NAME

       opencv_haartraining - train classifier

SYNOPSIS

       opencv_haartraining [options]

DESCRIPTION

       opencv_haartraining  is  training the classifier. While it is running, you can already get
       an impression, whether the classifier will be suitable or  if  you  need  to  improve  the
       training set and/or parameters.

       In the output:

       'POS:' shows the hitrate in the set of training samples (should be equal or near to 1.0 as
              in stage 0)

       'NEG:' indicates the false alarm rate (should  reach  at  least  5*10-6  to  be  a  usable
              classifier for real world applications)

       If  one  of  the  above  values gets 0 (zero) there is an overflow. In this case the false
       alarm rate is so low, that further training doesn't make  sense  anymore,  so  it  can  be
       stopped.

OPTIONS

       opencv_haartraining supports the following options:

       -data dir_name
              The directory in which the trained classifier is stored.

       -vec vec_file_name
              The   file   name   of   the   positive   samples   file   (e.g.   created  by  the
              opencv_createsamples(1) utility).

       -bg background_file_name
              The background description file (the negative sample set). It contains  a  list  of
              images into which randomly distorted versions of the object are pasted for positive
              sample generation.

       -bg-vecfile
              This option is that bgfilename represents a vec file with discrete  negatives.  The
              default is not set.

       -npos number_of_positive_samples
              The  number  of  positive  samples  used in training of each classifier stage.  The
              default is 2000.

       -nneg number_of_negative_samples
              The number of negative samples used in training  of  each  classifier  stage.   The
              default is 2000.

       Reasonable values are -npos 7000 -nneg 3000.

       -nstages number_of_stage
              The number of stages to be trained. The default is 14.

       -nsplits number_of_splits
              Determine the weak classifier used in stage classifiers. If the value is

              1, then a simple stump classifier is used

              >=2, then CART classifier with number_of_splits internal (split) nodes is used

              The default is 1.

       -mem memory_in_MB
              Available  memory in MB for precalculation. The more memory you have the faster the
              training process is.  The default is 200.

       -sym, -nonsym
              Specify whether the object class under  training  has  vertical  symmetry  or  not.
              Vertical  symmetry  speeds  up  training  process  and  reduces  memory  usage. For
              instance, frontal faces show off vertical symmetry. The default is -sym.

       -minhitrate min_hit_rate
              The minimal desired hit rate for each stage classifier. Overall  hit  rate  may  be
              estimated as min_hit_rate^number_of_stages.  The default is 0.950000.

       -maxfalsealarm max_false_alarm_rate
              The maximal desired false alarm rate for each stage classifier. Overall false alarm
              rate may be estimated as  max_false_alarm_rate^number_of_stages.   The  default  is
              0.500000.

       -weighttrimming weight_trimming
              Specifies  whether  and  how  much  weight  trimming should be used. The default is
              0.950000.  A decent choice is 0.900000.

       -eqw   Specify if initial weights of all samples will be equal.

       -mode {BASIC|CORE|ALL}
              Select the type of haar features set used in training.   BASIC  uses  only  upright
              features, while CORE uses the full upright feature set and ALL uses the full set of
              upright and 45 degree rotated feature set.  The default is BASIC.

              For more information on this see http://www.lienhart.de/ICIP2002.pdf.

       -h sample_height
              The sample height (must have the same value as used during creation).  The  default
              is 24.

       -w sample_width
              The  sample  width (must have the same value as used during creation).  The default
              is 24.

       -bt {DAB|RAB|LB|GAB}
              The type of the  applied  boosting  algorithm.  You  can  choose  between  Discrete
              AdaBoost (DAB), Real AdaBoost (RAB), LogitBoost (LB) and Gentle AdaBoost (GAB). The
              default is GAB.

       -err {misclass|gini|entropy}
              The type of used error if Discrete AdaBoost (-bt DAB)  algorithm  is  applied.  The
              default is misclass.

       -maxtreesplits max_number_of_splits_in_tree_cascade
              The maximal number of splits in a tree cascade. The default is 0.

       -minpos min_number_of_positive_samples_per_cluster
              The minimal number of positive samples per cluster. The default is 500.

       The   same   information   is   shown,   if  opencv_haartraining  is  called  without  any
       arguments/options.

EXAMPLES

       TODO

SEE ALSO

       opencv_createsamples(1), opencv_performance(1)

       More information and examples can be found in the OpenCV documentation.

AUTHORS

       This manual page was written  by  Daniel  Leidert  <daniel.leidert@wgdd.de>  and  Nobuhiro
       Iwamatsu <iwamatsu@debian.org> for the Debian project (but may be used by others).