Skip to content
GitLab
Menu
Projects
Groups
Snippets
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
bob
bob.pad.base
Commits
e3a9a96d
Commit
e3a9a96d
authored
Dec 02, 2016
by
Sushil BHATTACHARJEE
Browse files
doc. indentations fixed
parent
696aaa81
Pipeline
#5682
failed with stages
in 3 minutes and 44 seconds
Changes
3
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
bob/pad/base/evaluation/PadIsoMetrics.py
View file @
e3a9a96d
...
...
@@ -18,6 +18,8 @@ class PadIsoMetrics():
self
.
attack_name
=
'attack'
#attack_presentation_name #'attack'
def
save_scores_hdf5
(
self
,
outfile
,
scores_dict
):
""" saves input scores_dict dictionary in a hdf5 formatted file"""
h5out
=
bob
.
io
.
base
.
HDF5File
(
outfile
,
"w"
)
for
p
in
scores_dict
.
keys
():
...
...
@@ -33,6 +35,8 @@ class PadIsoMetrics():
del
h5out
def
load_scores_hdf5
(
self
,
infile
):
""" loads a hdf5 file, and trys to construct a dictionary of scores. Returns the score-dictionary."""
h5in
=
bob
.
io
.
base
.
HDF5File
(
infile
,
"r"
)
scores_dict
=
{}
...
...
@@ -55,15 +59,16 @@ class PadIsoMetrics():
""" computes EER threshold using the scores in the supplied dictionary
Input:
scores_dict: dictionary where each key is the name of the presentation ('real' or one attack-type),
and the corresponding value is a tuple: (scores, attack_potential).
'scores' should be a 1D numpy-array of floats containing scores
'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
Scores for 'real' presentations will not have an associated 'attack_potential',
so, if the value of a key is a tuple of length 1, the key-value pair is assumed
to represent a 'real'-presentation set.
and the corresponding value is a tuple: (scores, attack_potential).
'scores' should be a 1D numpy-array of floats containing scores
'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
Scores for 'real' presentations will not have an associated 'attack_potential',
so, if the value of a key is a tuple of length 1, the key-value pair is assumed
to represent a 'real'-presentation set.
Return:
tuple of three floats: (eer_threshold, far, frr). These are computed using functions from bob.measure.
"""
real_scores
=
None
attack_scores
=
None
assert
scores_dict
is
not
None
,
'no development score-set provided for computing EER'
...
...
@@ -93,16 +98,17 @@ class PadIsoMetrics():
""" computes HTER on test-set scores, using the supplied score-threshold.
Inputs:
scores_dict: dictionary where each key is the name of the presentation ('real' or one attack-type),
and the corresponding value is a tuple: (scores, attack_potential).
'scores' should be a 1D numpy-array of floats containing scores
'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
Scores for 'real' presentations will not have an associated 'attack_potential',
so, if the value of a key is a tuple of length 1, the key-value pair is assumed
to represent a 'real'-presentation set.
and the corresponding value is a tuple: (scores, attack_potential).
'scores' should be a 1D numpy-array of floats containing scores
'attack_potential' should be one of the 3 letters 'A', 'B', or 'C')
Scores for 'real' presentations will not have an associated 'attack_potential',
so, if the value of a key is a tuple of length 1, the key-value pair is assumed
to represent a 'real'-presentation set.
score_threshold: (float) value to be used for thresholding scores.
Return:
tuple of three floats: (hter, far, frr). These are computed using functions from bob.measure.
"""
assert
((
score_threshold
is
not
None
)
and
isinstance
(
score_threshold
,
(
int
,
long
,
float
))
),
'input score_threshold should be a number (float or integer).'
real_scores
=
None
...
...
@@ -132,6 +138,7 @@ class PadIsoMetrics():
def
_check_attack_potential
(
self
,
attack_potential
):
""" For now, we assume three levels of attack-potential: 'C'>'B'>'A' """
if
attack_potential
is
None
:
attack_potential
=
'C'
if
attack_potential
not
in
[
'A'
,
'B'
,
'C'
]:
...
...
@@ -144,13 +151,13 @@ class PadIsoMetrics():
""" computes BPCER on test-set scores, using either the supplied score-threshold,
or the threshold computed from the EER of the development set
Inputs:
scores: a 1D numpy-array of scores corresponding to genuine (bona-fide) presentations.
score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
scores: a 1D numpy-array of scores corresponding to genuine (bona-fide) presentations.
score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
Return:
floating-point number representing the bpcer computed for the input score-set
floating-point number representing the bpcer computed for the input score-set
"""
bonafide_scores
=
None
if
isinstance
(
scores
,
dict
):
#extract 'real' scores from dictionary
...
...
@@ -175,19 +182,18 @@ class PadIsoMetrics():
"""computes APCER as defined in ISO standard. For now, we assume three levels of attack-potential: 'C'>'B'>'A'
Inputs:
scores_dict: a dictionary where each key corresponds to a specific PAI (presentation-attack-instrument)
Keys corresponding to PAIs will have as value a list of 2 elements:
1st element: a 1D numpy-array of scores
2nd element: a single letter 'A', 'B', or 'C', specifying the attack-potential of the PAI.
scores_dict: a dictionary where each key corresponds to a specific PAI (presentation-attack-instrument)
Keys corresponding to PAIs will have as value a list of 2 elements:
1st element: a 1D numpy-array of scores
2nd element: a single letter 'A', 'B', or 'C', specifying the attack-potential of the PAI.
attack_potential: a letter 'A', 'B', or 'C', specifying the attack_potential at which the APCER is to be computed
score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
attack_potential: a letter 'A', 'B', or 'C', specifying the attack_potential at which the APCER is to be computed
score_threshold: a floating point number specifying the score-threshold to be used for deciding accept/reject.
Returns:
tuple consisting of 2 elements:
1st element: apcer at specified attack-potential
2nd element: dictionary of hter of individual PAIs that have attack-potential at or below input-parameter attack_potential.
tuple consisting of 2 elements:
1st element: apcer at specified attack-potential
2nd element: dictionary of hter of individual PAIs that have attack-potential at or below input-parameter attack_potential.
"""
attack_potential
=
self
.
_check_attack_potential
(
attack_potential
)
...
...
doc/conf.py
View file @
e3a9a96d
...
...
@@ -39,6 +39,7 @@ nitpicky = True
# Ignores stuff we can't easily resolve on other project's sphinx manuals
nitpick_ignore
=
[]
keep_warnings
=
True
# Allows the user to override warnings from a separate file
if
os
.
path
.
exists
(
'nitpick-exceptions.txt'
):
...
...
@@ -261,4 +262,4 @@ def member_function_test(app, what, name, obj, skip, options):
return
False
def
setup
(
app
):
app
.
connect
(
'autodoc-skip-member'
,
member_function_test
)
\ No newline at end of file
app
.
connect
(
'autodoc-skip-member'
,
member_function_test
)
doc/implemented.rst
View file @
e3a9a96d
...
...
@@ -44,6 +44,12 @@ Algorithms
.. automodule:: bob.pad.base.algorithm
Evaluation
~~~~~~~~~~
.. automodule:: bob.pad.base.evaluation
Databases
---------
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
.
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment