Updated the docs w.r.t. the new bob API.

doc/baselines.rst

@@ -147,7 +147,7 @@ performance:
 
 .. code-block:: sh
 
-   $ bob_eval_threshold.py <path-to>/verafinger/rlt/Nom/nonorm/scores-dev
+   $ bob bio metrics <path-to>/verafinger/rlt/Nom/nonorm/scores-dev --no-evaluation
    ('Threshold:', 0.31835292)
    FAR : 23.636% (11388/48180)
    FRR : 23.636% (52/220)
@@ -180,7 +180,7 @@ we obtained:
 
 .. code-block:: sh
 
-   $ bob_eval_threshold.py <path-to>/verafinger/mc/Nom/nonorm/scores-dev
+   $ bob bio metrics <path-to>/verafinger/mc/Nom/nonorm/scores-dev --no-evaluation
    ('Threshold:', 0.0737283)
    FAR : 4.388% (2114/48180)
    FRR : 4.545% (10/220)
@@ -213,7 +213,7 @@ we obtained:
 
 .. code-block:: sh
 
-   $ bob_eval_threshold.py <path-to>/verafinger/wld/NOM/nonorm/scores-dev
+   $ bob bio metrics <path-to>/verafinger/wld/NOM/nonorm/scores-dev --no-evaluation
    ('Threshold:', 0.240269475)
    FAR : 9.770% (4707/48180)
    FRR : 9.545% (21/220)
@@ -347,11 +347,11 @@ 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
-`markdet.py`:
+`bob_vein_markdet.py`:
 
 .. code-block:: sh
 
-   $ markdet.py --hidden=20 --samples=500 fv3d central dev
+   $ bob_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
@@ -367,7 +367,7 @@ 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``
+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
 :py:class:`bob.bio.base.preprocessor.Preprocessor`) and calculates a few
@@ -379,7 +379,7 @@ extracted ones. E.g.:
 
 .. code-block:: sh
 
-   $ compare_rois.py ~/verafinger/mc_annot/preprocessed ~/verafinger/mc/preprocessed
+   $ bob_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
@@ -399,12 +399,12 @@ metrics.
 Pipeline Display
 ================
 
-You can use the program ``view_sample.py`` to display the images after
+You can use the program ``bob_vein_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
+   $ bob_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
@@ -415,7 +415,7 @@ feature extractor):
    :scale: 50%
 
    Example RoI overlayed on finger vein image of the Vera fingervein database,
-   as produced by the script ``view_sample.py``.
+   as produced by the script ``bob_vein_view_sample.py``.
 
 
 .. figure:: img/binarized.*