` with the filter ``b`` by
-  applying it over an extended version of ``a``, expanded of ``b.shape`` on the
-  right and bottom, by repeating the values on ``a``'s borders.
-
-  The filter is then applied to the image considering only the ``valid`` range
-  with :py:func:`scipy.signal.convolve2d`.
-
-
-  Parameters:
-
-    a (numpy.ndarray): A 2-dimensional :py:class:`numpy.ndarray` which
-      represents the image to be filtered. The dtype of the array is supposed
-      to be 64-bit floats. You can also pass an 8-bit unsigned integer array,
-      loaded from a file (for example). In this case it will be scaled as with
-      :py:func:`bob.core.convert` and the range reset to ``[0.0, 1.0]``.
-
-    b (numpy.ndarray): A 64-bit float 2-dimensional :py:class:`numpy.ndarray`
-      which represents the filter to be applied to the image. The input filter
-      has to be rotated by 180 degrees as we use
-      :py:func:`scipy.signal.convolve2d` to apply it.
-
-  """
-
-  if a.dtype == numpy.uint8:
-      a = bob.core.convert(a, numpy.float64, (0,1))
-
-  shape = (a.shape[0] + b.shape[0] - 1, a.shape[1] + b.shape[1] - 1)
-  a_ext = numpy.ndarray(shape=shape, dtype=numpy.float64)
-  bob.sp.extrapolate_nearest(a, a_ext)
-
-  return scipy.signal.convolve2d(a_ext, b, 'valid')