ferns-demo
                               ----------

This  software demonstrates  Fern based  keypoint matching  for planar
object detection.  It also includes  template based tracking  code for
applications that demand even faster operation.

Homepage: http://cvlab.epfl.ch/software/ferns/index.php

Usage:
------
Type
$ make
to  compile  the  code  and   generate  the  executable 'ferns-demo'.

The code  depends on the OpenCV  library, so you  should first install
and  make sure  that  pkg-config can  locate  it.  To  test just  type
$ pkg-config  opencv --libs  --cflags
and  you  should get  a list  of compiler flags.

The executable  by default uses 'model.bmp'  and 'model.bmp.roi' files
to    train   a    Fern   based    classifier   and    save    it   to
'model.bmp.detector_data'. It  then tries to  open a webcam  and start
detecting the  object. You can  use the '-v'  flag to specify  a video
file  as  image  source. So  to  try  detection  on  a video,  type
$ ./ferns-demo -v mousepad.mp4

You can also use a sequence of images to test detection,
$ ./ferns-demo -s 'image%04.jpg'

To change  the model object just take  a photo of a  planar object and
save it.  You can specify a region  of interest by writing  a roi file
the same name  as your model file + '.roi'  extension. You can specify
the name of the image using the '-m' option,
$ ./ferns-demo -m book.jpg
which   will  use   'book.jpg'  and   'book.roi'  files   to  generate
'book.jpg.detector_data'

Type
$ ./ferns_demo -h
to see a list of command line options.

To  change parameters  for training  just edit  'main.cc' and  see the
header file for 'planar_pattern_detector_builder.h'.

Template Based Tracking:
------------------------
Tracking can be  done much faster than detection  but is less reliable
and needs  initialization and reinitialization in case  of failure. So
the executable has  different modes of operation that  can be switched
by pressing the number keys (0-3).

0: Detect when tracking fails or for initialization then track.
1: Track only
2: Detect only (DEFAULT)
3: Detect + track in every frame

The number  keys 4&5  can be  used to turn  on/off the  recognized and
 detected keypoints, respectively.

By default  the tracker is  trained using the  same model file  as the
detector and the tracker data is  saved in a file having the same name
as the model file + '.tracker_data' extension.

Windows:
--------
There is no automated way to compile the code under Windows. It should
be relatively easy to write one, since the source code depends only on
OpenCV (which has a Windows version) and a decent C++ implementation.