Commit cf9c1fe8 authored by André Anjos's avatar André Anjos 💬

More clean-up and simplifications

parent b5f5948e
Pipeline #2887 passed with stage
in 3 minutes and 34 seconds
...@@ -18,3 +18,4 @@ html/ ...@@ -18,3 +18,4 @@ html/
parts parts
sphinx sphinx
src/ src/
submitted.sql3
...@@ -3,8 +3,8 @@ ...@@ -3,8 +3,8 @@
from bob.bio.base.grid import Grid from bob.bio.base.grid import Grid
# our preferred grid setup for Idiap
grid = Grid( default = Grid(
training_queue='32G', training_queue='32G',
number_of_preprocessing_jobs=200, number_of_preprocessing_jobs=200,
......
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
from bob.bio.base.grid import Grid
grid = Grid(
grid_type = 'local',
number_of_parallel_processes = 4
)
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
# vim: set fileencoding=utf-8 : # vim: set fileencoding=utf-8 :
from ..preprocessors import FingerCrop from ..preprocessors import FingerCrop
nonorm = FingerCrop() none = FingerCrop()
he = FingerCrop(postprocessing='HE') he = FingerCrop(postprocessing='HE')
hfe = FingerCrop(postprocessing='HFE') hfe = FingerCrop(postprocessing='HFE')
circgabor = FingerCrop(postprocessing='CircGabor') circgabor = FingerCrop(postprocessing='CircGabor')
...@@ -85,27 +85,30 @@ Repeated Line-Tracking with Miura Matching ...@@ -85,27 +85,30 @@ Repeated Line-Tracking with Miura Matching
You can find the description of this method on the paper from Miura *et al.* You can find the description of this method on the paper from Miura *et al.*
[MNM04]_. [MNM04]_.
To run the baseline on the `VERA fingervein`_ database, using the ``nom`` To run the baseline on the `VERA fingervein`_ database, using the ``NOM``
protocol (called ``Full`` in [TVM14]_), do the following: protocol (called ``Full`` in [TVM14]_), do the following:
.. code-block:: sh .. code-block:: sh
$ ./bin/verify.py --database=vera --protocol=nom --preprocessor=none --extractor=repeatedlinetracking --algorithm=match-rlt --sub-directory="rlt" --verbose --verbose $ ./bin/verify.py --database=vera --protocol=NOM --preprocessor=nopp --extractor=repeatedlinetracking --algorithm=match-rlt --sub-directory="rlt" --verbose --verbose
.. tip:: .. tip::
If you have more processing cores on your local machine and don't want to If you have more processing cores on your local machine and don't want to
submit your job for SGE execution, you can run it in parallel by adding the submit your job for SGE execution, you can run it in parallel (using 4
options ``?``. parallel tasks) by adding the options ``--parallel=4 --nice=10``.
This command line selects and runs the following implementations for the This command line selects and runs the following implementations for the
toolchain: toolchain:
* Database: Use the base Bob API for the VERA database implementation, * Database: Use the base Bob API for the VERA database implementation,
protocol variant ``nom`` which corresponds to the ``Full`` evaluation protocol variant ``NOM`` which corresponds to the ``Full`` evaluation
protocol described in [TVM14]_ protocol described in [TVM14]_
* Preprocessor: Simple finger cropping, with no extra pre-processing and no * Preprocessor: Simple finger cropping, with no extra post-processing, as
histogram equalization, as defined in [LLP09]_ defined in [LLP09]_
* Feature extractor: Repeated line tracking, as explained in [MNM04]_ * Feature extractor: Repeated line tracking, as explained in [MNM04]_
* Matching algorithm: "Miura" matching, as explained on the same paper * Matching algorithm: "Miura" matching, as explained on the same paper
* Subdirectory: This is the subdirectory in which the scores and intermediate * Subdirectory: This is the subdirectory in which the scores and intermediate
...@@ -119,15 +122,18 @@ the detector error trade-off (DET) curve with the performance: ...@@ -119,15 +122,18 @@ the detector error trade-off (DET) curve with the performance:
.. code-block:: sh .. code-block:: sh
$ ./bin/bob_eval_threshold.py --scores <path-to>/vera/rlt/nom/nonorm/scores-dev --criterium=eer $ ./bin/bob_eval_threshold.py --scores <path-to>/vera/rlt/NOM/nonorm/scores-dev --criterium=eer
('Threshold:', 0.32023322499999995) ('Threshold:', 0.32023322499999995)
FAR : 24.318% (46866/192720) FAR : 24.318% (46866/192720)
FRR : 24.318% (107/440) FRR : 24.318% (107/440)
HTER: 24.318% HTER: 24.318%
$ ./bin/evaluate.py --dev-files <path-to>/vera/rlt/nom/nonorm/scores-dev --det det.pdf -l "vera-nom-mnm04" -rr $ ./bin/evaluate.py --dev-files <path-to>/vera/rlt/NOM/nonorm/scores-dev --det det.pdf -l "vera-nom-mnm04" -rr
The Recognition Rate of the development set of 'rlt' is 48.409% The Recognition Rate of the development set of 'rlt' is 48.409%
To view the DET curve stored in To view the DET curve stored in the output file, do the following (on a Linux
machine):
.. code-block:: sh
$ xdg-open det.pdf #to view the DET curve $ xdg-open det.pdf #to view the DET curve
......
...@@ -39,7 +39,7 @@ setup( ...@@ -39,7 +39,7 @@ setup(
# registered preprocessors # registered preprocessors
'bob.bio.preprocessor': [ 'bob.bio.preprocessor': [
'nonorm = bob.bio.vein.configurations.preprocessors:nonorm', 'nopp = bob.bio.vein.configurations.preprocessors:none',
'histeq = bob.bio.vein.configurations.preprocessors:he', 'histeq = bob.bio.vein.configurations.preprocessors:he',
'highfreq = bob.bio.vein.configurations.preprocessors:hfe', 'highfreq = bob.bio.vein.configurations.preprocessors:hfe',
'circgabor = bob.bio.vein.configurations.preprocessors:circgabor', 'circgabor = bob.bio.vein.configurations.preprocessors:circgabor',
...@@ -64,8 +64,7 @@ setup( ...@@ -64,8 +64,7 @@ setup(
# registered SGE grid configuration files # registered SGE grid configuration files
'bob.bio.grid': [ 'bob.bio.grid': [
'local = bob.bio.vein.configurations.grid.local:grid', 'idiap = bob.bio.vein.configurations.grid:default',
'demanding = bob.bio.vein.configurations.grid.demanding:grid',
], ],
}, },
......
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