diff --git a/doc/baselines.rst b/doc/baselines.rst index 1e3e80f01b20996d147b007b011a495eeab03d11..6509851b13f416be4a0d5403588629af08d118e0 100644 --- a/doc/baselines.rst +++ b/doc/baselines.rst @@ -147,7 +147,7 @@ performance: .. code-block:: sh - $ bob_eval_threshold.py /verafinger/rlt/Nom/nonorm/scores-dev + $ bob bio metrics /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 /verafinger/mc/Nom/nonorm/scores-dev + $ bob bio metrics /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 /verafinger/wld/NOM/nonorm/scores-dev + $ bob bio metrics /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.*