Commit f3657022 authored by Theophile GENTILHOMME's avatar Theophile GENTILHOMME
Browse files

Merge branch 'moving' into 'theo'

Moving biometrics-related functionality to bob.bio.base

See merge request !49
parents c89e5cb3 7ef58230
Pipeline #17712 passed with stage
in 30 minutes and 52 seconds
......@@ -7,9 +7,7 @@ from . import version
from .version import module as __version__
from . import plot
from . import load
from . import calibration
from . import openbr
import numpy
def mse (estimation, target):
......@@ -134,9 +132,7 @@ def recognition_rate(cmc_scores, threshold = None, rank = 1):
The input has a specific format, which is a list of two-element tuples. Each
of the tuples contains the negative :math:`\\{S_p^-\\}` and the positive
:math:`\\{S_p^+\\}` scores for one probe item :math:`p`, or ``None`` in case
of open set recognition. To read the lists from score files in 4 or 5 column
format, please use the :py:func:`bob.measure.load.cmc_four_column` or
:py:func:`bob.measure.load.cmc_five_column` function.
of open set recognition.
If ``threshold`` is set to ``None``, the rank 1 recognition rate is defined
as the number of test items, for which the highest positive
......@@ -172,9 +168,9 @@ def recognition_rate(cmc_scores, threshold = None, rank = 1):
Parameters:
cmc_scores (:py:class:`list`): A list in the format ``[(negatives,
positives), ...]`` containing the CMC scores loaded with one of the
functions (:py:func:`bob.measure.load.cmc_four_column` or
:py:func:`bob.measure.load.cmc_five_column`).
positives), ...]`` containing the CMC scores (i.e. :py:class:`list`:
A list of tuples, where each tuple contains the
``negative`` and ``positive`` scores for one probe of the database).
Each pair contains the ``negative`` and the ``positive`` scores for **one
probe item**. Each pair can contain up to one empty array (or ``None``),
......@@ -258,9 +254,7 @@ def cmc(cmc_scores):
The input has a specific format, which is a list of two-element tuples. Each
of the tuples contains the negative and the positive scores for one probe
item. To read the lists from score files in 4 or 5 column format, please use
the :py:func:`bob.measure.load.cmc_four_column` or
:py:func:`bob.measure.load.cmc_five_column` function.
item.
For each probe item the probability that the rank :math:`r` of the positive
score is calculated. The rank is computed as the number of negative scores
......@@ -281,10 +275,7 @@ def cmc(cmc_scores):
cmc_scores : :py:class:`list`
A list in the format ``[(negatives, positives), ...]`` containing the CMC
scores loaded with one of the functions
:py:func:`bob.measure.load.cmc_four_column`,
:py:func:`bob.measure.load.cmc_five_column`, or
:py:func:`bob.measure.load.cmc`.
scores.
Each pair contains the ``negative`` and the ``positive`` scores for **one
probe item**. Each pair can contain up to one empty array (or ``None``),
......@@ -345,9 +336,7 @@ def detection_identification_rate(cmc_scores, threshold, rank = 1):
Parameters:
cmc_scores (:py:class:`list`): A list in the format ``[(negatives,
positives), ...]`` containing the CMC scores loaded with one of the
functions (:py:func:`bob.measure.load.cmc_four_column` or
:py:func:`bob.measure.load.cmc_five_column`).
positives), ...]`` containing the CMC.
Each pair contains the ``negative`` and the ``positive`` scores for **one
probe item**. Each pair can contain up to one empty array (or ``None``),
......@@ -406,9 +395,9 @@ def false_alarm_rate(cmc_scores, threshold):
Parameters:
cmc_scores (:py:class:`list`): A list in the format ``[(negatives,
positives), ...]`` containing the CMC scores loaded with one of the
functions (:py:func:`bob.measure.load.cmc_four_column` or
:py:func:`bob.measure.load.cmc_five_column`).
positives), ...]`` containing the CMC scores (i.e. :py:class:`list`:
A list of tuples, where each tuple contains the
``negative`` and ``positive`` scores for one probe of the database).
Each pair contains the ``negative`` and the ``positive`` scores for **one
probe item**. Each pair can contain up to one empty array (or ``None``),
......
Failure To Acquire (FTA) in the development set is: 0.000%
Failure To Acquire (FTA) in the test set is: 0.000%
[Min. criterion: EER] Threshold on Development set: -4.830500e-03
| Development | Test
-------+-----------------+------------------
FMR | 6.731% (35/520) | 2.500% (13/520)
FNMR | 6.667% (26/390) | 6.154% (24/390)
FAR | 6.731% | 2.500%
FRR | 6.667% | 6.154%
HTER | 6.699% | 4.327%
[Min. criterion: Min. HTER] Threshold on Development set: 3.430500e-03
| Development | Test
-------+-----------------+------------------
FMR | 4.231% (22/520) | 1.731% (9/520)
FNMR | 7.949% (31/390) | 7.692% (30/390)
FAR | 4.231% | 1.731%
FRR | 7.949% | 7.692%
HTER | 6.090% | 4.712%
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1 1 probe_1 1
1 1 probe_2 1
1 2 probe_3 0
1 2 probe_4 0
1 2 probe_5 0
1 3 probe_6 0
1 3 probe_7 0
1 4 probe_8 0
1 5 probe_9 0
2 1 probe_1 0
2 1 probe_2 0
2 2 probe_3 1
2 2 probe_4 1
2 2 probe_5 1
2 3 probe_6 0
2 3 probe_7 0
2 4 probe_8 0
2 5 probe_9 0
3 1 probe_1 0
3 1 probe_2 0
3 2 probe_3 0
3 2 probe_4 0
3 2 probe_5 0
3 3 probe_6 1
3 3 probe_7 -0.1
3 4 probe_8 0
3 5 probe_9 0
1 1 probe_1 1
1 1 probe_2 1
1 2 probe_3 0
1 2 probe_4 0
1 2 probe_5 0
1 3 probe_6 0
1 3 probe_7 0
1 4 probe_8 0
1 5 probe_9 0
2 1 probe_1 0
2 1 probe_2 0
2 2 probe_3 1
2 2 probe_4 1
2 2 probe_5 1
2 3 probe_6 0
2 3 probe_7 0
2 4 probe_8 0
2 5 probe_9 0
3 1 probe_1 0
3 1 probe_2 0
3 2 probe_3 0
3 2 probe_4 0
3 2 probe_5 0
3 3 probe_6 1
3 3 probe_7 -0.1
3 4 probe_8 0
3 5 probe_9 10
1 1 probe_1 1
1 1 probe_2 1
1 2 probe_3 0
1 2 probe_4 0
1 2 probe_5 0
1 3 probe_6 0
1 3 probe_7 0
1 4 probe_8 0
1 5 probe_9 0
2 1 probe_1 0
2 1 probe_2 0
2 2 probe_3 1
2 2 probe_4 1
2 2 probe_5 1
2 3 probe_6 0
2 3 probe_7 0
2 4 probe_8 0
2 5 probe_9 0
3 1 probe_1 0
3 1 probe_2 0
3 2 probe_3 0
3 2 probe_4 0
3 2 probe_5 0
3 3 probe_6 1
3 3 probe_7 1
3 4 probe_8 0
3 5 probe_9 0
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S2
unknown-gallery.lst
unknown-probe.lst
MB 100 20 xV4

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