Implemented normalized-cross-correlation algorithm

bob/bio/vein/algorithm/Correlate.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+import numpy
+import skimage.feature
+
+from bob.bio.base.algorithm import Algorithm
+
+
+class Correlate (Algorithm):
+  """Correlate probe and model without cropping
+
+  The method is based on "cross-correlation" between a model and a probe image.
+  The difference between this and :py:class:`MiuraMatch` is that **no**
+  cropping takes place on this implementation. We simply fill the excess
+  boundary with zeros and extract the valid correlation region between the
+  probe and the model using :py:func:`skimage.feature.match_template`.
+
+  """
+
+  def __init__(self):
+
+    # call base class constructor
+    Algorithm.__init__(
+        self,
+        multiple_model_scoring = None,
+        multiple_probe_scoring = None
+    )
+
+
+  def enroll(self, enroll_features):
+    """Enrolls the model by computing an average graph for each model"""
+
+    # return the generated model
+    return numpy.array(enroll_features)
+
+
+  def score(self, model, probe):
+    """Computes the score between the probe and the model.
+
+    Parameters:
+
+      model (numpy.ndarray): The model of the user to test the probe agains
+
+      probe (numpy.ndarray): The probe to test
+
+
+    Returns:
+
+      float: Value between 0 and 0.5, larger value means a better match
+
+    """
+
+    I=probe.astype(numpy.float64)
+
+    if len(model.shape) == 2:
+      model = numpy.array([model])
+
+    scores = []
+
+    # iterate over all models for a given individual
+    for md in model:
+
+      R = md.astype(numpy.float64)
+      Nm = skimage.feature.match_template(I, R)
+
+      # figures out where the maximum is on the resulting matrix
+      t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
+
+      # this is our output
+      scores.append(Nm[t0,s0])
+
+    return numpy.mean(scores)

bob/bio/vein/algorithm/MiuraMatch.py

@@ -94,8 +94,6 @@ class MiuraMatch (Algorithm):
     if len(model.shape) == 2:
       model = numpy.array([model])
 
-    n_models = model.shape[0]
-
     scores = []
 
     # iterate over all models for a given individual
@@ -103,7 +101,7 @@ class MiuraMatch (Algorithm):
 
       # erode model by (ch, cw)
       R = md.astype(numpy.float64)
-      h, w = R.shape
+      h, w = R.shape #same as I
       crop_R = R[self.ch:h-self.ch, self.cw:w-self.cw]
 
       # correlates using scipy - fastest option available iff the self.ch and
@@ -127,6 +125,6 @@ class MiuraMatch (Algorithm):
       # normalizes the output by the number of pixels lit on the input
       # matrices, taking into consideration the surface that produced the
       # result (i.e., the eroded model and part of the probe)
-      scores.append(Nmm/(sum(sum(crop_R)) + sum(sum(I[t0:t0+h-2*self.ch, s0:s0+w-2*self.cw]))))
+      scores.append(Nmm/(crop_R.sum() + I[t0:t0+h-2*self.ch, s0:s0+w-2*self.cw].sum()))
 
     return numpy.mean(scores)

bob/bio/vein/algorithm/__init__.py

 from .MiuraMatch import MiuraMatch
+from .Correlate import Correlate
+from .HammingDistance import HammingDistance
 
 # gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+  """Says object was actually declared here, an not on the import module.
+
+  Parameters:
+
+    *args: An iterable of objects to modify
+
+  Resolves `Sphinx referencing issues
+  <https://github.com/sphinx-doc/sphinx/issues/3048>`
+  """
+
+  for obj in args: obj.__module__ = __name__
+
+__appropriate__(
+    MiuraMatch,
+    Correlate,
+    HammingDistance,
+    )
+
 __all__ = [_ for _ in dir() if not _.startswith('_')]

bob/bio/vein/tests/test.py

@@ -343,6 +343,25 @@ def test_miura_match():
   assert numpy.isclose(score_imp, 0.172906739278421)
 
 
+def test_correlate():
+
+  #Match Ratio method against Matlab reference
+
+  template_filename = F(('algorithms', '0001_2_1_120509-135338.mat'))
+  probe_gen_filename = F(('algorithms', '0001_2_2_120509-135558.mat'))
+  probe_imp_filename = F(('algorithms', '0003_2_1_120509-141255.mat'))
+
+  template_vein = bob.io.base.load(template_filename)
+  probe_gen_vein = bob.io.base.load(probe_gen_filename)
+  probe_imp_vein = bob.io.base.load(probe_imp_filename)
+
+  from ..algorithm.Correlate import Correlate
+  C = Correlate()
+  score_gen = C.score(template_vein, probe_gen_vein)
+
+  # we don't check here - no templates
+
+
 def test_assert_points():
 
   # Tests that point assertion works as expected