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Fixed bugs in facerec documentation

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This commit is contained in:
Andrey Kamaev 2012-07-02 11:46:17 +00:00
parent 76354287aa
commit 5d6c90e166
3 changed files with 58 additions and 58 deletions
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8
modules/contrib/doc/facerec/facerec_api.rst
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@ -46,7 +46,7 @@ I'll go a bit more into detail explaining :ocv:class:`FaceRecognizer`, because i
* So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see :ocv:func:`Algorithm::create`). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms. * So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see :ocv:func:`Algorithm::create`). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms.
* Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with :ocv:cfunc:`cvSetCaptureProperty`, :ocv:cfunc:`cvGetCaptureProperty`, :ocv:func:`VideoCapture::set` and :ocv:func:`VideoCapture::get`. :ocv:class:`Algorithm` provides similar method where instead of integer id's you specify the parameter names as text strings. See :ocv:func:`Algorithm::set` and :ocv:func:`Algorithm::get for details. * Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with :ocv:cfunc:`cvSetCaptureProperty`, :ocv:cfunc:`cvGetCaptureProperty`, :ocv:func:`VideoCapture::set` and :ocv:func:`VideoCapture::get`. :ocv:class:`Algorithm` provides similar method where instead of integer id's you specify the parameter names as text strings. See :ocv:func:`Algorithm::set` and :ocv:func:`Algorithm::get` for details.
* Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time. * Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time.
@ -209,7 +209,7 @@ Saves a :ocv:class:`FaceRecognizer` and its model state.
Every :ocv:class:`FaceRecognizer` overwrites ``FaceRecognizer::save(FileStorage& fs)`` Every :ocv:class:`FaceRecognizer` overwrites ``FaceRecognizer::save(FileStorage& fs)``
to save the internal model state. ``FaceRecognizer::save(const string& filename)`` saves to save the internal model state. ``FaceRecognizer::save(const string& filename)`` saves
the state of a model to the given filename. the state of a model to the given filename.
The suffix ``const`` means that prediction does not affect the internal model The suffix ``const`` means that prediction does not affect the internal model
state, so the method can be safely called from within different threads. state, so the method can be safely called from within different threads.
@ -255,7 +255,7 @@ Model internal data:
* ``labels`` The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. * ``labels`` The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
createFisherFaceRecognizer createFisherFaceRecognizer
------------------------- --------------------------
.. ocv:function:: Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX) .. ocv:function:: Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX)
@ -284,7 +284,7 @@ Model internal data:
createLBPHFaceRecognizer createLBPHFaceRecognizer
------------------------- -------------------------
.. ocv:function:: Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX); .. ocv:function:: Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX)
:param radius: The radius used for building the Circular Local Binary Pattern. The greater the radius, the :param radius: The radius used for building the Circular Local Binary Pattern. The greater the radius, the
:param neighbors: The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost. :param neighbors: The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost.

2
modules/contrib/doc/facerec/tutorial/facerec_save_load.rst
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@ -11,7 +11,7 @@ The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRec
call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a
:ocv:class:`FaceRecognizer`. :ocv:class:`FaceRecognizer`.
I'll adapt the Eigenfaces example from the :doc:`/facerec_tutorial`: Imagine we want to learn the Eigenfaces I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces
of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ store the of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_ store the
model to a YAML file and then load it again. model to a YAML file and then load it again.

2
modules/contrib/doc/facerec/tutorial/facerec_video_recognition.rst
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@ -24,7 +24,7 @@ I encourage you to experiment with the application. Play around with the availab
Prerequisites Prerequisites
-------------- --------------
You want to do face recognition, so you need some face images to learn a :ocv:class:`FaceRecognizer` on. I have decided to reuse the images from the gender classification example: :doc:`../facerec_gender_classification`. You want to do face recognition, so you need some face images to learn a :ocv:class:`FaceRecognizer` on. I have decided to reuse the images from the gender classification example: :doc:`facerec_gender_classification`.
I have the following celebrities in my training data set: I have the following celebrities in my training data set: