opencv_haartraining - train classifier

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

OpenCV                                              May 2010                              OPENCV_HAARTRAINING(1)

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.

       TODO

       opencv_haartraining - train classifier

opencv_haartraining supports the following options:

       -datadir_name
              The directory in which the trained classifier is stored.

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

       -bgbackground_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  notset.

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

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

       Reasonable values are -npos7000-nneg3000.

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

       -nsplitsnumber_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.

       -memmemory_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.

       -minhitratemin_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.

       -maxfalsealarmmax_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.

       -weighttrimmingweight_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.

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

       -wsample_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  (-btDAB) algorithm is applied. The default is
              misclass.

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

       -minposmin_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.

opencv_createsamples(1), opencv_performance(1)

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

opencv_haartraining[options]