diff --git a/bob/bio/vein/algorithm/Correlate.py b/bob/bio/vein/algorithm/Correlate.py
index e35310617c82d465e96a244b07494f229f1125e1..416e26c2fa74fb9f7e9bd569f27383fa9fd80e8f 100644
--- a/bob/bio/vein/algorithm/Correlate.py
+++ b/bob/bio/vein/algorithm/Correlate.py
@@ -4,10 +4,10 @@
import numpy
import skimage.feature
-from bob.bio.base.algorithm import Algorithm
+from bob.bio.base.pipelines import BioAlgorithm
-class Correlate(Algorithm):
+class Correlate(BioAlgorithm):
"""Correlate probe and model without cropping
The method is based on "cross-correlation" between a model and a probe image.
@@ -18,18 +18,34 @@ class Correlate(Algorithm):
"""
- def __init__(self):
-
- # call base class constructor
- Algorithm.__init__(
- self, multiple_model_scoring=None, multiple_probe_scoring=None
+ def __init__(
+ self,
+ probes_score_fusion="max",
+ enrolls_score_fusion="mean",
+ **kwargs,
+ ):
+ super().__init__(
+ probes_score_fusion=probes_score_fusion,
+ enrolls_score_fusion=enrolls_score_fusion,
+ **kwargs,
)
- def enroll(self, enroll_features):
- """Enrolls the model by computing an average graph for each model"""
+ def create_templates(self, feature_sets, enroll):
+ return feature_sets
- # return the generated model
- return numpy.array(enroll_features)
+ def compare(self, enroll_templates, probe_templates):
+ # returns scores NxM where N is the number of enroll templates and M is the number of probe templates
+ # enroll_templates is Nx?1xD
+ # probe_templates is Mx?2xD
+ scores = []
+ for enroll in enroll_templates:
+ scores.append([])
+ for probe in probe_templates:
+ s = [[self.score(e, p) for p in probe] for e in enroll]
+ s = self.fuse_probe_scores(s, axis=1)
+ s = self.fuse_enroll_scores(s, axis=0)
+ scores[-1].append(s)
+ return numpy.array(scores)
def score(self, model, probe):
"""Computes the score between the probe and the model.
@@ -49,21 +65,13 @@ class Correlate(Algorithm):
image_ = 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(image_, R)
+ R = model.astype(numpy.float64)
+ Nm = skimage.feature.match_template(image_, R)
- # figures out where the maximum is on the resulting matrix
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
+ # 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])
+ # this is our output
+ score = Nm[t0, s0]
- return numpy.mean(scores)
+ return score
diff --git a/bob/bio/vein/algorithm/HammingDistance.py b/bob/bio/vein/algorithm/HammingDistance.py
index ff38223a6d3eac13da316af2c69790c6b770e1ae..0ab27eee6cec98f4891ebd08da0c7a7ce73c4e0a 100644
--- a/bob/bio/vein/algorithm/HammingDistance.py
+++ b/bob/bio/vein/algorithm/HammingDistance.py
@@ -12,24 +12,22 @@ class HammingDistance(Distance):
base :py:class:`bob.bio.base.algorithm.Distance`.
The input to this function should be of binary nature (boolean arrays). Each
- binary input is first flattened to form a one-dimensional vector. The `Hamming
- distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ is then
+ binary input is first flattened to form a one-dimensional vector. The
+ `Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ is then
calculated between these two binary vectors.
The current implementation uses :py:func:`scipy.spatial.distance.hamming`,
which returns a scalar 64-bit ``float`` to represent the proportion of
mismatching corresponding bits between the two binary vectors.
- The base class constructor parameter ``is_distance_function`` is set to
- ``False`` on purpose to ensure that calculated distances are returned as
- positive values rather than negative.
-
+ The base class constructor parameter ``factor`` is set to ``1`` on purpose
+ to ensure that calculated distances are returned as positive values rather
+ than negative.
"""
- def __init__(self):
- from scipy.spatial.distance import hamming
-
+ def __init__(self, **kwargs):
super(HammingDistance, self).__init__(
- distance_function=hamming,
- is_distance_function=False,
+ distance_function="hamming",
+ factor=1,
+ **kwargs,
)
diff --git a/bob/bio/vein/algorithm/MiuraMatch.py b/bob/bio/vein/algorithm/MiuraMatch.py
index 774c8f239127af0286e26e803b5ff5cc388f4482..4e9ebace9bfb17612fc45bb2e64ae94662e65865 100644
--- a/bob/bio/vein/algorithm/MiuraMatch.py
+++ b/bob/bio/vein/algorithm/MiuraMatch.py
@@ -47,25 +47,35 @@ class MiuraMatch(BioAlgorithm):
self,
ch=80, # Maximum search displacement in y-direction
cw=90, # Maximum search displacement in x-direction
+ probes_score_fusion="max",
+ enrolls_score_fusion="mean",
+ **kwargs,
):
-
- # call base class constructor
- BioAlgorithm.__init__(
- self,
- ch=ch,
- cw=cw,
- # multiple_model_scoring = None,
- # multiple_probe_scoring = None
+ super().__init__(
+ probes_score_fusion=probes_score_fusion,
+ enrolls_score_fusion=enrolls_score_fusion,
+ **kwargs,
)
self.ch = ch
self.cw = cw
- def enroll(self, enroll_features):
- """Enrolls the model by computing an average graph for each model"""
+ def create_templates(self, feature_sets, enroll):
+ return feature_sets
- # return the generated model
- return numpy.array(enroll_features)
+ def compare(self, enroll_templates, probe_templates):
+ # returns scores NxM where N is the number of enroll templates and M is the number of probe templates
+ # enroll_templates is Nx?1xD
+ # probe_templates is Mx?2xD
+ scores = []
+ for enroll in enroll_templates:
+ scores.append([])
+ for probe in probe_templates:
+ s = [[self.score(e, p) for p in probe] for e in enroll]
+ s = self.fuse_probe_scores(s, axis=1)
+ s = self.fuse_enroll_scores(s, axis=0)
+ scores[-1].append(s)
+ return numpy.array(scores)
def score(self, model, probe):
"""Computes the score between the probe and the model.
@@ -85,57 +95,36 @@ class MiuraMatch(BioAlgorithm):
image_ = probe.astype(numpy.float64)
- if len(model.shape) == 2:
- model = numpy.array([model])
-
- scores = []
+ md = model
+ # erode model by (ch, cw)
+ R = md.astype(numpy.float64)
+ 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
+ # self.cw are height (>30). In this case, the number of components
+ # returned by the convolution is high and using an FFT-based method
+ # yields best results. Otherwise, you may try the other options bellow
+ # -> check our test_correlation() method on the test units for more
+ # details and benchmarks.
+ Nm = scipy.signal.fftconvolve(image_, numpy.rot90(crop_R, k=2), "valid")
+ # 2nd best: use convolve2d or correlate2d directly;
+ # Nm = scipy.signal.convolve2d(I, numpy.rot90(crop_R, k=2), 'valid')
+ # 3rd best: use correlate2d
+ # Nm = scipy.signal.correlate2d(I, crop_R, 'valid')
+
+ # figures out where the maximum is on the resulting matrix
+ t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
+
+ # this is our output
+ Nmm = Nm[t0, s0]
+
+ # 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)
+ score = Nmm / (
+ crop_R.sum()
+ + image_[t0 : t0 + h - 2 * self.ch, s0 : s0 + w - 2 * self.cw].sum()
+ )
- # iterate over all models for a given individual
- for md in model:
- # erode model by (ch, cw)
- R = md.astype(numpy.float64)
- 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
- # self.cw are height (>30). In this case, the number of components
- # returned by the convolution is high and using an FFT-based method
- # yields best results. Otherwise, you may try the other options bellow
- # -> check our test_correlation() method on the test units for more
- # details and benchmarks.
- Nm = scipy.signal.fftconvolve(
- image_, numpy.rot90(crop_R, k=2), "valid"
- )
- # 2nd best: use convolve2d or correlate2d directly;
- # Nm = scipy.signal.convolve2d(I, numpy.rot90(crop_R, k=2), 'valid')
- # 3rd best: use correlate2d
- # Nm = scipy.signal.correlate2d(I, crop_R, 'valid')
-
- # figures out where the maximum is on the resulting matrix
- t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-
- # this is our output
- Nmm = Nm[t0, s0]
-
- # 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
- / (
- crop_R.sum()
- + image_[
- t0 : t0 + h - 2 * self.ch, s0 : s0 + w - 2 * self.cw
- ].sum()
- )
- )
-
- return [numpy.mean(scores)]
-
- def score_multiple_biometric_references(self, biometric_references, data):
- if isinstance(biometric_references, list):
- return [self.score(model, data) for model in biometric_references]
- else:
- raise ValueError(
- "The model does not have the desired format (list, array, ...)"
- )
+ return score
diff --git a/bob/bio/vein/database/roi_annotation.py b/bob/bio/vein/database/roi_annotation.py
index aa7b550a9f2bc052380c6d8db2bd8474bd0b5b29..f888aa9a96d8428da381d61e5861f1c1c05a2c0c 100644
--- a/bob/bio/vein/database/roi_annotation.py
+++ b/bob/bio/vein/database/roi_annotation.py
@@ -20,7 +20,6 @@ class ROIAnnotation(TransformerMixin, BaseEstimator):
def _more_tags(self):
return {
- "stateless": True,
"requires_fit": False,
}
diff --git a/bob/bio/vein/database/utfvp.py b/bob/bio/vein/database/utfvp.py
index bc751026d7471197ef16fd7b4c92f1d2d85d37cd..a125724e30003b3d73fe151372695fdfb6919a7c 100644
--- a/bob/bio/vein/database/utfvp.py
+++ b/bob/bio/vein/database/utfvp.py
@@ -179,7 +179,7 @@ class UtfvpDatabase(CSVDataset):
),
ROIAnnotation(roi_path=rc.get("bob.bio.vein.utfvp.roi", "")),
),
- allow_scoring_with_all_biometric_references=True,
+ score_all_vs_all=True,
)
@staticmethod
diff --git a/bob/bio/vein/database/verafinger_contactless.py b/bob/bio/vein/database/verafinger_contactless.py
index 6730a5eac528b81c88a283085d23beab51109d6c..46e65851318bc3794a8b9fcd1806ca63230062d8 100644
--- a/bob/bio/vein/database/verafinger_contactless.py
+++ b/bob/bio/vein/database/verafinger_contactless.py
@@ -71,7 +71,7 @@ class VerafingerContactless(CSVDataset):
extension="",
reference_id_equal_subject_id=False,
),
- allow_scoring_with_all_biometric_references=True,
+ score_all_vs_all=True,
)
@staticmethod
diff --git a/bob/bio/vein/tests/test_tools.py b/bob/bio/vein/tests/test_tools.py
index 0f0570cc2cc3aada6ded9da2ebd911344ff38951..8a7a257cfa1ee1fef01bffae03166bdfeae0db4c 100644
--- a/bob/bio/vein/tests/test_tools.py
+++ b/bob/bio/vein/tests/test_tools.py
@@ -674,16 +674,16 @@ def test_hamming_distance():
# Tests on simple binary arrays:
# 1.) Maximum HD (1.0):
model_1 = numpy.array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0])
- probe_1 = numpy.array([[1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 0, 1]])
- score_max = HD.score(model_1, probe_1)
+ probe_1 = numpy.array([[1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1]])
+ score_max = HD.compare([model_1], [probe_1])[0, 0]
assert score_max == 1.0
# 2.) Minimum HD (0.0):
model_2 = numpy.array([0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1])
- probe_2 = numpy.array([[0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1]])
- score_min = HD.score(model_2, probe_2)
+ probe_2 = numpy.array([[0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1]])
+ score_min = HD.compare([model_2], [probe_2])[0, 0]
assert score_min == 0.0
# 3.) HD of exactly half (0.5)
model_3 = numpy.array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0])
- probe_3 = numpy.array([[0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1]])
- score_half = HD.score(model_3, probe_3)
+ probe_3 = numpy.array([[0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1]])
+ score_half = HD.compare([model_3], [probe_3])[0, 0]
assert score_half == 0.5