Section: User Commands (1)Updated: May 2008Local indexUp
NAME
opencv-performance - evaluate the performance of the classifier
SYNOPSIS
opencv-performance [options]
DESCRIPTION
opencv-performance
evaluates the performance of the classifier. It takes a collection of marked
up test images, applies the classifier and outputs the performance, i.e. number of
found objects, number of missed objects, number of false alarms and other
information.
When there is no such collection available test samples may be created from single
object image by the
opencv-createsamples(1)
utility. The scheme of test samples creation in this case is similar to training samples
In the output, the table should be read:
'Hits'
shows the number of correctly found objects
'Missed'
shows the number of missed objects (must exist but are not found, also known
as false negatives)
'False'
shows the number of false alarms (must not exist but are found, also known
as false positives)
OPTIONS
opencv-performance
supports the following options:
-data classifier_directory_name
The directory, in which the classifier can be found.
-info collection_file_name
File with test samples description.
-maxSizeDiff max_size_difference
Determine the size criterion of reference and detected coincidence.
The default is
1.500000.
-maxPosDiff max_position_difference
Determine the position criterion of reference and detected coincidence.
The default is
0.300000.
-sf scale_factor
Scale the detection window in each iteration. The default is
1.200000.
-ni
Don't save detection result to an image. This could be useful, if
collection_file_name
contains paths.
-nos number_of_stages
Number of stages to use. The default is
-1
(all stages are used).
-rs roc_size
The default is
40.
-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.
The same information is shown, if
opencv-performance
is called without any arguments/options.
EXAMPLES
To create training samples from one image applying distortions and show the
results:
