OPENCV_HAARTRAINING(1)						   User Commands					    OPENCV_HAARTRAINING(1)

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).

OpenCV								     May 2010						    OPENCV_HAARTRAINING(1)