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