+.TH "OPENCV\-HAARTRAINING" "1" "May 2008" "OpenCV" "User Commands"
+
+
+.SH NAME
+opencv-haartraining \- train classifier
+
+
+.SH SYNOPSIS
+.B opencv\-haartraining [options]
+
+
+.SH DESCRIPTION
+.PP
+.B 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.
+.PP
+In the output:
+.TP
+.RB \(aq POS: \(aq
+shows the hitrate in the set of training samples (should be equal or near to
+.I 1.0
+as in stage 0)
+.TP
+.RB \(aq NEG: \(aq
+indicates the false alarm rate (should reach at least
+.I 5*10-6
+to be a usable classifier for real world applications)
+.PP
+If one of the above values gets
+.IR 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.
+
+
+.SH OPTIONS
+.PP
+.B opencv\-haartraining
+supports the following options:
+
+.PP
+.TP
+.BI "\-data " dir_name
+The directory in which the trained classifier is stored.
+
+.TP
+.BI "\-vec " vec_file_name
+The file name of the positive samples file (e.g. created by the
+.BR opencv\-createsamples (1)
+utility).
+
+.TP
+.BI "\-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.
+
+.TP
+.BI "\-npos " number_of_positive_samples
+The number of positive samples used in training of each classifier stage.
+The default is
+.IR 2000 .
+
+
+.TP
+.BI "\-nneg " number_of_negative_samples
+The number of negative samples used in training of each classifier stage.
+The default is
+.IR 2000 .
+
+.PP
+Reasonable values are
+.BR "\-npos 7000 \-nneg 3000" .
+
+.TP
+.BI "\-nstages " number_of_stage
+The number of stages to be trained. The default is
+.IR 14 .
+
+.TP
+.BI "\-nsplits " number_of_splits
+Determine the weak classifier used in stage classifiers. If the value is
+.IP
+.BR 1 ,
+then a simple stump classifier is used
+.IP
+.BR >=2 ,
+then CART classifier with
+.I number_of_splits
+internal (split) nodes is used
+.IP
+The default is
+.IR 1 .
+
+.TP
+.BI "\-mem " memory_in_MB
+Available memory in
+.B MB
+for precalculation. The more memory you have the faster the training process is.
+The default is
+.IR 200 .
+
+.TP
+.B \-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
+.BR \-sym .
+
+.TP
+.BI "\-minhitrate " min_hit_rate
+The minimal desired hit rate for each stage classifier. Overall hit rate may
+be estimated as
+.IR "\%min_hit_rate^number_of_stages" .
+The default is
+.IR 0.950000 .
+
+.TP
+.BI "\-maxfalsealarm " max_false_alarm_rate
+The maximal desired false alarm rate for each stage classifier. Overall false
+alarm rate may be estimated as
+.IR "\%max_false_alarm_rate^number_of_stages" .
+The default is
+.IR 0.500000 .
+
+.TP
+.BI "\-weighttrimming " weight_trimming
+Specifies whether and how much weight trimming should be used. The default is
+.IR 0.950000 .
+A decent choice is
+.IR 0.900000 .
+
+.TP
+.B \-eqw
+Specify if initial weights of all samples will be equal.
+
+.TP
+.BI "\-mode {" BASIC | CORE | ALL "}"
+Select the type of haar features set used in training.
+.I BASIC
+uses only upright features, while
+.I CORE
+uses the full upright feature set and
+.I ALL
+uses the full set of upright and 45 degree rotated feature set.
+The default is
+.IR BASIC .
+.IP
+For more information on this see \%http://www.lienhart.de/ICIP2002.pdf.
+
+.TP
+.BI "\-bt {" DAB | RAB | LB | GAB "}"
+The type of the applied boosting algorithm. You can choose between Discrete
+AdaBoost (\fIDAB\fR), Real AdaBoost (\fIRAB\fR), LogitBoost (\fILB\fR) and
+Gentle AdaBoost (\fIGAB\fR). The default is
+.IR GAB .
+
+.TP
+.BI "\-err {" misclass | gini | entropy "}"
+The type of used error if Discrete AdaBoost (\fB\-bt DAB\fR) algorithm is
+applied. The default is
+.IR misclass .
+
+.TP
+.BI "\-maxtreesplits " max_number_of_splits_in_tree_cascade
+The maximal number of splits in a tree cascade. The default is
+.IR 0 .
+
+.TP
+.BI "\-minpos " min_number_of_positive_samples_per_cluster
+The minimal number of positive samples per cluster. The default is
+.IR 500 .
+
+.TP
+.BI "\-h " sample_height
+The sample height (must have the same value as used during creation).
+The default is
+.IR 24 .
+
+.TP
+.BI "\-w " sample_width
+The sample width (must have the same value as used during creation).
+The default is
+.IR 24 .
+
+.PP
+The same information is shown, if
+.B opencv\-haartraining
+is called without any arguments/options.
+
+
+.SH EXAMPLES
+.PP
+TODO
+.\" http://robotik.inflomatik.info/other/opencv/OpenCV_ObjectDetection_HowTo.pdf
+
+
+.SH SEE ALSO
+.PP
+.BR opencv\-createsamples (1),
+.BR opencv\-performance (1)
+.PP
+More information and examples can be found in the OpenCV documentation.
+
+
+.SH AUTHORS
+.PP
+This manual page was written by \fBDaniel Leidert\fR <\&daniel.leidert@wgdd.de\&>
+for the Debian project (but may be used by others).
projects / opencv / blobdiff