opencv_performance(1) [debian man page]
OPENCV_PERFORMANCE(1) User Commands OPENCV_PERFORMANCE(1) 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: opencv_performance -data trainout -info tests.dat SEE ALSO
opencv_createsamples(1), opencv_haartraing(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_PERFORMANCE(1)
Check Out this Related Man Page
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)