Use bob_bio_vein prefix on all scripts

doc/baselines.rst

@@ -254,7 +254,7 @@ correspond to the the equal-error rate on the development set, in percentage):
    Feature Extractor      Full     B    Nom   1vsall  nom
 ======================== ====== ====== ====== ====== ======
 Repeated Line Tracking    14.6   13.4   23.6   3.4    1.4
-Wide Line Detector         5.8    5.6    9.7   2.8    1.9
+Wide Line Detector         5.8    5.6    9.9   2.8    1.9
 Maximum Curvature          2.5    1.4    4.5   0.9    0.4
 ======================== ====== ====== ====== ====== ======
 
@@ -368,11 +368,11 @@ 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
-`bob_vein_markdet.py`:
+`bob_bio_vein_markdet.py`:
 
 .. code-block:: sh
 
-   $ bob_vein_markdet.py --hidden=20 --samples=500 fv3d central dev
+   $ bob_bio_vein_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
@@ -388,9 +388,9 @@ 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 ``bob_vein_compare_rois.py``
-compares two sets of ``preprocessed`` images and masks, generated by
-*different* preprocessors (see
+a couple of scripts available in this package. The program
+``bob_bio_vein_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
@@ -400,7 +400,7 @@ extracted ones. E.g.:
 
 .. code-block:: sh
 
-   $ bob_vein_compare_rois.py ~/verafinger/mc_annot/preprocessed ~/verafinger/mc/preprocessed
+   $ bob_bio_vein_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
@@ -420,12 +420,12 @@ metrics.
 Pipeline Display
 ================
 
-You can use the program ``bob_vein_view_sample.py`` to display the images after
-full processing using:
+You can use the program ``bob_bio_vein_view_sample.py`` to display the images
+after full processing using:
 
 .. code-block:: sh
 
-   $ bob_vein_view_sample.py --save=output-dir verafinger /path/to/processed/directory 030-M/030_L_1
+   $ bob_bio_vein_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
@@ -436,7 +436,7 @@ feature extractor):
    :scale: 50%
 
    Example RoI overlayed on finger vein image of the Vera fingervein database,
-   as produced by the script ``bob_vein_view_sample.py``.
+   as produced by the script ``bob_bio_vein_view_sample.py``.
 
 
 .. figure:: img/binarized.*

setup.py

@@ -50,11 +50,11 @@ setup(
         ],
 
       'console_scripts': [
-        'bob_vein_compare_rois.py = bob.bio.vein.script.compare_rois:main',
-        'bob_vein_view_sample.py = bob.bio.vein.script.view_sample:main',
-        'bob_vein_blame.py = bob.bio.vein.script.blame:main',
-        'bob_vein_markdet.py = bob.bio.vein.script.markdet:main',
-        'bob_vein_watershed_mask.py = bob.bio.vein.script.watershed_mask:main',
+        'bob_bio_vein_compare_rois.py = bob.bio.vein.script.compare_rois:main',
+        'bob_bio_vein_view_sample.py = bob.bio.vein.script.view_sample:main',
+        'bob_bio_vein_blame.py = bob.bio.vein.script.blame:main',
+        'bob_bio_vein_markdet.py = bob.bio.vein.script.markdet:main',
+        'bob_bio_vein_watershed_mask.py = bob.bio.vein.script.watershed_mask:main',
         ]
       },