Fix Hamming Distance module

bob/bio/vein/algorithm/HammingDistance.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-import bob.ip.base
-
-import numpy
-import scipy.signal
-
-from bob.bio.base.algorithm import Algorithm
-
-
-class HammingDistance (Algorithm):
-  """Finger vein matching: hamming distance
-  """
-
-  def __init__(
-      self,
-      # some similarity functions might need a GaborWaveletTransform class, so we have to provide the parameters here as well...
-      ch = 8,       # Maximum search displacement in y-direction
-      cw = 5,       # Maximum search displacement in x-direction
-  ):
-
-    # call base class constructor
-    Algorithm.__init__(
-        self,
-
-        ch = ch,
-        cw = cw,
-
-        multiple_model_scoring = None,
-        multiple_probe_scoring = None
-    )
-
-    self.ch = ch
-    self.cw = cw
-
-  def enroll(self, enroll_features):
-    """Enrolls the model by computing an average graph for each model"""
-    # return the generated model
-    return numpy.vstack(enroll_features)
-
-
-  def score(self, model, probe):
-    """Computes the score of the probe and the model
-         Return score - Value between 0 and 0.5, larger value is better match
-    """
-    I=probe.astype(numpy.float64)
-    R=model.astype(numpy.float64)
-    h, w = R.shape
-    crop_R = R[self.ch:h-self.ch, self.cw:w-self.cw]
-    rotate_R = numpy.zeros((crop_R.shape[0], crop_R.shape[1]))
-    bob.ip.base.rotate(crop_R, rotate_R, 180)
-    #FFT for scoring!
-    #Nm=bob.sp.ifft(bob.sp.fft(I)*bob.sp.fft(rotate_R))
-    Nm = scipy.signal.convolve2d(I, rotate_R, 'valid');
-    t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-    Nmm = Nm[t0,s0]
-    #Nmm = Nm.max()
-    #mi = numpy.argwhere(Nmm == Nm)
-    #t0, s0 = mi.flatten()[:2]
-    score = Nmm/(sum(sum(crop_R)) + sum(sum(I[t0:t0+h-2*self.ch, s0:s0+w-2*self.cw])))
-    return score
+# Calculates the Hamming distance (proportion of mismatching corresponding bits) between two binary vectors
+
+import bob.bio.base
+import scipy.spatial.distance
+algorithm = bob.bio.base.algorithm.Distance(
+    distance_function = scipy.spatial.distance.hamming,
+    is_distance_function = False  # setting this to False ensures that Hamming distances are returned as positive values rather than negative
+)
\ No newline at end of file