.. vim: set fileencoding=utf-8 :
.. date: Thu Sep 20 11:58:57 CEST 2012

.. _bob.bio.vein.baselines:

===============================
 Executing Baseline Algorithms
===============================

The first thing you might want to do is to execute one of the vein
recognition algorithms that are implemented in ``bob.bio.vein``.


Running Baseline Experiments
----------------------------

To run the baseline experiments, you can use the ``verify.py`` script by
just going to the console and typing:

.. code-block:: sh

   $ verify.py


This script is explained in more detail in :ref:`bob.bio.base.experiments`.
The ``verify.py --help`` option shows you, which other options you can
set.

Usually it is a good idea to have at least verbose level 2 (i.e., calling
``verify.py --verbose --verbose``, or the short version ``verify.py
-vv``).

.. note:: **Running in Parallel**

   To run the experiments in parallel, you can define an SGE grid or local host
   (multi-processing) configurations as explained in
   :ref:`running_in_parallel`.

   In short, to run in the Idiap SGE grid, you can simply add the ``--grid``
   command line option, without parameters. To run experiments in parallel on
   the local machine, simply add a ``--parallel <N>`` option, where ``<N>``
   specifies the number of parallel jobs you want to execute.


Database setups and baselines are encoded using
:ref:`bob.bio.base.configuration-files`, all stored inside the package root, in
the directory ``bob/bio/vein/configurations``. Documentation for each resource
is available on the section :ref:`bob.bio.vein.resources`.

.. warning::

   You **cannot** run experiments just by executing the command line
   instructions described in this guide. You **need first** to procure yourself
   the raw data files that correspond to *each* database used here in order to
   correctly run experiments with those data. Biometric data is considered
   private data and, under EU regulations, cannot be distributed without a
   consent or license. You may consult our
   :ref:`bob.bio.vein.resources.databases` resources section for checking
   currently supported databases and accessing download links for the raw data
   files.

   Once the raw data files have been downloaded, particular attention should be
   given to the directory locations of those. Unpack the databases carefully
   and annotate the root directory where they have been unpacked.

   Then, carefully read the *Databases* section of
   :ref:`bob.bio.base.installation` on how to correctly setup the
   ``~/.bob_bio_databases.txt`` file.

   Use the following keywords on the left side of the assignment (see
   :ref:`bob.bio.vein.resources.databases`):

   .. code-block:: text

      [YOUR_VERAFINGER_DIRECTORY] = /complete/path/to/verafinger
      [YOUR_UTFVP_DIRECTORY] = /complete/path/to/utfvp
      [YOUR_FV3D_DIRECTORY] = /complete/path/to/fv3d

   Notice it is rather important to use the strings as described above,
   otherwise ``bob.bio.base`` will not be able to correctly load your images.

   Once this step is done, you can proceed with the instructions below.


In the remainder of this section we introduce baseline experiments you can
readily run with this tool without further configuration. Baselines examplified
in this guide were published in [TVM14]_.


Repeated Line-Tracking with Miura Matching
==========================================

Detailed description at :ref:`bob.bio.vein.resources.recognition.rlt`.

To run the baseline on the `VERA fingervein`_ database, using the ``Nom``
protocol, do the following:


.. code-block:: sh

   $ verify.py verafinger rlt -vv


.. tip::

   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 (using 4
   parallel tasks) by adding the options ``--parallel=4 --nice=10``.

   Optionally, you may use the ``parallel`` resource configuration which
   already sets the number of parallel jobs to the number of hardware cores you
   have installed on your machine (as with
   :py:func:`multiprocessing.cpu_count`) and sets ``nice=10``. For example:

   .. code-block:: sh

      $ verify.py verafinger rlt parallel -vv

   To run on the Idiap SGE grid using our stock
   io-big-48-slots-4G-memory-enabled (see
   :py:mod:`bob.bio.vein.configurations.gridio4g48`) configuration, use:

   .. code-block:: sh

      $ verify.py verafinger rlt grid -vv

   You may also, optionally, use the configuration resource ``gridio4g48``,
   which is just an alias of ``grid`` in this package.



This command line selects and runs the following implementations for the
toolchain:

* :ref:`bob.bio.vein.resources.database.verafinger`
* :ref:`bob.bio.vein.resources.recognition.rlt`

As the tool runs, you'll see printouts that show how it advances through
preprocessing, feature extraction and matching. In a 4-core machine and using
4 parallel tasks, it takes around 4 hours to process this baseline with the
current code implementation.

To complete the evaluation, run the command bellow, that will output the equal
error rate (EER) and plot the detector error trade-off (DET) curve with the
performance:

.. code-block:: sh

   $ bob_eval_threshold.py <path-to>/verafinger/rlt/Nom/nonorm/scores-dev
   ('Threshold:', 0.32045327)
   FAR : 26.362% (12701/48180)
   FRR : 26.364% (58/220)
   HTER: 26.363%


Maximum Curvature with Miura Matching
=====================================

Detailed description at :ref:`bob.bio.vein.resources.recognition.mc`.

To run the baseline on the `VERA fingervein`_ database, using the ``Nom``
protocol like above, do the following:


.. code-block:: sh

   $ verify.py verafinger mc -vv


This command line selects and runs the following implementations for the
toolchain:

* :ref:`bob.bio.vein.resources.database.verafinger`
* :ref:`bob.bio.vein.resources.recognition.mc`

In a 4-core machine and using 4 parallel tasks, it takes around 1 hour and 40
minutes to process this baseline with the current code implementation. Results
we obtained:

.. code-block:: sh

   $ bob_eval_threshold.py <path-to>/verafinger/mc/Nom/nonorm/scores-dev
   ('Threshold:', 0.078274325)
   FAR : 3.182% (1533/48180)
   FRR : 3.182% (7/220)
   HTER: 3.182%


Wide Line Detector with Miura Matching
======================================

You can find the description of this method on the paper from Huang *et al.*
[HDLTL10]_.

To run the baseline on the `VERA fingervein`_ database, using the ``NOM``
protocol like above, do the following:


.. code-block:: sh

   $ verify.py verafinger wld -vv


This command line selects and runs the following implementations for the
toolchain:

* :ref:`bob.bio.vein.resources.database.verafinger`
* :ref:`bob.bio.vein.resources.recognition.wld`

In a 4-core machine and using 4 parallel tasks, it takes only around 5 minutes
minutes to process this baseline with the current code implementation.Results
we obtained:

.. code-block:: sh

   $ bob_eval_threshold.py <path-to>/verafinger/wld/NOM/nonorm/scores-dev
   ('Threshold:', 0.239141175)
   FAR : 10.455% (5037/48180)
   FRR : 10.455% (23/220)
   HTER: 10.455%


Results for other Baselines
===========================

This package may generate results for other combinations of protocols and
databases. Here is a summary table for some variants (results expressed
correspond to the the equal-error rate on the development set, in percentage):

======================== ====== ====== ====== ====== ======
       Toolchain              Vera Finger         UTFVP
------------------------ -------------------- -------------
   Feature Extractor      Full     B    Nom   1vsall  nom
======================== ====== ====== ====== ====== ======
Repeated Line Tracking    23.9   24.1   24.9   1.7    1.4
Wide Line Detector        10.2   10.2   10.5   2.3    1.7
Maximum Curvature          3.2    3.2    3.1   0.4    0.
======================== ====== ====== ====== ====== ======

In a machine with 48 cores, running these baselines took the following time
(hh:mm):

======================== ====== ====== ====== ====== ======
       Toolchain              Vera Finger         UTFVP
------------------------ -------------------- -------------
   Feature Extractor      Full     B    Nom   1vsall  nom
======================== ====== ====== ====== ====== ======
Repeated Line Tracking    01:16  00:23  00:23  12:44  00:35
Wide Line Detector        00:07  00:01  00:01  02:25  00:05
Maximum Curvature         03:28  00:54  00:59  58:34  01:48
======================== ====== ====== ====== ====== ======


Modifying Baseline Experiments
------------------------------

It is fairly easy to modify baseline experiments available in this package. To
do so, you must copy the configuration files for the given baseline you want to
modify, edit them to make the desired changes and run the experiment again.

For example, suppose you'd like to change the protocol on the Vera Fingervein
database and use the protocol ``full`` instead of the default protocol ``nom``.
First, you identify where the configuration file sits:

.. code-block:: sh

   $ resources.py -tc -p bob.bio.vein
   - bob.bio.vein X.Y.Z @ /path/to/bob.bio.vein:
     + mc         --> bob.bio.vein.configurations.maximum_curvature
     + parallel   --> bob.bio.vein.configurations.parallel
     + rlt        --> bob.bio.vein.configurations.repeated_line_tracking
     + utfvp      --> bob.bio.vein.configurations.utfvp
     + verafinger --> bob.bio.vein.configurations.verafinger
     + wld        --> bob.bio.vein.configurations.wide_line_detector


The listing above tells the ``verafinger`` configuration file sits on the
file ``/path/to/bob.bio.vein/bob/bio/vein/configurations/verafinger.py``. In
order to modify it, make a local copy. For example:

.. code-block:: sh

   $ cp /path/to/bob.bio.vein/bob/bio/vein/configurations/verafinger.py verafinger_full.py
   $ # edit verafinger_full.py, change the value of "protocol" to "full"


Also, don't forget to change all relative module imports (such as ``from
..database.verafinger import Database``) to absolute imports (e.g. ``from
bob.bio.vein.database.verafinger import Database``). This will make the
configuration file work irrespectively of its location w.r.t. ``bob.bio.vein``.
The final version of the modified file could look like this:

.. code-block:: python

   from bob.bio.vein.database.verafinger import Database

   database = Database(original_directory='/where/you/have/the/raw/files',
     original_extension='.png', #don't change this
     )

   protocol = 'full'


Now, re-run the experiment using your modified database descriptor:

.. code-block:: sh

   $ verify.py ./verafinger_full.py wld -vv


Notice we replace the use of the registered configuration file named
``verafinger`` by the local file ``verafinger_full.py``. This makes the program
``verify.py`` take that into consideration instead of the original file.


Other Resources
---------------

This package contains other resources that can be used to evaluate different
bits of the vein processing toolchain.


Training the Watershed Finger region detector
=============================================

The correct detection of the finger boundaries is an important step of many
algorithms for the recognition of finger veins. It allows to compensate for
eventual rotation and scaling issues one might find when comparing models and
probes. In this package, we propose a novel finger boundary detector based on
the `Watershedding Morphological Algorithm
<https://en.wikipedia.org/wiki/Watershed_(image_processing)>`. Watershedding
works in three steps:

1. Determine markers on the original image indicating the types of areas one
   would like to detect (e.g. "finger" or "background")
2. Determine a 2D (gray-scale) surface representing the original image in which
   darker spots (representing valleys) are more likely to be filled by
   surrounding markers. This is normally achieved by filtering the image with a
   high-pass filter like Sobel or using an edge detector such as Canny.
3. Run the watershed algorithm

In order to determine markers for step 1, we train a neural network which
outputs the likelihood of a point being part of a finger, given its coordinates
and values of surrounding pixels.

When used to run an experiment,
:py:class:`bob.bio.vein.preprocessor.WatershedMask` requires you provide a
*pre-trained* neural network model that presets the markers before
watershedding takes place. In order to create one, you can run the program
`markdet.py`:

.. code-block:: sh

   $ markdet.py --hidden=20 --samples=500 fv3d central dev

You input, as arguments to this application, the database, protocol and subset
name you wish to use for training the network. The data is loaded observing a
total maximum number of samples from the dataset (passed with ``--samples=N``),
the network is trained and recorded into an HDF5 file (by default, the file is
called ``model.hdf5``, but the name can be changed with the option
``--model=``).  Once you have a model, you can use the preprocessor mask by
constructing an object and attaching it to the
:py:class:`bob.bio.vein.preprocessor.Preprocessor` entry on your configuration.


Region of Interest Goodness of Fit
==================================

Automatic region of interest (RoI) finding and cropping can be evaluated using
a couple of scripts available in this package. The program ``compare_rois.py``
compares two sets of ``preprocessed`` images and masks, generated by
*different* preprocessors (see
:py:class:`bob.bio.base.preprocessor.Preprocessor`) and calculates a few
metrics to help you determine how both techniques compare.  Normally, the
program is used to compare the result of automatic RoI to manually annoted
regions on the same images. To use it, just point it to the outputs of two
experiments representing the manually annotated regions and automatically
extracted ones. E.g.:

.. code-block:: sh

   $ compare_rois.py ~/verafinger/mc_annot/preprocessed ~/verafinger/mc/preprocessed
   Jaccard index: 9.60e-01 +- 5.98e-02
   Intersection ratio (m1): 9.79e-01 +- 5.81e-02
   Intersection ratio of complement (m2): 1.96e-02 +- 1.53e-02


Values printed by the script correspond to the `Jaccard index`_
(:py:func:`bob.bio.vein.preprocessor.utils.jaccard_index`), as well as the
intersection ratio between the manual and automatically generated masks
(:py:func:`bob.bio.vein.preprocessor.utils.intersect_ratio`) and the ratio to
the complement of the intersection with respect to the automatically generated
mask
(:py:func:`bob.bio.vein.preprocessor.utils.intersect_ratio_of_complement`). You
can use the option ``-n 5`` to print the 5 worst cases according to each of the
metrics.


Pipeline Display
================

You can use the program ``view_sample.py`` to display the images after
full processing using:

.. code-block:: sh

   $ ./bin/view_sample.py --save=output-dir verafinger /path/to/processed/directory 030-M/030_L_1
   $ # open output-dir

And you should be able to view images like these (example taken from the Vera
fingervein database, using the automatic annotator and Maximum Curvature
feature extractor):

.. figure:: img/preprocessed.*
   :scale: 50%

   Example RoI overlayed on finger vein image of the Vera fingervein database,
   as produced by the script ``view_sample.py``.


.. figure:: img/binarized.*
   :scale: 50%

   Example of fingervein image from the Vera fingervein database, binarized by
   using Maximum Curvature, after pre-processing.


.. include:: links.rst
