"atomic bomb" commit. Reorganized OpenCV directory structure
This commit is contained in:
Vadim Pisarevsky
282
modules/haartraining/haartraining.cpp
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282
modules/haartraining/haartraining.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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/*
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* haartraining.cpp
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*
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* Train cascade classifier
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*/
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#include <cstdio>
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#include <cstring>
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#include <cstdlib>
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#include "cvhaartraining.h"
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int main( int argc, char* argv[] )
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{
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int i = 0;
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char* nullname = (char*)"(NULL)";
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char* vecname = NULL;
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char* dirname = NULL;
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char* bgname = NULL;
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bool bg_vecfile = false;
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int npos = 2000;
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int nneg = 2000;
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int nstages = 14;
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int mem = 200;
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int nsplits = 1;
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float minhitrate = 0.995F;
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float maxfalsealarm = 0.5F;
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float weightfraction = 0.95F;
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int mode = 0;
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int symmetric = 1;
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int equalweights = 0;
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int width = 24;
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int height = 24;
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const char* boosttypes[] = { "DAB", "RAB", "LB", "GAB" };
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int boosttype = 3;
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const char* stumperrors[] = { "misclass", "gini", "entropy" };
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int stumperror = 0;
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int maxtreesplits = 0;
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int minpos = 500;
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if( argc == 1 )
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{
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printf( "Usage: %s\n -data <dir_name>\n"
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" -vec <vec_file_name>\n"
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" -bg <background_file_name>\n"
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" [-bg-vecfile]\n"
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" [-npos <number_of_positive_samples = %d>]\n"
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" [-nneg <number_of_negative_samples = %d>]\n"
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" [-nstages <number_of_stages = %d>]\n"
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" [-nsplits <number_of_splits = %d>]\n"
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" [-mem <memory_in_MB = %d>]\n"
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" [-sym (default)] [-nonsym]\n"
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" [-minhitrate <min_hit_rate = %f>]\n"
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" [-maxfalsealarm <max_false_alarm_rate = %f>]\n"
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" [-weighttrimming <weight_trimming = %f>]\n"
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" [-eqw]\n"
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" [-mode <BASIC (default) | CORE | ALL>]\n"
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" [-w <sample_width = %d>]\n"
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" [-h <sample_height = %d>]\n"
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" [-bt <DAB | RAB | LB | GAB (default)>]\n"
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" [-err <misclass (default) | gini | entropy>]\n"
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" [-maxtreesplits <max_number_of_splits_in_tree_cascade = %d>]\n"
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" [-minpos <min_number_of_positive_samples_per_cluster = %d>]\n",
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argv[0], npos, nneg, nstages, nsplits, mem,
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minhitrate, maxfalsealarm, weightfraction, width, height,
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maxtreesplits, minpos );
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return 0;
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}
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for( i = 1; i < argc; i++ )
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{
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if( !strcmp( argv[i], "-data" ) )
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{
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dirname = argv[++i];
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}
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else if( !strcmp( argv[i], "-vec" ) )
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{
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vecname = argv[++i];
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}
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else if( !strcmp( argv[i], "-bg" ) )
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{
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bgname = argv[++i];
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}
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else if( !strcmp( argv[i], "-bg-vecfile" ) )
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{
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bg_vecfile = true;
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}
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else if( !strcmp( argv[i], "-npos" ) )
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{
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npos = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-nneg" ) )
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{
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nneg = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-nstages" ) )
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{
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nstages = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-nsplits" ) )
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{
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nsplits = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-mem" ) )
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{
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mem = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-sym" ) )
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{
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symmetric = 1;
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}
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else if( !strcmp( argv[i], "-nonsym" ) )
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{
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symmetric = 0;
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}
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else if( !strcmp( argv[i], "-minhitrate" ) )
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{
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minhitrate = (float) atof( argv[++i] );
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}
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else if( !strcmp( argv[i], "-maxfalsealarm" ) )
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{
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maxfalsealarm = (float) atof( argv[++i] );
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}
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else if( !strcmp( argv[i], "-weighttrimming" ) )
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{
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weightfraction = (float) atof( argv[++i] );
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}
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else if( !strcmp( argv[i], "-eqw" ) )
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{
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equalweights = 1;
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}
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else if( !strcmp( argv[i], "-mode" ) )
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{
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char* tmp = argv[++i];
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if( !strcmp( tmp, "CORE" ) )
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{
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mode = 1;
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}
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else if( !strcmp( tmp, "ALL" ) )
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{
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mode = 2;
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}
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else
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{
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mode = 0;
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}
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}
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else if( !strcmp( argv[i], "-w" ) )
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{
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width = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-h" ) )
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{
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height = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-bt" ) )
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{
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i++;
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if( !strcmp( argv[i], boosttypes[0] ) )
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{
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boosttype = 0;
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}
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else if( !strcmp( argv[i], boosttypes[1] ) )
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{
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boosttype = 1;
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}
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else if( !strcmp( argv[i], boosttypes[2] ) )
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{
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boosttype = 2;
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}
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else
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{
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boosttype = 3;
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}
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}
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else if( !strcmp( argv[i], "-err" ) )
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{
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i++;
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if( !strcmp( argv[i], stumperrors[0] ) )
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{
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stumperror = 0;
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}
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else if( !strcmp( argv[i], stumperrors[1] ) )
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{
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stumperror = 1;
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}
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else
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{
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stumperror = 2;
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}
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}
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else if( !strcmp( argv[i], "-maxtreesplits" ) )
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{
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maxtreesplits = atoi( argv[++i] );
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}
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else if( !strcmp( argv[i], "-minpos" ) )
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{
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minpos = atoi( argv[++i] );
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}
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}
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printf( "Data dir name: %s\n", ((dirname == NULL) ? nullname : dirname ) );
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printf( "Vec file name: %s\n", ((vecname == NULL) ? nullname : vecname ) );
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printf( "BG file name: %s, is a vecfile: %s\n", ((bgname == NULL) ? nullname : bgname ), bg_vecfile ? "yes" : "no" );
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printf( "Num pos: %d\n", npos );
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printf( "Num neg: %d\n", nneg );
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printf( "Num stages: %d\n", nstages );
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printf( "Num splits: %d (%s as weak classifier)\n", nsplits,
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(nsplits == 1) ? "stump" : "tree" );
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printf( "Mem: %d MB\n", mem );
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printf( "Symmetric: %s\n", (symmetric) ? "TRUE" : "FALSE" );
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printf( "Min hit rate: %f\n", minhitrate );
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printf( "Max false alarm rate: %f\n", maxfalsealarm );
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printf( "Weight trimming: %f\n", weightfraction );
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printf( "Equal weights: %s\n", (equalweights) ? "TRUE" : "FALSE" );
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printf( "Mode: %s\n", ( (mode == 0) ? "BASIC" : ( (mode == 1) ? "CORE" : "ALL") ) );
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printf( "Width: %d\n", width );
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printf( "Height: %d\n", height );
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//printf( "Max num of precalculated features: %d\n", numprecalculated );
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printf( "Applied boosting algorithm: %s\n", boosttypes[boosttype] );
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printf( "Error (valid only for Discrete and Real AdaBoost): %s\n",
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stumperrors[stumperror] );
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printf( "Max number of splits in tree cascade: %d\n", maxtreesplits );
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printf( "Min number of positive samples per cluster: %d\n", minpos );
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cvCreateTreeCascadeClassifier( dirname, vecname, bgname,
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npos, nneg, nstages, mem,
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nsplits,
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minhitrate, maxfalsealarm, weightfraction,
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mode, symmetric,
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equalweights, width, height,
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boosttype, stumperror,
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maxtreesplits, minpos, bg_vecfile );
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return 0;
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}
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