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bob
bob.bio.vein
Commits
6919292f
Commit
6919292f
authored
Oct 18, 2017
by
Vedrana KRIVOKUCA
Browse files
Fix Hamming Distance module
parent
700025b3
Pipeline
#13245
failed with stages
in 12 minutes and 11 seconds
Changes
1
Pipelines
1
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Inline
Sidebyside
bob/bio/vein/algorithm/HammingDistance.py
View file @
6919292f
#!/usr/bin/env python
# vim: set fileencoding=utf8 :
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 ydirection
cw
=
5
,
# Maximum search displacement in xdirection
):
# 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
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